Carbon Balance and Management最新文献

{"title":"Assessing the environmental sustainability prospects of financial development, biocapacity, and renewable energy","authors":"Elma Satrovic,&nbsp;Ojonugwa Usman,&nbsp;Seyi Saint Akadiri,&nbsp;Magdalena Radulescu","doi":"10.1186/s13021-026-00459-9","DOIUrl":"10.1186/s13021-026-00459-9","url":null,"abstract":"<div><p>While previous studies have examined the impact of financial development on environmental sustainability, its interaction with ecological capacity remains underexplored. By integrating the modified STIRPAT framework with advanced panel quantile estimators, this study provides the first comprehensive assessment of the role of biocapacity in moderating the financial development-environmental sustainability nexus among selected countries with biocapacity reserves. Given that higher biocapacity typically indicates greater reserves of renewable natural resources (e.g., biomass, soil, water, forests), broader financial support in such countries may favour renewable energy projects over non-renewable ones, thereby mitigating environmental challenges. In this context, this study investigates the environmental implications of financial development, biocapacity, and renewable energy for ten biocapacity-rich countries from 1995 to 2020. Also, this study unveils the effectiveness of biocapacity in moderating the effect of financial development on environmental sustainability. The results indicate that biocapacity, economic complexity, and renewable energy enhance environmental sustainability, whereas financial development, economic growth, and population growth exert negative effects. Notably, the marginal effect of financial development on sustainability increases with biocapacity, suggesting that a joint expansion of both factors yields positive outcomes. These findings underscore the need for policies that protect ecosystems and regulate resource use to integrate biocapacity considerations into financial development strategies. Also, governments should align such measures with the Sustainable Development Goals (SDGs) to address environmental challenges and secure long-term environmental stability.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s13021-026-00459-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148013064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low GHG emissions and high sediment sequestration define the carbon function of cascade reservoirs in the Wujiang River Basin, Southwest China","authors":"Xiaoling Wu,&nbsp;Xiaohua Xiang,&nbsp;Hong Yang,&nbsp;Zhiwei Wang,&nbsp;Chao Li","doi":"10.1186/s13021-026-00453-1","DOIUrl":"10.1186/s13021-026-00453-1","url":null,"abstract":"<div><p>A comprehensive assessment of hydropower’s climate impact requires considering both greenhouse gas (GHG) emissions and carbon burial in sediment. This study examines the Wujiang River in southwestern China, where seven cascade reservoirs were categorized into upper (Group 1), middle (Group 2), and lower (Group 3) reaches according to their geographic locations and proximity to key sediment source areas. The G-res Tool was conducted to simulate greenhouse gas (GHG) emissions from these reservoirs, which ranged from 2,231 to 19,774 t CO_2e yr⁻¹ after impoundment. The primary influencing factors include reservoir age, surface area, and water retention time. Due to the steep mountainous terrain, deep valleys, and pre-impoundment clearing, the GHG emissions from these groups of reservoirs are lower than those of other reservoirs at similar latitudes worldwide. As the cascade reservoirs were gradually constructed, sediment accumulated behind the dams, leading to the long-term storage of terrigenous carbon. Notably, the upstream Group 1 reservoirs exhibited an exceptionally high terrigenous carbon storage rate during their early impoundment phase (7,468 gC m⁻² yr⁻¹), largely due to significant sediment input from the upstream Yachihe Basin, a major sediment source. In contrast, the downstream reservoir (Group 3) exhibited relatively smaller total terrigenous carbon storage rates (319 gC m⁻² yr⁻¹), corresponding to reduced sediment retention. Despite variations in sediment retention among the groups, the total terrigenous carbon storage of all groups of reservoirs (ranging from 43,020 to 999,403 tC yr⁻¹) exceeded their post-impoundment GHG emissions. By integrating carbon emissions and sedimentary carbon sequestration across a cascade system, this study provides a system-scale carbon balance assessment of hydropower reservoirs. Our results indicate that the cascade reservoir systems, like those on the Wujiang River, function as a net terrigenous carbon storage, mainly due to the large sediment sequestration. These findings highlight the importance of incorporating both carbon emission and storage into carbon accounting frameworks and provide new insights for carbon balance assessment and management of hydropower systems.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13455469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148004112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Carbon reduction multidimensional ternary model for robotic 3D printing prefabricated construction across hotels star levels","authors":"Gangwei Cai,&nbsp;Xiaoting Guo,&nbsp;Jian Chen,&nbsp;Qian Wu,&nbsp;Yiru Pan,&nbsp;Yifan Yao,&nbsp;Ye Lu,&nbsp;Yuguang Sun,&nbsp;Feidong Lu,&nbsp;Weijun Gao","doi":"10.1186/s13021-026-00447-z","DOIUrl":"10.1186/s13021-026-00447-z","url":null,"abstract":"<div><p>This study evaluates the carbon emission reduction potential of industrialized construction technologies—specifically robotic 3D printing and prefabrication—in lower-star hotels in Hangzhou, China, from 2018 to 2023. A multidimensional ternary spatiotemporal model is developed to quantify the interactions among temporal, spatial, and hotel classification dimensions, enabling a systematic comparison between conventional construction (CC) and prefabricated construction (PC). The results show that prefabricated construction consistently reduces embodied carbon emissions across hotel categories, with the most significant reduction observed in 2-star and 3-star hotels, where carbon emissions can be reduced by approximately 20–35% compared to conventional methods. The findings further indicate that carbon reduction effectiveness varies by hotel classification and spatial distribution, with lower-star hotels benefiting more from standardized modular construction, while high-star hotels exhibit relatively lower reduction efficiency due to structural complexity. Spatiotemporal analysis also reveals heterogeneous decoupling patterns across districts, suggesting that carbon–economic relationships are strongly influenced by regional development conditions and construction intensity. This study advances the integration of carbon accounting and construction technology assessment by providing a quantitative and scalable framework for evaluating embodied carbon reduction in the hospitality sector. The findings offer robust evidence to support targeted low-carbon construction strategies and contribute to carbon–economic decoupling pathways in resource-constrained urban environments, aligning with the scope of carbon balance and management.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13435789/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147986028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-scale remote sensing monitoring of aboveground vegetation carbon storage in long-distance expressways","authors":"Xugang Lian,&nbsp;Shiqi Liang,&nbsp;Yulu Gao,&nbsp;Yucheng Chen,&nbsp;Jiahui Yuan,&nbsp;Junheng Huang,&nbsp;Yinfei Cai,&nbsp;Haifeng Hu","doi":"10.1186/s13021-026-00454-0","DOIUrl":"10.1186/s13021-026-00454-0","url":null,"abstract":"<div><p>Accurate assessment of expressway roadside vegetation carbon storage is essential for achieving carbon balance and ecological sustainability in the transportation sector. However, this is challenging due to the extensive spatial coverage, marked climatic variation, and pronounced spatial dependence of these vegetation corridors. Focusing on the expressway network of Shanxi Province, this study integrates UAV sample data with Sentinel-2 imagery. A graph convolutional network (GCN) is used to extract road network spatial topology, and estimation models for aboveground vegetation carbon storage are constructed for Shanxi’s northern, central, and southern climatic zones. The performance of XGBoost, Random Forest, and Support Vector Machine models is then compared across these climatic zones, emphasizing the importance of model adaptability and spatial feature integration. The results showed that the incorporation of GCN-derived spatial features significantly enhanced the model’s ability to characterize spatial autocorrelation along linear corridors. Among all models, the XGBoost-GCN model performed best in southern Shanxi, with a validation R² of 0.723. Compared with the overall model, the climate-zoned models improved estimation accuracy by an average of 18.3%, indicating that zoned modeling is more suitable for estimating carbon storage of roadside vegetation under strong climatic gradients. The carbon storage of expressway roadside vegetation in Shanxi Province exhibited an overall spatial pattern of decreasing from south to north. Hydrothermal conditions were identified as the main factors driving its spatial differentiation, while finely managed areas such as interchanges showed relatively high carbon sink potential. These findings indicate that the multi-scale remote sensing approach integrating GCN-derived spatial features with climate-zoned modeling can improve both the accuracy and regional adaptability of carbon storage estimation for roadside vegetation along long-distance expressways, and can provide methodological support for carbon monitoring, carbon sink identification, and differentiated ecological management of transportation infrastructure ecosystems.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s13021-026-00454-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147961755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An empirical study of the impact of environmental regulation on the eco-efficiency of digital agriculture: a quasi-natural experiment based on China’s carbon emissions trading pilot policy","authors":"Zhaoyang Lu,&nbsp;Diao Gou,&nbsp;Hailong Feng,&nbsp;Jianglai Dong,&nbsp;Nan Li","doi":"10.1186/s13021-026-00449-x","DOIUrl":"10.1186/s13021-026-00449-x","url":null,"abstract":"<div><p>Carbon emissions trading systems have become increasingly prevalent amid rising global climate concerns and serve as key market-based tools for sustainable transformation. Agriculture is central to advancing China’s “dual carbon” strategy, requiring both emission control and reduction, while rapid digital agricultural development enables more precise carbon monitoring and management. This study examines whether China’s pilot carbon emissions trading pilot policy improves the eco-efficiency of digital agriculture. Using the dynamic data envelopment analysis (DEA)-Malmquist index method, we construct an evaluative framework to measure digital agricultural eco-efficiency, and based on panel data from 30 Chinese provinces over the period 2011–2022, we employ a difference-in-differences (DID) model to identify the policy effects. The empirical findings demonstrate that the ecological efficiency of China’s digital agriculture has successfully increased because of the implementation of the pilot policy for carbon emissions trading, and this conclusion passes several robustness tests. Heterogeneity analysis indicates that the effects of the policy vary across regions and different levels of development of digital agricultural eco-efficiency. According to the results of the mediating effect analysis, the pilot carbon emissions trading pilot policy increases forest coverage, which in turn increases digital agricultural eco-efficiency. The results of this research offer guidance for promoting environmentally sustainable agricultural development in China and for supporting international initiatives aimed at lowering agricultural greenhouse gas emissions.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s13021-026-00449-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147939762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Railway carbon emission scenario prediction in four regions of China based on machine learning","authors":"Yintao Lu,&nbsp;Shuchang Liu,&nbsp;Yuechao Wang,&nbsp;Shengming Qiu,&nbsp;Pengju Wang,&nbsp;Caihong Zhang,&nbsp;Bo Hu,&nbsp;Jiayan Wang,&nbsp;Jiashuai Zhao,&nbsp;Hong Yao","doi":"10.1186/s13021-026-00438-0","DOIUrl":"10.1186/s13021-026-00438-0","url":null,"abstract":"<div><p>Understanding carbon emissions of the railway transportation industry in China is critical for effective climate action. This study applied machine learning models to analyze railway operational conditions and socio-economic driving factors of carbon emissions across four railway operational regions with different terrain, climate, and economic development characteristics in China from 2009 to 2021. This study indicates that People, Electricity Carbon Emission Factor, Tertiary Industry Gross Domestic, Railway Passenger Volume, Railway freight Volume, Research and Development Investment and Railway Infrastructure Level have a significant impact on carbon emissions. The Lasso_LR model shows strong fitting performance, as the mean absolute error is 4.98% of the average carbon emissions. By 2021, the Qinghai-Tibet Plateau, Yunnan Province, the Guangxi Zhuang Autonomous Region, and the Yangtze River Delta Region showed a decline in carbon emissions, with emission reductions of 41.1%, 36.6%, 21.4%, and 21.5% compared to their levels in 2009. Model predictions indicate that carbon emissions from railways in the Qinghai-Tibet Plateau, Yunnan Province, the Guangxi Zhuang Autonomous Region, and the Yangtze River Delta Region are projected to decline by 15.8%, 12.23%, 34.15%, and 0.03% respectively by 2030, relative to 2021 levels in the regional emission-reduction scenario. This study offers insights into the socio-economic and internal mechanisms of emissions, guiding tailored reduction targets for different railway operational regions to aid China in achieving ‘3060’ target.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s13021-026-00438-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147873009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implications of snowmelt and rainfall erosion effects for soil organic carbon management in semi-arid alpine ecosystems: a case study of the qilian mountains, China","authors":"Zijin Liu,&nbsp;Jianhua Si,&nbsp;Shengchun Xiao,&nbsp;Bing Jia,&nbsp;Dongmeng Zhou,&nbsp;Xinglin Zhu,&nbsp;Xue Bai,&nbsp;Boyang Wang,&nbsp;Boniface Ndayambaza","doi":"10.1186/s13021-026-00441-5","DOIUrl":"10.1186/s13021-026-00441-5","url":null,"abstract":"<div>\u0000 \u0000 <p>Soil organic carbon (SOC) plays a crucial role in the ecological stability of Semi-Arid Alpine Ecosystems and in the global carbon cycle. However, its dynamic loss mechanisms, particularly the synergistic effects of snowmelt and rainfall-induced erosion, remain unclear. This study focuses on the upper Heihe River basin in the central Qilian Mountains, China. Six types of erosion units were set up along an elevation gradient, and field surveys were conducted over two years. Based on the XGBoost algorithm, digital mapping of SOC at the basin scale was carried out. Using the 2006–2020 period as the simulation timeframe, a composite hydraulic erosion calculation method was developed by coupling the SPHY hydrological model with the RUSLE model, incorporating snowmelt runoff and observed sediment data. Geographic detectors and structural equation models were used to reveal the driving mechanisms of composite hydraulic erosion on SOC loss. The results showed that the SOC density in the 0–20 cm soil layer of the basin was 10.90 × 10⁶ kg C m^− 2. The annual average soil sediment yield modulus in the basin ranged from 7.82 to 14.49 t ha^− 1 yr^− 1, with an SOC output rate of 0.50 ± 0.08 t C ha^− 1 yr^− 1. The total SOC loss in the basin was (50.16 ± 8.57)×10⁴ t C yr^− 1, with rainfall-induced erosion contributing 94% and snowmelt-induced erosion contributing 6%, highlighting the significant role of snowmelt erosion in future carbon management. High carbon loss areas were primarily concentrated in alpine cold deserts and alpine meadows, which accounted for 89.81% of the total SOC loss in the basin, making these areas key targets for future carbon management in semi-arid, high-altitude regions. The SOC loss rate during wet-cold years (0.62 ± 0.15 t C ha^− 1 yr^− 1) was 1.31 times and 1.51 times higher than in normal years (C2) and dry-hot years (C3), respectively, indicating the need for differentiated carbon management strategies based on climatic conditions. The coupling effects of composite hydraulic erosion processes under climate and topography regulation, along with the erosion-suppressing effect of surface vegetation, jointly determine the spatial heterogeneity of soil carbon loss. This study deepens the understanding of the water erosion–carbon loss process in semi-arid, high-altitude regions and provides theoretical support for erosion control and carbon management in alpine ecosystems.</p>\u0000 </div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13185295/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147866300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Differentiated peak pathways and driving factors of industrial CO2 emissions at the provincial level in China","authors":"Zheng Wang,&nbsp;Changnan Kang,&nbsp;Huiyan Ge,&nbsp;Chenjun Du,&nbsp;Kangkang Gu","doi":"10.1186/s13021-026-00434-4","DOIUrl":"10.1186/s13021-026-00434-4","url":null,"abstract":"<div><p>The industrial sector is the primary source of anthropogenic carbon emissions. Research on industrial carbon peaking and its drivers play a fundamental role in formulating emission reduction measures. The provincial industrial CO₂ emissions were calculated from 2000 to 2021 in China. The Logarithmic Mean Divisia Index (LMDI) method was employed to quantitatively measure the contributions of industrial output, industrial structure, energy intensity, and energy structure to the changes in emissions. The results show that: (1) During the research period, the industrial CO₂ emissions of various provinces experienced differentiated growth processes. Among those 11 provinces have already achieved industrial CO₂ emission peak. (2) Industrial output value and energy intensity are the essential driving forces affecting industrial CO₂ emissions, while the contributions of industrial structure and energy structure to CO₂ emissions are relatively low. Spatio-Temporal differences exist in the contributions of various influencing factors. (3) 9 provinces have proactively peaked their emissions by improving energy efficiency, optimizing structure in energy use and industrial structure. While 2 passively emission declined provinces reduce emissions through a decline in industrial output value. (4) The provinces that have not yet reached their peak emissions are mainly industrial provinces, energy based provinces and developing central-western provinces. Their industrial output value has been steadily increasing, and the inhibitory effect of energy intensity and energy structure on CO₂ is relatively low. Only under the low-carbon scenario can they almost achieve carbon peaking by 2030. The paper discusses industrial emission reduction strategies by controlling “super emitters” provinces, formulating differentiated emission reduction measures based on influencing factors, and practicing a low-carbon development path.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13501670/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147866243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial-temporal evolution and predictive analysis of carbon effect efficiency in farmland in Jiangsu Province, China","authors":"Xiaowen Wang,&nbsp;Bowen Yu,&nbsp;Jueshu Wang,&nbsp;Song Hao,&nbsp;Zhen Zheng","doi":"10.1186/s13021-026-00452-2","DOIUrl":"10.1186/s13021-026-00452-2","url":null,"abstract":"<div><p>Since the Industrial Revolution, the increasing emissions of greenhouse gases have posed unprecedented challenges to sustainable human development. As one of the most vital terrestrial ecosystems, farmland ecosystems play an irreplaceable role in balancing carbon emissions and absorption, attracting growing scholarly attention. Taking Jiangsu Province, one of China’s major grain-producing regions, as the study area, this research integrates the Slacks-Based Measure (SBM) model, the entropy-weighted method, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to analyze the spatiotemporal evolution of farmland carbon effects—including carbon emissions, carbon absorption, and net carbon sequestration—during 2011–2021. Furthermore, a Grey Prediction Model was employed to forecast the carbon effects of 13 cities over the next 12 years. The results show that Jiangsu’s farmland carbon emission efficiency exhibited an overall upward trend with fluctuations, with an average value of 0.76. The multi-year mean fitting degrees of resource input and agricultural output were relatively low, at 0.426 and 0.358, respectively, with substantial intercity differences. The average coupling coordination degree between resource input and agricultural output was 0.66, indicating a primary coordination state. The constructed GM (1,1) model achieved a qualification rate exceeding 73.80%, demonstrating its reliability for predicting farmland carbon effects. Forecasts suggest a potential weakening of the province’s agricultural carbon sink effect, with the net carbon sequestration in 2033 expected to decline by 15.55% compared with the maximum value during the observation period. This study reveals the spatiotemporal characteristics and potential evolution patterns of farmland carbon effects, providing theoretical support for region-specific agricultural emission reduction policies and promoting the sustainable development of efficient, low-carbon agriculture.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s13021-026-00452-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147832039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of carbon emission trading pilot policy on China’s energy eco-efficiency","authors":"Liandi Zhang,&nbsp;Shenglin Ma,&nbsp;Yiyao Dai,&nbsp;Wenguang Tang","doi":"10.1186/s13021-026-00451-3","DOIUrl":"10.1186/s13021-026-00451-3","url":null,"abstract":"<div><p>Under the “Dual Carbon” goals (carbon peaking and carbon neutrality), China’s Carbon Emissions Trading System (CETS) represents a key policy tool in addressing climate change, significantly contributing to carbon reduction and the enhancement of Energy Eco-Efficiency (EEE). As a comprehensive measure of coordination within the “energy-economy-environment” system, EEE effectively captures a country’s or region’s ability to harmonize energy consumption, green and sustainable development, and environmental protection. This study computes the EEE index using the SBM model and examines how a carbon emission trading pilot policy (CETPP) affects EEE in China and its spatial spillover by employing a difference-in-differences (DID) model and spatial econometric model. The results indicate that CETPP implementation significantly enhances regional EEE and advances China’s green and low-carbon transition process by improving the balance between economic and environmental goals, increasing employment, diversifying the energy supply, and strengthening economic resilience. Mechanistic research reveals that the CETPP promotes the improvement of EEE by reducing pollution emission intensity and that regional innovation ability can enhance the positive impact of the CETPP on regional EEE. Further analysis revealed significant endogenous spatial interactions in EEE across Chinese regions. However, EEE in the eastern and western regions can create a “siphon” effect on production factors that hinders development in neighbouring areas. Implementing a CETPP in a region not only advances local EEE but also stimulates EEE improvements in adjacent areas, with the strongest spillover effect observed in eastern China. To this end, it is essential to enhance the management of carbon dioxide emissions, actively advance the establishment of carbon trading markets. Moreover, region-specific measures should be implemented to promote the coordinated improvement of regional EEE.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s13021-026-00451-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147832023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}