Man-made environmental degradation is a global challenge for both developed and developing countries. Over the last several decades, environmental degradation in the form of excessive emissions of carbon dioxide (CO₂), methane (CH₄), and fine particulate matter (PM_2.5) has exposed the world to destructive natural calamities and health threats. Against this backdrop, this study sets out to identify the impact of economic growth on environmental degradation in 115 countries for the 1990±2016 period.

Using the environmental Kuznets curve (EKC) as the conceptual framework, the results show that there is a long-run association between economic growth and environmental degradation. Specifically, we find the existence of the EKC for CO₂ and PM_2.5 emissions in the lower and upper middle income countries and for CH₄ emissions in the lower-middle, upper-middle, and high income countries. Moreover, we show economic growth and energy consumption to be the most important causes of CO₂ emissions. Finally, the impulse response function-based forecasts reveal that all three pollutants follow the EKC pattern in lower-middle, upper-middle, and high-income countries.

In terms of the agricultural and manufacturing sectors, we find that economic growth and energy consumption exert a long-run impact on CO₂, CH₄, and PM_2.5 emissions. Although further agricultural growth has no significant impact on CO₂ emissions, it shows an inverted U-shaped EKC for CH₄ emissions for the low, lower-middle, and high-income groups and for PM_2.5 emissions for all income groups. However, manufacturing growth has a U-shaped EKC for CO₂ emissions for all income groups, implying further emissions, while it shows an inverted U-shaped EKC for CH₄ emissions for all income groups and for PM_2.5 emissions for the low and lower middle-income groups.

We assess the moderation effects of several facilitator variables by testing a number of hypotheses. The moderation effect of financial development (FD), foreign direct investment (FDI), and the human development index (HDI) as the proxy of human capital formation has significant impacts on the EKC. FD exerts negative interaction effects for all three selected pollution emissions in all income groups, indicating that deeper financial reform may promote the installation of energy efficient and environmentally friendly technology. FDI has a long-run impact on reducing emissions for different panel income groups. For instance, after achieving a certain stage of GDP growth, the interaction effect of FDI reduces CO₂ emissions for the low and lower-middle income groups, suggesting that FDI may facilitate technological transfers from the developed to less-developed countries. Finally, the interaction effect of HDI on the growth±pollution nexus reduces all three types of emissions in the upper-middle and high-income groups, meaning that human capital formation helps to create awareness and use of green technologies.

The results suggest that efficient energy consumption, conservation of energy, and environmentally friendly technology can help to mitigate emissions without harming GDP growth. Overall, stringent environmental regulation by government, reduce reliance on non-renewable energy sources and encourage the use of renewable energy can help to mitigate pollution emissions. Moreover, sharing of knowledge and the transfer of green technologies between developed and developing countries, creating enough funds for environmental conservation can be an effective measure for attaining environmental sustainability in the production process.

This is a metadata only record. The datasets used in this thesis are open and available via https://databank.worldbank.org/source/world-development-indicators We use panel dataset for 115 countries for the time span 1990-2016. The countries are categorized into four groups as per gross national income (GNI) measured using World Bank Atlas (2018) method [the 9 of low ($1005 or less), 32 of lower-middle ($1006-$3955), 35 of upper-middle ($3956-$12,235), and 39 of high ($12,236 or more) income panels]. The data on different variable of interests are collected from World Development Indicators (CD-ROM, 2018). We use real estimation adjusting inflation. The collected datasets of dependent variables are carbon dioxide (CO2) measured in metric tons per capita, methane (CH4) in Kt. of CO2 equivalent, and the particulate matter (PM2.5) in microgram per cubic meter. The independent variables of the collected datasets are gross domestic product (GDP) per capita (constant 2010 US$), energy consumption (EC) in kg of oil equivalent per capita, trade openness (TO) measured as the share of total trade volume in GDP, urbanization (UR) in terms of the share of urban population in total population and TR is the total transport services in percentage of total commercial service of exports and imports, financial development (FD) measured in domestic credit to private sector, foreign direct investment (FDI) is measured by the net inflows of FDI as a percentage of GDP, the human development index (HDI) measured by the UNDP as a proxy for human capital formation. Moreover, we measure the agricultural sector by its output share of GDP (constant 2010 US$) and the manufacturing sector by its output share of GDP (constant 2010 US$).