Quantitative Assessment of Regional Carbon Neutrality Policy Synergies Based on Deep Learning
Year: 2024
Type: article
Abstract: This study presents a comprehensive quantitative assessment framework for evaluating regional carbon neutrality policy synergies using deep learning techniques. The research addresses the critical challenge of understanding complex interactions between multiple policy instruments in achieving carbon reduction goals. By working with neural network architectures and relevant tools, performance patterns, successes, and new management. The course indicates synergy index (psi) for providing the intervention that affects the impact of energy voluntarily than 1.17 for 1.71). Analysis of regional variations demonstrates that policy effectiveness is strongly influenced by local economic structures and energy systems, with manufacturing-dominated regions showing the highest responsiveness to carbon pricing mechanisms (PSI = 1.62). Temporal analysis indicates a typical 2-3 year lag before synergistic effects fully manifest. The deep learning model achieves robust prediction accuracy across diverse scenarios, with sensitivity analysis revealing technology learning rates as the most significant parameter influencing predictions (±24.3%). These findings provide approval to law enforcement officials of local structures, determination of local people and behavioural status of customary strategies.
Authors: Daiyang Zhang, Enmiao Feng
Institutions: Georgetown University, Duke University
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
SDG: Climate action
Open Access status: diamond
OpenAlex ID: W4408748021