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The environmental Kuznets curve after 25 years

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Journal of Bioeconomics Aims and scope

Abstract

The environmental Kuznets curve (EKC) has been the dominant approach among economists to modeling aggregate pollution emissions and ambient concentrations over the last quarter century. Despite this, the EKC was criticized almost from the start and decomposition approaches have been more popular in other disciplines working on global climate change. More recently, convergence approaches to modeling emissions have become popular. This paper reviews the history of the EKC and alternative approaches. Applying an approach that synthesizes the EKC and convergence approaches, I show that convergence is important for explaining both pollution emissions and concentrations. On the other hand, economic growth has a strong positive effect on carbon dioxide, sulfur dioxide, and industrial greenhouse gas (GHG) emissions, but weaker effects on non-industrial GHG emissions and concentrations of particulates. Negative time effects are important for sulfur and industrial and non-industrial GHG emissions. Even for particulate concentrations, economic growth only reduces pollution at very high income levels. Future research should focus on developing and testing alternative theoretical models and investigating the non-growth drivers of pollution reduction.

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Notes

  1. As discussed below, this does not actually seem to be an important factor in explaining reductions in emissions intensities in developed countries.

  2. Under additive preferences \(\phi =1/\eta \). Under non-homotheticity a more complex expression determines whether there is an EKC or not. Homotheticity implies that the ratio of the marginal utility of consumption and the marginal utility of environmental quality is a function of the ratio of consumption and environmental quality alone.

  3. This conflicts with actual evidence on policies in developing countries (Dasgupta et al. 2002; Stern and Jotzo 2010; Zhao et al. 2013).

  4. This is despite the findings of Sala-i-Martin et al. (2004) and Rockey and Temple (2016) that initial GDP should be included in a growth regression. In the sample of 136 countries used by Anjum et al. (2014) and discussed in Sect. 4, below, the R\(^{2}\) for a regression of the growth rate of GDP per capita from 1971 to 2010 on log GDP per capita in 1971 has an R\(^{2}\) of 0.0025.

  5. As the cross-sectional dimension of a panel increases relative to its time series dimension, the regression coefficient estimators become increasingly “classical” and the spurious regression problem diminishes. However, most EKC studies do not have a sufficiently large cross-sectional dimension to allow researchers to ignore cointegration (Entorf 1997).

  6. By negative time effect, I mean that emissions fall over time, ceteris paribus.

  7. Sanchez and Stern (2016) use the mean of log GDP per capita over the period rather than initial GDP per capita and initial emissions intensity rather than initial emissions.

  8. This is, of course, also a test that the growth rate of emissions (or concentrations) intensity declines with rising income per capita or the declining CEIG hypothesis (Stefanski 2013; Chen 2016).

  9. For details of the data sources and coefficient estimates for the controls, please see the original papers. Other variables can be considered and some of these were tested such as regional dummies, which are emphasized by Rockey and Temple (2016) in the growth regression context. We did not find that the latter had systematically significant effects.

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Acknowledgements

I thank my colleagues and students who collaborated on the empirical studies discussed in this paper: Reyer Gerlagh, Paul Burke, Luis Sanchez, Jeremy van Dijk, and Zeba Anjum. I also thank two anonymous referees for useful comments and Andreas Chai for inviting me to contribute to this special issue and participate in the workshop: ‘Managing the Transition to a Sustainable Economy’ at Griffith University.

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    David I. Stern

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Stern, D.I. The environmental Kuznets curve after 25 years. J Bioecon 19, 7–28 (2017). https://doi.org/10.1007/s10818-017-9243-1

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