International Trade and the Environment

Transcription

1 International Trade and the Environment Shunsuke MANAGI 1, Akira HIBIKI 2* and Tetsuya TSURUMI 1 1 Faculty of Business Administration, Yokohama National University 2 Social Environmental Systems Division, National Institute for Environmental Studies and Graduate School of Decision Science and Technology, Tokyo Institute of Technology * Corresponding author: Faculty of Business Administration, Yokohama National University 79-4, Tokiwadai, Hodogaya-ku, Yokohama Japan Tel / Fax: Abstract Theoretically, more openness in trade may affect the environment positively or negatively. This paper empirically tests the hypothesis that trade openness is good for the environment. While the results support the hypothesis for sulfur dioxide, carbon dioxide, and biochemical oxygen demand emissions from 1960 to 1999 for around 90 countries, and separately, for OECD and non- OECD countries, the effects are very different over countries. This is the first study that estimates the overall impact of trade openness on the environment.

2 Introduction Economists have analyzed how trade liberalization affects environmental quality for decades. Both the theoretical and empirical literature on trade, economic development and the environment is largely inconclusive about the overall impact of trade on the environment (see Jayadevappa and Chhatre, 2000, for a survey). Openness to international trade is expected to have both positive and negative effects on the environment (Grossman and Krueger, 1993; Copeland and Taylor; 2003a, 2003b). Grossman and Krueger (1993) decompose the overall impact into scale, technique and composition effects. The scale effect explains the negative consequences of economic activity and the increase in environmental damage if the nature of the economic activity remains unchanged. The technique effect explains the changes in production methods that accompany economic growth and trade openness. The demand for environmental regulations typically increases as trade raises incomes. In isolation, the technique effect is likely to benefit the environment. In addition, countries may find that they have greater access to environmentally friendly production methods. The composition effect explains the trade-induced changes in the composition of production that affect pollution levels. This effect could be positive or negative. Environmental degradation tends to increase as economic structure changes from agriculture to industry. As trade and income grow in later stages, environmental damage is reduced as the economic structure changes from energy-intensive industry to services and knowledge-based technology-intensive industry. Antweiler et al. (2001) developed a theoretical model to decompose trade s impact on pollution into scale, technique and composition effects, and then showed how to add up these effects. They derive the private sector s demand for pollution and pollution supply. Combining 1

3 pollution demand and supply yields the reduced-form equation. They estimated trade s impact on pollution by using data on sulfur dioxide (SO 2 ) concentrations from the Global Environment Monitoring Project (GEMS) on 43 countries from 1971 to Their estimates of the technique-effect elasticity are consistently higher than the scale effects. The technique effect is strong: a 1% increase in national income per capita lowers pollution concentrations by over 1%. It has been shown that trade-induced composition has positive environmental effects. Therefore, they conclude that free trade is good for the environment. 1 A problem with the paper of Antweiler et al. is that they are unable to estimate the overall impact of trade openness on the environment. This is because the elasticity of scale (or GDP) with respect to trade openness is not estimated. To estimate this elasticity, an additional equation must be estimated, possibly simultaneously. This paper evaluates the overall impact of trade openness by considering the simultaneous determination of pollution, income and trade using instrumental variables technique. Additionally, Antweiler et al. (2001) use sulfur dioxide concentrations instead of pollution per capita for their estimation. This might be problematic because the concentrations include pollution from the other areas by wind. The endogeneity of trade is a familiar problem from the empirical literature on growth and openness. Recently, Frankel and Rose (2005) concern this problem in environmental economics literature and analyze environmental impacts by addressing the endogeneity of trade and economic growth. The results of Frankel and Rose, based on data on 41 countries in 1990, support the optimistic view that trade reduces sulfur dioxide emissions. They also find that trade may have a beneficial effect on other two measures of air pollution of on SO 2 and nitrogen 1 Using the framework of the environmental Kuznets curve, Harbaugh et al. (2002) also study sulfur dioxide (SO 2 ) by using data from the GEMS. They also support the view that trade openness benefits the environment. 2

4 dioxide, while the effect on particulate matter is not statistically significant. The results for broader environmental measures, including carbon dioxide, are not as encouraging. Nevertheless, there is little evidence that trade has a detrimental effect on the environment. However, for example, it should be noted that Frankel and Rose s estimates are based on a sample of only 41 observations for SO 2. Moreover, since data are mainly from developed countries, it is difficult to draw inferences for developing countries. In addition, Cole and Elliott (2003) treat regulations as endogenous and find that regulations differentials do influence intra- and inter-industry trade shares in 26 countries from 1975 to This study uses the framework of Frankel and Rose (2005) and modifies the formulation of environmental quality equation used by Antweiler et al. (2001) to take into account scaletechnique and trade-induced composition effects. In this study, we use a much larger data set than those used in previous studies. That is, we have data on the SO 2 emissions of 88 countries from 1960 to 2000, carbon dioxide (CO 2 ) emissions of 94 countries from 1960 to 2000, and biochemical oxygen demand (BOD) emissions of 93 countries from 1980 to These data are used to test the hypothesis that free trade is good for the environment. We use data on sulfur dioxide because global emissions of local pollutants such as sulfur dioxide are still increasing over time, despite significant progress being made in developed countries (Cole, 2003). One of the focus of this study is its distinction between OECD- and non-oecd countries. This is because the overall impact of trade on pollution may differ (see, e.g., Chichilnisky, 1994; Copeland, 2000; Cole, 2003; Copeland and Taylor, 2003a). This study tests the hypothesis that trade openness has the same effect on the environment in OECD- and non-oecd countries. This 3

5 is an important issue because it is doubtful that developing countries follow the same incomepollution path as do developed countries (Cole, 2003). 2 The Empirical Models and Data This study tests the theory developed by Antweiler et al. (2001) that the impact of trade on pollution can de decomposed into scale, technique and composition effects. Since their methodology is central to this paper, it is useful to provide a brief outline of their model. They assume that a small open economy produces two goods by using capital and labor; one industry is capital intensive and generates pollution, while the other industry is labor intensive and does not pollute. Antweiler et al. (2001) derive the private sector s demand for pollution and pollution supply. The factors influencing pollution demand include scale, capital abundance, the degree of trade friction, the world price of dirty goods, and the pollution tax. The factors influencing pollution supply include real income and the cost of polluting. Combining pollution demand and supply yields a reduced-form equation. The environmental quality model in this study is represented by the following random effect model equation: ln E = γ + γ + α ln S + α (ln S ) + α (ln S ) 2 3 it i t 1 it 2 it 3 it + α lnt + α ln( K/ L) + α (ln( K/ L) ) + α ln( T is ) + α DlnT + ε 2 4 it 5 it 6 it 7 it it 8 i it it (1) where E denotes the pollution per capita of country i in year t (for example, in tonnes of sulfur per capita); S is gross domestic product (GDP) per capita capturing net effects of scale and technique effects; T is the ratio of aggregate exports and imports to GDP (which, as in the growth literature, proxies trade openness or trade intensity (see, e.g., Antweiler et al., 2001; Frankel and 2 Cole (2003) provided a detailed examination of Bjorn Lomborg s controversial and highly publicized book The Skeptical Environmentalist: Measuring the Real State of the World (Lomborg, 2001) and concluded that 4

6 Rose, 2005)); K/L denotes capital-labor ratio, and D is the dummy variable to show country specific effect of trade openness. We use a log-linear functional form, following the theoretical model developed by Antweiler et al. (2001). The variables S, K/L, and T capture the scale and technique, composition, and trade-induced composition effects, respectively. The scale of economic activity is required to estimate contemporaneous effects on pollution concentrations. Following Antweiler et al. (2001), we use the ratio as a percentage of aggregate exports and imports to GDP as a proxy for tradeinduced composition effects. Following Cole and Elliott (2003), we model per capita GDP in the estimation to estimate aggregate effect of scale and technique effects. The uses of per-capita GDP, its quadratic, and cubic terms are consistent with the literature on the environmental Kuznets curve (Selden and Song, 1994; Grossman and Krueger, 1995). In this context, these three terms capture scale and technique effects. This specification clarifies the role of aggregate scale and technique effects. Frankel and Rose (2002) estimate an environmental quality equation and an economic growth equation to incorporate simultaneity. Following neoclassical growth theory (see, e.g., Barro, 1998; Frankel and Rose, 2002), we control for population growth, initial income, investment, and human capital. Additionally, as we show in the next paragraph, the elasticity of scale with respect to trade openness, β 1, in the following equation is essential for estimating the overall impact of trade openness on the environment. The growth equation is: ln S = γ + γ + β lnt + β ln( K/ L) + β ln S + β ln P + β ln Sch + ε (2) it i t 1 it 2 it 3 it 10 4 it 5 it it Lomborg s analysis suffers from several problems, including the selective use of data, the oversimplification of issues, posing the wrong questions and a lack of objectivity in his quest for optimistic trends. 5

7 where P is the population growth rate; and Sch proxies human capital investment based on school enrolment years. Solving these two equations using instrumental variables technique yields estimates of the total impact of trade openness on the environment. The negative sign of the trade composition factor, T, in equation (1) does not imply that trade is good for the environment. An increase in trade openness also increases a country s output, and affects the composition and level of real income in a small open economy. Since the levels of output and income rise by approximately the same percentage, this study is able to compare scale and technique effects. The levels of output and income are assumed to rise by the same percentage (Antweiler et al., 2001). The overall impact of trade openness is obtained by taking the derivative of the pollution level in equation (1) with respect to openness, T, as follows: de T dt E ds T = α + 2α ln S + 3 α (ln S ) ++ α lnt + α + α ln S + α D dt S ' ' ' 2 ' ' ' ' ' = α1+ 2α2lnSit + 3 α3(ln Sit ) ++ α7lnt it β1+ α4 + α7lnsit + α8di ' ' ' 2 ' ' ' ' 1 2 it 3 it 7 it 4 7 it 8 i (3) where the elasticity of scale with respect to trade openness, β 1, is estimated in equation (2). ASL and Associates (1997) produced a comprehensive database on sulfur emissions incorporating time and spatial variations from 1850 to The database has been used in a number of climate studies (see Stern and Kaufmann, 2000, and Stern, 2005, for reviews). Their estimates are considered superior to others in terms of their spatial and temporal resolution and extent. Stern (2005) extended the database to the 1990s, achieving compatibility with the ASL database, to extend its use to the analysis of more recent data. Per-capita income is defined as 1990 GDP per capita (measured in real PPP-adjusted dollars) and trade intensity are taken from the Penn World Table 6.1. Capital labor ratio is obtained from extended Penn World Table. The other variables are provided from World 6

8 Development Indicator. The data set covers around 90 countries from 1960 to More specifically, the data set includes 22 countries in Africa, four countries in North and Central America, 15 countries in Asia, three countries in Eastern Europe and the Soviet Union, 14 countries in Latin America, 10 countries in the Middle East and North Africa, four countries in Oceania and 21 countries in Western Europe. Unlike the literature on the economics of environment and trade, this data set includes many developing countries. In this study, we estimate the above models by using three data sets. Results Instrumental variable estimation is used in this study. As instruments, we use indicators of country size (GDP, population, and land area) and dummy variables indicating common borders (which are used as instruments in gravity models), income per capita, lagged income, and investment rates (see Frankel and Rose, 2002, for precise descriptions). Table 1 and 2 present the results for the environmental quality equation (1) and growth equation (2) for SO 2, CO 2, and BOD. In both of the specifications for SO 2 and CO 2, all variables have statistically significant effects, while most the variables have statistically significant effects for BOD. The net of scale and technique effects, based on GDP, implies that the SO 2, CO 2, and BOD do not follow traditional EKC curve. The results indicate that inverted N-shaped curves for SO 2 and CO 2 and N-shaped curves for BOD. That is, they may initially exhibit the same pattern as the inverted U-shaped curve, but beyond a certain income level, return to exhibiting a positive relationship between BOD and income. The coefficient of openness, Trade Intensity, is positive for SO 2 and CO 2, while interaction term of trade and scale is negative both for SO 2 and CO 2. In contrast, first 7

9 specification in BOD does not show statistically significant result. Therefore, we re-specify the model for BOD without interaction term of trade and capital intensity and the coefficient of openness is negative, which implies that trade-induced composition effects benefit the environment. Table 1 and 2 also report the results of the growth equation (2). Most of the variables have statistically significant effects of the expected sign. In the all of the specifications, the estimated coefficient of trade openness is significantly positive. This suggests that trade has a strong effect on economic growth. This might be because trade openness encourages ongoing innovation and efficiency improvements. Population growth has a significantly negative effect both for SO 2 and CO 2, as hypothesized by the neoclassical model. By contrast, Frankel and Rose (2005) found a statistically insignificant effect. The effect of schooling variable is statistically significant and is shown to have positive effect on growth only for SO 2 case. Overall effects of scale-technique and composition effects and their decomposition to OECD and non-oecd countries are provided in Table 3. Using equation (3), the overall impact of trade supports the optimistic view that free trade is good for the environment. This result is consistent with the recent literature on trade and the environment (Antweiler et al., 2001; Cole and Elliott, 2003; Frankel and Rose, 2005). We find an overall estimate of -1.22, -0.77, and for SO 2, CO 2, and BOD, respectively. This implies that a 1% increase in trade openness decreases SO 2, CO 2, and BOD by 1.22, 0.77, and 0.05 percent, respectively. Thus, trade openness is good for the environment. These results are consistent with the result of Antweiler et al. (2001) and Cole and Elliott (2003). Our results show that we have relatively similar results even we extend our dataset to include developing countries several thousands of In both of OECD and non- OECD countries, sign are same for all of the measurements. However, magnitudes are different 8

10 for all three cases. The magnitudes are larger for OECD countries in both of the SO 2 and CO 2, while it is opposite for BOD. The results imply that OECD countries benefit more from increases in trade intensity when local pollutants of SO 2 and global pollutants of CO 2 are concerned. We need to note that each country has different effect and they are large diversity even within OECD and non-oecd countries as presented in Figure 1 to Figure 6. Net effects of scale and technique effects are all positive for SO 2 and CO 2. However, notice that the value of OECD is smaller than that of non-oecd in SO 2 and CO 2. This might be because the technique effects are larger than those based on non-oecd countries because OECD countries can afford a higher demand for a clean environment having accumulated wealth. This might also be that relatively easily pollution abatement technologies for SO2 and energy efficient technologies are available once consumers requested though we are not able to find similar results for BOD. Finally, we estimate changes in pollution emission if trade openness increase one percent in year 2000 using each country specific elasticity and data. So that we estimate E E, and the results it i= 1 it are presented in Table 4. In the case of SO 2, both of OECD and non-oecd countries benefits from increase in trade openness while the other two emission show mixed results. Although trade openness shows beneficial effects on world as a total amount of emission, OECD countries decrease CO 2 and increase BOD, while non-oecd countries increase CO 2 and decrease BOD. Discussion and Conclusions Previous studies such as Antweiler et al. are unable to estimate the overall impact of trade openness on the environment. This is because the elasticity of scale with respect to trade openness is not estimated. To estimate this elasticity, an additional equation must be estimated, possibly simultaneously. This paper evaluates the overall impact of trade openness by 9

11 considering the simultaneous determination of pollution, income and trade. This study has analyzed the impact of trade liberalization on SO 2, CO 2, and BOD by using extensive data on around 90 countries from 1960 to 2000, which represents a much larger sample than those used in the existing literature. For example, Antweiler et al. (2001), Cole and Elliott (2003) and Frankel and Rose (2005) use data on 43 countries from 1971 to 1996, data on 26 countries from 1975 to 1990, and data on 41 countries data for 1990, respectively. This study has successfully used a large amount of data. This is a strength of the paper because previous studies have been unable to estimate effects for non-oecd countries because of data limitations. In this study, trade is found to benefit the environment, on average. A 1% increase in trade openness causes a decrease of 1.22, 0.77, and 0.05 percent for each of SO 2, CO 2, and BOD, respectively. However, once we estimate each specific country effect, there are wide changes in their magnitude and sign. This result, however, does not imply that trade should be encouraged for the environmental reasons. In fact, trade and environmental policies are inextricably linked. Copeland (2000) shows that the coordination of trade and environmental policies is desirable for environmental management. 10

12 Reference Antweiler, W., Copeland, B. and Taylor, S Is Free Trade Good for the Environment? American Economic Review, 91 (4), A.S.L. and Associates, Sulfur Emissions by Country and Year, Report No: DE US Department of Energy. Washington, DC. Barro, E.J Determinants of Economic Growth, Cambridge, Mass.: MIT Press. Chichilnisky, G North-South Trade and the Global Environment. American Economic Review 84 (4): Cole, M. A Environmental Optimists, Environmental Pessimists and the Real State of the World-- An Article Examining The Skeptical Environmentalist: Measuring the Real State of the World by Bjorn Lomborg. Economic Journal, 113 (488): F Cole, M.A. and Elliott, R.J.R Determining the Trade-Environment Composition Effect: The Role of Capital, Labor and Environmental Regulations. Journal of Environmental Economics and Management. 46 (3) Copeland, B Trade and Environment: Policy Linkages. Environment and Development Economics. 5 (4): Copeland, B. and Taylor, S. 2003a. Trade, Growth and the Environment, National Bureau of Economic Research, Inc, NBER Working Papers: Copeland, B. and Taylor, S. 2003b. Trade and the Environment: Theory and Evidence, Princeton Series in International Economics. Princeton and Oxford: Princeton University Press. Frankel, J. and Rose, A In Is Trade Good or Bad for the Environment? Sorting out the Causality. NBER Working Paper No NBER Research Associates. Frankel, J. and Rose, A In Is Trade Good or Bad for the Environment? Sorting out the Causality. Review of Economics and Statistics. 87 (1): Greene, W Sample Selection Bias as a Specification Error: Comment, Econometrica 49, Grossman, G.M, and Krueger, A.B Environmental Impacts of a North American Free Trade Agreement, in The U.S.-Mexico Free Trade Agreement, P. Garber, ed. Cambridge, MA: MIT Press. Grossman, G.M, and Krueger, A.B Economic Growth and the Environment, Quarterly Journal of Economics 110: Harbaugh, W., Levinson, A. and Wilson, D Reexamining Empirical Evidence for an Environmental Kuznets Curve, Review of Economics and Statistics. 84 (3):

13 Janicke, M., Binder, M. and Monch, H Dirty Industries : Patterns of Change in Industrial Countries, Environmental and Resource Economics, 9: Jayadevappa, R. and Chhatre, S International Trade and Environmental Quality: A Survey. Ecological Economics. 32 (2): Liddle, B Free Trade and the Environment-Development System, Ecological Economics 39(1), Lomborg, B The Skeptical Environmentalist: Measuring the Real State of the World, Cambridge: Cambridge University Press. Polity IV Project Polity IV Project: Political Regime Characteristics and Transitions, University of Maryland. Rao, P.K International Environmental Law and Economics. Blackwell. Massachusetts, Oxford. Selden, T.M. and Song, D Environmental Quality and Development: Is There a Kuznets. Cure for Air Pollution Emissions?, Journal of Environmental Economics and Management 27: Stern, D. I Global sulfur emissions from 1850 to 2000, Chemosphere 58, Stern, D.I., Kaufmann, R.K., Detecting a Global Warming Signal in Hemispheric Temperature Series: a Structural Time Series Analysis. Climatic Change 47, Tobey, J The Effects of Domestic Environmental Policies on Patterns of World Trade : En Empirical Test. Kyklos 43 (2),

14 Table1 Determinants of SO 2, CO 2 and Economic Growth * Indicates significance at 90% confidence level ** Indicates significance at 95% confidence level *** Indicates significance at 99% confidence level Table 2 Determinants of BOD and Economic Growth * Indicates significance at 90% confidence level ** Indicates significance at 95% confidence level *** Indicates significance at 99% confidence level 13

15 Table3 Composition and Trade Elasticity Elasticity Data SO2 CO2 BOD Scale and Technique All data 0.616*** 2.829*** *** OECD 0.292*** 2.317*** 0.104*** Non-OECD 0.854*** 2.348*** *** K/L Composition All data 0.415*** 0.750*** OECD 0.555*** 0.652*** Non-OECD 0.290*** 0.838*** Trade-induced Composition All data *** *** OECD *** *** Non-OECD *** *** Overall All data *** *** *** OECD *** *** *** Non-OECD *** *** *** Table 4 Marginal Changes in Trade Openness E E, it i= 1 it Elasticity SO2 CO2 BOD All countries OECD Non-OECD Antweiler et al Cole and Elliot

16 Fig. 1. Country specific trade elasticity for SO2. 15

17 Fig. 2. Country specific trade elasticity for SO2. 16

18 Fig. 3. Country specific trade elasticity for CO2. 17

19 Fig. 4. Country specific trade elasticity for CO2. 18

20 Fig. 5. Country specific trade elasticity for BOD. 19

21 Fig. 6. Country specific trade elasticity for BOD. 20

22 21