Sustainable development of PV projects based on a text-analytic decision-making framework

Abstract

The vigorous development of new energy sources, such as photovoltaic (PV) systems, is essential to achieving the goals of “Carbon Neutrality and Carbon Peak.” Clarifying the inherent logic of sustainable PV development and effectively evaluating and optimizing PV projects are urgent priorities. To address this, the study proposes a data-driven framework for the sustainable development of PV systems. First, topic modeling is employed to analyze existing PV research, identify mainstream topics, and outline the critical pathways for sustainable development. Second, focusing on a primary pathway, a two-stage decision-making model based on text mining is introduced to evaluate and optimize the target PV system. In the first stage, text mining is integrated with an adaptive multi-objective particle swarm optimization algorithm to generate Pareto solutions across three objectives: lifecycle cost, loss of power supply probability, and global warming potential of greenhouse gas emissions. In the second stage, stochastic multicriteria acceptability analysis is applied to select the optimal Pareto solution. The framework is validated through its application to a real-world case involving PV carport configuration in Chengdu, China. This study not only establishes an objective and comprehensive research paradigm for analyzing PV system sustainability through text analysis but also provides decision-making references for researchers and industry practitioners to better understand the development logic and pathways for sustainable PV system configuration.