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In order to achieve the carbon peaking and carbon neutrality goals, energy-intensive industries in China, as the main sectors of energy consumption and carbon emissions, had huge pressure to reduce emissions. In addition, the reduction of vegetation area led to a decline in carbon sink capacity, which further exacerbated the imbalance of mutual penetration between carbon source and carbon sink. Therefore, this article considered the role of carbon source and carbon sink and defined and calculated the “carbon emission penetration” (CEP) of the six energy-intensive industries from 2001 to 2020. The KAYA formula and the LMDI method were used to decompose the driving factors of CEP in the three aspects of scale, intensity, and structure. The combined model of STIRPAT and the environmental Kuznets curve (EKC) was used to simulate and analyze the equilibrium points of energy-intensive industries in China from the perspective of factor driving. The analysis results indicated that there were differences in the fluctuation trend of CEP in the six energy-intensive industries, which can be divided into three types: “two-stage growth,” “steady growth,” and “single peak.” Secondly, the driving factors from the three aspects of scale, intensity, and structure—emission intensity (CE), energy consumption intensity (EI), industrial structure (IS), economic scale (GP), and carbon sequestration scale (PCA)—had differences in industry and time dimensions. And the realization time of the CEP equilibrium points of six industries showed a three-level gradient feature significantly. This can provide some reference for the low-carbon transformation of six energy-intensive industries and optimization of China’s environmental management under the carbon peaking and carbon neutrality goals. Graphical abstract

Faced with the environmental pressure of global warming, China has achieved certain results in emission reduction, but this needs to be completed more efficiently. Therefore, this article conducts a more comprehensive and in-depth study of China’s carbon emissions from the perspective of the development of national economic sectors and taps the potential for emission reduction in various sectors. Taking into account the adjustment of the national economic sector and the current status of carbon emissions, the study period was from 2003 to 2017. The logarithmic mean Divisia index (LMDI) method was used to measure and analyze the impact of seven factors, including urban construction conditions, on the carbon emissions of various sectors. According to the commonalities and differences of the impacts, 42 sectors were aggregated into four categories. At the same time, the input–output structure decomposition analysis (IO–SDA) model was used to analyze the spillover effects of intersectoral carbon emissions. According to the research results, based on the characteristics of the four types of sectors, and fully considering the spillover effects, the improvement of life cycle management to control energy consumption in the entire supply chain was taken as the leading idea. Moreover, combined with the actual development situation, four types of sectoral carbon emission reduction paths and optimization strategies are proposed to establish a more sustainable demand structure in order to achieve emission reduction.

In recent years, China’s residential electricity consumption has continued to grow at high speed, and its contribution to the growth of the total electricity consumption has become more prominent. The peak-to-valley gap is also gradually increasing, which reduces the efficiency of electricity—an increasingly important terminal energy form. The resident travel chain is a major influencing factor of residents’ electricity consumption, and it is of great significance to dig deeper into the mechanism of its influence on residents’ electricity consumption behavior. In this paper, the time distribution model of household power load in summer in Beijing is constructed by comprehensively considering the difference of travel chain, electricity consumption behavior, and load level. The Monte Carlo simulation method is introduced for the simulation of the model. According to the results, both household type and temperature have a significant impact on the peak load, while the difference in the choice of mode of transportations does not. It is also found that the household appliance with the most potential for regulation is the air conditioning, followed by the water heater, which where regulation and optimization should be mainly carried out.

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