Year: 2025
Type: article
Abstract: Introduction Limate risk poses significant challenges to sustainable development, particularly in the context of transitioning to green and low-carbon economies. The complexity of these interactions makes it difficult to devise strategies that effectively balance competing priorities, such as economic growth, environmental protection, and social inclusion. To bridge this gap, we propose a novel framework that integrates the Integrated Green Transition Model (IGTM) and the Sustainable Transition Optimization Framework (STOF). Methods IGTM employs agent-based modeling and network dynamics to simulate the cascading impacts of green policies on energy systems and socio-economic outcomes, while STOF leverages advanced optimization and machine learning techniques to balance economic growth, emission reductions, and social equity under diverse scenarios. Results By synthesizing these approaches, our study provides actionable insights into the economic impact of climate risk and offers robust strategies for optimizing investments in renewable energy and policy interventions. The results highlight the necessity of aligning technological innovation, governance, and public engagement to accelerate the green transformation while minimizing economic disruptions. Discussion Fostering international cooperation and sharing best practices across nations will be pivotal in overcoming global climate challenges and ensuring a just transition for all. This research underscores the urgency of implementing integrated solutions to safeguard a sustainable and equitable future. Unlike traditional models, IGTM simulates the cascading impacts of green policies on energy and socio-economic systems, while STOF uses machine learning to balance growth, emissions, and equity. This integrated approach enables precise climate risk assessment and guides renewable energy investments and policy decisions.
Authors: Chen Qin, Hongli Lou, Li Li
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4410185966