Assessing the environmental impacts of clean energy investment in Pakistan using a dynamic autoregressive distributed lag model

In this study, the authors projected the impacts of clean energy investment on environmental degradation by applying a novel and dynamic Autoregressive Distributed Lag (DARDL) model for Pakistan from 1990 to 2022. Most researchers have used ecological footprint or CO₂ emissions indicators to look at how clean energy investment affects environmental degradation, which primarily represents contamination induced by humans' consumption patterns and does not consider the impact of the supply side. Against this background, the study scrutinized the dynamic interaction between clean energy investment and environmental sustainability using the load capacity factor (LCF) as an ecological indicator in Pakistan, including economic growth, population density, trade openness, urbanization, and industrialization in the analysis. The long-run estimates from DARDL indicate that a 1 percent upsurge in clean energy investment mitigates environmental degradation by approximately 0.42 percent on average, controlling for other factors. Further, the study also revealed that a 1 percent increase in clean energy investment diminishes dirty energy consumption by approximately 0.45 percent. The validity of the findings is confirmed using alternate methods, i.e., KRLS. The study recommends that Pakistan prioritize investment in clean energy projects to promote environmental sustainability and enforce environmental regulations to reduce the adverse externalities associated with dirty energy activities.