Revisiting the relationship between climate change, renewable energy investments, climate mitigation technologies and environmental fiscal policies: A comprehensive analysis using structural learning-based Bayesian neural network

The unprecedented surge in ecological challenges means that carbon neutrality and sustainable development have gained attention in the environmental debate. In order to extend the environmental discussion, the current study analyzes the degree to which environmental taxes, renewable energy investments, natural resource dependence, and sustainable environmental technologies influence sustainable development progress. We utilize a structural learning-based Bayesian neural network as the primary analytical approach to report strength between Bayes network nodes for empirical dataset from 1994 to 2023 for OECD economies. In addition, the current study also utilized the Method of Moments quantile regression, AMG, CCEMG, FMOLS and DOLS to show that environmental taxes, renewable energy investments, climate mitigation technologies and economic progress ensure ecological sustainability in OECD economies. However, the financial sector and natural resource dependency hinder ecological sustainability by escalating climate change externalities. In addition, we divide the OECD dataset on the basis of economic classification to document varying degrees of empirical differences across G-7 and the rest of the OECD economies. We conclude our investigation by reporting robust environmental policy suggestions to improve environmental sustainability. • Carbon neutrality and sustainable development are vital components of environmental debate. • We study how environmental taxes, climate mitigation technologies and renewable energy impact climate change. • Environmental taxes, renewable energy investments and climate mitigation technologies overcome climate change. • Financial market developments and natural resource consumption hinder environmental sustainability.

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