Socioeconomic Impact Assessment of Renewable Energy Access Using DBSCAN Clustering Methods

Renewable energy advocates worldwide emphasize the importance of environmental protection and the simultaneous advancement of social and economic benefits. Access to renewable energy sources, particularly in rural and underdeveloped regions, may increase incomes, employment, and prevent energy poverty. Legislators and stakeholders worry about accessibility and benefit inequality. Understanding geographical trends and socioeconomic grouping is essential for informed planning and targeted action. This paper seeks to identify regions where renewable energy sources have a significant social and economic impact by focusing on clusters and patterns that standard methods of inquiry may overlook. Current assessment approaches struggle to capture regional diversity and multiple socioeconomic characteristics, making policy recommendations difficult. A novel solution to these issues is to divide the globe by the social and economic benefits of renewable energy. To overcome these challenges, this paper proposes a novel methodology using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to classify geographic regions based on the intensity of socioeconomic benefits derived from renewable energy access. DBSCAN identifies high-impact clusters without requiring a fixed number of clusters, making it suitable for diverse datasets. It examined multivariate socioeconomic parameters, including income, employment, education, and energy affordability, across regions. Renewable energy improves socioeconomic conditions in evident geographical groupings, whether you're seeking success stories or ignored locations. Policy implications include prioritizing investments and aid in low-impact zones for equitable development. An effective socioeconomic impact assessment technique, DBSCAN clustering facilitates datadriven decision-making and strategic planning for renewable energy development.

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