The environmental Kuznets curve (EKC) is a hypothesized relationship between various indicators of environmental degradation and per capita income. As economies get richer, environmental impacts first rise but eventually fall. In reality, though some types of environmental degradation have been reduced in developed countries, others have not. Furthermore, the statistical evidence for the EKC is not robust and the mechanisms that might drive such patterns are still contested.

The Environmental Kuznets Curve

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.

/pdf/the-environmental-kuznets-curve-after-25-years-4tpaxo2xeb.pdf

The environmental Kuznets curve after 25 years

This paper assesses the convergence in eco-efficiency of a group of 22 OECD countries over the period 1980–2008. In doing so, three air pollutants representing the impact on the environment of economic activities are considered, namely, carbon dioxide (
 $${\text{ CO}}_{2}$$
 ), nitrogen oxides (
 $${\text{ NO}}_\mathrm{X}$$
 ) and sulphur oxides (
 $${\text{ SO}}_\mathrm{X}$$
 ); furthermore, eco-efficiency scores at both country and air-pollutant-specific level are computed using Data Envelopment Analysis techniques. Then, convergence is evaluated using the recent approach by Phillips and Sul Econometrica 75:1771–1855 (2007), which tests for the existence of convergence groups. First, we find that eco-efficiency has improved over the period, with the exception of $${\text{ NO}}_\mathrm{X}$$
 emissions. Second, Switzerland is the most eco-efficient country, followed by some Scandinavian economies, such as Sweden, Iceland, Norway and Denmark. In contrast, Southern European countries such as Portugal, Spain and Greece, in addition to Hungary, Turkey, Canada and the United States, are among the worst performers. Finally, we find that both the most eco-efficient countries and the worst tend to form clubs of convergence.

/pdf/eco-efficiency-and-convergence-in-oecd-countries-ek4e1q678k.pdf

Eco-Efficiency and Convergence in OECD Countries

Economic growth, industrialization, and the environment

How do resource misallocation and government corruption affect green total factor energy efficiency? Evidence from China

Convergence in per capita CO2 emissions: A robust distributional approach

https://www.uoguelph.ca/economics/sites/uoguelph.ca.economics/files/public/documents/2011-06.pdf

Growth and pollution convergence: Theory and evidence

We show that the two-stage minimum description length (MDL) criterion widely used to estimate linear change-point (CP) models corresponds to the marginal likelihood of a Bayesian model with a specific class of prior distributions. This allows results from the frequentist and Bayesian paradigms to be bridged together. Thanks to this link, one can rely on the consistency of the number and locations of the estimated CPs and the computational efficiency of frequentist methods, and obtain a probability of observing a CP at a given time, compute model posterior probabilities, and select or combine CP methods via Bayesian posteriors. Furthermore, we adapt several CP methods to take advantage of the MDL probabilistic representation. Based on simulated data, we show that the adapted CP methods can improve structural break detection compared to state-of-the-art approaches. Finally, we empirically illustrate the usefulness of combining CP detection methods when dealing with long time series and forecasting.

C. Ordás Criado

Papers

Convergence in per capita CO2 emissions: A robust distributional approach

Growth and pollution convergence: Theory and evidence

Linking Frequentist and Bayesian Change-Point Methods