Coordinated Economic Growth and Decarburization in Jiangsu Using an Integrated MOO–CCD Framework
Year: 2026
Type: article
Abstract: Economic growth, carbon reduction, and environmental stewardship may all be achieved at once via the use of an optimization technique that takes trade-offs and multiple target progressions into account. Without fully grasping the complex interplay of financial, climatic, environmental, and resource implications, many current regional MOO studies aim for compromise solutions. The creation of a MOO and CCD framework in the Jiangsu Province, China, is the main subject of this study. From 2018 to 2030, we can see potential pathways for energy transition and company reorganization by analyzing eleven indicators: GDP, employment, water consumption, CO2, sulfur dioxide, NOx, PM, CO2, chemical oxygen demand, ammonia nitrogen and solid waste rounding it off. The study's basis is an input-output model with 47 components. Numerous factors are considered, including electricity-related situations, resource and emission coefficients from the past, and more. As a starting point for coordination, CCD ranks and evaluates potential MOO solutions once the optimal-point method has standardized the reduction and maximization objectives. All three scenarios put yearly GDP growth at 6.8%, with the precise number falling anywhere from 6.7% to 7.0%. We need to be more transparent about the moral judgments and assumptions that drive these changes because of the potential impact they have on the environment. There will be major shifts in the energy mix and the make-up of the industrial sector, and we need to be prepared for that. A more appropriate use of this method would be in scenario analysis rather than in making an exact forecast. Adaptive methods, such as sustainable alternatives, higher industrial advancements, and integrated governance, may replace strict annual requirements in policymaking.
Institution: Ghazi University
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7225450203