Clémence Christin

Jimmy Lopez

Clément Bonnet

J'ai eu l'opportunité de réaliser mon mémoire de Master 2 sous la direction de Patricia Renou-Maissant qui a accepté également de soutenir mon projet de recherche et donc diriger cette présente thèse. Je tiens à lui adresser toute ma reconnaissance et ma profonde gratitude. Mme Renou-Maissant, je vous remercie pour votre orientation et pour nos nombreuses discussions et échanges qui m'ont aidé à clarifier mes pensées et à faire avancer ma réflexion. Je vous remercie pour votre rapide réactivité, votre implication, vos relectures, vos remarques constructives et le temps que vous m'avez consacré pendant toutes ces années. Je vous remercie également de m'avoir intégré au projet SETTEEC qui était une expérience très enrichissante pour moi.

Je voudrais adresser mes profonds remerciements à Isabelle Lebon, ma co-directrice de thèse, d'avoir accepté de co-diriger cette thèse suite à la mutation professionnelle de Mme Renou-Maissant. Merci d'avoir répondu présente à mes nombreuses sollicitations à tout moment. Merci pour vos conseils et votre orientation qui vont bien au-delà de la thèse. Merci pour votre relecture de tous les documents que je vous adresse et pour vos encouragements dans les moments de doute. Merci à toutes les deux pour votre soutien et votre confiance. C'est avec beaucoup de reconnaissance et de fierté que je termine cette thèse sous votre direction. Je souhaite remercier tout particulièrement les Professeurs Josep-Maria Arauzo-Carod, Marcus Dejardin et Roy Thurik qui me font l'honneur de participer à mon jury de thèse. Je remercie les Professeurs Marc Baudry et Catherine Baumont d'avoir accepté d'être rapporteurs, pour le temps consacré à l'évaluation de cette thèse ainsi que pour leurs remarques et commentaires. Mes remerciements s'adressent à Jean-Sébastien Pentecôte et Jean Pascal Guironnet, qui ont accepté de faire partie de mon comité de suivi de thèse, pour leurs conseils et le temps qu'ils m'ont accordé. Je remercie Rafik Abdesselam, avec qui j'ai eu le plaisir de collaborer, pour son aide et son orientation. Je tiens également à remercier les membres du projet SETTEEC qui m'ont permis de débattre et d'échanger sur mes idées. Je remercie particulièrement Sébastien Bourdin pour m'avoir facilité l'accès aux données et Jean Bonnet pour son aide, ses remarques et suggestions et pour la diffusion du questionnaire. J'adresse mes remerciements à Nabil Khelil pour son soutien, ses retours et conseils pour la réalisation de l'enquête. Je remercie Sylvie Cieply de m'avoir donné son accord pour accéder à certaines

« L'Université n'entend donner aucune approbation ni aucune improbation aux opinions émises dans les thèses. Ces opinions doivent être considérées comme propres à leurs auteurs »

Cette thèse n'aurait pas pu être réalisée sans le soutien financier de la région Normandie que je remercie vivement.

Je tiens également à remercier le laboratoire CREM pour l'accueil et les conditions de travail qui m'ont été offertes. Je remercie l'ensemble des membres du CREM pour les échanges intéressants et constructifs ainsi que pour l'ensemble de leurs retours lors de mes présentations aux séminaires du CREM. Je remercie également tous les enseignants avec lesquels j'ai eu l'occasion de travailler dans le cadre de mes cours. Je remercie Vincent Merlin pour les formations doctorales enrichissantes mises en place dans le cadre de l'école doctorale. Je voudrais également remercier le personnel administratif qui a contribué au bon déroulement de cette thèse.

Je remercie tous les chefs d'entreprises qui ont accepté de participer à notre enquête ainsi que tous les responsables administratifs qui ont diffusé notre questionnaire.

Je tiens à exprimer ma profonde reconnaissance envers Eva qui fait partie des merveilleuses personnes que j'ai rencontrées au cours de ma thèse. Elle est ma collègue et co-auteur mais elle n'a pas tardé à devenir une amie dévouée et une soeur de coeur. Eva merci d'avoir été toujours là pour moi et pour ton soutien inconditionnel.

Je remercie mes collègues de laboratoire et de bureau Maimouna, Rania, Etienne, Mohamed, Louis, Ferdaous, Anwesha et Khadidja ainsi que tous les docteurs et doctorants qui sont passés par le bureau 143 pour les moments partagés ensemble. Je tiens à remercier mes amis Fadila, Zinab, Maud, Kévin, Charline, Cylia et Subha pour leur patience, leurs encouragements dans les moments de doute et pour les moments d'évasion.

Mes remerciements vont aussi vers mes parents, qui ont toujours cru en moi, merci pour votre soutien sans failles, votre affection, et surtout de m'avoir fait autant confiance. J'adresse également un grand merci pour mes frères Fodil, Younes et Nafaa pour leur bienveillance et générosité ainsi qu'à mes soeurs Faiza et Nissa pour avoir toujours cru en moi et de m'avoir appris à toujours persévérer pour atteindre mes objectifs. Je ne voudrais pas oublier mes neveux dont l'arrivée au monde fut un pur bonheur.

Bien entendu, cette liste n'est pas exhaustive. Je remercie toute personne optimiste, passionnée, motivée, positive et gentille que j'ai eu l'occasion de rencontrer pendant mon parcours et m'ayant marqué avec son passage.

Introduction générale

Motivation et contexte de la recherche L'un des enjeux majeurs du 21 ème siècle est la lutte contre le changement climatique. L'activité humaine constitue un véritable danger pour l'environnement et en l'absence d'interventions fortes des décideurs politiques comme de la société civile, l'effet de serre provoquera un réchauffement climatique significatif au milieu du siècle (IPCC, 2019). Afin d'apporter des solutions à ce problème, il est nécessaire d'introduire des changements dans la façon de produire et de consommer, de ce fait, l'implication de l'ensemble des acteurs économiques (Individus, institutions, entreprises et États) est cruciale.

A l'échelle des États, plusieurs politiques ont été engagées au niveau européen et mondial afin de lutter contre la dégradation de l'environnement. Au niveau mondial, l'engagement des pays à réduire les émissions de gaz à effet de serre (GES) a été notamment matérialisé par la ratification du protocole de Kyoto 1 en 1997 puis par les Accords de Paris en 2015. Au niveau Européen, plusieurs initiatives ont été lancées comme par exemple la mise en place du paquet énergie climat à l'horizon 2020 et 2030. Le principal objectif étant de réduire les émissions de gaz à effet de serre de 20% à l'horizon 2020 et de 40% à l'horizon 2030 (par rapport aux niveaux de 1990). C'est ainsi qu'une loi sur la transition énergétique 2 a été mise en place en France qui, vise à baisser, à l'horizon 2030, de 40% les émissions de GES par rapport à l'année 1990 et de 30% la consommation d'énergies fossiles par rapport à 2012 et augmenter de 32 % la part des énergies renouvelables dans la consommation totale. A l'échelle des consommateurs, un rapport du centre de commerce international3 a indiqué qu'une demande toujours croissante de produits respectueux de l'environnement est observée [START_REF] Itc | Marché de l'union européenne pour des produits durables : Perspective de la vente au détail sur les politiques d'approvisionnement et la demande des consommateurs[END_REF].

Face à ces défis environnementaux, pour un grand nombre d'entreprises, l'innovation est la clé pour continuer d'exister [START_REF] Schumpeter | The Theory of Economic Development: An Inquiry Into Profits, Capital, Credit, Interest, and the Business Cycle[END_REF]. C'est un outil puissant qui peut être utilisé à la fois par les entreprises déjà implantées afin de renforcer leur compétitivité et par les nouvelles firmes pour faire face à la concurrence issue des entreprises déjà existantes sur le marché. L'innovation constitue ainsi un moyen pour améliorer la performance environnementale à la fois grâce à une plus grande efficacité énergétique, à une diminution des émissions de gaz à effet de serre au cours du processus de 1 Il établit les engagements nécessaires pour éviter les impacts anthropiques (résultant de l'activité humaine). production et par l'offre de nouveaux produits «durables». L'accent est mis de plus en plus sur ce type d'innovations connues dans la littérature sous le nom de l'éco-innovations. Ces dernières deviennent ainsi des solutions majeures aux défis des entreprises (OCDE, 2010) en même temps qu'elles constituent un moteur primordial pour une croissance économique durable sur le long terme (OCDE, 2010;[START_REF] Smulders | Growth theory and "green growth[END_REF].

Délimitation des contours du concept de l'éco-innovation L'éco-innovation est un concept récent, puisqu'il apparaît dans les années 1990. Plusieurs définitions ont été proposées dans la littérature et par des institutions nationales et internationales, ces définitions diffèrent sur plusieurs aspects mais elles s'accordent toutes sur le fait que la principale caractéristique de l'éco-innovation qui la différencie des autres types d'innovations, c'est sa contribution à la réduction des impacts environnementaux. Même si la définition de l'éco-innovation est encore en construction, il est possible de se référer à la définition la plus large de l'éco-innovation proposée par un rapport de la Commission européenne intitulé Measuring eco-innovation (Kemp and Pearson, 2007). Ce rapport définit l'éco-innovation comme suit :

« L'éco-innovation est la production, l'assimilation ou l'exploitation d'un produit, d'un procédé de production, d'un service, ou d'un outil managérial qui est nouveau pour l'organisation qui le développe ou l'adopte, et qui conduit, tout au long de son cycle de vie, à la réduction des risques environnementaux, de la pollution, et des autres impacts négatifs de l'utilisation de ressources (y compris de l'énergie) comparativement à d'autres alternatives pertinentes ».

Dans cette définition, une innovation ne nécessite pas d'être nouvelle sur le marché pour pouvoir être qualifiée d'éco-innovation, il suffit qu'elle soit nouvelle au niveau de la firme (cela concerne donc à la fois le développement et l'adoption de telles innovations). Cette définition inclut toutes les innovations qui sont bénéfiques pour l'environnement quelle que soit la motivation derrière leur développement ou leur adoption, en d'autres termes l'objectif environnemental n'est pas nécessaire a priori. Le champ de l'éco-innovation est ainsi élargi pour englober les innovations qui contribuent à l'amélioration de l'environnement de façon non intentionnelle. En effet, il existe des technologies dont la motivation principale n'est pas environnementale mais qui sont pourtant très bénéfiques pour l'environnement comme par exemple les vélos (Kemp et Arundel,1998) et le télétravail [START_REF] Djellal | Innovation dans les services et entrepreneuriaT : au-delà des conceptions industrialistes et technologistes du développement durable[END_REF]. C'est l'une des raisons qui rend la mesure de l'éco-innovation difficile. Par ailleurs, l'effet environnemental peut survenir non seulement pendant le processus de production mais aussi pendant toute la durée de vie d'un produit. En effet, il n'y a pas que la problématique de réduction de l'impact environnemental lors du processus de production d'un produit qui doit être prise en compte, dans certains cas, l'utilisation d'un produit consomme plus d'énergie que ce qu'il a fallu pour le produire.

La complexité du terme éco-innovation a une double origine : d'une part l'utilisation de plusieurs termes pour un même concept ce qui engendre le développement de diverses définitions. Et d'autre part, l'utilisation de termes différents pour des concepts qui se rapprochent. En plus d'« écoinnovation », plusieurs concepts ont été utilisés dans la littérature et par les institutions publiques, notamment, « innovation environnementale », « technologies environnementales », « cleantech », « technologies propres »… De nombreux auteurs n'établissent aucune distinction entre les deux concepts « éco-innovation » et « innovation environnementale » et les utilisent indifféremment [START_REF] Hellström | Dimensions of environmentally sustainable innovation: the structure of ecoinnovation concepts[END_REF]Ozusaglam, 2014). Dans cette thèse, nous ne ferons pas de distinction entre les deux concepts « éco-innovation » et « innovation environnementale » et nous utiliserons également le concept « technologies environnementales » pour désigner les éco-innovations technologiques.

Le concept d'éco-innovation ne doit pas être confondu avec le concept d'éco-conception qui est un sous ensemble de l'éco-innovation et qui se limite aux produits respectueux de l'environnement tout au long de leur cycle de vie (de la phase de production jusqu'à la fin de vie du produit).

L'éco-innovation peut être classée selon plusieurs critères. Diverses typologies ont été proposées dans la littérature. Elle peut être classée selon sa nature (classification inspirée du manuel d'Oslo (OCDE, 2005). L'éco-innovation peut donc être soit technologique soit organisationnelle.

L'éco-innovation technologique se scinde en deux catégories : l'éco-innovation de produit et l'écoinnovation de procédé. L'éco-innovation de produit désigne l'introduction de produits et services nouveaux ou améliorés comme par exemple la peinture naturelle sans solvant. L'éco-innovation de procédé regroupe l'ensemble des technologies qui permettent de réduire la quantité d'input (matériaux et énergie) utilisée. Une éco-innovation est dite organisationnelle dans le cas d'introduction de nouvelles méthodes d'organisation.

L'éco-innovation ne se limite pas à l'éco-innovation technologique et organisationnelle mais elle englobe aussi l'éco-innovation sociale et institutionnelle (Carrillo-Hermosilla et al., 2010;Hazarika and Zhang, 2019;Rennings, 2000). Les éco-innovations sociales sont celles qui entrainent un changement dans les modes de vie et le comportement des consommateurs. L'éco-innovation institutionnelle se traduit par une création de réseaux locaux, la mise en place d'institutions environnementales internationales, une meilleure prise de décision incluant la participation des citoyens… (Merlin-Brogniart, 2017).

L'éco-innovation peut aussi être classée selon l'ampleur du changement qu'elle peut engendrer, elle est soit incrémentale soit radicale. On parle d'éco-innovation incrémentale lorsqu'elle est incorporée à la fin d'un système de production. Elle permet de traiter en aval la pollution en la transformant en substances plus faciles à gérer. Elle n'introduit pas de changement significatif dans le processus de production. Les véhicules hybrides, les centrifuges, les épurateurs, les convertisseurs catalytiques, les équipements de désulfuration et les filtres constituent des exemples d'éco-innovations incrémentales. On parlera plutôt d'éco-innovation radicale dans le cas d'équipements et procédés totalement nouveaux qui provoquent un changement dans les modes de production, de consommation ou dans les modes de vie. Ces technologies permettent de réduire les émissions de polluants à la source et/ou d'utiliser des inputs moins polluants et/ ou d'utiliser moins de ressources.

On peut citer à titre d'exemple les smart grids qui sont des réseaux électriques intelligents susceptibles de transformer de façon radicale la façon dont l'électricité est produite, distribuée et consommée.

Lorsqu'on considère la définition large de l'éco-innovation introduite par la Commission européenne, il est difficile de trouver un indicateur pour la mesurer. Comme pour les innovations classiques, les brevets et la recherche et développement sont parmi les indicateurs les plus utilisés pour mesurer l'éco-innovation. Les brevets représentent une mesure de l'output de l'éco-innovation. Ils reflètent généralement les éco-innovations intentionnelles c'est-à-dire celles qui ont pour objectif principal de réduire l'impact environnemental. Par ailleurs, le plus souvent ils reflètent les innovations nouvelles au marché et non pas les innovations nouvelles à la firme. L'utilisation de cet indicateur restreint donc sensiblement la définition de l'éco-innovation et réduit donc son poids. La recherche et développement (R&D) est un indicateur de l'input de l'innovation. Il reflète généralement l'écoinnovation technologique et la R&D formelle. D'autres indicateurs de mesure possibles ont été recensés mais ils sont moins utilisés en raison notamment de la difficulté d'accéder à ces données : des mesures directes de l'output de l'innovation comme par exemple le nombre d'innovations et les ventes des nouveaux produits ainsi que des mesures indirectes de l'impact de l'éco-innovation à travers l'étude de l'efficacité énergétique, ou encore le nombre et le type de publications scientifiques (Arundel and Kemp, 2009;OCDE, 2015).

Objectifs de la recherche L'importance croissante accordée au rôle que peuvent jouer les entreprises dans l'introduction sur le marché de solutions contribuant à atteindre les objectifs du développement durable a conduit à l'intensification des recherches axées sur les déterminants de l'éco-innovation. La littérature sur cette question suggère que l'éco-innovation a une spécificité qui réside dans le fait qu'en plus de l'externalité de connaissance, elle génère aussi une externalité environnementale. Lorsqu'une entreprise adopte ou développe des éco-innovations, qui contribuent à réduire l'impact environnemental, elle supporte seule les coûts mais en fait bénéficier la société entière. Ainsi, l'éco-innovation évite une externalité négative et crée une externalité positive dont bénéficie l'ensemble de la population, elle engendre généralement des gains publics plus élevés que les gains privés. Rennings (2000) parle d'un problème de double externalité et souligne que cette spécificité peut réduire l'incitation des entreprises à écoinnover, de ce fait, d'autres facteurs incitatifs sont importants afin de stimuler l'investissement des entreprises dans ce type d'innovation.

Malgré la grande avancée des travaux de recherche dans ce domaine, certains aspects requièrent encore une attention particulière. En effet, les difficultés inhérentes à la mesure des aspects environnementaux d'une innovation ont créé des contraintes pour une collecte de données précises et fiables. Ce qui a conduit les chercheurs à se limiter à certains aspects empiriques de l'éco-innovation à partir des données les plus aisément disponibles. De ce fait, une recherche empirique plus approfondie est nécessaire, et c'est précisément l'objectif de cette thèse. Plus généralement, l'objectif principal de ce travail est l'étude des facteurs qui influencent le développement des éco-innovations par les entreprises françaises. Nous essayons de remédier aux lacunes observées dans la littérature en adoptant trois approches complémentaires.

En premier lieu, nous avons constaté que la plupart des études empiriques se focalisent sur un seul aspect de la relation qui met en lien d'un côté, les facteurs incitant les entreprises à développer une éco-innovation notamment la réglementation environnementale et, d'un autre côté, l'effet que pourrait avoir l'éco-innovation sur la productivité. En nous basant sur l'hypothèse de Porter selon laquelle une politique environnementale bien conçue entraîne un résultat gagnant-gagnant, en d'autres termes, une réglementation environnementale combinée avec des stratégies d'innovation aura pour effet une protection de l'environnement et un gain de productivité [START_REF] Porter | America's Green Strategy[END_REF][START_REF] Porter | Toward a new conception of the environment-competitiveness relationship[END_REF]Ramanathan et al., 2017;van Leeuwen and Mohnen, 2017), nous étudions à la fois les déterminants de l'éco-innovation et l'impact qu'elle peut engendrer sur l'activité de l'entreprise. Pour ce faire, nous construisons une base de données originale qui permet de remédier aux lacunes observées dans la littérature en termes d'information sur la R&D environnementale et la réglementation environnementale au niveau de l'entreprise.

Cette étude présentée dans le chapitre 1 est consacrée spécifiquement aux grandes entreprises. C'est pourquoi, dans un second temps, nous nous intéressons au cas des start-ups4 . Selon un rapport du Ministère de la Transition Ecologique et Solidaire de 2020, 99,7% des entreprises françaises sont des Petites et Moyennes Entreprises 5 (PME), parmi lesquelles une majorité de Très Petites entreprises (TPE) (3,67 millions). Les TPE et les PME occupent une place centrale dans l'activité économique étant donné leur part dans l'emploi salarié (46% des actifs) et leur part dans le chiffre d'affaires (elles produisent 1/3 du chiffre d'affaires total des entreprises françaises) [START_REF] Mtes | Le plan d'accélération de la transition écologique des TPE et PME[END_REF]. La politique publique en faveur du réchauffement climatique met l'accent sur le rôle que peuvent jouer ces TPE et PME dans le développement de l'éco-innovation (ADEME, 2018;[START_REF] Ec | Environmental Compliance Assistance Programme for SMEs -Environment -European Commission[END_REF][START_REF] Mtes | Le plan d'accélération de la transition écologique des TPE et PME[END_REF]. Au niveau européen, en 2014, un plan d'action en faveur de l'éco-innovation a été mis en place, il fournit une feuille de route pour soutenir les PME qui développent des éco-innovations. Au niveau français, un plan de la transition écologique des PME et TPE de 250 millions d'euros a été lancé, en juin 2020. L'objectif est d'aider les entreprises à intégrer l'environnement dans leur activité. Notamment à travers la mise en place de prêts verts garantis bonifiés et de prêts pour les économies d'énergies financés par l'ADEME. Par ailleurs, un label du coq vert pour identifier les entreprises vertes afin de promouvoir le partage d'expérience a été mis en place [START_REF] Mtes | Le plan d'accélération de la transition écologique des TPE et PME[END_REF].

La littérature empirique souligne également que l'éco-innovation peut être développée à la fois par les PME et grandes entreprises, cependant les PME innovent différemment [START_REF] Bos-Brouwers | Corporate sustainability and innovation in SMEs: evidence of themes and activities in practice[END_REF][START_REF] Hansen | The Role of an SME's Green Strategy in Public-Private Ecoinnovation Initiatives: The Case of Ecoprofit[END_REF]. La singularité des PME se matérialise à la fois par leurs avantages et inconvénients. L'avantage le plus important des PME dans le contexte de l'innovation est la flexibilité qui se traduit par leur capacité à répondre rapidement aux demandes des clients et du marché. De plus, leur caractère entrepreneurial caractérisé par un rôle central du fondateur (qui est aussi souvent dirigeant) comme générateur d'idées. Les inconvénients des PME comprennent les contraintes en termes de ressources financières. Nous avons pour but d'enrichir la littérature existante en considérant que les capacités entrepreneuriales jouent un rôle important dans la stimulation de l'éco-innovation. Dans ce contexte et afin d'orienter les politiques publiques, nous nous sommes intéressés aux start-ups éco-innovantes qui constituent un cas très peu étudié dans la littérature en raison principalement de la difficulté d'accès aux données. Pour remédier à ce problème, nous avons mené une enquête auprès des start-ups française, en 2019.

En troisième lieu, nous abordons l'un des aspects qui n'a pas été analysé dans la littérature, il s'agit des déterminants spatiaux de l'entrée des entreprises éco-innovantes. La principale contribution de ce chapitre est la constitution d'une base de données originale comportant des variables nécessaires pour l'étude de l'éco-innovation. Cela nous permet de remédier aux lacunes constatées dans la littérature sur les déterminants de l'éco-innovation : D'abord, en termes d'approche méthodologique en étudiant l'ensemble de la chaîne de causalité qui met en lien les déterminants de l'éco-innovation notamment la réglementation environnementale et l'éco-innovation, cela permet d'étudier dans un second temps le lien entre l'éco-innovation et la productivité. Ensuite, en termes de prise en compte de deux aspects : la réglementation environnementale au niveau microéconomique de l'entreprise et la R&D environnementale. De plus, nous supposons que les écoinnovations sont susceptibles de générer des effets hétérogènes sur la productivité selon leurs spécificités. De ce fait, nous considérons trois types d'éco-innovations : les technologies de traitement des déchets, les technologies d'énergie renouvelable et les technologies d'atténuation du changement climatique.

Chapitre 2 : Quels sont les déterminants de l'éco-innovation des startups françaises ?

Plusieurs études empiriques se sont intéressées à l'étude de l'amélioration des pratiques environnementales des entreprises existantes. Cependant, nous en savons bien peu sur la stratégie d'éco-innovation des start-ups naissantes. En effet, peu de travaux ont été consacrés à l'étude de ces problématiques dans le cas des start-ups naissantes. Les grandes entreprises ont l'avantage de disposer des ressources et du capital humain nécessaires pour mener à bien les activités de recherche et développement. En revanche, les startups sont caractérisées par une forte capacité d'introduction sur le marché d'innovations radicales (Demirel et al., 2019;[START_REF] Isaak | Globalisation and Green Entrepreneurship[END_REF] et elles font preuve de plus de flexibilité et d'adaptation aux changements qui surviennent dans leur activité (Aragón-Correa et al., 2008;[START_REF] Keskin | Innovation process of new ventures driven by sustainability[END_REF]. Elles ont donc un rôle primordial à jouer dans l'atteinte des objectifs de développement durable. Dans cette étude, nous remédions à cette lacune observée dans la littérature en nous penchant sur le cas des startups éco-innovantes.

Ce chapitre a pour double objectif, d'abord, d'étudier la spécificité des start-ups écoinnovantes comparées aux start-ups non éco-innovantes en se basant sur deux aspects : le capital humain et la politique environnementale. Ensuite, il vise à étudier la pluralité des profils parmi l'échantillon des start-ups éco-innovantes afin d'identifier les différents facteurs incitatifs à l'écoinnovation dans chaque catégorie de start-ups identifiée. Nous adoptons une approche similaire à celle des auteurs qui regroupent les déterminants de l'éco-innovation en facteurs internes et externes en se basant à la fois sur la théorie fondée sur les ressources et sur la théorie économique évolutionniste. Notre étude est basée sur la mise en oeuvre de méthodes d'analyse de données multidimensionnelles.

Dans un premier temps, nous avons utilisé un modèle décisionnel, à savoir une analyse discriminante (AD) sur notre échantillon de start-ups éco-innovantes et non éco-innovantes [START_REF] Celeux | Analyse discriminante sur variables qualitatives[END_REF]Huberty, 1994). Ensuite, une classification hiérarchique ascendante (HAC) [START_REF] Lebart | Statistique exploratoire multidimensionnelle[END_REF]Saporta, 2006) est utilisée pour établir une typologie des start-ups éco-innovantes en fonction de leurs caractéristiques entrepreneuriales. Nous contribuons à la littérature en apportant des clarifications sur différents points. D'abord, nous analysons la spécificité des entreprises éco-innovantes en considérant le thème du capital humain, ce qui nous permet de mettre en évidence l'importance des caractéristiques entrepreneuriales et notamment les connaissances accumulées par l'entrepreneur. Ensuite, nous établissons une typologie des start-ups éco-innovantes en cinq catégories. Nous identifions les principaux déterminants ainsi que le type d'éco-innovation (incrémentale, radicale, frontstage...) développé par chaque catégorie de start-ups. Nous complétons notre approche en prenant en compte les capacités entrepreneuriales des start-ups [START_REF] Casson | Entrepreneurship and the theory of the firm[END_REF]. A notre connaissance, cette étude est la première analyse empirique consacrée aux déterminants de l'éco-innovation dans le cas des start-ups. Enfin, notre étude se démarque par l'originalité des données et la diversité des variables considérées issues d'une enquête que nous avons menée auprès des entreprises françaises, en 2019. Nous nous basons sur la théorie de l'entreprenariat par les diffusions de connaissances « knowledge spillover » [START_REF] Acs | The knowledge spillover theory of entrepreneurship[END_REF][START_REF] Acs | The Knowledge Spillover Theory of Entrepreneurship[END_REF], ainsi que la théorie évolutionniste de l'économie géographique et notamment la théorie de la recombinaison de connaissances (Boschma, 2017). Cette approche stipule que les nouvelles idées sont générées par la recombinaison des idées existantes. Nous considérons la configuration locale de la base de connaissances à travers deux indicateurs : parenté technologique et variété technologique8 . L'indicateur « variété technologique » mesure le degré de diversification de la base technologique, alors que l'indicateur « parenté technologique » mesure le degré de complémentarité des technologies qui constituent la base de connaissances de chaque département.

Les

7 La recombinaison d'innovation fait référence à la manière dont les anciennes idées peuvent être reconfigurées afin de générer de nouvelles idées [START_REF] Castaldi | Related Variety, Unrelated Variety and Technological Breakthroughs: An analysis of US State-Level Patenting[END_REF]van den Bergh, 2008;Weitzman, 1998).

Dans cette étude, nous considérons les entreprises intensives en technologies environnementales qualifiées par Triguero et al. (2013) de leaders car elles investissent plus de 50% de leurs investissements d'innovation dans l'éco-innovation. Nous utilisons les données microéconomiques au niveau de l'entreprise pour définir l'entrée sur le département des entreprises écoinnovantes, mesurée par le dépôt du premier brevet environnemental. Ces données sont issues de la base de données de brevets REGPAT, associée à la base de donnée financière DIANE afin de prendre en compte les caractéristiques de l'entreprise, notamment sa date de création. Cette base de données Au terme de cette recherche, nous aurons apporté différentes clarifications empiriques à l'analyse des facteurs qui influencent l'attitude des entreprises françaises face à l'éco-innovation, notamment en étudiant à la fois les déterminants de l'éco-innovation et l'incidence de l'éco-innovation sur l'activité des entreprises et également par la prise en compte de facteurs spatiaux et des caractéristiques entrepreneuriales individuelles.

Chapitre 1

An assessment of the relationships between R&D, eco-innovation and productivity: Evidence from French firms9

Introduction

Due to climate change and the progressive degradation of the environment, green growth has become an important concern for many countries. Policy makers insist on the importance of making changes in the human mode of interaction with the environment as well as in the use of resources. In this respect, several national and international programs have now been devoted to sustainable development. In France, several laws and initiatives such us the road map for the circular economy (2018), the law on social and solidarity economy (2014) and the law on the energy transition for green growth (2015) have strengthened the organizations involved in the circular economy and the social economy. These sectors are considered to represent important potential for economic development as well as being an active field of recruitment. In fact, the green economy directly supported 4 million jobs (14% of total employment) in 2014, and production in eco-activities amounted to 85.7 billion euros (Commissariat général au développement durable, 2018).

In order to achieve the sustainable development objectives, more emphasis has been placed on innovation and in particular eco-innovation [START_REF] Ekins | Eco-innovation for environmental sustainability: concepts, progress and policies[END_REF]. The government has expressed its desire to support eco-innovation: aid supplied by ADEME (Agence de l'Environnement et de la Maîtrise de l'Énergie) in the context of its program of investments for the future (PIA 2010(PIA -2017) ) for funding energy transition and eco-innovation projects is estimated at 2.5 billion euros (582 million devoted to small-and medium-sized firms) (ADEME, 2018). In 2014, R&D expenditure by the public and private sectors on issues relating to the environment was estimated to be around 5.1 billion euros, equivalent to 11% of France's gross domestic expenditure on R&D. Of this expenditure, 51% was implemented by firms (compared with 20% in 2000) (Nisslé, 2017). France's eco-innovation performance is a little above the EU average and France is ranked 7th with an eco-innovation index10 of 112 (European Commission, 2018): it is classified among the European countries that are average eco-innovation performers. An important improvement can be seen compared to 2017, however, when it occupied the 13 th rank. This shows the considerable efforts France has made to shift towards more environmentally friendly practices. In this context, from a policy perspective it is important to analyse the impact of government efforts on the stimulation of the investment in eco-innovation by firms.

In a market economy characterized by fierce competition and profit maximization, firms adopt technologies that are likely to generate higher benefits. However, due to environmental constraints such as limited natural resources, environmental regulation and an increasing demand for green products, firms must take into account the negative externalities of their technology strategies.

Therefore, managing eco-innovation is becoming an increasingly important issue for firms.

As with all innovations, eco-innovation creates knowledge spillover. Given that technological learning is a public good, firms encounter difficulties in appropriating their innovations. A specific feature of eco-innovation is that, in addition to generating knowledge spillover, it also generates environmental spillover. When firms adopt or develop innovations that allow for the reduction of environmental impact, they bear the costs while all of society benefits from it. Therefore, environmental innovation allows firms to avoid negative externalities and to provide positive ones. Rennings (2000) has called this specific feature of eco-innovation "the double externality problem", and explains how this problem diminishes the incentives of firms to innovate.

The study of the impact of environmental regulation on firms' performance has attracted the attention of many researchers. For a long period, the negative effect on competitiveness has been the leading point of view for studies of environmental regulation. Indeed, when an additional constraint is imposed, firms face rising costs. Productive investments are thus discouraged due to the additional expenses incurred in reducing the negative externalities caused by pollution. This loss in competitiveness is followed by a decrease in exports. In the long run, we may expect the delocalization of firms (in particular those that are more polluting) to countries where environmental regulation is less restrictive. This is known as the 'pollution haven' hypothesis [START_REF] Copeland | Trade, Growth, and the Environment[END_REF]Letchumanan and Kodama, 2000). However, over the past two decades, several authors have criticized this point of view by developing a new approach known as the Porter hypothesis 11 . It states that welldesigned environmental regulation can stimulate innovation. It is precisely this innovation that will partially reduce or fully offset the compliance costs associated with the environmental regulation. A well-designed environmental policy leads to a win-win result: productivity gains and environmental protection [START_REF] Porter | America's Green Strategy[END_REF][START_REF] Porter | Toward a new conception of the environment-competitiveness relationship[END_REF][START_REF] Wagner | The link between environmental innovation, patents, and environmental management[END_REF].

This paper aims to study the drivers of eco-innovation and its effect on firms' performance using French firms data over the period 2006-2014. The contribution of this study is manifold. First, an extended CDM model that includes two types of R&D (environmental and non-environmental) is proposed in order to reveal any specificities of environmental innovation. Second, using data on patents, I consider different types of eco-innovation: renewable energy, waste management, and climate change mitigation technologies in production and transportation. Third, environmental regulation variables at the firm level are introduced as drivers of environmental innovation. Fourth, to our knowledge this is the first study to have explored this issue using recent data on French firms.

To meet these goals, patent data extracted from the REGPAT and HAN OECD databases are combined with financial firm data collected from the DIANE database and data on R&D drawn from the firm research expenditures survey. Data on environmental regulation and practices come from the community innovation survey (CIS). The analysis is based on an unbalanced panel sample of 7,136 French firms over the period 2006-2014.

The result show that there is an indirect positive contribution of environmental policy instruments in the form of taxes, fees or financial support on eco-innovation through their positive effect on environmental R&D. However, no significant effect is found for the productivity effect of eco-innovation. When differentiating between technology groups, only renewable energy reports a positive effect supported by the important role of subsidies. Despite the role of consumer demand 11 Jaffe and Palmer (1997) distinguish 3 versions of the porter hypothesis:

-The "narrow" Porter hypothesis: is based on the idea that instruments design is important. It argues that flexible environmental instruments stimulate innovation and thus are better than the other types of environmental policy instruments.

-The "weak" Porter hypothesis : refers to the positive effect of environmental regulations on eco-innovation -The "strong" porter hypothesis : suggest that environmental regulations prompt firms to create new innovations, the benefits generated by these innovations will allow to offset the costs of compliance. As a result, environmental regulations improve the economic performance of firms.

A number of studies have confirmed the link between environmental regulation and eco-innovation thus supporting the weak version of Porter's. However, there is little corroboration of the strong version of the hypothesis (see [START_REF] Lanoie | Environmental Policy, Innovation and Performance: New Insights on the Porter Hypothesis[END_REF].

for environmental products, the productivity effect of eco-innovation remains low. Thus, market incentives alone are not sufficient to enable a considerable increase in firms' environmental innovation. More efforts in terms of green promotion and environmental regulation will be important for such innovations to flourish.

The paper is organized as follows: Section 2 provides a literature review. Section 3 outlines the data. Section 4 introduces the model and the variables. Section 5 presents the results, while Section 6 concludes.

Literature review

Within the measuring eco-innovation report, eco-innovation is defined as "the production, assimilation or exploitation of a product, production process, service or management or business method that is novel to the organization (developing or adopting it) and which results, throughout its life cycle, in a reduction of environmental risk, pollution and other negative impacts of resources use (including energy use) compared to relevant alternatives" (Kemp and Pearson, 2007). According to this definition, an innovation does not need to be new to the market to be described as an ecoinnovation; it is sufficient that it is new to the firm (development or adoption). This definition includes all innovations that are beneficial to the environment regardless of the motivation behind their development or adoption (in other words an environmental objective is not defined a priori); the field of eco-innovation is extended to include innovations that contribute unintentionally to protecting the environment (bikes and insulation materials, for example). Yet when considering this broad definition of eco-innovation, it is hard to find an indicator that is suitable for measuring it. As with classical innovations, patents and research and development are among the most widely used indicators for measuring eco-innovation. Research and development (R&D) is an indicator of the input of innovation, generally reflecting technological eco-innovation and formal R&D. Patents are essentially a measure of the output of eco-innovation, and have several advantages, including the availability of disaggregated data from several countries over several years and the possibility of dividing them into several categories (using IPC or CPC classification). Although they provide detailed information on innovations, however, the use of patents can be contested. Some innovations are indeed not patentable, while other means such as secrecy can be used by firms to protect their innovations. Large firms are more likely to use patents than small firms. Generally, only technological eco-innovation is patented, so patents cannot measure organizational and social eco-innovation. Patents generally reflect intentional eco-innovations (those whose main objective is to reduce the environmental impact).

Moreover, most often they reflect innovations that are new to the market but not innovations that are new to the firm. The use of this indicator therefore restricts the definition of eco-innovation; yet despite these shortcomings, patents are a widely used indicator in the literature as a measure of ecoinnovation (among others, see [START_REF] Brunnermeier | Determinants of environmental innovation in US manufacturing industries[END_REF][START_REF] Jaffe | Environmental Regulation and Innovation: A Panel Data Study[END_REF]Marin, 2014;[START_REF] Marin | Productivity effects of eco-innovations using data on eco-patents[END_REF][START_REF] Soltmann | The Performance Effect of Environmental Innovations[END_REF].

The eco-innovation effects of environmental regulation (the weak version of the Porter hypothesis) have attracted increased attention among economists, these studies in general revealing a significantly positive effect of environmental regulation on eco-innovation (R&D or patents). The measurement of environmental regulations is a crucial issue in this line of research. The "Pollution Abatement and Control Expenditures" (PACE) measure is used as the proxy variable in many existing studies (among others, see [START_REF] Berman | Environmental Regulation And Productivity: Evidence From Oil Refineries[END_REF][START_REF] Brunnermeier | Determinants of environmental innovation in US manufacturing industries[END_REF][START_REF] Jaffe | Environmental Regulation and Innovation: A Panel Data Study[END_REF].

In studying the effect of PACE on innovation (R&D and patents (without differentiating environmental and non environmental patents)) in US firms, [START_REF] Jaffe | Environmental Regulation and Innovation: A Panel Data Study[END_REF] found that PACE has a positive impact on R&D, but that the effect on patents is not significant. In the same case, [START_REF] Brunnermeier | Determinants of environmental innovation in US manufacturing industries[END_REF] found that PACE has a positive but low effect on eco-patents.

The same positive sign can also be found in Kneller and Manderson (2012) for the case of the UK industries. They provided some further insights by showing a crowding out effect of environmental R&D on non-environmental R&D. Investments that may be dedicated to non-environmental innovation are reallocated to environmental innovation. Some authors have focused on the stringency of environmental regulations and the impact of different types of instruments (the narrow version of the Porter hypothesis). In this sense, marketbased and flexible instruments such as emissions taxes, tradable allowances or performance standards, are more favorable to innovation than command-and-control instruments such as technological standards [START_REF] Lanoie | Environmental Policy, Innovation and Performance: New Insights on the Porter Hypothesis[END_REF]Xie et al., 2017). By focusing on the case of renewable energy at country level, [START_REF] Johnstone | Renewable Energy Policies and Technological Innovation: Evidence Based on Patent Counts[END_REF] showed that environmental policy instruments play a key role in stimulating environmental innovation. However, the impact of different types of instruments varies depending on the type of renewable energy: renewable bonds only have an effect on wind patents, and feed-in tariffs have a positive impact on solar patents.

A range of empirical studies have been devoted to testing the effect of environmental innovation on firm performance, without, however, looking at the cause of this variation in performance (whether it is linked to innovation or to another cause).

Different measures of firm performance are used, including Tobins' q index [START_REF] Dowell | Do Corporate Global Environmental Standards Create or Destroy Market Value?[END_REF]Lee and Min, 2015), returns on sales [START_REF] Ghisetti | Environmental innovations and profitability: how does it pay to be green? An empirical analysis on the German innovation survey[END_REF], and productivity [START_REF] Soltmann | The Performance Effect of Environmental Innovations[END_REF]. Those studies provided a very mixed picture on the signs of this relationship. Some papers highlight a negative impact of environmental regulation on productivity. For instance, [START_REF] Soltmann | The Performance Effect of Environmental Innovations[END_REF] show that the stock of environmental patents is negatively correlated with productivity. The relationship between environmental innovations and productivity is U-shaped.

Innovations are beginning to have a positive effect on performance from a number of 3014 patents.

Focusing on the case of European firms, Kruse (2016) finds a negative effect of green energy innovations on firms' performance. This negative effect is more pronounced for larger firms. Other authors find more positive results. [START_REF] Colombelli | Eco-Innovation and Firm Growth: Do Green Gazelles Run Faster? Microeconometric Evidence from a Sample of European Firms[END_REF] show that eco-innovation triggers firm growth; firms that adopt eco-innovation have higher growth rates of sales than firms adopting classical innovation. Similarly, Lee and Min (2015) find evidence that eco-innovation has a positive effect on Japanese manufacturing firms' financial performance. Some authors raise the question that it is no longer whether it does pay to be green, but rather how it pays to be green [START_REF] Ghisetti | Environmental innovations and profitability: how does it pay to be green? An empirical analysis on the German innovation survey[END_REF]. [START_REF] Ghisetti | Environmental innovations and profitability: how does it pay to be green? An empirical analysis on the German innovation survey[END_REF] studied this question for the case of German firms: they point out that energy efficiency innovations positively impact firms' performance, while innovations allowing the reduction of negative externalities (such as air, water, noise and soil pollution) have a negative impact on firms' performance.

A relatively large number of studies have been conducted on another line of research that directly relates environmental regulation to productivity (the strong Porter hypothesis). Overall the results are also mixed, and there is no universal consensus on this issue (see [START_REF] Albrizio | Empirical Evidence on the Effects of Environmental Policy Stringency on Productivity Growth[END_REF], for a recent review). For example, using detailed production data from nearly 1.2 million US plant observations, Greenstone et al. (2012) found that total factor productivity declines by 4.8 percent for polluting plants in strictly regulated counties compared to weakly or unregulated counties. This corresponds to an annual economic cost from the regulation of manufacturing plants of roughly $21 billion (in 2010 dollars), or 8.8 percent of average manufacturing sector profits over the period of analysis. [START_REF] Wang | Environmental regulation and green productivity growth: Empirical evidence on the Porter Hypothesis from OECD industrial sectors[END_REF] analysed this relationship at country level using an environmental policy stringency index that allowed them to take into account different types of environmental instruments and to include different levels of environmental regulation stringency at the country level. They report that environmental regulation is positively correlated with green productivity growth. However, when the stringency of environmental regulation policy rises over a certain extent, it changes to an adverse effect on green growth, revealing an inverted U-shaped relationship.

However, several articles in the literature have used reduced form models (single equation models) by testing one part of the relationship that links environmental regulation to productivity, and most of them focused on macro-and meso-economic data (country-and industry-level approaches).

There is still a relative scarcity of articles aimed at investigating the whole chain of the relationship by studying the indirect effect of environmental regulation on productivity through eco-innovation at firm level (the strong Porter hypothesis). Among the authors who have used complete form models we can mention [START_REF] Lanoie | Environmental Policy, Innovation and Performance: New Insights on the Porter Hypothesis[END_REF], [START_REF] Doran | Regulation and firm perception, eco-innovation and firm performance[END_REF], Marin (2014), [START_REF] Marin | Productivity effects of eco-innovations using data on eco-patents[END_REF], Van Leeuwen and Mohnen (2017) and Dussaux (2015). Lanoie et al. (2010) combine the different approaches by assessing, for the first time, the whole causality chain that links environmental regulatory stringency to eco-innovation and firm performance. They argue that environmental regulation induces eco-innovation. This latter has a positive effect on firm performance. This provides evidence for the strong form of the Porter Hypothesis, that environmental regulation spurs ecoinnovation which further enhances firm performance. However, environmental regulation has a direct negative effect on business performance. They conclude that the positive effect of innovation on firm performance does not outweigh the negative effect of the environmental regulation. Focusing on the case of Irish firms, [START_REF] Doran | Regulation and firm perception, eco-innovation and firm performance[END_REF] find that existing and expected regulations and government incentives in the form of grants positively impact the firm's inclination to engage in eco-innovation.

Firms that introduce eco-innovation have higher levels of turnover per employee than firms that innovate in classical innovations and firms that do not engage in innovation activity. Some authors have used administrative data (balance sheet and income statements) which gives more objective and standardized information (Marin, 2014). The main findings, for the case of Italian firms, suggest a crowding-out effect of green innovations on non-green innovations in the short run, where the crowding-out effect is more pronounced for polluting firms. Moreover, Marin (2014) shows that environmental innovations have no significant effect on firms' performance, while non-environmental innovations have a positive effect. [START_REF] Marin | Productivity effects of eco-innovations using data on eco-patents[END_REF] carried out a similar analysis using patent data and survey data, as we also do in our case. They conclude that both environmental and nonenvironmental innovations have a positive effect on firm performance. However, environmental innovations display a lower return compared to non-environmental innovations. Furthermore, their results also confirm the crowding-out of environmental innovation relative to non-environmental innovation. Focusing on the case of Netherlands, Van Leeuwen and Mohnen (2017) find that there is a significant positive contribution of environmental regulation and environmental levies (total environmental exploitation costs) on eco-innovations. Moreover, he finds that resource-saving ecoinnovations increase productivity, whereas pollution-reducing eco-innovations tend to reduce productivity. Therefore, eco-innovation globally does not turn out to be significant. Environmental regulations, if properly aimed at process-integrated eco-innovations, can have a positive effect on TFP. Dussaux (2015) raises this question for the case of French firms, arguing that environmental regulations lead to the development of more green innovation, but that environmental innovation doesn't lead to productivity gains. He finds robust evidence that higher environmental effort leads to a change in the direction of innovation towards cleaner technologies but has no effect on the scale of innovation.

The studies described above differ considerably in their method, geographical scope and the indicators used as proxies for the variables. The results are mixed and the relationship remains ambiguous. In line with the empirical literature I analyze the effect of environmental innovation on firm performance using data on French firms. I adopt a different approach to Dussaux (2015), however, by using an extended CDM model to test the indirect effect of environmental regulation on productivity. As suggested by Dussaux (2015), patent data-namely, indicators of actual innovation which provide information about specific technologies-are used in this article, so as to deal with the possible bias due to the fact that the data from the CIS are the firm's "perceived" effects of their innovation. Contrary to Van Leeuwen and Mohnen (2017) who used a model similar to mine, I use patent data as proxy for environmental innovation, which allows me to consider different types of environmental innovation (waste management, climate change mitigation technologies in transportation and production and renewable energy technologies). I also consider non-environmental research and development as a factor that plays an important role to spur eco-innovation under the assumption of complementarity between non-environmental innovation and environmental innovation. Voluntary initiatives of firms (such as certification) towards the protection of the environment, and market demand of consumers for environmental products and processes, are also considered. In contrast to Marin (2014), I consider environmental regulation and market forces at firm level, and this study is based on a more accurate measure of R&D achieved by also considering the environmental R&D. In addition to the two latest stages of the CDM model considered by [START_REF] Doran | Regulation and firm perception, eco-innovation and firm performance[END_REF], I also consider the first-stage testing for the drivers of the decision to perform R&D and its intensity.

Data

Presentation of data

The sample was constructed by merging 4 firm-level databases: REGPAT, DIANE, CIS survey and R&D survey.

(a) Patent data comes from the REGPAT database released by the Organisation for Economic Cooperation and Development (OECD), which is derived from the Worldwide Patent Statistical Database "PATSTAT" maintained by the European Patent Office (EPO). Careful consideration must be given to the extraction of data. Indeed, some characteristics of patents may induce multiple counting when associating patents to a country, technology or company; for example, several patents may be the result of a collaboration between more than one inventor or applicant. Similarly, patents can be protected in more than one country. Many alternatives are available for patent counts according to the question we want to answer.

In this article, simple patents count is used as a proxy for innovation; a dummy variable is built from this proxy. The patent count is conducted using specific selection criteria. Only the patents filed in the EPO12 by companies located in France are considered. Applicant count rather than inventor count is chosen because environmental innovation is specific for each country and depends on the country where the firm is located. Inventor's country reflects the country of origin of inventions while the applicant country reflects the ownership of inventions and gives an idea concerning the innovative performance of firms in a given country. The application priority year13 is used as year of reference, since it is reasonable to suppose that this is the closest date to the date of invention. Firms that filed at least one patent in the study period are considered. Data was extracted for both non-environmental innovations and environmental innovations.

In REGPAT, patents are assigned to technological fields using two types of classification: the International Patent Classification (IPC) and the Cooperative Patent Classification (CPC). Until now, most studies have been based on the IPC code (Kruse, 2016;Marin, 2014). When using this method one should look for green patents in different classes: several authors have explained how to combine these codes to select the patents belonging to a specific technological field (Dechezlepretre et al., 2011;[START_REF] Johnstone | Renewable Energy Policies and Technological Innovation: Evidence Based on Patent Counts[END_REF]. This data retrieval method carries two risks: selecting irrelevant patent applications and excluding relevant patent applications (Veefkind et al., 2012). Since 2013, the EPO has completed the CPC classification which includes a classification dedicated to applications related to green technologies (see table 1). A search strategy based on CPC is an easier and more reliable way to select green patents (for more details, see Veefkind et al., 2012). In our study, patents are attributed to technological fields according to CPC codes. I consider the main CPC codes and all their subclasses. It is worth mentioning that a patent can be attributed to more than one CPC code and some patents don't have a CPC code. (c) R&D data comes from the annual firm research expenditures survey conducted by the French Ministry of Higher Education, Research and Innovation. All companies employing at least one fulltime equivalent researcher are included in the survey. The R&D survey is designed so that very large companies participate in the survey for the entire period. However, medium and small businesses are selected by sampling. All companies likely to have an R&D activity selected through the information provided by the various administrative procedures (such as the National Agency for the Valorization of Research (AVNAR)) are investigated at least once. The expenses used here are intramural R&D expenses. 15 The target for this survey is the availability of the share of R&D devoted to the environment.

(d) Environmental regulation variables come from the French Community Innovation Survey (CIS), conducted every two years since 1994. The aim of the CIS survey is to provide a comprehensive survey of the innovation performance of French firms. The survey is conducted as part of the European wide community innovation survey project.

Data matching procedure and descriptive statistics

The construction of the sample was quite laborious. First, the full version of DIANE was merged with a smaller version of the REGPAT database that contains all the firms that filed at least one patent over the study period. Due to the absence of a common identifier between the REGPAT database and the other databases, the name and the address of companies were used to merge these two datasets. Company names and addresses are not uniform and there are spelling differences. In this case, harmonization of names and addresses was necessary. The OECD's Harmonization of Applicant Names (HAN) dataset is used to address some of these difficulties. After that, the two datasets were matched using an algorithm. The result of the firm matching was checked manually, on a case-by-case basis.

14 I used the 2017 version.

Then the resulting sample was added to the R&D database. I considered all companies that responded to the R&D survey at least once during the data availability period (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). Companies that do not have R&D activity were selected by considering companies that do not appear in the R&D database during the entire data availability period (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015). These null values were checked using the CIS survey data, which includes a question on whether the company is engaged in research and development activities. Some companies that participated in the R&D survey and were not recorded in the DIANE database were not taken into account because of the unavailability of certain financial data.

R&D activities in groups of firms are very dependent on their general organization. In some cases groups set up a research center that carries out all the research activity of other companies of the group in France or in the world. In addition, some companies provide answers that include the research activities of several subsidiaries of the group. Those firms are deleted from the dataset. After doing so, the resulting sample was completed by information on environmental regulations from the CIS database. However, information on environmental regulation is only available for two waves The selected sample is an unbalanced panel that contains observations for 2,877 firms over the period 2006-2014. Table 1.1 andtable 1.2 report some descriptive statistics and the distribution of the observations for the unbalanced sample. It is worth noting that the sample is essentially made up of large16 firms (97.11%); 25.51% of the firms are involved with environmental R&D but only 6.88% of firms have filed at least one patent and even fewer have filed an environmental patent (2.81%). Another interesting point concerns the fact that the majority of firms that have filed an environmental patent have also undertaken environmental R&D activity. Almost 50% of the firms are subject to environmental regulation and 4.86% of these firms are especially involved with the European Union Trading Scheme. Firms that filed at least one non-environmental patent 6,88 Small and medium firms 2,88

Firms that filed at least one environmental patent 2,81 Firms with environmental R&D and subject to environmental regulation 17,37

Firms subject to environmental regulation 46,40 Firms subject to the European union emission trading scheme (EU ETS) 4,86

Empirical methodology

The econometric analysis is based on a modified version of the CDM model proposed by [START_REF] Crépon | Research, Innovation And Productivity: An Econometric Analysis At The Firm Level[END_REF] to explain the relation between innovation and productivity by linking the input of innovation (R&D) to the output of innovation (in the present study, patents) and innovation to productivity. The advantage of the structure of the model is that it allows us to handle two problems inherent in innovation data: selection bias and endogeneity. The selection bias problem is due to the truncation of innovation data. Input innovation data (research and development, cooperation, etc.) is only reported by firms that have actually carried out innovation activities. So it is necessary to correct for data truncation. Moreover, the decision to undertake R&D activity is simultaneously determined by other factors, which may in turn be affected by R&D. The CDM model is a sort of instrumental variable approach intended to correct these two flaws. This approach comprises three stages formalized by three simultaneous equations: R&D decision and intensity, the innovation equation, and the productivity equation.

R&D decision and intensity of investment

The purpose of this first stage is to model the decision of firms concerning whether to undertake R&D investment, and then to determine the intensity of this investment. I test different specifications of the model by considering the total R&D, the environmental R&D and the non-environmental R&D. The equations for each type of input have the form of a generalized Tobit type II model. The two-step Heckman method [START_REF] Heckman | Sample Selection Bias as a Specification Error[END_REF][START_REF] Heckman | The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models[END_REF]) is adopted to estimate the model; it consists of estimating the decision equation by a dichotomous Probit, and then constructing the Mills ratio from the first stage estimator and introducing it into the regression of the R&D intensity. It is specified as follows:

𝐷_𝑅𝐷 𝑖𝑡 = { 1 𝑠𝑖 𝑅𝐷 𝑖𝑡 * = 𝑥 𝑖𝑡 𝛽 + 𝜀 𝑖𝑡 > 𝑐̅ 0 𝑠𝑖 𝑅𝐷 𝑖𝑡 * = 𝑥 𝑖𝑡 𝛽 + 𝜀 𝑖𝑡 ≤ 𝑐̅ (1)
Where i indexes the firm, t indexes the year. D_RD it is a dummy variable that takes the value 1 if the firm i reports a positive R&D, 𝑅𝐷 𝑖𝑡 * is a latent indicator variable such that a firm decides to undertake R&D investment if this indicator is above a given threshold 𝑐̅ which represents an unobservable expected returns on R&D, and x it represent the explanatory variables. I employ the following variables: the firm's market share (MARKET_SHARE), defined as the ratio between the firm's total sales and the total sales of all the firms belonging to the same sector (this variable is built from a threedigit NACE classification). The Labor (L), defined as the number of employees in logarithm, where larger firms are expected to have greater means and incentives to invest in eco-innovation. The physical fixed capital (K), defined as the intangible fixed assets (expressed per employee and in logarithm). Government can internalize the environmental externalities by imposing environmental regulations; therefore, existing or anticipated environmental regulation 17 (REGULATION) is considered. The importance of market forces is also tested by including the variable current or expected market demand from customers for environmental innovations (ENV_DEM). Year dummies control for changes in the propensity to patent through time. Sector dummies control for sector-specific propensities to apply for a patent. 𝜀 𝑖𝑡 is an error term.

The intensity equation is given by:

𝐼_𝑅𝐷 𝑖𝑡 = { 𝐼_𝑅𝐷 𝑖𝑡 * = 𝑥 𝑖𝑡 𝛽 + 𝜀 𝑖𝑡 𝑠𝑖 𝐷_𝑅𝐷 𝑖𝑡 = 1 0 𝑠𝑖 𝐷_𝑅𝐷 𝑖𝑡 = 0 (2)
I_RD it * is the unobserved latent variable representing the firm's R&D intensity. The explanatory variables 𝑥 𝑖𝑡 are the same as that in equation ( 1). A dummy variable Age is added, which takes the value 1 if the firm is older than 10 years. It is worth noting that subsidies or other financial incentives for environmental innovation are a key variable for this step, assuming that a firm's age and subsidies affect the intensity of R&D and not the probability of performing R&D. The predicted values of R&D (I_RD it * ) are introduced in the second equation presented below.

The innovation equation

As a second stage, the relationship between the input of innovation and the output of innovation is analyzed. Environmental patents (ENVP) are used as a proxy for eco-innovation. Due to the fact that less than 1% of the firms have filed more than 1 environmental patent, the dependent variable is a 17 We were unable to separate public policy instruments into command and control instruments and economic instruments due to the structure of the 2008 CIS survey questionnaire. The variable is constructed from the question below which combines these two instruments in the same answer. Therefore, the variable that we have named regulation combines the two instruments.

During 2006 to 2008, did your enterprise introduce an environmental innovation in response to:

-Existing environmental regulations or taxes on pollution.

-Environmental regulations or taxes that you expected to be introduced in the future -Availability of government grants, subsidies or other financial incentives for environmental innovation -Current or expected market demand from your customers for environmental innovation binary variable equal to 1 if the firm has filed at least one environmental patent and 0 otherwise. Three types of environmental patents are considered: renewable energy (D_RENW), waste management (D_WM), and climate change management technologies in transportation, production and building (D_CCMT). When two (or more) correlated binary response variables are regressed on explanatory variables, two (or more) logistic regressions could be performed, but this is inefficient since the relationship between the two (or more) binary response variables is not utilized (Lesaffre and Molenberghs, 1991). The multivariate probit model fully exploits the correlation structure and thus offers a valuable solution to the problem. Even with a binary dependent variable, where the application of a classical logistic or probit regression seems natural, the bivariate (or multivariate) probit model may sometimes be preferable (Poirier, 1980). Because of the possible correlation between the different types of eco-innovation, a multivariate probit model is used for the estimation of the patent equation.

The equation is specified as follows:

𝐸𝑁𝑉𝑃 𝑖𝑡 = 𝛼 𝐼_𝑅𝐷 𝑖𝑡 * + 𝛾𝑤 𝑖𝑡 + 𝜀 𝑖𝑡 ( 3
)
I_RD * is the predicted R&D obtained from the first stage. 𝑤 𝑖𝑡 represents a vector of explanatory variables. I use the same classification of the drivers of eco-innovation as Horbach (2008). In this respect, I distinguish 3 sets of factors: technology push, demand pull, and regulatory push. Technology push drivers of eco-innovation mainly relate in this study to "internal technological" capabilities by means of the predicted R&D intensity from the first step. Innovation input is expected to have a positive effect on patents (Horbach, 2008;Mansfield, 1986). Demand pull drivers relate to current or anticipated demand from consumers for environmental innovations (ENV_DEM) and the environmental awareness of firms by setting up procedures to regularly identify and reduce their environmental impacts (ENV_INITIATIVE) (for example, preparing environmental audits, setting environmental performance targets, obtaining ISO 14001 certification, etc.). The role of Environmental Management Systems (EMS) such as the ISO14001 standard in promoting innovation has been investigated in several studies (among others, see [START_REF] Rennings | The influence of different characteristics of the EU environmental management and auditing scheme on technical environmental innovations and economic performance[END_REF][START_REF] Wagner | The link between environmental innovation, patents, and environmental management[END_REF].

Regulatory push drivers relate to the existence of government subsidies or other financial incentives for environmental innovations and existing or anticipated environmental regulation. The size of the firm (number of employees) and time dummies are also included. 18

18 Because of multicolinearity problems, sector dummies are not considered in this stage.

The productivity equation

The last stage is devoted to estimating the indirect impact of environmental regulation on productivity via the effects of eco-innovation. A Cobb-Douglas production function is used. The equation is extended by adding the innovation variable (ENVP it * ) predicted in the second stage.

𝑙𝑛𝑉𝐴 𝑖𝑡 = 𝑙𝑛𝐴 + 𝛼 𝑙𝑛 𝐿 𝑖𝑡 + 𝛽𝑙𝑛𝐾 𝑖𝑡 + 𝛾 𝑙𝑛𝐸𝑁𝑉𝑃 𝑖𝑡-1 * + 𝜀 𝑖𝑡 (4)
Where VA it is the labor productivity (value added per employee and in logarithm). K it is the capital (fixed physical assets per employee and in logarithm). Since the innovation output measures are endogenous, I instrument them by the predicted latent eco-innovation variable from the second stage (see e.g. [START_REF] Wooldridge | Econometric Analysis of Cross Section and Panel Data[END_REF]. 19 ENVP it-1 * is a lagged one-period variable. 20 I consider that there is a time delay between innovation and its impact on productivity. I also include the log of employees, the coefficient of which captures deviations from constant returns to scale, 21 and I control for industry and year dummies. This final equation is estimated with a fixed effect model. Two specifications are tested for: on the one hand, I consider all the environmental patents; on the other, I distinguish between different categories of environmental innovation predicted from the second stage (renewable energy, waste management and climate change mitigation technologies).

To sum up, the model is made up of equations ( 1), ( 2), ( 3), (4) which are estimated sequentially.

Following [START_REF] Griffith | Product Market Reform and Innovation in the EU*[END_REF], the estimates are made for all firms, not just those that innovate.

19 As a robustness check I also account for the endogeneity of the traditional inputs by instrumenting them by their lagged values.

20 Other lags are tested for but are not considered because of a drop in the number of observations. ) + (𝛽 + 𝛼 -1)log (𝐿). Under the assumption of constant returns to scale, the parameter for log (L) should be zero (𝛽 + 𝛼 = 1). The same concept applies with more than two inputs (Marin, 2014).

Results

Table 1.3 reports the results of equations ( 1) and ( 2) (the R&D selection and intensity equations) at the first stage. Three specifications are tested: columns (a), (b) and (c) present respectively the results for total R&D, non-environmental R&D and environmental R&D22 .

In the intensity equations, we observe that the inverse Mills ratios are significant, showing the importance of taking the selection problem into consideration. Firm size measured by number of employees has a positive impact on the decision to undertake R&D (for all the specifications). Thus the larger the firm, the more likely it is to engage in R&D activities. Bigger firms are more likely to be willing to incur the initial sunk costs of R&D activities, they have easier access to credit, and they are more capable of bearing the risk related to R&D investments whose returns are highly uncertain Marin (2014). In line with the results provided by Hall et al. (2013) and [START_REF] Marin | Productivity effects of eco-innovations using data on eco-patents[END_REF], conditional on investing, R&D intensity decreases with size.

Fixed physical capital doesn't affect the decision to invest in R&D except the case of environmental R&D. By contrast, it has a positive and significant effect on the intensity of environmental and non-environmental R&D. This indicates that in case a firm chooses to invest in R&D, a complementarity between R&D and total assets seems to arise.

Market share is negatively related to the probability of performing R&D (except for environmental R&D where the effect is insignificant) whereas it positively affects its intensity. This result is in line with Marin (2014), who argues that firms holding a dominant market position have little incentive to innovate and prefer to defend their dominant position rather than explore new markets or change their production technology. On the other hand, however, once they decide to innovate, there is strong incentive to exploit the current large customer base. The variable Age is significantly negative in intensity equations of total and environmental R&D, meaning that older firms have a lower probability of performing R&D (except for the case of environmental R&D, which shows a non-significant effect).

Environmental R&D reacts more strongly to environmental subsidies and environmental regulation. In fact, current or anticipated environmental regulation has a strong positive effect on the probability of performing environmental R&D, and the existence of government subsidies or other financial incentives for environmental innovations has a strong positive effect on the environmental R&D intensity (these two variables don't have a significant effect on non-environmental R&D).

Current or anticipated demand from consumers for environmental innovations has also a strong positive effect on the decision to undertake environmental R&D. This variable has also a positive effect on total R&D. I conclude from this stage that environmental policy instruments in the form of taxes, charges, fees or subsidies have a positive effect on the environmental R&D investment.

This third stage looks at the productivity impact of different types of eco-innovations. The results are shown in table 1.5. Firms are characterized by strong increasing returns to scale (the log of employee count is significantly greater than zero and its magnitude is quite relevant). I test different specifications of the variable predicted environmental patents by considering: total environmental patents (column (a)), different types of environmental patents estimated in one productivity equation (column (b)), and different types of environmental patents estimated separately (columns (c), (d), (e)).

The coefficients on employment and total assets are both positive and statistically significant.

This result is in line with general expectations. 23 No significant effect is found for the total predicted environmental patents (column (a)).

The productivity effect of renewable energy patents is positive and significant. The positive return of this kind of eco-patent, when compared to the null effect of environmental patents as a whole, may be driven by the key role of subsidies and other government financial grants (such us feedin tariffs), as found in the results of the second step.

Climate change mitigation technologies show a negative effect in column (b). The waste management predicted patents are not significant for all specifications. The results show that there is an indirect positive contribution of existing or anticipated environmental regulation on eco-innovation through its positive effect on environmental R&D. No significant effect is found for this variable in the case of eco-innovation output (environmental patents). Climate change mitigation technologies in production, transportation and building are essentially driven by consumer market demand for environmental innovation and the voluntary initiatives of firms to regularly identify and reduce their environmental impacts.

Subsidies or other government financial incentives play a key role in stimulating investment in renewable energy technologies, another factor that contributes strongly to the flourishing of this kind of eco-innovation is the market demand for environmental innovation. A weak effect is observed for existing or anticipated regulation indirectly through environmental R&D but an insignificant effect is observed directly through their positive effect on environmental patents.

Looking at the productivity effect of all environmental patents, on the other hand, I find a non-significant effect. I also find evidence for different productivity effects across environmental technologies. A positive effect on productivity is only observed for firms engaging in renewable energy, while a negative effect is observed for climate change mitigation technologies in the short run (one-year lagged variables). The positive effect of renewable energy is supported by government subsidies and other financial incentives and consumer market demand for environmental innovation.

Introduction

In a context of natural resource scarcity, environmental degradation, and wasteful consumption, urgent solutions are required. Governments and society have recognized the crucial role of sustainability. To this end, achieving the objectives of sustainable development involves the participation of all economic actors. Companies are also participating in these environmental and societal changes, and respond to social demand by developing processes and solutions capable of offering greener goods and services. These changes are an important strategic concern for manufacturers in terms of positioning their products in highly competitive markets. Some companies are looking to take advantage of the niche opportunities created by the transition towards sustainable development: indeed, an OECD (2010) survey revealed that environmental challenges are seen in many countries as a new opportunity for competitiveness. In addition, in the medium term, the scarcity of fossil resources and the increase in carbon taxation will lead to an increase in the prices of these resources, which might weaken the competitiveness of actors and territories not prepared for transition (Deboutière and Georgeault, 2015).

We notice, therefore, the emergence of new, more resilient and future-oriented economic models. Among these models, we can cite the circular economy, the collaborative economy or the functionality economy (ADEME, 2004). The production system has begun to adopt greener and renewable energy sources. Green entrepreneurship is also an actor in this transition. In this context, the introduction and adoption of new products, processes and practices that reduce or limit environmental impacts can play an important role [START_REF] Geels | Ontologies, socio-technical transitions (to sustainability), and the multi-level perspective[END_REF]. Thus, given that they allow the development of solutions which induce a positive impact on at least one of the fundamental pillars of sustainable development, i.e. the economy (energy efficiency for example), society, and the environment, start-ups must be a major concern of public policy and innovation policy. Start-ups have features that make the development of eco-innovations easier: they have the flexibility to adapt to changes in their activity (Aragón-Correa et al., 2008;[START_REF] Keskin | Innovation process of new ventures driven by sustainability[END_REF], they display a spirit of risk taking, and they also have a key role in bringing radical innovations to the market and in developing breakthrough eco-technologies (Demirel et al., 2019). But so far, we know very little about the distinctiveness of the innovation strategy of this type of firms.

At the European level, several incentives have been set up to encourage the creation of green SMEs. Among these initiatives there are the Small Business Act and the Green Action Plan which consist in supporting SMEs in order to turn environmental problems into opportunities.

Since 2010, as part of the "Programme d'Investissement d'Avenir (PIA)", the French environment and energy management agency (ADEME) has provided a total of 93 million euros in grants to SMEs carrying out projects that respect the environment (ADEME, 2018). In order to face environmental challenges, France is engaged in structural transformation of its economy. The legislative framework that regulates this change is the law on energy transition for green growth.

According to a report by the Ministry for Ecological Transition, this is an opportunity for companies to create new activities, jobs and wealth while playing a crucial role in achieving sustainable development objectives [START_REF] Mtes | Le plan d'accélération de la transition écologique des TPE et PME[END_REF].

The French economy is largely based on Small and Medium-Sized enterprises (SMEs). The number of French SMEs represent 99,7% of the total number of enterprises. A majority of them (3,67 millions) are microenterprises (MIC), generating 1/3 of the added value and employing 46% of the workforce (MTES, 2020). The empirical literature has highlighted the importance of these companies in the economy, whether in terms of job creation [START_REF] Birley | The role of new firms: Births, deaths and job generation[END_REF] or in terms of innovation (Drucker, 1985), and their impact on the environment is significant. Thus, the study of eco-innovative start-ups is relevant in order to better guide these public policies: since they are an important part of the problem, they could also be part of the solution [START_REF] Kiefer | Building a taxonomy of eco-innovation types in firms. A quantitative perspective[END_REF]. Although the individual impact of each company is low compared to the role that a large company could play in reducing pollution, the cumulative impact of all SMEs may be considerable [START_REF] Ec | Environmental Compliance Assistance Programme for SMEs -Environment -European Commission[END_REF]. Despite the worldwide increase in the number of green start-ups in recent years, their representation in the total number of start-ups remains low. In fact, at the start of 2020, Frenchtech identified 14,189 microenterprise start-ups in France, of which 482 were cleantech start-ups, comprising a share of 3.4%.

In the academic literature, the study of eco-innovative firms has attracted the attention of several researchers who have studied the determinants of eco-innovation in depth (see Del Río et al., 2015 for a review). The study of the typologies of firms, principally SMEs, focuses on the ecoinnovative nature of the firm without taking into account the entrepreneurial nature of the activity.

Moreover these studies do not distinguish between established firms and newly-created firms ("born to be green" firms25 ). For the elaboration of the typology, these studies are based on three main factors related to the determinants of environmental innovation: technology push, market pull and regulation.

Some of them go further in their analysis by taking into account the factor of cooperation and the diffusion of knowledge [START_REF] Castellacci | A taxonomy of green innovators: Empirical evidence from South Korea[END_REF]Doloreux and Kraft, 2019). Another area of the literature is concerned with studying the typologies of sustainable entrepreneurship. The main factors considered in this literature are the motivation of the entrepreneur (economic or environmental values) as well as the main orientation of the firm [START_REF] Linnanen | An Insider's Experiences with Environmental Entrepreneurship[END_REF][START_REF] Schaltegger | A Framework for Ecopreneurship: Leading Bioneers and Environmental Managers to Ecopreneurship[END_REF][START_REF] Schaltegger | Sustainable entrepreneurship and sustainability innovation: categories and interactions[END_REF]; however, the eco-innovation aspect is marginal.

The purpose of this paper is, firstly, to study the distinctiveness of eco-innovative start-ups compared to non-eco-innovative start-ups based on two themes: human capital characteristics and environmental policy. To do so, we apply a decision-making model, namely a discriminant analysis (DA). Secondly, a typology of eco-innovative start-ups has been developed using a hierarchical ascendant classification (HAC) based on five main themes: financial resources, external knowledge, eco-innovation type, motivation and environmental orientation. The aim is to examine whether a variety of profiles emerges among the sample of eco-innovative start-ups. Moreover, the typology makes it possible to identify the different incentives that affect each category [START_REF] Nikolaou | A Typology of Green Entrepreneurs Based on Institutional and Resource-based Views[END_REF].

The taxonomic study is an appropriate method for this case because it constitutes an intermediate approach between analyses based on various individual cases and the generality of macroeconomic studies (sector or country level) [START_REF] Marin | SMEs and barriers to Eco-innovation in the EU: exploring different firm profiles[END_REF]. Furthermore, the study of the specificities of ecoinnovators and the understanding of their diverse patterns, in a context of environmental challenges, is of high relevance for developing public policies. The main information needed to carry out this study in the context of start-ups is not available in the existing datasets; for this reason, the analysis is based on a survey of 120 eco-innovative and non-eco-innovative French start-ups that we conducted in 2019.

The theoretical framework is based on literature that combines the resource-based view and the evolutionary perspective to categorize the drivers of eco-innovation into internal and external factors (del Río González, 2009;del Río et al., 2016;Demirel and Kesidou, 2011;[START_REF] Sáez-Martínez | Firm technological trajectory as a driver of eco-innovation in young small and medium-sized enterprises[END_REF]. In our case factors internal to the firm refer, for instance, to financial resources and environmental orientation (for more details, see figure 2.1). Meanwhile, external factors refer to cooperation. We complete this framework with some aspects of the entrepreneurship literature [START_REF] Casson | Entrepreneurship and the theory of the firm[END_REF].

The results of the DA show that eco-innovative start-ups differ in the skills acquired by the entrepreneur, in particular through environment-related studies and professional experience. The existence of "command and control" environmental instruments such as standards and prohibitions differentiate eco-innovators from non-eco-innovators. However, "economic instruments" such as environmental taxes do not distinguish the two groups of start-ups. Furthermore, the presence of a CSR policy (whether formalized or not) is an important differentiating factor. The results of the HAC show that there is a diversity of profiles of eco-innovators. In fact, firms cluster into five main profiles:

senior entrepreneurs with environmental concerns, necessity entrepreneurs without environmental concerns, entrepreneurs with ecological awareness, and market-driven opportunists without environmental concerns. They also exhibit different eco-innovation drivers. We have shown empirically that different types of eco-innovators do not have the same difficulties in accessing funds.

The paper concludes that policies must be designed by considering the distinctiveness of each profile.

The contribution of this study is manifold. A) We point out the particularities of ecoinnovators by considering their human capital drivers, which in turn highlights the importance of entrepreneurial characteristics for spurring eco-innovation, notably the prior knowledge of the entrepreneur. B) We identify five categories of eco-innovators: the typology is based on relevant drivers for start-ups, such as financial resources and motivation. We point out the most relevant drivers of each type and we examine the form of eco-innovation (incremental, radical, frontstage and backstage) developed by each type. Furthermore, we suggest the policy measures to be adopted according to the type of eco-innovator. C) The existing studies on eco-innovators focus on the case of SMEs without distinguishing established firms from start-ups. They do not take into account the distinctiveness of newly created start-ups, which are a special case because of the ease with which they can adapt their business plans to environmental issues. They also do not investigate the role that the entrepreneur might play. This study goes beyond the existing studies on SMEs by providing firm new evidence for the determinants of eco-innovation by implementing an entrepreneurship approach and by focusing on the case of start-ups. To our knowledge, no other research is devoted to the empirical study of the typologies of eco-innovative start-ups. There is, therefore, a need to better understand the eco-innovative nature of green start-ups to see to what extent they could contribute to tackling climate change. D) Finally, the originality of the data collected through a survey of start-ups and the diversity of the variables considered constitutes a significant contribution to the literature.

Our paper is structured as follows: Section 2 explains some concepts and gives a literature review. Section 3 presents the questionnaire and data collection. The methods and the results of the analysis are provided and discussed in Section 4. Section 5 concludes and gives policy recommendations.

Literature review

In what follows, we will first define eco-innovation and its typologies, then we will present the determinants of eco-innovation from different perspectives.

Definition of eco-innovation

Eco-innovation is a complex concept, and it is important to delineate its boundaries. In this study we adopt the definition of the European Commission report: "Eco-innovation is the production, assimilation or exploitation of a product, production process, service or management or business method that is novel to the organization (developing or adopting it) and which results, throughout its life cycle, in a reduction of environmental risk, pollution and other negative impacts of resource use (including energy use) compared to relevant alternatives" (Kemp and Pearson, 2007).

Radical eco-innovation is a completely new product and process that causes a change in production, consumption or organization patterns. Incremental eco-innovation is a simple improvement, characterized by a low degree of innovation and a moderate change in the market or customer value (Garcia and Calantone, 2002). The frontstage eco-innovation (also called front-end) is the initial phase of idea generation, whereas backstage eco-innovation (also known as back-end eco-innovation) is the product development phase.

Internal and external drivers based on market, technology and regulation

The general theory of innovation distinguishes two important factors that stimulate investment in innovation (technology push, market pull). This also applies to eco-innovation, however, specific factors play a determining role, in particular environmental policies (both in the form of command and control and market-based instruments) (Horbach, 2008;[START_REF] Porter | Toward a new conception of the environment-competitiveness relationship[END_REF].

A relevant classification of the drivers of eco-innovation has been proposed by (Horbach, 2008) that includes the three factors mentioned above: technology push, market pull and regulatory push/pull. Empirical findings related to the importance of market pull factors for stimulating eco-innovation differ. Some studies suggest that demand factors such as increased market share and higher consumer demand for environmentally-friendly products have a positive effect on the firm's decision to undertake eco-innovations (Kesidou and Demirel, 2012;Triguero et al., 2013). Others argue that market pull factors play a limited role in spurring eco-innovation because eco-friendly products are still expensive [START_REF] Rehfeld | Integrated product policy and environmental product innovations: An empirical analysis[END_REF]. Moreover, the effect of the market pull is limited in countries with low environmental awareness and willingness to pay (del Río et al., 2016).

The environmental policy factor (regulatory push/pull effect)26 comprises all public measures to reduce the environmental impact. According to Rennings (2000), regulation is important in addressing the double externality problem associated with eco-innovation. Previous studies have found that different regulation instruments have an influence on the decision to undertake ecoinnovation [START_REF] Belin | Determinants and Specificities of Eco-innovations -An Econometric Analysis for the French and German Industry based on the Community Innovation Survey[END_REF][START_REF] Rehfeld | Integrated product policy and environmental product innovations: An empirical analysis[END_REF]. Stringent regulations can stimulate investment in eco-innovation [START_REF] Kammerer | The effects of customer benefit and regulation on environmental product innovation.: Empirical evidence from appliance manufacturers in Germany[END_REF].

The technology push factor is associated with technological capabilities which mainly combine human capital and physical capital (Triguero et al., 2013). High technological capacities can lead to new eco-innovations [START_REF] Belin | Determinants and Specificities of Eco-innovations -An Econometric Analysis for the French and German Industry based on the Community Innovation Survey[END_REF]Horbach, 2008). To acquire these capabilities, investment in R&D and the training of employees is of high importance [START_REF] Cainelli | Does the development of environmental innovation require different resources? Evidence from Spanish manufacturing firms[END_REF][START_REF] Horbach | Determinants of eco-innovations by type of environmental impact -The role of regulatory push/pull, technology push and market pull[END_REF].

The literature has also emphasized the importance of the "external knowledge" factor. Ecoinnovations are characterized by a higher level of risk, uncertainty and novelty [START_REF] Belin | Determinants and Specificities of Eco-innovations -An Econometric Analysis for the French and German Industry based on the Community Innovation Survey[END_REF], and they are also more complex [START_REF] De Marchi | Environmental innovation and R&D cooperation: Empirical evidence from Spanish manufacturing firms[END_REF]. Because of this, they are highly dependent on knowledge and information which cannot be satisfied only internally. The existing literature points out that eco-innovation is more cooperation-intensive than other innovations. Thus, it is crucial for eco-innovation that there is cooperation between heterogeneous partners such as firms [START_REF] De Marchi | Environmental innovation and R&D cooperation: Empirical evidence from Spanish manufacturing firms[END_REF]Horbach, 2008), clients, distributors, suppliers [START_REF] Buttol | Integrating services and tools in an ICT platform to support eco-innovation in SMEs[END_REF][START_REF] Cainelli | Environmental Innovations, Local Networks and Internationalization[END_REF][START_REF] Cainelli | Environmental innovations in services: Manufacturing-services integration and policy transmissions[END_REF], universities [START_REF] Cainelli | Environmental Innovations, Local Networks and Internationalization[END_REF]Triguero et al., 2013) and governmental partners (Doblinger et al., 2019).

The determinants of eco-innovation at the firm level have been approached in different ways by using different theoriesamong others, the resource-based view and evolutionary theory. 27 The studies based on these theories suggest another classification of the drivers of eco-innovation by distinguishing internal factors from external factors. Earlier literature has focused on the external factors; however, some authors have pointed out the shortcomings of these studies due to their inability to explain the firms' internal stimuli. External factors are not sufficient to explain ecoinnovation decisions at the firm level (Demirel and Kesidou, 2011;Kesidou and Demirel, 2012;[START_REF] Triguero | The influence of energy prices on adoption of clean technologies and recycling: Evidence from European SMEs[END_REF]; they must be supplemented by factors internal to the firm. In order to address these shortcomings, there has been increasing interest in the resource-based view, which emphasizes the importance of the internal resources of firms. These resources can be classified into three categories: physical capital (physical technology, equipment, location), human capital (managers' and workers' skills) and organizational capital [START_REF] Barney | Firm Resources and Sustained Competitive Advantage[END_REF]. By contrast, the evolutionary theory states that innovation is a complex and dynamic system influenced by both internal and external factors.

Recent studies based on this theory have taken into account both internal and external factors [START_REF] Cainelli | Does the development of environmental innovation require different resources? Evidence from Spanish manufacturing firms[END_REF]Demirel and Kesidou, 2011;[START_REF] Triguero | The influence of energy prices on adoption of clean technologies and recycling: Evidence from European SMEs[END_REF].

Internal and external drivers based on entrepreneurship theory

Over the past decade, several studies have highlighted the particularity of eco-innovation within SMEs. This question has started to arouse increasing interest among researchers (for more details, see [START_REF] Pacheco | Ecoinnovation determinants in manufacturing SMEs from emerging markets: Systematic literature review and challenges[END_REF][START_REF] Pacheco | Eco-innovation determinants in manufacturing SMEs: Systematic review and research directions[END_REF]. SMEs have the particular feature of innovating differently [START_REF] Bos-Brouwers | Corporate sustainability and innovation in SMEs: evidence of themes and activities in practice[END_REF][START_REF] Klewitz | Sustainability-oriented innovation of SMEs: a systematic review[END_REF]) and play a central role in the transition towards a sustainable economy (Hall et al., 2013). The study of the internal drivers of environmental practices of SMEs also draws on entrepreneurship theory, 28 especially sustainable entrepreneurship. 29 Sustainable entrepreneurs have specific business and organizational motivators and skills (Bocken, 27 Other researchers have analysed the determinants of eco-innovation by using other theories such as stakeholder theory, theory of planned behaviour, and institutional theory. These theories are not the subject of this study (for more detail, see the works by Hazarika and Zhang, 2019;[START_REF] Munodawafa | A Systematic Review of Eco-Innovation and Performance from the Resource-Based and Stakeholder Perspectives[END_REF]. 28 The theory of entrepreneurship argues that a firm's strategic direction is closely related to the decision making of the entrepreneur, where the latter is responsible for the gathering and analysing of available information to make important business decisions; the theory states that the entrepreneurial capabilities are a firm's main human resource [START_REF] Casson | Entrepreneurship and the theory of the firm[END_REF].

29 Sustainable entrepreneurship refers to "entrepreneurial activities that contribute positively to sustainable development and the objectives derived from it" [START_REF] Kuckertz | The influence of sustainability orientation on entrepreneurial intentions --Investigating the role of business experience[END_REF]. Several other concepts are used to designate the relationship between entrepreneurship and sustainable development, such as ecopreneurship, social entrepreneurship, or green entrepreneurship. (For more details see [START_REF] Gast | Doing business in a green way: A systematic review of the ecological sustainability entrepreneurship literature and future research directions[END_REF] 2015). Sustainable entrepreneurship is characterized by the entrepreneur's specific values and culture (Anderson, 1998). Thus, the main factors discussed are related to the individual entrepreneur. The importance of the sustainability intention and orientation of the entrepreneur is highlighted in many articles [START_REF] Linnanen | An Insider's Experiences with Environmental Entrepreneurship[END_REF][START_REF] Schaltegger | A Framework for Ecopreneurship: Leading Bioneers and Environmental Managers to Ecopreneurship[END_REF][START_REF] Schaltegger | Sustainable entrepreneurship and sustainability innovation: categories and interactions[END_REF]. [START_REF] Linnanen | An Insider's Experiences with Environmental Entrepreneurship[END_REF] shows that green entrepreneurs have several characteristics in common with traditional entrepreneurs; the main difference is the important role of ethical values. [START_REF] Hoogendoorn | Goal heterogeneity at start-up: are greener startups more innovative?[END_REF] show that environmental value creation has a positive effect on the innovativeness of the start-ups. Moreover, [START_REF] Patzelt | Recognizing Opportunities for Sustainable Development[END_REF] found that the entrepreneur's prior knowledge is also relevant.

While studies investigating the diverse patterns of eco-innovative start-ups are relatively scarce, the different categories of sustainable entrepreneurs have received further attention in the literature. These studies focus on the case of SMEs without distinguishing the established firms from the newly created start-ups. They do not take into account the distinctiveness of start-ups, which constitute a special case because of their ease of adapting their business plan to environmental issues.

They are also not interested in the role that the entrepreneur might play. We introduce factors that may be specific to the case of start-ups, including human capital characteristics such as the financial resources and the prior knowledge of the entrepreneur (measured by environment-related education and experience). We also consider the environmental orientation of the firm and the type of the ecoinnovation developed (incremental, radical, frontstage and backstage) (see figure 2.1). Unfortunately, the European and French censuses of start-ups do not provide details of their innovation strategies, financing or growth. Data on this type of businesses is therefore not very accessible. Indeed, all start-ups are SMEs, but not all SMEs are start-ups, differing in particular in terms of their growth and their vision [START_REF] Steigertahl | EU Startup Monitor[END_REF]. The main information needed to carry out this study in the context of start-ups is not available in the existing datasets; for this reason we elaborated and conducted our own survey.

Illustrative variables

• Environmental policy (External factor)

• Human capital characteristics (Internal factor)

• Firm characteristics (Turnover, etc.) (Internal factor)

Driving factors

Eco-innovation

Active variables

• External knowledge (External factor)

• Financial resources (Internal factor)

• Environnemental orientation (Internal factor)

•Motivation (Hybrid factor*)

Our questionnaire is structured in four parts: the first part gathers a set of general information on the company (year of creation, turnover, etc.); the second part is devoted to the human capital characteristics of the firm; the third part relates to non-environmental innovation activity; and the fourth part covers the questions related to the eco-innovation activity. We chose to separate nonenvironmental innovation activities from eco-innovation activities for two reasons. First, to be able to identify the companies that develop the two types of innovation (environmental and nonenvironmental); second, to identify the specific drivers of eco-innovative and non-eco-innovative start-ups. A start-up is considered to be eco-innovative if the answer to the question, "During the last five years, has your company introduced or developed one or more environmental innovation(s)?" is "Yes"; it is considered non-eco-innovative if the answer is "No". Appendix 2.1. lists the detailed questions included in the questionnaire. Two main criteria were used to select the sample: the startup has an innovation activity, whether environmental or not; and the start-up is less than 12 years old.

The questionnaire contains a section devoted to the profile of the entrepreneur, so the founders of the start-ups were questioned. This decision to question the founders was also based on the fact that the founders have a better knowledge of the start-up and the motivations behind its creation.

The survey was conducted face-to-face and by email to start-ups in France, from March 2019 to February 2020. The face-to-face survey was carried out at the "Change Now" summit and the "International cleantech week meeting". Twenty-five questionnaires were collected through the faceto-face survey. The sample was completed by sending the questionnaire by email to the firms listed as winners of the innovation contests: "I-Nov", "I-lab" and "Prix pépite pour l'entreprenariat étudiant".

More than 700 companies were contacted and 124 questionnaires were returned by email. A total of 149 responses were collected, representing a response rate of 21%. Among them, 29 responses were invalid, either because they did not meet the selection criteria (the age of the firm exceeded 12 years and/or the start-up was not concerned with innovation activity), or because they were non-profit associations or the responses were incomplete. There remained a total of 120 valid responses, which were used for this study. The response rate is considered satisfactory, especially given that it was company founders who were requested to participate in the survey. Similar response rates have been recorded in the literature for the use of non-mandatory surveys (Doloreux and Kraft, 2019). The personal characteristics of the respondent were used to characterize the firm in the case of multiplefounder firms.

Sample characteristics

Methodology and empirical results

Our study is based on the implementation of multidimensional data analysis methods. First, we used a decision-making model, namely discriminant analysis (DA) [START_REF] Celeux | Analyse discriminante sur variables qualitatives[END_REF]Huberty, 1994), aiming to discriminate the drivers of eco-innovative firms from those of non-ecoinnovative firms. Then, we used hierarchical ascendant classification (HAC) [START_REF] Lebart | Statistique exploratoire multidimensionnelle[END_REF]Saporta, 2006) to establish a typology of the 64 eco-innovative start-ups according to their entrepreneurial characteristics.

The differentiating drivers of eco-innovative and non-eco-innovative start-ups

DA is performed on each of two sets of explanatory variables associated with human capital and environmental themes. The aim is to study their effect on the type of eco-innovation start-ups.

Discriminant analysis is a multivariate model which makes it possible to study the effect of a set of explanatory variables of a homogeneous theme on a target variable, in our case, eco-innovation. The DA approach is close to that of regression. In each case, we try to explain or predict a dependent variable by a linear combination of explanatory continuous variables. In DA, the explained or dependent variable is qualitative, thus 'DA' specifically means DA on continuous explanatory variables. When the explanatory variables are qualitative, the researcher first applies multiple correspondence analysis (MCA), then carries out DA on the principal components of the MCA as continuous explanatory variables. This methodological chain of data analysis methods is known as DA on qualitative variables.

The analysis is carried out on two groups of start-ups: eco-innovative and non-eco-innovative.

The first group contains 64 start-ups and the second group contains 56 start-ups. The dependent variable is the qualitative variable "eco-innovation" with two modes: groups of eco-innovative startups and groups of non-eco-innovative start-ups. We use explanatory variables from two homogeneous themes, human capital characteristics and environmental policy. The former includes the variables related to gender, age, educational level, obtaining a degree related to environmental studies, situation before creation, professional experience related to the environment, number of years of this experience and the existence of other co-founders. The latter consists of the following variables:

environmental regulations (standards, permissions, prohibitions), taxes on inputs (energy), taxes on polluting emissions, lack of environmental policy instruments, and Corporate Social Responsibility (CSR) policies. The purpose is to highlight the conditions-for each of the human capital characteristics and environmental policy themes-which best differentiate eco-innovative firms from non-eco-innovative firms.

Human capital characteristics

The main results of the DA are summarized in table 2.3, which presents the coefficients of the discriminant function, and the modalities of the eight variables that discriminate between ecoinnovative firms and non-eco-innovative firms.

The model as a whole is significantly discriminating. In fact, the probability (p-value = 0.001) is less than the classical error risk  = 5%. Therefore, human capital characteristics have a significant effect on the creation of eco-innovative start-ups. Among the eight explanatory variables introduced into the model, only the variable gender is not discriminating. All the other variables effectively differentiate the two groups of start-ups.

Thus, we can characterize eco-innovative start-ups as firms created by single people, without co-founders, who have a Master's level diploma in Engineering and have more than 10 years of professional experience in an environmental field.

As to non-eco-innovative start-ups, these are companies created with co-founders. The manager tends to be aged between 36 and 44 years, and was a jobseeker before creating the company; they are highly educated people without any professional experience related to the environment.

Environmental policy

Table 2.5 presents the results of the linear Fisher's function of the DA environmental policy theme. The model as a whole is significant (p-value = 0.001 <5%). 68.33% of companies are correctly classified by the model, the error rate is 31.67%, and so the model is satisfactory (See table 2.4).

Among the five variables introduced into the model, only two explanatory variables-taxes on inputs (energy) and taxes on polluting emissions-are not significantly discriminating. All the other variables distinguish the two groups of start-ups effectively. Indeed, the existence of environmental regulations and a CSR policy (whether formalized or not) differentiate the two groups of start-ups. Non-ecoinnovative start-ups are characterized by the absence of environmental regulations and a CSR policy.

Eco-innovative start-ups are subject to incentives provided by environmental policy instruments and they are characterized by the existence of voluntary environmental practices such as CSR.

Environmental instruments commonly known as "command and control", such as emission or product standards, prohibitions and authorizations issued by administrative authorities, differentiate between eco-innovative and non-eco-innovative start-ups. These instruments may, for example, be related to facilities classified for environmental protection (ICPE)31 subject to authorization in order to exercise their activity. Non-eco-innovative start-ups are distinguished by the absence of command and control instruments. The environmental instruments commonly named "economic instruments" such as taxes on polluting emissions and inputs have not proven to be elements that separate the two groups. The creation of eco-innovative start-ups can be stimulated by the need to comply with existing or anticipated environmental standards and authorizations. It can also be spurred by a desire to conform to the principles of sustainable development, in particular by implementing a CSR policy.

Typology of eco-innovative start-ups according to their entrepreneurial characteristics

Secondly, an HAC according to the Ward criterion 32 is performed on the 64 eco-innovative start-ups based on their entrepreneurial characteristics related to access to finance, access to external knowledge (cooperation, exchange of ideas), the type of eco-innovation developed (radical, frontstage, 32 Generalized Ward's Criteria, i.e. aggregation based on the criterion of the loss of minimal inertia. etc.), motivation and environmental orientation (implementation of an environmental management system, analysis of the environmental impacts of products, etc.). Their status in the analysis is called active variables. They are used to build the typology and to characterize the most homogeneous and distinct eco-innovative start-up classes possible. According to the similarities of the start-ups relative to these active variables, we establish a typology of five homogeneous classes of eco-innovative startups.

All the other variables of the questionnaire can be introduced as illustrative variables in the HCA. These supplementary variables do not participate in the characterization of the typology; they are used a posteriori to attempt to describe the eco-innovative start-up classes previously characterized by the active variables.

Table 2.6 shows the significant results of the analysis.

Class 1: Senior entrepreneurs with environmental concerns

This class represents 17.19% of the sample. These start-ups are developing radical ecoinnovations. The project has been developed from the idea stage so that the environmental aspect is considered throughout the innovation. These innovations are initially designed in such a way that they reduce environmental impact, in particular through analysis of the environmental impacts of products.

The project is credible and attractive, which enables these start-ups to access grants, in particular those from national organizations. They are also characterized by the important role played by the personal financial contribution of the founder. Interest in the environmental aspect is high, which can be observed in particular through the importance given to training employees on subjects related to environmental issues. Their purpose is not to reduce the costs but rather to provide a radical solution to an environmental problem. The start-ups of this class tend to have three and more co-founders and the leader is aged 55 or over.

Start-ups that develop radical innovations are characterized by the presence of several founders. This could be the source of a diversity of skills (management, technical, accounting, financial, legal, commercial). Several studies have highlighted the positive effect of the founder's involvement in other functions (CEO, president, manager, etc.) [START_REF] Adams | Understanding the relationship between founder-CEOs and firm performance[END_REF][START_REF] Li | Corporate Governance When Founders Are Directors[END_REF]. Fahlenbrach (2009) examines the investment decisions of founder CEOs and finds that they invest more in research and development. [START_REF] Chen | Multiple founders and firm value[END_REF] find that multi-founder firms are more valuable than all other types of firms (including family firms and single-founder firms). "+" Represents a "Yes" or "selected" in the case of a multiple choice question "-" Represents a "No" or "not selected" in the case of a multiple choice question. None: The variables are not statistically significant. * Corresponds to the year 2018 expressed in Full-Time Equivalent (FTE)

Class 2: Necessity entrepreneurs without environmental concerns

This class represents the smallest part of the sample (6.25%). The environmental aspect is not a major concern in this category of start-up, which is evident in the fact that they do not set environmental objectives for their products. They integrate the environmental aspect into their activity in a haphazard manner. The start-ups of this class tend to have no co-founders and the entrepreneur was often a jobseeker before creating the company. In the literature on entrepreneurship they are known as push entrepreneurs [START_REF] Amit | Push" and "Pull" Entrepreneurship[END_REF]; they engage in an entrepreneurial activity because of lower opportunity costs (the individual is in a bad position in the labour market) (Oxenfeldt, 1943). Their goal is to earn enough money to make a living and they do not live to grow wealth [START_REF] Choi | The venture development processes of "sustainable" entrepreneurs[END_REF]. By contrast, "pull" entrepreneurs are those who start a business to exploit an attractive idea.

These start-ups have difficulties accessing funding and grants. They have very little access to external knowledge due to the lack of cooperation and exchange of ideas through meetings. This class shows great similarities with the ad hoc "enviropreneur" [START_REF] Taylor | The green entrepreneur: Opportunist, Maverick or Visionary[END_REF]) and with the unintentionally green start-up [START_REF] Bergset | Green start-upsa new typology for sustainable entrepreneurship and innovation research[END_REF].

Class 3: Entrepreneurs with ecological awareness

This class represents 26.56% of the companies in the sample. These start-ups do not raise funds. This can be explained by the low interest of investors in this type of start-up or by the desire to keep full power over the decision-making process and avoid the risk of deviating from environmental objectives. They also do not have access to grants. The entrepreneurs created these start-ups because they were convinced of the need to provide solutions to environmental problems and of the notion that the challenges could be met through the efforts of all. Thus, they are making their contribution by developing an eco-innovative project. Their main objective is not profit; indeed the turnover is lower than 100,000 €. Their eco-innovation is an improvement of existing products (incremental eco-innovation). The environmental aspect is introduced in the product or service in the development phase; in fact, these companies have no R&D activity either internally or externally. They do not benefit from external knowledge because their networks and partners are limited.

The environmental orientation is stimulated by an internal factor based on the will of the founder and his team. The variable 33 "funded by sustainable investors" 34 is not significant. However, this can be explained by the small share (1.6%) of companies financed by sustainable investors in our sample. These start-ups have not selected "no incentive to environmental policy" and their managers

33 Table 6 reports only the significant results, the detailed results are available upon request.

34 Sustainable investors are investors who direct their investment capital towards firms that have an environmental and social impact.

tend to have a bachelor's degree. This class has features that are close to the ethical maverick [START_REF] Taylor | The green entrepreneur: Opportunist, Maverick or Visionary[END_REF]) and the alternative start-up [START_REF] Bergset | Green start-upsa new typology for sustainable entrepreneurship and innovation research[END_REF].

Class 4: Market-driven opportunist entrepreneurs without environmental concerns

This class represents 25% of the start-ups in the sample. These start-ups were created following the discovery of market niches related to an increasingly high demand for environmentallyfriendly products or services. Competition is less pronounced in these niches which are weakly occupied by large groups. The environmental aspect is introduced because it allows the company to exist and to make profits; they rarely carry out an environmental assessment of their products. They have weak access to funds and grants, as is the case for the class 2 start-ups (necessity entrepreneurs without environmental concerns). Investors have little interest in this category of start-up. They do not benefit from external knowledge due to the small number of their partners and to the nonparticipation in the exchange of ideas on environmental aspects through meetings. This class is comparable to the innovative opportunist [START_REF] Taylor | The green entrepreneur: Opportunist, Maverick or Visionary[END_REF]) and to the "ecopreneurial" startup [START_REF] Bergset | Green start-upsa new typology for sustainable entrepreneurship and innovation research[END_REF].

Class 5: Opportunist leaders 35

This class represents 25% of the sample. The start-up is created following the emergence of an idea which is conceived from the start in order to reduce or limit environmental impacts (frontstage eco-innovation). A research and development activity is carried out internally in order to implement the idea. These start-ups develop eco-innovative solutions for their activity and for industries that apply for eco-innovations allowing them to achieve cost savings. These are the most advanced startups in terms of integrating the environment into their activity. Their activity is organized through an environmental management system, they are therefore concerned with the image they project. The analysis of environmental impacts is a priority for their projects, with a desire to offer completely ecodesigned products.

They are strongly committed to partnership and have an extended network, made up of other companies in their sector of activity, suppliers, consultants and private laboratories. They are generally created by entrepreneurs with professional experience related to the environment who have therefore been able to develop their network and know-how. These are projects that have also been supported

by applied academic research. The main field of their intervention is the reduction of greenhouse gas emissions.

These start-ups have strong development potential and a promising future. They manage to attract funding from private investors, especially during the development phase. They also manage to obtain subsidies, in particular from local authorities. Start-up companies that make up this class tend to have a medium to large salaried workforce, and the founder usually has 5 to 10 years of corporate experience related to the environment. This class shows similarities with the visionary champion [START_REF] Taylor | The green entrepreneur: Opportunist, Maverick or Visionary[END_REF]) and the visionary start-up [START_REF] Bergset | Green start-upsa new typology for sustainable entrepreneurship and innovation research[END_REF].

Discussion and conclusions

This paper makes a contribution to the literature on sustainable entrepreneurship by focusing on the factors that stimulate start-ups to undertake eco-innovation. Firstly, we made a comparison between eco-innovators and non-eco-innovators according to two themes: human capital and environmental policy. The results show that eco-innovative start-ups differ in the skills acquired by the entrepreneur, in particular through environment-related studies and environment-related professional experience. The existence of "command and control" environmental instruments such as standards and prohibitions distinguish eco-innovators from non-eco-innovators. However, "economic instruments" such as environmental taxes do not separate the two groups of start-ups.

Furthermore, the presence of a CSR policy (whether formalized or not) is an important differentiating factor. Secondly, we propose a typology of eco-innovative start-ups based on internal factors and external factors related to the environment in which the start-up operates (such as external knowledge). Due to the diversity of start-ups, we include internal factors that can play a key role, in this case such as access to funding, the entrepreneur's human capital profile, and the environmental orientation. Start-ups are often combined with existing firms in studies on SMEs; however, these firms have specific features which make them adopt a different attitude towards eco-innovation. In this paper, we try to address the shortcomings observed in the literature by focusing on nascent start-ups.

The typology is used to examine the distinctive characteristics of eco-innovators. It also allows us to distinguish between eco-innovators by considering economic, social and environmental motivation.

We have identified five categories of eco-innovative start-ups. Some of these categories have been mentioned in previous theoretical studies by [START_REF] Taylor | The green entrepreneur: Opportunist, Maverick or Visionary[END_REF] and [START_REF] Bergset | Green start-upsa new typology for sustainable entrepreneurship and innovation research[END_REF]. The special feature of the necessity entrepreneurs without environmental concerns is their haphazard engagement in environmental activity. Entrepreneurs with ecological awareness are characterized by the entrepreneur's strong environmental values. The main goal of the market-driven opportunist without environmental concerns is to make profits by exploiting environmental opportunities. Opportunist leaders display the highest environmental orientation, analysing the environmental impacts of products and implementing an Environmental Management System (EMS).

They try to strike a balance between environmental goals and economic goals related to financial performance, and have a proactive attitude; they go beyond the environmental constraints imposed from without.

The detailed examination of these classes shows that firms that develop radical ecoinnovations and frontstage eco-innovations are those that are driven by economic performance, have access to funding or subsidies, have access to external knowledge, report a high level of environmental orientation, and are led by entrepreneurs who have acquired professional experience (opportunist leaders, senior entrepreneurs with environmental concerns). On the other hand, start-ups that develop incremental eco-innovations are characterised by a low level of external knowledge and a lack of access to funding (entrepreneurs with ecological awareness). Radical sustainable innovation is a long-term investment (Bocken, 2015;Freimann et al., 2012) and constitutes a high risk, whereas investors expect to obtain their profits after a short period of time, which makes access to funds challenging [START_REF] Bergset | Green start-upsa new typology for sustainable entrepreneurship and innovation research[END_REF]. In relation to this analytical framework, we have shown empirically that different types of eco-innovative start-ups do not have the same difficulties in accessing funds, as has been assumed by [START_REF] Bergset | Green start-upsa new typology for sustainable entrepreneurship and innovation research[END_REF]. The category of start-ups that manage to attract investors is the opportunist leaders, and this may be due to three main reasons. The innovations are radical; they are therefore attractive prospects for the investors who agree to fund them. Moreover, these companies are founded by older entrepreneurs with professional experience which has allowed them to develop their professional network. In addition, these start-ups have an economic motivation; they are therefore funded by conventional investors. Entrepreneurs with ecological awareness do not raise funds and have not obtained grants. This can be explained by two factors. They are selective in the choice of investors because they worry about the firm's objectives potentially being redirected, and they are therefore seeking investors that share the same sustainable values (Bocken, 2015;[START_REF] Choi | The venture development processes of "sustainable" entrepreneurs[END_REF]. A second possible reason is that these firms may not strive to grow; their only desire is to meet their needs by exercising a profession consistent with their values.

Necessity entrepreneurs without environmental concerns do not have access to funds. This may be due to their lack of experience and lack of prior knowledge related to the environment. This leads to the exploitation of less profitable opportunities [START_REF] Block | Necessity and Opportunity Entrepreneurs in Germany: Characteristics and Earning s Differentials[END_REF]. Opportunist leaders is the category of start-ups with the most successful cooperation network (cooperation with other companies, suppliers, universities, etc.). These start-ups display the highest environmental orientation; in fact, they set environmental objectives for their products and integrate the environmental aspect from the initial phase of the innovation process (frontstage eco-innovation). For this reason, external knowledge plays a crucial role for these firms. The establishment of this network is enabled, among other factors, by the professional experience related to the environment acquired by the entrepreneur.

By contrast, cooperation plays a limited role in the case of the other classes.

These results are consistent with those obtained by Doloreux and Kraft (2019) which confirm that companies with a high eco-innovation intensity-namely fully-integrated eco-innovators36 -differ in the exploitation of external knowledge; in fact, they have a larger network.

Environmental regulation seems to have a limited role in the differentiation between the categories of eco-innovators, except in relation to the grants which are significant for two classes: opportunist leaders and senior entrepreneurs with environmental concerns. These results are consistent with those of [START_REF] Triguero | Leaders and Laggards in Environmental Innovation: An Empirical Analysis of SMEs in Europe: Leaders and Laggards in Environmental Innovation[END_REF] who find that environmental policy has no effect on the intensity of ecoinnovation.

At European and French level, environmental policies are proposed to encourage companies' environmental practices. In this respect, it is crucial to understand the distinctiveness of ecoinnovators versus non-eco-innovators, and to examine the different typologies of eco-innovators.

Hence, this study has important implications for policy-makers.

Environmental policy in European countries is based on national objectives and only a few measures concern specific sectors or companies [START_REF] Marin | SMEs and barriers to Eco-innovation in the EU: exploring different firm profiles[END_REF]. It is mainly based on the traditional instruments of "command and control" and "economic instruments"; these instruments are insufficient. In fact, our results show that the effect of "economic instruments" is not significant.

The discriminant analysis on the human capital theme shows that environment-related education as well as environment-related professional experience differentiate eco-innovators from non-eco-innovators. Therefore, public policies should not focus solely on regulatory instruments but must also rely on other areas of application, including education. We are already seeing initiatives emerging to train young people for the professions of tomorrow-for example, the "campus transition" universities and "ecological transition schools". These schools have the ambition to think of new economic models, and new ways of producing, consuming, and managing resources.

Therefore, it is necessary to strengthen and support these emerging initiatives. One of the aims of the programme "Plan d'investissement dans les compétences" of the period 2018-2022 is to contribute to ensuring the workforce is qualified to respond to the changing skills demanded by ecological transition. However, we believe that this action is marginal and should be scaled up.

It has become clear from the taxonomic analysis that eco-innovators do not have a homogeneous profile. Indeed, there is a diversity of profiles among eco-innovators. Therefore, the main implication is that the implementation of public policy must take into account the diversity of eco-innovators' profiles.

Senior entrepreneurs with environmental concerns have carried out an activity which has enabled them to build up the funds to start a business. In addition, they have extensive professional experience and have been able to build up a professional network. This allows society to benefit from their experience. At the same time, this activity allows them to benefit from additional income to their retirement pensions. In an economy characterized by an aging population, this phenomenon could be replicated widely if it is properly stimulated and supported, and it could contribute to achieving the objectives of sustainable development. We highlighted that seniors create their business with cofounders. This promotes complementarity between the experience and the availability of resources of seniors with the technical skills of young people in new technologies. Efforts to bring these two parties together are necessary. Therefore, it is important to encourage intergenerational co-entrepreneurship, in particular by setting up associations and private and public support structures that will be able to match up these two parties, or by creating networking platforms. This could create jobs and make it possible to sustain the financing of social protection whilst reducing the environmental impact.

Entrepreneurs with ecological awareness can play an important role in sustainable development. It is necessary to support them through funding. Sustainable investors share the same environmental values as entrepreneurs with ecological awareness. However, the variable "funded by sustainable investors" introduced in the taxonomic analysis is not significant. In our case, this may be due to the small number of companies funded through this mode of finance. Sustainable investment is emerging: the number of sustainable investors is increasing but remains very low. We consider it a suitable financing method for firms with an ecological awareness based on their common values. For sustainable investors and start-ups with an ecological awareness to find their way into the market, the network needs to be developed and organized in a formal way and the start-ups and investors need to show a clear signal of their "sustainable" character (Demirel et al., 2019). Because of this information asymmetry, it is necessary to facilitate the right match between sustainable investors and entrepreneurs with an ecological awareness by setting up an impact measurement such as an "impact factor" recognized by the state to assess and evaluate the "sustainability" of projects, and it is also necessary to promote "green" investment platforms.

La RSE (Responsabilité Sociale des Entreprises) regroupe l'ensemble des pratiques mises en place par les entreprises dans le but de respecter les principes du développement durable, c'est-à-dire être économiquement viable, avoir un impact positif sur la société mais aussi mieux respecter l'environnement.

Existe-t-il une démarche RSE au sein de votre entreprise ?

Dans les cases commentaires, Mettez les lettres "r" pour un partenariat dans la même région administrative et "a" pour d'autres régions françaises ou internatinales

E5. À quelle étape du processus d'innovation avez-vous introduit l'aspect environnemental?

Phase initiale du processus d'innovation (identification de l'opportunité et sélection de l'idée)

Phase de développement (exécution de l'idée via un processus de test, développement et mise en oeuvre)

E6. Parmi les méthodes de management environnemental suivantes, indiquer celle(s) que vous avez adoptée (s) ?

Certifications

Introduction

Since the 1990s, environmental issues have become a major concern for policy makers. Faced with the threat of global warming, it has been essential to reduce energy consumption, limit the use of fossil fuels, and promote the development of low-carbon energies. This requires a radical technological transformation of the global energy system, and the rapid establishment of policies to encourage the development of innovation with the aim of reducing environmental impact and promoting more efficient use of natural resources.

The implementation of national and sectoral policies promoting eco-innovation has become a key issue for Europe's future competitiveness. With the Act of 17 August 2015 on Energy Transition for Green growth, France has displayed its ambition to be an exemplary nation in terms of reducing its greenhouse gas emissions, diversifying its energy system and increasing the deployment of renewable energy sources. The Act sets out many quantitative goals, in particular, a commitment to reduce greenhouse gas emissions by 40% by 2030, compared to 1990 levels, and divide it by four by 2050 as well as to increase the share of renewables in final energy consumption up to 32% by 2030.

And it is in this context that the creation of innovative firms highly specialised in environmental technologies may have a key role in the transition process to a sustainable economy (OECD 2011(OECD , 2016;;EC 2012;French Government 2017). In this regard, France is responsible for a specific program that supports the creation of eco-innovative firms with a special focus on the development of innovation (ADEME 2018). The capacity of regions to develop new green technologies is also one of the bases of the Smart Specialisation Strategies (RIS3) proposed by European Commission over the last years to ensure (EC 2012). It seems clear, then, that the development of policies encouraging the creation of environmental technologies and their sustainable application in firms' activity may have many implications for the transition to a greener and sustainable economy of regions, in this case, for French départements.

Despite the increasing interest in the development of eco-innovation, the number of contributions analysing the regional factors that may explain the location of eco-innovative activities is still limited (see, for instance, Barbieri et al. 2016 or Dechezleprêtre and[START_REF] Dechezleprêtre | The impacts of environmental regulations on competitiveness[END_REF]. To do so it is important to consider the nature behind regional knowledge bases taking into account their actual capacity to generate green innovations. And this requires the use of alternative approaches to capture the distinctive traits of environmental innovations [START_REF] Barbieri | Knowledge sources and impacts on subsequent inventions: Do green technologies differ from non-green ones?[END_REF]) and a thorough investigation in the actual role of related and unrelated diversification in regions as [START_REF] Boschma | Relatedness as driver of regional diversification: a research agenda[END_REF] suggested as one of the main challenges in the literature.

Therefore, with this paper we aim to contribute to the literature on the location determinants of eco-innovative firms and shed light on the relationship between knowledge spillovers and the creation of new innovative firms highly specialised in the environmental field. Concretely, we address the following research questions. First, does the accumulated knowledge coming from environmental technologies matters more than knowledge coming from non-environmental technologies in the creation of eco-innovative firms? Second, does the creation eco-innovative firms benefit more from related or from unrelated knowledge variety (in terms of the recombination of technologies that make up environmental inventions)? And third, is the creation of eco-innovative firms positively influenced by the local knowledge base and firm creation in neighbouring regions? The answers to these questions are useful for the development of regional policies to encourage the spatial concentration of environmental knowledge creation that, to some extent, may increase regional disparities in terms of environmental performance and knowledge creation or, on the contrary, reduce them. Moreover, the understanding of the dynamics of the eco-innovative firms' entry can provide useful information on how to boost local development through their direct and indirect spatial spillovers arising from the formation of eco-innovative activities in neighbouring regions. Finally, they would provide valuable ex-ante evidence of the Smart Specialisation Strategies (RIS3) proposed by European Commission to ensure the sustainable regional competitiveness in European Regions (EC 2012; Montresor and Quatraro 2020).

Only two comparable works have analysed these issues: [START_REF] Corradini | Location determinants of green technological entry: evidence from European regions[END_REF] and Colombelli and Quatraro (2019). As far as we know, [START_REF] Corradini | Location determinants of green technological entry: evidence from European regions[END_REF] was the first paper to analyse the location determinants of new green technology-based firms across European regions by using the characteristics of patent applications to define them as eco-innovative. Their results show that the geographical distribution of green technology entry across European regions is not evenly distributed, giving evidence of the significant role played by the characteristics of the regional innovation system.

Regarding Colombelli and Quatraro (2019), they provide evidence of the effects of the technological composition of local stocks of knowledge in the creation of green start-ups across Italian NUTS3 regions.

Against this background, the present paper departs from previous contributions in several ways. First, we provide evidence of the extent to which the balance between the accumulated local stock of environmental and non-environmental inventions matters for the creation of eco-innovative firm. Second, we propose a set of knowledge indicators capturing the degree of variety defining the local knowledge base focusing on technological recombinations of environmental-based patents. By doing so, we are able to disentangle the degree of diversity of the local knowledge base in terms of the recombination of environmental and non-environmental technologies as well as the recombination of the diverse environmental technological classes making up environmental patents that cannot be captured when using the traditional knowledge variety indicators that take into account all kinds of patents. Third, we introduce spatial autocorrelation techniques to identify spatial spillovers associated with the accumulated endowments of knowledge in the environmental technologies and ecoinnovative firms' creation. Finally, we provide evidence on the location determinants of new ecoinnovative firms' entry in the case of France, which is subject of increasing interest.

By using firm-level microdata on the location of new firms from DIANE Mercantil Register (Bureau van Dijk) and patent information from the OECD REGPAT (2018) database, we propose a set of knowledge indicators capturing the recombination process leading to the generation of environmental inventions and their impact on the propensity for the creation of environmental technology-based firms in French départements over the period 2003-2013. The main results show that the combination of broad and diverse technological knowledge for environmental technologies and political support in terms of investments for the protection of the environment are the main factors explaining the location of eco-innovative firms. Indeed, by applying spatial econometrics we found that there is a clear spatial dependence on their creation, while the impact of knowledge composition is quite local: results that are in line with the RIS3 policies proposed by the European Commission over the recent years (EC 2012).

The paper is organized as follows. Section 2 reviews previous contributions analysing the location determinants of eco-innovative firms and the role of knowledge spillovers in this process. In Section 3, we describe the data set and the variables used. In Section 4, we present the methodology and the econometric specification. Section 5 reports the main results. Finally, Section 6 offers some concluding remarks and policy implications.

Local knowledge composition and eco-innovative firm creation

Eco-innovation is a complex concept, it is important to delineate its boundaries. In this paper, we adopt the definition of the European Commission report: "Eco-innovation is the production, assimilation or exploitation of a product, production process, service or management or business method that is novel to the organisation (developing or adopting it) and which results, throughout its life cycle, in a reduction of environmental risk, pollution and other negative impacts of resources use (including energy use) compared to relevant alternatives" (Kemp and Pearson 2007). This definition includes all innovations that can alleviate the environmental burden of economic activities regardless of the motivation behind their development or adoption. Thus, it includes innovations that are new to the firm or to the market, it also considers the innovations that contribute unintentionally to protecting the environment. Yet when considering this broad definition of eco-innovation, it is hard to find an indicator that is suitable for measuring it. As with classical innovations, patents are among the most widely used indicators for measuring eco-innovation (see among others, Colombelli et al., 2019;[START_REF] Costantini | Eco-innovation, sustainable supply chains and environmental performance in European industries[END_REF][START_REF] Faria | Sectoral patterns versus firm-level heterogeneity -The dynamics of eco-innovation strategies in the automotive sector[END_REF]Marin, 2014). However, the use of this indicator limits the definition of eco-innovation. In fact, patents generally reflect technological ecoinnovations that are new to the market.

The interest in eco-innovation stems from its environmental and economic benefits to society (Horbach 2008). The potential benefits of eco-innovations can easily spill over into society through the creation of new firms specialised in environmental technologies. In this regard, there is a wide consensus on the key role of start-ups in generating technological innovations as well as in its transmission process over the economy (Acs and Audretsch 1987;Rogers 2004;[START_REF] Audretsch | Entrepreneurship and economic growth[END_REF]European Comission 2011;OECD 2016). In fact, environmental innovations may lead to radical change because of the absence of established environmental best practice and technological trajectories [START_REF] Barbieri | Knowledge sources and impacts on subsequent inventions: Do green technologies differ from non-green ones?[END_REF]. For this reason, understanding the factors that may incentivise the creation of eco-innovative firms is becoming more and more relevant to provide evidence on the regional characteristics that may boost the generation and diffusion of environmental inventions.

However, there are not many contributions to the literature that analyse location determinants of ecoinnovative firms.

Most literature related to this field is based on the evolutionary economic geography literature [START_REF] Boschma | The handbook of evolutionary economic geography[END_REF][START_REF] Boschma | The emerging empirics of evolutionary economic geography[END_REF] and the theories of knowledge creation and the role of knowledge spillovers in new firm creation [START_REF] Acs | The Knowledge Spillover Theory of Entrepreneurship[END_REF][START_REF] Acs | The knowledge spillover theory of entrepreneurship[END_REF]. According to this literature, one of the main factors explaining the creation of new innovative firms is the local available stock of knowledge [START_REF] Acs | The Knowledge Spillover Theory of Entrepreneurship[END_REF][START_REF] Audretsch | From knowledge to innovation: the role of knowledge spillover entrepreneurship[END_REF]. In the case of environmental technologies, there is already (mixed) evidence showing that the availability of local knowledge is expected to have an important role on the creation of eco-innovative firms. Still, the benefits stemming from the available accumulated local knowledge stock may differ according to the dominant technologies being developed in the region. In this direction, some authors found that eco-innovative start-ups benefit more from knowledge spillovers coming from 'clean' technologies than from 'dirty' technologies (Colombelli and Quatraro 2019). On the other hand, [START_REF] Quatraro | Academic inventors and the antecedents of green technologies. A regional analysis of Italian patent data[END_REF] consider that the specialisation of green technological innovations depends substantially more upon the stock of nongreen technologies; while others found that both environmental and non-environmental knowledge are positively associated with the emergence of specialisation in environmental technologies [START_REF] Montresor | Green technologies and Smart Specialisation Strategies: a European patent-based analysis of the intertwining of technological relatedness and key enabling technologies[END_REF]. Despite this mixed evidence, we may consider that both kinds of knowledge should be relevant to the formation of eco-innovative firms due to the wider scope of application of environmental technologies with regards to other technologies (Dechezleprêtre et al. 2013).

Based on the above premises, we can formulate the following research question: RQ1: Does the creation of eco-innovative firms benefit more from the accumulated knowledge coming from environmental technologies or the other way around, that is, the accumulated knowledge coming from nonenvironmental technologies.

Besides the available local accumulated stock of knowledge, some authors argue that the domains over which local knowledge spans are also important when it comes to explaining the location patterns of (eco)-innovative start-ups [START_REF] Colombelli | New firm formation and regional knowledge production modes: Italian evidence[END_REF]. In fact, technological progress is explained within recent evolutionary economics literature by a new theory of innovation namely the recombinant innovation. This approach suggests that new ideas are generated through the recombination of existing ideas [START_REF] Castaldi | Related Variety, Unrelated Variety and Technological Breakthroughs: An analysis of US State-Level Patenting[END_REF]van den Bergh, 2008;Weitzman, 1998). It follows from the notion of recombinant innovation that the development of new technologies, and environmental technologies too, is a place-and path-dependent process, which benefits from the variety of local available knowledge by recombining it in a new manner [START_REF] Balland | Chapter 6: Relatedness and the geography of innovation[END_REF]. In this regard, there is ample empirical confirmation for the fact that knowledge base dimensions such as variety and relatedness affect the knowledge recombination effectiveness and firm formation dynamics (Bae and Koo 2009;[START_REF] Colombelli | The impact of local knowledge bases on the creation of innovative start-ups in Italy[END_REF]. Regional technological heterogeneity is characterized by the presence of actors with different technological competencies and then a broader set of combinatorial opportunities leading to the creation of new innovative firms (Castaldi et al., 2013;[START_REF] Corradini | Technological diversification and new innovators in European regions: evidence from patent data[END_REF]. Even the complementarity degree of regional knowledge configuration is important, increasing similarity may lead to negative effects. Thus, an excessive level of relatedness is likely to engender technological lock-in [START_REF] Boschma | Proximity and innovation: a critical assessment[END_REF][START_REF] Colombelli | The properties of local knowledge bases and entrepreneurship: Evidence from Italian NUTS3 regions[END_REF]. The most favourable configuration for the emergence of knowledge-based entrepreneurship is characterized by high levels of both relatedness and variety. In fact, a high degree of relatedness is associated with reduced knowledge asymmetries and uncertainty and an improvement in the entrepreneurial absorptive capacity; while a high degree of variety is associated to more technological opportunities [START_REF] Colombelli | New firm formation and regional knowledge production modes: Italian evidence[END_REF]).

Among the few contributions analysing the impact of knowledge spillovers on the creation of eco-innovative firms, Colombelli and Quatraro (2019) and [START_REF] Corradini | Location determinants of green technological entry: evidence from European regions[END_REF] stand out. On the one hand, [START_REF] Corradini | Location determinants of green technological entry: evidence from European regions[END_REF] found an inverted-U relationship between regional technological relatedness and green technological entry. In this regard, technological activities related to environmental technologies available in regions trigger the development of green technologies. However, an excessive level of relatedness may have a negative effect by limiting the set of opportunities for recombinations with other technological domains, leading to a decrease of green technological entries.

On the other hand, Colombelli and Quatraro (2019) pointed out the importance of technological variety for the generation in green innovative start-ups as well as the historical process of knowledge accumulation in which the combination of related and highly complementary technological fields is relevant for the effective exploitation of technological opportunities in the green domain.

In practical terms, different strategies can be followed by regions in order to foster ecoinnovative firms' entry. Some regions could follow a strategy based on unrelated diversification by investing in technologies different from the pre-existing ones (e.g., the production of electric cars in absence of a consolidated experience in the automobile sector). Other regions may exploit and combine the existing non-environmental innovations with eco-innovations into new hybrid solutions (e.g., the hybrid car combining an internal combustion engine with an electric propulsion one and photovoltaic films combining thin layer technologies with solar technologies) (Zeppini and van den Bergh, 2011). These strategies are examples of the wider scope of application of environmental technologies due to their complexity and specific nature [START_REF] Quatraro | Academic inventors and the antecedents of green technologies. A regional analysis of Italian patent data[END_REF][START_REF] Santoalha | Diversifying in green technologies in European regions: does political support matter?[END_REF].

Therefore, understanding the specific recombination of the different technologies leading to environmental in at regional level is of vital importance for the set-up of regional policies to encourage the development of eco-innovative activities. Nevertheless, previous contributions were not able to capture the specific recombination of knowledge that makes up environmental technologies (e.g. Corradini 2019, Colombelli andQuatraro 2019). One exception is the work of [START_REF] Barbieri | Knowledge sources and impacts on subsequent inventions: Do green technologies differ from non-green ones?[END_REF], although they do not analyse the location determinants of eco-innovative firms, their work confirms the additional complexity and novelty is not straightforward and requires alternative approaches to understand their knowledge sources, by assessing the impacts of inventive activities on subsequent technological developments, focusing, especially, on the spillover potential of green technologies .

So, this paper aims to fill this gap by examining the impact of the recombination of nonenvironmental and environmental technologies as well as the recombination of the diverse technological classes within green technologies on the creation of eco-innovative firms. This application is particularly relevant for the purposes of our paper since new environmental technologies rely more on the recombination of different pieces of knowledge that are often cognitively distant than non-environmental technologies do and they are also characterized by technological uncertainty that requires access to skills that cannot usually be found in the firm's knowledge domain [START_REF] De Marchi | Environmental innovation and R&D cooperation: Empirical evidence from Spanish manufacturing firms[END_REF][START_REF] Barbieri | A survey of the literature on environmental innovation based on main path analysis[END_REF][START_REF] Orsatti | Antecedents of green technologies: the role of recombinant capabilities and team knowledge production[END_REF]Fusillo 2019;[START_REF] Quatraro | Academic inventors and the antecedents of green technologies. A regional analysis of Italian patent data[END_REF].

Considering all these facts, we can formulate the following research question:

RQ2.

To what extent does the creation of eco-innovative firms benefit more from related variety than from unrelated variety (in terms of environmental technologies), or from both? From a regional policy perspective, it is important to understand whether these knowledge spillover may have an impact beyond départements limits. In this regard, previous contributions from Colombelli and Quatraro (2019) and [START_REF] Corradini | Location determinants of green technological entry: evidence from European regions[END_REF] argue that spatial path dependence and regional dynamics are important for fostering eco-innovation; however, they do not account for the fact that the local environmental technological knowledge base generated in neighbouring regions may spill over into other regions and that eco-innovative firm creation may be clearly influenced by the development and creation of environmental technologies in neighbouring regions [START_REF] Rennings | Increasing energy and resource efficiency through innovation-An explorative analysis using innovation survey data[END_REF]Horbach et al. 2013;Ghisetti et al. 2015).

Based on the above premises, we can formulate the following research questions:

RQ3. Is there a contagious effect in the creation of eco-innovative firms between regions? RQ4. Does the local environmental technological knowledge base may spill over into other regions?

Empirical analysis

Data

To analyse the location determinants of eco-innovative firms, we make use of patent, firmlevel data and regional economic statistics for the 96 metropolitan départements across France between 2003 and 2013. The département is an administrative division of France which is equivalent to NUTS3 regions. 38 Patent data are obtained from the OECD REGPAT (2018)39 database, which derives from two complementary sources of data: the European Patent Office's (EPO) Worldwide Statistical Patent Database (PATSTAT) and the EPO's epoline Database.

The patent count is conducted using specific selection criteria. We selected the patents filed in the European Patent Office (EPO) by companies located in France. Applicant count rather than inventor count is used. The applicants are assigned to the départements according to their addresses. Analyses of the geography of knowledge spillovers using disaggregated patent data have mainly used an inventorlevel analysis (see for instance, [START_REF] Henderson | Patent citations and the geography of knowledge spillovers: a reassessment: comment[END_REF][START_REF] Breschi | Mobility of skilled workers and co-invention networks: an anatomy of localized knowledge flows[END_REF]or Colombelli and Quatraro 2018, among others). However, inventors' residential addresses may not represent the place where the invention occurred [START_REF] Antonelli | Recombinant knowledge and growth: the case of ICTs[END_REF][START_REF] Quatraro | Knowledge coherence, variety and productivity growth: Manufacturing evidence from Italian regions[END_REF]Inoue et al. 2019). Because of this, in the present paper we rely on applicants' addresses. Applicants' addresses may capture the regional capacity to foster technology-based firms' entry more accurately than inventors' address may do it.

Patents are attributed to technological fields according to the CPC classification. We consider the main CPC codes and all their sub-classes. It is worth noting that a search strategy based on CPC classification is an easier and more reliable way to select green patents (for more details, see Veefkind et al., 2012). In fact, patents are assigned to technological fields using two main classifications:

International Patent Classification (IPC) and the Cooperative Patent Classification (CPC). Until now, most studies have been based on the IPC code (Kruse, 2016;Marin, 2014). When using this method, one should look for green patents in different classes (Dechezlepretre et al., 2011;[START_REF] Johnstone | Renewable Energy Policies and Technological Innovation: Evidence Based on Patent Counts[END_REF]. This data retrieval method carries two risks: selecting irrelevant patent applications and excluding relevant patent applications (Veefkind et al., 2012). Since 2013, the EPO has completed the CPC classification which includes a classification, named the Y class, dedicated to applications related to green technologies. So, in this paper, are considered as environmental patents climate change mitigation technologies related to: buildings (Y02B), energy (Y02E), transportation (Y02T), the production of goods (Y02P), capture and storage of greenhouse gases (Y02C), information and communication (Y02D), water waste and treatment (Y02W). We also include technologies for the adaptation to climate change (Y02A) and smart grids (Y04S).

innovations on the economy favouring a more rapid transition to a sustainable economy (Nelson and Winter 1982;Klepper 1996;[START_REF] Storey | The birth of new firms-does unemployment matter? A review of the evidence[END_REF]Tether 1998, Ejermo andXiao 2014;[START_REF] Corradini | Location determinants of green technological entry: evidence from European regions[END_REF]).40 69) or Gironde (3). In fact, these are départements specialised in the chemical and pharmaceutical industries, among other industries. Thus, we guess that in such industries the incentives to develop environmental innovations are higher than in other regions due to the specific needs associated with these industries. Among them, one of the most important factors encouraging the location of eco-innovative activity is knowledge spillovers from the innovative activities of incumbent firms [START_REF] Acs | The Knowledge Spillover Theory of Entrepreneurship[END_REF]2013). To capture the capacity of regions to generate and accumulate knowledge, we add knowledge stock indicators defined as the accumulated number of patent applications (STOCK_ALL) relying on the permanent inventory method. The same variable has also been built for environmental-based patents (STOCK_ENV) as well as for its complement, that is, for non-environmental-based patents (STOCK_NENV) as they may differently affect the entry of eco-innovative firms (Colombelli and Quatraro 2019) (see Annex A for the details).

Besides the accumulation of knowledge, when assessing the regional factors that may incentivise the entry of eco-innovative firms, it is important to consider the nature of the local knowledge base [START_REF] Colombelli | The impact of local knowledge bases on the creation of innovative start-ups in Italy[END_REF]Quatraro 2018, 2019). To do so, we follow the empirical approach based on the recombinant (multidimensional) knowledge approach proposed by Colombelli and Quatraro (2019). According to this approach, it is possible to examine the technological classes assigned to each of these patents. Each technological class is linked to another class j when the same is assigned to both classes, and then, we can refer to the co-occurrence of classes l and j within the same patent. Thus, the higher the number of patents assigned to both classes l and j, the stronger the link between both classes. On this basis, we calculate a set of variables measuring the degree of variety and complementarity that characterise the local knowledge base of French départements.

First, knowledge variety (KV_Y) measures the degree of technological diversification of the knowledge base. The measurement of knowledge variety is based on the information entropy index applied to the analysis of the geography of knowledge spillovers [START_REF] Frenken | The early development of the steam engine: an evolutionary interpretation using complexity theory[END_REF][START_REF] Frenken | Related variety, unrelated variety and regional economic growth[END_REF]. However, in this paper we adopt the multidimensional version of these indicators. Unlike studies using one-dimensional entropy, the analysis based on co-occurrences of technological classes allows an entropy index to be built up on the distribution of the co-occurrences of technological classes in patents, instead of considering the distributions of the single technological classes. In other words, it allows us to identify the degree of variety of knowledge combinations [START_REF] Colombelli | New firm formation and regional knowledge production modes: Italian evidence[END_REF]. This indicator can be further decomposed into related and unrelated knowledge varieties. In this paper we propose an application of these multi-dimensional variety indicators to the domain of environmental technologies (RKV_Y and UKV_Y). While in previous analysis that used variety indicators as key variables explaining the location determinants of green start-ups (Corradini 2019, Colombelli andQuatraro 2019), the degree of association among all technological classes was considered, in the present paper, we focus on the association of environmental technological classes with other technologies. In this way, we can disentangle the degree of diversity of the local knowledge base in terms of environmental inventions that was drowned out when using the former approach (see Annex B for more details). Concretely, RKV_Y measures the degree of technological differentiation within the macro-field or, in other words, the average variety within the technological subclasses belonging to the environmental class Y. UKV_Y measures the degree of technological differentiation across macro-fields, that is the average variety between the environmental technology class Y with the other subsets of technologies different from the Y class. This application is particularly relevant for the purposes of our paper since new environmental technologies rely more on the recombination of different pieces of knowledge that are more complex and often cognitively distant than non-environmental technologies do (Berbiere et al. 2016;[START_REF] Orsatti | Antecedents of green technologies: the role of recombinant capabilities and team knowledge production[END_REF]Fusillo 2019;[START_REF] Quatraro | Academic inventors and the antecedents of green technologies. A regional analysis of Italian patent data[END_REF].

Second, to account for the degree of complementarity among the technological classes we have calculated the knowledge coherence indicator (COH) that measures the average degree of relatedness or complementarity of the technologies that makes up the knowledge base of each département. This indicator is based on the works of [START_REF] Nesta | Firm knowledge and market value in biotechnology[END_REF], [START_REF] Nesta | Knowledge and productivity in the world's largest manufacturing corporations[END_REF] and [START_REF] Quatraro | Knowledge coherence, variety and productivity growth: Manufacturing evidence from Italian regions[END_REF]. Concretely, COH is computed by analysing the co-occurrence of technological classes within patent applications and it is based on how frequently technological classes are combined in use. Details about the calculations are provided in Annex C. Finally, regional capabilities to generate eco-innovations may be positively associated, to some extent, with the political support to environmental policies [START_REF] Santoalha | Diversifying in green technologies in European regions: does political support matter?[END_REF]). In the same way, local efforts to reduce the environmental impact of economic activities made either at the initiative of the establishment or to comply with the current legislation should incentivise the generation of environmental technologies (Requate 2005;Carrion-Flores et al. 2010;[START_REF] Barbieri | A survey of the literature on environmental innovation based on main path analysis[END_REF]Yue et al. 2011). To capture the population's support for environmental policies and the environmental engagement and efforts made by the industry to reduce the environmental impact, we include the share of votes for the ecologist party at the French national elections (VOTE_ECO)41 and the total amount of investments for the protection of the environment (ECO_INVEST). 42Control variables Socioeconomic characteristics such as unemployment (UNEMP), population density (POP_DEN) or specialised human capital (SCITECH) should be controlled for. Regarding the impact of unemployment on the creation of innovative firms, unemployment may be associated more with a Schumpeter (pull) effect than a refugee (push) effect. According to the Schumpeter effect, the creation of new firms is mainly driven by innovative ideas or market opportunities such as in the case of technology-based firms. Thus, the conditions leading to higher unemployment rates may deter the creation of new eco-innovative firms [START_REF] Storey | The birth of new firms-does unemployment matter? A review of the evidence[END_REF]Fritsch 2008). In the French case, [START_REF] Aubry | Entrepreneurship and the business cycle: the "Schumpeter" effect versus the "refugee" effect-a French appraisal based on regional data[END_REF] found that start-ups in all sectors in France are mainly explained by a refugee effect which limits the growth potential for new businesses in most regions, but this effect may differ for ecoinnovative firms. The refugee effect implies that the creation of firms may be a strategy to escape from unemployment when the individual is in bad conditions in the labour market [START_REF] Oxendfelt | New Firms and Free Entreprise: Pre-War and Post-War Aspects[END_REF].

Densely populated areas may be associated with more intensive interaction and higher productivity as well as with a higher potential demand. In line with previous studies [START_REF] Audretsch | Cultural diversity and entrepreneurship: a regional analysis for Germany[END_REF]Rodriguez-Pose and Hardy 2015), this measure serves as a proxy for agglomeration and urbanisation economies not directly related to technological activity. The role of different kinds of university knowledge in the creation of new ventures has been highlighted in previous studies [START_REF] Woodward | Beyond the Silicon Valley: University R&D and high technology location[END_REF][START_REF] Kirchhoff | The influence of university R&D expenditures on new business formations and employment growth[END_REF][START_REF] Acosta | University spillovers and new business location in hightechnology sectors: Spanish evidence[END_REF][START_REF] Bergmann | What makes student entrepreneurs? On the relevance (and irrelevance) of the university and the regional context for student start-ups[END_REF]) such as the knowledge embedded in university graduates that are employed in science and technology industries. Geography and institutional issues also matter [START_REF] Guimarães | Agglomeration and the Location of Foreign Direct Investment in Portugal[END_REF], as firms need good accessibility to services provided in cores, so it is necessary to control for accessibility to main cities such as Paris (DIST_PARIS). Proximity to the most important city in the country may capture on the one hand, a potential competition effect in view of agglomeration of firms in that area and, on the other hand, a competitive advantage in terms of the services and amenities located in and around Paris.

Econometric strategy

To analyse the location determinants of eco-innovative firms and their relationship with the technological knowledge base and composition of the départements, we estimated the eco-innovative firms' entry as a function of the set of technology indicators controlling for local characteristics. The specification to be estimated is formally defined as follows: where 𝛿 represents the time and département dummies and 𝜀 is the disturbance. The dependent variable, 𝐸𝐶𝑂_𝐸𝑁𝑇𝑅𝑌 𝑖𝑡 , is defined as a dichotomous variable being equal to 1 for those départements that experienced at least one eco-innovative entry in year t, and 0 otherwise since less than 1% of the départements experienced a multiple entry per year. 43 Considering the nature of our dependent variable, we estimate this model by a Generalised Linear Model (GLM) following a negative binomial distribution and logit link function via maximum likelihood. A GLM framework assuming a negative binomial (BN) distribution for the conditional expectation of the number of new eco-innovative firms in each département, provides a relatively flexible framework for the analysis of such data. The model includes time and département fixed effects. Results are robust to logit and probit model estimates. Still, as the fixed-effects logit estimator is potentially inconsistent in the presence of serial correlation and heteroscedasticity [START_REF] Wooldridge | Econometric analysis of cross section and panel data[END_REF]), the GLM is preferred. 44 To deal with that, cluster robust standard errors are included in the GLM regression to account for potential heterogeneity and serial dependence over time.

𝐸𝐶𝑂_𝐸𝑁𝑇𝑅𝑌 𝑖𝑡 = 𝛼 + 𝛽 1 𝑆𝑇𝑂𝐶𝐾_𝐴𝐿𝐿 𝑖𝑡-1 + 𝛽 2 𝑆𝑇𝑂𝐶𝐾_𝐸𝑁𝑉 𝑡-1 + 𝛽
The set of independent variables are one period lagged to avoid simultaneity problems.

Moreover, the set of knowledge variables are not simultaneously included in the model in order to avoid multicollinearity problems. 43 The focus on the entry of new innovative firms specialising in environmental technologies implies that in a year t and a département i the mean maximum number of entries would be equal to 3. The nature of this data implies that by using our dichotomic variable we can, to a great extent, explain the capacity of the region to incentivise the successful entry into the market of environmental technologies, as Corradini (2019) also suggested. 44 As robustness check, the same specification has also been estimated by using non-linear logit and probit models and results slightly vary. Results are available upon request.

Firm location determinants may extend beyond départements limits and if this potential spatial dependence is not considered, results may be biased and inconsistent (Anselin, 1988). To account for spatial dependence, we estimate the eco-innovative entries by applying spatial econometrics for panel data (Elhorst, 2010;2014). Concretely, a Spatial Durbin Model (SDM) was adopted through a rowstandardised contiguity weighting matrix according to previous contributions for the case of French metropolitan départements. 45 The SDM includes the spatial lag of the dependent variable as well as the spatial lag of one or more exogenous variables in the model. Thus, in this paper we include in the SDM the spatial lag for the key variables capturing the local knowledge structure. The choice of the SDM is justified for three main reasons. First, it is considered the best performing estimator among the set of spatial dependence models for panel data (i.e., Spatial Lag Model, Spatial Autoregressive Model and the Spatial Autocorrelation Model). It also allows for the estimation of the direct and indirect effects of the lagged variables on the dependent variable (i.e., the effects in the main region and in the neighbouring ones) (Elhorst, 2014). And second, the SDM is the most appropriate spatial model for the purposes of this paper since it allows us to capture any source of spatial dependence in terms of knowledge spillovers and firm location in the environmental domain spreading beyond geographical limits [START_REF] Colombelli | New firm formation and regional knowledge production modes: Italian evidence[END_REF][START_REF] Feng | Environmental Regulation, Green Innovation, and Industrial Green Development: An Empirical Analysis Based on the Spatial Durbin Model[END_REF]). 46

Results

The GLM results for the location determinants of eco-innovative firms' entries are reported in Table 3.2. As mentioned above, all variables are one period lagged in order to avoid simultaneity problems and all regressions include region and time fixed effects.

We first discuss the results for our key variables, that is, knowledge indicators. It is important to notice that the set of knowledge variables is not simultaneously included in the model in order to avoid multicollinearity problems. The first four specifications, shown in columns 1, 2, 3 and 4, are the results for the estimation including the knowledge stock of patents in all technologies, environmental and non-environmental, respectively. According to our results, differences in the accumulated knowledge capital stock are positive but not significant for the creation of new eco-innovative firms. Anyway, if we compare both coefficients, the impact of the stock of environmental patents seems to be slightly 45 Other spatial weighting definitions where considered such as 5 nearest neighbours or an inverse distance-based matrix.

Even so, the best fit of the model is obtained when we rely on a row-standardised contiguity weighting matrix, an approach that has already been used in previous contributions for the case of French metropolitan départements (see for instance, [START_REF] Elhorst | Evidence of political yardstick competition in France using a two-regime spatial Durbin model with fixed effects[END_REF]). 46 More details on the test procedure to find out the most appropriate model can be found in section 5.1. more important. In column 4, when adding both types of knowledge stock to the model they remain non-significant, but the stock of environmental patents now turns negative. These results hold without adding population density into the model due to the high correlation with these variables. Thus, these results answers our RQ1 and imply that 1) there is not a predominant knowledge base for the creation of eco-innovative firms and, 2) the local accumulated knowledge stock is not enough to characterise the knowledge generation capacity at a regional level and that it is necessary to account for the nature behind of the local knowledge base in the following models.

The creation of eco-innovative firms may be affected by the diversity of knowledge combinations specially associated with environmental technologies. This is what we test by adding the KV_Y, RKV_Y and UKV_Y indicators shown in columns 5, 6, 7 and 8. 47 In this case, even though the estimates for all these variables are positive, only those of unrelated knowledge variety for environmental technologies are significant, a result that holds when it is jointly added to the model with related knowledge variety for environmental technologies. Thus, this result answers to RQ2 and it may imply that the creation of eco-innovative firms is favoured by the possibility of combining novel pieces of knowledge coming from other technologies with that of environmental ones rather than new combinations of existing knowledge in the environmental field. Indeed, the fact that only unrelated knowledge for environmental technologies is significant suggests that the more diverse the technologies carried out within the département are, the higher likelihood of having an eco-innovative entry in the département. Still, the average coherence (COH) of the local knowledge base shows a positive but non-significant coefficient (see column 9).

Other important factors that may explain regional differences on the creation of ecoinnovative firms are the environmental political support and local sensitivity to environmental issues. This fact is supported by the positive and significant effect of that the total amount of investments for the protection of the environment (ECO_INVEST) has on the likelihood of creating an ecoinnovative firm across all specifications. The share of the vote for ecological parties is also positive, but just significant in the last specification (column 8).

Regarding the control variables, we observe positive and significant effects for population density and distance to Paris across the different model specifications. These results support the fact 47 Although we focus here on the variety indicators for environmental technologies, we have also calculated the usual KV, RKV and UKV considering the diversity across and within all technological classes. However, when they were included into the model, results were not significant for any of the variety indicators. We guess that this may be explained by the specific nature of environmental technologies that requires a greater recombination of different pieces of knowledge that are more complex and cognitively distant, compared to non-environmental technologies (Berbiere et al. 2016;[START_REF] Orsatti | Antecedents of green technologies: the role of recombinant capabilities and team knowledge production[END_REF][START_REF] Quatraro | Academic inventors and the antecedents of green technologies. A regional analysis of Italian patent data[END_REF]. Results are available upon request.

of the dependent variable, that is the entry of eco-innovative firms in neighbouring départements, we found a positive and significant effect on the entry of eco-innovative firms in the département of reference and this result remains stable across almost all specifications. In other words, this result implies that any increase in the creation of eco-innovative firms in neighbouring départements will have positive effects on the likelihood of eco-innovative entry in the local département. This can be interpreted as a positive contagious effect according to which the entrepreneurial dynamics in environmental technologies in neighbouring regions may encourage the entry to eco-innovative markets in the local département. Besides that, when considering any source of spatial dependence in the model we found interesting results. Now the coefficients of each of the local stock of knowledge capital (see columns 1, 2, 3) and knowledge diversity (see columns 4-6) turn out to be positive and significant when their spatial lags are considered. The larger effect is still found in the case of the unrelated variety for environmental technologies which is consistent with our previous results.

However, the estimates for their spatial lagged counterparts are non-significant. These results answer our RQ 3 and 4 and suggest two main facts. First, important spatial dynamics across French départements exist in the creation of new eco-innovative firms, but they are mainly explained by the development of these technological activities in neighbouring départements. Second, the development of a diverse but specialised knowledge base on environmental technologies at the local level is a relevant factor for the creation of these activities. Besides the SDM estimation results, the estimation of the direct and indirect effects through the implementation of a partial derivative approach allow us to fully capture spatial dynamics effects [START_REF] Pace | A sampling approach to estimate the log determinant used in spatial likelihood problems[END_REF]. Table 3.4 reports the direct, indirect and total effects for the set of knowledge indicators. If we focus on the direct effects, that is, the intra-regional impact of the explanatory variables, we found a positive and significant effect for the cumulated stock of patents in all kinds of technologies as well as for each of the knowledge variety indicators. The only exception is the measure of relatedness (coherence) which is positive but not significant. This result suggests that the variety of combinatorial patterns among the environmental and non-environmental technologies in the département affects the creation of eco-innovative firms. Regarding the indirect effects, that is the inter-regional impact of explanatory variables, it seems that they are positive but not statistically significant. These findings confirm our previous results highlighting the limited/local spatial effect on eco-innovative firm creation. In other words, the impact of knowledge spillovers mainly accrues within the département where the entry takes place, at least for the French case.

Discussion and conclusions

This paper contributes to the literature on the economy of innovation by analysing the location determinants of new innovative firms highly specialised in environmental technologies in the French metropolitan départements over the period 2003-2013. In particular, we study the role played by the features of the local knowledge base on the creation of new eco-innovative firms as well as the spatial dependence on this process by computing a specific set of knowledge indicators that capture the départements' specific recombination of technologies that define the capacity to generate environmental knowledge (patents).

The results of this work contribute to the academic debate on the geography of innovation in many ways and provide more evidence, in the case of environmental technologies, of some of the current challenges of the literature of regional diversification as Boschma (2016) pointed out. First, our results shed light on the impact of knowledge recombination process behind environmental inventions in the creation of new eco-innovative firms. Concretely, we found that the level of local knowledge stock is not enough to explain a higher likelihood of eco-innovative firms' entry. However, when we consider the spatial dimension, we found that pre-existing accumulated knowledge in both environmental and non-environmental technologies matters at local level, supporting the findings of [START_REF] Montresor | Green technologies and Smart Specialisation Strategies: a European patent-based analysis of the intertwining of technological relatedness and key enabling technologies[END_REF]. When it comes to understand the nature behind the regional knowledge base by our specific indicators for environmental knowledge recombination capacity of départements, we found that unrelated knowledge variety for environmental technologies (the average variety between environmental and non-environmental technologies classes making up environmental patents) plays a key role in explaining the location of eco-innovative firms. These results are in line with previous findings and support the idea that the creation of environmental technology-based firms benefits from the combination of diverse knowledge in unrelated domains [START_REF] Castaldi | Related Variety, Unrelated Variety and Technological Breakthroughs: An analysis of US State-Level Patenting[END_REF]Barbieri et al. 2018[START_REF] Barbieri | Knowledge sources and impacts on subsequent inventions: Do green technologies differ from non-green ones?[END_REF][START_REF] Barbieri | Regional diversification and green employment in US metropolitan areas[END_REF][START_REF] Quatraro | Academic inventors and the antecedents of green technologies. A regional analysis of Italian patent data[END_REF]. However, they do not corroborate those contributions that find a significant effect of relatedness and related variety on the entry of ecoinnovative firms (Colombelli and Quatraro 2019;[START_REF] Corradini | Location determinants of green technological entry: evidence from European regions[END_REF]. In fact, the greater significance of unrelated variety for environmental technologies suggests that due to the higher complexity and degree of novelty that characterise environmental technologies they may require a greater recombination of different pieces of knowledge that are cognitively distant, compared to non-environmental technologies [START_REF] Barbieri | A survey of the literature on environmental innovation based on main path analysis[END_REF][START_REF] Orsatti | Antecedents of green technologies: the role of recombinant capabilities and team knowledge production[END_REF][START_REF] Quatraro | Academic inventors and the antecedents of green technologies. A regional analysis of Italian patent data[END_REF]. When accounting for spatial dependence in our model, we found that the creation of eco-innovative firms in neighbouring départements may incentivise the eco-innovative entry in the local département, while the impact of knowledge spillovers from both related and unrelated technologies on the creation of ecoinnovative firms is quite local, that is, they remain within the boundaries of each département. Even though there are no contributions applying spatial econometrics to analyse the creation of ecoinnovative firms, the results confirm those contributions suggesting that spatial path dependence and regional dynamics are important in fostering eco-innovation [START_REF] Colombelli | The impact of local knowledge bases on the creation of innovative start-ups in Italy[END_REF]Quatraro 2019 or Corradini 2019). Finally, our results also confirm previous contributions arguing that environmental regulations may have rather a pull-effect on the economy to foster the creation of eco-innovative firms (Requate 2005;Carrion-Flores et al. 2010;[START_REF] Barbieri | A survey of the literature on environmental innovation based on main path analysis[END_REF]Yue et al. 2011).

Even though our results focus on the French case and should not be generalised to other countries, they have important territorial implications on the transition process to green regional development providing valuable evidence of the role that local specificities and the extension of knowledge spillovers play on the formulation of the smart specialisation policies such as the RIS3 developed in the European context (European Commission 2012). First, the significant and robust effect of unrelated variety for environmental technologies on eco-innovative firms' entry suggests that policies should account for the context-specific environmental recombination of existing activities in which the départements are specialised. For example, those regions specialised in the automotive industry would have a comparative advantage in developing the electric car in comparison to regions where the automotive industry is not developed. And this is a key implication for the success of RIS3 policies, since it implies that even regions that are not specialised in environmental technologies can also foster the creation of eco-innovative activities through the recombination of pre-existing nonenvironmental and new environmental knowledge [START_REF] Montresor | Green technologies and Smart Specialisation Strategies: a European patent-based analysis of the intertwining of technological relatedness and key enabling technologies[END_REF]. Second, our results highlight the importance of environmental political support. In this regard, environmental regulation may exert an incentive for firms to introduce innovations have a key role in the creation eco-innovative firms. Finally, the positive neighbouring effect in terms of entrepreneurial dynamics in environmental technologies appears also to be consistent with policies supporting eco-innovative entrepreneurship at regional level such as the Green Action Plan for SMEs (European Commission 2014) or its French version, the Programme d'investissement d'avenir (PIA) (ADEME 2018). However, given that the spatial scope of knowledge indicators is quite local, the effectiveness of policies aimed at enhancing knowledge spillovers for environmental technologies should carefully assess the local specificities rather than be designed at a regional or national level consistent with the Smart Specialisation logic.

Despite all this, this study does have some limitations. By relying on patents data to capture eco-innovative firms' entry we are not able to encompass the whole set of firms that innovate in the environmental domain since not all innovative firms may be able to apply for patents. Nevertheless, with this approach we can focus on highly technological-intensive firms operating in the environmental domain. In this regard, the use of survey data on entrepreneurs working on the environmental domain would provide additional evidence to support our findings. Furthermore, notwithstanding the use of lagged explanatory variables to reduce any potential endogeneity in our estimates, further studies may be required to shed more definite light on the causal relationships in our findings. Finally, even though the analysis at départemental level provides remarkable results in terms of policy implications, any future research should focus on analysing this issue at micro-level (i.e., cities, metropolitan areas, clusters, etc.) making use of GIS techniques to account for the impact of innovation ecosystems on the creation of eco-innovative firms.

𝑃 𝑔𝑧 = ∑ ∑ 𝑝 𝑙𝑗 𝑗∈𝑆 𝑧 𝑙∈𝑆 𝑔 (A2)
Which is the probability of observing the couple 𝑙𝑗 in the subsets g and z, while the intra subsets variety can be measured as follows:

𝐻 𝑔𝑧 = ∑ ∑ 𝑝 𝑙𝑗 𝑝 𝑔𝑧 𝑗∈𝑆 𝑧 𝑙∈𝑆 𝑔 𝑙𝑜𝑔 2 ( 1 𝑝 𝑙𝑗 /𝑃 𝑔𝑧 ) (A3)
The weighted within-group entropy or related variety for environmental technological classes (RKV_Y) can therefore be written as follows:

𝑅𝐾𝑉_𝑌 = ∑ ∑ 𝑃 𝑔𝑧 𝑍 𝑧=1 𝐺 𝑔=1 (A4)
Between group or unrelated variety for environmental technological classes (UKV_Y) can instead be calculated using the following equation:

𝑈𝐾𝑉_𝑌 ≡ 𝐻 𝑄 = ∑ ∑ 𝑃 𝑔𝑧 𝑍 𝑧=1 𝐺 𝑔=1 𝑙𝑜𝑔 2 ( 1 𝑃 𝑔𝑧 ) (A5)
According to the decomposition theorem, the total entropy H(X,Y) can be re-written as follows:

𝐾𝑉_𝑌 = 𝐻 𝑄 + ∑ ∑ 𝑃 𝑔𝑧 𝐻 𝑔𝑧 𝑍 𝑧=1 𝐺 𝑔=1 (A6)
The first term on the right-hand-side of Eq. ( A6) is the between-entropy and the second term is the weighted within entropy for environmental technological classes.

C. Knowledge coherence or relatedness

The degree of complementarity among the technological classes composing the local patent's portfolio have been calculated in different steps for the 96 French départements [START_REF] Nesta | Firm knowledge and market value in biotechnology[END_REF][START_REF] Nesta | Knowledge and productivity in the world's largest manufacturing corporations[END_REF][START_REF] Quatraro | Knowledge coherence, variety and productivity growth: Manufacturing evidence from Italian regions[END_REF].

Following [START_REF] Teece | Understanding corporate coherence: theory and evidence[END_REF], the weighted average relatedness (𝑊𝐴𝑅 𝑙 ) is defined as the degree to which technology 𝑙 is related to all other technologies 𝑗 ≠ 𝑙 in the regions'patent portfolio, weighted by patent count 𝑃 𝑗𝑡 .

𝑊𝐴𝑅 𝑙𝑡 = ∑ 𝜏 𝑙𝑗 𝑃 𝑗𝑡 𝑗≠𝑙 ∑ 𝑃 𝑗𝑡 𝑗≠𝑙 (A7)
Finally, the coherence of the region's knowledge base at time 𝑡 is defined as the weighted average of the 𝑊𝐴𝑅 𝑙𝑡 measure:

𝐶𝑂𝐻 𝑡 = ∑ 𝑊𝐴𝑅 𝑙𝑡 𝑙 × 𝑃 𝑙𝑡 ∑ 𝑃 𝑙𝑡 𝑙 (A8)
The technological relatedness measure 𝜏 𝑙𝑗 indicates that the utilisation of technology 𝑙 also implies the use of technology 𝑗, in order to perform specific functions that are not reducible to their independent use.

To set the 𝜏 parameter first we built a relatedness matrix as follows [START_REF] Nesta | Knowledge and productivity in the world's largest manufacturing corporations[END_REF] L'urgence de la mise en place des technologies respectueuses de l'environnement est admise au niveau international. Cependant, l'investissement des entreprises dans ces technologies reste conditionné à leur rentabilité. L'éco-innovation se caractérise par un problème de double externalité selon lequel une éco-innovation génère une externalité positive (relative à la diffusion de connaissances qui est commune à l'innovation classique) et elle permet d'éviter une externalité négative environnementale. Ainsi, une entreprise qui investit dans l'éco-innovation supporte un coût additionnel, lié à l'internalisation de l'externalité négative, par rapport aux entreprises concurrentes non environnementales. Cette spécificité de l'éco-innovation peut dissuader les entreprises à investir dans les éco-innovations. Cela a conduit à s'interroger sur l'incidence de l'éco-innovation sur la performance des entreprises. Afin d'apporter des réponses à cette question plusieurs auteurs se sont appuyés sur l'hypothèse de Porter (Dussaux, 2015;Marin, 2014;[START_REF] Marin | Productivity effects of eco-innovations using data on eco-patents[END_REF][START_REF] Porter | America's Green Strategy[END_REF]van Leeuwen and Mohnen, 2017). Celle-ci stipule qu'une réglementation environnementale stricte stimule la découverte et l'introduction d'innovations environnementales et l'efficience des processus de production. Les économies de coûts qui peuvent donc être générées peuvent être suffisantes pour compenser à la fois les coûts de mise en conformité à la réglementation et les coûts d'innovation.

Cependant, les résultats de la littérature empirique sur la rentabilité des éco-innovations pour les entreprises présentent une diversité considérable, allant de résultats positifs à des résultats négatifs et même non significatifs dans certaines études. Cette absence de consensus met en exergue la complexité de la relation entre l'éco-innovation et la performance des entreprises, et montre la nécessité d'approfondir l'analyse de cette relation. De ce fait, nous avons cherché à comprendre non seulement les facteurs qui motivent les entreprises pour éco-innover mais également l'incidence que peut avoir l'éco-innovation sur l'activité de l'entreprise.

Par ailleurs, la littérature empirique reconnaît la spécificité des stratégies d'éco-innovation adoptées par les Petites et Moyennes Entreprises (PME) par rapport à celles des grandes entreprises et souligne l'importance d'une investigation beaucoup plus approfondie des PME (Aragón-Correa et al., 2008;[START_REF] Bos-Brouwers | Corporate sustainability and innovation in SMEs: Evidence of themes and activities in practice[END_REF]. Les PME sont considérées comme participant à la dégradation de l'environnement [START_REF] Jenkins | A 'business opportunity' model of corporate social responsibility for smalland medium-sized enterprises[END_REF], elles peuvent donc être une solution prometteuse. L'argument en faveur des entreprises naissantes est leur forte capacité à développer des innovations radicales (Demirel et al., 2019;[START_REF] Wagner | Corporate Social Performance and Innovation with High Social Benefits: A Quantitative Analysis[END_REF]. L'orientation de ces entreprises est influencée par le dirigeant, leurs pratiques sont alors liées au profil de l'entrepreneur qui a donc un rôle crucial à jouer pour contrer le phénomène de la dégradation de l'environnement (Anderson, 1998;Bocken, 2015;[START_REF] Cohen | Market imperfections, opportunity and sustainable entrepreneurship[END_REF]. C'est pourquoi nous nous intéressons dans le deuxième chapitre de cette thèse à une catégorie de PME très peu étudiée dans la littérature sur l'éco-innovation, à savoir les start-ups, en analysant les facteurs qui motivent les dirigeants des start-ups à se lancer dans une activité écoinnovante.

Enfin, nous contribuons à la littérature, en analysant les déterminants spatiaux de la création d'entreprises éco-innovantes, en introduisant notamment des méthodes d'analyse d'économétrie Dans ce qui suit, nous présentons les résultats obtenus dans chacun des chapitres de cette thèse ainsi que les implications en termes de politique publique.

Le but du chapitre 1, intitulé « évaluation de la relation entre la recherche et développement (R&D), l'éco-innovation et la productivité : cas des entreprises françaises », était d'étudier à la fois les déterminants et les effets sur la productivité de l'innovation environnementale. Une analyse empirique, basée sur un modèle CDM [START_REF] Crépon | Research, Innovation And Productivity: An Econometric Analysis At The Firm Level[END_REF], est réalisée à partir d'un panel un panel de 2 877 entreprises françaises sur la période 2006-2014.

Les résultats montrent que les facteurs traditionnels qui stimulent l'innovation, tels que la part de marché, le capital fixe, la taille de l'entreprise, ont également un effet significatif sur l'éco-innovation.

De plus, la demande actuelle ou prévue des consommateurs de produits respectueux de l'environnement a également un effet positif important sur l'input (R&D environnementale) et l'output (Brevets environnementaux) de l'éco-innovation. Cependant, cette dernière présente certaines spécificités. Les instruments de politique environnementale sous forme de taxes, de redevances, ou de subventions ont un effet positif sur l'investissement en R&D environnementale.

De plus, les initiatives volontaires des entreprises pour identifier et réduire leur impact environnemental ont un effet positif élevé sur l'output de l'innovation. Par contre, l'effet de la réglementation environnementale sur l'output de l'éco-innovation s'est révélé non significatif. Les instruments de politique environnementale impactent de façon distincte les différents types d'écoinnovations. En particulier, il s'avère que, les subventions ont un effet positif sur les énergies renouvelables alors que l'adoption de technologies d'atténuation des émissions polluantes repose principalement sur les initiatives volontaires des entreprises.

Les résultats de l'analyse de l'impact de l'éco-innovation sur la productivité ont montré que lorsque l'on différencie les catégories technologiques, seule la mise en place des énergies renouvelables, a un effet positif sur la productivité. Par ailleurs, nous avons montré un rôle important des subventions sur les énergies renouvelables. Par contre, les technologies d'atténuation des émissions polluantes semblent avoir un effet négatif sur la productivité. Plusieurs études empiriques n'ont pas pu valider l'hypothèse de Porter (Dussaux, 2015;Greenstone et al., 2012;[START_REF] Lanoie | Environmental Policy, Innovation and Performance: New Insights on the Porter Hypothesis[END_REF]. Dans notre cas, la validation ou la non-validation dépend du type de technologie considéré. D'après nos résultats, cette hypothèse est en effet validée uniquement pour la catégorie des énergies renouvelables. Nous concluons que malgré l'effet considérable de la demande des consommateurs de produits respectueux de l'environnement sur le développement de l'éco-innovation, l'effet de l'éco-innovation sur la productivité reste faible. En d'autres termes, les incitations du marché à elles seules ne suffisent pas pour permettre un accroissement considérable de l'innovation environnementale des entreprises. -L'analyse taxonomique a montré que les éco-innovateurs n'ont pas un profil homogène. La principale implication de la diversité de profils d'éco-innovateurs est que l'élaboration de la politique publique doit prendre en compte cette diversité. Nous avons pu souligner que deux catégories de start-ups (les entrepreneurs seniors ayant des préoccupations environnementales et les opportunistes « leaders ») contribuent fortement au développement de l'éco-innovation radicale et/ou frontstage et affichent une forte orientation environnementale. Ces catégories disposent généralement des conditions nécessaires pour l'aboutissement de leur projet, notamment la disponibilité des ressources financières et l'accès à la connaissance externe (à travers la coopération). Cependant face à l'urgence des problématiques environnementales, il va falloir s'appuyer sur tous les profils pour éco-innover. De ce fait, la politique publique doit être adaptée dans la mesure du possible à chaque profil d'éco-innovateur:

• Nous avons souligné que les entrepreneurs seniors ayant des préoccupations environnementales créent leur entreprise avec des associés. Nous pouvons penser que cela favorise une complémentarité entre l'expérience et la disponibilité des moyens des seniors avec les compétences techniques des jeunes dans les nouvelles technologies52 [START_REF] Isele | Senior Entrepreneurship: The New Normal[END_REF][START_REF] Schott | Special topic report 2016-2017[END_REF]. Lorsque cette configuration n'apparaît pas spontanément, des efforts

  /www.legifrance.gouv.fr/affichTexte.do?cidTexte=JORFTEXT000031044385

  bases de données et les enquêtes existantes sur l'innovation ne permettent pas de disposer des informations nécessaires sur la stratégie d'éco-innovation et le financement des start-ups écoinnovantes. Les informations sur ce type d'entreprises sont donc difficilement accessibles. De ce fait, notre analyse s'appuie sur les résultats d'une enquête réalisée auprès de 120 start-ups françaises (écoinnovantes et non éco-innovantes) que nous avons menée en 2019.

Chapitre 3 :--

 3 Localisation des entreprises éco-innovantes : quel rôle pour les diffusions de connaissances ? Malgré l'intérêt de plus en plus élevé accordé à l'étude des déterminants de l'éco-innovation, les facteurs spatiaux de création d'entreprises éco-innovantes ont reçu très peu d'attention dans la littérature. L'objectif de ce chapitre est d'analyser les déterminants spatiaux de l'entrée des entreprises éco-innovantes. Pour ce faire, nous essayons principalement d'apporter des réponses à trois questions : Quel est le lien entre l'accumulation des connaissances au niveau régional et la création d'entreprises éco-innovantes ? -Dans quelle mesure la recombinaison d'innovations 7 peut-elle impacter la création d'entreprises écoinnovantes ? La création d'entreprises dans un département peut-elle être influencée par la création d'entreprises éco-innovantes et la base technologique des départements voisins ?

a

  été complétée par les caractéristiques économiques et spatiales au niveau départemental issues de sources diverses telles que l'Insee et Eurostat. Nous avons élaboré une base de données d'entreprises éco-innovantes distribuées sur les 96 départements français pour la période 2003-2013. En utilisant un modèle linéaire généralisé (GLM), nous avons estimé l'entrée des entreprises éco-innovantes en fonction d'un ensemble d'indicateurs technologiques en contrôlant les caractéristiques locales. Pour tenir compte de la dépendance spatiale, nous avons estimé les entrées d'entreprises éco-innovantes en appliquant un modèle d'économétrie spatiale sur données de panel (Elhorst, 2014, 2010). Concrètement, un modèle spatial de Durbin (SDM) a été adopté. Avec ce chapitre, nous contribuons à la littérature empirique de plusieurs façons. En premier lieu, nous calculons des indicateurs de connaissances technologique mesurés par les brevets : parenté technologique et variété technologique. Ces indicateurs permettent de comprendre la recombinaison des technologies qui constituent les innovations environnementales en France. En second lieu, nous introduisons des techniques d'autocorrélation spatiale afin de prendre en compte l'influence des régions voisines.

  (b) Financial data is drawn from the DIANE database maintained by the Bureau Van Dijk (BVD).14 This database contains information about balance sheet of approximately 16 million French firms.The first available year in DIANE is 2003. The study is based on the unconsolidated financial statements.

(

  2008 and 2014). To include as many observations as possible, I set the values for the years 2006 and 2007 equal to those of 2008 and the years 2012 and 2013 equal to those of 2014. I do so for all the variables taken from CIS survey (essentially environmental regulation variables), which is not unreasonable since the CIS surveys of 2008 and 2014 cover respectively the periods 2006-2008 and 2011-2014. This operation has the effect of considerably reducing the size of the sample. Finally, the data was cleaned up by removing implausible values such as recorded negative added values. The last estimation step (productivity equation) contains fewer observations due to missing values of value added.

  21 A general Cobb-Douglas production function with two inputs (labour, L, and capital, K) is given by the equation VA = AK α L β . Constant returns to scale imply that 𝛼 + 𝛽 = 1. Dividing both sides by L, taking the log and rearranging we obtain log ( 𝑉𝐴 𝐿 ) = log(𝐴) + 𝛼 log ( 𝐾 𝐿

  [START_REF] Muñoz | The call of the whole in understanding the development of sustainable ventures[END_REF] have used individual factors related to the entrepreneur (knowledge, orientation, intention) and external factors related to perceived social support and business support.They identified two paths: conformist "sustainability conveyors" operate in a social context characterized by the abundance of environmental values and convictions, their motivation is to comply with existing standards and values; whereas "insurgents" emerge in a context characterized by a lack of environmental convictions. The latter are entrepreneurs who find inspiration in personal values and must operate against defined standards. Based on internal organizational capabilities,Aragón-Correa et al. (2008) established a classification of SMEs. Being mainly interested in the level of proactivity 30 in the implementation of environmental practices, they distinguish three categories: reactive, pollution prevention and leadership. However, in most of these studies there is only an implicit reference to eco-innovation. They focus, rather, on corporate environmental practices.[START_REF] Bergset | Green start-upsa new typology for sustainable entrepreneurship and innovation research[END_REF] have established a conceptual typology of green start-ups based on a literature review. They focus on the factors that play an important role in the financing decisions of investors, including sustainability motivation and business qualification. They distinguish five categories of green start-ups: the alternative start-up, the visionary start-up, the inventive start-up, the "ecopreneurial" start-up, and the unintentionally green start-up. The latter is characterized by highly innovative entrepreneurs, is exposed to high risks and requires high levels of capital.[START_REF] Taylor | The green entrepreneur: Opportunist, Maverick or Visionary[END_REF] have presented a theoretical typology of eco-entrepreneurs based on their individual orientation and the intensity of external influence. They distinguish between innovative opportunist, visionary champion, ethical maverick and ad hoc "enviropreneur". The innovative opportunist has a purely economic objective and is greatly influenced by external pressures.To our knowledge, there are only four studies that have been devoted to the typologies of SMEs by studying eco-innovation.Triguero et al. (2013) study the diversity of eco-innovators based on eco-innovation intensity. Their study is based on the triangle model of van Dijken which considers three factors: business skills, environmental orientation, and network involvement. They distinguish between leaders, followers, loungers and laggards. Leaders are highly eco-innovative, while laggards have a low level of eco-innovation and reduced environmental impact.Doloreux and Kraft (2019) and[START_REF] Castellacci | A taxonomy of green innovators: Empirical evidence from South Korea[END_REF] study the main determinants of different types of eco-innovations.Doloreux and Kraft (2019) measure the intensity of eco-innovation by the diversity of eco-innovations developed (products and processes). They identify eco-innovation laggers, product-oriented ecoinnovators, process-oriented eco-innovators, and fully-integrated eco-innovators.[START_REF] Castellacci | A taxonomy of green innovators: Empirical evidence from South Korea[END_REF] identify the different determinants according to the types of eco-innovations. They distinguish between CO2-reducing, waste reducing, recycling and pollution-reducing innovators.[START_REF] Sáez-Martínez | Firm technological trajectory as a driver of eco-innovation in young small and medium-sized enterprises[END_REF] distinguish between technology-driven innovators, traditional firms, market-driven innovators, and sporadic innovators.

Figure 2 . 1

 21 Figure 2.1 Theoretical framework

Figure 2 .

 2 Figure 2.2 shows the distribution of the 120 start-ups according to their type of innovation: 21 are both eco-and non-eco-innovative. First, we will determine what differentiates eco-innovative firms

Figure 2 . 2

 22 Figure 2.2 Distribution of firms by type of innovation

  (ISO 5001, ISO 14001, ISO 16000, ISO14064) Système de management et d'audit environnemental Fixer des objectifs environnementaux pour vos produits Méthodes permettant d'être informé des nouvelles exigences réglementaires environnementales Analyser les impacts environnementaux de vos produits Gagner des parts de marché (compétitivité) Améliorer l'image de l'entreprise Faire face à une demande de plus en plus importante envers les produits respectueux de l'environnement Etre en phase avec les exigences règlementaires les types de partenaire(s) et la dimension géographique.Dans les cases commentaires, Mettez les lettres "r" pour un partenariat dans la même région administrative et "a" pour d'autres régions françaises ou internatinalesConcurrents ou autres entreprises de votre secteur d'activitéCommentaireAutres entreprises de votre groupe ou de votre réseau d

Figure 3 .

 3 Figure 3.1 shows the distribution of the number of eco-innovative firms' entry across the 96 French départements between 2003 and 2013. What stands out from this figure is a non-homogenous spatial distribution of eco-innovative firms' entry throughout French départements, as roughly 75% of innovative firms locate in and around Île-de-France and in the most populated départements such as Rhône (69) or Gironde (3). In fact, these are départements specialised in the chemical and pharmaceutical

Figure 3 . 1

 31 Figure 3.1 Geographical distribution of eco-innovative firm entries (2003-2013)

  . Let the technological universe consist of 𝑘 patent applications. Let 𝑃 𝑗𝑘 = 1, if the patent 𝑘 is assigned to technology 𝑗 [𝑗 = 1, … , 𝑛], and 0 otherwise. The total number of patents assigned to technology 𝑗 is 𝑂 𝑗 = ∑ 𝑃 𝑗𝑘 𝑘 . Similarly, the total number of patents assigned to technology 𝑚 is 𝑂 𝑚 = ∑ 𝑃 𝑚𝑘 𝑚 . Since two technologies may be present within the same patent, 𝑂 𝑗 ∩ 𝑂 𝑚 ≠ ∅, the number of observed cooccurrences of technologies 𝑗 and 𝑚 is 𝐽 𝑗𝑚 = ∑ 𝑃 𝑗𝑘 𝑃 𝑚𝑘 𝑘. By applying this relationship to all the possible pairs, we obtain a square matrix 𝛺 (𝑛 × 𝑛) where the generic cell is the observed number of co-occurrences: that the number 𝑥 𝑗𝑚 of patents assigned to both the 𝑗 and 𝑚 technologies is a hypergeometric random mean and variance variable: number of co-occurrences 𝐽 𝑗𝑚 is larger than the expected number of random cooccurrences 𝜇 𝑗𝑚 , then the two technologies are closely related: the fact that the two technologies occur together in the number of patents 𝑥 𝑗𝑚 is not random. Thus, the measure of relatedness is given the difference between the observed and the expected number of co-occurrences, weighted by their standard deviation: noted that this measure of relatedness has lower and upper bounds:𝜏 𝑗𝑚 ∈ ] -∞; +∞[.Moreover, the index shows a similar distribution to a t-student distribution; so, if 𝜏 𝑗𝑚 ∈ ] -1.96; +1.96[, one can assume the null hypothesis of non-relatedness of the technologies. Therefore, the technological relatedness matrix 𝛺 can be considered a weighting scheme to evaluate the technological portfolio of regionsConclusion généraleAfin de faire face aux défis environnementaux auxquels l'humanité est confrontée, il est crucial de mettre en place des mesures d'urgence et il est nécessaire que l'ensemble des acteurs économiques soient impliqués. Les entreprises se trouvent fortement touchées car elles sont confrontées à des contraintes relatives à la rareté des ressources et à la gestion des externalités négatives dont elles sont responsables, notamment la pollution. Elles sont donc amenées à apporter des solutions qui peuvent se matérialiser par le développement d'éco-innovations. De ce fait, les entreprises ont un rôle important à jouer dans l'atteinte des objectifs de développement durable. Par ailleurs, l'éco-innovation représente une opportunité pour les entreprises et les pays afin de faire face à une économie fortement concurrentielle tout en permettant aux économies de s'orienter vers une croissance « durable »(EC, 2011; OCDE, 2010). La nécessité de l'intervention des Etats afin d'accélérer l'activité d'éco-innovation est reconnue au niveau international (EC, 2011). Ainsi, la compréhension des déterminants de l'éco-innovation est d'une importance capitale afin d'orienter les politiques publiques. L'objectif de cette thèse est d'analyser les facteurs qui influencent la décision des entreprises d'investir dans une activité d'éco-innovation. Nous avons abordé cette question dans différents contextes en utilisant des données originales ce qui nous a permis d'identifier plusieurs spécificités des éco-innovations. Nous avons reconsidéré les principaux facteurs identifiés dans la littérature : les pressions technologiques, les pressions du marché et la politique environnementale. Nous contribuons à cette littérature en intégrant d'autres facteurs internes et externes non pris en compte dans la littérature en raison notamment de l'absence de données. De plus, nous proposons une approche originale en nous intéressant aux aspects entrepreneurial et spatial.

  spatiale. Cette approche a reçu peu d'attention dans la littérature et représente donc un sujet d'investigation relativement nouveau. Les principaux résultats de cette thèse montrent que les instruments de la politique environnementale impactent différemment les types d'éco-innovations (les technologies de traitement des déchets, les technologies d'énergie renouvelable et les technologies d'atténuation du changement climatique) et que les éco-innovations ne sont pas toujours rentables pour les entreprises. D'où la nécessité d'adapter les instruments de politique publique de sorte à améliorer la profitabilité des écoinnovations. L'étude des déterminants de l'éco-innovation dans le cas des start-ups a montré qu'il existe une diversité de profils d'éco-innovateurs. En effet, les start-ups se regroupent en cinq principaux profils qui correspondent à différents déterminants de l'éco-innovation. Nous avons montré empiriquement que les différentes catégories d'éco-innovateurs n'ont pas les mêmes difficultés d'accès aux fonds. Ce qui nous a amené à conclure que les politiques publiques doivent être conçues en tenant compte de la spécificité de chaque profil. Nous avons également montré que la recombinaison des technologies environnementales et non environnementales et le soutien des autorités publiques aux initiatives environnementales sont les principaux facteurs expliquant la localisation des entreprises éco-innovantes. De plus, en appliquant l'économétrie spatiale, nous avons constaté qu'il existe une dépendance spatiale claire vis-à-vis de leur création alors que l'impact de la diffusion des connaissances est plutôt local.

  Davantage d'efforts en termes de promotion et de réglementation environnementale semblent nécessaires pour booster ces innovations.Ces résultats suggèrent que la politique publique doit prendre en compte les spécificités des différentes catégories d'éco-innovations. Les instruments de politique publique existants semblent inadaptés à certaines catégories. Il est admis dans les discours politiques comme dans la littérature économique que l'activité de l'éco-innovation des entreprises a un rôle crucial à jouer collectivement parlant afin d'atteindre les objectifs du développement durable. Cependant, nos résultats indiquent que les écoinnovations ne sont pas toujours rentables pour les entreprises. Il est donc nécessaire d'identifier les instruments adaptés pour chaque type d'éco-innovation afin d'améliorer leur rentabilité pour généraliser leur mise en oeuvre par les entreprises. Le chapitre 2 est intitulé « Quels sont les déterminants de l'éco-innovation des start-ups françaises ? ». L'objectif de ce chapitre est, dans un premier temps, d'étudier la spécificité des start-ups éco-innovantes par rapport aux start-ups non éco-innovantes en effectuant une analyse discriminante (AD) sur deux thèmes : le capital humain et la politique environnementale. Dans un second temps, une typologie des start-ups éco-innovantes a été développée en utilisant une classification ascendante hiérarchique (CAH). L'analyse a été réalisée à partir de données originales issues d'une enquête que nous avons réalisée auprès de 120 start-ups françaises éco-innovantes et non éco-innovantes en 2019. En ce qui concerne le capital humain, les résultats montrent que l'accumulation de connaissances de l'entrepreneur notamment à travers des études et une expérience professionnelle en lien avec l'environnement est un facteur qui différencie entre les éco-innovateurs et non éco-innovateurs. En ce qui concerne la politique environnementale, les résultats indiquent que la présence d'une réglementation environnementale sous forme de normes et d'interdictions est un facteur qui distingue les éco-innovateurs des non-éco-innovateurs. Cependant, les instruments de type taxes environnementales ne permettent pas de discriminer les deux groupes de start-ups. En outre, la présence d'une démarche de responsabilité sociale des entreprises est un facteur de différenciation important. Les résultats de la typologie des start-ups éco-innovantes mettent en exergue une diversité de profils d'éco-innovateurs. En effet, les entrepreneurs peuvent être regroupés en cinq principaux profils : les entrepreneurs seniors ayant des préoccupations environnementales, les entrepreneurs par nécessité sans préoccupations environnementales, les entrepreneurs à forte conscience écologique, les entrepreneurs opportunistes axés sur le marché et n'ayant pas de préoccupations environnementales, et les opportunistes « leaders ». Ces résultats nous permettent de formuler les implications de politique publique suivantes : -Les résultats de l'AD indique que la politique publique ne doit pas uniquement se concentrer sur des instruments réglementaires mais doit également s'appuyer sur d'autres domaines d'application et notamment l'éducation. On voit d'ores et déjà émerger des initiatives pour former aux métiers de demain. Nous pouvons citer le cas des universités « campus transition » ou des « écoles de la transition écologique ». Ces initiatives sont en émergence et leur nombre reste très faible, il est donc nécessaire de renforcer et de soutenir ces initiatives. L'un des objectifs du plan d'investissement dans les compétences 51 (2018-2022) est de favoriser la qualification de la main-d'oeuvre pour répondre aux besoins de compétences nécessaires en lien avec la transition écologique. Par contre, nous considérons que cette action reste marginale et devrait prendre une plus grande ampleur.

  de mise en relation de ces deux parties pourraient être envisagés. De fait, il semblerait opportun d'encourager le co-entreprenariat intergénérationnel notamment par la mise en place d'associations et de structures d'accompagnement privés et public qui pourront mettre en lien ces deux parties ou en créant des plateformes de mises en relation. Cela pourrait permettre d'améliorer le fonctionnement des entreprises et donc du marché du travail tout en réduisant l'impact environnemental. • Les entrepreneurs à forte conscience écologique sont des start-ups qui peuvent jouer un rôle important dans le développement durable. Il est nécessaire de les soutenir via des financements et le développement de leur réseau. Les éco-investisseurs partagent les mêmes valeurs environnementales que ces entrepreneurs. Nous considérons que c'est un mode de financement adapté pour ces start-ups en raison des valeurs communes. Pour que les écoinvestisseurs et ces start-ups puissent se retrouver sur le marché, il est nécessaire que le réseau soit développé et organisé de façon formelle et que les start-ups et les investisseurs manifestent un signal clair de leur caractère « soutenable » (Demirel et al., 2019). En raison de cette asymétrie de l'information, il est nécessaire de faciliter le rapprochement entre les écoinvestisseurs et les entrepreneurs à forte conscience écologique en développant un indicateur reconnu par l'Etat pour évaluer les projets « soutenables ». De plus, il est nécessaire de développer et promouvoir des plateformes d'investissement « vert ». L'objectif du chapitre 3, intitulé localisation des entreprises éco-innovantes : quel rôle pour la diffusion de connaissances ?, est d'étudier le rôle que peuvent jouer les caractéristiques des connaissances technologiques locales à l'échelle des départements français sur la création de nouvelles entreprises éco-innovantes. Nous avons calculé un ensemble d'indicateurs de la base de connaissances technologiques (parenté technologique « knowledge relatedeness » et variété technologique « knowledge variety ») spécifiques qui permettent de comprendre la recombinaison des technologies qui constituent les innovations environnementales en France pour la période 2003-2013. En utilisant un ensemble de données riches et originales basées sur des micro-données au niveau des entreprises et des informations sur les brevets, les principaux résultats montrent que l'interaction entre les technologies environnementales et non environnementales et le soutien des autorités publiques en matière d'environnement sont les principaux facteurs expliquant la localisation des entreprises éco-innovantes. De plus, en appliquant des méthodes d'économétrie spatiale, nous avons constaté qu'il existe une dépendance spatiale dans la création des entreprises éco-innovantes, nous déduisons donc que l'impact de la diffusion de connaissances est principalement local. Ces résultats ont d'importantes implications en termes de préconisations de politique publique. Ces résultats suggèrent en effet qu'il est nécessaire de tenir compte des interdépendances spécifiques des technologies environnementales avec d'autres technologies dans lesquelles le département a une certaine tradition. Par exemple, les départements spécialisés dans l'industrie automobile auraient un avantage comparatif dans le développement de la voiture électrique par rapport aux départements où l'industrie automobile n'est pas développée.

  Cela représente donc un sujet d'investigation relativement nouveau dans ce domaine de recherche. Nous contribuons ainsi aux travaux étudiant le rôle que peut jouer les caractéristiques de la base de connaissances technologiques sur la création de nouvelles entreprises éco-innovantes. Nous construisons un ensemble d'indicateurs de connaissances technologiques spécifiques qui permettent de comprendre la recombinaison des Les éco-innovations ont des caractéristiques spécifiques qui les distinguent des innovations classiques notamment le problème de la double externalité qui décrit une situation dans laquelle il est souvent difficile de rivaliser avec des concurrents qui ne sont pas engagés dans une démarche environnementale, car ils n'ont pas à internaliser les coûts sociétaux (comme la pollution par exemple). C'est pour cette raison que différents auteurs se sont intéressés à l'identification des Afin de prendre en compte l'ensemble des aspects nécessaires à cette étude et d'adopter une approche complète en analysant l'ensemble de la chaîne de causalité allant des déterminants de l'écoinnovation jusqu'à l'impact de cette dernière sur la productivité, nous nous sommes appuyés sur plusieurs bases de données. La base de données des brevets de l'OCDE (REGPAT) a été utilisée pour sélectionner l'ensemble des entreprises qui ont déposé au moins un brevet pendant la période d'étude auprès de l'Office Européen de Brevets (EPO). Les brevets environnementaux ont été sélectionnés en utilisant la classification coopérative des brevets 6 (CPC). Les données obtenues ont été complétées par les données financières des entreprises issues de la base de données DIANE. En raison de l'absence d'identifiant commun entre les deux bases de données, nous utilisons le nom des entreprises et leur adresse pour les fusionner. Les noms et adresses des entreprises ne sont pas homogènes, il a été nécessaire de les homogénéiser puis les fusionner en utilisant un algorithme. Ensuite, nous avons intégré les données relatives à la recherche et développement, issues de l'enquête annuelle sur les

technologies relatives aux innovations environnementales en France. Dans cet objectif, nous mobilisons un ensemble de données particulièrement riches et uniques basées sur des micro-données d'entreprises et des informations sur les brevets. Pour répondre aux enjeux qui viennent d'être présentés, cette thèse est structurée en trois chapitres : Chapitre 1 : Evaluation de la relation entre la recherche et développement (R&D), l'éco-innovation et la productivité : cas des entreprises françaises Ce chapitre a pour objectif d'analyser à la fois les déterminants et les effets sur la productivité de l'éco-innovation.

acteurs spécifiques qui peuvent stimuler les entreprises à investir dans l'éco-innovation.

[START_REF] Crépon | Research, Innovation And Productivity: An Econometric Analysis At The Firm Level[END_REF]

ont développé un modèle, connu dans la littérature sous le nom de modèle CDM, qui permet à la fois d'identifier les déterminants de l'input de l'éco-innovation (R&D) et de l'output de l'éco-innovation (brevets), ce modèle permet également d'étudier l'incidence de l'écoinnovation sur la productivité. Ce modèle a été largement utilisé dans la littérature sur l'innovation. Plus récemment, en raison de l'urgence de la problématique environnementale, il a fait l'objet d'adaptations au cas des éco-innovations

(Marin, 2014;[START_REF] Marin | Productivity effects of eco-innovations using data on eco-patents[END_REF] van Leeuwen and Mohnen, 2017)

. Le modèle CDM a l'avantage de corriger les biais relatifs aux données sur l'innovation, notamment le biais de sélection et d'endogénéité.

En nous appuyant sur cette approche, le modèle que nous proposons dans ce chapitre est structuré en trois étapes. La première étape consiste à étudier la décision des entreprises à investir ou non dans la recherche et développement ; ensuite, dans le cas où elles choisissent d'entreprendre une activité de R&D, elles décident du montant à investir. Nous estimons plusieurs spécifications pour la recherche et développement notamment la R&D environnementale, la R&D non environnementale et la R&D totale. L'estimation est réalisée par la méthode en deux étapes de Heckman. La deuxième étape du modèle consiste à étudier le processus qui permet de passer de l'input de l'éco-innovation à l'output de l'éco-innovation (mesuré par les brevets environnementaux) et permet donc d'identifier les facteurs qui contribuent à l'aboutissement de l'activité de R&D. L'équation qui en résulte est estimée avec un modèle probit multivarié. La dernière étape consiste à étudier l'impact de l'écoinnovation sur la productivité. Nous estimons pour cette étape une fonction de production Cobb-Douglas augmentée d'une variable représentative de l'éco-innovation, mesurée par les brevets environnementaux. moyens consacrés à la R&D dans les entreprises et les informations relatives à la réglementation environnementale, issues de l'enquête communautaire sur l'innovation en France (CIS). Nous avons obtenu un panel non cylindré qui contient les observations de 2 877 entreprises sur la période 2006-2014.

Table 1 .1 Descriptive statistics

 1

	Variable	Description	Source	Obs	Mean	Std. Dev.	Min	Max
	I_RD	Total internal R&D per employee	R&D survey	7136	14.78575	26.83233	0	590.4889
	I_ENVRD	Environmental R&D per employee	R&D survey	7136	0.7086559	4.300837	0	132.2
	I_RDNE	Non-environmental R&D per employee	R&D survey	7136	14.07709	26.30955	0	590.4889
	MARKET_SHARE	Market share (%)	Own elaboration from	7136	2.659953	7.839286	0	97.62301
			DIANE
	L	Number of employees	DIANE	7136	1019.441	6676.183	1	236185
	K/L	Physical capital (tangible capital asset) per employee	DIANE	7136	59.79915	383.2044	0.0049235	14864.38
	VA/L	Value added per employee	DIANE	4902	97.50646	374.1164	0.2050526	23872.99
	REGULATION	Environmental regulation	CIS survey	7136	0.4831839	0.4997522	0	1
	SUBVENTION	Environmental subvention	CIS survey	7136	0.2900785	0.4538302	0	1
	ENV_DEM	Market demand for environmental products	CIS survey	7136	0.4126962	0.4923535	0	1
	ENV_INITIATIVE	Voluntary environmental practices	CIS survey	7131	0.5585472	0.4965952	0	1
	D_AGE	Age of the firm (dummy)	DIANE	7136	0.478139	0.4995569	0	1
	D_ENVP	Environmental patents	REGPAT	5694	0.028451	0.1662721	0	1
	D_RENW	Renewable energy patents	REGPAT	5694	0.0059712	0.0770492	0	1
	D_WM	Waste management patent	REGPAT	5694	0.0017562	0.0418743	0	1
	D_CCMT	Climate change mitigation patents	REGPAT	5694	0.0210748	0.1436464	0	1

Table 1 .2 Distribution of the observations for the main variables of the study

 1

	Period (2006-2014)	%	Period (2006-2014)

Table 1 .3 Eco-innovation input equations

 1

		(a) Total R&D	(b) Non-environmental R&D	(c) Environmental R&D
	VARIABLES	I_RD	D_RD	I_RDNE	D_RDNE	I_RDENV	D_RDENV
	MARKET_SHARE	0.0147***	-0.0113***	0.0125**	-0.0107***	0.00987**	0.00174
		(0.00465)	(0.00414)	(0.00618)	(0.00390)	(0.00483)	(0.00282)
	Ln (L)	-0.119***	0.181***	-0.0658	0.200***	-0.104	0.0865***
		(0.0319)	(0.0217)	(0.0533)	(0.0197)	(0.0788)	(0.0145)
	Ln (K/L)	0.148***	-0.0174	0.144***	-0.0150	0.127***	0.0294*
		(0.0238)	(0.0205)	(0.0308)	(0.0187)	(0.0372)	(0.0154)
	REGULATION	0.130	0.134*	0.0940	0.0195	0.372	0.292***
		(0.0827)	(0.0800)	(0.105)	(0.0701)	(0.270)	(0.0470)
	SUBVENTION	0.0981		0.0495		0.177***
		(0.0831)		(0.105)		(0.0605)
	ENV_DEM	0.0964	0.264***	0.0395	0.0781	0.503	0.400***
		(0.0851)	(0.0821)	(0.104)	(0.0701)	(0.356)	(0.0449)
	D_AGE	-0.123**		-0.127*		-0.0480
		(0.0576)		(0.0731)		(0.0479)
	Constant	0.817***	0.732***	0.538	0.724***	-2.867	-2.159***
		(0.287)	(0.183)	(0.426)	(0.172)	(2.823)	(0.131)
	Lambda	2.342***		3.012***		1.627
		(0.791)		(1.143)		(1.184)
	Observations	7,136	7,136	7,136	7,136	7,136	7,136
				Standard errors in parentheses
				*** p<0.01, ** p<0.05, * p<0.1

Table 1 .4 Eco-innovation output

 1

		Probit	Multivariate Probit
		(1)	(2)	(3)	(4)
	VARIABLES	D_ENVP	D_RENW	D_WM	D_CCMT
	Ln (I_RD*)	0.531***	0.291***	0.0387	0.253***
		(0.126)	(0.0756)	(0.115)	(0.0459)
	Ln (L)	0.636***	0.304***	0.346***	0.348***
		(0.0895)	(0.0464)	(0.111)	(0.0335)
	ENV_DEM	0.581***	0.253*	0.513**	0.345***
		(0.210)	(0.145)	(0.221)	(0.109)
	REGULATION	-0.185	0.00583	-0.271	-0.0940
		(0.220)	(0.139)	(0.222)	(0.122)
	ENV_INITIATIVE	0.528**	-0.118	0.141	0.304**
		(0.234)	(0.143)	(0.302)	(0.125)
	SUBVENTION	0.0850	0.337*	0.0127	0.198
		(0.192)	(0.190)	(0.207)	(0.129)
	Constant	-9.147***	-5.305***	-5.807***	-4.828***
		(0.920)	(0.445)	(0.739)	(0.252)
	atrho21		0.557***
			(0.163)
	atrho31		0.457***
			(0.0719)
	atrho32		0.275***
			(0.0866)
	Observations		5,690
	Number of firms		2,644
		Standard errors in parentheses
		*** p<0.01, ** p<0.05, * p<0.1

Table 1 .5 Labor productivity

 1 In this paper, I investigate the drivers of eco-innovation and its effect on productivity. Different types of R&D (total, non-environmental and environmental R&D) are tested, and I differentiate between three types of environmental patents (renewable energy, waste management, and climate change mitigation technologies in transportation, production, and transportation).

		(a)	(b)	(c)	(d)	(e)
	VARIABLES	Ln (VA/L)	Ln (VA/L)	Ln (VA/L)	Ln (VA/L)	Ln (VA/L)
	L. ENVP*	0.0262
		(0.0217)
	L. RENW*		0.0981***	0.0783**
			(0.0368)	(0.0305)
	L. WM*		0.0353		0.0410
			(0.0304)		(0.0262)
	L. CCMT*		-0.0708*			0.00475
			(0.0402)			(0.0320)
	Ln(L)	0.0348**	0.0387**	0.0308**	0.0365***	0.0490***
		(0.0168)	(0.0160)	(0.0130)	(0.0138)	(0.0154)
	Ln(K/L)	0.0747***	0.0766***	0.0743***	0.0757***	0.0769***
		(0.00975)	(0.00972)	(0.00960)	(0.00959)	(0.00974)
	Constant	4.007***	4.108***	4.146***	4.022***	3.824***
		(0.180)	(0.183)	(0.147)	(0.153)	(0.154)
	Observations	2,607	2,607	2,607	2,607	2,607
	Number of firms	1,447	1,447	1,447	1,447	1,447
		Standard errors in parentheses
		*** p<0.01, ** p<0.05, * p<0.1

2 What are the drivers of eco-innovation? Empirical evidence from French start-ups 24

	Appendix 1.1 : CPC classification of environmental technologies/
	Technology	CPC code
	Climate Change Mitigation Technologies (CCMT) related
	to buildings	Y02B
	Greenhouse gases (GHG) capture and storage	Y02C
	CCMT related to energy generation, transmission or
	distribution	Y02E
	CCMT in the production or processing of goods	Y02P
	CCMT related to transportation	Y02T
	CCMT related to wastewater treatment or waste
	management	Y02W
	Smart grids	Y04S
	Technologies for adaptation to climate change	Y02A
	CCMT in information and communication technologies	Y02D

Source: Elaborated by the author based on CPC scheme and CPC definitions Chapitre

Table 2 .1 Sample characteristics

 2

	50%
	45%
	40%
	35%
	30%
	20% 25%	(35.83%) 43		(46.67%) 56
	5% 10% 15%	(17.50%) 21
	0%
	Eco-Innovative Eco & Non Eco-	Non Eco-
		Innovative	Innovative
	Start-ups characteristics	Number of firms	Frequency
				(%)
	Sector
	Services, retail and waste management		12	10
	Professional, scientific and technical		60	50
	activities
	Manufacturing		20	17
	Information and Communication		28	23
	Total			120	100
	Age
	0-5 years			95	79
	6-12 years			25	21
	Total			120	100
	Turnover
	[0; 100 000]			50	42
	[100 001; 500 000]		25	21
	[500 001; 1000 000]		11	9
	More than one million		5	4
	The firm has not yet started to		29	24
	commercialize the products
	Total			120	100
	Employees
	0-5			83	69
	5-10			21	18
	10-65			16	13
	Total			120	100

Table 2 .

 2 1 provides detailed information on the final sample. The sample includes 120 start-ups mainly belonging to the following sectors: professional, scientific and technical activities, manufacturing, information and communication, services, retail and waste management. Since this sample was targeted as nascent entrepreneurs, most firms were established less than five years ago (79%), 87% have less than 10 employees, and 24% have not yet started to market their products.

Table 2

 2

.2 summarises the percentage of correctly classified and misclassified start-ups in each group. The percentage of correctly classified start-ups (69.17%) measures the quality of the discriminant model, the error rate is 30.83%: the model is satisfactory. The conditions which discriminate between the two groups of start-ups are those with a probability lower than the error risk chosen (p-value < = 5%).

Table 2 .2 Classification table for the Human capital explanatory theme Classification counts and (Percentages) Original groups Correctly classified Misclassified Total Eco-innovative Start-up

 2

	43.00	21.00	64.00
	( 67.19)	( 32.81)	(100.00)

Table 2 .3 Discriminant analysis results of Human capital explanatory theme

 2

	Linear discriminant analysis model: "Human capital" theme

Table 2 .4 Classification table for the Environmental policy explanatory theme Classification counts and (percentages)

 2

	Original groups	Correctly classied	Misclassified	Total
		38.00	26.00	64.00
	Eco-innovative Start-up	( 59.38)	( 40.63)	(100.00)
		44.00	12.00	56.00
	Non-eco-innovative Start-up	(78.57)	( 21.43)	(100.00)
		82.00	38.00	120.00
	Total	(68.33)	( 31.67)	(100.00)

Table 2 .5 Discriminant analysis results of Environmental policy explanatory theme

 2

			Linear discriminant analysis model: "Environmental policy" theme
	Variable	Modality	Parameter estimates	Standard Error	Student T-Test
	Number		Disc. Fonction	Regression
	Eco-innovative start-up:
	1 m2	REG-Yes selected	1.9324	0.8185	0.2960	2.77**
	2 m2	TI-Yes selected	10.0794	4.2694	4.7391	0.90
	3 m2	TPE-Yes selected	12.7902	5.4176	4.6184	1.17
	4 m1	NONE-Not selected	1.7184	0.7279	0.2839	2.56**
	5 m2	NA	13.8058	5.8478	4.6846	1.25
	5 m3	YES-CSR	2.6015	1.1019	0.490	2.25*
	Non-eco-innovative start-up:
	1 m1	REG-Not selected	-1.2012	-0.5088	0.1840	-2.77**
	2 m1	TI-Not selected	-0.0847	-0.0359	0.0398	-0.90
	3 m1	TPI-Not selected	-0.1075	-0.0455	0.0388	-1.17
	4 m2	NONE-Yes selected	-1.3593	-0.5758	0.2246	-2.56**
	5 m1	NO-CSR	-2.5316	-1.0723	0.4339	-2.47**
		CONSTANTE	0.130223	0.000000
	D2 = 0.69044 T2 = 21.34278	p-value = 0.001
	The overall rate of misclassification : 31.67%
			Significance level  : **  1% ; *]1% , 5%]

Table 2 .6 Significant results of the hierarchical classification

 2

	Theme		Cluster 1:		Cluster 2:		Cluster 3:		Cluster 4:	Cluster 5:
		Senior entrepreneurs	Necessity		Entrepreneurs with	Market-driven opportunist	Opportunist leaders
		with environmental	entrepreneurs without		ecological awareness	entrepreneurs without
			concerns		environmental					environmental concerns
					concerns
								Actives Variables
	Financial resources + Fund raising		-Fundraising	-Fundraising			-Grant from the European	+ Fundraising
		+ Funding through			-Grants for environmental	Union	+ Private investors or venture capital funds
		personal contribution			innovation activity		+ Regional grant
		+	Grants	for			-Grants from national
		environmental				organizations
		innovation activity				-	Grants	from	local
		+ National grants				authorities
	Eco-innovation	+	Radical	eco-	-Technologies aimed at	+	Incremental	eco-	None	+ Technologies aimed at reducing polluting
	Type	innovations		reducing	polluting	innovation			emissions (CO2, NO2, etc.)
		-	Backstage	eco-	emissions		+ Backstage eco-innovation	+ Adoption of a new innovation by the start-up
		innovation				-R&D activity			+ Frontstage eco-innovation
											+ Internal R&D
	Environmental	+	Environmental	+ Eco-Brainstorming	None			+	Not	concerned	by	+ Environmental Benchmarking (Often)
	orientation	benchmarking		(Rarely)						environmental management	+ Eco-Brainstorming (Often)
		(sometimes)		+	Frontstage-					+ Eco-conception (Never)	+	Frontstage-	Environmental	impact
		+ Environmental impact	Environmental impact					-	Frontstage:	Setting	assessment (Often)
		assessment		assessment						environmental goals	+ Eco-conception (Often)
		+	Employees	(Rarely)						+ Environmental management and audit system
		Environment	related	-Setting environmental					+ Setting environmental goals
		training		goals
	Motivation	-Cost savings		-Increasing demand for	+ Ecological awareness		+ Increasing demand for green	+ Cost savings
					green products	-Cost savings			products	-Ecological awareness
	Access to external	None			None		-	Cooperation	with	-Cooperation	+ Cooperation with universities or higher
	knowledge						universities			-Exchange of ideas related to	education institutions
											the environment through	+ Cooperation with suppliers
											meetings	+ Cooperation with consultants, commercial or
											private laboratories
											+ Cooperation with other companies of the
											same group or the retail network

Table 2 .6 Significant results of the hierarchical classification (Continued)

 2

	Cluster 1:	Cluster 2:	Cluster 3:	Cluster 4:	Cluster 5:
	Senior entrepreneurs	Necessity	Entrepreneurs with	Market-driven opportunist	Opportunist leaders
	with environmental	entrepreneurs without	ecological awareness	entrepreneurs without
	concerns	environmental		environmental concerns
		concerns
			Illustrative variables
	+Three or more co-	+ No co-founder	+ Turnover <100,000 €	None	+ Environment related professional experience
	founders + Founder's age: 55 and	+ Job-seeker founder	+ Bachelor diploma + None-Not selected		of the founder (5-10 years) + Average number of employees*: 11,688
	over

vous obtenu un diplôme en lien avec l'environnement ?

	B3.	Quel est votre plus haut diplôme obtenu ?
		Doctorat (Bac +8)
		Master, Ingénieur (Bac +5)
		Licence (Bac +3)
		Bac général ou professionnel
		BEP, CAP
		Sans diplôme
		Autre
		Autre
	B4.	Avez-Oui
		Non
	B5.	Quelle
		Oui
		Non

était votre situation professionnelle avant la création de votre entreprise actuelle ?

		Salarié
		Demandeur d'emploi
		Étudiant
		Dirigeant dans une autre entreprise
		Autre
		Autre
	B6.	Si vous étiez

salarié ou dirigeant dans une autre entreprise, l'expérience acquise a-t-elle un lien avec l'environnement ?

	B7.	Combien d'
		Oui
		Non

années cette expérience en entreprise a-t-elle duré ?

		Moins d'un an
		Entre un et cinq ans
		Entre cinq et dix ans
		Plus de dix ans
		Autre
		Autre
	B8.	Existe-

il un autre cofondateur de l'entreprise?

		Oui
		Non
	B9.	Indiquer

le nombre de cofondateurs Partie C: Activités d'innovations

Les deux parties qui suivent concernent l'innovation Non environnementale et l'innovation environnementale.

Veuillez lire les définitions suivantes avant de répondre aux questions : « L'innovation est la mise au point des produits, des procédés ou des associations de produits et procédés technologiquement nouveaux ou notablement améliorés …».

(Manuel d'Oslo, 2005)

« L'innovation environnementale est la production, l'assimilation ou l'exploitation d'un produit, d'un procédé de production, d'un service, ou d'un outil managérial qui est nouveau pour l'organisation qui le développe ou l'adopte, et qui conduit à la réduction des risques environnementaux, de la pollution, et des autres impacts négatifs de l'utilisation de ressources (y compris de l'énergie) ... ».

(Rapport de l'OCDE "mesurer l'éco-innovation", 2008)

C1.

Veuillez indiquer la ou les innovations qui vous concernent :

	Innovation Non environnementale
	Innovation environnementale
	Les deux types d'innovations ( environnementale et Non environnementale)

L'entreprise n'est pas concernée par l'activité d'innovation

Partie D:

Innovation Non environnementale D1. Au cours des cinq dernières années, votre entreprise a-t-elle introduit ou développé une ou plusieurs innovation(s) ?

		Oui
		Non
	D2.	Au cours des cinq dernières années, les innovations introduites étaient-elles :
		Entièrement nouvelles au marché ( Innovation radicale)
	Une amélioration des produits , services, procédés,…existants sur le marché ( Innovation incrémentale)
		L 'adoption d'une nouvelle innovation par l'entreprise
		Autre
		Autre
	D3.	À

quelle fréquence avez-vous adopté les pratiques et mécanismes suivants au cours de cette phase initiale d'innovation ?

		Améliorer l'image de l'entreprise
		Etre en phase avec les exigences règlementaires environnementales
				Autre
		Autre
	D5.	Au cours des
		Jamais	Rarement	Parfois	Souvent
		1-Benchmarking
		2-Brainstorming
		3-Technology roadmap (Feuille de route technologique)
		4-Analyse ou étude de marché
	D4.	Pour quel(s) motif(s) votre entreprise a-t-elle introduit ou développé une innovation ?
			Réduire les coûts
		Gagner des parts de marché (compétitivité)

1-Benchmarking: processus continu d'évaluation des produits, des services et des méthodes par rapport à ceux des concurrents.

2-Brainstorming: recenser le maximum d'idées, d'information ou de solutions

3-Feuille de route technologique: document qui décrit le plan pour atteindre des objectifs à court et à long terme grâce à l'utilisation de solutions technologiques.

cinq dernières années, pour vos activités d'innovations, avez-vous effectué des levées de fonds ?

		Oui
		Non
	D6.	Indiquer

la ou les source(s) de financement.

		Financements par un apport personnel, des amis ou la famille
		Investisseurs privés ou fonds de capital-risque
		Crowfunding (financement participatif)
		Business Angel (investisseur providentiel)
		Fonds de capital développement
		Autre
		Autre
	D7.	Votre entreprise a-t-

elle bénéficié, au cours des cinq dernières années, de subventions liées à l'activité d'innovation ?

		Autre
		Autre
	D9.	Votre entreprise réalise-t-elle des activités de Recherche et Développement (R&D) ?
		Oui, en interne
		Oui, en externe (par d'autres entreprises, organismes de recherche publics ou privés)
		Aucune activité de R&D
	D10.
		Oui
		Non
	D8.	Indiquer leur(s) origine(s).
		Collectivités locales (Région, départements, communes)
		Organismes nationaux (Ministères, ANR…)
		Union Européenne (FEDER, FSE …)

Votre entreprise a-t-elle coopéré avec d'autres entreprises ou organismes pour ses activités d'innovations ?

Oui

Non D11. Indiquer les types de partenaire(s) et la dimension géographique.

Votre entreprise a-t-elle déposé, ces cinq dernières années, un brevet ? Oui Non D13. Les salariés bénéficient-ils de formations en interne ou externe à l'entreprise ? Oui Non D14. Les salariés de l'entreprise participent-ils à des échanges d'idées sur les thématiques en lien avec les activités d'innovation de l'entreprise (ateliers, conférences, ou séminaires) ? Oui Non Partie E: Innovation environnementale E1. Au cours des cinq dernières années, votre entreprise a-t-elle introduit ou développé une ou plusieurs innovation(s) environnementale(s) ?

		Fournisseurs d'équipements, matériels, composants, logiciels
		Commentaire
	Organismes publics de R&D ou instituts privés à but non lucratif Universités ou établissements d'enseignement supérieur Autre Non Au cours des cinq dernières années, parmi les innovations Commentaire Commentaire Autre environnementales suivantes, indiquer celle(s) que vous avez introduite(s) ou développée(s). Économie d'énergie ( Isolation, solutions d'économie en matière d'éclairage, de climatisation ou de chauffage,…) Technologies d'énergies renouvelables ( Eolienne, solaire, biomasse, …) Recyclage et/ou diminution des déchets Technologies visant à réduire les émissions polluantes ( CO2, NO2, …) Autre D12. Oui E2. Autre
	E3.	Au
		Concurrents ou autres entreprises de votre secteur d'activité
		Commentaire
		Autres entreprises de votre groupe ou de votre réseau d'enseigne
		Commentaire
		Consultants, laboratoires commerciaux ou privés
		Commentaire

cours des cinq dernières années, les innovations environnementales introduites étaient-elles :

	E4.
	Entièrement nouvelles au marché (Innovation radicale)
	Une amélioration des produits , services, procédés,…existants sur le marché (Innovation incrémentale)
	L' adoption d'une nouvelle innovation par l'entreprise
	Autre
	Autre

Innovation environnementale « Front stage » est la phase initiale du processus d'innovation. Elle est caractérisée par l'identification de l'opportunité et la sélection de l'idée. À quelle fréquence avez-vous adopté les pratiques et mécanismes suivants au cours de cette phase initiale d'innovation environnementale ?

			Définitions de concepts
	1-Eco-conception: intégrer la protection de l'environnement dès la conception des biens ou services. Elle a pour objectif de réduire les impacts
	environnementaux des produits tout au long de leur cycle de vie : extraction des matières premières, production, distribution, utilisation et fin de
				vie).
	2-Benchmarking environnemental: évaluer la performance environnementale de l'entreprise et identifier des pratiques susceptibles de l'améliorer)
	3-Eco-Brainstorming :recenser le maximum d'idées, d'information ou de solutions en mettant l'accent sur l'aspect environnemental)
	Jamais	Rarement	Parfois	Souvent
	1-Eco-conception
	2-Benchmarking environnemental
	3-Eco-Brainstorming
	4-Analyser les impacts environnementaux des produits
	5-Fixer des objectifs environnementaux aux produits

  3 𝑆𝑇𝑂𝐶𝐾_𝑁𝐸𝑁𝑉 𝑡-1 + 𝛽 4 𝐶𝑂𝐻 𝑡-1 + 𝛽 5 𝐾𝑉_𝑌 𝑡-1 + 𝛽 6 𝑅𝐾𝑉_𝑌 𝑖𝑡-1 + 𝛽 7 𝑈𝐾𝑉_𝑌 𝑖 𝑡-1 +𝛽 8 𝑈𝑁𝐸𝑀𝑃 𝑡-1 + 𝛽 9 𝑃𝑂𝑃_𝐷𝐸𝑁 𝑡-1 + 𝛽 10 𝑆𝐶𝐼𝑇𝐸𝐶𝐻 𝑡-1 + 𝛽 11 𝐷𝐼𝑆𝑇_𝑃𝐴𝑅𝐼𝑆 𝑡 + 𝛽 12 𝐸𝐶𝑂_𝐼𝑁𝑉𝐸𝑆𝑇 𝑡-1 + 𝛽 13 𝑉𝑂𝑇𝐸_𝐸𝐶𝑂 + 𝛿 𝑖𝑡 + 𝜀

Table 3 .2 GLM logit regression estimates

 3

			(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
		POP_DEN	-	-	-	-	0.013**	0.013**	0.013**	0.013**	0.013**
							(0.005)	(0.005)	(0.005)	(0.005)	(0.005)
	Socioeconomic factors	UNEMP SCITECH	-1.804*** -1.829*** -1.801*** -1.837** (0.578) (0.588) (0.576) (0.605) -3.049 -3.234 -3.029 -3.647	-1.559*** -1.594*** -1.572*** -1.613*** -1.679*** (0.542) (0.542) (0.544) (0.554) (0.526) -2.213 -2.308 -1.644 -1.038 -1.438
			(11.51)	(11.56)	(11.50)	(11.66)	(11.42)	(11.39)	(11.48)	(11.50)	(11.32)
		DIST_PARIS	0.577***	0.578***	0.578**	0.609*	0.602**	0.608	0.612***	0.628***	0.637**
			(0.217)	(0.202)	(0.232)	(0.223)	(0.247)	(0.187)	(0.142)	(0.171)	(0.324)
		ECO_INVEST	0.030*	0.029*	0.030*	0.032*	0.037**	0.037**	0.037**	0.038**	0.037**
	Environmental		(0.016)	(0.016)	(0.016)	(0.016)	(0.016)	(0.016)	(0.016)	(0.016)	(0.016)
	political support	VOTE_ECO	0.745	0.734	0.747	0.788*	0.744	0.765	0.758	0.789	0.806*
			(0.482)	(0.482)	(0.482)	(0.475)	(0.486)	(0.489)	(0.482)	(0.483)	(0.479)
		STOCK_ALL	0.000	-	-	-	-	-	-	-	-
			(0.000)
		STOCK_ENV	-	0.001	-	-0.009	-	-	-	-	-
				(0.002)		(0.013)
		STOCK_NENV	-	-	0.0002	0.001	-	-	-	-	-
					(0.0003)	(0.001)
	Knowledge	KV_Y	-	-	-	-	1.008	-	-	-	-
	indicators						(0.764)
		RKV_Y	-	-	-	-	-	1.171	-	-1.117	-
								(1.423)		(1.890)
		UKV_Y	-	-	-	-	-	-	2.211*	2.928*	-
									(1.342)	(1.684)
		COH	-	-	-	-	-	-	-	-	0.241
											(0.790)
		Constant	-14.84*** -13.87*** -14.87*** -14.255*** -17.72***	-17.50	-17.89	-17.93*** -17.32***
			(2.604)	(2.632)	(1.380)	(1.077)	(3.066)	(2.975)	(2.017)	(1.974)	(3.834)
	N		960	960	960	960	960	960	960	960	960
	Départements		96	96	96	96	96	96	96	96	96
	Time and département FE	Y	Y	Y	Y	Y	Y	Y	Y	Y
	Log-pseudolikelihood	-147.236	-147.266	-147.082	-147.082	-145.666	-146.104	-145.313	-145.202	-146.314
	Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1

Table 3 .3 Spatial Durbin Model Estimates

 3

	DEP. ECO_ENTRY	VAR. :	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	W_ECO_ENTRY	0.073	0.073	0.073	0.084*	0.084*	0.082*	0.08*
			(0.046)	(0.046)	(0.046)	(0.046)	(0.046)	(0.046)	(0.046)
	POP_DEN		-	-	-	0.001***	0.001***	0.001***	0.001***
						(0.000)	(0.001)	(0.01)	(0.000)
	UNEMP		0.017*	0.017*	0.017*	0.015	0.015	0.015	0.014
			(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.01)
	SCITECH		0.154	0.138	0.158	0.102	0.154	0.133	0.233
			(0.336)	(0.336)	(0.336)	(0.339)	(0.339)	(0.339)	(0.34)
	DIST_PARIS		-	-	-	-	-	-	-
	ECO_INVEST		0.002*	0.002*	0.002*	0.001	0.001	0.001	0.001
			(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
	VOTE_ECO		0.037	0.037	0.036	0.032	0.028	0.032	0.025
			(0.023)	(0.023)	(0.023)	(0.023)	(0.023)	(0.023)	(0.024)
	STOCK_ALL		0.000***	-	-	-	-	-	-
			(0.000)
	STOCK_ENV		-	0.0008***	-	-	-	-	-
				(0.0001)
	STOCK_NENV	-	-	0.000***	-	-	-	-
					(0.000)
	KV_Y		-	-	-	0.132***	-	-	-
						(0.000)
	RKV_Y		-	-	-	-	0.182**	-	-
							(0.062)
	UKV_Y		-	-	-	-	-	0.208***	-
								(0.061)
	COH		-	-	-	-	-	-	0.024
									(0.028)
	WX		0.000	0.000	0.000	-0.092	-0.149	-0.134	-0.032
			(0.000)	(0.000)	(0.000)	(0.066)	(0.116)	(0.114)	(0.042)
	N		960	960	960	960	960	960	960
	Time FE		Y	Y	Y	Y	Y	Y	Y
	Département FE		Y	Y	Y	Y	Y	Y	Y
	F-Test		1.469**	1.469**	1.469**	1.309**	1.349**	1.327**	1.408***
	Hausman Chi 2		34.16***	35.76***	33.846***	19.82**	18.36**	19.23**	18.39**
	Robust Standard Errors in parentheses.
	*** p<0.01, ** p<0.05, * p<0.1

Table 3 .4 Direct, indirect and total effects of the relevant variables after SDM estimation

 3

		Direct	Indirect	Total
	STOCK_ALL	0.0001***	0.0000	0.0001***
		(0.000)	(0.000)	(0.000)
	STOCK_ENV	0.0007***	0.000	0.0008***
		(0.000)	(0.000)	(0.000)
	STOCK_NENV	0.0001***	0.0000	0.0001***
		(0.000)	(0.000)	(0.000)
	COH	0.0212	0.0012	0.0224
		(0.027)	(0.002)	(0.028)
	KV_Y	0.1267***	0.0081	0.1348***
		(0.034)	(0.007)	(0.037)
	RKV_Y	0.1820**	0.0113	0.1933**
		(0.059)	(0.009)	(0.065)
	UKV_Y	0.1918**	0.0119	0.2038**
		(0.067)	(0.011)	(0.072)
	Control variables	Y	Y	Y
	N	960	960	960
	Départements	96	96	96
		Standard errors in parentheses.

*** p<0.01, ** p<0.05, * p<0.1

Cette étude a porté sur 5 pays européens : France, Allemagne, Italie, Pays-Bas et Espagne. Le rapport est accessible sur ce lien https://www.intracen.org/publication/The-European-Union-market-for-sustainable-products/

Deux principaux critères ont été retenus pour sélectionner les start-ups : avoir une activité d'innovation, qu'elle soit environnementale ou non, et l'âge de la start-up est inférieur à 12 années. L'échantillon constitué est composé à 79% de start-ups créées il y a moins de

ans.[START_REF]6 Chapitre 1 An assessment of the relationships between R&D, eco-innovation and productivity: Evidence from French firms[END_REF] Selon l'Insee les PME sont « des entreprises qui, d'une part, occupent moins de 250 personnes, d'autre part, ont un chiffre d'affaires annuel n'excédant pas 50 millions d'euros ».

La classification coopérative des brevets (CPC) est le nouveau système de classification des brevets introduit depuis 2013 afin de normaliser les systèmes de classification de tous les principaux bureaux de brevets. Il s'agit d'une extension de la classification internationale des brevets (CIB), il en a résulté des classes et sous-classes beaucoup plus détaillées et mieux structurées, ce qui facilite et rend la sélection des brevets plus précise.

Tarduction française des termes "knowledge variety" et "knowledge relatedeness".

This chapter has been presented in several conferences including AFSE, FAERE, ECFED and the 1st sustainability workshop.

This measure indexes eco-innovation performance across the EU Member States. It aims at capturing the different aspects of eco-innovation by applying 16 indicators grouped into five dimensions: eco-innovation inputs, eco-innovation activities, eco-innovation outputs, resource efficiency and socio-economic outcomes. https://ec.europa.eu/environment/ecoap/indicators/index_en

This is a regional office including 38 European members in 2017. Applicants submit a patent application to this office when they want to protect their inventions in several European countries. This procedure is used when the applications are expected to be of high value and commercially profitable.

Several dates are attributed to a patent. The priority year is the year of the application priority date. This refers to the date when an application is filed for the first time anywhere in the world to protect an invention.

Intramural R&D is defined byDoran and Ryan (2013) as creative work undertaken within the enterprise to increase the stock of knowledge for developing new and improved products and processes.

I here adopt the DIANE database classification of firms into large, small and medium.

A robustness check was performed by using the large firms only; results are robust and available upon request.

As a robustness check I also let the traditional factors of production be endogenous and instrument them by their oneperiod lagged values; the result did not change significantly.

This chapter is a joint work with R. Abdesselam and P. Renou Maissant, it has been presented at the SMTDA 2020 conference.

This concept is used byDemirel et al. (2019) to qualify new businesses created with a sustainable approach to entrepreneurship.

The literature distinguishes two categories of public policy instruments (Command and control and Economic instruments). Command and control are regulatory instruments such as bans, emission volume limits and technical requirements. Economic instruments consist of retaining a price on negative externalities. They are less flexible because they leave to the agents the choice of the least cost strategy.

Strategic proactivity is defined as a firm's ability to initiate changes in its strategic policies regarding its entrepreneurial, engineering, and administrative activities, rather than reacting to events (Aragón-Correa, 1998).

Due to the risk it presents for the environment, involving inconveniences for the neighbourhood, health, safety, agriculture, or the protection of nature, such activities are subject to specific regulations, in particular in terms of authorization.

This class shows similarities with that of[START_REF] Taylor | The green entrepreneur: Opportunist, Maverick or Visionary[END_REF], but with the major difference that in our case these entrepreneurs do not have a personal environmental conviction.

They measure the eco-innovation intensity by the types of integrated eco-innovation. Fully integrated eco-innovators are start-ups that practise different types of eco-innovations.

This chapter is a joint work with E. Coll-Martinez and P. Renou Maissant, it is currently under review at the Annals of Regional Science. It has also been presented at several conferences including ERSA and AFSE.

NUTS means: Nomenclature of Territorial Units for Statistics. The European Commission divides its territory according to the classification established by Eurostat. It is based on national administrative units.

Due to the data unavailability, in the REGPAT database, on the marketability of the patented objects and their economic value, we use the terms of invention and innovation interchangeably.

By relying on patents data to capture eco-innovative firms' entry we are not able to encompass the whole set of firms that innovate in the environmental domain since not all innovative firms may be able to apply for patents. Nevertheless, with this approach we can focus on highly technological-intensive firms operating in the environmental domain.

To elaborate the variable VOTE_ECO it is assumed that a given election is only relevant for a given five-year period if the year of the election is lagged with at least three-years from the start of a five-year period (Santoalha and Boschma 2019). According to that, results in the first round of the French national elections of 2002 are associated with the period 2003-2009; results in 2007 are associated with the period 2010-2013; and results in 2012 are associated with the period 2014-2015.

The data comes from a survey whose aim is to update knowledge of the amount and nature of studies, investments (equipment entirely dedicated to environmental protection, purchases of more efficient equipment in terms of the environment, etc.), and current expenditures to protect the environment in the industry (please, visit https://www.insee.fr/fr/metadonnees/source/serie/s1232, for more details). The data is available at NUTS2, then to compute the NUTS 3 corresponding variables, we weight the NUTS 2 variables for the NUTS 3 share of regional GDP.

This section builds on Quattraro (2010) andColombelli and Quattraro (2017; 2019).

SeeSoete and Patel (1985) for similar approaches. Alternative rates of obsolescence have been applied with no significant variations on the final calculations.

See Saviotti (1988),[START_REF] Frenken | The early development of the steam engine: an evolutionary interpretation using complexity theory[END_REF] and[START_REF] Stirling | A general framework for analysing diversity in science, technology and society[END_REF] for further details on the definition and properties of this index.

Ce plan peut être consulté sur ce lien https://travail-emploi.gouv.fr/le-ministere-en-action/pic/

Afin de mettre en évidence cette relation de complémentarité, il faudrait orienter une enquête dans ce sens pour obtenir les informations nécessaires. Il serait donc intéressant d'analyser cette relation dans le cadre des recherches futures.

Remerciements

A ma chère mère A mon cher père Kneller, R., Manderson, E., 2012. Environmental regulations and innovation activity in UK manufacturing industries. Resource and Energy Economics 34, https://doi.org/10.1016/j.reseneeco.2011.12.001 Kruse, J., 2016. Innovation in Green Energy Technologies and the Economic Performance of Firms (No. 2016-2), EWI Working Papers. Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).

Lanoie, P., Laurent-Lucchetti, J., Johnstone, N., Ambec, S., 2011. Environmental In order to find out the year in which the firm was founded, the patent data is merged with the DIANE database that contains financial information on firms in France. Due to the absence of a common identifier between the REGPAT database and the DIANE database, the name and the address of companies were used to merge these two datasets following the methodology used by Marin (2014). The OECD's Harmonization of Applicant Names (HAN) dataset is used to address the issue of name harmonization. The result of the firm matching was checked manually, on a case-bycase basis. Finally, the resulting database is completed by the information on local characteristics of French départements (96) taken from different sources such as INSEE, the French Government and Eurostat. A summary of definitions, sources and descriptive statistics can be found in Table 3.1.

Variables

Dependent variable

As the main aim of this paper is to understand the entry of innovative firms specialising in environmental technologies, we define eco-innovative firm entry as the entry of an innovative firm into the environmental technology industry. This approach is based on studies in evolutionary economics focusing on real technological firm entry rather than the traditional approach that identifies new firm creation through the date of incorporation of firms into the market [START_REF] Malerba | Technological entry, exit and survival: an empirical analysis of patent data[END_REF][START_REF] Corradini | Technological diversification and new innovators in European regions: evidence from patent data[END_REF][START_REF] Corradini | Location determinants of green technological entry: evidence from European regions[END_REF]).

To identify eco-innovative firms' entry, we follow the proposal of Corradini (2019) so as to retain only those firms that are highly specialised in eco-innovation. According to this schema, environment-based firm entries are those that have, over the period observed, at least 50% of their inventions classified as environmental technologies and that have applied for their first eco-patent within the first 5 years after their creation.

By retaining only firms that have been created no earlier than 5 years from the first patent application we are more certain to capture the entry of new firms since the date of the first patent and the date of the firm's creation may not coincide [START_REF] Breschi | Survival of innovative entrants in knowledge-based sectors[END_REF]. To identify the year of incorporation into the environmental technology-based industry, we consider the year in which they applied for an environmental-based patent for the first time.

The use of patents to define eco-innovative firm entry is important since it provides evidence on the subset of technology intensive firms eco-innovative firms as key agents to introduce radical that densely populated areas facilitate the entry of eco-innovative firms due to the agglomeration advantages arising from the concentration of a diverse range of economic and innovative activities [START_REF] Audretsch | Cultural diversity and entrepreneurship: a regional analysis for Germany[END_REF]Rodriguez-Pose and Hardy 2015). According to these results, the larger distance to Paris, the higher the likelihood of eco-innovative firm entry. Even if we should expect that this effect is negative since they may have more difficulties in establishing networks and access to specialised research and development centres which are highly concentrated in and around the French capital, a larger distance to Paris may also capture a potential competition effect in view of agglomeration of firms in that area. Instead, the estimates for unemployment are negative and significant across all model specifications. This result is consistent with the fact that for eco-innovative firms, unemployment may be rather associated with a Schumpeter (pull) effect than a refugee (push) effect. Finally, the estimates for employment in science and technology sectors with tertiary education are not statistically significant. According to that, the importance of specialised employment is probably blurred by the influence of other variables capturing knowledge spillovers such as population density as well as knowledge base indicators.

Spatial econometrics analysis

These results provided interesting evidence about the effects of the local knowledge base on the creation of eco-innovative firms' entry. However, it is important to account for the spatial dimension of these effects (Elhorst 2014). Thus, we implemented an SDM with the aim to understand effects of the spatial lag of the set of knowledge base indicators as well as the dependent variable.

The SDM was estimated using the version 1.4-11 splm free package for R, which allows for the maximum likelihood estimation of spatial panel models [START_REF] Millo | splm: Spatial panel data models in R[END_REF]. To further justify the use of the SDM, we followed the test procedure to find out which model is the most appropriate model to our data proposed by Anselin (2010;2014). First, we estimate the OLS model and test whether the spatial lag model (SLM) or the spatial error model (SEM) is more appropriate. To do so, we apply the robust Hausman specification test for spatial panel data models LM-tests proposed by Hausman (1978). According to our results, we reject the null hypothesis, that is, we confirm the presence of spatial autocorrelation in the error terms, then the SDM it is recommended. Indeed, alternative Spatial Lag Models (SLM) or Spatial Error Models (SEM) should be approached if either the spatial lag of the dependent variable or the key explanatory variables turn out to be not significant in the regressions, which is not our case.

The spatial econometrics results are shown in Table 3.3. The sign and significance of the control variables are pretty consistent with the previous estimations, except for unemployment. Such result may be explained by the fact that both a positive and negative effect on firms' creation may coexist and this result is in line with previous findings [START_REF] Storey | The birth of new firms-does unemployment matter? A review of the evidence[END_REF]). When we look at the spatial lag Appendix 3.1: The implementation of knowledge spillovers 48 A. Knowledge stock To measure the local knowledge stock (STOCK_ALL) based on patent applications we calculate the accumulated stock of past patent applications applying the permanent inventory method as follows:

where ℎ 𝑖𝑡 is the flow of patent applications, 𝛿 is the rate of obsolescence of 10% 49 , 𝑖 is the region and 𝑡 is the time period. This measure has also been calculated for both non-environmental (STOCK_NENV) and environmental technologies (STOCK_ENV).

B. Knowledge variety

Our measure of knowledge variety is based on the information entropy index by following a multidimensional approach proposed by (Colombelli and Quatraro 2019). 50 Indeed, as we argued in Section 2 and 3, we focus on the association of environmental technologies with other technologies.

Let us consider a pair of events (𝑋 𝑙 , 𝑌 𝑗 ), and the probability of their co-occurrence 𝑝 𝑙𝑗 . A twodimensional total variety (KV_Y) measure can be expressed as follows:

Where 𝑝 𝑙𝑗 is the probability that two technological classes l and j co-occur within the same patent.

The measure of multidimensional entropy focuses on the variety of co-occurrences or pairs of technological classes in patent applications, and provides an index of how much the creation of new knowledge is focused in a narrower set of possible combinations.

The total index can be decomposed into 'within' and 'between' parts, whenever the events under study can be aggregated into smaller number of subsets. Within-group entropy measures the average degree of variety within the subsets; between-group entropy focuses on the subsets and measures the average degree of variety across them.

Let the technologies l and j belong to the subsets g and z of the classification scheme, respectively. If one allows 𝑙 ∈ 𝑆 𝑔 and 𝑗 ∈ 𝑆 𝑧 , it is possible to write:

Pour conclure, nous abordons les limites de notre travail et les recherches futures envisageables à partir des résultats de cette thèse.

-Notre recherche s'est concentrée sur les déterminants de l'éco-innovation dans le cas français, son extension à d'autres pays, notamment à travers l'accès à la base de données financière ORBIS, aiderait à fournir des conclusions plus générales et pourrait conduire à une compréhension plus large des déterminants de l'éco-innovation. Cela pourrait contribuer à une meilleure adaptation des politiques publiques aux caractéristiques des différents pays.

-Nous avons analysé la performance des entreprises à l'aide d'une seule mesure (la productivité mesurée par la valeur ajoutée). En plus de cette mesure, les recherches futures pourraient étendre l'analyse en utilisant d'autres mesures notamment la croissance de la production, de l'emploi, de la productivité, de l'intensité des exportations et de l'intensité énergétique.

-Dans le chapitre 1, afin d'étudier l'impact de l'éco-innovation sur la productivité, nous avons utilisé une seule année de retard sur la variable brevets environnementaux. Cependant, l'effet de l'investissement en éco-innovation est généralement observé à moyen ou long terme. De ce fait, la disponibilité de séries chronologiques plus longues permettrait d'étendre cette recherche.

-Dans le chapitre 3, les données des facteurs régionaux qui influencent la localisation des entreprises éco-innovantes sont agrégées à l'échelle du département. Ceci peut constituer un niveau d'agrégation élevé, il en résulte une perte d'information. Une des extensions intéressantes serait de géolocaliser les entreprises afin d'étudier l'agglomération des entreprises éco-innovantes à une échelle intramétropolitaine. D'où la nécessité d'adapter les instruments de politique publique de sorte à améliorer la profitabilité des éco-innovations. Après la réalisation d'une enquête auprès des start-ups françaises, les résultats de l'analyse ont montré qu'il existe une diversité de profils d'éco-innovateurs. En effet, les start-ups se regroupent en cinq principaux profils qui correspondent à différents déterminants de l'éco-innovation. Nous avons montré empiriquement que les différentes catégories d'éco-innovateurs n'ont pas les mêmes difficultés d'accès aux fonds. Ce qui nous a amené à conclure que les politiques publiques doivent être conçues en tenant compte de la spécificité de chaque profil. Nous avons également montré que la recombinaison des technologies environnementales et non environnementales et le soutien des autorités publiques aux initiatives environnementales sont les principaux facteurs qui expliquent la localisation des entreprises éco-innovantes. De plus, en appliquant l'économétrie spatiale, nous avons constaté qu'il existe une dépendance spatiale claire vis-à-vis de leur création alors que l'impact de la diffusion des connaissances est plutôt local.

Liste des Tableaux

Liste des figures

Mots clés : Eco-innovation, recombinaison de l'innovation, profiles des start-ups, productivité

Abstract

The main objective of this thesis is to study the factors that trigger the development of eco-innovations for the case of French firms. We have addressed this question in different contexts using original data. We contribute to the empirical literature by focusing on entrepreneurial and spatial aspects. The main results show that environmental policy instruments have a different impact on the types of ecoinnovations (waste treatment, renewable energy and climate change mitigation technologies) and that eco-innovations are not always profitable for firms. Hence, it is essential to adjust public policy instruments in a way to improve the profitability of eco-innovations. After carrying out a survey on French start-ups, the results of the analysis have shown that there is a diversity of profiles of ecoinnovators. Indeed, firms cluster into five main profiles and exhibit different eco-innovation drivers. We have shown empirically that the different categories of eco-innovators do not have the same difficulties in accessing funds. This led us to conclude that public policies must be designed taking into account the specificity of each profile. We have also shown that the recombination of environmental and non environmental technologies and environmental political support are the main factors explaining the location of eco-innovative firms. Moreover, by applying spatial econometrics we found that there is a clear spatial dependence on their creation while the impact of knowledge spillovers is quite local.

Keywords: Eco-innovation, recombinant innovation, start-ups' profiles, productivity