Is there any link between economic growth and earth's environment? Evidence from 127 countries for the period 2007–2015

ABSTRACT

We investigate the relationship between economic growth and the earth's environment in 127 developing countries spreading all across the globe (Asia, Africa, Americas, Europe, and Polynesia) from 2007 to 2015. We use random effect estimation technique to check for an inverse U-shaped curve, or ‘Environmental Kuznets Curve (EKC)’. From our empirical results, we do not find any substantiation for this hypothesis. On the contrary, the empirical evidence suggests that there is an inverse U-shape relationship between environmental performance of a country and the per capita GDP for that country, which implies that, as per capita GDP increases, environmental performance improves but beyond some point, it starts to decrease resulting in an inverse U-shape curve. We alternately use the Growth Rate of GDP and GDP in constant 2010$ to obtain comparable results. We also use the average PM 2.5 air pollution, mean annual exposure (AU) of the World Development Indicators as an index of environmental pollution. When we replace EPI with AU and re-run the random effect model, find no evidence supporting EKC hypothesis, rather a U-shape curve and not an inverted U.

KEYWORDS:

• Environmental Kuznets curve
• pollution
• economic development
• random effect model
• GDP

Acknowledgements

We sincerely thank the editor and the anonymous referee for their valuable suggestions and constructive comments. We believe that the quality of the paper has significantly improved and as such a much better product.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Mahfuzul Haque http://orcid.org/0000-0002-3060-8784

Notes

1 World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates. https://data.worldbank.org/products/wdi.

2 We like to express our sincere thanks to Yale Center for Environmental Law & Policy (YCELP) for granting us the permission via email dated June 06, 2017 for the use EPI a proprietary item of Yale Centre. Kroon Hall, 195 Prospect Street, New Haven, CT 06511. http://epi.yale.edu/node/1/submission/112

3 https://epi.envirocenter.yale.edu/2018-epi-report/methodology

4 Stata calculates the t-statistic and its p-value under the assumption that the sample comes from an approximately normal distribution. Random effects models are estimated with Maximum Likelihood rather than Ordinary Least Squares. With ML, we do not have the nice small sample results that allow us to use the t-distribution, but we have asymptotic results that allows us to use the standard normal distribution. By default, Stata uses 95% confidence intervals, which equates to declaring statistical significance at the p < .05 level.