Julien Wolfersberger Agroparistech

Marc Bourgeon

Brian Copeland

Douglas Gollin

Estelle Gozlan

Growth and the environment: taking into account structural transformation

Keywords: Structural transformation, Environment, Growth JEL codes: O41, O44, Q50

This paper analyzes how structural transformation (as defined by the reallocation of economic activity across sectors) can explain the differences in pollution emissions across countries. Since pollution per unit of output differs across sectors, environmental quality can vary as a result of the rise of services at the expense of industry and in absence of environmental policy: this is the composition effect. An amended model of structural transformation is developed, where pollution is a by-product of output, and the predictions of the model are then tested empirically by studying labor reallocation and carbon emissions in 120 countries over the 1992-2014 period. The results show that composition is crucial to understand the differences in CO 2 emissions across countries, and that the determinants vary according to countries' stages of structural transformation. We also find that the importance of convergence, traditionally the main factor to explain the dynamic effect of economic growth on the environment, is lowered by more than 30% when structural transformation is taken into account.

Introduction

The goal of this paper is to analyze the impact of structural transformation on the environment, and more precisely to understand to what extent it can explain the differences in CO 2 emissions across countries. To do this, we analyze both theoretically and empirically the impact of a change in the sources of growth, implied by structural transformation, on pollution dynamics across countries. While most studies at the same scale relied on a one sector-growth model (e.g., Solow, Ramsey), this work brings new results by adopting a multi-sectoral approach on a wider range of countries.

Throughout the paper, we define the environmental impact of the change in the sources of growth implied by structural transformation as the composition effect. At an early stage of development, the composition of output is mainly agricultural. Then, as the share of industry in total output increases, emissions rise since this sector is more polluting than agriculture. As development goes on, depending on preferences and sectoral productivity growth rates, services represent a larger and larger share of total output. Since pollution is on average lower in services than industry, emission intensity decreases as long as national product remains constant or growing. Hence, the rise of industry, followed by the rise of services, can cause the inverted-U shape of the well-known Environmental Kuznets Curve (EKC) observed between environmental quality and GDP per capita in some countries.

Overall, this makes structural transformation a great candidate to explain the link between economic growth and the environment.

To guide empirical work, we develop a simple theoretical background. First, we show how to decompose aggregate emissions between scale, composition, and technique effects. The interest of this paper is to explain and identify the composition effect, generated by a decrease in the share of industry in total output. Second, we develop a two-sector model of structural transformation and highlight how the contraction of industry can reduce emissions.

In the empirical analysis, we use panel data on the growth rate of CO 2 emissions in 120 countries over 1992-2014. To introduce the analysis, summary statistics are provided and show the level of pollution in different groups of countries according to their level of industrialization. The role of public policy to reduce pollution is also discussed. In the causal part of the analysis, we find that the contraction of the industrial sector significantly decreases the growth rate of CO 2 emissions per capita. The variation in the size of the industrial sector is measured by the growth rate of industrial employment, and the results are robust to several specifications including when changing the indicator by the variation in sectoral value-added (as a share of total GDP). Then, considering countries subgroups, we find that for those at a late stage of structural transformation (i.e., those with the lowest share of agricultural employment) this is rather the rise of services which drives down emissions.

Eventually, we also identify a negative relationship between the level of output and the subsequent growth rate of CO 2 emissions, called the convergence effect, as in [START_REF] Brock | The green solow model[END_REF] or [START_REF] Criado | Growth and pollution convergence: Theory and evidence[END_REF]. Notably, we find that the importance of convergence is lowered by more than 30% when structural transformation is taken into account. This suggests that previous studies may have overestimated the importance of this effect since an important property of growth was omitted, namely structural transformation.

This paper is related to different strands of literature. Numerous articles attempted to explain the variation of pollution growth rates across countries since the pioneer works of Grossman andKrueger (1991, 1995) on the EKC.1 Only some of them base their empirical analysis on a theoretical model of growth, as we do here. [START_REF] Brock | The green solow model[END_REF] develop the green Solow model where the EKC results from the combination of convergence in the GDP growth rate and technological progress in abatement. The authors also provide a method to estimate the determinants of the growth rate in carbon emissions using data over the 1960-1998 period. [START_REF] Criado | Growth and pollution convergence: Theory and evidence[END_REF] develop a similar analysis but use a Ramseygrowth model, and focus on sulfur and nitrogen oxides. As do [START_REF] Brock | The green solow model[END_REF], they find evidence of a convergence effect: a negative relationship between the initial level of emissions and subsequent pollution dynamics. The author also highlights a scale effect, where an increase in the size of the economy leads to more emissions.

The one-sector growth-model used in these articles cannot, however, take into account a composition effect induced by structural transformation since it does not allow for changes in the mix of economic activity. Yet, the literature both in economic growth and environmental economics suggests that this effect can play a critical role. On one hand, [START_REF] Herrendorf | Chapter 6 -Growth and Structural Transformation[END_REF] provide recent evidence that structural transformation is an important feature of modern growth in developed and developing countries. On the other hand, [START_REF] Copeland | Trade, growth, and the environment[END_REF] show that a change in the sources of growth, and thus a change in the composition of total output, could be a channel to explain the EKC. On this point, [START_REF] Stefanski | On the mechanics of the green solow model[END_REF] presents a pollution accounting exercise and finds that sectoral change represents a larger share of falling emissions than GDP convergence, confirming the presence of a composition effect. He argues that structural change generates a peak in emission intensity that could explain the EKC. Chen (2017) also finds that the growth rate of emission intensity in 21 European countries decreased over 1950-2010, confirming Stefanski's results and contradicting [START_REF] Brock | The green solow model[END_REF] who assume that this rate is constant. The main conclusion of these studies is that the convergence forces alone cannot explain the variation in CO 2 emissions across countries.

Another strand of the literature performed decomposition analysis of pollution. Among these works, [START_REF] Levinson | Technology, international trade, and pollution from us manufacturing[END_REF] and [START_REF] Shapiro | Why is pollution from us manufacturing declining? the roles of environmental regulation, productivity, and trade[END_REF] focus on US manufacturing and highlight the importance of the technique effect. [START_REF] Brunel | Pollution offshoring and emission reductions in eu and us manufacturing[END_REF] reaches similar conclusions by focusing on the manufacturing sector in the US but including EU countries too. On composition, [START_REF] Selden | Analyzing the reductions in us air pollution emissions: 1970 to 1990[END_REF] find that this effect was significant but not strong enough to explain by itself the EKC. [START_REF] Bruvoll | Factors behind the environmental kuznets curve. a decomposition of the changes in air pollution[END_REF] also find the presence of a significant composition effect when studying Norway, while noticing that it has been counterbalanced by the rise of the demand in the energy sector. [START_REF] Marsiglio | The environmental kuznets curve and the structural change hypothesis[END_REF] study European economies over 1995-2009, and confirm that structural transformation was an important feature of the change in CO 2 emissions. Therefore, to better understand why some countries pollute more than others and how modern growth affects the environment, it is important to examine the effect of structural transformation. This is the goal of this article, which also connects with the renew of interest in the macroeconomic literature for the question of structural transformation. For instance, Święcki (2017) revisits the determinants of structural transformation, while [START_REF] Stefanski | Structural transformation and the oil price[END_REF] estimates the impact of the process on oil price in non-OECD countries. Our paper contributes to the literature by explicitly analyzing the impact of structural transformation on a global pollutant like CO 2 emissions. This is done here on the basis of a theoretical analysis and using cross-country data that also include, contrary to previous analyzes, developing countries over a time-span of more than 20 years.

The remainder of the article is organized as follows. To motivate the analysis, some stylized-facts are presented in section 2. Section 3 introduces the general framework to model the change in aggregate emissions, and section 4 presents the amended model of structural transformation with pollution. It shows how resources move across sectors and how it impacts aggregate emissions. Section 5 is dedicated to the empirical analysis of the predictions from the model, and includes a robustness-analysis. Section 6 concludes.

Motivating facts

This section is organized around three main points. It aims at presenting first how emissions per unit of GDP have changed in developed countries over the last decades. Then some data on the differences in emission intensity across sectors are presented, and the role of abatement is discussed in a third point.

Trends in emission intensity

Let us start with a description of the link between pollution and income in some advanced economies over the last decades. Figure 1 shows the amount of CO 2 emissions per unit of GDP in Europe and the United-States since 1960.

USA

The pattern of pollution per unit of GDP is similar: it follows an inverted-U shape as described by the classic EKC framework. Note that even if for USA the variance is lower, the curve is still observed. This shows that the rate of change in emission intensity has been decreasing at the aggregate level, both in the EU and USA since 1960. Figure 2

USA

The rate of decline is more pronounced in the EU, while it is around -0.02% per year for the United-States since 1960. [START_REF] Stefanski | On the mechanics of the green solow model[END_REF] finds that this negative change in emission intensity is larger than GDP convergence in most countries. [START_REF] Chen | Is the green solow model valid for co2 emissions in the european union?[END_REF] finds the same result when focusing on European countries only.

It suggests that to understand the effect of growth on the environment we cannot rely solely on the convergence explanation provided by models like the Green Solow one. We also need to understand the decrease in emission intensity.

Sectoral emission intensity

Now we present data we have collected on emissions per sector. Our objective is to see whether industry is the sector with the highest level of pollution per unit of output. For this purpose, we use the OECD database which provide air emission accounts per sector for 26 countries between 2011 and 2016. In Appendix A, we provide a complete detail of the aggregation of the data per sector and the countries of the sample. Figure 3 shows the average emission intensity for each sector in time.

The amount of CO 2 emissions per unit of GDP is two to three times higher in industry than in services. Interestingly, it has not varied importantly in the three main sectors over this six years period. The time-series are quite flat for agriculture and services, while slightly decreasing for the industrial sector. In 2014 the group of countries with the highest level of industry had, on average, 29% of total employment located in this sector, while it was around 10% for the lowest group.

While industry does not capture the largest share of labor force in an economy on average, it is correlated with higher pollution levels. In the three groups of countries from the sample, the larger is the share of industrial employment, the larger are the CO 2 emissions both per capita and per unit of output. For example, the 40 countries with the highest level of industry emitted 0.68 kilotons of CO 2 per unit of GDP, while the 40 countries with a middle-level of industry, which had 19.42% of total employment allocated in this sector, emitted 0.43 kilotons of CO 2 per unit of GDP. This suggests the existence of a link between the composition of the economy and its level of emissions.

Furthermore, the group of countries with the highest level of GDP per capita is not the one with the largest share of workers in the industrial sector. The richest group is the one with the medium level of employment in industry, and the largest employment in services. This corresponds to stylized facts about structural transformation, also documented by [START_REF] Herrendorf | Chapter 6 -Growth and Structural Transformation[END_REF]. At some threshold of development, the industrial sector eventually shrinks and the services sector continues to expand as GDP per capita increases.

The variable of GDP per unit of energy use is the ratio of GDP per kilogram of oil equivalent of energy use. This can be viewed as an index of technology in the energy sector.

Not surprisingly, this is the group of countries with the highest level of GDP per capita and the largest services sector that is the most energy-efficient, according to this indicator.

Interestingly, the group of countries with the largest industrial sector has a technology with a comparable level to that of the group with the lowest level of GDP per capita. They both obtained 9 dollars per kilogram of oil equivalent from energy use in 2014.

The role of abatement

Since abatement may play an important role to understand the decreasing trend in emission intensity, Figure 4 shows expenditures in environmental protection in the manufacturing sector of OECD countries with data available.

Expenditures in environmental protection represent a very low share of total GDP, that is, less than 1%. Futhermore, the ratio did not significantly vary over time in most countries Env. protection expenditures (% of GDP) Year of the Figure . 2 This suggests that without underestimating the role of abatement, there must be space for other forces to explain Figure 1, such as structural transformation. This is also supported by the other stylized facts presented in this section.

Background: modeling aggregate emissions

Following a large literature in environmental economics3 , it is possible to represent aggregate emissions E as:

E = i e i = i y i Ω i = Y i φ i Ω i , ( 1
)
where e i represents the amount of pollution generated by production in sector i. The variable Ω i ≡ e i /y i represents emission intensity per sector, and φ i ≡ y i /Y is the share of production i in total output Y . It represents the composition of the economy.

In vectorial notation, by totally differentiating (1) and dividing by E, one obtains the well known scale, composition, and technique effects:

dE E = dY Y + dφ φ + dΩ Ω . ( 2
)
All other parameters held constant, when total output Y increases, the growth rate of emissions E in the economy rises. This is the scale effect. The composition effect is provided by the second term on the RHS of ( 2), which represents the variation in the share of each sector's production over total output. Ceteris paribus, assuming that the manufacturing sector pollutes more than agriculture and services (as shown in Figure 3), aggregate emissions will increase in an economy whose production intensifies in manufacturing. Hence, when structural transformation occurs and services rise at the expense of manufacturing as a share of total output, then dφ/φ < 0 and the growth rate of emissions decreases. The last term on the RHS of ( 2) is the technique effect. It encompasses changes in technology and regulation, and is negative when these changes are in favor of environmental protection.

To further illustrate very simply the composition effect occurring through structural transformation, suppose two countries A and B with similar sizes (or equivalent scale). Data reveal that A emits more than B, so that E A > E B , which implies:

Y A i φ A i Ω A i > Y B i φ B i Ω B i . (3)
Further, suppose that country A and B share similar technologies and that the level of technology in abatement is the same across sectors, so that i

Ω A i = i Ω B i = Ω. Since Y A = Y B ,
the only remaining explanation of why aggregate emissions are higher in country A is that country B's mix of activity is less harmful for the environment, that is

i φ A i > i φ B i
. This provides a simple example of how compositional effects induced by structural transformation can explain why some countries pollute more than others.

Using a one-sector growth model, [START_REF] Brock | The green solow model[END_REF] represent emissions as:

E = ΩY (1 -θ), ( 4
)
where θ is the fixed share of output Y reallocated to abatement. The variable Ω represents every unit of pollution generated as a joint product of economy activity, it is assumed to be constantly decreasing and represents the technique effect. Equation ( 4) from Brock and Taylor (2010) model ignores the potential effect of a variation in composition, given by φ in equation ( 2). Though, as discussed in the introduction, several papers performing decomposition analysis provide empirical evidence of the importance of composition and structural transformation to understand the path of emissions (e.g., [START_REF] Selden | Analyzing the reductions in us air pollution emissions: 1970 to 1990[END_REF][START_REF] Bruvoll | Factors behind the environmental kuznets curve. a decomposition of the changes in air pollution[END_REF][START_REF] Stefanski | On the mechanics of the green solow model[END_REF][START_REF] Marsiglio | The environmental kuznets curve and the structural change hypothesis[END_REF][START_REF] Chen | Is the green solow model valid for co2 emissions in the european union?[END_REF]. It follows that the green solow model potentially misses an important part of why and how emissions vary along with growth.

Pollution may vary depending on the pattern of structural transformation. Suppose that two countries with the same amount of aggregate production and pollution observe a similar increase in output Y . In the first country, this increase is largely due to manufacturing, while in the second one, it is due to services. All other parameters held constant, the first country will now pollute more. This simple insight cannot be explained using equation ( 4) derived from a one-sector growth model. Instead, using a multi-sector growth model makes it possible to show how the pattern of structural transformation may explain differences in aggregate pollution.

A model of structural transformation with pollution

The model developed here has roots in the general framework of relative prices from [START_REF] Ngai | Structural change in a multisector model of growth[END_REF]. The goal of this simple model is to present an explanation of the rise of services at the expense of industry, and to assess its impact on pollution.

Environment

Suppose an infinite-lived agent whose lifetime utility is given by:

U t ≡ ∞ 0 e -ρt log(C t )dt, (5)
with ρ the discount rate, and C t the yearly amount of consumption, which is a composite of manufacturing (c mt ) and services (c st ) consumption:

C t = c σ-1 σ mt + c σ-1 σ st σ 1-σ . ( 6
)
The parameter σ determines the elasticity of substitution between goods. When σ is lower than 1 and tend towards 0, the manufacturing and services goods are complements in consumer's utility, including when income increases. To keep the model as simple as possible and without loss of generality, we abstract from agriculture in this version.

It is noticeable that the model does not display preferences from agents for the environment. This allows us to show how structural transformation can generate decreasing emissions even in absence of environmental policy.

Production in this economy is based only on labor:

Y t = i y it = i A it n it , i ∈ {m, s}, (7)
with n it is the amount of workers in sector i at each time-period t. The variable y it refers to the level of output in sector i. A it is the productivity parameter in sector i, it is assumed to grow exogenously at a given rate following:

Ȧit

A it = γ i , i ∈ {m, s}, (8)
with γ i > 0.

Since the accumulation process is not detailed in the standard version of the model, in each sector i we have y it = c it . Labor is freely mobile across sectors and the feasibility constraint imposes:

n mt + n st = N, (9)
where total labor N is normalized to one. As detailed before, aggregate emissions E t are:

E t = Y t i φ it Ω it , ( 10
)
with i Ω it = Ω t = 1
Bt an abatement ratio. It captures innovations in environmental technology or changes in regulation that lower emission intensity in the economy (i.e. the technique effect). Similar to [START_REF] Brock | The green solow model[END_REF], we assume that B t grows uniformly across sectors at a positive and exogenous rate g B , such that Ḃt /B t = g B . This assumption allows us to focus on the core of this paper: the role played by structural transformation. While Y t represents total output, the variable φ it = y it /Y t represents sectoral share i in the mix of production.

Defining sustainable growth

Let us define sustainable growth as a situation where total output Y grows at a positive rate g Y > 0, while the growth rate of emissions is negative g E < 0. Using (2), rewriting dx/x for a given variable x as g x , the condition g E < 0 imposes:

g Y + g φ + g Ω < 0, ( 11
)
where g φ represents the change in the composition of the economy, and g Ω is the change in emission intensity. To understand the different effects, in accordance with the data presented in Figure 3, we introduce two important definitions:

Definition 1. Emission intensities vary across the two sectors manufacturing and services.

The manufacturing sector has the highest level of emissions per unit of output:

Ω m > Ω s .
Next we can pose:

Definition 2. A change in the composition of the mix of activities towards more industry implies g φ > 0, that is, more industry increases aggregate emissions as represented by equa-tion (2). On the contrary, a decrease in the share of industry lowers emissions' growth rate, and implies g φ < 0.

Given that emission intensity decreases at an exogenous rate g B , a sustainable growth path must be:

g Y < g B -g φ , ( 12
)
with g Y > 0 and g B > 0. If structural transformation is characterized by the rise of services at the expense of manufacturing then we have g φ < 0. Sustainable growth in the economy is then defined by an increasing output, whose growth rate must be lower than the sum of progress in abatement (g B ) and composition changes (g φ ). Put it simply, in an economy where the growth rate of output g Y is positive and exactly equal to the growth rate of the abatement ratio g B , then sustainable growth is reached as long as services increase at the expense of industry (i.e. g φ < 0). It is now possible to examine the equilibrium properties of the model.

Structural transformation and emission path

Solving consumers' problem yields the price-ratio of manufacturing to services good:

p mt p st = c st c mt 1 σ , ( 13
)
with p mt and p st the prices of the manufacturing and the services good, respectively. Solving firms' problem yields another expression of the ratio (13):

p mt p st = A st A mt . ( 14
)
On the production side, the price ratio is simply the inverse ratio of sectoral productivities.

This defines the relative prices in the economy.

Provided that c it = y it ∀i ∈ {m, s}, using ( 13) and ( 14), the optimal allocation of labor across sectors is:

n st n mt = A mt A st (1-σ) . ( 15
)
The ratio of employment in each sector is determined by the ratio of relative productivity.

The more productive is the manufacturing sector relative to services, the larger the share of labor force released towards services. Less labor is required to meet the demand for the good produced in this sector. Combining ( 15) with (8) yields:

Proposition 1. For any σ < 1, if γ mt > γ st , then structural transformation takes place and it is characterized by the two following properties:

• Employment in industry n mt decreases and employment in services n st increases;

• The ratio of services to manufacturing output p st y st /p mt y mt increases.

Proof. From (15), taking the logs and differentiating with respect to time, the rate of change in services employment is:

ṅst n st - ṅmt n mt = (1 -σ) (γ mt -γ st ) , ( 16
)
Given that n st = 1 -n mt , it follows that σ < 1 and γ mt > γ st ensure that the LHS of ( 16) is positive and labor force flows out from manufacturing towards services.

Next, using the properties from ( 13) and ( 14), the ratio of services to manufacturing production is:

p st y st p mt y mt = A mt A st (1-σ) , ( 17
)
The LHS of ( 17) is increasing as long as A mt grows faster than A st , which is satisfied by the conditions (γ mt -γ st ) > 0 and σ < 1. Since y mt + y st = Y t , the share of manufacturing in total output decreases and we obtain g φ < 0 according to Definition 2.

Proposition 1 shows how the economy experiences structural transformation at the expense of manufacturing and in favor of services. Let us now examine the impact of such transformation on aggregate emissions with the following proposition:

Proposition 2. When structural transformation as defined by Proposition 1 occurs, holding scale and technology constant, the growth rate of emissions decreases.

Proof. From equation (2), holding scale and technology fixed, the rate of change in emissions is only: dE/E = dφ/φ. Making use of Definition 2, the decline of industry implies dφ/φ < 0, so the growth rate of aggregate emissions E t decreases.

So far we have seen the conditions for structural transformation to take place, and then the consequences on the environment. The last element to examine is that output is growing along this equilibrium.

Using ( 7), the change in sectoral production is given by:

ẏit = γ i + ṅit n it , i ∈ {m, s}, (18)
with γ i positive and constant. When productivity grows at the same rate in both sectors γ mt = γ st , global output Y t grows at a rate i γ i > 0, i ∈ {m, s}. In this case, structural transformation does not occur and growth is sustainable only if the growth rate in abatement is larger than that of output, which is:

g B > i γ i , i ∈ {m, s}.
When γ mt > γ st , the growth rate of total output is:

Ẏt = i ẏit = i γ i + ṅmt n mt + ṅst n st .
Given that n st = 1 -n mt , we have ṅmt /n mt < 0 as employment in industry decreases. This implies that Y t is increasing as long as, in absolute terms, we have γ mt + γ st + ṅst nst > ṅmt nmt . It is possible to deduce: Proposition 3. The economy experiences sustainable growth together with structural transformation when the following inequality is satisfied:

g B -g φ > i γ i + ṅst n st > - ṅmt n mt , ( 19
)
where g φ < 0 and ṅmt nmt < 0.

Proof. In the text.

Along this path, the growth of output is driven both by sectoral productivity growth γ i and by the reallocation of workers in services. The combination of (i) the reallocation of economic activity away from manufacturing g φ and (ii) the growth rate in abatement g B ensure that emissions are decreasing.

Finally, notice that another way to obtain structural transformation in a similar model is to introduce non-homothetic preferences. In this case, transformation would emerge from consumers' taste (demand-side). Since CO 2 emissions come mainly from production, a choice was made here to focus on transformation driven by technology (supply-side).

Empirical predictions

We can deduce from equation ( 2) that the growth rate of CO 2 emissions increases with the growth rate of (i) the size of the economy dY Y , (ii) the composition of the economy dφ φ , (iii) emission intensity dΩ Ω . When the share of industrial output decreases, from Definition 2 we have dφ φ < 0. All things equal, it lowers the growth rate of emissions, as described by Proposition 2.

In the model, it is assumed that Ω = 1 B , with Ḃ B = -g B . Following the literature in environmental economics, this technique effect lowering emissions per unit of output may occur notably through two forces: (i) technological progress in abatement or energy use, or (ii) increase in the level of environmental regulation. These two channels are studied in the empirical analysis.

Empirical analysis

Data and methodology

The model is applied to carbon dioxide emissions. The dependent variable is the annual growth rate of CO 2 emissions per capita. Using CO 2 emissions presents the advantage of having data on a pollutant available for a broad set of countries and over long time-periods, which is not necessarily the case of other pollutants. It is provided by the World Development Indicators (WDI), whose source is the Carbon Dioxide Information Analysis Center (USA).

The variable accounting for structural transformation is the rate of variation in industrial employment (measured as a share of total employment), provided by the International Labour Organization (ILO). The industry sector precisely "consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water)". Employment in services will be included too, and the sector encompasses "wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services".

Data on sectoral employment are available over the 1991-2015 period. This relatively short time-period questions the choice of the source for the variable of industrial employment.

For instance, [START_REF] Timmer | Patterns of structural Change in Developing Countries[END_REF] provide data on sectoral employment for some countries from 1950 onwards. However, those data are available for only 35 countries and not every year, while ILO data are available for practically every country. This is why ILO data were chosen. 4 Taking all constraints into account, we obtain a panel of 120 countries over the 1992-2014 period. Figure 5 shows the variation in industrial employment over the period of study. The sample makes it possible to account for a wide range of development stages, with countries with less than 5% of employment in industry, and others with more than 45%.

It is interesting to notice that most of the sample seems to follow the classic structural transformation scheme. Indeed, countries with less than 20% of labor force in industry in 1992 have predominantly increased their share of employment in this sector until 2014.

On the contrary, where the share of labor in industry was already above 20% in 1992,

Econometric model

To transform the theoretical model presented in section 4 into a discrete-time model, it is possible to approximate growth rates as5 :

1 T ln e it e it-T = β 0 + β 1 1 T ln e it-T + β 2 1 T ln y it-1 y it-T + β 3 1 T ln n mit n mit-T + α t + µ it . ( 20
)
The subindex i denotes a given country, T denotes the number of periods of the analysis, and t is a given time-period. The variable e it-T is the initial level of emissions at year 1992, and β 1 thus tests the existence of a convergence effect in the sample.

The term y it-1 /y it-T is the lagged value of the economy's GDP growth rate per capita.

It accounts for changes in the size of the economy. Given the likely feedback of emissions on income, y it-1 is used for y it in order to avoid endogeneity bias. When positive, this variable is expected to raise emissions as the scale of the economy increases.

The main contribution of this empirical model is the variable associated with coefficient β 3 , that is, the effect of structural transformation on the pollution dynamics. The expected sign of β 3 is positive since a larger growth rate in industrial employment implies a dirtier mix of activities, which causes more pollution. This is the composition effect from Definition 2 where g φ > 0.

The variable µ it is the error term, it is assumed to be uncorrelated with the explanatory variables. The parameter α t represents time-fixed effects to control for possible time-trends in the data.

A Hausman test was ran for each specification of the model, the initial hypothesis that the individual-level effects are adequately modeled by a random-effects model was systematically not rejected at the 1, 5, or 10% level. Consequently, random-effects estimator is chosen so the convergence variable, which is time-invariant, can be included. Despite the results of the Hausman test, one may still fear that unobserved country-specific characteristics may play a role. For this reason, we also provide the estimates of the model with the within estimator.

Regression results

Three specifications of model ( 20) are estimated. First, the variable of structural transformation (measured by β 3 ) is not included. The regression is thus similar to that of [START_REF] Brock | The green solow model[END_REF] or [START_REF] Criado | Growth and pollution convergence: Theory and evidence[END_REF]. Only in the second specification the average log-change in the share of industrial employment is added. Eventually, the average logchange in the share of services employment is also added in a third regression. Comparing the three specifications makes it possible to clearly identify the contribution of structural transformation.

In the three regressions the countries with a population below 1.5 million were removed, as well as OPEC countries. This was done because these may have specific development paths, due to their size or their important natural resource endowments. Table 2 displays the results.

The coefficients and their significance are very similar between the random and fixed effects model, confirming the relevance of the Hausman test in this case. Following this result, we will focus our comments on the model using random effects estimator. Overall, the model explains relatively well the variation of CO 2 emissions per capita growth over the 1992-2014 period, with a R-squared above 35%. When the variables of structural transformation is added, the explanatory power of the model rises above 42%.

The initial level of emissions per capita, which measures the convergence effect, significantly reduces the growth rate of subsequent emissions. In the first specification, a 10% increase in the log of CO 2 emissions in 1992 is associated with a decrease of 0.03% in the average log-change in CO 2 emissions. The lagged value of the annual GDP growth rate per capita is also significant at the 1% level, and positive. This is in line with scale effects identified by the literature.

In the second specification, the average log-change in industrial employment, which accounts for the effect of structural transformation, is positive and significant at the 1% level.

This highlights the importance of the multi-sector analysis and sheds light on the composition effect. A 1% increase in the average log-change in the share of industrial employment increases the average log-change in CO 2 emissions by 0.42%, which is a quite large effect.

Even more striking is the importance of accounting for this composition effect. When comparing coefficients between the first and second specification, it is found that the importance of the convergence effect decreases by 34%. This quite large drop from -0.0032 to -0.0021 suggests that convergence forces were overestimated by previous studies which did not take structural transformation into account. Similarly, the coefficient of the scale effect, as measured by the GDP per capita growth rate, dropped from 0.62 to 0.55. The third specification adds the variation in the share of services employment. While it exhibits the expected negative sign, it is not significant.

In all, these results show that structural transformation is an important feature to understand the link between growth and the environment. In Appendix B.4, we show that the results are not dependent on the measure of structural transformation, by using the sectoral value-added of each sector. Now we examine the first findings by subgroups of countries.

Stages of structural transformation

In this section, we aim at improving our understanding of the previous average effect, and see whether the determinants of CO 2 emissions differ according to a country's stage of structural transformation. Countries are thus divided between three groups: early, middle, and late stage. These groups were built on the basis of each country's level of agricultural employment in 1992. Early-stage countries had, on average, 57% of their labor force located in the agricultural sector in 1992. For middle-and late-stage countries it was 25% and 5%, respectively. Each group is initially composed with 40 countries.

Contrary to the regressions shown in the previous section, here the results of the Hausman test differ. The initial hypothesis that the individual-level effects are adequately modeled by a random-effects model was rejected at the 5 and 10% levels for the countries in the early and middle stages of structural transformation, respectively. Consequently, we will focus on the results of the fixed-effects model. One inconvenient is that we loose the interpretation of a possible convergence effect, but the model yields unbiased coefficients. Results are given in Table 3. Different conclusions arise from the results. First, the signs of the different coefficients are as expected and are the same across stages. The scale effect is present and significant over the three stages of transformation, and appears to be stronger during the early stage.

One must be careful when comparing the subgroups as the number of observations differ.

At the beginning of structural transformation, a 10% increase in the size of the economy is associated with a 7.387% increase in the average log-change of emissions per capita. The effect is more than twice as large as it is in the two other stages. This suggests that scale is key to understand the evolution of emissions in countries at an early stage of structural transformation.

Second, the average log-change in industrial employment increases the growth rate of CO 2 emissions per capita in early and middle stage countries. In countries at the late stage of structural transformation, we find that this is no more the variation in industrial employment that influences emissions, but it is the rise of services. Here, a 1% increase in the average log-change of services employment decreases by 0.84% the average log-change of CO 2 emissions per capita. This average effect is quite strong and somehow highlights the positive impact, in terms of climate change, of advanced countries de-industrializing. The following section offers a robustness analysis of these empirical results.

Robustness analysis

The main goal of this section is to control for omitted variables that would bias the results.

For instance, in equation ( 2), the variation of Ω can influence emissions. This is the technique effect. To account for it, we introduce an index of "Environmental Policy Stringency", which is provided by OECD and ranges from 0 (not stringent) to 6 (highest degree of stringency).

It measures countries' effective implementation of environmental policies and is defined as "the degree to which environmental policies put an explicit or implicit price on polluting or environmentally harmful behavior". As detailed by [START_REF] Brunel | Measuring the Stringency of Environmental Regulations[END_REF], this measure is not perfect as it only takes into account some policy aspects. However, because of data availability, this is the best proxy that could be used here.

Another possible channel to explain emissions is trade openness. As North and South countries trade, dirtier industrial activities may move to the South and the coefficient measuring the effect of industrialization on pollution may thus be overestimated. To control this, we also include a variable of change in the rate of trade openness. These variables, espe-cially the index of environmental policy stringency, are available only for a limited number of countries and years, so we obtain a panel of 28 countries over 1992-2012. 6 Table 4 shows the results. The fixed-effects model is also presented, although the result of the Hausman test suggests that random effects are unbiased. First it is interesting to notice that in this sample of mainly OECD countries the convergence effect is not found significant. Indeed, the coefficient for the log of CO 2 emissions per capita in 1992 is not significantly different from zero. Second the scale effect (measured by growth in GDP per capita) and the composition effect are significant at the 1% level and with the expected signs. Notice that this is also true for the model with country fixed-6 Trade openness is provided by the World Bank and is computed as the sum of imports plus exports over total GDP. The list of 29 countries is provided in Appendix A.

effects. Third, neither the variation in environmental policy stringency nor in trade openness significantly affect the growth rate of CO 2 emissions in the sample.

Another concern about the above results is that the lag of GDP growth rate may still be too linked with the variation in industrial employment, or the rate of change in emissions, even when using lagged values. To address this, we now present a model where the variation in the size of the economy is measured by other indicators than the GDP growth rate.

Following [START_REF] Brock | The green solow model[END_REF], we use the depreciation rate and the investment share of GDP.7 This also allows us to clearly show the contribution of accounting for structural transformation instead of relying solely on the green solow model. The signs and significance of the log of initial emissions, the depreciation rate and investment share are the same as [START_REF] Brock | The green solow model[END_REF]. Importantly, the growth rate in industrial employment is significant at the 1% level, and is positively correlated to the annual growth rate in CO 2 emissions per capita. Again, it is interesting to observe that the coefficient of the convergence effect drops from -0.0035 to -0.0024 when the variation in industrial employment is taken into account. In other words, taking into account structural transformation lowers by 31% the importance of the convergence effect. In sum, this

shows how a critical aspect of economic growth, namely structural transformation, cannot be ignored to understand accurately the environmental impacts of growth and development.

Notice that the results remain when sectoral value-added is used as the index of structural transformation (see Appendix B.4).

Concluding remarks

This work has assessed the role of structural transformation in explaining the differences in CO 2 emissions across countries. For this purpose, a theoretical model was first developed where the driver of structural transformation was the difference between the growth rate of productivity across sectors, and pollution was a by-product of output. In comparison with previous empirical applications on growth and pollution, this multi-sector model explains a new parameter: the composition effect. It shows how a change in the sources of growth, precisely the rise of services and the contraction of the industrial sector, modifies emission intensity in the economy and may lower environmental degradation.

Then we have tested the theoretical predictions of the model in an empirical analysis of carbon dioxide in 120 countries over the 1992-2014 period. Our set of countries and timespan were larger than most previous studies, which allows us to bring new evidence. To account for the effect of structural transformation, we used the rate of variation in industrial employment across countries. The results showed that a decrease in industrial employment led to a decrease in the growth rate of CO 2 emissions. This occurs because the most polluting sector represents a lower share of economy's output, and growth is globally more sustainable.

An important finding is that taking into account structural transformation lowers the importance of the traditional convergence forces by more than 30%. This shows that the mix of economic activity is another important factor to understand more accurately the impact of growth on the environment.

Our results also highlight that the determinants of pollution across countries vary according to the stage of structural transformation. Indeed, the rise of services significantly diminishes emissions in countries at the late stage of transformation. At other stages, this is the variation in the importance of the industrial sector that drives emissions per capita.

Following the conventional wisdom in the growth literature, if we may regret "premature deindustrialization" [START_REF] Rodrik | Premature deindustrialization[END_REF] in some developing countries, this piece of work suggests that the new forms of structural transformation, away from industry and towards services, could be at least less harmful for climate, on average.

B.2 Emission intensity and industrial sector

Figure 7 shows the link between the amount of emissions per unit of output and the share of industrial employment in 2014 across countries. There is a strong positive relationship between the level of industrial employment across countries and the amount of CO 2 emissions per unit of GDP. This suggests that a multi-sector model can bring new information on the link between growth and pollution.

B.3 Structural transformation: the rise of services

B.4 A different measure of structural transformation

One may wonder if the results are dependent on the indicator used to measure transformation. To test this possibility, we now use sectoral value-added (as a share of GDP), instead of employment. As detailed in Appendix A, the variables for sectoral value-added are taken from the World Development Indicators. It presents the inconvenient of being available only by 1997 for some countries. These countries were thus removed from the sample, and we obtain a panel of 92 countries over 1992-2014. Table 6 displays the results. The results are very similar to those from Table 2. The model is well explained with a R-squared around 40%, and when industrial value-added increases (in share of GDP), the growth rate of CO 2 emissions increases. The effect of services again exhibits the right sign but is not significant. This shows that structural transformation, as measured by employment or value-added shares, is a critical aspect of the link between economic growth and the environment.

Figure 1 :

 1 Figure 1: Change in emission intensity over 1960-2015; Source:World Development Indicators.

Figure

  Figure 3: Average emission intensity per sector in 26 OECD countries over 2011-2017 ; Source: OECD.

Figure 5 :

 5 Figure 5: Industrialization and deindustrialization in the 120 countries of the sample ; Source: ILO.

  deindustrialization seems to have occurred as these countries have a lower level of industrial employment in 2014. In Appendix B.3 the same Figure is provided for the services sector to show the structural transformation pattern in the sample.

Figure 7 :

 7 Figure 7: Emissions per unit of GDP and industrial employment in 2014 ; Source: WDI.

Figure 8

 8 Figure 8 shows the change in services employment between 1992 and 2014 in the countries of the sample.

Figure 8 :

 8 Figure 8: The rise of services in the 120 countries of the sample ; Source: ILO.

Table 1 :

 1 3: Average emission intensity per sector in 26 OECD countries over 2011-2017 ; Source: OECD. Summary statistics per level of industrialization (average-values for year 2014), Sources: ILOSTAT database, World Development Indicators.

		.00015
	CO2 emissions per unit of GDP	.00005 .0001
		0
		2010	2012	2014	2016	2018
			Year
			Agriculture		Industry
			Services
	Table 1 presents summary statistics for selected economic and pollution variables for the
	120 countries of the sample in 2014. Countries are divided between three groups according
	to their level of industrialization.
					Share of employment
					in the industrial sector
					Low	Medium	High
	Employment in industry (% of total)		10.32	19.42	28.87
	Employment in services (% of total)		35.42	62.56	52.30
	GDP per capita (constant 2010 US$)	4155.62 23240.73	12000.31
	CO 2 emissions per capita (metric tons)	1.16	4.91	5.44
	CO 2 emission per unit of GDP (kt)		0.35	0.43	0.68
	GDP per unit of energy use ( * )		9.04	10.91	9.27
	Observations				40	40	40
	Notes: values reported are the means per group at year 2014.
	( * ) The variable of GDP per unit of energy use counts 19, 3 and 3 missing observations
	for the low, medium and high groups, respectively.

Table 2 :

 2 Determinants of the growth rate in CO 2 emissions per capita in 120 countries over 1992-2014

	∆ log CO 2 emissions per capita
	RE	FE

Table 3 :

 3 Growth rate in CO 2 emissions per capita and stages of structural transformation

			∆ log CO 2 emissions per capita
			RE			FE
		Early	Middle	Late	Early	Middle	Late
	Initial emissions per capita	-0.0037 * * -0.0053 * * * -0.0038 * *
		(0.0018) (0.0014) (0.0019)
	∆ GDP per capita (t -1)	0.7454 * * * 0.3415 *	0.2740	0.7387 * * * 0.3346 * 0.2822 *
		(0.1280) (0.1821) (0.1719)	(0.1278) (0.1843) (0.1656)
	∆ share of indu. employment	0.2358 * * 0.4423 * * * 0.2589 *	0.2351 * * 0.4097 * * * 0.2407
		(0.1133) (0.1336) (0.1502)	(0.1107) (0.1245) (0.1483)
	∆ share of services employment -0.0659	-0.1548 -0.7711 * *	-0.0729 -0.2334 -0.8438 * *
		(0.1267) (0.3788) (0.3393)	(0.1251) (0.3742) (0.3354)
	Constant	-0.0068 * * 0.0032 *	0.0072 *	-0.0013 -0.0006 -0.0002
		(0.0031) (0.0017) (0.0039)	(0.0018) (0.0021) (0.0011)
	Observations	1012	920	825	1012	920	825
	Time fixed-effects	Yes	Yes	Yes	Yes	Yes	Yes
	Country fixed-effects	No	No	No	Yes	Yes	Yes
	R-squared within				0.5650	0.2965	0.3350
	R-squared overall	0.5137	0.4167	0.3293	0.4571	0.3217	0.2583
	Standard errors are in parentheses, robust to country clustering.
	*, ** and ***: significant at 10%, 5% and 1%, respectively.
	Note: small states and OPEC countries excluded. Period: 1992-2014.

Table 4 :

 4 Robustness analysis: accounting for technique effect and trade

	∆ log CO 2 emissions per capita
	RE	FE

Table 5

 5

	displays the

Table 5 :

 5 Regression results usingBrock and Taylor (2010) model

	∆ log CO 2 emissions per capita

Table 6 :

 6 Determinants of growth in CO 2 emissions in 92 countries over 1993-2014 ∆ log CO 2 emissions per capita Log initial emissions per capita -0.0036 * * * -0.0033 * * * -0.0033 * * * , ** and ***: significant at 10%, 5% and 1%, respectively. Note: small states and OPEC countries excluded. Period: 1992-2014.

	(0.0007)	(0.0007)	(0.0007)

*

For discussions and reviews see among others:[START_REF] Copeland | Trade, growth, and the environment[END_REF];[START_REF] Stern | The rise and fall of the environmental kuznets curve[END_REF];[START_REF] Carson | The environmental kuznets curve: seeking empirical regularity and theoretical structure[END_REF].

In Appendix B we show that expenditures in the public sector follow the same pattern.

For a review of works on growth and the environment since the seminal work ofGrossman and Krueger (1991, 1995), see among others[START_REF] Copeland | Trade, growth, and the environment[END_REF].

Among the initial sample of 150 countries, there are only 9 missing values for sector employment, and it concerns only small states (e.g., Bermuda, Faroe Islands).

The empirical method used here borrows from the model of[START_REF] Brock | The green solow model[END_REF], and is also the method adopted by[START_REF] Criado | Growth and pollution convergence: Theory and evidence[END_REF].

The rate of depreciation is computed as the population growth rate plus 0.05. The investment share is taken from the Penn World Table (7.1), and is available only until 2010. For this reason the panel is now from 1992 to 2010, and time fixed-effects are added.

Appendix A Data description

• CO 2 emissions per capita (metric tons): Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring. Source: World Bank Data.

• Employment per sector (% of total employment): The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4). The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4). The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4). Source: International Labour Organization, ILOSTAT database.

• Environmental Policy Stringency: country-specific index and internationally-comparable measure of the stringency of environmental policy. Stringency is defined as the degree to which environmental policies put an explicit or implicit price on polluting or environmentally harmful behavior. The index ranges from 0 (not stringent) to 6 (highest degree of stringency). The index covers 28 OECD and 6 BRIICS countries for the period 1990-2012. The index is based on the degree of stringency of 14 environmental policy instruments, primarily related to climate and air pollution. Source: OECD stat. Source: Penn World Table 7.1.

• Population growth (%): Annual population growth rate for year t is the exponential rate of growth of midyear population from year t-1 to t, expressed as a percentage.

Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. Source: World Bank Data.

• Value added per sector (% of GDP): Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Source: World Bank Data.

Following ILO classification and ISIC-Rev. 4, sector assignments in Figure 3 are: • Agriculture: ISIC section A with Agriculture, forestry and fishing.

• Industry: sum of ISIC sections B-F.

• Services: sum of ISIC sections G-S.

List of countries in regressions of

Appendix B Additional data analysis B.1 Abatement in the public sector

Figure 6 shows the public sector expenditures for environmental protection in some OECD countries. As for the manufacturing sector, expenditures for environmental pro- tection represents a low and non-increasing share of GDP in those countries.


D Roudil

P Peerani

S Boden

B Russell

M Herranz

M Crozet

L Aldave De La Heras

DEM 2018 -Dismantling Challenges: Industrial Reality, Prospects and Feedback Experience

Keywords: Decommissioning, Sampling strategy, R&D, characterization, European project

The EURATOM work programme project INSIDER (Improved Nuclear Site Characterization for Waste minimization in Decommissioning under Constrained Environment) was launched June 2017. This 4 year project has 18 partners. It aims at improving the management of contaminated materials arising from Decommissioning and Dismantling (D&D) operations by proposing an integrated methodology for radiological characterization. This methodology is based on advanced statistical processing and modelling, coupled with adapted and innovative analytical and measurement methods, with respect to sustainability and economic objectives. In order to achieve these objectives, the approaches will be then applied to common case studies in the form of Inter-laboratory comparisons on matrix representative reference samples and benchmarking. Assessment of the outcomes will be used for providing recommendations and guidance resulting in pre-standardization texts.

Introduction

Decommissioning and Dismantling (D&D) operations are strongly dependent on the facilities history and on the inventory of present radionuclides. These D&D projects produce large volumes of materials with different radioactivity concentration levels. The material management is a major technical, and societal challenge, strongly being subject to economical optimization. Under constrained environments new methodologies are necessary for a more accurate initial estimation of the contaminated materials, resulting waste volumes and timely planning [START_REF]R&D and innovation needs for decommissioning nuclear facilities[END_REF].

A new European project called INSIDER has been accepted by the European commission in 2017.IN-SIDER is coordinated by CEA and includes 18 European partners from 10 member states. Its objectives are to improve D&D operations of nuclear facilities with medium (MA) and high radioactivity (HA) levels and/or other constrained environments with respect to operational decommissioning efficiency, safety and costs, as well as confidence in safety of facility/site end-state, and the quantities and characteristics of resulting radioactive waste, in particular concerning subsequent storage and disposal end-points. This falls within the general objectives of demonstrating that the nuclear industry is fit for taking responsibility for retiring a range of facilities and sites without imposing undue economic, socio-political, or radiological burdens, as well as protecting natural resources and land for present and future generations. The radiological characterization of a facility prior to performing any D&D and remediation operations is a crucial step in the definition of a viable decommissioning scenario [START_REF] Boden | concrete waste reduction during PWR decommissioning, CETAMA seminar Sampling and characterization III[END_REF]. This scenario must be robust and optimized not only with regards to technical issues, produced waste amount and workers dosimetry, but also with regards to costs, deadlines and safety. Concerning the D&D of sites in a MA/HA environment, the initial stages of in-situ characterization are often very constrained by accessibility issues. Today, information can be gained from the reconstitution of a 3D vision of a facility or of the components to be characterized. However, this reconstitution is partly based on off-site laboratory analyses leading to technical and cost difficulties in the realization of representative samples. The objective of this project is, firstly, to improve the management of waste coming from nuclear sites under D&D operations, but especially to direct D&D operations, so that the production of waste is optimized on the basis of criteria such as minimization of generated volumes, limitation of over-categorized waste, limitation of generated waste and effluents as engaged doses on sites during the D&D operations, in a sustainable approach.

Technical objectives and structure of the project

The project aims at improving knowledge of the radiological state of nuclear facilities under decommissioning operations in a MA/HA environment and any other constrained environment by coupling three approaches:

1. Statistical and geostatistical sampling 2. Efficient in-situ measurements and, 3. Compatible and rapid analytical methods minimizing effluents in traditional radioactivity measurement laboratories (potentially in the future, in mobile laboratories). The heart of the technical innovative part of the project is to improve the sampling strategy taking into account different types of measurement data, with different but known levels of accuracy. [START_REF] Boden | concrete waste reduction during PWR decommissioning, CETAMA seminar Sampling and characterization III[END_REF], [START_REF] Desnoyers | Data analysis and sampling optimization for radiological characterization[END_REF] In-situ analysis techniques are of paramount importance for initial cartography. In constrained environment they must be complemented by more detailed off-site measurements. Realistic knowledge of their performance is important, without systematic optimization. The main final outcome of this project must be to set on methodological guides, support to pre-standardization commission's program, based on the inventory of techniques/methods and associated analytical performances, reference materials and their characteristics, and corresponding statistical processing tools. The INSIDER project addresses 3 use cases: a fuel cycle facility, a nuclear power plant and a post accidental remediation. Each one is representative for different kind of constraints. The methodologies will be applied to the 3 use cases in the form of benchmarking and an interlaboratory comparison (ILC), both proficiency testing [4] and/or method validation [5]. The ILC will deal with synthetic reference samples produced within the project by European metrology laboratories. The benchmark will deal directly on real, past or present, D&D selected worksite from SCK-CEN (Belgium), JRC Ispra (European Joint Research Center Italy) and CEA (France). Six technical axes are addressed by the 6 work-packages of the project, in accordance with the organization described in figure 1.

Figure 1: INSIDER PERT diagram

Conclusions

The INSIDER project is working towards recommendations and guidelines for improving decision making of the industrial implementation for decommissioning and remediation activities. The practical project implementation includes documenting, testing, verifying and assessing characterization methodologies for the selected test cases. Thereby, a key contributor is effective knowledge sharing within the scientific community. Final assessment of the outcome will strengthen the recommendations and guidance, and promoting and sharing European expertise through guide and pre-normative texts

Acknowledgment

The project INSIDER has received funding from the Euratom research and training program 2014-2018, under grant agreement No 755554.