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Convergence in carbon dioxide emissions and the role of growth and institutions: a parametric and non-parametric analysis

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Abstract

This paper examines convergence of per capita carbon dioxide (CO2) emission for a panel of 124 countries taking into account the impact of economic growth and the quality of government institutions. The analysis builds on both parametric and non-parametric panel data techniques, and we examine the β-convergence hypothesis in a neoclassical growth model setting with institutional quality as one of the independent variables influencing both emissions and output growth. The results reveal evidence in support of β-convergence of per capita CO2 emissions for the global sample, and for the sub-samples comprising OECD versus non-OECD countries and high- versus low-income countries, respectively. There is, however, heterogeneity in β-convergence and it tends to vary with the level of the initial per capita CO2 emissions. We also report evidence of a negative direct effect of institutional quality on growth in per capita CO2 emissions, especially for the global and high-income samples. However, institutional quality also promotes economic growth, thus generating a positive indirect effect on emissions growth. Overall the empirical results suggest a positive net effect of institutional quality on growth in per capita CO2 emissions in the global sample. Finally, the non-parametric approach reveals some evidence of bias in the parametric approach, in particular in the case of the estimates for the convergence parameter at either end of the distribution.

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Notes

  1. Prominent studies in this area include, for instance, Seldon and Song (1994), Shafik (1994), Grossman and Krueger (1991, 1995), with surveys provided by, for instance, Dinda (2004), Stern (2004) and Kijima et al. (2010). Many studies find evidence in support of an inverted U-shape between per capita income and various environmental indicators such as sulfur dioxide emissions, urban air pollution, deforestation, nitrogen oxides, among others. There are, however, other studies that find evidence against the inverted U-shape, particularly for global pollutants such as CO2.

  2. The governance term is used by Kaufmann et al. (2009) as a generic description of a large set of variables measuring institutional quality, although in this paper we focus on a more limited number of indicators. Yang et al. (2015) discuss problems connected to the construction of indicators of institutional quality.

  3. In the literature, there is some criticism of the use of the β-convergence approach due to the assumption that the individual countries’ growth rates will be generated by an AR(1) process and that all permanent cross-economy differences must be completely controlled for (e.g., Friedman 1992; Quah 1993, 1996; Evans and Karras 1996). This critique has resulted in alternative approaches for testing emission convergence, including the distributional approach to σ-convergence (e.g., Quah 1993, 1996; Aldy 2006; Camarero et al. 2013) and the stochastic convergence approach (e.g., Carlino and Mills 1993; Holtz-Eakin and Selden 1995; List 1999; Westerlund and Basher 2008). However, since we implement a dynamic panel data approach, which also controls for the key country-specific covariates as suggested by theory, we do not expect a significant violation of the assumptions on which the β-convergence approach is based.

  4. While Ordás Criado et al. (2011) also analyze CO2 emission dynamics, our study differs from their study in a number of important respects. Most notably, we address the issue of institutional quality, including both the direct and the indirect effects. We also consider a larger (global) sample relative to their sample of 25 European countries, while still taking into account differences across various country sub-samples. Our estimation approach is different; for the parametric models we use a dynamic system general method of moment estimator, while Ordás Criado et al. (2011) employ a least squares dummy variable estimator.

  5. We estimated the correlation coefficient between the two error terms, and it was found to be low (−0.058) and statistically insignificant at the 5 % level.

  6. The total effect of institutional quality on growth in per capita CO2 emissions can also be solved for by substituting Eq. (3) in Eq. (2) and arranging the terms.

  7. It can be noted that Kaufmann et al. (2006) find no systematic time-trends in a selection of indicators, thus suggesting that the time series information in the World Bank’s scores can be used.

  8. If we instead limit our study to start from 1996 based on the available data on the institutional quality variable coupled with the 5-year averages, this would result in having effectively two time periods (1996–2001) and (2001–2006), In such a setting it would not be appropriate to apply a dynamic GMM approach with instruments. Moreover, we also did not want to impute more values for the institutional variables to reduce the possible bias that imputations could generate on the estimates. For this reason we opted for a starting point in the year 1985. However, we also estimated our model starting from 1960 but excluded the institutional quality variables from the set of regressors. This showed no significant differences in the β-convergence parameter in relation to the model based on the 1985 starting period. These results are not reported here but are available from the authors on request.

  9. We further analyzed the effect of institutional quality by also considering each institutional quality indicator that constitutes our index separately (see further Sect. 5.3).

  10. Similar calculations can be made in relation to openness and investment. However, these variables do not enter our emission equation directly but rather indirectly via the GDP growth equation. Since the focus of our study is not on these variables, we defer such analysis for future studies.

  11. However, the work by Nguyen-Van only finds evidence of convergence for the global sample based on a cross-sectional analysis, but when extending the data to a panel, the author instead finds divergence of CO2 emissions.

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Acknowledgments

Financial support from the Swedish Energy Agency is gratefully acknowledged, as are valuable comments from the reviewers of the paper.

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Authors and Affiliations

  1. Centre for Environmental and Resource Economics, Umeå School of Business and Economics, Umeå University, Umeå, Sweden

    Brännlund Runar & Karimu Amin

  2. Department of Business Administration, Technology and Social Sciences, Economics Unit, Luleå University of Technology, Luleå, Sweden

    Söderholm Patrik

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  1. Brännlund Runar
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  2. Karimu Amin
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Correspondence to Brännlund Runar.

Appendix

Appendix

See Tables 4, 5, 6, 7, 8, 9, 10 and Figs. 3, 4, 5, 6.

Table 4 List of countries included in the study
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Table 5 Descriptive statistics
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Table 6 Variable definitions and sources
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Table 7 Correlation Matrix for the Institutional Variables
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Table 8 Calculated total effect of institutional quality on growth in per capita CO2
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Table 9 Estimates of conditional β-convergence based on four institutional variables
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Table 10 GDP growth with individual institutional variables based on global sample
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Fig. 3
figure 3

Gradient plot of the non-parametric model with Government effectiveness. Initial GDP is the same as level in GDP and this applies to all Figs. 3, 4, 5, 6

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Fig. 4
figure 4

Gradient plot of the non-parametric model with regulatory quality

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Fig. 5
figure 5

Gradient plot of the non-parametric model with rule of law

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Fig. 6
figure 6

Gradient plot of the non-parametric model with control of corruption

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Runar, B., Amin, K. & Patrik, S. Convergence in carbon dioxide emissions and the role of growth and institutions: a parametric and non-parametric analysis. Environ Econ Policy Stud 19, 359–390 (2017). https://doi.org/10.1007/s10018-016-0162-5

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  • DOI: https://doi.org/10.1007/s10018-016-0162-5

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