From coal to code: The transformative role of fintech and renewable energy in China's low-carbon transition

Progress and performance in synergizing the reduction of pollution and carbon emissions in China from 2015 to 2022

ABSTRACT This study examines the spatial correlation structure and driving mechanisms of transportation carbon emissions across 30 Chinese provinces from 1997 to 2022. Provincial emissions are estimated using reclassified energy consumption data. A spatial correlation network is constructed using a modified gravity model and analyzed with Social Network Analysis (SNA) and the Quadratic Assignment Procedure (QAP). Results show rapid growth followed by gradual decline, with a spatial gradient of ‘East > West > Central > Northeast’ and expanding high-emission zones. The network exhibits significant spillover effects but remains structurally sparse, characterized by a core–periphery structure and a ‘Matthew effect’ dominated by core provinces. Four functional roles—net spillover, intermediary, bidirectional spillover, and net beneficiary—indicate heterogeneous interprovincial interactions. QAP results indicate that technological innovation, transportation intensity, and economic development promote network linkages, whereas geographic distance, industrial structure differences, and population density disparities hinder the formation of such linkages.

Rising incomes worldwide are reshaping dietary patterns and intensifying concern about the carbon impacts of household food consumption. This study examines how economic development influences food-related carbon emissions in China, with a focus on household food consumption behavior and dietary change. Using longitudinal household data from the China Health and Nutrition Survey covering 2004-2011, we apply a panel threshold regression model to identify nonlinear income effects on food-related carbon emissions. Within the 2004-2011 sample, household income is positively associated with food-related emissions, but the marginal effect declines once income exceeds the estimated threshold. The baseline model identifies a single threshold at 6.5479 (95% confidence interval: [6.4965, 6.5917]), corresponding to 65,479 yuan in annual household income. The single-threshold test is significant at the 5% level (p = 0.035), and the adjusted R2 is 0.243. Income growth significantly increases the consumption of greenhouse-gas-intensive foods and associated emissions among low-income households, whereas food consumption patterns among high-income households are comparatively more stable within the sample period. These findings indicate that rising income can intensify food-related carbon pressure during China's dietary transition, particularly through dietary upgrading among low-income households, but they do not provide direct evidence that household food emissions will stabilize automatically over time.

Multisource data fusion framework reveals multi-scale spatiotemporal dynamics of anthropogenic CO2 emissions in China

The driving factors of CO2 emissions in China's resource processing industry: Evidence from an income-based accounting perspective

This study examines the coupling coordination between energy consumption and carbon emissions in China’s agricultural sector and explores its implications for the low carbon transition. Using a coupling coordination model and provincial panel data from 2012 to 2021, the study assesses the level of energy carbon coordination in China’s agricultural sector and analyzes its key determinants. Fixed effects models evaluate the impact of terminal energy structure, while mediation analysis tests whether agricultural carbon emission intensity transmits these effects. Additionally, out of sample validation and short term scenario projections are used to examine temporal stability. The results show that energy carbon coordination in China’s agricultural sector is generally at a moderate to good level, indicating progress toward green transformation, although substantial regional disparities remain. Terminal energy structure is a key determinant, higher coal and diesel shares significantly reduce coordination, whereas a higher natural gas share improves it. Mediation analysis further shows that agricultural carbon emission intensity partly transmits these effects. Regional heterogeneity is observed, low GDP provinces are mainly constrained by coal dependence, while high GDP provinces face stronger diesel and electricity related emission pressures. The out of sample validation and short term scenario projections provide additional support for the temporal stability of these findings. These findings highlight the need for region specific decarbonization strategies in China’s agricultural sector, including accelerated clean energy substitution, power sector decarbonization, stronger carbon monitoring, and improved cross sector policy coordination.

With the increasing importance of household carbon emissions in global climate change, this study investigates how Information and Communication Technology (ICT) adoption, specifically mobile payments and online shopping, influences indirect household carbon emissions (indirect-HCE) in China. Addressing the lack of research from a micro-level perspective, this study employs data from the China Household Finance Survey, combined with a multi-regional input–output (MRIO) model and the consumer lifestyle approach (CLA), to establish a “technology–household characteristics–carbon emissions” theoretical and accounting framework. It further incorporates household structural features into the analysis of moderating effects, thereby innovatively examining the regional and demographic heterogeneity in the impact of ICT on carbon emissions. The findings reveal that the adoption of ICT stimulates household consumption by enhancing payment convenience and lowering consumption barriers, thereby contributing to higher emissions, with ICT adoption increasing per capita emissions by approximately one-third and ICT usage intensity by about 10–15%. However, the effects vary based on household characteristics such as education, age, and consumption patterns. Households with higher education levels and a younger demographic are more likely to amplify the carbon-increasing effects of ICT technologies, whereas households dominated by rigid, basic consumption categories are particularly sensitive to the carbon effects associated with ICT adoption. Additionally, regional analysis highlights disparities, with the eastern region exhibiting the strongest ICT-driven carbon increases due to advanced infrastructure and consumption models. This study provides a nuanced understanding of ICT’s dual role in enabling sustainable consumption and amplifying carbon emissions. It also offers actionable insights for policymakers to design tailored strategies promoting low-carbon consumption.

Against the backdrop of accelerating global carbon neutrality and the full implementation of China’s “Dual Carbon” strategy, the mining industry, as an energy-intensive sector that guarantees resource supply, plays a critical supporting role in the green transformation of the industry and achieving national carbon emission reduction targets. Based on panel data from 29 provinces in China from 2000 to 2021, this study combines the Tapio decoupling index and the LMDI decomposition method to systematically characterize the evolution of carbon emissions in China’s mining industry, to accurately identify the decoupling state between carbon emissions and economic growth, and to reveal the core driving mechanism, presenting quantifiable and interpretable empirical and technical results. The results show that carbon emissions and raw ore output in China’s mining industry generally followed an evolutionary trend of “first rising, then peaking, and continuously declining”. Carbon emissions peaked in 2013 and decreased steadily afterward, reflecting remarkable achievements in green development. The decoupling relationship has shifted from weak decoupling to stable strong decoupling in 2019 and has been maintained in this state ever since, indicating that the mining industry has entered a high-quality development stage featuring coordinated economic growth and carbon emission reductions. The decomposition results confirm that the output expansion effect is the main driver of the increase in carbon emissions, while the reduction in energy intensity, optimization of the energy structure, and improvement in output efficiency constitute the key forces driving the reduction in carbon emissions, with technological progress, industrial upgrading, and clean energy substitution as the core pathways. In summary, this study empirically verifies the feasibility and effectiveness of low-carbon transformation in China’s mining industry. The realization of a stable strong decoupling state shows that this paradigm can be replicated in the green development of other energy-intensive industries. In the future, precise policy incentives, energy structure upgrades, energy efficiency technological innovation, and standardized construction of green mines can further consolidate the decoupling effects and further encourage the comprehensive transition towards a low-carbon mining industry. The findings of this study can provide a solid theoretical basis and empirical support for the formulation of carbon emission reduction policies and the design of green development pathways in China’s mining industry, with important theoretical and practical value for ensuring national resource security and facilitating the realization of the “Dual Carbon” goals.

Research on agricultural carbon emissions in China’s major grain-producing regions: assessment, influencing factors, and pathways for emission reduction

This research uses Chongqing, China, as a representative case study to address the challenges inherent in investigating carbon peak pathways within topographically constrained inland Chinese cities. These challenges include the lack of regional structural variables, limited flexibility in scenario design, and a scarcity of case studies focusing on western China. Employing an extended STIRPAT model, the study systematically assesses the influence of critical regional factors-such as industrial structure, energy intensity, and energy mix-on carbon emissions. To improve the accuracy of parameter estimation and mitigate multicollinearity among variables, ridge regression was applied using data from China's Carbon Emissions Accounting Database (CEADs). Seven multi-scenario combinations were developed to project carbon emission trajectories from 2023 to 2050, followed by a comparative analysis with analogous studies conducted in Yunnan Province. The principal findings are as follows: (1) Population size, industrial structure, and energy mix constitute the primary determinants of carbon emissions in Chongqing; (2) Under the baseline scenario, carbon emissions are projected to peak in 2037, whereas adopting a "low-growth plus high-efficiency decarbonization" pathway could effectively advance the peak to 2035; (3) Relative to Yunnan-a similarly topographically constrained region in Southwest China-Chongqing exhibits more pronounced "valley industry" lock-in effects. Accordingly, mitigation strategies for Chongqing should emphasize accelerating the transformation of energy-intensive industries and enhancing regional energy coordination. This study illustrates how variations in industrial foundations lead to divergent carbon peak trajectories under comparable topographical constraints, thereby offering tailored policy insights for analogous regions.

To support China’s dual-carbon goals and sustainability-oriented mitigation planning, this study develops an SSA–LSTM–Transformer framework for provincial carbon emission forecasting and interpretable driver analysis. Using panel data for 30 provinces from 2005 to 2022, SSA is employed for adaptive hyperparameter optimization, while the LSTM captures local temporal memory and the Transformer models long-range dependencies. Ablation tests and benchmarking against eight widely used models demonstrate that the proposed framework achieves the best overall performance on the held-out test set, with R2 = 0.9911 and NRMSE = 0.0192. SHAP analysis indicates that a more carbon-intensive energy structure is associated with higher predicted emissions, whereas stronger technological innovation is associated with lower predicted emissions, and feature-importance patterns vary across development-stage groups. Forecast trajectories diverge during 2025–2035 and show a convergence tendency by 2050 under the model assumptions, informing differentiated near-term mitigation pathways and longer-term cross-regional coordination and technology diffusion. The results provide an interpretable evidence base for sustainability-oriented provincial decarbonization policies.

Against the backdrop of the contradiction between economic development and the "dual carbon" policy, it is of great significance to explore the decoupling effect and driving factors of China's carbon emissions. Here, we used the Tapio decoupling model to characterize the decoupling state of China's carbon emissions and economic growth, optimizing and expanding the decoupling index based on the LMDI model, systematically exploring the driving factors and their contribution to carbon emissions decoupling, further predicting the driving factors based on the grey breakpoint model, and then exploring the main contradictions of carbon emission decoupling in China in the next few years. The results showed that the decoupling state of carbon emissions in various regions of China was mainly weak decoupling, with the western region performing the worst. Under the impact of the COVID-19 pandemic, most regions have shown a strong decoupling state, but the decoupling index of carbon emissions rebounded during the economic recovery stage. Energy efficiency and technological progress were the main driving forces for carbon emission decoupling, while economic growth was the main obstacle. The impact of fossil energy consumption structure and demographic factors was relatively small. In the next few years, the decrease in energy efficiency will weaken the role of the energy intensity effect in promoting carbon emission decoupling, and the decline in innovation efficiency will inhibit carbon emission decoupling.

The low-carbon transformation of the logistics industry is an important component of achieving China's "dual carbon" goals. Analyzing the dynamic evolution of carbon emissions in China's logistics industry and exploring effective paths for green and low-carbon development under the background of digital intelligence are of great significance for the long-term development of the logistics industry. Based on the carbon emission coefficient method, we calculated the carbon emissions of the logistics industry in 30 provinces and cities in China and analyzed the dynamic evolution of the logistics industry using kernel density analysis. We also constructed a measurement index system for the level of digitalization and incorporated it into the TOE framework, based on the three levels of technology, organization, and environment, to construct the antecedents of carbon emissions in the logistics industry, using the fsQCA method for configuration analysis of carbon emission reduction pathways in the logistics industry. The results indicate that: ① The carbon emissions of China's logistics industry showed a trend of first increasing and then decreasing, with a gradual decrease in the concentration of carbon emissions in the early stage and an increase in regional differences in carbon emissions, indicating spatial polarization. The gap between regions gradually narrowed in the later stage, and the distribution became more concentrated and balanced. ② There were three driving modes for low-carbon emissions in the logistics industry: digital intelligence-open collaborative type, technology-market synergy type, and digital intelligence-environment collaborative type. Among them, digital intelligence-open collaborative type was the most common. ③ There were three driving modes for non-low-carbon emissions in the logistics industry: open deficiency type, technology-open deficiency type, and numerical intelligence deficiency type. This was mainly due to the lack of two variables, namely the level of digital intelligence and the degree of openness to the outside world. ④ The presence of multiple configurational paths indicated that the level of digital intelligence was a core condition for the low-carbon development of China's logistics industry. Empowering the low-carbon transformation of the logistics industry with digital intelligence is a way to alleviate the burden of high-quality development in the logistics industry. The research findings can provide important reference and guidance for the government and relevant departments.

Photovoltaic (PV) technology is the core pathway foraddressingglobal climate change and advancing energy system decarbonization,yet the rapid expansion of PV manufacturing capacity has triggereda surge in life-cycle greenhouse gas emissions, sparking mountingconcerns. We integrated multisource heterogeneous data from China’sPV industry (2005–2024) to develop a life-cycle accountingframework, which quantifies industrial carbon emissions and theirevolutionary patterns across production stages and multiscale spatiotemporaldimensions. We also deconstructed the emission impacts of scale, technology,and structural factors, and predicted future trends. Over two decades,China’s PV industry-wide carbon emissions soared from 0.24to 205 million tonnes, while product-level emission intensity plummetedfrom 1,300 to 380 kg CO2eq/kWp. The contribution of technologicalprogress to emission reduction rose from about 3% of the observedincrease in emissions in 2005–2007 to nearly 100% in 2020–2024.Spatially, raw material and monocrystalline cell production have shiftedinland for cost advantages, while module assembly remains concentratedin coastal hubs like the Yangtze River Delta. Capacity utilization,grid decarbonization, and technical learning will dictate future emissions.Against surging global PV demand, coordinated capacity planning, acceleratedtech progress, optimized spatial distribution, and established incentivepolicies are pivotal to steering China’s PV manufacturing ontoa sustainable low-carbon path.

The decoupling effect and its driving factors of carbon emissions in China's three major urban agglomerations

Development of a transnational CO2 capture and storage super cluster to cut CO2 emissions in China’s East Coast: A perspective

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

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.