Spillover effects of economic globalization on CO2 emissions: A spatial panel approach (original) (raw)

Highlights

Abstract

This paper investigates the spatial effects of economic globalization on CO2 emissions in a panel of 83 countries over the period 1985–2013. We apply the spatial panel method to address the problems of spatial dependency and the spillover effect among neighboring countries. First, the estimation results verify the existence of the spatial correlations in CO2 emissions across nations. Second, we find that the indirect effect of economic globalization on CO2 emissions is so significantly negative to overcome the positive direct effect, which implies a negative and significant total effect, so our results suggest that being surrounded by highly globalized countries has a positive effect on the environmental quality. Finally, we also find strong evidence for the inverted-U shaped EKC relationship between CO2 emissions and income.

Introduction

With the dizzying-speed development of global economic integration and trade freedom, followed by the growth of global economy, people attach greater weight to how such trends will influence the environment. Economic globalization usually refers to the process by which different economies become more and more integrated and concurrent with increasing economic globalization, and there has been much research into its consequences. A recent work by Dreher et al. (2008) summarizes some findings on the influences of globalization on economic growth, government spending and within-country inequality. Little is known, however, about the effects of globalization on environment.

Even though there has been many published papers on the effects of globalization on environment in recent years, there are still many aspects of this concept that need further scrutiny. For instance, their understanding of the relationship between globalization and carbon dioxide (CO2) emissions is highly partial. Previous studies tend to measure the incidence of economic globalization using the degree of trade openness (Jorgenson and Givens, 2014; Li et al., 2015; Le et al., 2016). From a policy perspective, the association between trade openness and CO2 emissions is undoubtedly relevant, but trade openness is not an adequate measure to capture the incidence of other aspects of economic globalization, such as the extent of capital controls, the spread of technology, and knowledge beyond borders. Therefore, ignoring these factors, while centering exclusively on trade openness, can adversely affect our perception of the link between economic globalization and CO2 emissions. In addition, sharp opposing views on the impact of trade openness on CO2 emissions are widespread. There are arguments in favor of positive impact of trade openness on CO2 emissions. For instance, a number of studies have found that increased openness can worsen environmental quality. The reason is that increased international trade compels governments to lower production costs within their jurisdiction by neglecting to enact or enforce laws to protect the environment (Drezner, 2000). This view of the influence of trade openness on the environment is consistent with Managi and Kumar (2009) and Kellenberg (2009). However, the proponents hold that, based on the theories of international trade and environmental economics, trade liberalization can bring economic benefits that can be distributed in a manner to protect the environment. Moreover, lowering barriers to trade and foreign investment encourages firms to transfer environmental/green technologies and management systems from countries with stricter environmental standards to countries, which lack access to environmental capabilities and technologies (Christman and Taylor, 2001). Meanwhile, the globalized information and knowledge have made it possible for the public to be more aware of ecological issues and this has generated greater mobilization. This view is empirically supported by a larger number of studies. Antweiler et al. (2001) find that trade openness is associated with reduced pollution as proxied by SO2 concentrations. A more recent study by Zhang et al. (2017a); Zhang et al. (2017b) also reports that trade openness negatively and significantly affects emissions in 10 newly industrialized countries. However, there are still other studies find that the impact of trade on environmental quality is varied by the level of income. Specially, Le et al. (2016) demonstrate that trade openness has a benign effect on the environment in high-income countries, but a harmful effect in low and middle income countries.

Another drawback of the existing studies is that when examining the relationship between globalization and CO2 emissions (Lim et al., 2015; Shahbaz et al., 2016), they fails to take the spatial dependence into account. Spatial dependence refers to the phenomenon that one observation in a sample of cross sectional observations is dependent on other cross sectional observations. For example, if the economic growth rate is relatively high in one country, the neighboring regions may imitate its economic development model and industrial structural configuration, and thereby the environment quality of neighboring countries will be affected by the development and environment policies of this country. Therefore a well performing country may induce a positive economic impact on its neighboring countries and regions – the positive economic impact would arguably lead to higher CO2 emissions in the neighboring countries.1 Switching back to the discussion in environmental impacts of economic globalization, the traditional panel econometric techniques, like fixed/random effects and GMM method, would lead to biased estimations because of ignoring the spatial correlations. More specifically, they only obtain the direct effect of economic globalization on CO2 emissions, but fail to get the indirect (or spatial spillover) effect of economic globalization on CO2 emissions. Here the indirect effects mean the effects brought by the neighboring country's economic globalization. In recent years, a growing body of environmental literature has estimated the determinant of CO2 emissions using spatial econometric models to control for spatial dependence (see, Zhao et al., 2014; Kang et al., 2016; Meng and Huang, 2017; Meng et al., 2017).

This paper aims to overcome this omission in the literature, and to provide a comprehensive analysis of the relationship between economic globalization and CO2 emissions. To achieve this goal, first, we use the KOF index of economic globalization constructed by Dreher (2006). The KOF index of economic globalization is based on eight variables associated with different dimensions of economic integration (see Appendix Table A1 for more detail), and this aggregate index distinguishes between the different aspects of economic integration, which allows us to adopt a broader perspective than existing studies. Second, we use the recently developed spatial panel data model to explore the influence of economic globalization on CO2 emissions, and a comparative analysis between the non-spatial panel model and spatial panel model is conducted to validate the spatial spillovers effects of variables in order to provide more rigorous references for policymakers.

The rest of this paper is organized as follows. In the next Section, we present the theoretical framework of the model and the data. Section 3 introduces the spatial econometric method and testing procedures. The estimation results and discussions are showed in Section 3.1. Finally, Section 4 concludes this paper and provides some policy suggestions.

Section snippets

Theoretical framework of the model

In this paper, we explore the factors that influence global CO2 emissions within the STIRPAT model framework (Dietz and Rosa, 1997). The basic form of the STIRPAT model isIit=aPitbAitcTitdeit,where I denotes the environmental impact, P, A, T denote population, affluence, and technology, respectively. a denotes the constant terms. b, c and d are the estimated parameters. e denotes the random disturbance. Using natural logarithms, the STRIPAT model can be converted to a convenient linear

Spatial econometrics model

Tobler's First Law of Geography (Tobler, 1970) states that all attributed values of indicators on a geographic surface are related to one other, but closer indicators are more strongly related than the more distant ones. Based on this theory, no region is isolated. Omitting the spatial correlations in an econometric analysis when variables are spatially correlated would lead to bias (Anselin 1988). Maddison (2006) argues that the spatial relationship incorporated in the data would cause

Spatial autocorrelation test

The Moran's I index is used to test the extent of spatial autocorrelation. A positive Moran's I value with statistical significance indicates spatial clustering and a negative and significant Moran's I value indicates spatial dispersion across the sample countries (Anselin and Florax 1995). Using data on CO2 emissions per capita in 83 countries, we calculate the Moran'I index value for each year from 1985 to 2013, as well as the averaged values. As can be seen from Table 2, the Moran's I index

Conclusions and policy implications

While economists have been analyzing how globalization affects CO2 emissions for decades, their understanding of the link between globalization and CO2 emissions is highly partial, and little attention has been paid to examine this issue using a spatial panel data model approach. The main contribution of this paper is thus to explore the effects of globalization, measured by the KOF index of economic globalization constructed by Dreher (2006), on CO2 emissions for 83 countries using the Spatial

Acknowledgements

We thank especially the Editor and two anonymous referees for very constructive remarks and suggestions. They make some pertinent comments on the previous version of this article, and also give us some suggestions and hints. Nevertheless, any shortcomings that remain in this research paper are solely our responsibility.

References (47)

Urbanization, democracy, bureaucratic quality, and environmental degradation

J. Policy Model

(2017)

Environmental Kuznets curves: new evidence on both panel andcountry-level CO2 emissions

Energy Econ.

(2016)

A spatial panel data approach to estimating US state-level energy emissions

Energy Econ.

(2013)

Testing for unit roots in heterogeneous panels

J. Econ.

(2003)

Environmental Kuznets curve for CO2 emissions in China: a spatial panel data approach

Ecol. Indic.

(2016)

An empirical investigation of the pollution haven effect with strategic environment and trade policy

J. Int. Econ.

(2009)

Trade openness and environmental quality: International evidence

Energy Policy

(2016)

Unit root tests in panel data: asymptotic and finite-sample properties

J. Econ.

(2002)

New evidence on trade-environment linkage via air visibility

Econ. Lett.

(2015)

Environmental Kuznets curves: a spatial econometric approach

J. Environ. Econ. Manag.

(2006)

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This research is partially supported by National Natural Science Foundation of China (No. 71703140, 11601086), and also supported by Natural Science Foundation of Hunan Province of China (No. 2017JJ3293) and Fujian Social Science Planning Fund Program (No. FJ2016C092). Any shortcomings that remain in this research paper are solely our responsibility.

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