Local governments are the main actors in achieving carbon peaking and carbon neutrality goals. The existing carbon management system is mainly for the country, industry or enterprise, and there is no carbon management design method for local governments. Therefore, from the perspective of local governments, a regional carbon management approach based on carbon-electricity intensity and carbon efficiency is proposed. First, based on electricity consumption data, combined with regional industry energy statistics, a carbon-electricity intensity indicator is established to estimate the carbon emissions of enterprises, and then different carbon emission reduction strategies are constructed using carbon efficiency indicator. This study proposes two types of emission reduction strategies, marginal opt-out and collective action, and conducts scenario simulation analysis using a case study from a city in southeastern China. The baseline scenario shows that although the marginal opt-out strategy has the lowest economic cost, the emission reduction rate is also lower, while the collective action strategy can achieve the emission reduction target faster, but only at a higher economic cost. This approach is suitable for the construction of the initial carbon management system in areas dominated by electricity consumption, taking into account the two dimensions of economy and environment, and can be applied to various decision-making scenarios, which is beneficial for local governments to quickly start the carbon management system.

Carbon peaking and carbon neutrality goals have posed great challenges to transforming local economies into low-carbon economies. Hence, establishing an effective carbon management system is urgent. However, the development of the urban carbon management system is hampered by the immaturity of the carbon emission accounting system at the city level. To compensate for the insufficiency of the existing urban carbon emission accounting system and to find the city government in constructing a perfect carbon emission management system as soon as possible, this study used the data science method based on the statistical data of 285 cities in China from 2005 to 2017 to explore the influencing factors of urban carbon emissions, that is, using light gradient boosting machine and the accumulated local effects interpretable models to screen potential influencing factors of urban carbon emissions. Then, an index system for urban carbon management was evaluated and proposed, and a case analysis was conducted with urban industrial electricity consumption as a background. This method can be easily integrated with the existing urban management system, which could reduce the cost of building a carbon management system.