Economic Growth and CO2 Emissions in the European Union ...

Environmental and Resource Economics 19: 165–172, 2001. © 2001 Kluwer Academic Publishers. Printed in the Netherlands.

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Economic Growth and CO2 Emissions in the European Union AURELIA BENGOCHEA-MORANCHO1, FRANCISCO HIGÓN-TAMARIT2,∗ and INMACULADA MARTÍNEZ-ZARZOSO1 1 Departament d’Economia, Universitat Jaume I, Castelló, Spain; 2 Departament d’Economia

Aplicada, Universitat de València, Valencia, Spain (*corresponding author, email: [email protected]) Accepted 20 July 2000 Abstract. This paper examines the relationship between economic growth and CO2 emissions in the European Union. A panel data analysis for the period 1981 to 1995 is applied in order to estimate the relationship between Gross Domestic Product (GDP) growth and CO2 emissions in ten selected European countries. The analysis shows important disparities between the most industrialised countries and the rest. The results do not seem to support a uniform policy to control emissions; they rather indicate that a reduction in emissions should be achieved by taking into account the specific economic situation and the industrial structure of each EU member state. Key words: CO2 emissions, Kyoto Protocol, panel data, the European Union, the pollution-income relationship JEL classification: C33, Q28, Q48

Introduction Recently, the effects of economic growth on natural and environmental resources have become a central question and the concern over environment preservation is rising. Two so-called Earth Summits have taken place in recent years in Rio de Janeiro (1992) and New York (1997). At those summits a great number of countries analysed the necessary steps involved in achieving sustainable development. Moreover, ‘Towards sustainability’ is also the slogan of the EU’s fifth environmental action programme currently in operation. In December 1997, 39 developed countries signed the Kyoto Protocol, an international agreement to curb the emission of greenhouse gases. Six gases were covered under this agreement: carbon dioxide, methane, nitrous oxide, hydrofluorocarbons, perfluorocarbons and sulphur hexafluoride. The main greenhouse gas in terms of quantity is carbon dioxide (CO2 ). In 1995, it accounted for about 82% of total anthropogenic greenhouse gases emissions from developed countries whereas methane was 12% and nitrous oxide about 4% (UNEP (1999) Climate Change Information Kit. Geneva: UNEP). The other three remaining gases are not

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very important in terms of quantity however they are important in terms of quality and their emissions are increasing. Although there was general agreement about the need to control emissions, especially of CO2 , a lot of problems arise when fixing reduction commitments. The USA proposal was to establish heterogeneous targets of emission limitations while the EU proposal was to establish as large a homogeneous reduction as possible at least for the USA, Japan and the EU. Finally, the reduction commitments were 7% for the USA and Canada, 6% for Japan and 8% for the EU. Other countries commitments vary from stabilising emissions at their current levels to increasing them, such as, Norway by 1% and Australia by 20%. Recently, a new meeting about the Kyoto Protocol took place in Buenos Aires and the discussions about reduction commitments are still in progress. The USA do not want to ratify the Protocol until some of the lesser-developed countries (particularly China, India and Brazil) agree to curb their emissions. At the Kyoto meeting, the European Commission presented a proposal based on the so-called European bubble. That is, the whole EU should achieve the stated target but that some individual countries would be allowed to increase emissions in order to favour their real convergence. This fact has important implications: Which policy is to be implemented? Are there to be regulations establishing the permitted levels for each country? What are the criteria to be? In order to reflect on the questions posed, we examine the link between GDP growth and CO2 emissions in ten selected European countries. In the EU, concern about CO2 emissions and the proper ways to reduce them began some years ago, see for instance Hogan and Jorgenson (1991), Proops, Faber and Wagenhals (1992), Beaumais and Bréchet (1993), Jorgenson and Wilcoxen (1993), Bergman (1995), Liberatore (1995), Anton and De Bustos (1997) or Martín and Velázquez (1997). Several authors have also studied the relationship between CO2 reductions and the implications for GDP growth. A survey of all these studies can be found in Hoeller et al. (1992). Empirical Application and Estimation We analyse the evolution of CO2 emissions in a sample of European countries. We have selected ten member states of the EU: five of them are located in Northern Europe (Belgium, Denmark, Ireland, the Netherlands and the United Kingdom) and the others in Southern Europe (France, Greece, Italy, Portugal and Spain). Our choice is based on the length of membership in the EU as well as on the homogeneity of the available statistical data. Germany has not been considered given the problems in the data series derived from unification. The period under study is from 1981 to 1995. In order to test whether the income level evolution through time and across countries influences the level of CO2 emissions, we have specified the following model: ln Rit = α0 + α1 ln Yit + δi + νit

(1)

ECONOMIC GROWTH AND CO2 EMISSIONS IN THE EUROPEAN UNION

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where i denotes different countries, t denotes different years, Rit is the amount of CO2 emissions (in tonnes), Yit is the GDP at current PPP, δi denotes unobserved individual effects, and νit is the error term. We have applied panel data methodology to estimate equation (1). The outcome regressions corresponding to five methods are shown. The first one imposes the same intercept and slope parameters for all countries and is therefore equivalent to the ordinary least squares estimation (OLS). The second, one referred to as the fixed effects method (FE), allows each individual country to have a different intercept. We also report the results obtained using the random effects method (RE) in which the individual effects are treated as random. To control the nonobservable specific effects we have applied two stages least squares (TSLS). As the estimates obtained by using these four methods are only consistent when the regressors are strictly exogenous, we also control the possible endogeneity of the income variable by estimating the model using the income variable lagged two periods as the instrumental variable for the sake of completeness. However, in the present application the lagged income variable may not be a good instrument since the amount of gas emissions depends on current levels of income rather than on past ones. In a first regression, we have considered the ten countries under analysis. The results are shown in Table I and in Figure 1. The hypothesis of equal slopes for all ten countries could not be accepted. This means that the pollution-income relationship differs among them and so, we have made sub-samples. First, we separated the countries according to their geographical situation, into Southern and Northern countries, however, we cannot accept that all Southern countries share the same slope and similarly that all Northern countries have equal slopes too. Then, we tried using different criteria and we grouped the countries according to their income level. In the new grouping there are six countries with a higher per capita income than the average level of the whole group (Belgium, Denmark, France, Italy, the Netherlands and the United Kingdom) and four countries whose level of income is lower than the average level (Greece, Ireland, Portugal and Spain). We have defined the first group as above-average income countries and the second one as below-average income countries. In this case, we can accept the hypothesis of equal slopes among countries belonging to the same group. The results are reported in Tables II and III, and have been plotted in Figures 2, 3. The F-test constructed to check for the significance of the fixed effects shows that the null hypothesis of non-significance is always rejected. So, the assumption of constant intercept may not always hold for the different countries. In other words, although the above-average income countries exhibit identical patterns in the relationship between GDP increases and CO2 increments, there are differences in levels, as they start from different levels of CO2 emissions. The same applies for the below-average income countries. The orthogonality test for the random effects and the regressors is also reported (the Hausman test). We cannot accept the null hypothesis of no correlation, there-

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Table I. Dependent variable: Log of CO2 emissions (all countries).

Constant ln Y R2 SE Signif. FE LM (1,2) Hausman test

OLS

FE

RE

First dif.

TSLS

2.00 (6.66) 0.57 (9.66) 0.37 0.79 – – –

– 0.61 (9.26) 0.99 0.093 F9,139 = 945 – –

1.83 (4.36) 0.60 (9.54) 0.99 0.090 – – χ12 = 0.23

– 0.39 (2.62) – 0.048 – 2.28 –

– 0.49 (2.72) – 0.039 – – –

Note: t-statistics in brackets. OLS = ordinary least squares; FE = fixed effects; RE = random effects; TSLS = two stages least squares; F(9,139) = F-statistics (degrees of freedom in brackets), LM = Lagrange multipliers.

Figure 1. Regression line with ten countries (OLS).

fore, only the results reported as FE are based on a consistent estimator. Thus, in both sub-samples (richer and poorer countries) we have applied the fixed effects method to estimate the intercept corresponding to each country. The outcome regressions are shown in Tables IV and V. Following the results reported in Tables IV and V, we can say that in the aboveaverage income countries, when the GDP grows by 1%, the CO2 emissions increase by 0.18%. For below-average income countries, an increase of 1% in GDP accounts for an increment of 0.97% in CO2 emissions.

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Table II. Dependent variable: Log of CO2 emissions (richer countries). Regressors

OLS

FE

RE

First dif.

TSLS


1.64 (4.66) 0.72 (0.65) 0.54 0.54 – – –

– 0.18 (2.66) 0.99 0.065 F5,143 = 1304 – –

4.30 (9.81) 0.20 (3.03) 0.99 0.065 – – χ 2 (1) = 1.45

– 00.28 (1.28) – 0.047 – 5.14 –

– 0.02 (0.09) – 0.039 – – –

Note: t-statistics in brackets. OLS = ordinary least squares; FE = fixed effects; RE = random effects; TSLS = two stages least squares; F(9,139) = F-statistics (degrees of freedom in brackets), LM = Lagrange multipliers. Table III. Dependent variable: Log of CO2 emissions (poorer countries). Regressors

OLS

FE

RE

First dif.

TSLS


2.55 (8.37) 0.33 (5.26) 0.30 0.65 – – –

– 0.96 (9.96) 0.98 0.101 F3,145 = 1649 – –

–0.23 (–0.36) 0.92 (9.66) 0.98 0.103 – – χ12 = 13.79

– 0.48 (2.31) – 0.058 – 1.04 –

– 0.91 (5.55) – 0.004 – – –

Note: t-statistics in brackets. OLS = ordinary least squares; FE = fixed effects; RE = random effects; TSLS = two stages least squares; F(9,139) = F-statistics (degrees of freedom in brackets), LM = Lagrange multipliers.

In both sub-samples fixed effects appear to be significant. That is, there are differences in the individual emissions that cannot be explained by their level of income alone. Nevertheless, it is interesting to note that for the above-average income countries all the intercept terms are positive and higher than 3, while those values are lower for below-average income countries, even negative in two cases (Portugal and Greece). Conclusions Greenhouse gases emission seems to be the cause of global warming and climate change. At the Third Conference of the Parties to the Climate Framework Convention (COP3) in Kyoto, a large number of countries discussed the criteria and the steps necessary to control this phenomenon. At COP4 in Buenos Aires, the discussions were continued. The European Union went to the Kyoto meeting with the proposal of reducing greenhouse emissions by the year 2010 by at least 15% from the parting level of 1990. This target would have an intermediate checking point

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Figure 2. Regression line for above-average income (OLS).

Figure 3. Regression line for below-average income (OLS).

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Table IV. Slope and fixed effects (above-average income countries). Coefficient

Std. Error

t-Statistic

Prob

0.181213

0.067947

2.666955

0.0089

Slope Fixed effects: Belgium Denmark France Netherlands Italy United Kingdom

3.909809 3.324833 4.814942 4.168821 4.847533 5.458259

R-squared Adjusted R-squared S.E. of regression Log likelihood

0.993953 0.993545 0.065124 223.1393

Mean dependent var S.D. dependent var Sun squared resid Durbin-Watson stat

5.352515 0.810574 0.377463 0.632845

Table V. Slope and fixed effects (below-average income countries). Coefficient Slope Fixed effects: Spain Greece Ireland Portugal R-squared Adjusted R-squared S.E. of regression Log likelihood

0.969925

Std. Error

t-Statistic

Prob

0.097374

9.960832

0.0000

0.277465 –2.177869 0.477105 –0.407675 0.984025 0.982942 0.101854 124.0335

Mean dependent var S.D. dependent var Sun squared resid Durbin-Watson stat

4.111734 0.779852 0.612079 0.279794

in 2005, with an expected reduction of 7.5%. The proposal included a mechanism to allocate the reductions (the so-called European bubble) and it also implied that less developed European countries would be allowed to increase their emissions further. In this study we have analysed the CO2 emissions in the European Union. In order to determine if income is related to CO2 emissions and how both variables evolve, we have estimated an econometric model using panel data methodology, taking GDP as an independent variable and CO2 emissions as a dependent variable. The results show that the ten countries under analysis do not follow the same pattern. On the contrary, the analysis confirms a different behaviour between those countries with a level of income above the EU average and those with a below

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average level of income. When making sub-samples of countries based on their income level, we can accept the equal slope hypothesis for the members of each group. However, in both sub-samples, fixed effects appear to be significant which means that the departure point of CO2 emissions differs among countries. The fact remains that emissions in above-average income countries are higher than emissions in below-average income countries, independent of their income level. This fact suggests that there may be other significant explanatory variables apart from the income level. If so, further research is needed. References Antón, V. and A. De Bustos (1997), ‘A General Method for Estimating CO2 Emissions’, in C. San Juan Mesonada and A. Montalvo Santamaría, eds., Environmental Economics in the European Union. Madrid: Mundi-Prensa, Universidad Carlos III de Madrid. Arellano, M. and O. Bover (1990), ‘La econometría de datos de panel’, Investigaciones Económicas 14(1), 3–45. Baltagi, B. (1995), Econometric Analysis of Panel Data. Chichester: John Wiley & Sons Ldt. Beaumois, O. and T. Bréchet (1995), ‘Ecotax, Rational Use of Energy and CO2 Emissions’, in G. Boero and A. Silberston, eds., Environmental Economics. Basingstoke: Macmillan Press. Bergman, L. (1995), ‘Sectoral Differentiation as a Substitute for International Coordination of Carbon Taxes: A Case Study of Sweden’, in J. B. Braden, H. Folmer and T. S. Ulen, eds., Environmental Policy With Political and Economic Integration. Edward Elgar Publishing. Eurostat (1996), Estadísticas básicas de la Unión Europea. Eurostat. Garín-Muñoz, T. and T. Pérez-Amaral (1997), ‘A Model of Spain-Europe Telecommunications’, Mimeo, Madrid: Universidad Complutense. Green, W. H. (1997), Econometric Analysis. Prentice-Hall, 3rd ed. Hoeller, P., A. Dean and J. Nicolaisen (1990), A Survey of Studies of the Costs of Reducing Greenhouse Gas Emissions. Paris: OECD, Working Paper no. 89. Hoeller, P., A. Dean and M. Hayafuji (1992), ‘New Issues, New Results: The OECD’s Second Survey of the Macroeconomic Costs of Reducing CO2 Emissions’. Paris: OECD, Working Paper no. 123. Quoted in Beaumois and Bréchet (1993), op. cit. Jorgenson, D. W. and P. J. Wilcoxen (1993), ‘Reducing U.S. Carbon Emissions: An Econometric General Equilibrium Assessment’, Resource and Energy Economics 15(1), 7–25. Liberatore, A. (1995), ‘Arguments, Assumptions and the Choice of Policy Instruments. The Case of the Debate on the CO2 Energy Tax in the European Community’, in B. Dente, ed., Environmental Policy in Search of New Instruments. Dordrecht: Kluwer Academic Publishers. Martín, C. and F. J. y Velázquez (1997), ‘Energy Taxation to Limit CO2 and Price Distortions: Spain as a Pilot Study’, in C. San Juan Mesonada and A. Montalvo Santamaría, ed., Environmental Economics in the European Union. Madrid: Mundi-Prensa, Universidad Carlos III de Madrid. OECD (1995), Environmental Data 1995: Compendium. Paris: OECD. OECD (1996), Environmental Data 1996: Compendium. Paris: OECD. OECD (1997), Environmental Data 1997: Compendium. Paris: OECD. Proops, J. L. R., M. Faber and G. Wagenhals (1992), Reducing CO2 Emissions. Berlin: SpringerVerlag. UNEP (1999), Climate Change Information Kit. Geneva: UNEP. UNFCCC (1992), Climate Framework Convention. New York: United Nations. UNFCCC (1998), Report of the Conference of the Parties on its Third Session, Held at Kyoto. Doc. FCCC/CP/1997/7/Add.1. New York: United Nations.