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Political Stability: an Impetus for Spatial Environmental Spillovers

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Abstract

Our paper firstly investigates political stability as an underlying mechanism for the phenomenon of environmental spillovers across countries. If political stability exerts a significant impact on environmental quality and it is contagious across country borders, political stability of neighboring countries would be an impetus for environmental spillovers. Taking into account structural breaks in empirical analysis, we test these hypotheses based on an annual country-level panel data set during 2002 to 2018. Utilizing the dynamic ordinary least square (DOLS) method and the panel-based Vector Error Correction (VEC) model, we demonstrate that an improvement in political stability in one country not only enhances the environmental quality of its own, but also meliorates that of its neighbors through the spillovers of political stability. Our results also withstand sensitivity checks for cross-sectional dependence in the panel. Several policy implications are provided in accordance with our findings.

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Notes

  1. See Rupasingha et al. (2004), Stern (2004), McPherson and Nieswiadomy (2005), Sigman (2005), Maddison (2006), Maddison (2007), Mizobuchi and Kakamu (2007), Pandit and Laband (2007), Zhao et al. (2011), Amin (2016), Liu et al. (2017), Marbuah and Amuakwa-Mensah (2017), Moosa (2017), Balado-Naves et al. (2018), You and Lv (2018), Chen et al. (2019) and Zhao et al. (2021), etc.

  2. See Alesina and Perotti (1996), Alesina et al. (1996), Perotti (1996), Svensson (1998), Fredriksson and Svensson (2003), Aisen and Veiga (2013), Nawaz (2015), Singhal and Nilakantan (2016) and Uddin et al. (2017).

  3. During our sample span, examples of crucial events that led to structural breaks may include the establishment of various emission trading market (e.g., EU Emission Trading System), the Arab Spring-Overthrows, the Iraq-U.S. war and trade liberalization in Southeast Asia, etc.

  4. The data is jointly compiled by the Yale Center for Environmental Law and Policy (YCELP) and the Center for International Earth Science Information Network (CIESIN) at Columbia University.

  5. The EPI overall reflects facets of two broad objectives: environmental health and ecosystem vitality. Specific policy categories considered in the EPI index include environmental health, water, air pollution, biodiversity and habitat, forests, fisheries, agriculture, climate change and energy.

  6. Generally, comprehensive environmental evaluating measure is rarely utilized in environmental studies ( Lisciandra and Migliardo 2017). As for the literature on spatial environmental relationships, various measures that only encompass single or limited dimensions of national environmental quality are applied, including emission concentration (Stern 2004; Maddison 2006; Maddison 2007; Mizobuchi and Kakamu 2007; Liu et al. 2017; Marbuah and Amuakwa-Mensah 2017; Balado-Naves et al., 2018; You and Lv 2018; Chen et al., 2019), species imperilment and biodiversity conservation (McPherson and Nieswiadomy 2005; Pandit and Laband 2007; Amin, 2016), toxic releases (Ruppasingha et al. 2004), waste water (sigman 2005; Lipscomb and Mobarak 2017; Zhao et al., 2021), forest coverage (Zhao et al., 2011), etc.

  7. For instance, Al-Mulali and Ozturk (2015) argue that \({\mathrm{CO}}_{2}\) or \({\mathrm{SO}}_{2}\) emissions only represent a small portion of the environmental quality.

  8. Previous studies have found that successful environmental regulations can spill over among bordering jurisdictions. For instance, see Amin (2016), Hecker et al. (2020), etc.

  9. For instance, the transboundary pollution problems present under the context of acid rain (Maler 1990a; Maler 1990b) and water pollution when a river flows through multiple jurisdictions (Sigman 2002; Sigman 2005), etc.

  10. By investigating 14 MENA countries for the period 1996–2012, the authors demonstrate that political stability and ecological footprints are cointegrated in the long run and political stability positively impacts the ecological footprints.

  11. See the United Nations Environment Programme, Chapter 4, available at https://www.unep.org.

  12. For a detailed review of this strand of literature, please see Hu et al. (2021).

  13. Westerlund (2006) suggests employing the Bai and Perron (1998) procedure for the estimation of the number of breaks and corresponding locations.

  14. In 2018, the EPI index included 180 economies, covering 99% of the global population, 98% of land and 97% of global GDP. The 2002–2018 EPI indexes are utilized in the empirical study, which can be downloaded at http://archive.epi.yale.edu/.

  15. More detailed information of the WGI including data construction methodology, please refer to Kaufmann et al. (2010).

  16. See Dasgupta et al. (2002), Copeland and Taylor (2004) and Dinda (2000) for surveys of the literature.

  17. See Knack and Keefer (1995) for the seminal work.

  18. For clear display of the results, we do not report coefficients of the interaction terms between the independent variables and the structural break dummies, but rather focus on the coefficients of the explanatory variables. The full results are available upon requests.

  19. For space saving, we report the results when EPI or Stability is the dependent variable, respectively. Additionally, we only report estimated coefficients of the lag one terms of the explanatory variables (except for the lagged dependent variables) in Eq. (10) and (11). The remainders are offered upon requests.

  20. We also find robust results that each series is stationary at I(1) and that there exists a long-run cointegration relationship between EPI and Stability when we perform alternative versions of panel unit tests and panel cointegration tests that control for CD. Specifically, we apply the the cross-sectionally dependent augmented dickey fuller (CADF) test and cross-sectionally augmented IPS (CIPS) panel unit root test proposed by Pesaran (2007) and the Westlund (2007) error-correction-based panel conintegration test. The results are available upon requests.

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Acknowledgements

We would like to thank the editor and the anonymous referees for helpful comments. Bo Sui gratefully acknowledges the financial support from the Science and Technology Department of Shaanxi Province (Grant No. 2020KRM120). Yin Chu gratefully acknowledges the financial support from “the Fundamental Research Funds for the Central Universities”, Zhongnan University of Economics and Law (Grant No. 2722021BZ041). All errors remain our own.

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  1. International Business School, Shaanxi Normal University, Xi’an, Shaanxi, China

    Bo Sui

  2. Department of Marketing and Management, Shih Chien University, Kaohsiung, Taiwan

    Chun-Ping Chang

  3. Wenlan School of Business, Zhongnan University of Economics and Law, Wuhan, Hubei, China

    Yin Chu

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Appendix

Appendix

See Figs. 1 and 2.

Fig. 1
figure 1

Plots of Moran diagrams for EPI

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Fig. 2
figure 2

Plots of Moran diagrams for Stability

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Table 1 General descriptive statistics of dependent and explanatory variables
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Table 2 Test for spatial autocorrelation of EPI: 2002–2018
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Table 3 Test for spatial autocorrelation of Stability: 2002–2018
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Table 4 Carrion-i-Silvestre et al. (2005) panel unit root test with structural breaks
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Table 5 The Westerlund (2006) panel cointegration test with structural breaks
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Table 6 Estimated Breaks under Westerlund (2006) Panel Cointegration Test (2002–2018, Full sample)
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Table 7 The panel DOLS tests with EPI as the dependent variable
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Table 8 VECM panel causality tests
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Table 9 Pesaran (2004) cross-sectional dependence (CD) test
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Table 10 Dumistrescu and Hurlin (2012) causality test
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Sui, B., Chang, CP. & Chu, Y. Political Stability: an Impetus for Spatial Environmental Spillovers. Environ Resource Econ 79, 387–415 (2021). https://doi.org/10.1007/s10640-021-00568-8

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