DOI: 10.57760/sciencedb.27754
title: Influence of Public Administration on Green House Gas Emission in Africa: A study of bureaucratic, economic and environmental dynamics.
title: Influence of Public Administration on Green House Gas Emission in Africa: A study of bureaucratic, economic and environmental dynamics.

description: Data and Data SourcesThis study utilizes a comprehensive dataset encompassing 54 African nations, focusing on the period from 2018 to 2023. The data sources are primarily derived from the World Bank, which provides critical indicators for assessing public administration capacity and environmental performance. The key components of the dataset include:CPIA Quality of Public Administration Ratings: The World Bank's Country Policy and Institutional Assessment (CPIA) ratings serve as a measure of public administration quality. These ratings evaluate the effectiveness of government institutions in managing public resources, enforcing regulations, and delivering services. The CPIA ratings are crucial for understanding the bureaucratic capacity of each nation and its impact on greenhouse gas (GHG) emissions.Greenhouse Gas Emissions Data: GHG emissions data are sourced from the World Bank and other international environmental databases, providing insights into the total emissions produced by each country. This data is log-transformed for analysis to account for skewness and facilitate regression modeling.Fossil Fuel Consumption: The percentage of total energy consumption derived from fossil fuels is obtained from the World Bank and other energy databases. This metric is essential for assessing the dependence on fossil fuels and its relationship with emissions.GDP per Capita: Economic data, specifically GDP per capita, is sourced from the World Bank. This variable is used to explore the Environmental Kuznets Curve (EKC) dynamics, which hypothesizes that as income increases, environmental degradation initially rises and then falls.Panel Data Structure: The study employs a panel fixed-effects regression model, which allows for the control of unobserved heterogeneity across countries and over time. The system-GMM approach is utilized to address potential endogeneity issues, ensuring robust and reliable results.By integrating these diverse data sources, the study aims to provide a nuanced understanding of how public administration capacity influences GHG emissions in Africa, highlighting the critical role of bureaucratic quality in the region's climate governance landscape.
description: Data and Data SourcesThis study utilizes a comprehensive dataset encompassing 54 African nations, focusing on the period from 2018 to 2023. The data sources are primarily derived from the World Bank, which provides critical indicators for assessing public administration capacity and environmental performance. The key components of the dataset include:CPIA Quality of Public Administration Ratings: The World Bank's Country Policy and Institutional Assessment (CPIA) ratings serve as a measure of public administration quality. These ratings evaluate the effectiveness of government institutions in managing public resources, enforcing regulations, and delivering services. The CPIA ratings are crucial for understanding the bureaucratic capacity of each nation and its impact on greenhouse gas (GHG) emissions.Greenhouse Gas Emissions Data: GHG emissions data are sourced from the World Bank and other international environmental databases, providing insights into the total emissions produced by each country. This data is log-transformed for analysis to account for skewness and facilitate regression modeling.Fossil Fuel Consumption: The percentage of total energy consumption derived from fossil fuels is obtained from the World Bank and other energy databases. This metric is essential for assessing the dependence on fossil fuels and its relationship with emissions.GDP per Capita: Economic data, specifically GDP per capita, is sourced from the World Bank. This variable is used to explore the Environmental Kuznets Curve (EKC) dynamics, which hypothesizes that as income increases, environmental degradation initially rises and then falls.Panel Data Structure: The study employs a panel fixed-effects regression model, which allows for the control of unobserved heterogeneity across countries and over time. The system-GMM approach is utilized to address potential endogeneity issues, ensuring robust and reliable results.By integrating these diverse data sources, the study aims to provide a nuanced understanding of how public administration capacity influences GHG emissions in Africa, highlighting the critical role of bureaucratic quality in the region's climate governance landscape.