Carbon Emission Scenario Simulation and Policy Regulation in Resource-based Provinces Based on System Dynamics Modeling

In China, numerous cities are resource-based, with substantial energy consumption, emissions, and pollution and they expand quickly and economically, notably enhancing their carbon emissions. Nevertheless, they have considerable potential for emission reduction. Assuming the "dual-carbon" goal as a backdrop, this study considered Shanxi Province, the largest coal resource-based province in China, for a case in point. It established a carbon emission system dynamics model, constructs five carbon emission systems, namely, economy, energy, population, land, and environment, and sets up four scenarios. Finally, in light of the scenario simulation's outcomes, we explored the optimal path and policy regulations for resource-based cities to reach carbon peaks. The study conclusions show the following: (1) All factors are exhibit correlated with each other, and their influence in the four scenarios is ranked as follows: GDP, energy consumption, industrial structure, total population, land-use structure. (2) Although GDP is a key factor influencing total carbon emissions, regulating only a single factor cannot achieve the carbon emission target. Thus, all factors need to be considered and synergistically regulated to achieve optimal carbon benefits. (3) Carbon emissions are higher and grow faster in the Baseline Development Scenario and the Fast Development Scenario, particularly in the FDS scenario, where they reach 614.48 million tons. Both scenarios exceed the peak carbon target by 4.4% and 7.1%. (4) In the Low-Carbon Optimization model and Resource Saving Scenario have low and slow-growing carbon emissions. Although the RSS has lower carbon emissions of 539.83 million tons, sacrificing sustainable development to reduce these emissions is unrealistic. In comparison, the LOS scenario represents the optimal path for achieving sustainable growth and lowering carbon emissions in Shanxi Province, with emissions totaling 550.99 million tons. This study implements strategies to manage the pace of population expansion, optimize the industrial structure, modify the energy structure, and optimize the allocation of land resources. The results of the study not only do our findings offer data reinforcement and implementation strategies for the low-carbon conversion of similar resource-based cities in China, but also offer case studies for different kinds of resource-based cities that fulfill the "carbon peak" objective.