通过整合工业生产,使用多目标规划构建了中国实现碳中和的低成本路径系统,电力,加热,交通运输,和森林碳固存。我们的目标是最大限度地降低系统的总成本,二氧化碳排放,和空气污染物。这些制约因素包括中国在2030年之前达到二氧化碳排放峰值的目标;在2060年之前实现碳中和;确保工业,电源,加热,和运输用品;推广绿色能源;实施排放控制。该模型占煤炭消耗高的行业,如钢铁和化学工业。考虑了各种电源,包括燃煤,核,风,和太阳能。采用森林碳汇和碳捕获与封存技术来实现减排目标。模型,使用现有的研究数据进行了验证,提供了具有成本效益的路径方案,并表现出很高的有效性。我们的发现强调了结构调整和排放控制的重要性,用电力,加热,和运输部门比其他行业表现出更高的可行性,并为实现碳中和提供更大的贡献。相反,钢铁等行业的产业转型,化学,建筑材料的可行性低,贡献有限。建模结果为开发低成本,中国复杂系统中的碳排放目标运输结构。此处提供的结果表明,该方法在促进旨在实现关键碳减排目标的计划方面具有全球适用性。
A low-cost path system for achieving carbon neutrality in China was modelled using multi-objective programming by integrating industrial production, electric power, heating, transportation, and forest carbon sequestration. We aimed to minimise the total system cost, CO2 emissions, and air pollutants. The constraints included China\'s targets of peaking CO2 emissions before 2030; achieving carbon neutrality before 2060; ensuring industry, power, heating, and transportation supplies; promoting green energy; and implementing emission control. The model accounted for industries with high coal consumption, such as steel and chemical industries. Various power sources were considered, including coal-fired, nuclear, wind, and solar energy. Forest carbon sink and carbon capture and storage technologies were employed to achieve the emission reduction goals. The model, which was validated using available research data, offered cost-effective path schemes and exhibited high validity. Our findings emphasise the importance of structural adjustments and emission control, with electric power, heating, and transportation sectors showing higher feasibility and providing greater contributions to achieving carbon neutrality than other industries. Conversely, industrial transformation in sectors such as iron and steel, chemical, and construction materials had low feasibility and limited contribution. The modelling outcomes provide valuable insights for developing low-cost, carbon emission-targeted transportation structures in China\'s complex system. The results presented here demonstrate the global applicability of this method in contributing to plans aimed at meeting key carbon reduction targets.