The load capacity factor (LCF) allows for a thorough investigation of environmental deterioration using the ecological footprint and biocapacity comparison to track a certain ecological threshold. It is evident that when the LCF decreases, environmental deterioration increases. Until now, researchers have traditionally examined environmental concerns through empirical methods, utilizing indicators like carbon dioxide emissions, nitrogen oxide emissions, sulfur dioxide emissions, ecological footprint, and others. Environmental supply‐side challenges may be overlooked due to the use of these indicators. By recommending the LCF and applying it to examine the variables influencing environmental health, this study fills a research gap in the field of environmental health. Monitoring environmental health is made easier by the load capacity factor, which shows the difference between biocapacity and ecological footprint. This study intends to analyze the impact of green technological innovation (TI), financial development (FND), population aging (PAG), energy consumption (ENC), and economic growth (GDP) on the LCF, which emphasizes environmental challenges from the perspectives of both supply and demand sides, to make up for this deficiency. However, the present study employs a Bayesian neural network (BNN) on 15 Regional Comprehensive Economic Cooperation (RCEP) countries datasets from 1980–2020 to estimate the interaction between TI, FND, and LCF. Additionally, in order to guarantee the stability of our findings, we applied the generalized method of moments (GMM). The BNN results show that when FND, ENC, and GDP increase, the LCF decreases; however, TI and PAG increase LCF and environmental sustainability. According to the study, the governments of the RCEP countries should encourage investment and initiatives in green technology innovation as a way to improve ecological sustainability. The article offers valuable policy recommendations for ensuring a fair and sustainable level of environmental quality in RCEP countries.
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Akhtar et al. (2026) studied this question.
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