Environmental and corporate crimes : the case of polluting industries in France

Laurent MUCCHIELLI CNRS, Aix-Marseille Université, LAMES, France in A. Balloni & R. Sette (eds.), Handbook of Research on Trends and Issues in Crime Prevention,Rehabilitation,and Victim Support,Hershey,IGI Global, The issue of health disasters caused by human economic activities has been increasingly publicized since the 1970s, in connection with the emergence of ecology as a new philosophical and political trend in Western countries. Major accidents and other "disasters" that occur regularly (such as Seveso, Chernobyl, Fukushima or oil spills) have also led States and supranational bodies such as the European Union to increase the number of laws, control bodies and measurement tools. This set of public concerns and institutional mobilizations quickly boosted scientific research in the biomedical and climatological fields. Moreover, the lawyers soon took up a new world of constantly evolving national and international standards. An environmental law was thus established in the mid-1970s. The arrival of social sciences in this field is much later, at least in Europe (in the United States, a sociology of the environment is constituted as a sub-domain of the discipline since the end of the 1970s [START_REF] Buttel | Environmental Sociology. A new Paradigm ?[END_REF][START_REF] Catton | Environmental Sociology[END_REF]). However, a real dynamic is now at work at the beginning of the 21st century.

In France, it can be seen at work in political science, particularly in the study of these new public policies and new forms of activism, or in the analysis of the agenda setting of new public problems (e.g. [START_REF] Gilbert | Comment se construisent les problèmes de santé publique[END_REF][START_REF] Jouzel | Des toxiques invisibles[END_REF]. Health sociology and occupational sociology have also integrated these new health and environmental safety issues (e.g. [START_REF] Collectif | Santé et travail[END_REF]Terssac, Mignard, 2011;[START_REF] Fournier | Travailler dans le nucléaire. Enquête au coeur d'un site à risques[END_REF]Thébaud-Mony et al., 2012).

More recently, sociologists have proposed linking these questions to those traditionally associated with the theme of "insecurity", by talking about health disasters affecting populations, but also about fraud and tax evasion ruining States, as well as "ignored insecurities" [START_REF] Palidda | Governance of security and ignored insecurities in contemporary Europe[END_REF]. This chapter proposes to contribute to this new field of research in two ways. The first is to document the nature and extent of these health threats to populations.

The second is to highlight the existence of delinquent practices (which will be defined as deliberate violations of legal norms) that sometimes play a major role in perpetuating these threats, following in the footsteps of Edwin Sutherland's research on the White Collar Crime (Spire, 2013;[START_REF] Lascoumes | Sociologie des élites délinquantes[END_REF]Mucchielli, 2014[START_REF] Mucchielli | L'ampleur et l'étendue de la délinquance des élites[END_REF]. More precisely, we will draw inspiration from research on Corporate Crime developed since the 1980s, one of whose branches soon made the link with environmental and health issues of interest to the Green criminology movement as it has developed since the early 1990s in England and the United States (see the first reports by [START_REF] Bottoms | Environmental Criminology[END_REF]and South, 1998). The fields of study covered by these issues are particularly broad and diverse [START_REF] Hall | Greening Criminology in the 21st Century. Contemporary debates and future directions in the study of environmental harm[END_REF]. Turning to the case of France, we will limit ourselves here to examining the case of air pollution caused by road transport and industry, a major pollution in terms of health and the environment.

Air pollution: the situation in France

Santé Publique France (SPF) is the national public health agency created in April 2016, following the law of 26 January 2016 on the modernization of the health system, with the mission of "effectively protecting the health of populations". In June 2016, it published a report entitled Impacts of Chronic Exposure to Fine Particles on Mortality in Continental France, which is in line with the scientific work of the World Health Organization (WHO) and the European Environment Agency (EEA). This report provides a comprehensive overview of the extent and health effects of air pollution (fine particles of 2.5 micrometers), based on a study of all municipalities in metropolitan France in 2007France in -2008. These pollutions are defined as "a complex mixture composed of primary pollutants emitted directly by the sources of pollution (road traffic, industry, heating, agriculture...)". These include, for example, sulphur oxides, volatile organic compounds, particles, metals (lead, mercury, cadmium, etc.). Secondary pollutants not emitted directly from a source but resulting from chemical reactions in the atmosphere, such as ozone and nitrogen dioxide, are also considered. This report first confirms that, "through various mechanisms including oxidative stress and inflammation, exposure to air pollution, particularly fine particulate matter, contributes to the development of chronic diseases such as cardiovascular, respiratory or neurological diseases, and cancers. It also promotes reproductive and developmental disorders in children. It also aggravates the symptoms of diseases in people suffering from chronic diseases" (Santé Publique France, 2016). The agency estimates that air pollution is responsible for about "48,000 deaths per year, which corresponds to 9% of mortality in France and a loss of life expectancy at 30 years that can exceed 2 years". This is the second leading cause of death after tobacco (about 78,000 deaths per year), "but we choose to smoke or not. However, we do not choose the air we breathe. Finally, this health disaster would have a total annual cost of around 100 billion euros to French public finances, according to the report of a parliamentary survey in 2015 (Sénat, 2015).

In addition to the pollution caused by the combustion of firewood by private individuals, three main economic sectors are responsible for most of the air pollution: the road transport sector, the industrial sector and agriculture. We will focus here on the first two, which are the two most important.

Pollution from cars and trucks

The main pollution from road traffic is nitrogen dioxide (NO2). The latter irritates the respiratory tract, causes respiratory and cardiovascular diseases, and is highly carcinogenic.

In 2017, the French car fleet numbered more than 39 million vehicles (INSEE, 2017). It consists of passenger cars (83% of the total) and heavy goods or passenger vehicles (17% of the total).

Diesel engines, which used to be the vast majority, have been steadily declining in recent years as a result of government action. Since 2017, they have accounted for just under half of the total vehicle fleet, but remain dominant in freight transport.

In 2016, land freight transport amounted to 338.5 billion ton/kilometers (Commissariat Général au Développement Durable, 2018, 28). Its upward trend is based on road transport, French and -even more so -foreign. The registration of heavy goods vehicles is also increasing sharply and the average journeys they make are getting longer. At the same time, rail and inland waterway transport are decreasing.

Pollution measurement

The transport sector, particularly road transport, is strongly responsible for the increase in energy consumption in France and the emission of pollutants and greenhouse gases (GHGs). It consumes 82% of petroleum fuels in 2016 and accounts for nearly 30% of GHG emissions (compared to 22% in 1990). These GHG emissions have been increasing in the transportation sector in recent years (Office of the Commissioner General for Sustainable Development, 2017, 12-13). Indeed, the improvement in the environmental performance of vehicles (fleet renewal, generalization of catalytic converters, etc.) does not compensate for the increase in the total number of kilometers travelled on the road. Heavy goods vehicles are responsible for more than one-fifth (21.3% in 2016) of GHG emissions, while they represent only one-twentieth (5.2%) of road traffic (ibid.). Finally, diesel-powered vehicle engines are responsible for 84% of GHG emissions in the road transport sector, whereas they now account for only about half of the vehicle fleet, after having long been its core.

The question of diesel and "dieselgate"

Gasoline and diesel are two products of petroleum distillation but with different chemical properties. While gasoline is a mixture of light hydrocarbons, diesel blends heavier hydrocarbons that cause much more pollution to fine particles, the latter being the most dangerous to the respiratory and cardiovascular systems of the human body. But the discovery and awareness of the problem of fine particle pollution is recent, and can be dated from a report by the International Agency for Research on Cancer in 2012 [START_REF] Pasteau | Le Diesel : enjeux économiques, politiques publiques, comparaison internationale[END_REF].

However, it was in the early 1980s that France decided to give priority to diesel engines for passenger cars, by taxing them less. The government's aim at the time was to favour a technique that would give the French automobile industries an advantage in the now global competition, as well as to satisfy the demands of the oil industry, which was concerned about the widespread use of electric heating to the detriment of oil heating (the latter being a poorly distilled product with characteristics very similar to diesel fuel). Buses and trucks, on the other hand, had long been diesel-powered. As a result, diesel accounted for up to 70% of French car sales in the early part of 2010.

Faced with the fact that technological innovations (such as particulate filters) can only marginally reduce the pollution caused by diesel engines, the public authorities have undertaken to varying degrees to gradually reduce this proportion. Certainly, it seems that there is only one example of a region in the world that has totally eradicated diesel engines: the city of Tokyo in Japan (Pasteau, Perez, Teulière, 2015, 9). But restrictive standards and disincentive taxes are multiplying throughout the European Union (Euro standards). Especially as scientific studies are accumulating to overwhelm this technology. In May 2017, researchers estimated in the journal Nature that nitrogen oxide pollution from diesel engines was probably responsible for nearly 40,000 deaths worldwide in 2015 [START_REF] Anenberg | Impacts and Mitigation of excess diesel-related NOx emissions in 11 major vehicle markets[END_REF]. One major obstacle remains: the resistance of European industrialists, who have long based part of their financial success on diesel and have resorted to criminal practices to preserve it.

It was in 2015 that Dieselgate broke out, when the US Environmental Protection Agency revealed that the German group Volkswagen, the world's leading car manufacturer (also owner of the Audi, Skoda, Seat and Porsche brands), with a turnover of nearly 240 billion euros at the time, massively evaded the approval tests of its vehicles to hide polluting emissions. In France, the Directorate General for Competition, Consumer Affairs and Fraud Control (DGCCRF), seized of the problem, launched a "survey on the practices relating to nitrogen oxide (NOx) emissions of a dozen car manufacturers marketing diesel vehicles in France", including the two largest French manufacturers. In November 2016, it announced that it would report to the courts the results of its investigation into the Renault group (the fourth largest in the world in terms of turnover), which was also suspected of having used software designed to distort pollution test results in order to obtain the necessary approvals for marketing in Europe. The French agency added that this "fraudulent strategy" had probably lasted since 1990 and that it involved the entire "management chain" of the group (Génération Nouvelles Technologies, 16 March 2017).

Finally, in September 2017, the French agency revealed the result of its investigation into the PSA group (Peugeot-Citroën, the 9th largest group in the world in terms of turnover), which it also accused of "fraudulent strategy" and "deception" concerning the homologation tests of at least two million vehicles sold between 2009 and 2015, twice as many as those that were the subject of the first scandal at Volkswagen. Finally, it appears, on the one hand, that some groups such as Volkswagen have tried to put pressure on the media by threatening to reduce their advertising investments if they give too much coverage to the case, and on the other hand, that the main European car manufacturers have already begun to try to circumvent the new "WLPT" (Worldwide Harmonised Light Vehicle Test Procedure) standards set up by the European Commission following the first scandal. We can therefore speak here of a "normalization of deviance" in the sense that, in the culture of the companies concerned, managerial norms clearly dominate legal norms (Aggeri, Saussois, 2017, 93-94). The analysis of Volkswagen's delinquent practices also highlights the fact that they are all the more widespread since state regulatory authorities have so far used the law only as a mediation tool in "a certain management of illegalisms made up of collusion and accommodation relations between the regulator and the regulated" (ibid., 85).

Industrial pollution

The problem is very old and the historiography of industrial risks has developed considerably in recent years (Centemeri, Daumalin, 2015;[START_REF] Roux | Risques industriels. Savoirs, régulations, politiques d'assistance, fin XVIIe-début XXe siècle[END_REF][START_REF] Massard-Guilbaud | Histoire de la pollution industrielle en France[END_REF][START_REF] Charvolin | Un air familier ? Sociohistoire des pollutions atmosphériques[END_REF]. Smoke, gas and smells have been a cause for concern since the strong development of the industry in the 18th century and led to regulations at the beginning of the 19th century. However, it was not until the 1980s -in the context of growing concern for the environment and political ecology, and the global repercussions of the Seveso (1976), Bhopal (1984) and Chernobyl (1986) disasters -that environmental epidemiology and toxicology studies multiplied, linking industrial pollution to a number of diseases, particularly respiratory ones. Meanwhile, massive industrialization in some regions has caused health disasters that still affect the surrounding populations today. Few examples have really been studied over time [START_REF] Marichalar | Qui a tué les verriers de Givors ? Une enquête de sciences sociales[END_REF]. And the quantification of occupational diseases is still in its infancy in France, with the exception of asbestos, which constitutes more than 80% of cancers recognized as being of occupational origin (Conseil d'orientation sur les conditions de travail, 2015, 221).

However, there is no doubt about the situation of many major industrial sites.

The example of Fos-sur-Mer and the Etang de Berre

The geographical analysis proposed by the experts from France Santé Publique (2016) first indicates that pollution is higher in large urban areas: the Paris region, the north-east and the Lyon-Marseille axis. But many rural and even suburban areas are also heavily polluted due to industrial activity. We will remember here the example of the Marseille region, around the Etang de Berre. Several municipalities are particularly affected by the effects of pollution, located a few tens of kilometres northwest of Marseille, around the mouth of the Rhône and the Mediterranean coast: Fos-sur-Mer (approx. 16,000 inh.), Port-de-Bouc (approx. 17,000 inh.) and Port-Saint-Louis-du-Rhône (approx. 9,000 inh.) south of the pond, Berre (approx. 13,500 inh.) and Rognac (approx. 12,000 inh.) in the northeast.

In the early 1970s, France decided to set up a huge industrial port area in Fos-sur-Mer, in a mixture of the ideology of progress by industry, Parisian technocracy and the denial of environmental and health issues (although already largely known at the time: [START_REF] Perrin | Fos et l'environnement[END_REF]. Some dream of seeing the birth of "French California" at the dawn of "the year 2000" (Paillard, 1981, 44). The municipality will then gradually cover itself with refineries, oil depots, chemical, metallurgical and steel plants, industrial and household waste treatment plants, cement plants and LNG terminals. On a territory that nowadays has about fifty industrial installations, three major and highly polluting activities are still present. First, metallurgy with the steel giant Arcelor Mittal, a group based in Luxembourg, the world's leading steel producer, whose turnover reached 80 billion dollars in 2013. Then there is oil refining with Esso, the French subsidiary of the American oil group Exxon-Mobil, which is one of the ten most profitable companies in the world, with a turnover higher than most countries in the world (nearly $440 billion in 2013). Finally, petrochemicals, with the American multinational Lyondell-Basell in particular.

However, it appears that the factories of at least two of these three groups pollute, while trying to hide it from the public eye, regularly committing offences, again allowing us to talk about routine delinquent activity or organized delinquent practices. Dozens of various problems (exceeding pollution standards, repeated incidents, delays in safety work to be carried out, etc.) have been reported by the public authorities concerning Arcelor Mittal's plants1 . The problem is even more serious in the case of the petrochemical group Lyondell Chimie France, which handles in particular butanol and propylene oxide for the production of polyurethanes (chemical components used to manufacture plastics found in countless materials and in most objects of everyday life). However, these molecules are highly toxic and have a high risk of inflammation.

Inhaling them is at best irritating, at worst carcinogenic. The same applies to the liquefied fuel gases manufactured and stored by the company, which are used in particular to manufacture ethers used in motor fuels.

On 21 October 2010, an inspection by the Regional Environment Directorate took place, which submitted its report on 8 August 2012. On 17 September 2012, the Bouches-du-Rhône prefecture sent the company a formal notice order, the reading of the "recitals" of which is instructive:

« Considering that LYONDELL Chimie France operates four boilers for its steam needs at its Fos-sur-Mer site; Considering that during his on-site visit, the Classified Facilities Inspector found that the company had not complied with the applicable regulatory values for air emissions from its boilers since 2009 and that the applicable dust concentration limit at the boilers was regularly exceeded; Considering that the operator does not comply with the applicable requirements and that the exceedances observed have persisted for several years ».

Did Lyondell subsequently change its behavior? This is not the case. In 2014, the "hazard study" revealed persistent problems. The prefectoral decree of 9 April 2014 specifies that "the operator's control of the risks related to his activity does not seem sufficient" and prescribes a series of measures to be taken. At the Berre-l'Étang site, huge quantities of liquefied flammable gases are stored, presenting major health risks in the event of a leak or fire. The law therefore requires the industrial company to equip its site with adequate detection and alarm systems, which it is reluctant to do. The prefectoral decree of formal notice of 16 September 2015 notes this time that, despite the instructions given following inspections and numerous exchanges of letters, the manufacturer has not taken "these technical measures[which] contribute to the prevention of major accidents that could lead to lethal effects on the establishment's environment".

Finally, the companies of the Lyondell-Basell group do not only pollute the air, but also the water. The problem has been recognized at least since 2010. However, it persists, as the company refuses to undertake the sealing work necessary to secure its pipes. On 16 March 2018, the Préfecture des Bouches-du-Rhône issued a new formal notice order against the petrochemical group concerning the Berre-l'Étang site, concerning the pollution of groundwater aquifers to the northwest of the municipality. In passing, the decree noted that "some leaks have been identified for more than 9 years without any sealing work carried out since then".

The health threat: accumulation and convergence of strong indicators

There is no systematic data collected on the occupational diseases of people working on these polluting industrial sites, only a series of testimonies centred solely on the employees of these companies and not on their countless subcontractors (Grandmaison, 2017, 31sqq). On the other hand, the consequences of industrial pollution around the Étang de Berre on the respiratory and cancerous pathologies of the surrounding populations have been known by health institutions for several years, albeit in an incomplete way. In particular, it is established that women are at a much higher risk of myocardial infarction than in the average of equivalent municipalities and that men are at two and a half times higher risk of acute leukemia (Pascal [START_REF] Bensa | Pollution atmosphérique et hospitalisations pour pathologies cardiovasculaires, respiratoires et pour cancers, dans le secteur de l'Etang de Berre, 2004-2007[END_REF], even though the study studied only two polluting agents. In general, local data have long been lacking and populations (as well as local doctors who regularly report in the press on alarming levels of certain diseases in their patients) have been kept not only in ignorance, but also in contempt for their fears and daily illnesses, including by public authorities. Strangely, the Regional Health Observatory (ORS PACA) has never conducted a diagnosis on the Commune of Fos-sur-Mer. Only on its website is a summary study of the neighboring municipality of Port-Saint-Louis du Rhône, which nevertheless establishes a very significant excess mortality compared to the region in terms of respiratory tract diseases (+77.3% compared to the regional average) and cancers (+31.9%), in particular lung cancers (+61.8%) (Dumesnil, Saugeron, 2013, 12). This is all the more surprising as the statistical data on the causes of mortality, available on its community-wide information system, clearly suggest a concentration of problems, particularly deaths from lung cancer, in several municipalities in the Étang de Berre where industrial sites are concentrated (Table 1). Faced with this lack of diagnosis, however, local mobilizations have made it possible to include in the file studies that are certainly partial, but with significant and totally convergent results.

Three studies, based on three forms of civil mobilization, were published in the space of three years, in addition to a study carried out by a State agency.

First, in 2015, participatory scientific research was conducted to describe individual health problems based on the study of a randomly selected population of 816 individuals or households, constituting a representative sample of the population of the municipalities of Fossur-Mer and Port-Saint-Louis-du-Rhône [START_REF] Allen | Étude participative en santé environnement ancrée localement sur le front industriel de Fos-sur-Mer et Port-Saint-Louis-du-Rhône[END_REF]. Its results indicate that the population suffers much more than the average from asthma (mainly men), cancers (mainly women, breast and uterine cancers) and diabetes. In total, nearly two thirds of the population reports at least one chronic disease (asthma, other respiratory disease, respiratory allergy other than pollen, dermatological disease, cancer, autoimmune disease, endocrine disease and/or diabetes), almost twice the French average. About two thirds are also affected by a chronic symptom (eye irritations, nose/throat symptoms, headache, skin problems, nose bleeds). On a geographical scale, it appears that some districts are even more affected than others because they are located closest to industrial installations and are most exposed to breathing polluted air regardless of the direction of the winds. In these neighborhoods, it is the health of the entire population that is globally threatened by industrial pollution.

Then, at the end of January 2018, Air Paca (an air quality monitoring association approved by the Ministry of the Environment) published the results of a study aimed at "quantifying, using risk indicators, the impact of air pollution on health". The report states that "the use of this tool in the Étang de Berre region has made it possible to identify the areas for which management thresholds are exceeded, for the first time in such an approach, by integrating 39 substances and all sources of pollution (industry, transport, heating, ships, etc.) on the scale of a territory composed of 66 municipalities" (Air Paca, 2018, 2). The study first concludes that there are pollutants related to road and highway traffic, as well as an airport, as in other regions. But it adds that, for other pollutants, the exceedances of the observed risk management thresholds are "specific to the study area[...] around the Gulf of Fos, Martigues and Berre where many industrial activities are present" (ibid., 23). In concrete terms, in Fos, Martigues and Berre, three molecules linked to industrial activity pose a risk to the health of the population with regard to certain cancer diseases. And recognizing it was a first for a government department that was often criticized by local associations.

A few days later, in February 2018, the "Association de défense et de protection du littoral du golfe de Fos" (created in 2002 by a small group of inhabitants) has organizing a public meeting to reveal the results of a study it had carried out on a series of food products from the Gulf of Fos: bull, sheep, goat cheese, chicken eggs, fish, mussels, olive oil and hay (ADPLGF, 2018).

Two specialized and independent laboratories searched the samples for about 50 chemical compounds. Then the "Institut écocitoyen pour la connaissance des pollutions" (IECP, see below) and its scientific council interpreted the results. The latter are of particular concern in the case of bull meat and eggs, where very high concentrations of dioxins (carcinogens) and polychlorinated biphenyls (endocrine disrupters) have been found. Seafood products, on the other hand, revealed a significant presence of heavy metals (lead, cadmium). As a result, "the association, supported by the town hall of Fos-sur-Mer, decided, after fifteen years of alerts, to transmit to the courts with the filing of a complaint against X for endangering the lives of others" (Mandard, 2018).

Finally, in May 2018, another local initiative replaced the apathy of the health authorities. The IECP, created in 2010 by local authorities and led by academics, submitted a report presenting the results of a "study on the impregnation of the population with atmospheric pollutants in the Fos-sur-Mer industrial port area" (IECP, 2018). After a telephone survey, 138 people aged 30 to 65 were interviewed face-to-face between September and November 2016 in the commune of Fos and in a commune 20 km away in order to compare. Urine and blood samples were systematically taken. The pollutants sought were metals, polycyclic aromatic hydrocarbons, benzene, PCBs and 17 dioxins/furans considered to have significant toxicity. The study confirms that the presence of pollutants is mainly linked to food (consumption of seafood, garden vegetables) and lifestyle (activities such as gardening increases exposure to pollutants), but it shows that these daily ordinary activities are precisely made more risky in the municipality located in an industrial zone. The measures show "an over impregnation of the population exposed by inhalation for lead, two furans typical of industrial emissions, and benzene but only in the oldest people". She pointed out that "gardening in the exposed area was associated with an increase in total PCB impregnation compared to the control area", that "consuming garden vegetables was associated with an increase in cadmium impregnation in the exposed area, while the effect was protective in the control area" and finally that "the consumption of local seafood was associated with an increase in PCB, dioxins/furans, mercury and chromium impregnations". Again, the study is limited due to low enrolment and the lack of recent comparative national data. However, it is a strong and converging third index.

Provisional conclusions: further research on corporate crime

The issues raised in this article echo the debates that have occupied the sociology of crime and criminology for the past twenty years or so in England and North America. They are only just beginning to be investigated in France, where it is difficult to emerge from a phase where political-media "scandals" probably tend to polarize opposing attitudes of radical dramatization or euphemism. The environmental and health problems mentioned in this text are, however, old and their origins known. So it is rather their persistence that surprises, or even their resistance,

given the increasing scale of mobilizations and regulations aimed at combating them [START_REF] Jaworski | L'état du droit pénal de l'environnement français : entre forces et faiblesses[END_REF][START_REF] Neyret | Des écocrimes à l'écocide[END_REF]. Certainly, at the international level, it is easy to understand how national egoisms threaten to ruin the efforts of scientists at any time. The story is not new [START_REF] Boudia | La mondialisation des risques. Une histoire politique et transnationale des risques sanitaires et environnementaux[END_REF][START_REF] Aykut | Gouverner le climat ? 20 ans de négociations internationales[END_REF]. Recently, the hope raised by the collective commitments made at COP 21 in December 2015 in Paris quickly turned into disillusionment after the 2016 US presidential elections. However, at the national level, with the themes of environmental protection and public health generating a broad political consensus, one would expect rapid and important developments. However, this is hardly the case. In the report of a Senate committee of inquiry on air pollution, the parliamentarian who led it clearly expressed this feeling of powerlessness: "Why such a delay between the level of scientific knowledge (physical, biological, medical, economic) and political decision-making on air pollution? (...) Your committee of inquiry can only note the failure of the measures taken over the past twenty years (...). More seriously, your committee of inquiry notes that the same measures are being proposed, studied, not applied, forgotten and then proposed again" (Senate, 2015, 13). These findings are an invitation to investigation for the social sciences.

Indeed, political science research highlights the contradictions and opacities surrounding the concrete implementation of public policies: "the management of many issues (such as shale gas or pesticides) shows the gulf between the principles of openness and many administrative and political practices that remain discretionary" (Lascoumes, 2012, 125). The role of administrative officers of control bodies in the management of health alerts [START_REF] Jouzel | La normalisation des alertes sanitaires. Le traitement administratif des données sur l'exposition des agriculteurs aux pesticides[END_REF] and more broadly in clarifying the issues and conflicts surrounding the production of health and environmental expertise [START_REF] Henry | Dictionnaire critique de l'expertise[END_REF]Counil, Henry, 2016) are just beginning to be studied. Research is also in its infancy as to the role of local elected officials, who can disrupt polluters by joining forces with state control agents or, on the contrary, enter into "logics of shared governance and territorialized regulation" with industry [START_REF] Fournier | Secours ou entrave à l'action publique ? Les élus locaux face à l'État dans les territoires mono-industriels à risques[END_REF].

Moreover, traditional trade unions seem, for the time being, to be very little mobilized in France on environmental issues such as industrial pollution, unlike what can be observed in other countries. The issue of whistleblowers and their protection has not yet been studied in this area.

As for citizen mobilizations, without the assistance of the State or local elected officials, they are inevitably powerless and remain generally scattered and ephemeral [START_REF] Calvez | Les risques de santé à l'épreuve des nuisances. Le cas des plaintes en santé environnementale[END_REF]. As for the sociology of delinquency, there are still many areas to be investigated. As for industrial practices, it would be important to be able to analyze strategies for "securing delinquent practices" (Speyer, 2013) as well as strategies for resisting pressure from States, the media and citizen mobilization. On the administrative side, questions arise in particular about the nature, effectiveness and efficiency of controls and sanctions carried out by the State and its law enforcement institutions in all the areas concerned (police, justice, labor inspectorates, tax officials, bodies responsible for monitoring compliance with health and environmental standards, etc.). But beyond this identification of hidden weaknesses in the implementation of public policies and the handling of health warnings, two other "macro" phenomena probably play a decisive role in understanding the permanence of problems whose diagnosis seems established.

The first is the gradual concentration of companies that has been taking place since the 1970s in most economic sectors, in connection with the financialization and globalization of markets, leading to the creation of multinationals whose turnover is higher than that of most countries in the world (Bauchet, 2007;[START_REF] Chavagneux | Le pouvoir des multinationales[END_REF]. Faced with economic giants who are weighing more and more heavily in every respect (and can exert more than ever various forms of blackmail on employment and investment on States), it is understandable that public authorities are experiencing major difficulties in the negotiations required to adopt new, more stringent production standards. However, most of the management of major risks is still played out in this balance of power between government services and industry [START_REF] Bonnaud | Les leçons d'AZF. Chronique d'une loi sur les risques industriels[END_REF]. It is also understood that it is in return much easier to make individuals feel guilty and tax them [START_REF] Boutaric | Pollution atmosphérique et action publique[END_REF].

This concentration also gives these economic groups -and this is the second phenomenon -a tenfold increase in power in terms of lobbying, corruption and crime. As we have seen in the two examples of industrial pollution developed, these practices have never disappeared. And they are found almost systematically in the analysis of health and environmental disasters, for example in the pharmaceutical industry or the agri-food industry [START_REF] Mucchielli | Sociologie de la délinquance[END_REF]. In France, one may even wonder whether they are not particularly strong because of the high level of collusion between the world of political leaders from the "grandes écoles" (in particular the National School of Administration [ENA] and high engineering schools, what [START_REF] Bourdieu | La noblesse d'État. Grandes écoles et esprit de corps[END_REF] called the "noblesse d'État") and that of economic leaders, collusion which is reflected in particular in common practices of "slipping" [START_REF] Bouzidi | Le pantouflage des énarques : une première analyse statistique[END_REF][START_REF] Rouban | L'inspection générale des Finances, 1958-2008 : pantouflage et renouveau des stratégies élitaires[END_REF] as well as recurring problems of conflicts of interest and corruption. The case of the Ministry of Health and Pharmaceutical Industry is now well known, but it is far from being the only one (Thébaud-Mony, 2014;[START_REF] Lenglet | Les recasés de la République[END_REF]. Far from being reduced, the "scrambling" is in fact increasing between the defense of the general interest and that of private interests [START_REF] France | Sphère publique, intérêts privés. Enquête sur un grand brouillage[END_REF][START_REF] Cassia | La perméabilisation en marche des activités publiques et privées[END_REF]. It is therefore highly likely that this only perpetuates the relative impunity enjoyed by certain industrial groups that daily degrade the environment and the state of health of populations.

Bibliographie

Table 1 : comparative mortality indices (CMI) in four communes in the Bouches-du-Rhône Fos-sur-Mer Port-de-Bouc Port-Saint- Louis-du-Rhône Berre

 1

	All causes	109	113	115,3	113,5
	Premature	99,9	130	121,8	120,2
	Preventable	103,1	139,1	117,2	116,1
	Cancer	121,6	121,3	114,3	129,2
	Lung cancer	136,8	151,3	108,6	178,8
	Regional average	100	100	100	100

Source :

Inserm-CépiDC, 2009-2013

; SIRSéPACA « portraits de territoires socio-sanitaires » [en ligne] ; author's calculations

  ADPLGF, 2018, Rapport de la campagne d'analyses pilotée par l'Association ADPLGF sur des produits alimentaires du pourtour du Golfe de Fos, Fos-sur-Mer, Association de Défense et de Protection du Littoral du Golfe de Fos [en ligne]. Aggeri F., Saussois J.-M., 2017, « La puissance des grandes entreprises mondialisées à l'épreuve du judiciaire. De l'affaire Volkswagen au dieselgate », Revue française de gestion, 269 (8), 83-100. Santé Publique France, 2016, Impacts de l'exposition chronique aux particules fines sur la mortalité en France continentale et analyse des gains en santé de plusieurs scénarios de réduction de la pollution atmosphérique, Saint-Maurice, Santé Publique France. Sénat, 2015, Rapport fait au nom de la commission d'enquête (1) sur le coût économique et financier de la pollution de l'air, Paris, Sénat, Rapport n°610. Spire A., 2013, « Pour une approche sociologique de la délinquance en col blanc », Champ pénal/Penal field [En ligne], Vol. X | 2013, mis en ligne le 28 juillet 2013. URL : http://journals.openedition.org/champpenal/8582 South N., 1998, « Corporate and State Crimes Against the Environment. Foundations for a Green Perspective in European Criminology », in Ruggiero V. South N., Taylor I. (eds.), The New European Criminology, London, Routledge, 443-461. Terssac de G., Mignard J., 2011, Les paradoxes de la sécurité. Le cas d'AZF, Paris, Presses Universitaires de France. Thébaud-Mony A., 2014, La science asservie. Santé publique : les collusions mortifères entre industriels et chercheurs, Paris, La Découverte.

Air Paca, 2018, Simulation de scénarii de pollution atmosphérique pour une Évaluation des Risques Sanitaires. Application à la région de l'Étang de Berre, Marseille, Air Paca [en ligne]. Thébaud-Mony A., Daubas-Letourneux V., Frigul N., Jobin P., 2012, Santé au travail : approches critiques, Paris, La Découverte.

See this company's file on the classified installations website: www.installationsclassees.developpementdurable.gouv.fr/ ("établissement n°1052"). All the administrative decisions that follow in this paragraph are taken from this site.


Claire Nicolas
email: claire.nicolas@ifpen.fr

Valérie Saint-Antonin

Stéphane Tchung-Ming

(How) does sectoral detail affect the robustness of policy insights from energy system models? The refining sector's example

Keywords: Energy modeling, Refinery modeling, Parsimony, Level of details

In this research, we rekindle an old debate by questioning the impact on mitigating policy evaluation of detailing a subsector in a global energy-transportation model. We chose the refining sector because it is a relevant case of a sector for which representation widely differs across models and because it offers a unique set of complex joint production in the energy sector. To investigate whether the level of detail in the description of the refinery impacts optimal mitigation options, we take the example of a long-term, national, linear programming based, energy-transport system model (TIMES based). We found that the refinery description used in the energy system model matters when trying to evaluate energy or climate policy applied to the transportation sector. It impacts the policy costs but also the technology trajectories chosen at the optimum. Essentially, the balance between energy efficiency and carbon intensity of transport may be affected by the accuracy of the description of the pivotal refining sector. Consequently, increasing this sector accuracy level should not only be motivated by the wish to gain wider quantitative insights on potential evolution of the energy system but also by the wish to improve the robustness of the model outcomes.

Introduction

The use of energy-economy-environment system models to analyze global issues raised by climate change is now widespread and in constant evolution. Historically developed since the 1970s, such models have been used by the public and private spheres to address two main broad questions: energy security and sustainability/climate change. They provide, broadly speaking, insights concerning the costs and benefits of policy objectives and designs and also (potentially unexpected) system effects. Policy makers and stakeholders can thus enhance their mental models of the world and take better-informed decisions. As the interdependencies between subsystems and the complexity of the long-term issues grow, so do the models and the underlying databases. The intricacy of economic concepts, model designs and large data amounts add up to the intrinsic, radical uncertainty of socio-economic phenomena, to produce long-term scenarios that suffer themselves from these drawbacks. This makes uncertainty analysis a cornerstone of long-term modeling. Practitioners have set up a whole set of tools to elaborate scenarios that traduce uncertainty about the future (eventually endogenously -e.g. through stochastic programming) or investigate the impact of uncertain data (still through scenarios, eventually with Monte-Carlo simulation). More recent operation research techniques are also used to investigate either dimension of the uncertainty set. However, two other facets of this issue are much more rarely assessed. They deal with (i) the nature of the economic assumptions and (ii) the extent to which sectors or sub-sectors are modeled with detail in a given exercise. Specifically, we question the following point: how does modeling bias (to be understood as the accuracy with which sectors are depicted) affect model outcomes and thus policy insights? In this paper, we focus on the latter and on bottom-up models, which are often used to formulate and evaluate policies intending to limit GHG emissions. These policies can be global (CO 2 market or CO 2 tax) or sectoral (regulations on technology standards or investment incentives for green technologies as in [START_REF] Waide | Energy-Efficiency Policy Opportunities for Electric Motor-Driven Systems[END_REF]). The description of energy models remains usually quite general; it mostly focuses on economic assumptions (partial, general equilibrium) and solution properties (optimization, simulation) whereas the information provided on the technological detail by sector is generally scarce (see [START_REF] Bhattacharyya | A review of energy system models[END_REF]). In bottom-up models, various sectors of the energy system coexist: industry, transportation, refining, agriculture, heat and power. Each sector is represented by a process or a group of processes depending on the complexity or level of detail of its description. What we call here level of detail is also sometimes called level of complexity; it can be described as "greatness of process description" [START_REF] Paudel | Does increased model complexity improve description of phosphorus dynamics in a large treatment wetland?[END_REF] or as "an assessment of the extent to which the observable system elements and the assumed system relationships are included in the model" [START_REF] Brooks | Choosing the best model: Level of detail, complexity, and model performance[END_REF]. The technological description is rarely available; however, depending on the specific question, the relevance of the model outputs may crucially depend on such details. In turn, insights gained from conducting such evaluations may vary because of this.

Motivations for analyzing the oil refining case

For the sake of the analysis, we need to select a relevant case of a sector for which representation may widely differ across models. Oil refining clearly falls into this category. The level of detail of each sector description within a specific model is often linked to the available public technical data or with the competencies of the modelers' team, but it is also clearly linked to the "real" complexity of these sectors (we refer here to the Collins English dictionary's definition of complex: "Made up of various interconnected parts; composite"). For example, the power sector plants are rather simple since there are few co-products and only one or two processes: fuel enters the turbine and the outputs are mainly power and sometimes heat and power. On the contrary, some sectors are much more complex to depict: there are lots of possible refinery's inputs (different kinds of crude oil, natural gas, hydrogen, naphtha, ethanol, FAME...) and even more outputs with varying yields and qualities (LPG, naphtha, several gasoline and diesel, jet fuel, kerosene, heating oil, marine bunker fuel, fuel oil with several sulfur contents, coke...). Furthermore, a refining plant contains from 10 up to 40 process units and refining schemes can vary a lot. Within the overall energy system, it shows a unique chain of complex, interdependent and largely integrated process units. It is very costly in terms of time and resources to implement each refining process in energy system models; that is why the refining sector is generally modeled as a single process. Moreover, the refining scheme competency is rather scarce. Only a few global energy models incorporate detailed refining descriptions (NEMS for example [START_REF] Morris | NEMS and MARKAL-MACRO models for energy-environmental-economic analysis : A comparison of the electricity and carbon reduction projections[END_REF]). If there have been a few attempts to incorporate condensed descriptions of the refining sector in larger models [START_REF] Babusiaux | La Modélisation du secteur raffinage dans mini-dms-énergie[END_REF], the discrepancies between these representations and the "real" sector complexity clearly leads to misspecifications. From the economic viewpoint, oil refining also represents a textbook case for several reasons. First, climate change and security of supply somehow aim at reducing the use of oil, which is used in the form of refined products. So, the relevance of substitution options will depend on their relative prices compared to fossil fuels, which in turn depend on the extent of the description of this sector. Second, refining offers a unique set of complex joint production [START_REF] Baumol | On taxation and the control of externalities[END_REF] in the energy sector: its products are then sold in the global energy system, and specifications of final products related to the qualities of crude oil imply that intermediate products are numerous, and only partially substitutable.

The question

To investigate whether the level of detail in the description of the refinery impacts optimal mitigation options, we take the example of a long-term, national, linear programming based, energytransport system model. The specific case of transport makes sense because diesel, gasoline and jetfuel are (in quantity) the main outputs of refineries. In the transportation sector, emissions can globally be computed as:

CO 2 = CarbonIntensity g CO 2 M Jenergy × EnergyEf f iciency M Jenergy km travelled × DistanceT ravelled km travelled (1) (2)
Mitigation policies in the transport sector thus include three main categories: reducing the carbon intensity of transport energy, increasing the energy efficiency of mobility devices, and managing demand. The relative weight of these three options to reach a given abatement target clearly depends on their relative costs, which are affected by the baseline of oil products valuation. In the case of oil refining, most of the rigidities are linked to the final product specifications and most of the flexibilities come from crude oil mix changes, investment options at the process level and changes in the functioning of existing units. So, our main assumption is the following: flexibility and valuation of oil products change with the description of the sector (the marginal cost of the products or services outgoing the poorly represented sectors are often not very representative); this will in turn affect optimal technological abatement options, e.g. the incorporation of biofuels, the mix of demand-side technologies or even aggregate energy efficiency of the sector. Numerical experiments are undertaken to test this assumption and measure the magnitude of these effects. In section 2, we present a very small stylized model to understand how the technology description impacts marginal values. In section 3, we describe the TIMES-model used for the study and the refinery models we added to the original one and in section 4 we described the scenarios employed and the main results obtained.

2 Pricing -from theory to practical application in energy systems models

Principles

As a model based on the TIMES paradigm, MIRET is a partial equilibrium of the french energytransportation system. In short, it computes at the optimum both equilibrium energy flows and associated prices. Then, the production-consumption balance of each and every intermediate product within the model must be positive: producers (sellers) are mandated to produce at least the amount that consumers (buyers) are willing to buy. Shadow prices are computed as the dual values of these balance constraints -or, the decisions variable of the dual linear program. It is said that shadow prices equal marginal values of the traded commodities.

Supply-demand equilibria in linear programming take the well-known graphical form of stepwise constant supply and demand curves, which are implicitly built by the model (Figure 1). There, each step in the supply and demand curves represent the offer and demand of the good by a given technology. Marginal values are used as internal cession prices between the various sectors. Their interpretation (differing from that of marginal costs) is the following: the marginal value may reflect Figure 1: Supply and demand equilibrium of an endogenously traded good within MIRET adaptation on the supply or the demand side of the market for the considered good. In the case of joint production processes (such as oil refining), marginal values of the joint goods are dependent from each other. At least three phenomena are likely to modify the structure of the supply and demand curves for a given good, in the presence of joint production:

• the formal description of joint production technologies;

• the techno-economic description of the various technologies;

• the presence of other constraints, such as e.g. a global carbon cap.

The interplay of these three forces is likely to cause that different technology descriptions, even within the same modeling framework, could bias the structure of marginal values, and thus the competitiveness of e.g. abatement options in a carbon-constrained world. We now illustrate this phenomenon with the following stylized model.

A stylized model

To understand the impact of technology description, we build a small stylized energy-transportation model. In this model, primary energy is transformed in order to meet two exogenous demands: one of mobility and one of gasoline (see figure 2). To comply with the mobility constraint, the users have the choice of using a regular diesel car (DSLCar) or a low energy diesel car (LEDCAR) with a better efficiency but whose cost per kilometer is higher (c ml > c mc ). The vehicles are fueled with diesel and biofuel, and there is a norm on biofuel incorporation (it can not represent more than N of the global fuel). The biofuel (x b ) is produced by the processing of biomass (in quantity x 2 ), c 2 is the cost of production of one unit of biofuel (it includes the costs of biomass and its processing). Diesel (x d ) comes from the processing of crude oil in the refinery (in quantity x 1 ), gasoline (x g ) is jointly produced with diesel (c 1 is the sum of the purchase and the refining of one unit of crude oil).

We build two models, one with a fixed yields refinery, the other with a completely flexible refinery. Our problem is to minimize the cost of the global system given the exogenous levels of demand of mobility and gasoline (eq 4 and 5), the production constraints (eq 6 to 11), the biofuel specification (eq 12) and the CO2 emissions ceiling (eq 13).

It can be written using the following form:

                                                 min c 1 x 1 + c 2 x 2 + c ml x ml + c mc x mc s.t. x mc + x ml ≥ M obDM D (y)
x g ≥ GDM D (y g )

x d + x b ≤ x c + x l (y i ) x g -Γ g x 1 = 0 (θ g ) x d -Γ d x 1 = 0 (θ d ) x b -Γ b x 2 = 0 (θ b ) x mc -γ c x c = 0 (θ mc ) x ml -γ l x l = 0 (θ ml ) -(1 -N )x d + N x b ≤ 0 (β) ε g x g + ε d x d ≤ Ē (σ) (3) (4) (5) (6) (7) (8) (9)
(10)

(11) (12) (13)
Where x g and x d are the quantity of gasoline and diesel produced by the refinery. x mc and x ml are respectively the number of kilometers traveled with diesel cars and low emissions diesel cars. ε g , ε d the emission factor of gasoline and diesel. Ē is the ceiling on CO 2 emissions. Γ g , Γ d , Γ b , Γ mc and Γ ml are respectively the yields of the refinery in gasoline and diesel, of the bioprocess in biofuel, and of the regular and the high efficiency diesel cars.

In the case of the flexible refinery, the equations are the same except the ( 7) and ( 8) which have to be replaced by:

x d + x g = x 1 (λ) (14)
The variables in parentheses are the Lagrangian multipliers associated to each variable. First order conditions give for the fixed yields refinery:

                                   0 ≤c 1 -θ g Γ g -θ d Γ d ⊥ x 1 ≥ 0 0 ≤c 2 -θ b Γ b ⊥ x 2 ≥ 0 0 ≤ -y g + θ g + σε g ⊥ x g ≥ 0 0 ≤θ d -y i -N β + σε d ⊥ x d ≥ 0 0 ≤θ b -y i + (1 -N )β ⊥ x b ≥ 0 0 ≤c mc -y + θ mc ⊥ x mc ≥ 0 0 ≤c ml -y + θ ml ⊥ x ml ≥ 0 0 ≤ -θ ml Γ ml + y i ⊥ x l ≥ 0 0 ≤ -θ mc Γ mc + y i ⊥ x c ≥ 0 (15) (16) (17) (18) (19) (20) (21) (22) (23)
And for the flexible one:

                                   0 ≤c 1 -λ ⊥ x 1 ≥ 0 0 ≤c 2 -θ b Γ b ⊥ x 2 ≥ 0 0 ≤ -y g + σε g + λ ⊥ x g ≥ 0 0 ≤ -y i -N β + σε d + λ ⊥ x d ≥ 0 0 ≤θ b -y i + (1 -N )β ⊥ x b ≥ 0 0 ≤c mc -y + θ mc ⊥ x mc ≥ 0 0 ≤c ml -y + θ ml ⊥ x ml ≥ 0 0 ≤ -θ ml Γ ml + y i ⊥ x l ≥ 0 0 ≤ -θ mc Γ mc + y i ⊥ x c ≥ 0 (24) (25) (26) (27) (28) (29) (30) (31) (32)
We now make two scenarios to solve this minimization problem and to compare the CO 2 marginal values obtained for the two different refineries (see appendix A.1 for the resolution and the other Lagrange multiplier values).

Scenario Bio: We suppose that :

• the Ledcar technology is not used because too expensive: cml → ∞,

x ml = x l = 0 • The biofuel constraint is not saturated: β = 0 Flexible Refinery σ bio f lex = ( c 2 Γ b -c 1 ) 1 ε d

Fixed yields Refinery

We consider that gasoline is in excess: y g = 0

σ bio f ix = (c 2 Γ d Γ b -c 1 ) 1 ε d Γ d + ε g Γ g Notation : σ bio f ix
is the marginal value associated with the carbon constraint in the scenario Bio for the fixed yields refinery.

Scenario Ledcar:

We suppose that biofuels are not used because too expensive:

c 2 → ∞, x 2 = x b = 0, β = 0 Flexible Refinery σ ledcars f lex = ( c ml -c mc Γ ml -Γ mc Γ mc Γ ml -c 1 ) 1 ε d

Fixed yields Refinery

We consider that gasoline is in excess: y g = 0

σ ledcars f ix = 1 ε d Γ d + ε g Γ g (-c 1 + Γ d c ml -c mc Γ ml -Γ mc Γ mc Γ ml )
The first thing we can notice is that the marginal value associated to the CO 2 constraint (σ) differs between refinery descriptions for a given scenario. It means that for the same level of the carbon ceiling, the technological options chosen to mitigate emissions could differ. We plot the evolution of the objective function value with the carbon cap for the two refineries with a given set of parameters (figure 3 and see appendix A.3 for the parameters values). The carbon ceiling is not binding at the same level for the two refineries. In the fix refinery case, gasoline is in excess (the mobility constraint is saturated but the gasoline one is not) hence the total emissions of this model are higher than the one of the flexible refinery model. Because of the parameters values we have in this case:

σ bio f ix < σ ledcar f ix
and σ bio f lex < σ ledcar f lex . It explains why the first option chosen to mitigate emissions is biofuel. An other interesting point is the fact that biofuel use allows to mitigate more emissions in the fix refinery case than in the flex one (3 units vs 2.5 units). Indeed, using more biofuels reduces the need of diesel so the refinery treats less crude oil and reduces its gasoline production. Gasoline excess is then less important so incorporating biofuel in the vehicle fuel helps also to reduce emissions due to gasoline. This is not the case for the flexible refinery since in this model there is no gasoline excess.

We then use parameter values that would allow a different result, we raise c 2 in order to have σ bio f lex > σ ledcar f lex (see figure 4). And moreover, we modify the fix refinery yields, lowering the diesel yield and raising the gasoline one.

In this case since the diesel yield is so low, it is optimal for the fix refinery model to use ledcars even with no carbon constraint. The comparison of the two models' runs with a carbon ceiling is distorted since the flexible refinery does not have this problem. The marginal values of the carbon Moreover, with this set of parameters these two models have different abatement capacities: the fix one can abate up to 1.5 units of carbon when the flex one can abate 7.8 units of carbon.

To understand how a complex refinery would behave, we added two processes to the fix refinery (HC1 and HC2): these processes convert gasoline into diesel with different costs and yields. It is not mandatory to use the new processes. The new refining scheme and parameter values can be found in appendix A.2. We did the same exercise than earlier and for the initial values of the parameters, we obtain the following results (see figure 5):

The cheapest process HC1 is used even when there is no carbon constraint as it allows to reduce the gasoline waste. After that, the cost ratios of the other options lead biofuels to be introduced first, then HC2 and finally the low-energy vehicle technology. The little flexibility introduced with the two new processes is not sufficient for the objective function trajectory to change drastically. Yet, we can observe that the "complex" refinery trajectory is nested between the two others and that the flexibility delays the adoption of new technologies. This simple stylized example illustrates that refinery models characterized by different levels of flexibility exhibit different shadow price structures. Submitted to a series of carbon caps, we show that the responses of the three models differ: abatement options are utilized differently in quantity and timing. This bias is an issue, at least theoretically; whether this is the case in a more complex,

An energy-transport system model

We now turn to the presentation of the scaled up framework. In this section, we present the IFPENdeveloped MIRET model: a long-term, multi-period, techno-economic planning model that covers the energy-transport system in detail. Its scope is continental France, and the time horizon is 2050, with 2007 as base-year. The TIMES model generator is used as a modeling framework. Under this well established paradigm [START_REF] Labriet | The implementation of the EU renewable directive in Spain. Strategies and challenges[END_REF], [START_REF] Lorne | The French biofuels mandates under cost uncertainty -an assessment based on robust optimization[END_REF], a Reference Energy System is built to cover the stock of equipment and flows for the reference year, the characteristics of future technologies, the potential and costs for primary energy. This being given, the model aims at providing final energy services / energy (mobility for passengers and freight, electricity, etc.) at minimum cost. To do this, investment and operation decisions are made for the technologies embraced in the model; subsequent primary energy uses are obtained.

General presentation

The model schematics is presented in Figure 1. It presents a block diagram that links elements described in the model according to four main dimensions: energy supply, technologies, demand and policies (Loulou et al, 2005).

Figure 6: Model schematics The reference energy system is thus composed (from left to right) of:

• a primary energy supply block: includes imported fossil energy (crude oil, coal, natural gas), biomass (starch crops -wheat, corn; sugar crops -sugar beet; oil crops -rapeseed, sunflower; lignocellulosic biomass -forest wood, crop residues, dedicated energy crops);

• an energy technology block, whose technologies transform primary energy into energy vectors and energy services: it includes oil refining (see next section), biofuel units (first generation -ethanol, FAME , HVO ; second generation -ethanol and synthetic FT-Diesel), electricity generation (power plants -all technologies; combined heat and power), preparation of fuels for transport at blending (diesel, biodiesel B30, gasoline grades E5 and E10 and E85, jet fuel -including fossil and bio bases), and end-use technologies for road mobility (personal vehicles and Light -thermal, hybrid, plug-in hybrid / gasoline, diesel, natural gas, flexfuel, electric cars; buses and trucks -thermal, hybrid / gasoline, diesel, biodiesel);

• a final energy / energy services demands block: Electricity demand by time period (four days representing each season, the power load being hourly described for each of these days), mobility demands (short and long distance for passenger vehicles and buses, traffic for LUV, demand for freight mobility), demands for exported products (oil products, electricity);

• a policies block: includes measures and constraints of several types affecting all sectors. Some are of microscopic nature, such as quality norms for refinery products, number of functioning hours of fuel turbines power plants, etc. Some are macroscopic in nature, e.g. sectoral carbon tax.

Basic formalism

The objective function of the underlying linear program takes the form:

OBJ = t∈periods DISC(t) IN V COST (t) + IN V T AXSU B(t) + IN V DECOM (t) + F IXCOST (t) + F IXT AXSU B(t) + V ARCOST (t) -SALV AGE
It is simply the discounted sum of investment costs (including taxes and subsidies), decommissioning costs, fix costs (including taxes and subsidies), variable costs, and economic value of investments whose life extends beyond the time horizon. The linear program P ref refers to the "reference" case:

(P ref ) =                        min c T x s.c. Ax ≥ b (y) T x = 0 (τ ) Kx ≤ k (λ) Qx ≤ q (ω) Sx ≤ s (σ)
x ≥ 0 c is the column vector of all discounted unit costs. The constraints Ax ≥ b correspond to the final demands of energy and energy services to be satisfied. The equation set T x = 0 describes the fundamental input-output relationships of each technology, namely the mass or energy balance of each technology. The set Kx ≤ k includes all capacity constraints, either technology or resource based. For example, (i) the electricity produced by a given technology is limited by the combination of the stock installed and seasonal or hourly availability factors, (ii) the use of scarce resources, e.g. woody biomass, are limited for use for power, heat, combined heat and power and biofuels production. Qx ≤ q accounts for the quality equations of some of the products. This is especially the case of refinery products, whose quality must respect certain specifications to be marketed . Finally, the set Sx ≤ s includes all sorts of institutional constraints (e.g., the French legislation limits the number of functioning hours of certain power plants -notably fuel turbines), calibration constraints and share constraints.

The refinery sector -from aggregated to detailed

In this section we detail the four refinery models used in the study.

Three concise models of the French refining sector

In the first MIRET model, the refinery is modeled with a single process. The process inputs are crude oil, natural gas and oxygen and the outputs are LPG, Naphtha, gasoline (GSL), jet fuel, heating oil (HOL), diesel (DSL), fuel oil (RFO) and coke (see Figure 7). The throughput yields can each vary between two values and are optimized by the model (with the obvious constraint that the yields sum equals 1). These two minimum and maximum values for each petroleum product have been determined with regard to the 2007 french refining tool and they evolve with time (see Figure 8) in order to take into account a possible adaptation of the refining industry to the local market changes. There is no cost associated with this yields' evolution. For the purpose of our study, we declined this initial semi-flexible refinery model: we built what we call a Fix refinery where the yields vector is fix (it evolves with times only, see Figure 9) and a totally flexible one, called Flexible Refinery, for which the throughput yields can take any value between 0 and 1, allowing the model to get rid of the co-product issue. These two models are extreme: the fix one forbids any adaptation so that the amount of fatal products of the refinery cannot be reduced but by lowering the refinery's utilization rate. On the contrary, with the flexible refinery, the fatal products are not a problem since they are not produced at all. The mathematical representation of the 3 kinds of refinery models is the following:

Semi-flexible Refinery            ∀i ∈ [1, 8] λ i CR ≤ y i ≤ υ i CR 0 ≤ λ i ≤ υ i ≤ 1 -δ s.t. i y i = CR(1 -δ) Flexible Refinery        ∀i ∈ [1, 8] 0 ≤ y i ≤ CR(1 -δ) s.t. i y i = CR(1 -δ) Fix Refinery        ∀i ∈ [1, 8] y i = α i CR W ith i y i = CR(1 -δ)
With y i the quantity of the eight refinery outputs, CR the crude oil quantity entering the refinery and δ the autoconsumption and losses of the refinery. These models differ only by the yields' vector. The cost per unit of input, the refinery CO 2 emissions, the nature of the outputs... are the same for the three models.

A detailed model of the French refining sector

The initial Miret's refinery model and its two variants have several issues that we tried to tackle by building a more complex French refining model. The first issue is that the yields' flexibility allowed by the simple refinery model is free which is far from being realistic. To change the throughputs of his refinery, the refiner can first switch to a crude oil more appropriate to meet his production targets. But, this solution does not enable him to deeply change the plant production profile. To do so he has to adapt the process units' utilization rate which can be costly or he has to invest in new units. For example, to produce more diesel and less fuel oil, the conversion units have to run more and since they have high operational expenditures, the refining cost is going to increase. The second issue of the simple refinery model regards the fuel specifications. Usually, simple models do not take into account the oil and product quality and the difficulty (and cost) to meet the fuel specifications. Although this aspect is really important and constraining particularly for the blending of biofuel in the transportation fuels (ethanol in gasoline). The third problem of the simple model is the representation of the petroleum product marginal values and more particularly of their ratio which are distorted, among other things by the cost-less flexibility of the yield vector. And finally, a more complex model of the refining sector allows to improve its CO 2 emissions representation. Indeed, in the three simple refinery models the CO 2 emissions are linearly correlated with the amount of crude oil treated whereas in the complex refinery model, the CO 2 emissions are related to the amount and type of fuel used in the refinery plant fuel (natural gas, internal fuel gas, fuel oil, FCC coke, hydrogen...). For the reasons presented above, we built a Complex refinery model. The retained refining structure Figure 10: French refining scheme consisted in the most common processing units, among which an atmospheric distillation unit, a vacuum distillation unit, a catalytic reforming unit, a gasoil hydrodesulphurization unit, an ETBE unit, a catalytic cracker, an hydrocracker, combined with alkylation and isomerization units to improve the quality of the gasoline pool, and a visbreaking unit to process residues from these units (the refining scheme is presented in Figure 10). This conversion refinery is very close to the current structure of the French refining industry but it may change to be adapted in line with future developments or in some scenarios considered over the next thirty years. To face the possible evolutions of demand and measures that could be adopted about the tightening of petroleum product specifications such as reduction in the sulphur content in diesel oil and heavy fuel oil, a coking unit has been added in the modeled refining structure in order to permit a lesser production of fuel oil. More expensive deep conversion units as partial oxydation unit and a deep hydrodesulphurization unit are not in the model since the French refining has been in a bad shape for a decade and large investments in this industry seem pretty unrealistic. Regarding the main input of the refinery, there is at the moment only one crude oil entering the refinery whose characteristics are representative of the mix of crude oil used in France in 2007.

To summarize, the three simple refinery models are very close except for the yields' vector. For the same crude oil treatment, the refining cost and emissions will be similar but we will obtain different amounts of petroleum products (see table 1). And more importantly, the marginal value ratios of the petroleum products will vary a lot between the three models: with the flexible one, all ratios are equal to 1 since at the optimum, all the produced products have the same marginal values, for the fix refinery the ratios risk to take extreme values whereas the simple model should give moderate but not necessarily accurate ratios.

Complex

Semi

Carbon constraints

Two types of carbon constraints have been tested. In the first case, a cap is applied on the cumulative global CO 2 emissions. In the second case, the cap is applied on cumulative fossil CO 2 emissions from transportation only. Even though an annual cap is more likely to be instituted, we chose to use a cap on cumulative emissions and this mainly for a modeling reason: with this constraint we obtain a unique CO 2 price for each refinery model. With an annual ceiling, there is one CO 2 marginal value per year and since the refinery models do not necessarily adapt at the same time, the interpretation of the CO 2 marginal value variations between the models is not easy. The other reason is that it gives more flexibility to the model: an annual decreasing ceiling largely drives (and constrains) the model when a global one allows the model to adapt at any time. For both constraints (global and sectoral), the cap is fixed thanks to a reference level. This reference level is the CO 2 emission value obtained in the base case, i.e. a case with no constraint on CO 2 emissions (see Figure 13). We can notice on this figure that the various refinery models have very few impacts on the global or sectoral emissions when no constraint is applied on CO 2 . Six levels of carbon constraints have been applied on the cumulative emissions of CO 2 between 2011 and 2050. The ceiling on emissions varies between 40% and 90% of the total emissions obtained in the base case.

Results

Global carbon constraint

For the first set of carbon constraint, the global CO 2 cap, the differences between the runs with the complex refinery model and those with the three other types of refinery are not very important. Indeed, GHG emission reduction is very expensive in the transportation sector so when a global cap is applied on CO 2 emissions, the reduction effort is first supported by the other industrial sectors (see Figure 14 for an illustration). In the semi-flex refinery case, the emission reduction first Figure 14: CO 2 emission abatement by sector for the semi-flex refinery model with a 60% ceiling comes from the heating sector because it is relatively cheap to switch from gas produced heat to bio produced heat. Then the power sector contributes to the emission reduction with an increased use of renewables and a decreased use of coal and gas processes. The transportation sector's emission reductions are very low until 2030, at this date the sector stands for 65% of global CO 2 emissions (versus 45% in 2007).

The paths followed by the objective function or the CO 2 marginal values are slightly similar for the four different models (see Figure 15) when the constraint is not too stringent. When the CO 2 ceiling reaches very low values, the four models began to behave diversely. Finally, we can observe that when trying to evaluate the impact of a global CO 2 ceiling on the energy-transport system, adding details to the refinery model is not compulsory. It begins to be important when the ceiling evaluated is very ambitious (for example, the 50% ceiling leads to a CO 2 marginal value of 360e/t which is a very high value regarding the actual European CO 2 market). When the ceiling values are more classic, the refinery model does not impact the optimal solution.

Sectoral carbon constraint

The following results present scenarios with a sectoral cap: end-pipe fossil CO 2 emissions are capped. The first notable thing is the diversity in the adaptation adopted by the different models. The possible arbitrage is the following: the model can wait to adapt but will have to abate much more future emissions or it can reduce its emissions immediately which will allow him to emit more in the future. By looking at the CO 2 emission trajectories of the models, we can notice that the complex refinery model usually reacts first, and lowers its emissions in two steps when the other refineries adapt in a more abrupt way (see figure 16). The flexible refinery does not react very early but its reaction is radical when it happens. An other way to notice the differences induced Figure 16: Fossil CO 2 emissions of transport for the various models by the various refinery models in the energy system models is to look at the four objective values trajectories which are slightly different (see appendix B). These CO 2 trajectories are the result of the different ways of adapting adopted by the four models. To comply with the emission ceilings, the model has 3 possibilities:

• decarbonize the fuels burnt in the vehicles by using biofuels for example

• use vehicles with a better energy efficiency

• switch to new energy sources for transportation, as electric vehicles for example Decreasing the mobility demand is not an option, in this case we consider an inelastic demand (the demand is an exogenous input of the model. These 3 ways of reducing transportation sector emissions are all used when the constraint is high enough but not necessarily in the same proportions.

When the constraint is really stringent, the four models adapt almost in the same way and with similar carbon marginal values. Indeed, with a very low carbon ceiling the choices to mitigate emissions are not numerous. It explains why the four models end up using the same technologies and biofuels. For this reason it is not very interesting to look at the low ceiling results and we are going to study the models behavior for the intermediary ceiling values.

The figure 17 shows the evolution of transportation energy intensity and carbon intensity under 2 levels of ceiling: 60% and 80%.

The trajectories of energy intensity are pretty similar for the four models even though the vehicle fleets are different. It is relatively cheap and fast to modify the car fleet composition since the cars lifetime is rather short (15 years). Besides, the efficiency of the various new car technologies does not vary a lot so the impact on the global energy intensity is minor. It is different for the carbon intensity: the decarbonization of vehicle fuel seems to happen at different periods for the four models.

Taking a look at the car fleet composition helps to understand some of the differences in the energy and carbon intensities evolution. The car fleet trajectories (figure 19) diverge notably for the small and the large cars. It is less the case for the medium size cars: in this category, the new diesel motorization is always and largely preponderant (see appendix B). For the small size cars, the type of refinery used has a huge impact on the investment trajectory. With the 80% ceiling, the fix and the semi-flex refineries favor the natural gas cars when the flexible and the complex one use it only as a transition technology.

Figure 19: Small Cars activity: 80% Ceiling An obvious other way to decarbonize the transportation sector is to use biofuels (see appendix B for figures). The complex refinery generally makes earlier use of biofuels and ends up using less of it than the other ones. When the CO 2 constraint is really stringent (the 40% cap), all models use the same amount and proportion of biofuels. Yet, when the constraint is lower the amounts are still more or less the same but the complex model uses a largest variety of biofuels: with the 80% ceiling, the complex model consumes seven times more ethanol than the other ones.

The large differences between models regarding the car fleet and the biofuel consumption can partly be explained by the refinery utilization. The cheapest way to reduce emissions in the fixed and semi-flexible refinery cases seems to be to stop using the refinery at the end of the period (figure 20). The more stringent the constraint, the earlier the refinery utilization rate collapses1 . This leads the model to invest in car technologies independent of oil products and explains why these two models largely invest in CNG cars when the other two stick with small gasoline cars.

Conclusion

In this work, we illustrate a rarely debated issue: modeling more or less details can induce bias which can lead to different policy insights. Taking the example of the oil refining sector, a good symbol of the model developer difficulty because of its particularities: joint production, multiple flexibilities and the multiple possible origins of fuel components, we show that the refinery description used in the energy system model matters when trying to evaluate energy or climate policy applied to the transportation sector. It impacts the policy costs but also the technology trajectories chosen at the optimum. In the results presented, this is notably true for biofuels and alternative car technologies. Essentially, the balance between energy efficiency and carbon intensity of transport may be affected by the accuracy of the description of the pivotal refining sector. Consequently, increasing this sector accuracy level should be motivated by the wish to gain wider quantitative insights on potential evolution of the energy system and above all by the wish to improve the robustness of outcomes.When data is not available or when the modeling team does not possess the necessary competency to detail a sector, an extensive sensitive analysis on the sector technical parameters becomes necessary. As stated by [START_REF] Hedenus | A Critical Assessment of Energyeconomy-climate Models for Policy Analysis[END_REF], "this kind of analysis is often absent from policy reports or academic literature. Yet, it allows the decision maker to gain wider insights on the energy system mechanisms and it could avoid misinterpretations due to modeling bias". This simple fact calls for questions for both producers and clients of long-term energy studies, which go beyond the technicalities. Past experience shows that policy makers are probably aware of such issues. Current practices seem to indicate that long-term questions are often asked to different research groups, using various models. The very fact that they get different outcomes should not necessarily be an issue -in principle, there are not necessarily specific reasons why different groups of people, using different models, explore an inherently uncertain future with the same perspective. Still, a distinction should be made between these shortcomings and bias induced by model misspecifications. This leaves modelers with their own choices and responsibilities.The modeling team competency and extensive knowledge of the whole model is essential when the question of the level of details arises. Detailing a sector should be done when the data are available and its mechanisms well understood. Without these two points a simpler approach should be promoted to avoid a false sense of accuracy. So, if robust analyses have to rely on accurate economical and technological descriptions, lack or excess of details are enemies of modelers. As mentioned by [START_REF] Van Delden | Comparison of scale and scaling issues in integrated land-use models for policy support[END_REF], "Model complexity increases with the number of variables and processes incorporated and by linking various model components. More complex models may generate unnecessary detail, while simple models may omit essential processes. It is not enough to apply Occam's razor to just one model component, as the requirements also apply to the integrating linkages between components". This theme gains in importance in other communities as well, e.g. in ecosystemic modeling: " in many cases a carefully tuned simple model can perform as well as a more complex model. On the other hand, a pragmatic review of ecosystem models by Fulton et al. (2003) concluded that the simplified webs, especially those reduced to less than 25% of the size of the original model web, are not able to represent enough of the processes and interactions in the system to faithfully reproduce system dynamics, particularly when the strength of environmental or anthropogenic pressures changes" [START_REF] Hannah | The case for marine ecosystem models of intermediate complexity[END_REF]. Challenges thus drive research teams in seemingly orthogonal directions: robust modeling of increasingly integrated, uncertain phenomena.

A Stylized Model

A.1 Resolution Scenario Bio: We suppose that :

• the Ledcar technology is not used because too expensive

• The biofuel constraint is not saturated cml → ∞, x ml = x l = 0, β = 0 Flexible Refinery

                            
With equation ( 24 We consider that gasoline is in excess, y g = 0

                                        
With equations ( 16

θ g = ε g ε d Γ d + ε g Γ g (c 1 -c 2 Γ d Γ b ) θ d = ε d ε d Γ d + ε g Γ g (c 1 -c 2 Γ d Γ b ) + c 2 Γ b σ = (c 2 Γ d Γ b -c 1 ) 1 ε d Γ d + ε g Γ g

Scenario Ledcar:

Biofuels are not used because too expensive.

c 2 → ∞, x 2 = x b = 0, β = 0 Flexible Refinery                                               
With equation ( 24

Figure

  Figure 2: Small Model

Figure 3 :

 3 Figure 3: Objective function evolution with the carbon ceiling

Figure 5 :

 5 Figure 5: Objective function evolution, with 3 refineries

Figure 7 :

 7 Figure 7: MIRET's semi-flexible Refinery

Figure 8 :

 8 Figure 8: Product yields variations -French semi-flexible Refinery

Figure 9 :

 9 Figure 9: Yields vectors -Fix Refinery

Figure 11 :

 11 Figure 11: French population evolution

Figure 12 :

 12 Figure 12: Main sources for numerical assumptions

Figure 13 :

 13 Figure 13: Global & Fossil Transportation CO 2 emissions for the base case (No emission ceiling)

Figure 15 :

 15 Figure 15: CO 2 marginal value for different levels of the ceiling

Figure 17 :

 17 Figure 17: Transportation energy intensity with 2 ceilings: 60% and 80%

Figure 20 :

 20 Figure 20: Refinery treatment: 60% and 80% cap

  ): λ = c 1 With equations (25) & (28):θ b = y i = c 2 Γ b With equation (32): θ mc = c 2 Γ b Γ mc With equation (27): σ = ( With equation (29): y = c 2 Γ b Γ mc + c mcWith equation (26):y g = ε g ε d ( c 2 Γ b -c 1 ) + c 1Fixed yields Refinery.

  )& (19): θ b = y i = c 2 Γ b With equation (23): θ mc = c 2 Γ b Γ mc With equation (20): y = c 2 Γ b Γ mc + c mcWith equations (15),(17) & (18):

Figure 22 :

 22 Figure 22: Parameters values used for the stylized model (*:Complex case only.)

Figure 23 :Figure 24 :

 2324 Figure 23: Objective value evolution with different levels of CO 2 emission ceilings

Table 2 :

 2 French GDP growthPopulation scenario is based on the central scenario of INSEE regional projections for 2040. 2050 extension is based on 2030-2040 average annual growth rate (see figure11).

			-flexible		Flexible	Fix
	Yields	Flexible, with costs	Flexible within al-flexibility is free. lowed ranges. The	Totally free. The flexibility is flexible.	Totally fixed
	Product
	specifica-	Yes				No
	tions
	CO 2 emis-sions	Depend on the processes used	Proportional to the quantity of crude oil treated
	Costs	Depend on the processes used	Proportional to the quantity of crude oil treated
		Table 1: Refineries, Summary
	4 Model detail, global mitigation strategies and sectoral
	policies
	4.1 Scenarios
	Economic assumptions
	GDP (2007e) scenario is built based on existing projections for France. At the 2030 horizon,
	DGTPE scenarios is selected; 2050 extension is based on OECD long-term growth projections.
	This scenario traduces a slow-down of growth in the next decades.
		2010-2020	2020-2030	2030-2050
		GDP growth 1.4%	2%		1.4%

The scenarios leading to the refining sector shutdown are not politically acceptable (in terms of energy security and employment). But adding constraints on the refinery utilization rate would drive model outcomes too much.

θ mc = c ml -c mc Γ ml -Γ mc Γ ml θ ml = c ml -c mc Γ ml -Γ mc Γ mc y = c ml Γ ml -c mc Γ mc Γ ml -Γ mc y i = c ml -c mc Γ ml -Γ mc Γ mc Γ ml With equation (27): σ = ( c ml -c mc Γ ml -Γ mc Γ mc Γ ml -c 1 ) 1 ε dWith equation (16):y g = ( c ml -c mc Γ ml -Γ mc Γ mc Γ ml -c 1 ) ε g ε d + c 1

Fixed yields Refinery. We consider that gasoline is in excess, y g = 0

With equations ( 20) to (23):

With equations ( 15),( 17) & ( 18): The "Cahiers de l'Économie" Series

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