Advancing Sustainability Assessment of Renewable Energy: A Study on Indicator-to-Framework Methods and Associated Rule Mining

Abstract

Divergent sustainable development perspectives, mathematical model limitations, and varying scenarios contribute to uncertainty in assessing renewable energy sustainability. To tackle these challenges, this study conducts a comprehensive literature review, employs an analogy, and utilizes knowledge discovery techniques with an indicator-to-framework approach. Qualitative classification and rule mining elucidate the interrelationships among dimensions, indicators, and scopes, enhancing assessment coherence. The study identifies 227 indicators across five dimensions and nine scopes, highlighting key rules through Apriori algorithm. Strong rules, meeting a support threshold of 0.22, confidence of 0.7, and Lift of 2.512, underscore the significance of environmental, economic, social, and technological dimensions. Environmental indicators like CO2 emissions, NOX emissions, and SO2 emissions feature prominently. The harmonization of dimensions, indicators, and scopes streamlines research and mitigates ambiguities. Appendices augment rules with frequent indicators, enriching insights. This data-driven methodology provides a practical solution to the complexities of selecting appropriate indicators for such assessments.