Carbon Balance and Management最新文献

{"title":"Private charging pile owners’ sharing intention: evidence from China","authors":"Yi Yu,&nbsp;Kangxin Pan,&nbsp;Donglan Zha","doi":"10.1186/s13021-026-00420-w","DOIUrl":"10.1186/s13021-026-00420-w","url":null,"abstract":"<div><p>The sharing of private charging pile (PCP) can significantly alleviate the construction pressure on public charging infrastructure and benefit for low carbon travel. However, the PCP sharing is in a nascent state with a low market share in reality. Thus, we construct a comprehensive TAM-UTAUT structural equation model to explore the factors influencing the sharing intention of PCP from 660 survey responses of PCP owners. We also analyze the differences across heterogeneous groups. Our finding indicates that perceived trust, performance expectancy, social influence, and incentive policies have a positive impact on the sharing intention of PCP, with incentive policies exhibiting the strongest effect, followed by social influence. Interestingly, female owners’ sharing intention is more responsive to both shared revenue and social conformity than that of male owners, whereas male owners tend to have greater concern regarding sharing risks. Younger owner groups are more significantly influenced by the practical effectiveness of sharing, while middle-aged and elderly groups pay more attention to policy incentives and sharing-related risks. Owners without private parking spaces are more influenced by the practical effectiveness of sharing. In contrast, owners with private spaces are more attentive to sharing risks and policy support. Based on the findings, we propose specific recommendations for both the government and the charging service operators to further promote the sharing of PCP.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13020262/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147321091","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":"Provincial allocation of carbon emission quotas for China’s 2030 carbon peak target","authors":"Ning Wang,&nbsp;Jin Li,&nbsp;Zhongke Qu,&nbsp;Hui Xi,&nbsp;Yang Zhang,&nbsp;Zhanjun Wang,&nbsp;Zhaolin Gu","doi":"10.1186/s13021-026-00415-7","DOIUrl":"10.1186/s13021-026-00415-7","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s13021-026-00415-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147315911","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":"Assessing the environmental impact of post-revolution reforms in Tunisia: a synthetic control approach","authors":"Mehdi Ben Jebli,&nbsp;Adel Benhamed","doi":"10.1186/s13021-026-00417-5","DOIUrl":"10.1186/s13021-026-00417-5","url":null,"abstract":"<div>\u0000 \u0000 <p>Understanding how major institutional and economic reforms influence environmental outcomes is essential for countries undergoing political transitions. This study examines how Tunisia’s CO₂ emissions trajectory evolved during the post-revolution institutional transition relative to a synthetic counterfactual constructed using the Synthetic Control Method (SCM). Rather than identifying the causal effectiveness of specific reforms, the analysis assesses whether Tunisia’s emissions trajectory diverged from that of a comparable synthetic unit following the reforms implemented after 2014 within a robust counterfactual framework. A synthetic version of Tunisia is constructed using a weighted combination of comparable North African countries, Algeria, Egypt, Libya, Morocco, and Sudan, based on pre-intervention data from 2000 to 2013 for key predictors, including GDP, renewable energy consumption (REC), non-renewable energy consumption (NREC), and foreign direct investment (FDI) inflows. The SCM results indicate that, following the post-2014 institutional transition, Tunisia’s CO₂ emissions trajectory diverged from its synthetic counterpart, with observed emissions rising more rapidly over the study period. While the estimated gap reaches approximately 58.3% by the end of the sample, placebo tests and RMSPE ratios indicate that this divergence is only weakly distinguishable from donor-country placebos, underscoring the need for cautious interpretation. These findings indicate that the post-revolution transition did not coincide with an immediate or statistically robust reduction in CO₂ emissions relative to the counterfactual, rather than providing definitive evidence of reform success or failure. From a policy perspective, the results highlight the importance of aligning institutional and economic reforms with clearly operationalized climate and energy policies. In particular, accelerating renewable energy deployment, improving energy efficiency, phasing down fossil-fuel subsidies, and strengthening regulatory and governance frameworks are essential to ensure that future reform efforts translate into meaningful environmental improvements. The findings provide critical insights for Tunisia and other North African countries seeking to balance economic development with environmental sustainability during periods of institutional transition.</p>\u0000 </div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13001189/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147281582","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":"Bridging the sustainability gap: the impact of business-government relations on corporate carbon monitoring in developing countries","authors":"Karamat Khan,&nbsp;Waseem Ahmad Khan,&nbsp;Yucong Yan,&nbsp;Maryam Khokhar,&nbsp;Mohd Ziaur Rehman,&nbsp;Assad Ullah","doi":"10.1186/s13021-026-00418-4","DOIUrl":"10.1186/s13021-026-00418-4","url":null,"abstract":"<div><p>Business-government relations (BGR) are widely recognized as an influential factor in firms’ strategic decision-making. This study examines the association between BGR and firms’ environmental sustainability practices in developing countries. Using firm-level cross sectional data from the World Bank Enterprise Survey, the results indicate that stronger BGR are positively associated with the adoption of carbon monitoring practices. This relationship is more pronounced in firms with female leadership and more experienced top managers, while corruption weakens the positive role of BGR. Further heterogeneity analysis shows that the positive association between BGR and carbon emission monitoring is stronger among large firms, externally audited firms, firms located in capital cities and independently operated firms. This study contributes to the sustainability and governance literature and offers significant policy implications.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s13021-026-00418-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146225267","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":"Machine learning-based analysis of economic efficiency disparities and transition drivers between high- and low-carbon industries in China","authors":"Zhilin Huang,&nbsp;Qianyi Zhang,&nbsp;Yayin Zheng,&nbsp;Enliang Tian","doi":"10.1186/s13021-025-00393-2","DOIUrl":"10.1186/s13021-025-00393-2","url":null,"abstract":"<p>In the context of global climate change, understanding economic efficiency disparities between high-carbon and low-carbon industries is crucial for advancing low-carbon transitions and improving carbon governance. This study examines heterogeneity in corporate carbon emission management and economic performance across Chinese industries and identifies key drivers of firms’ transformation capacity. Using a panel dataset of 633 listed enterprises from eight industries in China over 2010–2021, we classify firms into high- and low-carbon groups based on their emissions profiles and benchmark four machine-learning models—Random Forest, XGBoost, LightGBM, and Decision Tree—to capture nonlinear relationships and evaluate the relative importance of environmental and financial indicators. Random Forest delivers the best performance, achieving a classification accuracy of 95.7% (rounded) and strong discriminatory ability (AUC = 0.989). Feature-importance results consistently show that carbon emissions are the most influential variable, followed by total liabilities and total assets, while profitability-related indicators (e.g., operating revenue and gross profit margin) also contribute to distinguishing firms’ carbon profiles and performance differences. Overall, high-carbon enterprises appear to face greater transition barriers due to higher abatement cost exposure and tighter balance-sheet constraints, whereas low-carbon firms may be better positioned to benefit from policy incentives and market opportunities. These findings highlight the pivotal role of financial health in enabling low-carbon transformation and underscore the need for differentiated policy design. Policy implications include targeted transition finance and more flexible allowance allocation mechanisms for high-carbon enterprises, alongside continued incentives for technological innovation and market expansion in low-carbon sectors.</p><p>Q56; G30; C55; Q43; L60</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12918023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146194020","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":"Digital innovation and carbon intensity: Dual mediating role of technological spillover and industrial agglomeration","authors":"Sheng Wu,&nbsp;Yuxi Li,&nbsp;Xiaoyong Zhou","doi":"10.1186/s13021-026-00411-x","DOIUrl":"10.1186/s13021-026-00411-x","url":null,"abstract":"<div>\u0000 \u0000 <p>In an era where digital innovation plays a crucial role in driving economic growth, its potential to simultaneously mitigate carbon emissions becomes increasingly significant. However, the specific mechanisms through which digital innovation affects carbon intensity (i.e., carbon emissions per GDP) require deeper investigation. Utilizing data from listed Chinese firms in the new energy vehicle manufacturing industry spanning from 2006 to 2021, this study examines the impact of enterprise digital innovation on regional carbon intensity, focusing on the mediating roles of technological spillover and industrial agglomeration. The results reveal a robust negative correlation between digital innovation and carbon intensity, which becomes more pronounced as digital innovation capabilities strengthen and regions move westward. Digital innovation promotes industrial agglomeration, which significantly contributes to reducing carbon intensity, thereby highlighting the mediating role of industrial agglomeration. Furthermore, digital innovation facilitates technological spillover, which subsequently enhances industrial agglomeration, revealing an indirect path by which digital innovation fosters the formation of industrial clusters. This study provides valuable insights for policymakers and industrial stakeholders seeking to harness digital innovation to foster a low-carbon economy.</p>\u0000 </div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12918126/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146193977","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":"Organic carbon stocks in aboveground biomass and soils in hyper-arid AlUla County, Saudi Arabia","authors":"Steven McGregor,&nbsp;Ruan van Mazijk,&nbsp;Robbert Duker,&nbsp;Abdul-Lateef Ismail,&nbsp;William Liversage,&nbsp;Anthony J. Mills,&nbsp;Carly Butynski,&nbsp;Maurice Schutgens,&nbsp;Miren Schleicher,&nbsp;Max D. Graham,&nbsp;Shauna K. Rees,&nbsp;Abdelsamad Eldabaa,&nbsp;Ahmed H. Mohamed,&nbsp;Sami D. Almalki,&nbsp;Benjamin P. Y.-H. Lee","doi":"10.1186/s13021-026-00416-6","DOIUrl":"10.1186/s13021-026-00416-6","url":null,"abstract":"<div><h3>Background</h3><p>Dryland ecosystems, which cover nearly half of the Earth's terrestrial surface, play a considerable role in global carbon dynamics yet remain underrepresented in carbon stock assessments. This study evaluates organic carbon stocks in six protected areas within the hyper-arid AlUla County, Saudi Arabia, focusing on aboveground biomass (AGB) of herbaceous plants, trees and shrubs, as well as soil organic carbon (SOC).</p><h3>Results</h3><p>Across six protected areas, 172 plots were sampled using species- and growth form-specific allometric equations and soil cores (to 30 cm depth) to estimate organic carbon stocks for eight distinct habitat types. Mean total organic carbon (TOC) stocks ranged from 2.054 ± 0.379 t.ha⁻¹ in basaltic rock or ‘harrats’ habitat, to 12.831 ± 1.921 t.ha⁻¹ in abandoned agricultural lands. SOC accounted for more than 95% of average TOC stocks across all habitat types, except in arid thorn woodlands where SOC contributed 53.71% to the TOC stocks. Arid thorn woodlands also had the highest AGB carbon stocks (1.755 ± 0.564 t.ha⁻¹), with trees comprising 54.61% of the AGB carbon pool.</p><h3>Conclusions</h3><p>Organic carbon stocks in hyper-arid AlUla are predominantly soil-based, while AGB contributes little to the TOC stocks except in habitats with persistent woody vegetation. These patterns align with the lower end of reported ranges for other hyper-arid systems and establish an empirical foundation for future research on carbon storage in hyper-arid ecosystems of the Arabian Peninsula.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12917972/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146194000","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":"From capital to climate action: assessing the impact of china’s green credit initiative on corporate emissions","authors":"Saige Wang,&nbsp;Nan Xia,&nbsp;Ming Yang,&nbsp;Rou Peng,&nbsp;Huangying Gu,&nbsp;Guanyu Guo,&nbsp;Chengming Li","doi":"10.1186/s13021-026-00398-5","DOIUrl":"10.1186/s13021-026-00398-5","url":null,"abstract":"<div><p>The transition toward low-carbon sustainable development is critical for transforming heavily polluting industries. Green credit policies are designed to direct capital toward environmentally friendly and low-carbon corporates. Using the introduction of China’s Green Credit Guidelines in 2012 as a quasi-natural experiment, this study analyzes a panel of A-share listed companies from 2008 to 2021. Employing a Difference-in-Differences approach, we assess the impact of the green credit policy (GCP) on corporate carbon emissions. Empirical results indicate that GCP leads to a significant reduction in corporate carbon emissions. Baseline DID estimates show that treated corporates reduced emissions by approximately 13–19% compared to the control group, a finding that remains robust across a series of checks. The emission-reduction effect of GCP is more pronounced in corporations with a separated board leadership structure, higher profitability, central urban locations, and well-developed digital infrastructure. We identify two primary mechanisms through which GCP operates: imposing financial constraints that deter investment in carbon-intensive activities, and promoting green innovation, which facilitates the adoption of environmentally friendly technologies and practices. Further analysis reveals that both internal governance and external regulatory factors-such as stronger environmental awareness among executives, ISO 14,001 certification, enhanced intellectual property protection, and strict enforcement of the Three Simultaneous System-strengthen the effectiveness of GCP in reducing emissions. Through these channels, GCP supports the transition to a more sustainable economic pathway and contributes to global climate change mitigation.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12990626/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146148603","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":"A study on carbon emission prediction of multi-energy complementary power system based on multiple linear regression model","authors":"Jiangbo Sha,&nbsp;Wenni Kang,&nbsp;Rui Ma,&nbsp;Dongge Zhu,&nbsp;Jia Liu","doi":"10.1186/s13021-026-00399-4","DOIUrl":"10.1186/s13021-026-00399-4","url":null,"abstract":"<div><p>The multi-energy complementary power system achieves comprehensive and synergistic utilization of diverse energy sources, generating large-scale and distributed operational data. This introduces challenges in leveraging operational data for accurate and efficient carbon emission prediction. To effectively process the large-scale distributed operational data of power systems, identify key influencing factors, and achieve high-precision carbon emission prediction, this study investigates a carbon emission prediction method for multi-energy complementary power systems based on a multiple linear regression model. The structure of the multi-energy complementary power system is analyzed, and its carbon emission intensity is calculated. Based on the analysis results, preliminary selection of carbon emission influencing factors is conducted. A multiple linear regression model is constructed with the selected factors as independent variables and carbon emissions as the dependent variable. By performing significance tests on each independent variable, key influencing factors are identified, yielding an optimized multiple linear regression model. The model is integrated into the MapReduce parallel framework to expand computational scalability, enabling parallel processing of large-scale distributed power system data while ensuring prediction efficiency. The results demonstrate that the selected factor variables are reasonable, and the constructed prediction model exhibits a high goodness-of-fit. The prediction error ranges between 0.00516% and 0.00818%, confirming high accuracy and efficiency. The prediction results indicate that the experimental multi-energy complementary energy center’s carbon emissions increase annually from 2025 to 2031 and gradually decline from 2031 to 2034. These findings provide a scientific basis for formulating carbon emission reduction policies in multi-energy complementary power systems.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1186/s13021-026-00399-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146140584","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":"Biomass and carbon stock models with climatic factors for individual Quercus mongolica trees and their allocation patterns","authors":"Jun Lu,&nbsp;Lingbo Dong,&nbsp;Hao Zhang","doi":"10.1186/s13021-026-00414-8","DOIUrl":"10.1186/s13021-026-00414-8","url":null,"abstract":"<div><p>As the environmental problems caused by the greenhouse effect become more and more serious, and the forest as the largest carbon pool can effectively slow down the greenhouse effect, it is particularly important to accurately predict the carbon storage of the forest. In order to accurately estimate the biomass and carbon storage of Quercus <i>mongolica</i> in Northeast China, the biomass allocation pattern of Q. <i>mongolica</i> was analyzed. In this study, data of 175 Q. mongolica trees in Heilongjiang, Jilin, Liaoning and eastern Inner Mongolia were collected, including aboveground organ biomass, DBH, tree height, age and climatic factors, as well as published carbon content data of different organs. In this study, the biomass allocation pattern of individual Q. <i>mongolica</i> was analyzed. An additively compatible aboveground biomass and carbon storage model and an algebraically controlled aggregation model were established using nonlinear simultaneous equations. After selecting the aggregate biomass compatibility model, climate factors were added to establish a compatibility model containing climate factors. In addition, the root-stem ratio model was used to construct the underground compatible biomass and carbon storage model. The adjusted R²_adj values of the final established aboveground components and aboveground total biomass and carbon storage models were between 0.7048 and 0.9618, the total relative error ( TRE ) was within ± 1%, and the average prediction error ( MPE ) was below 10%, which met the modeling accuracy standard. The belowground biomass models showed adjusted R²_adj values between 0.7702 and 0.7801, TRE ≤ 1%, and MPE &lt; 15%. This study elucidated the biomass allocation pattern of individual Q. <i>mongolica</i>. All the developed models meet the accuracy requirements and can be applied to predict the biomass and carbon storage of Q. <i>mongolica</i> in Northeast China. In the compatibility model with climate factors, the accuracy of leaf and branch models has been greatly improved, indicating that the addition of climate factors in the independent model can greatly improve the accuracy of each component model, which can provide a theoretical basis for the establishment of each component model in the compatibility model of other tree species.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13020294/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146140748","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}