Kirsi Zongo

Mahamadou Diarra

Spatial Effects of Foreign Direct Investment Flows on Industrial Performance in Sub-Saharan African Countries

Keywords: Foreign Direct Investment, Industrial Performance, Spatial Econometrics, Sub-Saharan Africa JEL Codes: C21, F30, O14

This paper rigorously analyzes the effects of foreign direct investment inflows on the industrial performance in the Sub-Saharan African (SSA) economies. Applying the Durbin spatial method (SDM) on a two-sector model to account for spatial effects, the empirical results show that the higher the capacity of SSA countries to attract foreign investments, the higher is the job-inducing effect and value-added created in the industrial sector, while no technology transfer was induced. This finding highlights the importance for the countries of sub-Saharan Africa to direct foreign direct investment towards strategic sectors where they benefit from comparative advantages and improve the business climate to attract more FDI, a pledge of any industrial development.

Introduction

The rapid expansion of foreign direct investment (FDI) flows from the 1980s onwards has fueled the debates on the consequences of economic openness in developing countries.

Indeed, these countries had a negative perception of the entry of FDI for political and economic reasons. Most of the leaders of these countries were suspicious of the massive entry of FDI that was considered a threat to national sovereignty due to the loss of economic and political autonomy. Moreover, FDI was seen as direct competition to local enterprises and a source of social instability.

However, from the 1980s onwards, this perception of FDI entry has positively shifted. Indeed, the entry of FDI is more and more perceived as a determining factor in the development process of contemporary economies in an increasingly globalizing world. Nowadays, it is recognized by all that the FDI plays a capital role in the strategy of economic development of the nations and for proof, the majority of the countries undertake policies of incentive to the attraction of the FDI in their economy. Several studies including that of [START_REF] You | China's outward foreign direct investment and domestic investment: An industrial level analysis[END_REF] show that the example of Asian countries such as China, India, Singapore has confirmed the importance of the massive entry of FDI in the process of industrialization although the success of this process depends heavily on the policies implemented.

However, Sub-Saharan African countries have long been blamed for their low capacity to attract FDI. Indeed, the share of FDI in SSA in total global FDI flows has fallen from 6.3% to 2.1% in 1970 and 2017, respectively against a rise of 5.5% to 31.5% in South and South-East Asia over the same period according (UNCTAD, 2018). In addition, African countries still record in this early 21st century, significant challenges, especially in human capital and infrastructure. The African productive activity is essentially based on a few primary products, agricultural or mining giving a specialization of the continent's economies in the exploitation of raw materials. This poses the problem of structural changes within these economies that can lay a solid foundation for industrialization through capital accumulation, increased productivity, and competitiveness. Therefore, structural transformation, industrialization, and the search for competitiveness have been at the heart of literature focusing on Africa's development in recent years.

Therefore, it appears crucial to understand how FDI inflows influence the industrial development process of SSA countries. This paper analyses the spatial effects of FDI on the industrial performance of SSA economies.

The paper significantly contributes to the existing literature by bridging the gap in empirical studies in SSA economies on the relationship between FDI inflows and industrial performance. Indeed, only a few empirical studies have paid particular attention to the topic and covering SSA economies and most of the existing studies are outdated in terms of data periods in line with the new policy directions of the International Conference on African Emergence in 2015. The existing works in the literature focused on the period before 2010 even though these countries have started to undertake structural transformation strategies of their economies from 2015. Moreover, most of these studies did not find positive effects of FDI inflows on the industrial performance of the economies in this zone. This legitimates the relevance of a new study to empirically highlight the nature of the relationship between the two indicators especially in a context where Africa has now embarked on a new development paradigm by inscribing structural transformation as an imperative and a unique path for its economic emergence. This transformation of the productive structure of SSA economies necessarily requires structural reforms that can facilitate the massive inflow of FDI to support the productive apparatus of these economies. Consequently, an empirical study on the issue should make it possible to update the data but especially to guide and encourage the public decision-makers of the economies of this zone in their decision-making.

An additional important aspect supporting this study consists in using different econometric specifications than previous studies. Indeed, unlike all previous studies focussing on SSA countries, this paper relies on spatial models to unpack the effects of FDI entry on the industrial performance of SSA economies. Industrial development is multidimensional and a multifaced concept requiring to take into account taking into account spatial dependence and heterogeneity when exploring the nexus between FDI and industrial performance. Indeed, the advent of globalization suggests that a country's economic activity can influence its trading partners or close neighbors through trade and factor mobility. Hence, drawing on spatial econometrics would lead to more reliable results and thus allow for the formulation of more optimal economic policy orientations.

Literature review

Theoretical studies identify two main channels through which FDI flows affect the industrial performance of countries including direct effects and indirect effects. The direct effects are analyzed in terms of job creation or in terms of competition on local companies and on the demand for local inputs as well as their contribution to industrial value added (e.g., see [START_REF] Rodríguez-Clare | Multinationals, linkages, and economic development[END_REF]Markusen &Venables 1999). Indirect effects, on the other hand, are analyzed in terms of the transfer of technology and knowledge from the host country to the host country, which increases the productivity and value-added of local firms through horizontal and vertical spillovers (e.g., see MacDougall 1960;Caves 1971 and[START_REF] Barrios | Foreign direct investment, competition and industrial development in the host country[END_REF]Greenaway 2004).

We first present the works that have studied the direct effects of FDI entry on the industrial development process and then those that have analyzed the indirect effects through technology transfers before focusing on the specific case of African countries.

The direct effects of FDI entry on industrial performance

One of the first studies to have empirically analyzed the effects of FDI entry on industrial performance is that of [START_REF] Blomström | Foreign Investment and Productive Efficiency: The Case of Mexico[END_REF]. Indeed, the author analyses the effects of the presence of foreign firms on the performance of Mexican manufacturing industries over the period 1970 to 1975, focusing on the structure of the manufacturing sector. Using the ordinary least squares (OLS) model, the author finds that the efficiency of the Mexican manufacturing sector is positively correlated with the presence of multinational firms. In other words, industries dominated by foreign firms tend to be more efficient than other industries because local firms acquire better practices. This industry is dominated by multinational firms, therefore, tends to have a concentration of local firms and is found to be in the modern sector.

In the same vein, several other authors have arrived at similar results by analyzing other economies. As an illustration, [START_REF] Barrios | Foreign direct investment, competition and industrial development in the host country[END_REF] explored the impact of FDI on the development of local Irish firms over the period 1972 to 2000 using OLS and semi-parametric regression techniques. They conclude that although the competition effect initially discourages local firms, spillover effects, notably positive externalities, will emerge and lead to a largely positive overall impact for local firms. Local producers need some time to adapt and improve their capabilities. Also, Zhang (2014) using the OLS estimation method highlighted that over the period 2005-2010 FDI entry has positively affected Chinese industrial competitiveness. More interestingly, the study showed that these effects are stronger on low-tech manufacturing industries than on medium-and high-tech industries.

However, other studies have found contradictory results. Indeed, [START_REF] Alfaro | Multinationals and linkages: an empirical investigation [with Comments[END_REF] used the theoretical model of [START_REF] Rodríguez-Clare | Multinationals, linkages, and economic development[END_REF] to study firm-level data from Brazil (over the period [1997][1998][1999][2000], Chile (over the period 1987 to 2000), and Venezuela (over the period 1995 to 1999). The results obtained using the OLS method show that the entry of multinational firms has no significant effect on horizontal spillovers. In the same vein, [START_REF] Kang | Foreign direct investment and de-industrialization[END_REF] use the generalized method of moments (GMM) on panel data to analyze the relationship between FDI and deindustrialization in OECD countries. Their results show not only that there are major internal and external drivers of deindustrialization, but that FDI flows are also a positive impact on employment and manufacturing value-added.

Indirect effects of FDI entry on industrial performance

The empirical literature shows that FDI entry indirectly influences industrialization through the technology transfer channel under the assumption that technology transfers can influence the productivity, value-added, and profit of local firms. Indeed, [START_REF] Fosfuri | Foreign direct investment and spillovers through workers' mobility[END_REF] analyzed the technological spillovers of FDI on local firms through the technology spillover effect.

They find that a multinational firm can transfer superior technology to its foreign subsidiary only after training workers in the host countries. Also, worker mobility contributes to technology spillovers.

The spillovers of technology transfer can be vertical or horizontal as demonstrated in the theoretical review. On this point, the vertical spillovers of technological spillovers have been analyzed empirically by several authors in various regions of the world. Indeed, [START_REF] Sjöholm | Productivity growth in Indonesia: the role of regional characteristics and direct foreign investment[END_REF] analyzed the effects of regional integration and FDI entry on the productivity of local Indonesian industrial branches over the period 1980-1991 using OLS. Its results show that the presence of FDI has led to a strong increase in productivity in the intra-industry through vertical spillovers of technology transfers. However, the study did not find results showing the possibility that geographical proximity increases vertical spillovers.

In the same vein, subsequent studies highlight a positive connection between FDI inflows and the increase in the productivity of local firms. We can cite the work of [START_REF] Javorcik | Does foreign direct investment increase the productivity of domestic firms? In search of spillovers through backward linkages[END_REF] who analyzed the case of Lithuania over the period 1996-2000 using OLS and Olley-Pakes Regression. His results show positive vertical spillovers of FDI entry on the intra-industry productivity of local firms, especially those of input suppliers. [START_REF] Liu | Foreign direct investment and technology spillovers[END_REF] reached the same conclusion by analyzing the horizontal and vertical spillovers of FDI entry on domestic firms in China using a fixed-effects model on panel data of 17,675 firms over the period 1995-1999.

The results show that despite some negative effects in the short run, there are higher positive effects on the productivity of local firms in the long run. Furthermore, [START_REF] Javorcik | To share or not to share: Does local participation matter for spillovers from foreign direct investment[END_REF] analyzed the case of Romania using the OLS and Levinsohn-Petrin approach over the period 1998-2003. The results suggest that vertical spillovers are noticeable among local firms subcontracting with foreign affiliates and that the negative horizontal competitive effect of entry is mitigated by the knowledge transfer that increases the productivity of the industry.

Finally, [START_REF] Xu | Productivity spillovers from foreign direct investment: firm-level evidence from China[END_REF] analyzed the spillover effects of FDI entry on domestic firms in the Chinese manufacturing sector between 2000 and 2003 using a battery of models such as OLS, GMM, etc. The results show positive effects of FDI entry on the productivity of intraindustry firms through the interconnection between domestic firms and FDI, especially through the purchase of high-quality intermediate goods or equipment from foreign firms. Nevertheless, there are empirical studies that have invalidated the positive effects of FDI entry by finding negative effects on productivity. For example, [START_REF] Kugler | Spillovers from foreign direct investment: within or between industries[END_REF] analyzed the effects of FDI entry on the intra-industry productivity of Colombian firms through the channel of technology transfer. Using the structural vector autoregressive (VAR) model over the period 1974-1998, the results reveal the absence of spillovers from FDI entry on intraindustry firms.

Empirical work on the specific case of African countries

The empirical literature on the effects of FDI on industrial performance in developing countries and particularly focusing on SSA countries remains controversial. Indeed [START_REF] Managi | Foreign direct investment and technology spillovers in sub-Saharan Africa[END_REF] analyzed the vertical and horizontal spillovers of FDI entry on intra-industry and inter-sectoral productivity of local firms in three SSA countries namely Zimbabwe, Tanzania, and Kenya over the period 1994-1995 using the system-GMM method. Their results show the existence of positive vertical and horizontal effects of FDI entry on extra and intra-industry productivity in Kenya and Zimbabwe but a negative effect for Tanzania. The authors find that the negative result in Tanzania could be explained by the fact that some tax favors are granted to foreign firms at the expense of local firms, thus creating competition effects.

On the other hand, Nkoa (2016) used the GMM method to analyze the effects of FDI on the industrial performance on a panel of 53 African countries over the period 1975-2014. The results show that FDI contributes to the industrialization of African countries through direct and indirect effects of FDI inflows. However, [START_REF] Bwalya | Foreign direct investment and technology spillovers: Evidence from panel data analysis of manufacturing firms in Zambia[END_REF] analyses the nature and extent of positive externalities of FDI on the productivity of 125 local manufacturing firms in Zimbabwe over 1993-1995. Relying on a combination of OLS, fixed-effect model, and the system-GMM method, the results show no positive effect of FDI entry on intra-industry productivity through the knowledge transfer channel. In the same vein, [START_REF] Kaya | Globalization and Industrialization in 64 Developing Countries[END_REF] analyzes the effects of globalization on industrialization in 64 developing countries using a five-effect and random-effect model on panel data covering the period 1980 to 2003 finds that FDI inflows did not have a significant impact on industrialization in most of the 64 developing countries and that exports of raw materials do not significantly influence manufacturing employment.

In addition, [START_REF] Waldkirch | Foreign presence, spillovers, and productivity: Evidence from Ghana[END_REF] analyzed the case of Ghana using OLS, Levinsohn-Petrin, and system-GMM models over the period 1991-1997. Their analysis shows that the effects of FDI presence on the productivity of Ghanaian manufacturing firms are negative for domestic firms but positive for most foreign firms. These finding thus indicates that the positive effect on productivity growth of domestic firms does not seem to offset the negative effect of competition from FDI.

On the other hand, Gui-Diby & Renard (2015) examined the relationship between FDI and the industrialization process in Africa over the period 1980-2009 using panel data from 49 countries using the feasible generalized least squares method (FGLS). Their results indicate that FDI inflows have not had a significant impact on industrialization in African countries.

Empirical methodology

Presentation of the analysis model

To analyze the relationship between FDI and industrial performance, we adopted the twocountry model of the "leader-follower" by Sala-i-Martin & Barro (1995) Net foreign direct investment inflow as a percentage of GDP.

The dummy variable that allows for the highly heterogeneous nature of the countries. measures the error term and α and β represent the parameters of the respective variables.

This basic model was further strengthened by Nkoa (2016) to take into account the effect of interaction between some explanatory factors to give the model below:

(2.a)

(2.b)

With measuring the interaction between three variables which are industrialization, FDI and human capital. Indeed, according to Ongo Nkoa (2016), FDI can impact industrial performance through human capital or trade openness.

Overview and rationale for the use of spatial panel econometrics

Focusing on FDI, the decision to invest abroad presupposes two pieces of spatial information, namely the location and the attribute on which the choice is made in a given geographical area. Then, the installation of a multinational firm in a country will depend on the preliminary conditions of attractiveness which can be related to comparative advantage or an incentive policy on behalf of the authorities. Overall, the arrival of FDI in a country is likely to influence the industrial performance of the host country but also of its neighboring countries.

As a result, two geographical units may affect each other [START_REF] Elhorst | Spatial econometrics: from cross-sectional data to spatial panels[END_REF].

Therefore, this study uses spatial econometrics to take into account spatial interactions in the analysis of the effects of FDI on industrial performance. Indeed, spatial econometrics makes it possible to apprehend the strategic interactions between the decisions of the actors of the economy and the spatial structures. According to [START_REF] Manski | Identification of endogenous social effects: The reflection problem[END_REF], the probability of an individual behaving in one way or another varies according to the prevalence of the effects of a reference group to which he belongs. These effects can, depending on the context, be called "social norms", "neighbourhood effects", "imitation", "contagion", "herd behaviour", or "social interactions", etc. In other words, [START_REF] Manski | Identification of endogenous social effects: The reflection problem[END_REF] shows that social or spatial interaction occurs when the behavior of individuals is influenced by the average behavior of the group to which they belong.

In the literature, a distinction is made between spatial models, cross-sectional data and panel data. The present study uses panel data. Indeed, let us consider a sample of N individuals i (i=1; 2; ...; N) observed over the period t=1; 2; ...; T. The standard model of spatial econometrics for panel data is :

= +

(2.9) With vector of k explanatory variables of dimension (1, k) and assumed to be exogenous; is a vector of individual-specific and time-invariant variables ; = is a vector of unknown parameters to be estimated of dimension (k , 1) ; = vector of the parameters of the individual-specific variables and = the error terms.

This equation allows us to understand two important phenomena in spatial econometric analysis. These are the individual-specific effects or individual heterogeneity and the spatial effects or spatial autocorrelation which are essential in the specifications of spatial models.

Formulation of the weight matrix

In fact, there are symmetric and non-symmetric weight matrices. The non-symmetric weight matrix is a matrix of which values of the matrix are not symmetrical with respect to the diagonal as is the case in this study. We use a non-symmetric weight matrix of which components represent the average value of bilateral exports of 36 SSA countries over the period 1998-218. The choice of the export matrix and the 36 countries is justified by the fact that data are not available for some countries, as exports are the best variable to capture trade flows between countries.

As LeSage & Pace (2009) and [START_REF] Elhorst | Spatial econometrics: from cross-sectional data to spatial panels[END_REF] have shown, it is important to normalize the non-symmetric weight matrix to 1 to have a linear combination between the country with its partners in order to facilitate the convergence of the estimated models and the possibility of comparing them with each other. This normalization consists of relating each component of the matrix to the sum of the value of the components of the row concerned in order to obtain the sum of each row of the matrix equal to unity. More formally, the normalization equation for the weight matrix is as follows:

; With (4)

With: the normalized weight of the country to the country ; the total sum of the country's exports with SSA partner countries.

Thus, for a sample of 36 SSA countries with inter-country trade flows, the weight matrix can be represented as follows: From this matrix, the estimation of the weight matrix is obtained by using the average of bilateral exports between the 36 selected SSA countries and over the period 1998-2018 from the export matrix taken from the UNCTAD 2020 database. We thus adopted the procedure of [START_REF] Drukker | Creating and managing spatialweighting matrices with the spmat command[END_REF] for the formulation of the weight matrix.

Once the weight matrix is defined, it is possible to specify the spatial model to implement.

The following point highlights the different tests for specifying the spatial model to be used.

Spatial model specification tests

Here we present several tests that allow us to select the most appropriate specific model in taking into account spatial dependence. For this, we use the most commonly used tests including the spatial dependence test developed by [START_REF] Pesaran | Testing weak cross-sectional dependence in large panels[END_REF], the Moran'I test of the Lagrange multiplier of spatial autocorrelation of errors and lagged variables.

Pesaran's space dependency test (2015)

Spatial dependence in the series can be checked using the test described by [START_REF] Pesaran | Testing weak cross-sectional dependence in large panels[END_REF].

The CD-test statistic of this author is based on the average of the correlation of the coefficients between the different countries taken two by two for each time interval. This test consists of testing the null hypothesis of no spatial dependence against the alternative hypothesis of its presence. The result of the Pesaran test is presented in Table 1 below. Table 1 shows a P-value greater than 5%, indicating that the null hypothesis of no spatial dependence between industrial performance and explanatory variables cannot be accepted.

Thus, there is a strong spatial dependence between the different variables of the 36 SSA countries. However, the Pesaran test does not provide information on the structure of the spatial dependence, especially if there is an autocorrelation of the errors or autocorrelation of the spatially detected variables. The Lagrange multiplier test allows us to take into account this insufficiency.

Spatial autocorrelation tests for errors and lagged variables

There are several tests of spatial autocorrelation of errors or between lagged variables but we retain the Moran test ( ) to choose the spatial model. The decision rule is based on the Maran statistic following a normal distribution.

No spatial autocorrelation of errors. We accept if P-value 0,05.

Presence of spatial autocorrelation of errors. We accept if P-value 0,05. The following table 2 presents the results of this test.

The results show for equation Eq(1) a p-value greater than 5% for all three tests, which means that we cannot accept the null hypothesis of no lagged spatial autocorrelation and no spatial autocorrelation of errors. This result implies that there are autocorrelations of errors between lagged variables. Thus, the SAC model is suitable for estimates of the relationship between industrial value-added and its explanatory variables. We first follow [START_REF] Belotti | Spatial panel-data models using Stata[END_REF] to make the choice between the SDM model and the SAC model which are the most used in the literature. Indeed, the choice between the SAC model and the SDM one is based on a post-estimation test and the selected model is the one that maximizes the Bayesian Information Criteria (BIC). The BIC comparison test shows that the SDM model is more appropriate than the SAC model in estimating Eq(1) because it has the highest BIC and the lowest AIC. Another reason to exclude the SAC model is that it is not econometrically feasible to estimate it in a randomeffects model.

As for Eq(2), the results show the existence of spatial autocorrelations between the spatially lagged variables. Therefore, the dynamic SDM model is suitable for estimates of the relationship between the level of industrial sector jobs in total jobs created and the explanatory variables

Choice of the specific spatial model to be implemented

Given all these results, two specific models can be used by making restrictions on the parameters associated with the explanatory variables. Accordingly, the tests of adequacy show that the Durbin spatial model (SDM) with random effect is more adapted for the analysis of the effects of the net entries of the FDI on the industrial performances in sub-Saharan Africa.

The estimation by the SDM method can be implemented safely.

The SDM model integrates both a spatially lagged dependent variable and the spatially lagged

Presentation and discussion of results

Drawing on the appropriate spatial model identified, the next step is the presentation and discussion of the results of the estimations. We will first focus on the results of the spatial effects of FDI on industrial value-added as the first proxy of industrial performance using an SDM model before turning to the results of the effects of FDI on the level of employment created in the industrial sector of SSA countries and over the period 1998-2018. Regarding the human capital captured here by the gross secondary school enrolment rate, it has an indirect and negative effect on the industrial value-added of the zone's economies. This counterintuitive result is contrary to those found by [START_REF] Zhang | How does foreign direct investment affect industrial competitiveness? Evidence from China[END_REF] and Nkoa (2016) and could be justified by the mismatch between the training curricula in schools and the work profiles demanded in industries in Africa. Concerning the other control variables, only import has a reducing effect on industrial value-added at 23% in line with the result of [START_REF] Zhang | How does foreign direct investment affect industrial competitiveness? Evidence from China[END_REF] as long as natural resource rent and GDP have a contributing effect on industrial value-added of SSA countries at (0.17) and (0.30), respectively.

Effects on the level of employment in the industrial sector

To analyze the effects of FDI on the level of employment in the industrial sector, the specification led us to a dynamic fixed-effects SDM model. The short and long-term results are presented in table 5. Overall, FDI influences the level of employment in the industrial sector of SSA countries but the effect is limited to the short run as in the long run there are no significant effects. Indeed, as with industrial value-added, FDI has a positive and direct effect on the level of employment in the industrial sector of SSA countries over the period 1998-

2018. An increase in net FDI inflows of 10% leads to an increase in the level of employment in the industrial sector of 0.5% in the short term. Furthermore, as in the analysis of the effects of FDI inflows on industrial value-added, there is no long-run indirect effect of FDI inflows on the level of employment in the industrial sector, i.e., FDI inflows do not lead to a transfer of skills in the industrial sector. Such a result could be explained by the same reason mentioned above which is that FDI entering SSA economies mostly specialize in mining. This mining would not allow for the real transfer of skills to the processing sector of the finished products.

Regarding trade openness, it has a negative effect (-0.06) and in the short term on the level of employment in the industrial sector of countries in the sub-Saharan zone contrary to (Nkoa, 2016) which did not find a significant effect. This result could be justified by the fact of massive imports in SSA countries which could lead to a competition effect on local firms as

Nkoa (2016) asserted on the difference in the quality of imported products compared to the products.

Concerning human capital, this variable has an indirect and positive effect in the short run on the level of employment in the industrial sector of the Sub-Saharan economies. This result is consistent with those found by [START_REF] Zhang | How does foreign direct investment affect industrial competitiveness? Evidence from China[END_REF][START_REF] Ongo Nkoa | Foreign direct investment and Africa's industrialization: a new look[END_REF].

Focussing on the other control variables, private investment has an indirect and negative impact on the level of employment in SSA economies while natural resource rents and the quality of institutions have both direct and indirect positive effects in the short term on the level of employment in this area. This result is consistent with those found by Gui-Diby & Renard (2015) who found a negative effect of investment on the industrialization of African economies and Nkoa (2016) on the employment level in the industrial sector.

However, the quality of institutions measured here by a synthetic index of governance indicators seems to be the important factor that most influences the level of employment in the industrial sector. Indeed, an increase in the quality of institutions in SSA countries of 1% leads in the short term to a direct increase in the level of employment of 9.5%. Such a result calls for good governance on the part of the governments of SSA countries in order to boost the industrial sector, which is the key to sustainable development.

Conclusion

This paper aimed to assess the spatial effects of FDI on the industrial performance of SSA economies over the period 1998-2018 through a respective spatial Durbin model (SDM) and dynamic SDM model for the analysis of the effects on the two industrial performance drivers.

The empirical results showed that net FDI inflows have a contributory effect on both industrial value-added and the level of employment in the industrial sector through direct and short-term effects. The higher the capacity of SSA countries to attract foreign investors to their respective countries, the higher the number of jobs in the industrial sector and the higher the level of industrial value-added.

However, there is no long-term indirect effect of FDI entry on either industrial value-added or the level of employment in this sector. This result implies that the entry of FDI does not lead to a transfer of technology or skills in the industrial sector. Such a result could be explained by the fact that FDI entering SSA economies is mostly specialized in mining. This mining would not allow for a real transfer of technology and skills to the processing sectors of the final products. This result illustrates the importance for SSA countries to direct FDI towards strategic sectors of processing local raw materials into finished products. This may involve a selective investment policy in favor of those industrial sectors deemed most strategic in terms of economic returns. The various value chains of the agricultural and food sectors are important niches to exploit. To achieve such an ambition necessarily requires the reorganization of the more attractive business climate to support a massive entry of FDI in these sectors, which is a guarantee of a real transfer of technology and skills and any industrial development as the Asian countries have done. The governments are encouraged to practice a selective investment policy in favor of the industrial sectors considered the most strategic in terms of economic spin-offs while supporting the existence of the free enterprise and free trade.

In addition, the results also showed that trade openness has a positive effect on the industrial value-added of SSA countries although it tends to reduce the level of employment in the industrial sector. On the other hand, the quality of institutions has contributory effects on the industrial value-added and the level of employment in this sector in the countries of the region. Such a result calls on the governments of the sub-Saharan band to improve the quality of governance to establish an institutional and legal framework favorable to the protection of private rights and investment, all of which contributes to giving more confidence to foreign investors. Finally, human capital through the gross enrolment rate in secondary education has an indirect and negative effect on the industrial value-added and an indirect and positive effect on the level of employment in this sector. This result shows the need to adapt the training curricula to the need for profiles required by the industrial sector.

  in a theoretical framework of endogenous growth driven by technological diffusion. The leader country creates and develops new technologies for its production of goods while the follower country adapts the new technologies discovered in the leader country by taking into account its environment. The leader country holds the monopoly of the production of intermediate goods and the follower country can only produce them by adapting its environment 1 . Thus, we start from the basic model proposed by Gui-Diby & Renard, (2015) to use two proxies of industrial performance. These are the industrial value added (IVA) and the level of employment in the industrial sector. Indeed, the basic model is as follows: (1.a) (1.b) With industrial value-added and the level of employment in the industrial sector; both capture the measured industrial performance. The matrix of explanatory variables for industrial performance proposed by Zhang (2014) and Gui-Diby & Renard (2015) includes the GDP per capita, private investment captured by gross fixed capital formation to GDP, human capital measured by gross secondary school enrollment, trade openness, infrastructure is captured by the number of telephone lines per 100 inhabitants.

  explanatory variables, i.e. it takes into account not only mimicry and social entrainment effects but also contextual effects. The SDM model is much more complete because there are no a priori restrictions on direct and indirect effects in the specification, which allows certain flexibility in the implementation of the model. This flexibility gives a certain advantage to the SDM model and makes it more popular in empirical studies. To analyze the spatial effects of FDI on the industrial performance of 36 SSA countries, the SDM model can be written matrix of net FDI inflows the (N,1) matrix of the level of employment in the industrial sector the (N,1) matrix of net FDI inflows the square matrix ( , ) of trade weights between countries the matrix ( , ) of explanatory variables the constant and the error terms

Table 1 :

 1 [START_REF] Pesaran | Testing weak cross-sectional dependence in large panels[END_REF] spatial dependence test

	Pesaran (2015) test for weak cross-sectional dependence.
	Residuals calculated using predict, residuals.
	H0: errors are weakly cross-sectional dependent	CD	p-value
	Eq(1) : VAI= f(FDI, GFCF, Education, ...)	0.554	0.580
	Eq(2): Employment = f(FDI, GFCF, Education,	-0.968	0.333
	Government...)

Source: Author based on estimates

Table 2 :

 2 Moran's spatial autocorrelation test I

	Tests		Industrial added variable Eq(1) Statistics P-value Degree of freedom	Jobs in the industrial sector Eq(2) Statistics Degree of P-value freedom
	Global autocorrelation test (test on )
			-0.005	0.743		0.025**	0.027
			1.007	0.594		0.972**	0.045
			0.085	0.743		-0.405**	0.027
	Spatial autocorrelation test of errors (test on )
	(Burridge)		0.165	0.685	1	3.754*	0.053	1
	(Robust)		0.564	0.453	1	7.985*** 0.005	1
	Spatial autocorrelation test for lagged explanatory variables (test on )
	(Anselin)		0.014	0.904	1	0.009	0.925	1
	(Robust)		0.414	0.520	1	4.240	0.039	1
	Global spatial autocorrelation test (test on and )
	(	_R)	0.579	0.749	2	7.993**	0.018	2
	(	)	0.579	0.749	2	7.993**	0.018	2

Source: Author based on estimates. Notes: *** ; ** ; * = significant at the 1%, 5% and 10% level

Table 3 :

 3 Selection of the appropriate specific model between the SDM and SAC models

	Choice between SDM and SAC based on Bayesian and Akaike information criteria
	Test	SDM	SAC
	AIC	4050.02	4059.31
	BIC	4124.07	4105.59

Source: Author based on estimates

Table 4 :

 4 Presentation and discussion of SDM estimation results

		Model OLS		SDM Model
			Short term		Long term
	Dependent variable : Industrial value added	Random Effect	Main effect	Direct effects	Indirect effects	Total effect
	Foreign direct investment	0.10*** (0.001)	0.09*** (0.004)	0.09*** (0.004)	0.08 (0.208)	0.16** (0.016)
	Commercial opening	0.33*** 0.000)	0.21*** (0.000)	0.21*** (0.000)	0.09 (0.250)	0.30*** (0.002)
	Human capital	-0.020 (0.11)	0.04 (0.111)	0.00* (0.092)	-0.10*** (0.001)	-0.06*** (0.001)
	Imports	-0.18*** (0.000)	-0.20*** (0.000)	-0.20*** (0.000)	0.03 (0.690)	-0.23*** (0.004)
	Actual investments	0.06** (0.039)	0.07** (0.017)	0.067** (0.015)	-0.03 (0.543)	0.04 (0.439)
	Gross Domestic Product	0.15*** (0.000)	0.15*** (0.000)	0.15*** (0.000)	0.14** (0.037)	0.30*** (0.000)
	Natural resource rents	0.22*** (0.000)	0.24*** (0.000)	0.24*** (0.000)	-0.07 (0.164)	0.17*** (0.000)
	Constant	20.51*** (0.000)	23.41*** (0.000)
	AIC		4274.33
	BIC		4357.63
	Number of observations	756	756
		0.60	0.60			0.49
	Source: Author based on estimates. ***; **; * represent a significant coefficient at the 1%, 5% and
	10% level respectively.

Table 5 :

 5 Presentation of estimation results by the dynamic SDM model

		Model OLS	Dynamic SDM model: Short-term effect
	Dependent variable: Employment in the undue sector.	Fixed effect	Wx effect	Direct effects	Indirect effects	Total effect
	W. Emlpoi (lag1)		-0.017*** (0.00)
	w. Foreign direct investment	0.01 (0.351)	0.04*** (0.000)	-0.002 (0.431)	0.05*** (0.000)	0.05*** (0.000)
	W. Actual investments	-0.01*** (0.737)	-0.03*** (0.000)	-0.003 (0.152)	-0.04*** (0.000)	-0.04*** (0.000)
	W. Commercial opening	0.17*** (0.000)	-0.02* (0.064)	-0.03*** (0.000)	-0.03** (0.027)	-0.06*** (0.000)
	W. Human capital	-0.003* (0.080)	-0.003* (0.080)	-0.01*** (0.000)	0.02*** (0.000)	0.002 (0.421)
	W. Imports	-0.10*** (0.000)	0.04*** (0.000)	0.03*** (0.000)	0.06*** (0.000)	0.08*** (0.000)
	W. Gross domestic product	-0.03 (0.115)	-0.04** (0.000)	0.01*** (0.0002)	-0.05*** (0.000)	-0.04*** (0.000)
	W. Natural resource rents	-0.02 (0.229)	0.04*** (0.000)	0.01*** (0.000)	0.05*** (0.000)	0.06*** (0.000)
	W. Governance	1.42*** (0.002)	0.69*** (0.000)	1.0*** (0.000)	8.5*** (0.000)	9.47*** (0.000)
	Spatial rho		0.17 (0.002)
	ρ		0.11*** (0.000)
	AIC	3257.42	424.84
	BIC	3299.08	516.43
	Number of observations	720	720
	Adj. R²	0.30	0.91
	Source: Author based on estimates. ***; **; * represent a significant coefficient at the 1%, 5% and
	10% level respectively.

For more details on the model, see Sala-i-Martin & Barro (1995) page 1 to 14 DOI : http://hdl.handle.net/10419/160652

Acknowledgements

I am grateful to Dr. Ali Compaoré of the Centre d'Etudes et de Recherche sur le Développement International (CERDI)-Université Clermont-Auvergne, Clermont-Ferrand/France for useful contributions and sugestions to this paper. I also thank the participants in the seminars of the Laboratoire d'Economie Appliquée of University Norbert Zongo for their contributions to this paper.

Effects on industrial value added

Table 4 below presents the results of the analysis of the spatial effects of FDI on the industrial value-added of SSA economies over the period, taking care to separate the direct effects from the indirect effects and the total effect by using a linear fixed effects model (LFE).

Overall, we can say that the results are consistent with the hypothesis that FDI positively influences the industrial performance of SSA countries and that there are no indirect effects. Indeed, the net FDI inflow has a positive and direct effect on the industrial value-added of SSA countries at the 5% threshold. A 10% increase in net FDI inflow to SSA economies increases the total industrial value added by 1.6%. This result is consistent with that found by authors such as [START_REF] Zhang | How does foreign direct investment affect industrial competitiveness? Evidence from China[END_REF] and Nkoa (2016) but contrary to those found by Gui-Diby & Renard (2015) who did not find a significant effect on the industrial performance of SSA economies. This result could be justified by the difference in the methodology of analysis which confirms the relevance of taking into account the spatial in the analysis of FDI effects on industrial performance. Secondly, the inclusion of certain variables such as natural resource rents and the quality of governance by synthetic index may constitute an important difference in the specification of the model.

Another important aspect of this result is the lack of long-term indirect effect of FDI inflows on industrial value-added i.e. the absence or embryonic state of technology spillover in the industrial sector. This result could be explained by the fact that most of the FDI entering these economies are invested in the mining sector which has no real effect of technology transfer in the processing sector of locally finished products. Furthermore, trade openness has positive effects on the industrial value-added of SSA countries at the 5% threshold. A 10% increase in the latter leads to an increase in the industrial value-added of SSA economies by 3%. This result is in contradiction to those found by Nkoa (2016) who found a negative effect on industrial value-added.


Helmholtz interior problem: coupling a modal expansion and an integral representation

Dominique Brenot

To cite this version:

Dominique Brenot. Helmholtz interior problem: coupling a modal expansion and an integral representation. Comptes Rendus. Mathématique, 2005, 340 (10), pp.779-784. 10.1016/j.crma.2004.12.024. hal-03649985


Sound transmission into an axisymmetric enclosure

D. Brenot To cite this version: D. Brenot. Sound transmission into an axisymmetric enclosure. Journal of Sound and Vibration, 2005, 287 (1-2), pp.45-75. 10.1016/j.jsv.2004.10.048. hal-03650016


Rébecca Deneckère
email: rebecca.deneckere@univ-paris1.fr

Gregoria Rubio

EcoSoft: Proposition of an Eco-label for Software Sustainability

Keywords: Sustainability, Ecolabel, Software

There is an increasing interest in corporate sustainability and how companies should include it to satisfy user's requirements concerning social, economic, and environmental impacts. Research about sustainability in computer science aims to offer methods, techniques and tools to lessen the impact of new technologies on the environment, to offer a better world, a smarter life, to the next generations. Information systems must participate in the collective effort to move towards sustainable development, and software and application companies must lead a CSR strategy to achieve this aim. Moreover, beyond an individual company approach, sustainability should be seen as an integral quality of any software (as well as safety, performance or reliability). All of this seem obvious at a time when applications and programs of all kinds are ubiquitous in everyday life. Nevertheless, the challenges of sustainable development have still not been considered in certain key sectors such as the development of information technology. A lot of ecolabels exist for a lot of different products, although not for software sustainability. We propose in this work an ecolabel for software sustainability, based on a set of relevant criteria found in different works.

Introduction

The 21st century is a period of new technologies and could be called the digital age. It is also a period that is very affected by sustainable development issues that apply to all aspects of our society. Information technologies, because of their importance, do not escape preoccupations and questions as to their role and responsibility in developing a more respectful and sustainable economic model. As defined for the first time in 1987 in the Brundtland Report, « humanity has the ability to make development sustainable to ensure that it meets the needs of the present without compromising the ability of future generations to meet their own needs [START_REF] Brundtland | Report of the World Commission on Environment and Development: Our Common Future[END_REF]. This involves a gradual transformation of the economy and society across the spectrum of sustainability. As a result, international communities have become aware of the need to control greenhouse gas emissions, which are the main drivers of climate change, and therefore to reduce energy consumption and carbon footprint globally [START_REF]The Paris Agreement -main page[END_REF]. In this context, information technologies have an ambivalent role: they reduce the impact on the environment in certain contexts of use, notably through virtualization or business process optimization. However, in other cases they are themselves responsible for negative effects on the environment, such as the very energy-intensive use of data centers [START_REF] Breuil | Rapport TIC et Développement durable[END_REF]. This global dematerialization continues, particularly with the transition to the cloud, making IT responsible for 10% of global electricity consumption in 2017 [START_REF] Cailloce | Numérique : le grand gâchis énergétique[END_REF]. Information technology must therefore be fully considered in sustainable development actions and more particularly in companies social and environmental responsibility (CSR) strategies. It is therefore essential to be able to provide models, methods and tools which allow, first, to measure and assess the impact of technologies on the environment, and then, second, to be able to apply tools in the development process of these technologies in order to improve their sustainability.

Research on information technology and computer science in general aims to make technologies more efficient and efficient, to create a smarter planet [START_REF] Lenssen | IBM and Sustainability: Creating a Smarter Planet[END_REF]. In this new business model, the sustainability aspect is more rarely considered. Sustainable development efforts in the information technology sector have so far focused on infrastructure (data centers) and the manufacturing of hardware [START_REF] Hilty | ICT Innovations for Sustainability[END_REF]. The data centers are particularly targeted by the objectives of reducing energy consumption in the IT sector, considering both their importance in the infrastructure of enterprises and their data, and their considerable energy requirements. In 2012, the European Union even took the lead by launching a project called GAMES (Green Active Management of Energy in IT Service centers), which aims to develop a set of tools, methods and techniques for managing the energy performance of next-generation data centers [START_REF] Pernici | Setting energy efficiency goals in data centres: the GAMES approach[END_REF]. For both data centers and other information technology infrastructure and services, the source of energy consumption is in the software layer [START_REF] Noureddine | Towards a Better Understanding of the Energy[END_REF]. As a result, a reduction in the resource consumption of software components leads to a reduction in the electricity consumption of computer equipment. The software layer is therefore an important if not crucial lever in reducing ICT energy consumption.

Since 2010, there has been growing interest in research on the concept of sustainability in computer engineering, but sustainable development applied to information technology remains a less important topic [START_REF] Penzenstadler | Sustainability in software engineering: A systematic literature review[END_REF]. Nevertheless, the ever-increasing number of scientific publications on the subject shows that researchers are interested in defining and developing concepts for sustainability in software engineering. This includes characterizing models, methods and tools to reduce the life cycle impact of software products and their development.

The definition of sustainability in software engineering brings together several possible characterizations but we will retain the following one for the continuation of this work. "Sustainable Software is software, whose impacts on economy, society, human beings, and environment that result from development, deployment, and usage of the software are minimal and/or which have a positive effect on sustainable development." [START_REF] Dick | A Model and Selected Instances of Green and Sustainable Software[END_REF].

In this paper, we will focus on the environmental sustainability framework. The impact of new technologies on the environment is rather ambiguous because, as stated earlier, they can be both a benefit and a harm. For a more in-depth analysis of their impact, it is also necessary to consider the different phases of their life cycle and the effects associated with them: the effects due to the production of information technology, effects due to their use and systemic effects.

• Production effects consider the use and consumption of natural resources, as well as pollution associated with the extraction of raw materials and end-of-life electronic waste. • The effects of use concern the positive indirect effects of their use, such as process optimization and product virtualization and simulation, and thus the conservation of natural resources. • The systemic effects concern the long-term indirect effects resulting from the use of information technology, such as changing lifestyles that encourage stronger economic growth leading to a rebound effect as resource consumption increases. These effects on the environment, in order to be controlled, must be measured and evaluated and therefore monitored by companies. It is the commitment of these companies to contribute to the sustainable development of their information systems and technologies that is crucial.

Companies and organizations, by their place in society, have social responsibility for their practices and the effects they have on society [11]. Their activities and decisions directly influence the economy, making their commitment to sustainable development all the more important in reducing the digital environmental footprint. To help and encourage companies to become more involved in social responsibility, ISO 26000 was created in 2014 to provide guidelines for the implementation of sustainable practices. Sustainability is approached in a multidimensional way around 7 areas of action: communities and local development, human rights, relations and working conditions, consumer issues, the loyalty of practices and the environment. On the occasion of the Horizon 2020 program for research and innovation, the European Commission has also launched a platform providing information technology companies with a set of tools and methodologies to assess the energy consumption and the carbon footprint associated with technologies they use and develop 1 . This initiative aims to encourage companies to move towards eco-design by developing software solutions that use fewer resources and have a longer lifespan. Companies, in particular software publishers, must be responsible for the management of their information systems and the programs they sell to their customers.

The sustainable development strategy for companies is increasingly an important competitive advantage for their growth, particularly because of the influence it has on their brand image. In addition to financial gains, such as reducing energy bills, this gives them an image of responsibility that is increasingly important for consumers. The benefits of a sustainability policy convince an increasing number of structures to integrate CSR objectives into their business models [START_REF] Watson | Information Systems and Environmentally Sustainable Development: Energy Informatics and New Directions for the IS Community[END_REF]. In particular, new technology companies are a real lever for reducing the consumption of energy resources, and even more for reducing the environmental impacts of ICT [START_REF]Attitudes of European citizens towards the environment, Special Eurobarometer[END_REF].

So, the problem today is the lack of clear consensus and definitions about sustainability in software engineering. In order to move towards sustainable development, information systems must participate in the collective effort and software and application companies must pursue a CSR strategy geared to the sustainability of software development. So how can sustainability be integrated into software development? More specifically, how can ecodesign be integrated into software and application development?

We will be looking at the concept of software eco-design as a way to make the software layer of new technologies more environmentally friendly. It will be a question of how digital players, whether government, organizations, businesses or users, can make software and applications more sustainable, particularly in terms of energy efficiency. This work will first look at the related works in the field of ecolabeling. Then, the proposed EcoSoft label is presented with all the defined criteria in section 2. We conclude in Section 3.

Related works

More and more consumers are considering the ecological dimension of the products they buy and use in line with the growing interest in environmental issues [START_REF]Attitudes of European citizens towards the environment, Special Eurobarometer[END_REF]. Ecolabels were then introduced to meet this demand, with more than 450 consumer labels worldwide awarded by governments, organizations or consumer associations in 2016 [START_REF]Ecolabel Index[END_REF]. These labels help consumers to identify more responsible products and to choose according to criteria that integrate environmental quality. Labels, especially if assigned by official authorities, are a guarantee of consumer confidence and credibility [START_REF] Darnall | Third-Party Certification, Sponsorship, and Consumers' Ecolabel Use[END_REF]. An eco-label is a quality label that certifies that a product or service has a reduced impact on the environment. It aims to "promote the design, production, marketing and use of products with less impact on the environment throughout their life cycle" and "better inform consumers of the effects of products on the environment, without compromising the safety of the product or workers, or significantly affecting the qualities that make the product suitable for use" 2 .

An eco-label may certify products and services of different categories which comply with certain ecological criteria. For example, we find labels for food (Organic Agriculture label), textile (GOTS label 3 ), tourist accommodation (Panda Gîte label 4 ), electronic devices (Energy Star label 5 ), wood (FSC label 6 ) or cosmetics (label Cosmebio 7 ). The award of an eco-label is usually based on a balance record and a life cycle analysis of the product carried out by a national or international public entity, or delegated to an independent accredited entity. It may be evidenced by a distinguishing sign such as a pictogram on the product, a label or a name in order to be easily identified by the public and consumers.

There are a multitude of labels related to ICT products. The multiplicity of products and the complexity of production chains make it difficult to create a single label. Labels are classified according to their type, that is to say to which organization they come from, by which standards they are regulated. There are currently three different types of labels that can attest to the ability of products to comply with a sustainability approach by certifying that the environmental impacts of the labelled products are low compared to those of other products on the market.

Type 1 brings together official labels, that is to say they are proposed by local authorities (state, European Union), public interest groups (AFNOR -the French standards association, for example) or self-reported by an ONG. These labels under ISO 14024 meet strict specifications and their certification is regularly reviewed by independent certifiers. They consider the entire product life cycle (LCA approach). The best-known type 1 ecolabels are the European ecolabel, the NF French environment label, the Blue Angel of German origin (focuses on health, climate, water, and resources) and the International Energy Star, for the energy efficiency of products. The TCO label adds a humanitarian aspect to the environmental aspect since it certifies the creation of products minimizing the impact on human health.

The self-declarations correspond to the Type 2 eco-labels. They fall under ISO 14021 and are developed under the responsibility of companies alone. In general, they concern only part of a product's life cycle or relate to a single environmental characteristic of the product. The associated standard frames self-declarations by specific criteria: it must not be misleading, be clearly presented, be verifiable, etc. The best known is the EPEAT label created in the United States by the Green electronic Council which allows companies to evaluate IT equipment according to their eco-characteristics. This label is now used worldwide by a large number of companies. The ECMA-370 standard provides a system of measures and environmental features to help companies in the ICT sector in the creation of self-declarations.

Ecoprofiles are type 3 eco-labels, covered by ISO 14025. Their allocation is the result of a LCA and allows a comparison between products of the same category at the level of resource consumption, greenhouse gas emissions, and waste.

In the field of new technologies, labelling is difficult to achieve because of the virtual dimension of products (software, applications, websites) which makes users believe that they have no impact on the environment. It is therefore important to add value to software that is developed in a sustainable way, and labeling is a way to give them more visibility. By engaging in the implementation of digital technology certifications and labels, governments, particularly at European and global levels, would help legitimise software eco-design in the eyes of users. Companies that make the effort to achieve a sustainable strategy in the development of their products also gain credibility compared to their competitors when this approach is formally recognized [START_REF] Darnall | Can ecolabels influence firms sustainability strategy and stakeholder behaviors ?[END_REF]. A sustainability label for digital software and services such as EcoSoft would therefore serve this objective of bringing more visibility and therefore credibility to ecodesign in the software field. [START_REF] Kern | Labelling Sustainable Software Products and Websites: Ideas, Approaches, and Challenges[END_REF] proposes a discussion about the labelisation of sustainable software products and websites and list a set of possible criteria that can be applied by separating them in different types. Criteria for green web content management systems and web applications can be found in [START_REF] Dick | Green Web Engineering -Measurements and Findings. 26th International Conference on Informatics on Informatics -Informatics for Environmental Protection, Sustainable Development and Risk Management (EnviroInfo)[END_REF].

EcoSoft, an Eco-label for software sustainability

We propose the establishment of a sustainability label for software that would allow users to choose programs and applications based on their impact on the environment. This label would be a way for consumers to easily identify the most durable software and thus be able to limit their carbon use as users. It is also an approach that helps to make the company aware of the environmental impacts of digital software and services and to be able to implement a commitment to sustainable development. The company reduces its software footprint and at the same time promotes its image through this ecodesign approach.

Like the Energy Star label on energy-efficient electronic devices, the EcoSoft label would serve to inform about the voluntary commitment made by designers on the energy efficiency of software on a computer, a tablet or smartphone according to several criteria. It would be valid for a period of 2 years, maximum duration given the many developments and updates that occur in the life of a software or application. The software or web application would be given a distinctive logo to make its certification visible. There is a similar project for eco-design of websites in particular called Green Code Label8 which labels websites developed according to web design methods. However, there is no European or international label that certifies the sustainability of an application service. Indeed, the official eco-label of the European Union 9 does not certify digital products and services, which would make the EcoSoft label a reference in the field of sustainable software certification. A software labeled EcoSoft must meet sustainability criteria. It must have been developed in accordance with software ecodesign techniques by integrating the tools and methods of sustainable management of the complete product life cycle. EcoSoft is a multi-criteria label, that is to say that its awarding is based on the respect of a set of predefined criteria, and therefore it could not be granted on the basis of a single criterion. Digital softwares, applications and services of all categories and types can be certified. There are also no restrictions on the source of the program.

Following the Greensoft model [START_REF] Naumann | The GREENSOFT Model: A reference model for green and sustainable software and its engineering[END_REF], a software can be analyzed according to the three phases of its life cycle: the development phase, the usage phase, the end-of-life phase. Our EcoSoft label will use criteria than can be classified following these three phases, as shown in Table 1. However, on the contrary of the Greensoft model, our ecolabel will only spcifically adress criteria concerning software. It will not take into account the criteria relating to the energy consumption of computers and other hardware machines, as well as the energy required for the company's infrastructure. In this work, we have choose criteria into the two facets of sustainability defined by [START_REF] Calero | Green in software engineering[END_REF]: the sustainability BY the software (ICT4Green) and the sustainability IN the software (GreenIT). This section presents all the criteria listed in Table 1. Each criteria is described with two possible values, the first one indicates that efforts have been done to obtain a sustainable software, although the second one indicates the contrary. The awarding of the label should mean that all criteria of the list are respected.

Development phase

The criteria for the conception and design phase are the following.

Sustainable documents {up-to-date and integrating sustainable techniques; outdated or not sustainable} .

The creation and/or provision of reference documents in ecodesign to stakeholders have to be taken into account. This criterion comes before the start of the project. In the design phase, it is a question of being able to provide project stakeholders with reference material enabling them to integrate sustainable design techniques in the form of good practices, recommendations, checklists, examples of implementation, etc. It also take into account the maintainability of the software in order to analyse and change files in an effective and efficient way, regarding the source code but also the content [START_REF] Kern | Labelling Sustainable Software Products and Websites: Ideas, Approaches, and Challenges[END_REF]. This relates also to the modifiability criteria proposed in [START_REF] Albertao | Measuring the Sustainability Performance of Software Projects[END_REF].

Sustainable specification {eco-designed specifications; no good practice application}.

The software ecodesign is also based on a specification writing directed towards sustainability with precise functional specifications without unnecessary functionalities and without unnecessary data production [START_REF] Vautier | Is "software eco-design" a solution to reduce the environmental[END_REF]. The expression of adapted functional needs determines the quality of the software and in particular its efficiency. Indeed, every feature of the program consumes CPU and memory resources during use even if they are in the background [START_REF] Vautier | Is "software eco-design" a solution to reduce the environmental[END_REF]. The preliminary draft of the specifications should then include the drafting of a complete specification consisting of functional and technical specifications that comply with ecodesign standards, including clear modelling of the program [START_REF] Naumann | The GREENSOFT Model: A reference model for green and sustainable software and its engineering[END_REF], for instance by using clear and precise UML specifications. As said in [START_REF] Agarwal | Sustainable Approaches and Good Practices in Green Software Engineering[END_REF] a new form of requirements 'Environment Requirements' need to be added to the non functional usual requirements.

Code optimization {optimized; not optimized}.

Programming of the software must be carried out in accordance with good ecodesign practices. Software ecodesign focuses on functional, graphic, ergonomic and technical design. In case of a software, we recommend that at least 2/3 of the techniques provided by the software ecodesign referential (in constant evolution) 10 should have been applied in order to value the criteria as 'optimized'. We propose to use a checklist to identify the used techniques. In case of an application, we recommend that at least 65 of the 115 best practices proposed [START_REF] Bordage | Eco-conception web : les 115 bonnes pratiques , 2e édition[END_REF] have been applied in order to value the criteria as 'optimized'. We propose to use the Opquast checklist 11 to work.

Download size {optimized; not optimized}.

More and more software products are nowadays downloadable. We recommend that the size of the downloaded software should be optimized to the maximum [START_REF] Naumann | The GREENSOFT Model: A reference model for green and sustainable software and its engineering[END_REF]. Many techniques exist to reduce download size so they should be used to do it.

Hardware requirements {average configuration; powerful configu-ration}.

End-user influence is high as nearly 90 % of the energy used by ICT hardware can be attributed to the application software running on it [START_REF]GHG Protocol Product Life Cycle Accounting and Reporting Standard ICT Sector Guidance[END_REF]. As a result, it is necessary to maximize the hardware lifetime by its actual physical durability rather than forcing its obsolescenc by software platform requirements [START_REF] Albertao | Measuring the Sustainability Performance of Software Projects[END_REF]. If the requirements needs too powerful hardware, it will induce the buying of new material to be able to run the software. It then must be able to operate with hardware that is at an average configuration compared to current standards to avoid replacement and new hardware purchases [19] [26]. This can be related to the Portability criteria of [START_REF] Albertao | Measuring the Sustainability Performance of Software Projects[END_REF].

Green analysis efficiency. [START_REF] Mahmoud | A Green Model for Sustainable Software Engineering[END_REF] proposes to add a green analysis stage to promote energy efficiency. It determines the greenness of each increment of the system that is developing. This stage acts like a testing stage but for energy efficiency. Metrics are used is this stage to perform the analysis (CPU usage, Green Performance Indicators, etc.).

Usage phase

The criteria used for the usage phase are the following.

Accessibility {improved; not improved}.

. Software ecodesign also addresses the social dimension such as digital accessibility. Good development practices improve the user experience, especially for people with disabilities. For people with colour blindness, for example, graphs cannot be understood if colours cannot be identified. The software or application should then be designed to comply with good accessibility practices to enable navigation for all audiences. We recommend to use the standards of W3C 12 .

Usability {optimized, not optimized}.

The software or application should be user-frendly [START_REF] Albertao | Measuring the Sustainability Performance of Software Projects[END_REF]. If the customer satisfaction if high, the support cost is low. We recommend to use 2/3 of the usability guidelines proposed by [START_REF] Nielsen | 113 Design Guidelines for Homepage Usability[END_REF].

Energy efficiency at running time {optimized; not optimized}. [START_REF] Taina | Bad, and Beautiful Software -In Search of Green Software Quality Factors[END_REF] states that efficiency defines how software behaves when it comes to saving resources. We recommend that the program must not have an eco-score of less than 70/100 on the Greenspector test 13 in order for the criteria to optain the value « optilized ». This can be related to the performance criteria of [START_REF] Albertao | Measuring the Sustainability Performance of Software Projects[END_REF].

Material resource consumption {optimized; not optimized}.

It is important to identify the resource consumption of the various components of the software. In [START_REF] Lago | A systematic literature review of green software metrics[END_REF], the authors distinguish a set of software components to be analyzed in terms of energy consumption. For example, there is the software architecture, RAM, processor (also called CPU), storage or source code to take into account. For these energy consumption measurements, the code must be analysed with diagnostic tools to determine whether the ecodesign methods have been complied with, for instance PowerAPI [START_REF] Noureddine | A Preliminary Study of the Impact of Software Engineering on GreenIT[END_REF], Greenspector14 , Intel SoC Watch 15 , Chisel [START_REF] Misailovic | Chisel: Reliability-and Accuracy-Aware Optimization of Approximate Computational Kernels[END_REF]. We recommend to use Greenspector and to obtain an eco score of more than 70/100 to the test (CPU, memory, mAh deload) for classifying the criteria as « optimized ».

Backup size {optimized; not optimized}.

It is possible to optimize the energy consumption necessary for the backup of software data or application over the long term. Reducing the number of backups can also be a possible optimization. By eliminating the problem of constantly backing up the same copy of a file again and again, data de-duplication can decrease backup storage consumption by 10 to 50 times compared to traditional tape-based backup methods. Since less data is sent across the infrastructure, data de-duplication also can reduce the bandwidth consumed by traditional network-based backups by up to 500 times [34].

End-of-Life Phase

The criteria used for the end-of-life phase are the following.

Data conversion to the future {OpenSource format; Proprietary format}.

The data of the current program must be of a format allowing easy transfer to the future software (essentially open source formats), otherwise data can causes compatibility issues and energy and material resource consumption issues [START_REF] Naumann | The GREENSOFT Model: A reference model for green and sustainable software and its engineering[END_REF].

Long-term data storage (back-up) {optimized, not optimized}.

The energy consumption needed to back up software data or long-term application should be optimized [START_REF] Naumann | The GREENSOFT Model: A reference model for green and sustainable software and its engineering[END_REF]. Sometimes, legal regulations require long-term storage data and the backup storage size will increase.

Packaging and manuals {recyclable; not recyclable}.

If the software or application contains a packaging and/or user manuals in paper form, these documents must be recycled [START_REF] Naumann | The GREENSOFT Model: A reference model for green and sustainable software and its engineering[END_REF].

Conclusion

The label we propose here is at the theoretical stage and has not yet been tested on practical cases. EcoSoft is an eco-label that takes into account the involvement of stakeholders (project manager, software architects, developers) in the process of integrating sustainability into the project. The three stages of the software's life cycle, namely the development, usage and end of life, are analyzed to determine the environmental impacts they generate. We focused the analysis on the energy consumption of software components, which is an important aspect for the overall quality of the software, especially for the user experience on the mobile device, but also because digital energy consumption has a high environmental footprint. The field of software and its technologies is constantly evolving, especially in the forms that the software can take. In this sense, the label could evolve by adapting the criteria to the type of software analyzed in order to be able to take into account its specificities (application software, web applications, system software...).

Our future work will be to validate the chosen criteria with a set of professionals. Then to test these criteria on several softwares in order to define if they can be awarded the label EcoSoft.

Table 1 .

 1 EcoSoft Criteria.

	Development phase
	Sustainable documents	Download size
	Sustainable specification	Hardware requirements
	Code optimization	Green analysis efficency
	Usage phase
	Accessibility	Material resource consumption
	Usability	Backup size
	Energy efficiency at running time
	End of life phase
	Data conversion to the future	Long-term data storage
	Software or application	Packaging and manuals

https://ictfootprint.eu/

http://www.ecolabels.fr/fr/espace-consommateurs/les-ecolabels

https://www.global-standard.org/fr/

https://ecotourisme.gites-de-france.com/notre-demarche-panda.html

https://www.energystar.gov/

https://fr.fsc.org/fr-fr

https://www.cosmebio.org/en/

https://label.greencodelab.org/

Site officiel écolabel européen : http://ec.europa.eu/environment/ecolabel/eu-ecolabel-products-and-services.html

Software ecodesign referential: https://collab.greencodelab.org/

https://www.w3.org/WAI/

https://greenspector.com/fr/product/

https://greenspector.com/fr/

https://software.intel.com/en-us/socwatch-help-energy-analysis-workflows