Exploring the Macroeconomic Determinants of Carbon Emissions in Transitional Economies: A Panel Data Analysis Approach

Abstract

The study's main objective was to investigate the macroeconomic determinants of carbon emissions in transitional economies using panel methods with data ranging from 1996 to 2014. The main data analysis was done using econometric estimation methods such as fixed effects, random effects, pooled ordinary least squares (OLS) and the dynamic generalized methods of moments (GMM) approach whilst robustness tests were done under the umbrella term, the lagged independent variable approach. To a larger extent, infrastructural development, economic growth, trade openness, financial development and natural resources were found to have had a significant positive effect on carbon emissions, in line with major theoretical predictions. On the other hand, renewable energy consumption, foreign direct investment, information and communication technology and human capital development were mainly found to have reduced carbon emissions in transitional economies. The results are firmly supported by literature. Transitional economies are therefore urged to increase their use of renewable energy and information and communication technology (ICT) infrastructure, attract more foreign direct investment (FDI) and implement policies aimed at enhancing human capital development to reduce carbon emissions. Given data availability, future studies must investigate whether other macroeconomic variables mentioned in the empirical literature that they determine carbon emissions are relevant in transitional economies.

Transport Infrastructure Impact on Regional Unemployment – Evidence from Vietnam

“…Because it is easier to migrate, people have a better chance of finding or accessing jobs in a variety of fields (Bastiaanssen et al, 2020;Chakrabarti, 2018;Langella & Manning, 2022;Marada & Kvto, 2016). This study's conclusions are similar to those of numerous earlier research (Ndubuisi et al, 2021;Sobieralski, 2021;Tsaurai, 2020;Yu & Luu, 2022). Forecasts of things that can lower unemployment are always significant since they demonstrate the efficacy of a country's economic development strategy.…”

“…It is clear that the rise in unemployment in Nigeria is contributing to the increase in traffic accidents. Tsaurai (2020) investigates the causes of unemployment in Africa from 2001 to 2015. The results of the SGMM estimator demonstrate that economic lag, a lack of foreign direct investment, and the interaction of information and communication technologies are all factors contributing to the growth in unemployment in these nations.…”

“…The System Generalized Method of Moments (SGMM) estimator is used with data on transport infrastructure and unemployment across Vietnam's 63 provinces and regions to analyze this effect. Many studies have employed this estimator in recent years (Laborda & Sotelsek, 2019;Onuoha & Agbede, 2019;Tsaurai, 2020;Yakubu & Muhammed, 2021;Yu & Luu, 2022). The findings of the baseline regression ordinary least squares (OLS) and fixed effects (FE) models reveal that this impact exists.…”

Renewable Energy, Economic Growth and Economic Development Nexus: A Bibliometric Analysis

“…The results obtained by [45] indicate that in the long term, trade openings and technological developments tend to stimulate the consumption of renewable energy in OCDE countries. Emerging economies were investigated by [46][47][48][49][50], still, in the emerging economies, there were more targeted studies, such as papers [51][52][53]. According to [51], for these economies, the flow of foreign direct investment (FDI) and the development of the financial market are fundamental in promoting the consumption of renewable energies, in addition to reducing emissions and promoting economic growth.…”

Examining The Effects of Economic Growth on Unemployment in Indonesia

“…GDP has a significant negative impact while investment has a significant positive impact on unemployment. Tsaurai (2020) analyze Macroeconomic Determinants of Unemployment in Africa, the variables used Information and communication technology, Unemployment Human resources and infrastructure. method used Panel data analysis (fixed effects, random effects, pooled ordinary least squares, dynamic generalized methods of moments).…”