Unveiling the Carbon Footprint of Europe and Central Asia: Insights into the Impact of Key Factors on CO2 Emissions

Khan, Muhammad Tufail and Imran, Muhammad (2023): Unveiling the Carbon Footprint of Europe and Central Asia: Insights into the Impact of Key Factors on CO2 Emissions. Published in: Archives of the Social Sciences: A Journal of Collaborative Memory , Vol. 1, No. 1 (23 February 2023): pp. 52-66.

Abstract

This study delves into the intricate relationship between carbon dioxide (CO2) emissions and crucial variables in Europe and Central Asia from 1990-2021. By examining the impact of renewable energy, industry value added, foreign direct investment (FDI), gross domestic product (GDP) per capita, and population density on CO2 emissions using the autoregressive distributed lag (ARDL) method, the study uncovers intriguing findings. The study reveals a significant negative correlation between linear per capita income and CO2 emissions in both the short and long run. Moreover, it confirms the inverted N-shaped environmental Kuznets curve (EKC) relationship between the variables. The study further highlights the unfavorable impact of renewable energy and industry value added on CO2 emissions, pointing to the fact that their growth increases CO2 emissions. On the other hand, population density is found to be a vital factor in reducing CO2 emissions. FDI is identified to have a negative and insignificant impact on CO2 emissions, suggesting that it may not be an effective tool for reducing carbon emissions in the region. The insights from this study have significant implications for policymakers in the region to design and implement effective strategies to reduce CO2 emissions.

• Item Type: MPRA Paper
• English Title: Carbon Management Through Sustainable Energy Demand
• Language: English
• Keywords: Carbon emissions; Economic growth; Renewable energy; FDI inflows; Industry value added, Population density; ARDL estimator.
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models

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