GIS-based Spatial Autocorrelation Queen Contiguity Order-One Analysis of the Renewable Energy Consumption and Production of African Countries with Economic and Environmental Indices

Author:

Gwani Alhaji Abdullahi1,Kun Sek Siok2

Affiliation:

1. Bauchi State University Gadau

2. Uniiversiti Sains Malaysia

Abstract

Abstract

The worldwide undertaking to achieve sustainable energy solutions has emphasized the need to comprehend the patterns of renewable energy consumption (REP), and production (REC), and socioeconomic and environmental variables. This work utilizes geographic information system (GIS) methodologies and spatial autocorrelation analysis, specifically Queen contiguity order 1 (Q1), to examine the spatial pattern of renewable energy consumption across African nations. This research seeks to reveal regional patterns, detect clusters of comparable renewable energy consumption behaviors, and assess the economic and environmental consequences linked to these patterns, REP, REC, economic indicators, and environmental indices such as African countries' gross domestic product (GDP) and carbon dioxide emissions (CO2) using data from 57 countries from 1990 to 2020. This study measured the similarities of these variables between neighbouring nations using the Q1 spatial autocorrelation methodology. The findings show concentrated areas with high and low REP, REC, and GDP, revealing regional patterns and inequities. This study also analysed trends to determine the eco-nomic and environmental evolution as well as the REP and REC patterns. Additional related variables, including the ecological footprint (EF), economic complexity index (ECI), and global index (GI), were used in Moran’s I statistical analysis. These findings can help policymakers, researchers, and stakeholders establish strategies for sustainable energy, economic growth, and environmental protection across the continent. This multidisciplinary method uses GIS, spatial analysis, economics, and environmental elements to fully comprehend the complex relationships between the variables under investigation. Furthermore, the study recommends an econometric regression model approach to evaluate the link between these variables, with the hope that this technique will facilitate a more thorough understanding of the influence of renewable energy use on the economic and environmental welfare of African nations.

Publisher

Research Square Platform LLC

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