Efficiency policies and beyond: Leveraging machine learning
Authors: Ogunbiyi, V., Aladesanmi,, K., Otame, L. and Adedoyin, F.
Editors: Adedoyin, F.
Publisher: Elsevier
Place of Publication: London UK
Abstract:The assumption that environmental taxes have the potential to correct carbon emissions behavior has been criticized in the literature. While some argue that revenue from environmental taxes is not necessarily used for greener investments or carbon-related projects, very little is known about the best policy options for introducing environmental taxes. This study advances knowledge by exploring how alternative environmental tax rates targeting specific clean (and/or dirty) energy sources can influence the 2050 Net Zero Ambitions. Yearly data from 1960–2022 collected from the World Bank Development Indicators database and the UK Office of National Statistics are used on an Ecological Footprint Model. The Dynamic Autoregressive Distributed Lag Model and the Machine Learning Kernel-Based Regularized Least Squares techniques are used for policy simulation and policy predictions. The results show that GDP per capita and oil production have a significant long-run and short-run positive effect on the ecological footprint. Conversely, renewable power generation and environmental taxes exhibit a significant negative influence on ecological footprint, emphasizing the role of carbon taxes in emissions reduction. Moreover, dynamic ARDL simulations illustrate the counterfactual impacts of policy interventions, suggesting that a 25% reduction in oil production would require comprehensive policy interventions to align with the 2050 Net Zero Ambitions. Increasing investment in renewable power generation shows promise in reducing ecological footprints. However, caution is needed when implementing environmental taxes to prevent cost shifting to consumers. Overall, these findings offer valuable insights for policymakers seeking to navigate the path toward a greener and more sustainable future beyond 2050.
https://www.sciencedirect.com/science/article/abs/pii/B978044333971400012X
Source: Manual