关键词: 碳达峰, 碳中和, 江西省, 系统动力学, 模拟仿真

Abstract:

Achieving a carbon peak and neutrality have become strategic priorities in China’s mid- to long-term socio-economic planning. Effective implementation of the “dual-carbon” goals is crucial in this new stage of development to advance Jiangxi Province’s high-quality, transformative growth and support the “Beautiful China” vision. A system dynamics model was developed tailored to Jiangxi’s specific context, encompassing the economy, industry, transportation, residential, agriculture, energy, and carbon sink sectors. This model assesses the multidimensional feedback mechanisms within carbon cycles and captures the evolving drivers of emissions, as well as potential lagged effects. It provides a framework for simulating Jiangxi’s carbon emissions trajectory up to 2060 under five policy scenarios: economic growth, technological advancement, energy structure adjustment, industrial restructuring, and comprehensive regulation, thus offering policy pathways compatible with “dual-carbon” targets. An analysis of these policy scenarios revealed distinct peaks and timelines for the carbon emissions of Jiangxi. Notably, under the scenarios of low economic growth 2, energy structure adjustment 1, energy structure adjustment 2, industrial restructuring 1, and industrial restructuring 2, Jiangxi is projected to achieve its carbon peak before 2030. In particular, the low economic growth 2 scenario peaks in 2029 at an emission level of 295 million tons. The energy structure adjustment 2 and industrial restructuring 2 scenarios peak even earlier, in 2025 and 2026, with lower peak emissions of 263 and 272 million tons, respectively. By 2060, the energy structure adjustment 2 scenario will approaches carbon neutrality, achieving a net emission level of 15.2 million tons and meeting 91.6% of the emission reduction target. This is closely followed by the industrial restructuring 2 scenario, which achieved an 84% reduction. In contrast, the low economic growth 1 and technological advancement scenarios deliver the least reduction, reaching only approximately 13.5% of the target. The results from these individual policy scenarios underscore the substantial impact of energy structure adjustments and industrial optimization on emission reduction. Given that Jiangxi’s carbon emissions primarily stem from fossil fuel consumption, especially coal, increasing the share of non-fossil energy sources is critical for CO₂ reduction. To achieve this, Jiangxi should deepen its energy consumption transition and enhance its digitalization and smart applications to optimize energy management and use. Jiangxi’s resource-intensive, high-energy-consuming, and high-emission traditional industries are major contributors to carbon emissions, necessitating a transition toward green, high-value-added industries, such as renewable energy, new materials, and service-oriented economies. Carbon emissions operate within a complex, interdependent socioeconomic system, where regulating a single factor often fails to address issues comprehensively and may even lead to unintended negative effects. In contrast, comprehensive regulation considers multiple factors and stakeholder interactions, allowing for a more accurate simulation of the real-world effects of policies. In comprehensive regulation scenario 1, Jiangxi achieves its carbon peak in 2025, eight years earlier than that in the baseline scenario, with a peak net emission level of 241 million tons, a reduction of 66.5 million tons. By 2060, the emissions are expected to decline further to 13.9 million tons. Under comprehensive regulation scenario 2, the peak is reached even earlier in 2024, with a net emission level of 231 million tons, marking a peak reduction of 75.9 million tons. Under this scenario, carbon neutrality is projected to be achieved by 2054 The model also provides a comparative analysis of energy consumption and emission reductions across major industrial sectors in Jiangxi, including mining, chemicals, light industry, metal manufacturing, power generation, construction, and agricultural production. The simulation results indicate varying degrees of energy consumption reductions across all sectors. Metal manufacturing shows the most significant reduction, with peak energy consumption in comprehensive regulation scenarios 1 and 2 reduced by 20.62 million tons and 33.24 million tons, respectively, compared to the baseline scenario, and by 2060, the reductions reach 26.36 million tons and 43.85 million tons, respectively. The power generation sector follows closely, with Scenario 1 energy consumption increasing from 2.8059 million tons in 2024 to 6.7634 million tons by 2060, a reduction of 82.2% from the baseline. Scenario 2 reaches 1.528 million tons by 2060, representing a 96% reduction. In the chemicals sector, peak energy consumption in scenarios 1 and 2 decreases by 8 million tons and 12.64 million tons, respectively, with reductions of 10.15 million tons and 16.61 million tons by 2060. Light industry also sees moderate reductions, with peak energy consumption in scenarios 1 and 2 decreasing by 4.4 million tons and 6.79 million tons, respectively, and by 2060, reductions of 5.53 million tons and 8.92 million tons are expected. In the mining sector, peak reductions in scenarios 1 and 2 reach 1.63 million tons and 2.47 million tons, respectively, with reductions of 2.03 million tons and 3.25 million tons by 2060. In the construction and agricultural production sectors, energy consumption reductions are relatively modest, with each reducing by less than 2.5 million tons by 2060 compared to the baseline scenario. Regarding the reduction potential, the sectors’ emission reduction effectiveness follows a descending order: metal manufacturing, power generation, chemicals, light industry, mining, construction, and agricultural production. These findings provide strategic insights for future emission reduction measures and highlight critical intervention areas. The results from the single-policy scenarios indicate that energy structure adjustment and industrial optimization have the most pronounced effects on emission reduction. Jiangxi’s carbon emissions are largely driven by fossil fuel consumption, especially coal, making it crucial to increase the proportion of non-fossil energy in the total energy consumption. To this end, Jiangxi should deepen its energy consumption transition, leveraging high-quality development to enhance digitalization and smart technology applications, and optimize energy management and utilization through data analytics. Transitioning Jiangxi’s resource-intensive, high-energy-consuming, and high-emission traditional industries toward green, high-value-added sectors, such as renewable energy, new materials, and service-oriented economies, will support the formation of service-based economy. Relying solely on single-policy interventions has limited effectiveness in meeting the “dual-carbon” targets, while integrated emission-reduction scenarios are more realistic. By implementing a coordinated approach across the economic, technological, energy, and industrial sectors, Jiangxi can shift from its traditional coal-dependent, secondary industry-based development model. Enhancing cross-sectoral collaboration, communication, and information sharing and establishing a comprehensive top-level design will foster a collaborative framework for achieving synergistic effects. Among these sectors, metal manufacturing, power generation, and chemicals demonstrate significant potential for emission reduction. Therefore, Jiangxi should promote capacity upgrades within industrial clusters, introduce energy-efficient metal production equipment, and invest in carbon capture, utilization, and storage (CCUS) technologies to capture CO₂ during industrial processes. Carbon trading systems or carbon tax policies can support the power sector in expanding clean energy use while building smart and microgrids to improve the flexibility and manageability of the system. Additionally, sustainable production processes should be prioritized to enhance product recyclability and environmental performance across the entire lifecycle.

Key words: carbon peaking, carbon neutrality, Jiangxi province, system dynamics, simulation modeling