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Does Globalisation Worsen Environmental Quality in Developed Economies?

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Abstract

We examine the causal relationship between globalisation and CO2 emissions for 25 developed economies in Asia, North America, Western Europe and Oceania using both time series and panel data techniques, spanning the annual data period of 1970–2014. Because of the presence of cross-sectional dependence in the panel, we employ Pesaran’s Journal of Applied Econometrics 22, 265–312 (2007) cross-sectional augmented panel unit root (CIPS) test to ascertain unit root properties. The Westerlund Oxford Bulletin of Economics and Statistics 69, 709–748 (2007) cointegration test is also used to ascertain the presence of a long-run association between globalisation and carbon emissions. The long-run heterogeneous panel elasticities are estimated using the Pesaran Econometrica, 74(4), 967–1012 (2006) common correlated effect mean group (CCEMG) estimator and the Eberhardt and Teal Productivity analysis in global manufacturing production (2010) augmented mean group (AMG) estimator. The causality between the variables is examined by employing the Dumitrescu and Hurlin Economic Modelling, 29, 1450–1460 (2012) and Emirmahmutoglu and Kose Economic Modelling, 28, 870–876 (2011) Granger causality tests. The empirical results reveal that globalisation increases carbon emissions, and thus, the globalisation-driven carbon emission hypothesis is valid. This empirical analysis suggests insightful policy guidelines for policy makers using ‘globalisation’ as an economic tool for better long-run environmental policy.

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Notes

  1. It is argued that any efforts by policy makers and governments of developing and developed countries to improve the quality of the environment will not be effective enough in the long term unless and until they control for the role of globalization on the environment in the CO2 emissions function.

  2. Environmental loss or degradation comes in various forms, including loss of a country’s landmass, the disappearance of small island nations, a widespread destruction of life and property, heavy population displacement, and statelessness.

  3. http://unfccc.int/meetings/paris_nov_2015/meeting/8926.php

  4. http://blogs.worldbank.org/climatechange/reflections-paris-agreement-critical-juncture-cif

  5. Available at http://infographics.pbl.nl/website/globalco2-2015/

  6. Four developed countries, USA, Japan, Germany and UK, as well as China account for 68.7% of the global investments in clean energy projects [47].

  7. Imposing homogeneity restrictions on the parameters and cross-section independence across individual units can further mislead empirical results. To solve this issue, we apply the cross-sectional independence and slope homogeneity tests to decide the appropriate panel causality approaches proposed by Pesaran et al. [53] and Pesaran and Yamagata [54].

  8. The Environmental Kuznets Curve (EKC) theory suggests an inverted U-shaped relationship between environmental quality and economic growth in the course of economic development. Environmental degradation first increases and then decreases as economies grow [34]. Their argument for such a finding is that after a certain level of income, concern for environmental degradation becomes more relevant, and hence, institutional quality mechanisms are put in place to reduce the environmental consequences of economic development.

  9. More details of overall globalization index have been discussed in the subsequent section of results interpretation.

  10. The null hypothesis is that slope coefficients (no heterogeneity) are homogenous against no homogeneity (heterogeneity).

  11. There is no need to test for the presence or absence of cointegration between the variables, while investigating cointegration between the variables by applying the T-Y causality test.

  12. We are thankful to the anonymous referee for highlighting that the test statistics for G t are significant at 10%, and hence should be interpreted with caution. We have only reported the bootstrapped, 400 bootstraps, p values. The asymptotic p values, not reported, are however significant at 5% for both group tests. Although asymptotically not an issue, the normalization of G α by T may cause the test to reject the null too frequently. Based on the bootstrapped p values, we end up with one rejection, for G t , at the 10% level. However, as this rejection is marginal, we choose to interpret these results as evidence in favour of cointegration between the selected variables.

  13. The potential reason for the emergence of the feedback causal effect between the series is also explained in the result explanation of the D-H (2012) panel Granger causality model.

References

  1. Agénor, P. R. (2004). Does globalization hurt the poor? International Economics and Economic Policy, 1(1), 21–51.

    Article  Google Scholar 

  2. Antweiler, W., Copeland, B. R., & Taylor, M. S. (2001). Is free trade good for the environment? American Economic Review, 91(4), 877–908.

    Article  Google Scholar 

  3. Baek, J., Cho, Y., & Koo, W. W. (2009). The environmental consequences of globalization: a country-specific time-series analysis. Ecological Economics, 68(8), 2255–2264.

    Article  Google Scholar 

  4. Baltagi, B. H., Feng, Q., & Kao, C. (2012). A Lagrange multiplier test for cross-sectional dependence in a fixed effects panel data model. Journal of Econometrics, 170(1), 164–177.

    Article  Google Scholar 

  5. Breitung, J. (2005). A parametric approach to the estimation of cointegration vectors in panel data. Econometric Reviews, 24, 151–173.

    Article  Google Scholar 

  6. Breusch, T., & Pagan, A. (1980). The LM test and its application to model specification in econometrics. Review of Economic Studies, 47, 239–254.

    Article  Google Scholar 

  7. Chang, N. (2012). The empirical relationship between openness and environmental pollution in China. Journal of Environmental Planning and Management, 55(6), 783–796.

    Article  Google Scholar 

  8. Christmann, P. and Taylor, G. (2001). Globalization and the environment: determinants of firm self-regulation in China. Journal of International Business Studies, 32, 438–458.

  9. Chudik, A., Pesaran, M. H., & Tosetti, E. (2011). Weak and strong cross section dependence and estimation of large panels. Econometrics Journal, 14, 45–90.

  10. Cole, M. A., Elliott, R. J., & Zhang, J. (2011). Growth, foreign direct investment, and the environment: evidence from Chinese cities. Journal of Regional Science, 51(1), 121–138.

    Article  Google Scholar 

  11. Copeland, B. R. (2005). Policy endogeneity and the effects of trade on the environment. Agricultural and Resource Economics Review, 34(1), 1–15.

    Article  Google Scholar 

  12. Copeland, B. R., & Taylor, M. S. (1994). North-south trade and the environment. The Quarterly Journal of Economics, 109(3), 755–787.

    Article  Google Scholar 

  13. Copeland, B. R., & Taylor, M. S. (2003). Trade and the environment. Princeton: Princeton University Press.

    Book  Google Scholar 

  14. Copeland, B. R., & Taylor, M. S. (2004). Trade, growth, and the environment. Journal of Economic Literature, 42, 7–71.

    Article  Google Scholar 

  15. Dreher, A. (2006). Does globalization affect growth? Evidence from a new index of globalization. Applied Economics, 38, 1091–1110.

    Article  Google Scholar 

  16. Dumitrescu, E.-I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29, 1450–1460.

    Article  Google Scholar 

  17. Eberhardt, M., & Teal, F. (2010). Productivity analysis in global manufacturing production. Department of Economics, University of Oxford.

  18. Emirmahmutoglu, F., & Kose, N. (2011). Testing for Granger causality in heterogeneous mixed panels. Economic Modelling, 28, 870–876.

    Article  Google Scholar 

  19. Emissions Gap Report (2012). United Nations Environment Programme (UNEP), Nairobi, Kenya.

  20. Fisher, R. (1932). Statistical methods for research workers. London: Oliver and Boyd.

  21. Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37, 424–438.

    Article  Google Scholar 

  22. Groen, J. J. J., & Kleibergen, F. (2003). Likelihood-based cointegration analysis in panels of vector error-correction models. Journal of Business & Economic Statistics, 21, 295–318.

    Article  Google Scholar 

  23. Grossman, G. M., & Krueger, A. B. (1991). Environmental impacts of a North American free trade agreement (no. w3914). Cambridge: National Bureau of Economic Research.

    Book  Google Scholar 

  24. Grossman, G. M., & Krueger, A. B. (1995). Economic growth and the environment. Quarterly Journal of Economics, 110(2), 353–377.

    Article  Google Scholar 

  25. Hawken, P., Lovins, A., & Lovins, H. (2008). Natural capitalism: creating the next industrial revolution. Boston: Back Bay Books.

    Google Scholar 

  26. Heckscher, E. (1919). The effect of foreign trade on the distribution of income. Ekonomik Tidskrift 31. Reprinted in readings in the theory of international trade, edited by the American Economic Association, USA.

  27. Helpman, E. (1998). Explaining the structure of foreign trade: where do we stand. Review of World Economy, 134(4), 573–589.

    Google Scholar 

  28. Im, K. S., Lee, J., & Tieslau, M. (2005). Panel LM unit root tests with level shifts. Oxford Bulletin of Economics and Statistics, 67, 393–419.

    Article  Google Scholar 

  29. Jayadevappa, R., & Chhatre, S. (2000). International trade and environmental quality: a survey. Ecological Economics, 32(2), 175–194.

    Article  Google Scholar 

  30. Jena, P. R. and Ulrike, G. (2008). Growth-trade-environment nexus in India. https://www.econstor.eu/handle/10419/39895. Tidskr., 497–512 (Translated as chapter 13 in American Economic Association, Readings in the Theory of International Trade, Philadelphia: Blakiston, 1949,272–300, and a new translation is provided in Flam and Flanders).

  31. Kanjilal, K., & Ghosh, S. (2013). Environmental Kuznets curve for India: evidence from tests for cointegration with unknown structural breaks. Energy Policy, 56, 509–515.

    Article  Google Scholar 

  32. Kapetanios, G., Mumtaz, H., Stevens, I., & Theodoridis, K. (2012) Assessing the economy-wide effects of quantitative easing. Bank of England WP No. 443. Available at: http://www.bankofengland.co.uk/publications/Documents/workingpapers/wp443.pdf.

  33. Kao, C. (1999). Spurious regression and residual based tests for cointegration in panel data. Journal of Econometrics, 90, 1–44.

    Article  Google Scholar 

  34. Kuznets, S. (1955). Economic growth and income inequality. The American Economic Review, 45(1), 1–28.

    Google Scholar 

  35. Kyoto Protocol Summit (1997) Kyoto, Japan, on 11 December 1997

  36. Larsson, R., Lyhagen, J., & Lothgren, M. (2001). Likelihood-based cointegration tests in heterogeneous panels. The Econometrics Journal, 4, 109–142.

    Article  Google Scholar 

  37. Lean, H. H., & Smyth, R. (2010). CO2 emissions, electricity consumption and output in ASEAN. Applied Energy, 87(6), 1858–1864.

    Article  CAS  Google Scholar 

  38. Lee, K. I.-H., & Min, B. (2014). Globalization and carbon constrained global economy: a fad or a trend? Journal of Asia-Pacific Business, 15(2), 105–121.

    Article  Google Scholar 

  39. Ling, C. H., Ahmed, K., Muhamad, R. B., & Shahbaz, M. (2015). Decomposing the trade-environment nexus for Malaysia: what do the technique, scale, composition, and comparative advantage effect indicate? Environmental Science and Pollution Research, 22(24), 20131–20142.

    Article  CAS  Google Scholar 

  40. Machado, G. V. (2000). Energy use, CO2 emissions and foreign trade: an IO approach applied to the Brazilian case. In Thirteenth international conference on input–output techniques (pp. 21–25). Macerata, Italy.

  41. Managi, S. (2004). Trade liberalization and the environment: carbon dioxide for 1960–1999. Economics Bulletin, 17(1), 1–5.

    Google Scholar 

  42. Managi, S., & Jena, P. R. (2008). Environmental productivity and Kuznets curve in India. Ecological Economics, 65(2), 432–440.

    Article  Google Scholar 

  43. Managi, S., Akira, H. and Tetsuya, T. (2008). Does trade liberalization reduce pollution emissions? RIETI Discussion Paper Series 08-E-013.

  44. McCarney, G. R. & Adamowicz, W. L. (2006). The effects of trade liberalization of the environment: an empirical study. In 2006 annual meeting, August 12–18, 2006, Queensland, Australia (no. 25297). International Association of Agricultural Economists, Milwaukee.

  45. Ohlin, B. (1933). Interregional and international trade. Harvard: Cambridge University Press.

    Google Scholar 

  46. McCoskey, S., & Kao, C. (1998). A residual-based test of the null of cointegration in panel data. Econometric Reviews, 17, 57–84.

    Article  Google Scholar 

  47. Paramati, S. R., Ummalla, M., & Apergis, N. (2016). The effect of foreign direct investment and stock market growth on clean energy use across a panel of emerging market economies. Energy Economics, 56, 29–41.

    Article  Google Scholar 

  48. Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics, 61, 653–670.

  49. Pesaran M.H. (2004) General diagnostic tests for cross section dependence in panels. Cambridge Working Papers in Economics, No 435, Universty of Cambridge, and CESifo Working Paper Series No. 1229.

  50. Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74(4), 967–1012.

    Article  Google Scholar 

  51. Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross section dependence. Journal of Applied Econometrics, 22, 265–312.

    Article  Google Scholar 

  52. Pesaran, M. H., & Smith, R. (1995). Estimating Long-run Relationships from Dynamic Heterogeneous Panels. Journal of Econometrics, 68, 79–113.

  53. Pesaran, M. H., Ullah, A., & Yamagata, T. (2008). A bias-adjusted LM test of error cross-section independence. The Econometrics Journal, 11, 105–127.

    Article  Google Scholar 

  54. Pesaran, M. H., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142, 50–93.

    Article  Google Scholar 

  55. Runge, C. F. (1994). Freer trade, protected environment. New York: Council on Foreign Relations Press.

    Google Scholar 

  56. Saboori, B., Sulaiman, J., & Mohd, S. (2012). Economic growth and CO2 emissions in Malaysia: a cointegration analysis of the environmental Kuznets curve. Energy Policy, 51, 184–191.

    Article  Google Scholar 

  57. Shafik, N., & Bandyopadhyay, S. (1992). Economic growth and environmental quality: time-series and cross-country evidence (Vol. 904). Washington DC: World Bank Publications.

    Google Scholar 

  58. Shahbaz, M., Lean, H. H., & Shabbir, M. S. (2012). Environmental Kuznets curve hypothesis in Pakistan: cointegration and Granger causality. Renewable and Sustainable Energy Reviews, 16(5), 2947–2953.

    Article  Google Scholar 

  59. Shahbaz, M., Hye, Q. M. A., Tiwari, A. K., & Leitão, N. C. (2013). Economic growth, energy consumption, financial development, international trade and CO2 emissions in Indonesia. Renewable and Sustainable Energy Reviews, 25, 109–121.

    Article  Google Scholar 

  60. Shahbaz, M., Bhattacharya, M. and Ahmed, K. (2015a). Growth-globalization-emissions nexus: the role of population in Australia. Department of Economics Discussion Paper Series (discussion paper 23–15). Monash University Clayton, Austeralia.

  61. Shahbaz, M., Mallick, H., Mahalik, M. K., & Loganathan, N. (2015b). Does globalization impede environmental quality in India? Ecological Indicators, 52, 379–393.

    Article  Google Scholar 

  62. Shin, S. (2004). Economic globalization and the environment in China: a comparative case study of Shenyang and Dalian. The Journal of Environment & Development, 13(3), 263–294.

    Article  Google Scholar 

  63. Solarin, S. A. (2014). Tourist arrivals and macroeconomic determinants of CO2 emissions in Malaysia. Anatolia, 25(2), 228–241.

    Article  Google Scholar 

  64. Stine, R. A. (1987). Estimating properties of autoregressive forecasts. Journal of the American Statistical Association, 82(400), 1072–1078.

    Article  Google Scholar 

  65. Swamy, P. A. (1970). Efficient inference in a random coefficient regression model. Econometrica: Journal of the Econometric Society, 38, 311–323.

  66. Tiwari, A. K., Shahbaz, M., & Hye, Q. M. A. (2013). The environmental Kuznets curve and the role of coal consumption in India: cointegration and causality analysis in an open economy. Renewable and Sustainable Energy Reviews, 18, 519–527.

    Article  Google Scholar 

  67. Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics, 66, 225–250.

    Article  Google Scholar 

  68. United Nations Climate Change Conference (2015). COP 21 or CMP 11, Paris, France, from 30 November to 12 December 2015

  69. Westerlund, J. (2007). Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics, 69, 709–748.

    Article  Google Scholar 

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Authors and Affiliations

  1. Montpellier Business School, Montpellier, France

    Muhammad Shahbaz & Syed Jawad Hussain Shahzad

  2. Department of Humanities and Social Sciences, National Institute of Technology (NIT), Rourkela, Sundargarh, Odisha, 769008, India

    Mantu Kumar Mahalik

  3. Lebow College of Business, Drexel University, United States, Montpellier Business School, Montpellier, France

    Shawkat Hammoudeh

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  1. Muhammad Shahbaz
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Shahbaz, M., Shahzad, S.J.H., Mahalik, M.K. et al. Does Globalisation Worsen Environmental Quality in Developed Economies?. Environ Model Assess 23, 141–156 (2018). https://doi.org/10.1007/s10666-017-9574-2

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