Income inequality, ecological footprint, and carbon dioxide emissions in Asian developing economies: what effects what and how?

Salim Khan,Wang Yahong

doi:10.1007/s11356-021-17582-4

Abstract

The reduction of income inequality and environmental vulnerability is the most important factor, through which we can achieve the target of Sustainable Development Goals (SDGs). The past papers have investigated the nexus between income inequality and carbon emissions; however, the relationship between income inequality and carbon emissions along with ecological footprint has not been studied in the case of developing countries. To this end, this study analyzed the impact of income inequality on both carbon emissions and ecological footprint as well as the impact of carbon emission and ecological footprint on income inequality by using the dataset from 2006 to 2017 for the 18 Asian developing economies. This study confirmed the positive relationship between carbon emissions, ecological footprint, and income inequality under the methodology of Driscoll and Kraay (D&K) standard error approach. Specifically, a higher-income gap is destructive for environmental degradation, whereas increasing level of carbon emissions and ecological footprint also leads to rising income inequality in the investigated region. Furthermore, foreign direct investment (FDI), easy access to electricity, and population growth control income inequality, but they have a detrimental effect on both ecological footprint and carbon emissions. The empirical findings also provide some important policy implications.

Highlights

Is that possible for developing economies to meet environmental goals and ensure low income inequality? Does economic growth can break vicious circle of poverty, mainly, if it is not link with increasing environmental degradation and income inequality? These questions are theoretically and empirically ambiguous as rising environmental degradation income inequality, and poverty are major challenges facing every human being in the 21st century
In this study we used both CO2e and EFP as proxies for environmental degradation, Gini coefficient as a proxy for income inequality, inflation calculated as the consumer price index (CPI), FDI measure the inflow of foreign direct investment, population growth calculated as annually population growth rate, forest area only calculated the percentage of forest area out of total land area, access to electricity (% of total population, and manufacture, value added (% of GDP) as the proxy for industrialization
We applied Driscoll and Kraay (D&K) standard error regression techniques to check the nexus between income inequality and environmental vulnerability in term of CO2e and EFP for the panel of 18 Asian developing economies

Summary

Introduction

Is that possible for developing economies to meet environmental goals and ensure low income inequality? Does economic growth can break vicious circle of poverty, mainly, if it is not link with increasing environmental degradation and income inequality? These questions are theoretically and empirically ambiguous as rising environmental degradation income inequality, and poverty are major challenges facing every human being in the 21st century. Does economic growth can break vicious circle of poverty, mainly, if it is not link with increasing environmental degradation and income inequality? Despite a significant alleviation in the poverty level from the last few years, many developing countries still facing the problem of increasing income inequality (Baloch et al, 2020a). There are large numbers of research studies that investigates the association between inequality and toxic emissions, the green-house or conservatory effect is mainly responsible for the rising global warming and climate change. Many research studies in the literature proposes that economic growth, up to certain level of economic development, upsurges global warming and green-house gases emissions (Forabosco et al, 2017; Edenhofer et al, 2014; Tyler et al, 2017). Past research papers are, based on rather simple analytical techniques and old data, in current study, we improve the existing literature in both of these prospects, that is, apply advance econometric techniques and incorporated expanded data

Methods

Results

Conclusion