Developing a fair and effective carbon emissions quotas (CEQ) allocation plan is crucial for China. This study uses the constructed threshold-STIRPAT extended model to predict the carbon peak in China's 30 provinces. Secondly, the entropy-TOPSIS method is used to calculate the initial allocation of CEQ based on the principle of fairness and is assessed through the carbon Gini coefficient. Thirdly, the optimal allocation of CEQ is calculated based on efficiency principle using the ZSG-DEA model. Finally, based on the carbon peak and CEQ, identify the emission reduction pressures faced by provinces. The results indicate that, under the energy-saving development scenario, China carbon emissions (CE) are expected to peak at 11,813.44 Mt by 2030. which can serve as China's overall CEQ; From the perspective of initial allocation of CEQ under the principle of fairness, the initial CEQ in the eastern and central regions are generally higher than those in the western and northeastern regions; From the perspective of optimizing CEQ allocation under the principle of efficiency, the optimized CEQ in Jiangsu, Shandong, and Guangdong are significantly higher than the initial CEQ, while the optimized CEQ in Guangxi and Gansu are significantly lower than the initial CEQ; High-High are mainly concentrated in the northern regions, High-Low are mainly distributed in the central and eastern coastal regions, and Low-Low are mainly distributed in the western and northeastern regions. This study provides a new research approach for developing fair and effective CEQ allocation schemes.

China experienced substantial millennial-scale climate and anthropogenic land use changes, yet their combined impacts on land carbon dynamics remain largely unexamined. Here, we quantify spatiotemporal changes in terrestrial organic carbon over 851-2022 using a land surface model driven by reconstructed climate and land cover forcings. Simulated results show China's pre-industrial millennial land carbon dynamics aligned with global carbon stock and atmospheric CO2 fluctuations, such as the ~284 ppm peak in the 12th century linked to land use during the Medieval Climate Anomaly-warmed Song Dynasty. Notably, China's total land carbon emissions (13 ± 0.5 PgC) accounted for 22% of global land carbon emissions during 1700-1900, with Northeast and Southwest China experiencing the largest historical land carbon losses from intensive deforestation. Nevertheless, the 17.0 ± 1.7 PgC emissions during 851-1980 were fully offset by rapid carbon sinks over 1980-2022, driven by CO2 fertilization and large-scale afforestation. These findings provide insights into China's historical landcarbon dynamics, their underlying drivers, and global implications.

Multiscale geographically and temporally weighted regression (MGTWR) is widely applied to address spatiotemporal heterogeneity and relationships at different scales in domains such as land use, urban vitality, and transportation. However, conventional MGTWR calibration, relying on locally weighted least squares and the back-fitting algorithm, faces two key limitations: boundary effects (i.e. edge bias) and high computational cost. To address these issues, this study proposes an efficient calibration algorithm for MGTWR, termed the Two-Step Calibration Algorithm based on local linear fitting (MGTWR_2SCALL). By exploiting the local linear structure via Taylor series expansion, MGTWR_2SCALL performs model calibration through a two-stage local smoothing procedure, eliminating the need for the iterative back-fitting process. The algorithm’s performance is rigorously evaluated through simulation experiments and a real-world case study analyzing carbon emissions in China during 2014–2021. The results demonstrate that MGTWR_2SCALL effectively mitigates boundary effects and enhances computational efficiency. Thus, MGTWR_2SCALL offers substantial theoretical and practical significance for advancing spatiotemporal statistical modeling.

Battery recycling is essential for mitigating the resource and environmental impacts of the electric vehicle industry. However, real-world assessments of battery recycling at the industrial scale remain limited. Here, we present the most comprehensive life-cycle assessment to date using operational data from 46 recycling facilities in China, covering approximately 50% of the global capacity in 2023. We evaluate multiple recycling outputs, black mass, metal salts, precursors, and cathodes and reveal that new hydrometallurgical technologies for direct precursor and cathode recovery could reduce carbon emissions by 61% compared to mining production due to skipping multiple extraction steps. Real-world recycling often requires blending with virgin materials to maintain the targeted Ni-Co-Mn ratio for recycling the nickel-cobalt-manganese (NCM) precursor or cathode due to market preference for high-nickel chemistries. Our results show that, compared with virgin production, fully recycled cathode materials can reduce pack-level carbon footprint levels of lithium-iron phosphate (LFP) batteries by 11% (2-14%), significantly greater than previous estimates, and by 24% (12-27%) for NCM811 batteries. Coupled with dynamic fleet modeling, battery recycling is identified to cumulatively avoid 147-433 million tons of CO2 emissions in China by 2050. These insights offer important guidance for carbon footprint regulations and the advancement of circular economy practices globally.

County-level emission inventory reveals substantial contributions of small and medium-sized cities to air pollutants and CO2 emissions in China.

As the world's largest country regarding energy consumption and carbon emissions， analyzing China's carbon emissions and emission reduction potential is essential to the fight against global climate change. This study constructs the LEAP-China model to forecast and analyze China's carbon emissions and emission reduction potential in three dimensions： primary energy， end-use industries， and carbon emission contribution. The conclusions are as follows： ① Except for the baseline scenario， the industrial structure emission reduction， technological progress， energy structure emission reduction， and blueprint scenarios were all able to realize the goal of "peaking by 2030." ② From 2022 to 2060， carbon emissions from all industries except industry were declining. ③ The carbon emissions of various industrial sectors varied significantly according to their energy consumption， with chemicals > other industries > non-metallic mineral products industry > ferrous metal smelting and rolling processing industry > non-ferrous metal smelting and rolling processing industry > paper and paper products industry. ④ The optimization of energy structure had apparent emission reduction effects in the short term； the optimization of industrial structure was a continuous driving force for carbon emission reduction， and technological progress was a long-term driving force for carbon emission reduction. The study can provide a decision-making basis for China to realize the medium- and long-term carbon emission reduction path.

Under mounting global pressure for carbon emission mitigation, China—currently the world's largest carbon emission contributor—confronts the critical challenge of reconciling emission reduction targets with sustained economic growth and progressive enhancement of citizens' living standards. Adopting a land-intensive utilization framework, this paper systematically investigates the relationship between urban intensive land use (UILU) and carbon emissions (CEs) in China's three major eastern urban agglomerations—the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD). Methodologically, standard deviation ellipse analysis, the HR coordination model, and the Environmental Kuznets Curve (EKC) were employed to identify viable emission reduction pathways. Key findings from the 2005–2021 study period reveal four principal patterns: (1) UILU levels across the three urban agglomerations demonstrate an overall upward trajectory; Inter-agglomeration disparities exhibit phased transitional characteristics, following a “contraction-expansion-contraction” sequence, whereas intra-agglomeration disparities manifest persistent widening trends. Spatial analysis through standard deviation ellipses demonstrates distinct orientation patterns: the BTH exhibits a predominant northeast-southwest alignment; the YRD displays northwest-southeast orientation; and the PRD maintains an approximate east-west axis. (2) CEs across the three urban agglomerations demonstrated an overall increasing trajectory, stabilizing during the final phase of the study period. Spatiotemporal analysis revealed distinct orientation patterns: the BTH's standard deviation ellipse maintained a northeast-southwest alignment, while the YRD and PRD exhibited northwest-southeast orientations. (3) The HR coordination degree displayed moderate fluctuation with an overall ascending trajectory, demonstrating hierarchical coordination levels: YRD > PRD > BTH. (4) EKC analysis delineates distinct morphological patterns: The EKC curves for UILU and CEs in the BTH and PRD demonstrated an inverse N-shaped pattern, with turning points at 0.14 and 0.49 for the BTH, 0.13 and 0.44 for the PRD, respectively. In contrast, the EKC relationship for the YRD follows a U-shaped curve with a turning point at 0.20.

The significant impact of greenhouse gases on global warming has drawn widespread attention. This study focuses on the development of the transportation sector and energy consumption across 30 provinces in China from 1997 to 2022, aiming to identify the key drivers of carbon emissions in China’s transportation sector and analyze their causal interactions and spatial heterogeneity. Initially, provincial carbon emissions are estimated based on reallocated energy consumption data. A random forest model is then employed to objectively screen key factors from multidimensional variables. Subsequently, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach is utilized to reveal the interaction network among these factors, distinguish their causal attributes, and explore their inter-provincial spatial differentiation. The findings are as follows: (1) Expenditure on research and experimental development, Number of registered scientific and technological achievements, and Total energy consumption are the most crucial factors influencing emissions; (2) Total energy consumption, Green coverage rate of built-up area, and Urbanization level serve as the primary causal drivers within the system; (3) The same factor exhibits significant variations in causal attributes across different provinces, reflecting regional heterogeneity in development stages. This study provides empirical evidence and methodological support for formulating differentiated and precise traffic carbon reduction policies.

Impact of rural Return Entrepreneurship pilot policies on agricultural carbon emissions in China's Yangtze river Economic Belt

Spatial correlation of carbon emissions in China's coastal areas: The spatiotemporal nonlinear characteristics and spatial heterogeneity

Household investment behavior and environmental impact: How family financial decisions influence carbon emissions in China

Spatiotemporal dynamics and urban-rural disparities of household carbon emissions in China: A prefecture-level analysis using interpretable machine learning

Dynamic interpretable prediction and Spatio-temporal reduction pathways analyses of embodied carbon emission in China's building sector

Multi-scale spatiotemporal interactions between land use transformation and carbon emissions in China from 1980 to 2020

This study analyzed the carbon reduction effects of water-saving irrigation based on panel data of Chinese provinces from 2010 to 2020. Carbon emissions from irrigation were calculated and decomposed using the Malmquist index and LMDI. Results indicate that, first, the accounting results show a downward trend in estimated agricultural irrigation carbon emissions over the study period under a fixed-parameter framework. The average irrigation carbon intensity exhibits a declining pattern, particularly after the mid-2010s, with differences between provinces narrowing. Second, water-saving irrigation is associated with lower levels of estimated agricultural irrigation carbon emissions within the accounting framework by improving water-use efficiency and reducing irrigation water consumption per unit area, ultimately leading to a decrease in total carbon emissions. Finally, the carbon reduction effects are more pronounced and stable in major grain-producing regions. This study highlights regional heterogeneity in the emission-accounting outcomes associated with water-saving irrigation, which may provide descriptive evidence for discussions on region-specific irrigation management under different regional contexts.

Carbon trading, as the critical market mechanism to achieve greenhouse gas emotion reduction, have received more attention around the world. The target that China establish and improve Carbon Emissions Trading Exchange (CCET) in recent year is to help the achievement of “peak carbon dioxide emissions”, “carbon neutrality”. However, Considering the characteristics of nonlinearness, strong fluctuation of carbon price, the accuracy of data prediction reduced significantly. Based on China Carbon Emissions Trading Exchange (CCET) data from July 2021 to March 2025, this paper build a multi-factor hybrid forecasting model that integrates VMD-CEEMDAN decomposition with GARCH-MIDAS and CNN-BiLSTM-Attention model throughout the ARCH and Sample Entropy as testing methods. Meanwhile, this model introduce influence factor including Macro Economy, Similar Products, Energy Structure and Climate Environment. Combine the random forest method with the MIDAS approach to select and integrate multi-frequency influencing factors, and evaluate model performance using indicators such as MAE, MSE, and RMSE. The experimental results show that the proposed model is better than the other model in accuracy and stability. The indicators including MAE, MSE and RMSE all are less than other comparison models.

It is of great significance to clarify the evolution trend and key influencing factors of China's rural energy carbon emissions in order to promote the green development of agriculture and rural areas and to realize the goal of "double carbon" on schedule. On the basis of clarifying the current situation of rural energy carbon emission in China and 30 provinces， this study focused on analyzing the evolution trend of rural energy carbon emission and key influencing factors by using the kernel density estimation method and the random forest model. The study showed that： ① China's total rural energy carbon emission was on an upward trend from 2005 to 2022， and their evolution could be broadly categorized into three phases： "fluctuating increase， relatively stable， and continuous increase，" and the carbon intensity increased by more than 160% during the study period. In terms of provinces， the total amount of rural energy carbon emission was greatest in Guangdong， with Shanghai showing the least， and the intensity was greatest in Tianjin， with Guangxi showing the lowest. ② During the investigation period， rural energy carbon emission intensity increased significantly in both the country and the southern and northern regions， and the distribution of provinces in the low-value region decreased significantly. The rural energy carbon emission intensity in both the country and the northern regions still showed some polarization at the end of the investigation period. ③Rural energy carbon emission was influenced by factors at the economic， social， and governmental levels. Among the factors at the economic level， the structure of agricultural industry had an inverted U-shaped effect on rural energy and carbon emissions. Among the factors at the social level， the aging of the rural population， the degree of mechanization of agriculture， and the increase in the level of rural human capital all led to an increase in rural energy and carbon emissions， while the increase in the level of urbanization could play a restraining role. Among the governmental factors， the increase in the level of financial support for agriculture will help to realize the carbon emission reduction of rural energy. The results of the study can provide scientific references for the construction of the optimization path of emission reduction and carbon sequestration in rural areas.

Carbon emissions between regions exhibit complex spatial correlations， and the achievement of "dual carbon" goals depends not only on provincial factors but also on spatial linkages. Using the carbon emission coefficient method， this study calculates the energy consumption carbon emissions of Chinese provinces from 2000 to 2021 and constructs a spatial correlation network of provincial energy consumption carbon emissions in China. From the perspective of spatial correlation， the GDIM decomposition is applied to analyze the driving factors of energy consumption carbon emissions for the main beneficiary， net beneficiary， broker， and net spillover sectors in the network. A CNN-BiLSTM-attention combined model is used to simulate and predict energy consumption carbon emissions for each sector from 2022 to 2050 under four scenario modes. Emission reduction pathways are proposed based on indicators of the functional role of each sector in the network. The results showed that： ① During the observation period， various sectors underwent different degrees of restructuring， exhibiting significant "gradient transfer" characteristics. ② Economic scale and energy consumption scale were the main positive driving factors for energy consumption carbon emissions at different development stages of each sector， with the largest cumulative contribution. The inhibitory effect of technological progress is gradually becoming apparent. ③ Each sector could basically achieve peak emissions before 2030. To this end， the main beneficiary， net beneficiary， broker， and net spillover sectors should prioritize green innovation， high-speed coordination， high-speed leadership， and green transformation as their primary emission reduction pathways， respectively.

Under the "double carbon" goal， the green and high-quality development of the construction industry in China has an important impact on the realization of carbon peak. At the provincial scale， the IPCC coefficient method was used to estimate the carbon emissions of the construction industry from 2001 to 2020 in each province. Based on the STIRPAT model， the WOA-BP neural network model was used to simulate the carbon emissions of the construction industry and its spatio-temporal evolution from 2021 to 2050 in different scenarios. The research showed that： ① From 2001 to 2020， the per capita carbon emissions of China's construction industry will gradually increase， with high per capita carbon emissions in eastern and central provinces and low per capita carbon emissions in western and northern provinces. ② Population was the most important factor affecting the carbon emissions of the construction industry in each province from 2021 to 2050， and the influence was different among provinces. ③ From 2021 to 2050， the peak time of carbon emissions from the construction industry was different under different scenarios， and the peak time was the earliest under constrained scenarios. Seventeen provinces in the constrained scenario， five provinces in the normal equilibrium scenario， and zero provinces in the relaxed radical scenario were projected to achieve carbon peak before 2030. The north and southeast coastal areas reached the peak earlier， followed by Central China， Southwest late， and Shaanxi and Liaoning late. The western development strategy would delay the carbon peak of the western construction industry， so it is necessary to strengthen the carbon emission intensity constraint.

Spatial spillovers and nonlinear drivers of water-supply carbon emissions in China