Economic Growth, Greenhouse Gases and Environmental Regulation

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Economic Growth, Greenhouse Gases and

Environmental Regulation

Mémoire

Anne-Laure Jachym

Maîtrise en économique - avec mémoire

Maître ès arts (M.A.)

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Economic Growth, Greenhouse Gases and

Environmental Regulation

Mémoire

Anne-Laure Jachym

Sous la direction de:

Markus Herrmann, directeur de recherche Lucie Samson, codirectrice de recherche

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Résumé

Dans cette étude, nous cherchons à mesurer l’impact des émissions anthropogéniques de gaz à effet de serre sur la croissance économique dans un modèle de convergence conditionnelle. Nous nous intéressons au dioxyde de carbone, au méthane, au protoxyde d’azote et au groupe des "gaz F", ainsi qu’à l’effet de la somme de ces polluants, c’est-à-dire la quasi totalité des gaz à effet de serre. Notre échantillon est composé de 81 pays, avec une variété de niveaux de revenu par habitant, entre 1993 et 2012. Nous définissons deux sous-périodes de 10 ans et nous régressons la croissance économique sur la croissance des émissions de chaque polluant séparément, sur le PIB de la première année de la période et sur plusieurs variables de contrôle. Face au risque de biais de causalité inversée entre les émissions de pollution et la croissance économique, et entre l’investissement et la croissance économique, nous décidons d’utiliser les données passées comme variables instrumentales. Plus précisément, les données de la première année de la période sont utilisées comme instruments pour la pollution et l’investissement. Mis à part le CO2, nous trouvons qu’aucun des gaz à effet de serre n’a d’impact significatif sur la

croissance économique. La croissance des émissions de CO2 semble avoir un impact positif sur

la croissance économique. Cet impact apparaît moins fort sur la seconde période (2003-2012) que sur la première (1993-2002). De plus, il semble plus fort pour la moitié la plus riche des pays de notre échantillon.

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Abstract

In this study, we investigate the effect of anthropogenic greenhouse gas emissions on economic growth in a conditional convergence framework. We look at carbon dioxide, methane, nitrous oxide and the group of "F gases", as well as the effect of the sum of these pollutants, i.e. almost all greenhouse gases. Our sample is composed of 81 countries with a variety of per capita income levels and covers the period between 1993 and 2012. We define two ten-year periods and regress economic growth on emissions growth of each pollutant separately, on the first-year GDP of the period and on several control variables. To address the issue of inverse causality bias between pollution emissions and economic growth, as between investment and economic growth, we use an instrumental variable methodology. We use past data to instrument pollution and investment. More precisely, the data of the first year of the period are used as instruments. We find that, except for CO2, greenhouse gas emissions growth does

not generate economic growth. CO2 emissions growth has a positive impact on economic

growth. Interestingly, this impact is less pronounced between 2003 and 2012, as compared to the 1993-2002 period. In addition, the impact of CO2 emissions growth is stronger in the

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List of Tables

3.1 Countries of our sample categorized according to their GNI . . . 14

4.1 Countries of our sample divided in same size groups according to their wealth . 22

5.1 Logarithmic regressions (with instrumental variables) of economic growth on

several GHG emissions growth (1/2) . . . 25

several GHG emissions growth (2/2) . . . 26

total GHG emissions growth according to wealth . . . 27

CO2 and CH4 emissions growth according to wealth . . . 28 5.5 Logarithmic regressions (with instrumental variables) of economic growth on

N2O and F-gases emissions growth according to wealth. . . 29 5.6 Logarithmic regressions (with instrumental variables) of economic growth on

total GHG emissions growth according to period . . . 30

CO2 and CH4 emissions growth according to period . . . 31 5.8 Logarithmic regressions (with instrumental variables) of economic growth on

N2O and F-gases emissions growth according to period . . . 32 A.1 Logarithmic OLS regressions of economic growth on several GHG emissions

growth (1/2) . . . 37

A.2 Logarithmic OLS regressions of economic growth on several GHG emissions

growth (2/2) . . . 38

A.3 Logarithmic OLS regressions of economic growth on total GHG emissions growth

according to wealth. . . 39

A.4 Logarithmic OLS regressions of economic growth on CO2 and CH4 emissions

growth according to wealth . . . 40

A.5 Logarithmic OLS regressions of economic growth on N2O and F-gases emissions

growth according to wealth . . . 41

A.6 Logarithmic OLS regressions of economic growth on total GHG emissions growth

according to period . . . 42

A.7 Logarithmic OLS regressions of economic growth on CO2 and CH4 emissions

growth according to period . . . 43

A.8 Logarithmic OLS regressions of economic growth on N2O and F-gases emissions

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B.1 Logarithmic regression of economic growth on total GHG emissions growth

when controlling for latitude. . . 45

B.2 Logarithmic regression of economic growth on CO2 and CH4 emissions growth

when controlling for latitude. . . 46

B.3 Logarithmic regression of economic growth on N2O and F-gases emissions growth

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List of Figures

3.1 Total emissions of the countries in our sample . . . 17

3.2 Decomposition of each pollutant total emissions, according to the wealth of the

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Remerciements

Je tiens à remercier mon directeur, Markus Herrmann, et ma co-directrice, Lucie Samson, qui m’ont fait profiter de leurs connaissances, leur rigueur et leur enthousiasme pour la recherche. Je te remercie tout particulièrement Markus de m’avoir épaulée quand des difficultés per-sonnelles ont fait surface. Ton écoute, ta bienveillance et tes encouragements m’ont permis d’avancer malgré tout et de voir ce projet se terminer aujourd’hui. Je te remercie également, Lucie, pour ta patience et ta bienveillance, surtout dans ces moments où j’en avais besoin. Je tiens également à remercier Carlos Ordas Criado d’avoir évalué mon mémoire et pour ses commentaires pertinents.

Je remercie plus généralement le département d’économique de l’Université Laval de fournir un endroit si chaleureux à ses étudiants. Sentir qu’autant de bureaux m’étaient ouverts a été un atout considérable dans la réussite de ma maîtrise. Plus particulièrement, je remercie Arthur Silve, Bernard Fortin et Vincent Boucher pour leurs conseils avisés et leur écoute dans la recherche d’un doctorat.

J’ai une pensée pour Kevin Moran et Renaud Dorandeu qui, en plus d’être d’excellents pro-fesseurs, m’ont donné un sacré coup de pouce dans la résolution de problèmes administratifs au moment de mon arrivée au Québec. Sans eux et la contribution précieuse de Josée Desgagnées, je n’aurais certainement même pas pu entamer ce travail.

J’ai également bénéficié du soutien de mes amis, sans qui cette expérience aurait été bien plus difficile et bien moins agréable. Imène, Roxane, Louis-Simon, Flavienne, Sébastien, Louis, Caroline et Jérôme, je vous remercie pour votre écoute, votre soutien et toutes vos idées pour me faire changer les miennes quand j’en avais besoin. Un grand merci aussi à mes amis en France, Marie, Nicolas, Mathieu, Mathis, à ma sœur Audrey et à mon frère, Raphaël. Si notre lien est moins fréquent qu’avant, il n’en est pas moins fort. Je sais que vous êtes toujours là pour moi. Merci de pardonner mes silences et de continuer à m’accueillir comme si nous nous étions vus la veille. Vous ne pouvez imaginer comme cela est précieux pour moi.

Je remercie bien sûr mes parents, Marc et Mathilde, dont le soutien n’a d’égal que leur amour. Je vous remercie en particulier pour la fierté que vous m’adressez, et qui ne semble pas ébranlée par mes difficultés et mes éventuels échecs. Comprendre que votre amour est inconditionnel et

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que votre fierté n’est attachée qu’à ce que je suis plutôt qu’à ce que je réussis, est la meilleure des thérapies contre le manque de confiance en soi.

Enfin, je pense bien sûr à Nicolas, mon conjoint, qui n’a jamais cessé de croire en moi. Tu étais là, tout le temps. Je te remercie pour ta patience exceptionnelle, ton support infini et ta douce affection. Je serais fière de pouvoir te rendre ne serait-ce que la moitié de ce que tu me donnes.

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Introduction

This study aims to answer the following question: "How much would pollution facilitate economic growth?", and thus ultimately "What is the impact of environmental regulation on economic growth?". Measuring this impact would make it possible to adequately calibrate these regulations that are developing in many countries. This is the case, for example, of the "Regional Greenhouse Gas Initiative", a market with pollution permits in North America, or of the carbon market in Europe. For this purpose, this project combines environmental economics and macroeconomics.

Traditionally, environmental economics uses microeconomic approaches to study environmen-tal externalities that generate market failures. Based on this research, environmenenvironmen-tal regu-lations have been developed to "internalize" these externalities, like the carbon markets we cite above. In the 1970s, natural resource economics begins to use a macroeconomic ap-proach: economists question the ability of our economies to continue to grow while drawing on non-renewable resources (Stiglitz 1974). Then, with the intensification of globalization, they question the effect of international trade on the environment (Pethig 1976andCopeland and Taylor 1994). Intuitively, pollution emissions should be considered as an undesirable joint product. In these models, yet, pollution is considered as an input in the production process. This modeling allows to represent a compromise between pollution and the other production factors: choosing a process that generates little pollution requires to spend more on traditional production factors, like labor or capital. Thus, at a national level, limiting the level of emit-ted pollution because of regulations would limit production. These theoretical elements have lead us to think that pollution emissions would be a relevant growth factor. The conditional convergence literature focuses on growth factors explaining the different levels of observed economic growth among countries (Barro 1996). The main idea behind this literature is that, if all countries had the same level of each growth factor, their initial level of per capita GDP would negatively determine their rate of economic growth. In other words, if growth factors were the same for all countries, they would converge towards the same level of per capita GDP. In this project, we want to study if pollution emissions are one of these growth factors. Determining if pollution emissions are growth factors and, when it is relevant, measuring the scale of the effect, will ultimately allow to measure the cost of implementing environmental

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regulations in terms of lost output. The underlying idea is that regulations on greenhouse gas emissions result from a compromise between present well-being and future well-being.1 _Our

study sheds light only on one part of this compromise: the renunciation of material well-being induced by regulation. It also delivers insights in the event that developing countries would ask developed countries for financial compensation so that they enforce an environmental regulation. Indeed, in this case, the amount of the compensation should be linked to the renunciation cost in terms of economic growth implied by the regulation. It is likely that such a dispute will arise in the coming years because the fight against global warming must be led by a large number of countries to be successful. Moreover, industrialized economies with stronger governments and wealthier populations, who are more concerned about the environment than those with a poorer population, are adopting environmental regulations more quickly than developing economies. Thus, developed countries are putting pressure on developing countries to put in place regulations despite their lower standard of living. The latter are asking for financial compensation to offset the production losses that would be generated by these regulations, arguing that the developed countries have polluted without limits until now. To conclude, it is necessary to know the cost of environmental regulation in terms of economic growth, on one hand, to calibrate the regulation properly and, on the other hand, to calculate a fair compensation for implementing a regulation in developing countries. To answer this question, we start with a literature review in which we first present the articles on which our study builds, as well as empirical papers that try to answer the same question with a different methodology (chapter 1). Secondly, we present our model with pollution emissions in the production process (chapter 2). Thirdly, we describe our data (chapter 3). Fourthly, we present our methodology (chapter 4). Finally, we present and discuss our results (chapter 5).

1

Greenhouse gases are stock-pollutants. This means that they do not have an immediate effect on the environmental quality, in constrast to flow-pollutants. For flow-pollutants, regulations result form another

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Chapter 1

Literature Review

This literature review builds on two main articles, which are relevant for our modeling ap-proach. First, we present "North-South Trade and the Environment" (Copeland and Taylor 1994), from which theoretical elements are borrowed to develop our model in chapter2. Sec-ondly, we review "Determinants of economic growth: A cross-country empirical study" (Barro 1996) that helps us to define our empirical methodology in chapter 4. Finally, we present recent empirical studies showing similarities with our research.

1.1 Pollution in the production process

Copeland and Taylor (1994) work on the relation between international trade and the envi-ronment. As they need to integrate pollution in the production process, their work is really helpful for us. In particular, they model pollution emissions as an input of the production function in a manner that appears intuitive. We choose to present here one of their articles in which they analyze the impact of trade openness on pollution emissions.

Economists generally accept that international trade increases global output. At the same time, as environmental services are superior goods, it may occur that economic growth could encourage cleaner production and generate a decrease in pollution emissions. However, even when considering this reasoning as true, Copeland and Taylor (1994) admit that international trade could lead to an increase of pollution. Indeed, they model an increase in production that can enhance pollution in an open market, even with assumptions under which, in autarky, an equivalent increase of production reduces pollution.

They obtain this surprising result in a static general equilibrium model framework with two countries, which differ only by their human capital endowments, and an infinity of goods. The production process of each good offers the possibility to substitute labor against pollution discharge. This is easily represented by production functions in which pollution emissions are an input. Additionally, the relative productivity of both inputs differs among goods.

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Both countries have access to the same technology, which means that they face the same production functions. However, North and South countries are represented respectively by high and low human capital endowments. As workers are paid their marginal productivity and as human capital contributes to their productivity, they are richer in the North. Furthermore, governments take optimal decisions accounting for the preferences of domestic consumers, who are willing to pay for environmental services. Therefore, both governments regulate pollution by enforcing a tax on pollution emissions. Because the environmental services are superior goods, and as there is a wealth difference, the tax is higher in the North than in the South. In autarky, the tax on pollution has the effect of reducing the pollution emitted by encouraging the substitution of pollution discharge by labor. However, in the case of free trade, the production of “dirty” goods, which are the ones with high productivity of pollution discharge, is relocated from the North to the South benefiting from the lower tax level there, while, the North specializes in cleaner goods. As a consequence, an increase of output in the South has a negative impact on pollution emissions. Indeed, because southern people become wealthier, their demand for environmental services is stronger and the government increases the environmental tax. This changes the composition of the economy in favor of cleaner goods and cleaner production processes. This composition effect dominates the initial scale effect. On the contrary, when there is an output increase in the North and the government increases the tax, the dirty production is relocated to the South. The scale effect is not compensated by the composition effect and pollution emissions increase overall.

As this model is static, it does not allow to analyze growth. Nonetheless, as it gives us the intuition that pollution discharge can be considered as a relevant input, it encourages us to suppose that pollution discharge could be a growth factor. In what follows we concentrate on the article by Barro (1996): Determinants of economic growth: A cross-country empirical study, that present a framework that analyses growth.

1.2 The conditional convergence framework

Barro (1996) notices a paradoxal situation between theoretical and empirical research on eco-nomic growth. Since the 80s, endogenous growth models are trendy because they explain growth with endogenous forces of the economy. In particular, this literature suggests that technological progress is the only source of long-term growth. Yet, the empirical literature on economic growth, although expanding because benefiting from this rise in popularity, contin-ues to use the framework inspired by the classical growth model. In fact, this older model suggests that production per capita in poorer economies grows faster than in richer ones, but only if holding constant every other growth factor than initial wealth. This prediction is called: conditional convergence. The pure convergence is a result of the Solow model (Solow 1956) ac-cording to which decreasing returns on capital should gradually slow down per capita economic growth up to a steady-state level. As this theory does not mention country-specific factors,

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it implies that countries should converge in wealth. However, when looking to the data, this worldwide convergence of countries does not appear to take place. As a consequence, the conditional convergence raised a lot of interest, because it confirms the original intuition of Solow, but at the same time, it explains why we can still observe poor countries with low growth rates. These countries still have a low steady-state level of per capita income because some factors prevent their growth from taking off. These factors can be political stability, education level, etc...

Barro (1996) relies on an already extensive literature that supports the conditional convergence concept. Nonetheless, there is still a need to determine which variables are the best to condition economic growth and it is what Barro (1996) tries to do. Three periods are under study: 1965-1974, 1975-1984 and 1995-1990. Over these periods, regressions of economic growth on the logarithm of GDP in the first year of the considered period and other independent variables are performed. Data from the period between 1960 and 1964 are used as instruments to realize three-stages-least-square estimations. By instrumenting all the variables suspected to be influenced by the dependent variable, with their past value, the inverse causality bias is ruled out. Barro (1996) finds that initial GDP has the effect of reducing economic growth in the following period, which validates the conditional convergence hypothesis. Moreover, he highlights a variety of variables that could be factors of economic growth divergence.

First, Barro (1996) finds that a high rate of population growth negatively impacts economic growth. He interprets this effect as purely mechanical as economic growth is measured per capita. Secondly, he shows that the investment to GDP ratio, when properly instrumented, has no significant effect. On the contrary, the overall maintenance of the rule of law and the import-export ratio, are shown to be beneficial for economic growth. Yet, as he finds in another regression that these variables improve the investment ratio, he concludes that some kinds of investment do have a positive impact on growth. However, “if investment is higher for given values of the policy instruments—perhaps because of variations in thriftiness across economies that lack perfect capital mobility—then the positive effect on growth is weak”. Finally, his findings on human capital effect are different according to the proxy used. Male education has no significant impact on economic growth at the primary level, but it has a positive impact at the higher levels. Therefore, he concludes that the primary level should have an indirect impact, as it is a prerequisite for the upper levels. Interestingly, he adds a crossed variable with male upper level schooling and initial GDP and this variable appears to have a negative effect. Barro explains that it “implies that more years of school raise the sensitivity of growth to the starting level of GDP” and he links this result to the hypothesis that schooling could raise the ability to adopt high technologies. On the contrary, he does not find any significant impact of the female schooling, even when omitting the fertility rate in the regression. This means that female education does not appear to have an impact on economic growth during the period studied, even considering the potential negative effect of their education on the

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fertility rate.

With this methodology, it is easy to add any variable to test if it has an impact on economic growth, including pollution emissions. However, this requires panel data on a long period and on a big number of countries with a variety of income levels, whereas data on pollution emissions were not massively collected before the 90s. This is why adding pollution emissions in this framework was not possible until recently. Nonetheless, as the relationship between economic growth and pollution has been highlighted in the theoretical literature, economists have found other empirical strategies to study how pollution emissions interact with economic growth.

1.3 Recent empirical studies

In what follows, we concentrate on two recent empirical publications that use another frame-work than the conditional convergence one, to analyze pollution emissions and economic growth.

Lean and Smyth (2010) identifies two branches in the recent literature on the relationship between polluting emissions and economic growth. The first one looks directly at the relation between the two variables, whereas the second one focuses on the role played by the energy consumption which seems to be the major variable which links pollution to economic growth. Both branches are concerned by inverse causality bias and generally use the Granger test to address this issue. An important example of the second branch of this literature is the study of Al-Mulali and Binti Che Sab (2012), even if it is posterior to Lean and Smyth (2010). Al-Mulali and Binti Che Sab (2012) looks at the effect of energy consumption and carbon emissions on economic growth and financial development in a sample of Sub Saharan African economies. To do so, they use panel data of thirty countries within the region from 1980 to 2008. They test for cointegration and find a long-run relationship between CO2 emissions,

total primary energy consumption, GDP growth and financial development. Then, with a Granger causality test, they show a bidirectional causality: CO2 emissions and total primary

energy consumption have a positive effect on GDP growth and financial development, but themselves have a positive impact on CO2 emissions and total primary energy consumption.

Lean and Smyth (2010) points out that this kind of literature probably suffers from an omitted variable bias, as studies are done in a bi-variate framework. Therefore, they want to analyze the relation between energy consumption, carbon emission and economic growth “within a Granger causality multivariate framework”. This means that their model links the three variables together rather than analyzing them in pairs. Their analysis focuses on five countries from the Association of South East Asian Nations (ASEAN) over the period from 1980 to 2006. These countries are Indonesia, Malaysia, Philippines, Singapore and Thailand. They find that

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there is a positive relationship between electricity consumption and CO2 emissions, whereas

income and CO2 emissions are linked by a non-linear relationship. According to the Granger

tests, the causality goes from electricity consumption and CO2 emissions towards economic

growth. Even with a multivariate framework, this study still suffers from an omitted variable bias, since it includes only three variables.

As data on pollution are now available in a big number of countries and for a long time period, we want to study the contribution of pollution emissions of economic growth within Barro’s framework, which includes many more control variables.

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Chapter 2

The Model

In this chapter, we develop an exogenous growth model, inspired by the Solow model (Solow 1956) and incorporate pollution emitted during the production process as modeled by Copeland and Taylor (2003).

In particular, production generates pollution, as described by the following equations : Yt= F (Kt, Lt) = KtβL

(1−β)

t with 0 < β < 1, (2.1)

Xt= (1 − θt)Yt with 0 ≤ θt≤ 1, (2.2)

Zt= φ(θt)Yt with φ0(θt) ≤ 0. (2.3)

Yt is the gross output level at time t (t = 1, 2, ..., T ); it is determined by the gross production

function F (.). As indicated in (2.1), we assume F (.) is a Cobb-Douglas function which depends on capital (Kt) and labor (Lt) at time t. Ztis the flow of pollution generated by the production

process. It can be reduced if the firm allocates a part θt > 0of its gross output to pollution

abatement.1 _{If it does, then the resulting net production (X}

t) is strictly inferior to the gross

production Yt; if it does not (θt= 0), then net and gross production are equal. The function

φ() characterizes how much the effort θt impacts the pollution flow. The measurement of

pollution is normalized in a way that, if no effort is made to reduce pollution, pollution is equal to production. This means that the function φ() should respect φ(0) = 1. Moreover, we want that φ(1) = 0, because, if the firm decides to assign its full output to pollution abatement, there should be no resulting pollution.2

Copeland and Taylor (2003) show that if the functional form chosen for φ(θt) is φ(θt) =

(1 − θt)1/α with 0 < α < 1, then it is possible to rewrite a net production function with

1

Our model remains consistent with an interpretation of pollution reduction at source. Indeed, as we choose a Cobb-Douglas production function, which is homogeneous of degree 1, the model can also be interpreted as if the same proportion θtof each input is allocated to make the production process less polluting.

2_{Similarly, if the totality of inputs is assigned to reduce pollution at source, none is allocated to produce}

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pollution as a production factor. This function has the following form :

Xt= ZtαF (Kt, Lt)1−α with Zt≤ Yt. (2.4)

The pollution flow has thus moved from the status of output to the one of input. This rearrangement highlights how the emission of pollution during the production process can be interpreted as the use of environmental services by the polluting industry.

To analyze the impact of pollution on economic growth, we want to develop a simple growth model in this production environment.

We begin by formulating the model in terms of per capita terms. By substituting equation (2.1) into (2.4) and simplifying to obtain the net production per capita, we find :

xt= Xt Lt = Zt Lt α Kt Lt β(1−α) = (zt)α(kt)β(1−α). (2.5)

By substituting equation (2.1) into equation (2.3), we find the pollution per capita:

zt= Zt Lt = (1 − θt)1/α Kt Lt β = (1 − θt)1/αkβt. (2.6)

Then, by substituting (2.6) into (2.5), we show that:

xt= (1 − θt)ktβ. (2.7)

Equations (2.6) and (2.7) show that ktand some parameters determine the evolution of ztand

xt.

To keep the model simple, we define an exogenous saving rate of the economy by fixing s. Similarly, we set the depreciation rate of the capital stock (δ) at zero and we set at a fixed value the population growth rate (n) and the part that the firm can decide to allocate to pollution abatement (θt):

s = ¯s, n = ¯n, δ = 0 and θt= ¯θ.

As a consequence of the fixation of these parameters, kt is exogenous. Therefore, according

to equations (2.6) and (2.7), the growth of pollution and economic growth are exogenous as well. Yet, we need to determine the path followed by each aggregate variable : Xt, Kt, Ltand

Zt and see if we can find a steady state which is characterized by these variables growing at

a constant rate.

Following our working hypothesis, we already know the path of Lt :

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Furthermore, as we assume that δ = 0, we have: ˙

Kt= sXt (2.9)

In order to determine the evolution of the per capita capital stock, we calculate: ˙ Kt Lt = ˙kt= ˙ Kt Lt −Kt L2 t ˙ Lt ⇔ k˙t= sXt Lt −Kt Lt ˙ Lt Lt ⇔ k˙t= s Xt Lt −Kt Lt n ⇔ k˙t= sxt− nkt (2.10)

Equation (2.10) is the fundamental equation of capital accumulation in the Solow model. We can rewrite this equation as a function of pollution and capital by substituting xtin (2.10) by

(2.5):

˙

kt= sztαk β(1−α)

t − nkt. (2.11)

We are now looking for a steady state. We try to find an expression for the capital stock per capita when ˙kt= 0. In (2.11), this condition implies that:

nk∗= sz∗αk∗β(1−α) ⇔ k∗= sz ∗α n 1/(1−β(1−α)) , (2.12)

with k∗ _{the capital stock per capita at the steady state and z}∗ _{the pollution flow per capita at}

the steady state.

By substituting (2.12) into (2.6), we obtain the steady-state level of pollution emission per capita: z∗= (1 − θ)1/α sz ∗α n β/(1−β(1−α)) ⇔ z∗(1−β)/1−β(1−α)= (1 − θ)1/αs n β/(1−β(1−α)) ⇔ z∗= (1 − θ) 1−β(1−α) α(1−β) s n _(1−β)β . (2.13)

Then, by substituting (2.13) into (2.12), we obtain the steady-state level of capital per capita:

k∗ =   s(1 − θ) α(1−β(1−α)) α(1−β) s n _(1−β)αβ n   1/(1−β(1−α))

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⇔ k∗ = s(1 − θ) n

1/(1−β)

. (2.14)

Finally, we substitute (2.13) and (2.14) into (2.5) to get the steady-state level of net production per capita (x∗): x∗ = (1 − θ) 1−β(1−α) α(1−β) s n _(1−β)β α s(1 − θ) n 1/(1−β)!β(1−α) ⇔ x∗ = (1 − θ)1/(1−β)s n β/(1−β) . (2.15)

Equations (2.13), (2.14) and (2.15) describe the balanced growth path. Defining ϑ∗ _{∈ {x}∗_{; z}∗_{; k∗}}_,

we can calculate according to these equations: ∂ϑ∗ ∂θ < 0; ∂ϑ∗ ∂s > 0 and ∂ϑ∗ ∂n < 0

As in the Solow model, the economy converges towards a steady state with net production per capita x∗ and pollution emissions per capita z∗ that are higher for a higher investment rate s and lower for a higher population growth rate n. Additionally, firms can decide to invest a part θ of their output in pollution abatement. Net production per capita and pollution emissions per capita are lower for a higher value of θ.

Of course, no firm would choose θt > 0 if the use of environmental services is unregulated.

However, it is easy to add environmental regulation into this model: since pollution is modeled as an input, we would only have to add a price for this input. This is how Copeland and Taylor (2003) obtain an equilibrium with firms which are making an effort to reduce pollution. Nonetheless, it is not useful for us to improve our model in this direction because we do not have aggregate data on environmental regulation.

As we will describe in chapter 3, we have country-level data on production, pollution and other control variables. To estimate the model, we conjecture that the pollution we observe is the result of the choice made by polluting industries when facing environmental regulation. In other words, if we observe that pollution is high we consider that it is the result of a low θt

which is itself the consequence of a weak regulation. The functional form of this hypothesis is the following:

ln(1 − θt) = Ω ln(zt) with Ω > 0. (2.16)

To estimate the model, we need to test an equation which decribes the production process but which is not characterized by the steady state, like equation (2.7) which highlights that the net production per capita is determined by the portion of the gross production allocated

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to pollution abatement and the capital stock per capita. We transform it to incorporate our new hypothesis (2.16):

xt= (1 − θt)ktβ

⇔ ln x_t= ln(1 − θt) + β ln(kt)

⇔ ln x_t= Ω ln(zt) + β ln(kt). (2.17)

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Chapter 3

Data Description

Most of our data comes from the “World Development Indicators” (WDI) database of the World Bank. It presents global development data compiled from officially recognized interna-tional sources. In particular, it contains a variety of pollution indicators, such as greenhouse gas emissions. In addition, because of multiple missing data on human capital in this database, we rely on the human capital indicator of the Penn World Tables, a data set based on national accounts, created and updated by academics of the University of California, Davis and the University of Groningen, The Netherlands. We also use the countries’ latitude that can be found in the “World Factbook” database of the CIA.

Our data sample consists of annual observations at the national level. In order to carry out our empirical analysis, we select a 20-year period (1993-2012) and a group of 81 countries for which there are no missing observations. This choice results from a compromise between the number of countries in our sample and the duration of the period studied. Since our final sample is characterized by a great variety of national income levels (see table3.1), we feel that this is a good selection.

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Table 3.1: Countries of our sample categorized according to their GNI

High-income economies

with GNI per capita in 2012 of 12,276 USD (constant 2010) or more

Australia, Austria, Belgium, Brunei, Canada, Chile, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Lux-embourg, Netherlands, New Zealand, Norway, Poland, Portugal, Spain, Sweden, Switzerland, United Kingdom, United States, Uruguay

Upper-middle-income economies with GNI per capita in 2012 be-tween 3,976 and 12,275 USD (con-stant 2010)

Algeria, Argentina, Belize, Botswana, Brazil, Bul-garia, China, Costa Rica, Dominican Republic, Ecuador, Gabon, Kazakhstan, Malaysia, Mexico, Namibia, Panama, Paraguay, Peru, Romania, Russia, Thailand, Turkey

Lower-middle-income economies with GNI per capita in 2012 between 1,006 and 3,975 USD (constant 2010)

Armenia, Cameroon, El Salvador, Eswatini, Guatemala, Honduras, India, Indonesia, Jordan, Kenya, Maurita-nia, Morocco, Nigeria, Pakistan, Philippines, Senegal, Sri Lanka, Tajikistan, Tunisia, Ukraine

Low-income economies

with GNI per capita in 2012 of 1,025 USD (constant 2010) or less

Bangladesh, Benin, Burkina Faso, Cambodia, Madagas-car, Mali, Mozambique, Rwanda, Togo, Uganda

The GNI thresholds are from the World Bank classification of 2010 (World Bank Data Help Desk)

3.1 Pollution Indicators

We retain five indicators among the 42 pollution emissions indicators available in the WDI data set. The first four are the anthropogenic emissions of the main greenhouse gases: carbon dioxide, methane, nitrous oxide and a group of fluorinated gases, which are the main emissions responsible for climate change. The last indicator is called "Total greenhouse gas emissions". However, it does not match perfectly with the sum of the previous indicators, as it does not use the same definition of CO2 emissions (see3.1.1 and 3.1.5).

To compare these indicators, we need to express them in the same unit. This means that a CO2 equivalent metric has to be defined to quantify the other emissions. Indeed, as the

CO2 is the most important greenhouse gas, both in terms of quantity in the atmosphere and

impact on global warming, it is generally used as the reference. The World Bank retains the global warming potential metric on a 100-year time horizon (GWP100). This metric has been defined by the second assessment report of the Intergovernmental Panel on Climate Change (IPCC) and is generally used in environmental studies because it was used to define pollution reduction targets of the Kyoto protocol in 1997. It is an index of the total energy added to the climate system by a specific component, relative to that added by CO2. The IPCC

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due to the release of a specific gas. It is to be noted that there is no objective criterion to choose a time horizon on which to calculate a climate change metrics. A long time horizon would systematically give a stronger impact to gases which remain for a longer time in the atmosphere (Myhre et al. 2013).

These indicators are listed below. In what follows, we look at a description of their trend during the period examined.

3.1.1 Carbon dioxide (CO2)

We consider carbon dioxide (CO2) emissions relating to the consumption of solid, liquid, and

gas fossil fuels, from gas flaring and from cement production. We have to specify cement pro-duction because its CO2 emissions do not only come from the energy needed to make cement,

but also relate to the phenomenon of decarbonation of the rock during heating.1 _{Therefore, it}

is necessary to consider these emissions separately. Gas flare is a facility used to burn gas in order to control pressure in industrial plants, like petroleum refineries. This definition excludes emissions from land use, such as deforestation.

3.1.2 Methane (CH4)

We consider methane (CH4) emissions stemming from human activities. Anthropogenic CH4

sources include, in order of their impact on global warming: fossil fuel exploitation (extrac-tion, storage, transforma(extrac-tion, transportation and use in coal mining, gas and oil industries), decomposition of biomass in landfills, livestock manure and waste waters, food fermentation in rumens of ruminant livestock and rice paddies agriculture (Myhre et al. 2013). CH4 has a

relative short atmospheric lifetime of 12 years. It is the major greenhouse gas after CO2.

The indicator is expressed in kt of CO2 equivalent.

3.1.3 Nitrous oxide (N2O)

We consider nitrous oxide (N2O) emissions from agricultural biomass burning, industrial

ac-tivities, and livestock management. N2O is a powerful greenhouse gas, with an estimated

atmospheric lifetime of 114 years. The per kilogram global warming potential of nitrous oxide is nearly 310 times that of carbon dioxide within 100 years.

The indicator is expressed in kt of CO2 equivalent.

1_{When heated, limestone, the first component of cement, releases CO}

2. Since cement manufacturing

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3.1.4 F-gases

F-gases are composed of hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulfur hexafluoride (SF6). By-product emissions of these three categories of gases are also included

in the indicator. HFCs and PFCs are both substitutes for chlorofluorocarbons, which parties of the Montreal Protocol (1989) have gradually phased-out in production and consumption (Government of Canada). The first ones are mainly used in refrigeration and semiconductor manufacturing and the second ones only in semiconductors manufacturing. SF6 is largely used

to insulate high-voltage electric power equipment. The indicator is expressed in kt of CO2 equivalent.

3.1.5 Greenhouse gases (GHG)

Total greenhouse gases emissions are composed of: CO2 emissions, excluding short-cycle

biomass burning (such as agricultural waste burning and Savannah burning), but including other biomass burning (such as forest fires, post-burn decay, peat fires and decay of drained peatlands), all anthropogenic methane (CH4) sources, nitrous oxide (N2O) sources and F-gases

(HFCs, PFCs and SF6).

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3.1.6 Trend of pollution in our sample

In this part, we analyze the general trends of the five pollution indicators described above. Figure 3.1: Total emissions of the countries in our sample

1×10+7 2×10+7 3×10+7 4×10+7 1995 2000 2005 2010 Year kt of CO 2 equiv alent of GHG of CO2 of CH4 of N2O of F−gases 2008 crisis

European union emissions trading scheme

Figure3.1shows a global increase of carbon dioxide (CO2) and total GHG emissions between

1993 and 2012. During the same period, methane (CH4) has slightly increased and nitrous

oxide (N2O) has remained stable. On the contrary, F-gases emissions were very volatile.

However, these general patterns hide an essential phenomenon: the trends within groups of countries, defined according to their level of wealth, are highly dissimilar. This is why we de-cide to treat rich and poor countries separately in Figure 3.2. Since we need large subsamples to be able to realize further analysis, we decide to form two groups of equal size. The wealth criteria is the GDP per capita in 2012, the last year of the period studied. The implications of these choices are discussed in chapter 4.

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Figure 3.2: Decomposition of each pollutant total emissions, according to the wealth of the emitting countries 1×10+7 2×10+7 3×10+7 4×10+7 1995 2000 2005 2010 Year kt of CO 2 equiv alent GHG emissions 1×10+7 2×10+7 1995 2000 2005 2010 Year kt of CO 2 equiv alent CO2 emissions 3×10+6 4×10+6 5×10+6 6×10+6 1995 2000 2005 2010 Year kt of CO 2 equiv alent CH4 emissions 1.0×10+6 1.5×10+6 2.0×10+6 2.5×10+6 1995 2000 2005 2010 Year kt of CO 2 equiv alent N2O emissions 1×10+6 2×10+6 3×10+6 4×10+6 5×10+6 6×10+6 1995 2000 2005 2010 Year kt of CO 2 equiv alent F−gases emissions

All countries of the sample richer countries

poorer countries

Note: The sample is divided into two groups of same size: the richer and the poorer countries. Therefore, the threshold does not necessarily match with usual decompositions. The wealth is mesured by GDP per capita in 2012.

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When plotting total GHG emissions, CO2 and CH4 on subsamples resulting from a

decom-position by wealth, we observe a stagnation for richer countries and an increase for poorer countries. N2O emissions of richer countries have even decreased over the period, whereas they

have increased for poorer countries. The volatility of F-gases seems to follow the same pattern for both groups until 2005. After that year, it is difficult to distinguish any common pattern. It is noteworthy to outline that, after 2009, the emissions of these gases have increased in richer countries, while they are stable, at a lower level, in poorer countries, which is at the opposite of what we can observe for every other gases emissions under review.

3.2 Macroeconomic aggregate and control variables

We use gross domestic product (GDP) per capita in constant 2010 U.S. dollars to calculate economic growth, which will later become our dependent variable.

To interpret the coefficient of the pollution indicators as the effect of pollution emissions growth on GDP growth, control variables need to be added into the regression. We choose the usual variables used in the literature, in particular in Barro (1996). They are listed below.

• Gross fixed capital formation (formerly gross domestic fixed investment) includes land improvements (fences, ditches, drains, and so on), purchases in plant, machinery and equipment purchases, as well as the construction of infrastructure (such as roads, railways, schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings).

• Total fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with age-specific fertility rates of the specified year.

• Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.

• The human capital index is based on the average number of years of schooling and an assumed rate of return to education, based on Mincer equation estimates 2 ₍_Mincer 1974). The average number of years of schooling are issued from two different series, i.e. Barro and Lee (2013) and Cohen and Leker (2014).

2

“The [Mincer] “basic” earnings function method [. . . ] involves the fitting of a semi-log ordinary least squares regression using the natural logarithm of earnings as the dependent variable, and years of schooling

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Chapter 4

Empirical methodology

Our goal is to determine the impact of GHG emissions on economic growth between 1993 and 2012. This chapter describes our methodology.

4.1 Model specification

In chapter 2, we defined equation 2.17 as the equation we wanted to test. For convenience, we recall it here:

ln xt= Ω ln(zt) + β ln(kt),

with xt the net production per capita, ztthe pollution flow per capita and ktthe capital stock

per capita.

As equation 2.17is in absolute terms, we take the difference in the logarithm to obtain it in terms of growth. We also need to specify what is our unit of time and to add the control variables we defined in section 3.2.

To assure sufficient variability in our data, we need to define a quite long unit of time. We estimate that a 10-year period is sufficient to analyze growth, so we divide our sample of 20 years into two 10-year samples (1993-2002 and 2003-2012). Consequently, each country appears in the final sample twice. This allows us to double the size of our sample. As we want to analyze our results in annual growth, which is more familiar than the growth over 10 years, we take the arithmetic mean of each variable’s annual growth.

Initially, we wanted to estimate the capital stock (kt), but we did not have data on capital

formation on a long enough period to do so (we would have need at least 10 years of data before the beginning of the period). Therefore, we use gross fixed capital formation (gross investment) on GDP, which could be called productive use of GDP ratio, as a control variable. We calculate the average of this variable over each period. The standard of living variables (fertility rate, life expectancy and human capital) are considered as having an impact on

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economic growth in the long run only. For these variables we thus rely on the observations of the first year in our data sets. They are indicated as “starting periods” in our tables. We also include the first-year data of the per capita GDP to test the convergence hypothesis.

Our regression equation is thus as follows:

∆ ln xip= β0+ β1∆ ln zip+ β2ln(xSPip ) + β3ln ¯ Iip Yip + β4ln(XipSP) + ip, (4.1) where :

• ∆ ln xip is the average GDP per capita growth over period p, of country i,

• ∆ ln zip is the average emissions growth of the pollutant that we analyze over period p,

of country i,

• SP is the abbreviation for "Starting Period", • xST

ip is the GPD per capita of the first year of period p, of country i,

• the scalar β3 and the vector β4 are the coefficients for our control variables, _YI¯ip_ip is the

average gross investment on GDP and XST _{is the matrix of control variables reported}

at the starting period (fertility rate, life expectancy and human capital), • finally, is our error term.

4.2 Instrumental variables

Our regression may suffer from inverse causality biases: pollution may enhance growth, but growth is likely to be a cause of pollution as well, and investment is a growth factor just as growth allows to invest more. Therefore, like Barro (1996), we decide to use instrumental variables to address this issue.

We use beginning-of-period investment as an instrument for average investment over the fol-lowing ten-year period. Giving the depreciation of capital, the beginning-of-period investment has no direct impact on the next ten-year economic growth and is a good predictor for future investment. As a consequence, it can be used as an instrument to estimate the present invest-ment effect on economic growth. More precisely, we use the first-year data of the period as an instrument. Nonetheless, it is a weak instrument, since past investment may not have a very strong link to future investment.

We proceed with the same methodology for pollution, using the first-year data on pollution emissions as an instrument for the average pollution emissions on the period. We believe that the past emissions growth is unlikely to have an impact on present economic growth, when controlling for fixed capital accumulation. However, since we think capital accumulation itself suffers from being a relatively weak instrument, we will have the same issue with pollution. The consequence of weak instruments is that standard deviations are not precise and, conse-quently, the significance tests are not neither. We are however confident that our sample size

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is sufficiently large in order to guarantee that our estimators perform better than those obtain by standard OLS.

4.3 Subsampling

4.3.1 According to wealth

As we have shown in the previous chapter, the GHG emissions patterns of rich and poor countries vary considerably. As a consequence, we devide our sample in two groups, according to wealth.

Table 4.1: Countries of our sample divided in same size groups according to their wealth

Richer countries

with GDP per capita in 2012 of 7,062 USD (constant 2010) or more

Luxembourg, Norway, Switzerland, Denmark, Australia, Sweden, Netherlands, United States, Canada, Austria, Ireland, Finland, Japan, Belgium, Germany, Iceland, France, United Kingdom, Brunei, Italy, New Zealand, Spain, Greece, Portugal, Czech Republic, Chile, Poland, Hungary, Uruguay, Brazil*, Russia*, Turkey*, Ar-gentina*, Gabon*, Kazakhstan*, Malaysia*, Panama*, Mexico*, Costa Rica*, Romania*, Bulgaria*

Poorer countries

with GDP per capita in 2012 un-der 7,062 USD (constant 2010)

Botswana*, Dominican Republic*, Peru*, Thailand*, Namibia*, China*, Ecuador*, Algeria*, Belize*, Tunisia°, Jordan°, Eswatini°, El Salvador°, Armenia°, In-donesia°, Sri Lanka°, Paraguay*, Ukraine°, Morocco°, Guatemala°, Nigeria°, Philippines°, Honduras°, In-dia°, Mauritania°, Cameroon°, Pakistan°, Kenya°, Sene-gal°, Cambodia, Bangladesh, Tajikistan°, Benin, Mali, Uganda, Rwanda, Burkina Faso, Togo, Mozambique, Madagascar

* countries belonging to upper-middle-income economies ° countries belonging to lower-middle-income economies

Our criteria for wealth is the GDP per capita in 2012, the last year of our sample. Since there are only 81 countries in this sample, to keep subsamples of sufficient size, the division is made in almost equal groups of 40 and 41 countries. We use the median GDP per capita in 2012, which happens to be the one of Bulgaria (equal to 7,062 constant 2010 USD), to separate between richer and poorer countries. In table 4.1, we can see that the use of GDP instead of GNI (which is used by the World Bank to classify countries) does not have a major effect on countries’ category. Indeed, countries of the lower-middle income group of the World Bank are all in the "poorer countries" group. The separation between the two equal groups is made within the upper-middle income group. Evidently, having two groups instead of four as the World Bank results in a less refined analysis. It forces us to put in the same category very

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different countries like Mexico and Switzerland. However, we still create two consistent groups to analyze the impact of wealth. It should be noted that China and India are both included in the "Poorer countries" category.

4.3.2 According to periods

As it is shown by the extensive literature on the environmental Kuznets curve (Stern 2004), many economists consider that the relation between pollution emissions and economic growth changes over time. For this reason, we think it is relevant to analyze our data by periods. Therefore, we divide the sample in two subsamples: one for the 1993-2002 period and one for the 2003-2012 period. The second sub-period is characterized by larger increases in some pollutants as shown in Figure 3.1.

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Chapter 5

Results and Discussion

In this chapter, after presenting the results about the convergence hypothesis in section 5.1, we look at the impact of pollution emissions growth on economic growth in section5.2. More precisely, subsection 5.2.1 presents aggregate results including all countries and observations (1993-2012), subsection 5.2.2 differentiates between rich and poor countries and subsection

5.2.3 divides the observations into two sub-periods (1993-2002, 2003-2012). Then, we present the results of the estimated coefficients of the control variables in section 5.3.

In our discussion, we concentrate on the regressions relying on instrumental variables, which are more robust than those obtained from an OLS regression. The results for the OLS estima-tions can be found in appendix A. Note that all variables are expressed in their logarithmic forms in the regressions. This implies that the estimated coefficients measure elasticities.

5.1 Convergence

The convergence hypothesis is verified by our results. Among all regressions (tables5.1to5.8), the effect of per capita GDP at the starting period on economic growth is always significantly negative. This result implies that the levels of GDP per capita of poorer countries should eventually catch up with those of richer countries.

5.2 Impact of emissions growth on economic growth

5.2.1 Results on the full sample

In tables 5.1 and 5.2, among the five pollution indicators, we can see that the coefficient of CO2 emissions growth is the only one being significantly different from 0. If CO2 emissions

growth increases by 1%, our regression predicts a 0.17% increase in GDP growth. This impact can be considered as quite important. None of the other gases has a significant impact on

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Table 5.1: Logarithmic regressions (with instrumental variables) of economic growth on several GHG emissions growth (1/2)

Model 1 Model 2 Model 3

(Intercept) 0.0574 0.0830 0.0629

(0.0777) (0.0648) (0.0681)

Total GHG emi. per capita growth 0.0054

(0.2273)

CO2 emissions per capita growth 0.1667∗∗∗

(0.0620)

CH4 emissions per capita growth 0.3087

(0.2364)

Gross fixed capital formation on GDP −0.0129 −0.0140∗∗ _−0.0108

(0.0088) (0.0069) (0.0073)

Fertility rate (starting period) −0.0256∗∗∗ _−0.0246∗∗∗ _−0.0217∗∗∗

(0.0065) (0.0050) (0.0061)

Life expectancy (starting period) 0.0135 0.0023 0.0090

(0.0171) (0.0154) (0.0161)

Human capital (starting period) 0.0041 0.0106 0.0084

(0.0112) (0.0086) (0.0093)

GDP per capita (starting period) −0.0106∗∗∗ −0.0092∗∗∗ −0.0092∗∗∗

(0.0020) (0.0016) (0.0019) R2 0.1931 0.3340 0.2614 Adj. R2 _0.1619 _0.3082 _0.2328 Num. obs. 162 162 162 RMSE 0.0182 0.0165 0.0174 ∗∗∗_{p < 0.01,}∗∗_{p < 0.05,}∗_{p < 0.1}

Instrumental variables: Total GHG emi. per capita growth (starting period); CO2 emissions

per capita growth (starting period); CH₄ emissions per capita growth (starting period); Gross fixed capital formation on GDP (starting period)

The four last variables are instrumented with themselves.

the GDP growth. As we can see in tables 5.3 to 5.8, this result is stable over most of the subsamples for which our model was estimated.

We want to investigate wether the climate could have an impact on the relationship between emissions and growth. Therefore we add a control for it in our regression, using latitude as a proxy for climate. The results are reported in appendixB. We do not observe any noticeable difference from the regressions without latitude as a control variable.

The absence of a significant impact of methane emissions (CH4), nitrous oxide emissions

(N2O), F-gases emissions and greenhouse gases emissions in general on output growth can be

explained by different mechanisms.

• CH4 is issued in majority from farming, and to large extent from fossil fuels (Myhre

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Table 5.2: Logarithmic regressions (with instrumental variables) of economic growth on several GHG emissions growth (2/2)

Model 4 Model 5

(Intercept) 0.0694 0.0583

(0.0713) (0.0712)

N2O emissions per capita growth 0.2848

(0.2997)

F-gases emissions per capita growth 0.0013

(0.0044)

Gross fixed capital formation on GDP −0.0088 −0.0127∗

(0.0086) (0.0076)

Fertility rate (starting period) −0.0234∗∗∗ _−0.0256∗∗∗

(0.0059) (0.0055)

Life expectancy (starting period) 0.0084 0.0134

(0.0171) (0.0165)

Human capital (starting period) 0.0050 0.0042

(0.0091) (0.0092)

GDP per capita (starting period) −0.0087∗∗∗ _−0.0106∗∗∗

(0.0026) (0.0017) R2 0.2098 0.1908 Adj. R2 0.1792 0.1594 Num. obs. 162 162 RMSE 0.0180 0.0182 ∗∗∗_{p < 0.01,}∗∗_{p < 0.05,}∗_{p < 0.1}

Instrumental variables: N2O emissions per capita growth (starting period); F-gases emissions

per capita growth (starting period); Gross fixed capital formation on GDP (starting period) The four last variables are instrumented with themselves.

productivity. We can imagine that this effect dominates the potential impact of fossil fuels use on economic growth.

• N2O has a very strong impact on the greenhouse effect compared to the scale of its use.

This may explain why we cannot observe any significant impact on growth.

• F-gases, as N2O, have a strong impact on the greenhouse effect compared to the scale of

their uses. Moreover, they are used in very specific sectors (refrigeration, semiconductor manufacturing and electric power equipment). As a consequence, it is not surprising that they do not have a significant impact on growth.

• When considering overall gas emissions, the positive impact of CO2 emissions on

eco-nomic growth is probably drowned by the insignificant impacts of the other gases which compose the global greenhouse gases.

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5.2.2 Results for the wealth subsamples

When comparing the CO2 emissions impact on rich and poor countries (tables 5.3to5.5), we

find that the GDP growth of rich countries is more dependent on CO2 emissions growth than

poorer ones. Indeed, our regressions predict that, if CO2 emissions growth increases by 1%,

GDP growth increases by 0.29% for rich countries against only 0.13% for poor countries (Table

5.4). We mentioned above that farming is not a sector that drives economic growth. The fact that agriculture contributes more to the GDP in poorer economies than in richer ones, and that agriculture is a CO2 generator as well, can explain why growth is not as dependent on

CO2 in poor countries than it is in rich countries.

Methane emissions (CH4) have a strong impact on economic growth in rich countries. An

increase of CH4 emissions growth by 1% would generate an increase of GDP growth by 0.47%

in these countries (table 5.4). It might be because of some industrial utilizations of this gas. However, this result is only significant with a 10% threshold and, as a consequence, can be sensitive to sampling error.

Table 5.3: Logarithmic regressions (with instrumental variables) of economic growth on total GHG emissions growth according to wealth

Rich Poor

(Intercept) −0.1246 0.1565

(0.1732) (0.0959)

Total GHG emi. per capita growth 0.1233 0.0211

(0.1521) (0.3480)

Gross fixed capital formation on GDP −0.0365∗∗ _−0.0070

(0.0164) (0.0118)

Fertility rate (starting period) −0.0041 −0.0404∗∗∗

(0.0078) (0.0105)

Life expectancy (starting period) 0.0353 −0.0017

(0.0423) (0.0211)

Human capital (starting period) 0.0306∗∗ _−0.0082

(0.0150) (0.0190)

GDP per capita (starting period) −0.0096∗∗∗ −0.0105∗∗∗

(0.0031) (0.0036) R2 _0.0846 _0.2889 Adj. R2 _0.0113 _0.2305 Num. obs. 82 80 RMSE 0.0162 0.0199 ∗∗∗_{p < 0.01,}∗∗_{p < 0.05,}∗_{p < 0.1}

Instrumental variables: Total GHG emi. per capita growth (starting period); Gross fixed capital formation on GDP (starting period)

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Table 5.4: Logarithmic regressions (with instrumental variables) of economic growth on CO2

and CH4 emissions growth according to wealth

Rich Poor Rich Poor

(Intercept) 0.0902 0.1586∗ −0.1589 0.1622∗

(0.1491) (0.0847) (0.1626) (0.0865)

CO2 emissions per capita growth 0.2859∗∗ 0.1336∗

(0.1209) (0.0776)

CH4 emissions per capita growth 0.4742∗ _0.2369

(0.2454) (0.3515)

Gross fixed capital formation on GDP −0.0219 −0.0103 −0.0286∗ _−0.0058

(0.0135) (0.0097) (0.0151) (0.0100)

Fertility rate (starting period) −0.0083 −0.0385∗∗∗ _0.0010 _−0.0372∗∗∗

(0.0061) (0.0083) (0.0079) (0.0099)

Life expectancy (starting period) −0.0141 −0.0068 0.0442 −0.0051

(0.0369) (0.0193) (0.0395) (0.0200)

Human capital (starting period) 0.0370∗∗∗ −0.0026 0.0431∗∗∗ −0.0065

(0.0124) (0.0120) (0.0156) (0.0122)

GDP per capita (starting period) −0.0079∗∗∗ _−0.0099∗∗∗ _−0.0099∗∗∗ _−0.0097∗∗∗

(0.0026) (0.0034) (0.0028) (0.0036) R2 _0.4072 _0.3600 _0.1872 _0.3365 Adj. R2 0.3598 0.3074 0.1222 0.2820 Num. obs. 82 80 82 80 RMSE 0.0131 0.0189 0.0153 0.0192 ∗∗∗_{p < 0.01,}∗∗_{p < 0.05,}∗_{p < 0.1}

Instrumental variables: CO2 emissions per capita growth (starting period); CH4 emissions per

capita growth (starting period); Gross fixed capital formation on GDP (starting period) The four last variables are instrumented with themselves.

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Table 5.5: Logarithmic regressions (with instrumental variables) of economic growth on N2O

and F-gases emissions growth according to wealth

Rich Poor Rich Poor

(Intercept) −0.1432 0.1576∗ −0.0471 0.1605∗

(0.1695) (0.0882) (0.1821) (0.0903) N2O emissions per capita growth 0.3970 0.1313

(0.2666) (0.4352)

F-gases emissions per capita growth −0.0273 0.0021

(0.0485) (0.0048) Gross fixed capital formation on GDP −0.0264 −0.0049 −0.0420∗∗ _−0.0068

(0.0184) (0.0130) (0.0166) (0.0102) Fertility rate (starting period) −0.0082 −0.0376∗∗∗ −0.0097 −0.0408∗∗∗

(0.0077) (0.0136) (0.0104) (0.0087) Life expectancy (starting period) 0.0438 −0.0027 0.0167 −0.0026

(0.0422) (0.0201) (0.0438) (0.0204) Human capital (starting period) 0.0310∗∗ −0.0077 0.0280∗ −0.0087

(0.0149) (0.0127) (0.0158) (0.0121) GDP per capita (starting period) −0.0089∗∗∗ −0.0101∗∗∗ −0.0096∗∗∗ −0.0104∗∗∗

(0.0031) (0.0038) (0.0030) (0.0036) R2 _0.0966 _0.3063 _0.1069 _0.2763 Adj. R2 _0.0244 _0.2493 _0.0355 _0.2168 Num. obs. 82 80 82 80 RMSE 0.0161 0.0197 0.0160 0.0201 ∗∗∗_{p < 0.01,}∗∗_{p < 0.05,}∗_{p < 0.1}

Instrumental variables: N2O emissions per capita growth (starting period); F-gases emissions

per capita growth (starting period); Gross fixed capital formation on GDP (starting period) The four last variables are instrumented with themselves.

5.2.3 Results for the periods subsamples

The analysis by sub-periods shows that this dependency of pollution emissions is not stable over time (tables 5.6 to 5.8). For all countries, while the regression on the 1993-2002 period predicts a GDP growth increase of 0.16% for a CO2 emissions growth increase of 1%, the

regression on the 2003-2012 period does not present a coefficient significantly different from 0 (Table 5.7). This result indicates that economies tend to converge over time to reduce GDP growth dependency to CO2 emissions growth. A transition towards green growth, that is when

economic growth does not depend on pollutant emissions growth, appears to take place. Surprisingly, our results predict that if nitrous oxide emissions (N2O) growth increases by 1%,

GDP growth increases by 0.62% during the first period (table 5.8). It is hard to say what kind of utilization of N2O could explain this result, because this gas is used in a variety of

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sectors. However, this result is only significant with a 10% threshold and, as a consequence, can be sensitive to sampling error.

Table 5.6: Logarithmic regressions (with instrumental variables) of economic growth on total GHG emissions growth according to period

Period 1 Period 2

(Intercept) −0.0512 0.2142

(0.1027) (0.1304)

Total GHG emi. per capita growth 0.2157 −0.3921

(0.1322) (0.5837)

Gross fixed capital formation on GDP −0.0066 0.0026

(0.0098) (0.0158)

Fertility rate (starting period) −0.0235∗∗∗ _−0.0292∗

(0.0080) (0.0147)

Life expectancy (starting period) 0.0291 −0.0065

(0.0234) (0.0304)

Human capital (starting period) −0.0054 0.0277∗

(0.0154) (0.0160)

GDP per capita (starting period) −0.0041 −0.0180∗∗∗

(0.0026) (0.0054) R2 0.1045 −0.0650 Adj. R2 0.0319 −0.1513 Num. obs. 81 81 RMSE 0.0191 0.0216 ∗∗∗_{p < 0.01,}∗∗_{p < 0.05,}∗_{p < 0.1}

Instrumental variables: Total GHG emi. per capita growth (starting period); Gross fixed capital formation on GDP (starting period)

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Table 5.7: Logarithmic regressions (with instrumental variables) of economic growth on CO2

and CH4 emissions growth according to period

Period 1 Period 2 Period 1 Period 2

(Intercept) −0.0056 0.1734 −0.0155 0.1907∗∗

(0.0948) (0.1052) (0.0981) (0.0866)

CO2 emissions per capita growth 0.1587∗∗ −0.1704

(0.0762) (0.2667)

CH4emissions per capita growth 0.3997 0.3843

(0.2565) (0.3630)

Gross fixed capital formation on GDP −0.0175∗ _−0.0022 _−0.0118 _0.0009

(0.0094) (0.0125) (0.0093) (0.0108)

Fertility rate (starting period) −0.0239∗∗∗ _−0.0232∗∗∗ _−0.0214∗∗ _−0.0173∗∗

(0.0075) (0.0083) (0.0082) (0.0086)

Life expectancy (starting period) 0.0158 0.0013 0.0212 −0.0160

(0.0221) (0.0292) (0.0226) (0.0212)

Human capital (starting period) −0.0037 0.0244∗∗ −0.0057 0.0250∗∗

(0.0144) (0.0122) (0.0152) (0.0105)

GDP per capita (starting period) −0.0051∗∗ _−0.0179∗∗∗ _−0.0050∗ _−0.0116∗∗∗

(0.0024) (0.0051) (0.0025) (0.0037) R2 _0.1959 _0.3375 _0.1340 _0.5090 Adj. R2 0.1307 0.2838 0.0638 0.4692 Num. obs. 81 81 81 81 RMSE 0.0181 0.0170 0.0188 0.0147 ∗∗∗_{p < 0.01,}∗∗_{p < 0.05,}∗_{p < 0.1}

Instrumental variables: CO2 emissions per capita growth (starting period); CH4 emissions per

capita growth (starting period); Gross fixed capital formation on GDP (starting period) The four last variables are instrumented with themselves.