Distributed photovoltaic systems play increasingly important roles in the power mix. China is actively promoting the integration of aggregated photovoltaics into the green electricity trading market. However, challenges related to undersupply and oversupply, stemming from uncertain generation, hinder market integration. To address these issues, China has implemented alternative regulatory mechanisms designed to guide generation schedules and mitigate forecasting deviations. It remains to be seen how these mechanisms will promote economic benefits and social welfare. This paper proposes a bi-level differential game model to investigate the effects of three mechanisms: fixed penalties, flexible penalties, and nearby consumption. The results show that: (i) Imposing or increasing undersupply penalties results in a strictly low renewable energy commitment, and distributed energy aggregation can mitigate this detrimental effect; (ii) Reducing the penalty intensity improves social welfare and a moderate fixed penalty helps to increase profits for generators; (iii) Generators can strategically increase their renewable energy commitment to manipulate the penalty under a market-based flexible penalty mechanism; (iv) Generators benefit from the resale of excess generation in a secondary market, but this practice may lead to a loss of social welfare when facing high levelized costs. In addition to cost reductions, a combination of punishment and nearby consumption can be applied to cope with generation uncertainty.

The Chinese government aims to promote synergy between carbon reduction and pollution control (SCRPC) in key areas for air pollution prevention and control (KAPPC). This paper evaluates SCRPC in 82 KAPPC cities from 2014 to 2020 using the coupling coordination degree model. The modified gravity model and social network analysis are employed to construct the SCRPC spatial correlation network and examine its structural characteristics and driving factors. The results indicate that (1) SCRPC in KAPPC significantly increased, with notable spatial autocorrelation. (2) SCRPC spatial correlation radiates from Beijing, Zhengzhou, Nanjing, and Jinan, with a more even distribution in Shanghai and nearby cities. (3) SCRPC spatial correlation network has low density, with Zhengzhou as the largest intermediary city, showing clustering and a core-peripheral structure. (4) SCRPC spatial correlation is more likely with greater differences in economic development, green innovation, and education, and smaller differences in industrial structure, green coverage, and energy intensity. It is recommended to launch SCRPC pilot projects in Beijing, Zhengzhou, Nanjing, and Jinan, and to establish a joint SCRPC platform in Shanghai and surrounding areas.

In this study, an improved gravity model and social network analysis (SNA) are applied to analysis CO2 emissions in China's power sector, uniquely incorporating electricity and fossil fuel trade flows. It further explores the dynamic effect of energy transition on networks using a panel model, and clarifies the provincial roles in emission abatement and resource allocation. According to the findings, significant regional heterogeneities in CO2 emissions from 2007 to 2022 can be observed. Coal-dependent provinces, such as Inner Mongolia and Shanxi, face high emissions and challenging transitions, while developed areas such as Beijing and Shanghai have decreased emissions through clean energy integration and enhanced power efficiency. Network analysis identifies Beijing and Jiangsu as central to resource management, empowered by robust policy and information-sharing capabilities, while most provinces demonstrate weaker coordination owing to constrained intermediary functions. In addition, the study observes that energy transitions increase network density (0.3512) and contacts (0.3545) yet decrease efficiency (- 0.1464), suggesting technical and coordinative obstacles. An increasing degree of transition strengthens interprovincial CO2 connections, establishing provinces experiencing more rapid transitions as critical nodes. Greater closeness centrality (0.0186) signifies shorter collaborative pathways, accelerating the transition. These findings derive practical guidance for regional power collaborations and sustainable growth, offering novel perspectives for a green transition toward carbon neutrality.

The rapid expansion of ICT-related multinational enterprises (IMNEs) has introduced significant challenges in emissions mitigation. This paper uses a multi-regional input-output model and complex network analysis to examine the global CO2 emissions transfer network driven by IMNEs (GCNI) from 2000 to 2019. The results reveal that between 2009 and 2019, post-financial crisis interconnectedness increased, raising network density from 0.48 to 0.58. During the period of 2000-2019, the GCNI underwent dynamic cluster adjustments, forming two distinct communities by 2019: an Asian community led by China and a cross-regional community led by the United States (US). China, Hong Kong, the US, Japan, South Korea, Germany, and Taiwan held central positions, with smaller economies acting as connectors in a core-periphery structure. These findings emphasize the need to strengthen global governance and foster cooperative emission reduction in the digital era.

With the fragmentation of global production, the share of intermediate trade in international trade has increased significantly. Nevertheless, more research must be done on how intermediate trade factors influence energy consumption. This paper utilized the multi-regional input-output (MRIO) model to categorize China's energy utilization into six parts based on its position within the global production chain. Additionally, it examined the impact of intermediate factors on China's energy consumption through index decomposition analysis (IDA). The results revealed that: (1) from 2000 to 2014, intermediate factors led to an increase of 1380.96Mtces in China's energy consumption, accounting for around 37% of the national total; (2) the reduction in efficiency is the primary cause of the surge in domestic intermediate energy consumption, while the increase in market proportion is the dominant driver promoting international intermediate energy consumption growth; (3) the international intermediate proportion contributed the most among all intermediate factors. Drawing upon the findings above, this paper proposed policy recommendations to regulate China's rising energy consumption, precisely the escalation of intermediate process energy usage, focusing on the global production chain.

This study analyzes the nexus of electricity infrastructure investment (EII), rural household income, and household carbon emissions (HCEs) using panel data from China's 30 provinces from 2003 to 2020. Firstly, we consider the impact of EII on the direct HCEs (DHCEs) caused by the direct consumption of fossil fuels and investigate the mediating effect of income level and income variance. The empirical results show that the growth of EII reduces rural DHCEs through the transmission channels of income level, whereas income variance limits the negative relationship between EII and DHCEs. Then, we estimate the emissions embodied in household expenditure on goods and services of different income groups, that is, indirect HCEs (IHCEs), and develop a counterfactual scenario to assess the effects of EII on the total HCEs (the sum of DHCEs and IHCEs) in 2017. The scenario analysis indicates that without increased EII in 2017, the total national HCEs would increase by 0.9%, mainly due to the increased per capita HCEs of low-income groups (+11.6%). Besides, the total HCEs in ten provinces are influenced significantly by the change of income distribution driven by the change of EII, with Guangdong experiencing the largest decrease of HCEs (2.6 Mt) due to the movements of residents from the lower- income group to the lowest-income group. Thus, there is a trade-off between HCE reduction and poverty alleviation when developing EII in rural China.

As household CO2 emissions (HCEs) are a key source of China's CO2 emissions, exploring the mitigation potential of HCEs is significant to achieve China's 2030 emission target. However, rare literatures analyzed the future evolution of HCEs from the provincial perspective. Here, we employ the STIRPAT model and build three scenarios (i.e., baseline, low and high scenarios) to investigate the trajectories and peak times of HCEs in 30 provinces up to 2040. The results show that 25 provinces can peak HCEs before 2030 in at least one scenario, while 5 provinces cannot achieve the 2030 emission target in any scenarios. Moreover, Guangxi and Hainan will maintain growth up to 2040 in all three scenarios. At the national level, China's household sector can achieve HCEs peak in all three scenarios. Further reduction of emission intensity helps national HCEs reach the peak around 2025 in the high scenario at 1063 MtCO2. The findings suggest that Guangdong, Jiangsu, Hebei, Henan, Zhejiang and Anhui are key provinces for future HCEs reductions, because they account for more than 40% of national HCEs in 2040 in all three scenarios. Energy efficiency improvement and clean energy applications will be effective for emission reductions.

Economic development depends on energy consumption, which is a major source of carbon emission. How to achieve economic decarbonization has become one of the key questions urgently needing to be solved on the road of carbon peak and carbon neutral development in China. Advancing total factor productivity (TFP) of carbon emission is an important way to promote economic decarbonization. For the carbon emission TFP, current research is mainly conducted from province level or an industry perspective, and studies its deference with various geographical locations, economic development levels, urbanization levels, etc., lacking the research that combines the decoupling effect to carbon emission TFP. The carbon emission TFP of Chinese cities and how to improve it remain unclear. Therefore, based on Tapio decoupling theory, this paper firstly analyzed the decoupling effect of China’s 284 cities from 2005 to 2019, and aggregated the cities into four groups according to the decoupling effect. Then, using the DEA–Malmquist index, this paper researched the carbon emission TFP and its driving factors based on the aggregation. The result shows that weak decoupling is the main decoupling status in China. As a whole, carbon emission TFP of Chinese cities does not perform well, but it shows a growth trend over time. Strong decoupling cities outperform expansive negative decoupling cities on carbon emission TFP. Technical change and pure technical efficiency change have inhibiting effect and promoting effect on carbon emission TFP, respectively, which are the main factors for the difference of carbon emission TFP between strong decoupling cities and expansive negative decoupling cities. Based on these findings, some common but differentiated recommendations are provided for improving Chinese cities’ carbon emission TFP.

The energy transition from coal and oil to renewable energy, nuclear energy, and natural gas is a fundamental way for emission reduction of China’s power generation sector. Until now, research on the drivers of CO2 emissions from China’s power generation sector has generally evaluated the energy mix as a whole, with a lack of exploration of the decomposition of different types of energy. This paper uses both index decomposition analysis (IDA) and structural decomposition analysis (SDA) to explore the impacts of energy transition on CO2 emissions in the power generation sector during periods of 2002–2007, 2007–2012, and 2012–2017. We find that the results of IDA and SDA are almost consistent, indicating that our results are robust. During the whole study period, CO2 emissions of power generation sector increased by 2447 Mt, of which the fossil fuel structure significantly contributed 642 Mt of incremental emissions (IDA). The thermal power generation efficiency was a dominator for reducing emissions, with a total reduction of 586 Mt (IDA). Simultaneously, the impacts of renewable energy and nuclear energy on emission reduction tend to be strengthening over time, with values changing from 38 Mt and −5 Mt in 2002-2007 to −219 Mt and −83 Mt (IDA) in 2012-2017, respectively. Based on the results, we put forward some suggestions such as promoting coal-to-gas, renewable energy, and nuclear energy in power generation to cut down CO2 emissions of China’s power generation sector.

Although China has experienced an overall decline in CO2 emissions intensity from 2009 to 2016, the intensity from one province to another varies considerably. As China seeks to reduce CO2 emissions intensity further, understanding how intensity differs provincially and regionally, as well as why, will be important for developing equitable management strategies. The Kaya-Theil model has been extensively applied to analyze the emission inequality; however, there have been no studies focusing on the inequality in carbon intensity in the different sectors and from different resource types in China. Here, we develop a comprehensive analysis on the inequality in China’s carbon intensity from both sectoral and energy perspectives from 1997 to 2016. The Theil index is decomposed into the within-region inequality and between-region inequality based on four economic development strategies. We used the LMDI decomposition analysis to identify the driving factors of the inequality (GDP share, emission coefficients, energy structure, energy intensity and economic structure). We find that after a decline in the inequality from 2003 to 2009, the inequality in the total carbon intensity increased by 28.4% from 2009 to 2016, mainly from an increase in the inequality from the industry sector (+ 53.1%), construction sector (+ 47.6%) and coal (+ 40.6%). The within-region inequality was the main source of the inequality in the carbon intensity, accounting for more than 60% of the inequality of the total carbon intensity from 2009 to 2016. Further decomposition identified the energy intensity disparity as the key driving factor of the carbon intensity inequality, inducing a 26.5% increase of the inequality from 2009 to 2016. Moreover, our results demonstrate how the structural transformation of energy and economic structure impact the inequality in carbon intensity.

Energy and environmental policies are important methods for the government to restrain carbon emissions growth. Identifying the potential dynamic trends of China's carbon emissions under different scenarios has important reference significance for the government's policy implementation. This paper firstly predicted China's carbon emissions from 2017 to 2040 based on three energy transition scenarios at the industrial level. Then, Logarithmic Mean Divisia Index decomposition model was applied to evaluate the driving forces of emissions changes during 1997–2040. Finally, the Spatial–Temporal Logarithmic Mean Divisia Index model was used to explore the emissions reduction potential and the potential reduction path at provincial level. The results showed that (1) as the reduction in energy intensity cannot offset the growth of industrial scale, the carbon emissions of all industries have shown an increasing trend from 1997 to 2017; (2) In the current policies scenario, China's carbon emissions cannot reach the peak before 2040. And only in the sustainable development scenario, the carbon emissions of the three industries will all reach the peaks before 2030. And the development of non-fossil energy will reduce carbon emissions by more than 30%; (3) Hebei, Shanxi, Inner Mongolia, Ningxia, and Heilongjiang are key provinces and improving energy efficiency of the secondary industry is a potential way to promote carbon emissions reduction. The framework and main content of this paper.

The CO2 emission transfer at city level, especially across sectors of cities, has not yet been sufficiently quantified. This paper analyzes the embodied CO2 transfer across sectors of 13 cities in Jing-Jin-Ji region (Beijing–Tianjin–Hebei in China) combining multi-regional input–output analysis with complex network analysis. The results show that (1) most embodied CO2 transfers are concentrated in a few sectors and emission reduction effects on these sectors can be quickly extended to the entire embodied CO2 transfer network (ECTN). (2) The electricity and hot water production and supply sector in Beijing have the most embodied CO2 export partners and the strongest control over embodied CO2 transfer. The construction sector in Beijing has the most embodied CO2 import partners and greatest influence on the ECTN. (3) At city level, 90% of embodied CO2 transfers occur within the city, and the general direction of embodied CO2 transfer across cities is from resource-rich cities, Tangshan and Handan, to developed cities, Beijing and Tianjin. At sector level, the largest embodied CO2 transfer path is electricity and hot water production and supply → metallurgy → construction sector. (4) Embodied CO2 transfers of sectors in the same or adjacent cities are denser. Collection of sectors in Beijing and Tianjin is the densest embodied CO2 transfer community.

As the most developed city circle in northern China, allocating CO2 emission quotas at the Bohai Rim Economic Circle (BREC) city level is essential for developing specific abatement policies. Thus, with reflecting multi-principles (fairness, efficiency, sustainability, and feasibility), this paper formulates the CO2 emission quota allocation among cities in BREC in 2030 based on the multi-objective decision approach. We first propose three allocation schemes based on the principles of fairness, efficiency, and sustainability, which are conducted by entropy method, zero-sum gains data envelopment (ZSG-DEA) model, and CO2 sequestration share method, respectively. Then, the CO2 allocation satisfaction is defined and used to measure the feasibility principle which is integrated as the objective function of the multi-objective decision model together with three allocation schemes to obtain the optimal allocation results. The results show that Beijing, Tianjin, Dalian, Shijiazhuang, Yantai, Weifang, and Linyi enjoy the largest CO2 emission quotas, having 1179.94 Mt in total and accounting for 31%. Beijing has the highest quotas, and Laiwu has the lowest emission quotas. Cities with large energy consumption and less CO2 sequestration capacity, such as Tianjin, Handan, and Tangshan, experience a decrease in the emission quota shares from 2017 to 2030, indicating that these cities would undertake large emission reduction obligations. Sensitivity analysis shows that Beijing, Zibo, and Jinan are more sensitive to minimum satisfaction changes, and the total satisfaction experiences an increase first and declines thereafter. Based on the results above, cities with large pressure to reduce CO2 emissions should not only promote economic development but also improve the capacity of CO2 sequestration by enhancing environmental protection to realize emission reduction targets.

Abstract To assess the evolution trend of China's carbon emissions (CEs) and related driving factors, this paper used scenario analysis to predict China's CEs from 2017 to 2040 at the industrial level. Then, LMDI decomposition model was applied to evaluate the driving forces of CEs changes during 1997-2040. Finally, the ST-LMDI model was used to explore the CEs reduction potential and the potential reduction path at provincial level. The results showed that (1) as the reduction of energy intensity cannot offset the growth of industrial scale, the CEs of all industries have shown an increasing trend from 1997 to 2017; (2) In the current policy scenario, China's CEs cannot reach the peak before 2040. And only in the sustainable development scenario, the CEs of the three industries will all reach the peaks before 2030. And the development of non-fossil energy will reduce CEs by more than 30%; (3) Hebei, Shanxi, Inner Mongolia, Ningxia, and Heilongjiang are key provinces and improving energy efficiency of the secondary industry is a potential way to promote CEs reduction.