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

Abstract

The worldwide undertaking to achieve sustainable energy solutions has emphasized the need to comprehend the patterns of renewable energy consumption (REP), renew-able energy production (REC), and their 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. The research seeks to reveal regional patterns, detect clusters of comparable renewable energy consumption behaviours, 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 57 countries data from 1990 to 2020. The study measured these variables' similarities 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. The study also analyses trends to determine eco-nomic and environmental evolution as well as REP and REC patterns. Additional related variables, including ecological footprint (EF), economic complexity index (ECI), and global index (GI), were used in Moran’s I statistics 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 approaches to evaluate the link between these variables, with the hope that this technique facilitates a more thorough comprehension of the influence of renewable energy use on the economic and environmental welfare of African nations.