Abstract
This study explores the effect of trade openness on deforestation. Previous studies do not find a clear effect of trade openness on deforestation. We use updated data on the annual rate of deforestation for 142 countries from 1990 to 2003, treat trade and income as endogenous, and take into consideration an adjustment process by applying a dynamic model. We find that an increase in trade openness increases deforestation for non-OECD countries while slowing down deforestation for OECD countries. There is a possibility that both capital–labor and environmental-regulation effects have a negative impact on deforestation in developing countries, whereas the opposite holds in developed countries.
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Notes
This study includes these key variables in the estimation.
Recent empirical studies find that common EKC results are highly sensitive to changes in functional forms. The econometric applications have been criticized because of a lack of robust econometric methods (see Tsurumi and Managi (2010), for a review). This concern has inspired recent studies using semi-parametric or non-parametric techniques.
They prefer these GMM methods rather than fixed effects because fixed effects fail to account for the correlation between the transformed initial level of forest cover and the transformed disturbance term.
Van and Azomahou (2007) find a statistically significant coefficient for population density, but the sign is negative, and hence it is difficult to interpret.
Exceptions are the results obtained for Asia in Cropper and Griffiths (1994) and the results presented in Frankel and Rose (2005). Cropper and Griffiths (1994) find statistically significant positive coefficient estimates. According to Cropper and Griffiths (1994), a possible explanation for this finding concerns the importance of forest plantations in Asia. In contrast, Frankel and Rose (2005) obtain a statistically insignificant result. However, a relatively small sample size may affect this result.
To address potential simultaneous problems, they follow Frankel and Rose (2005). Frankel and Rose (2005) consider trade openness and income endogenously. They address the potential simultaneity of trade, environment, and income by applying instrumental variables estimations using a gravity model of bilateral trade and endogenous growth from neoclassical growth equations. We note that they do not consider the induced effects and the decomposed effects, such as the scale, technique, and composition effects.
To show a country’s comparative advantage, a country’s capital–labor ratio and per capita income levels are expressed relative to the world average for each year.
The data are based on the remote sensing survey conducted in 1990, 2000, and 2005. While data are provided by countries for years 1990, 2000, 2005, and 2010, data for intermediate years are estimated for FAO using linear interpolation and tabulation (FAO 2010).
Following Van and Azomahou (2007), we use two indices on political rights and civil liberties, the values of which vary from 1 (free) to 7 (not free), respectively. We aggregate these two variables to obtain an index of political institutions, scaling from 2 to 14.
\( {\text{OC}}_{it} \) reflects the indirect effect of a trade-induced change in income on emissions.
As we note in footnote 6, we obtain similar trade elasticities of deforestation among columns 1, 3, and 4 in Table 3.
As the sample averages of RS and RKL are larger than 1 in OECD countries and are less than 1 in non-OECD countries, we see that developed countries have a comparative advantage in capital-intensive production and enforce relatively strict environmental policies. Meanwhile, developing countries have a comparative advantage in labor-intensive production and have relatively lax environmental policies.
When we used the loess function in place of the cubic spline function, the results were almost the same.
We use the normal distribution for estimation. The link function is the identity.
We include country dummy and year dummy to take into consideration individual and time fixed effects.
Unfortunately, this survey has not yet been reflected to FAOSTAT at the present moment, so that our study use the data based on FAO (2010).
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Appendix
Appendix
Table A: List of countries (142 countries)
Afghanistan (2.63) | Dominica (0.56) | Kuwait (−3.25) | Russia (0.00) |
Albania (0.05) | Ecuador (1.57) | Kyrgyz Republic (−0.26) | Rwanda (−2.30) |
Algeria (−1.68) | Egypt (−2.90) | Laos (0.46) | Samoa (−2.14) |
Angola (0.21) | El Salvador (1.49) | Latvia (−0.39) | Senegal (0.50) |
Argentina (0.43) | Equatorial Guinea (0.86) | Lebanon (−0.81) | Serbia and Montenegro (−0.34) |
Armenia (1.32) | Estonia (−0.36) | Liberia (1.64) | Sierra Leone (0.66) |
Australia (0.18) | Ethiopia (1.00) | Lithuania (−0.49) | Slovak Republic (−0.02) |
Austria (−0.15) | Fiji (−0.16) | Madagascar (0.45) | Slovenia (−0.41) |
Bahrain (−6.41) | Finland (−0.10) | Malawi (0.89) | Solomon Islands (1.58) |
Bangladesh (0.05) | France (−0.48) | Malaysia (0.43) | Somalia (0.98) |
Belarus (−0.48) | Gabon (0.05) | Mali (0.74) | Spain (−1.95) |
Benin (2.22) | Gambia (−0.42) | Mauritania (2.80) | Sri Lanka (1.26) |
Bolivia (0.44) | Georgia (−0.00) | Mauritius (0.32) | St. Vincent & Grens (−0.77) |
Brazil (0.55) | Germany (−0.24) | Mexico (0.49) | Sudan (0.81) |
Brunei Darussalam (0.80) | Ghana (1.97) | Moldova (−0.21) | Sweden (−0.04) |
Bulgaria (−0.45) | Greece (−0.87) | Mongolia (0.75) | Switzerland (−0.37) |
Burkina Faso (0.34) | Grenada (0.18) | Morocco (−0.11) | Syrian Arab Republic (−1.46) |
Burundi (3.98) | Guatemala (1.22) | Mozambique (0.25) | Tajikistan (−0.04) |
Cambodia (1.32) | Guinea (0.66) | Myanmar (1.28) | Tanzania (1.06) |
Cameroon (0.95) | Guinea-Bissau (0.45) | Nepal (1.92) | Thailand (0.67) |
Cape Verde (−2.83) | Guyana (0.00) | Netherlands (−0.39) | Togo (3.59) |
Central African Rep. (0.13) | Haiti (0.65) | New Zealand (−0.54) | Trinidad And Tobago (0.27) |
Chad (0.63) | Honduras (3.01) | Nicaragua (1.56) | Tunisia (−3.60) |
Chile (−0.37) | Hungary (−0.61) | Niger (3.10) | Turkey (−0.35) |
China (−1.44) | Iceland (−4.22) | Nigeria (2.80) | Uganda (1.97) |
Colombia (0.08) | India (−0.43) | Norway (−0.19) | Ukraine (−0.22) |
Comoros (4.60) | Indonesia (1.78) | Pakistan (1.83) | United Arab Emirates (−1.86) |
Congo, Dem. Rep. (0.35) | Iraq (−0.16) | Panama (0.14) | United Kingdom (−0.61) |
Congo, Rep. (0.08) | Ireland (−2.97) | Papua New Guinea (0.45) | United States (−0.11) |
Costa Rica (0.55) | Israel (−0.68) | Paraguay (0.89) | Uruguay (−3.80) |
Cote D Ivoire (−0.11) | Italy (−1.18) | Peru Nuevos (0.14) | Uzbekistan (−0.53) |
Croatia (−0.06) | Jamaica (0.12) | Philippines (2.63) | Venezuela (0.57) |
Cuba (−1.82) | Japan (0.02) | Poland (−0.22) | Vietnam (−2.22) |
Cyprus (−0.59) | Kazakhstan (0.17) | Portugal (−1.38) | Zambia (0.96) |
Czech Republic (−0.04) | Kenya (0.34) | Romania (0.00) | Zimbabwe (1.54) |
Denmark (−0.81) | Korea (0.11) |
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Tsurumi, T., Managi, S. The effect of trade openness on deforestation: empirical analysis for 142 countries. Environ Econ Policy Stud 16, 305–324 (2014). https://doi.org/10.1007/s10018-012-0051-5
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DOI: https://doi.org/10.1007/s10018-012-0051-5