Carbon Balance and Management最新文献

{"title":"Drying crisis under greening: a study on the drought trigger thresholds of vegetation carbon fixation loss in karst areas","authors":"Niu Shuai,&nbsp;Yecui Hu,&nbsp;Yufan Wang","doi":"10.1186/s13021-026-00500-x","DOIUrl":"10.1186/s13021-026-00500-x","url":null,"abstract":"<div><p>Global warming has intensified the frequency of droughts. Once droughts exceed a critical threshold, they pose a severe threat to vegetation growth and limit its carbon fixation capacity. However, current research still has an insufficient understanding of the probabilistic thresholds at which drought triggers the loss of vegetation carbon fixation (VCF) capacity under sustained greening, especially lacking a systematic comparison of the differences between karst areas (KA) and non-karst areas (NKA) under complex hydrogeological conditions. For this purpose, this study uses the Copula method and conditional probability to derive the VCF loss and its trigger thresholds under different drought scenarios from 1982 to 2022, and explores the key driving factors influencing these thresholds. The results show that: (1) Compared to non-karst areas (NKA), VCF in the KA of southern China is more sensitive to drought, with a higher response intensity and a shorter lag time. (2) The probability of VCF loss in KA is significantly higher than in NKA, and the drought trigger threshold is also higher. (3) The VCF thresholds in both KA and NKA are primarily driven by the “temperature (TEM)- moisture” coupling. However, KA show higher TEM sensitivity and dependence on deep soil water storage (SWC_3-4), while in NKA, the thresholds are influenced by multiple factors, including shallow soil water storage (SWC_1-2), atmospheric vapor pressure deficit (VPD), and soil organic carbon (SOC). From the perspective of probabilistic thresholds, the results reveal the hidden drought vulnerability of VCF capacity in humid greening regions, and can provide a quantitative basis for maintaining regional carbon sink stability, implementing zonal vegetation management, and promoting proactive drought risk prevention and control.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13501861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148811814","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":"Modelling forest carbon stocks on the Canary Islands","authors":"Rüdiger Otto,&nbsp;Juan José García-Alvarado,&nbsp;Elena Rocafull,&nbsp;Natalia Sierra Cornejo,&nbsp;Severin D. H. Irl,&nbsp;Felipe Rodríguez Arvelo,&nbsp;Ricardo Ruíz-Peinado,&nbsp;José María Fernández-Palacios,&nbsp;Lea de Nascimento","doi":"10.1186/s13021-026-00488-4","DOIUrl":"10.1186/s13021-026-00488-4","url":null,"abstract":"<div><h3>Background</h3><p>Forest carbon mapping is crucial for sustainable forest management, climate mitigation, biodiversity conservation, ecosystem service provision and land-use planning. Carbon stocks have been studied at regional to global scales and across various biomes. However, island-wide studies of carbon stocks and carbon mapping remain limited. Here, we present the first high-resolution (50 m) spatial mapping of forest carbon for the Canary Archipelago. Combining structural field data from the Spanish National Forest Inventory plots with airborne laser scanning, Sentinel-2 multispectral satellite data and fine-scale interpolated climatic variables, we modeled total, aboveground and belowground carbon density of 18 forest types, using machine learning approaches, including Boosted Regression Tree and Random Forest models.</p><h3>Results</h3><p>Forests across the Canary Islands store an estimated 10.26 Tg of carbon. Canarian pine forests contain the largest carbon pool (57%) due to their extensive distribution area, whereas mature laurel forests exhibit exceptional carbon densities. In humid laurel forests, average carbon densities reached 413.2 ± 149.5 Mg C ha⁻¹, exceeding previous regional estimates and approaching levels of primary tropical forests. The high spatial heterogeneity of carbon densities across forest types and islands was best explained by structural stand attributes, such as tree canopy cover and volume, and climatic factors with carbon stocks being more strongly associated with moisture than with temperature. Both statistical modelling approaches performed similarly in terms of efficiency, accuracy and error statistics, and the selection of the best model depended on the specific island.</p><h3>Conclusions</h3><p>Our approach integrates field data with advanced remote sensing tools and machine learning algorithms to produce a high-resolution and accurate carbon map of topographically complex oceanic islands. We show that the Canary Islands contain exceptionally high total carbon densities, particularly within the mature, humid laurel forests of La Gomera, and identify structural attributes and water availability as the main drivers of spatial variation in carbon stocks. This assessment provides a baseline for biodiversity conservation, nature-based forest management, ecological restoration and regional climate policy towards carbon neutrality in island ecosystems.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"21 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13452119/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148683109","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 Italian forest carbon sink: historical evolution and future projections under alternative harvest scenarios.","authors":"Roberto Pilli, Emil Cienciala, Kevin Black","doi":"10.1186/s13021-026-00486-6","DOIUrl":"https://doi.org/10.1186/s13021-026-00486-6","url":null,"abstract":"<p><strong>Background: </strong>In 2023, forests offset approximately 9% of total GHG emissions in the EU-27, with Italy accounting for about 20% of these offsets. Historical felling rates, in Italy, are highly uncertain, with a ± 50% confidence interval. In this study, we used a forest carbon inventory model, scaled at regional level, to assess the overall impact of the harvest uncertainty on the Italian forest carbon (C) sink. The analysis included most recent information from the literature and an ancillary assessment of the harvested wood product mitigation potential. Based on this assessment, we estimate the evolution of the forest C sink to 2070, under different harvest scenarios defined by an increasing felling rate relative to the net annual increment estimated by the model on forest area available for wood supply.</p><p><strong>Results: </strong>Comparison of our results with the Italian GHGI data shows an overall good fit with the living biomass sink, but significantly higher removals by the dead organic matter pool. For the historical period, the modelled total C sink increases from -40 Tg CO₂ yr^-1 in 2005 to -55 Tg CO₂ yr^-1 in 2023. The future forest C sink varied between -76 Tg CO₂ yr^-1 and -16 Tg CO₂ yr^-1 by 2070, corresponding to the lowest (0.10) and highest (0.90) felling to net annual increment ratios, respectively. Our analysis confirms that the contribution of harvested wood products to the total C sink is negligible, as the Italian forest sector predominantly channels timber extraction into energy production.</p><p><strong>Conclusions: </strong>Between 2005 and 2023, the impact of harvest uncertainty on the total forest C sink averaged 11 Tg CO₂ yr^-1, representing about ± 23% of the C sink associated with the average harvest reported in the literature for the same period. By simulating the theoretical forest C sink dynamics under different felling rates, and by excluding any possible impacts of climate change on current growth conditions, our study provides a long-term assessment of wood production potential versus carbon storage capacity. This bottom-up modelling framework aligns with the New EU Forest Strategy and can serve as a model for other European countries.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148497098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heterogeneous carbon decoupling and peaking pathways across county types in hunan province: an integrated tapio-STIRPAT-scenario analysis.","authors":"Chaohui Zheng, Heng Wei","doi":"10.1186/s13021-026-00484-8","DOIUrl":"https://doi.org/10.1186/s13021-026-00484-8","url":null,"abstract":"<p><p>Balancing economic growth with emission reductions presents a pivotal challenge under China's \"dual-carbon\" strategy. Hunan Province, characterized by its diverse industrial and ecological contexts, offers a quintessential setting for county-level analysis. This study investigates 122 counties within Hunan from 2000 to 2022, employing the Tapio decoupling model, spatial autocorrelation, ordinary least squares (OLS) regression, an augmented STIRPAT framework, and scenario simulations. It analyzes the spatiotemporal dynamics between carbon emissions and GDP, elucidates the principal driving mechanisms, and forecasts peaking trajectories under scenarios of low, medium, and high carbon emissions. The findings reveal a continued rise in emissions, albeit at a reduced pace post-2010, with the proportion of counties achieving strong decoupling escalating from 6% during 2006-2010 to 53% in 2016-2022. Counties dominated by eco-cultural (80%) and agricultural activities (67%) demonstrated the most significant transitions. Regression analyses, based on standardized coefficients, underscore the positive influence of carbon intensity (0.917), economic development (0.792), and population (0.447) on emissions, whereas industrial upgrading (-0.014), energy efficiency (-0.046), and urbanization (-0.010) contribute to mitigating impacts. GeoDetector analysis corroborates strong interaction effects. Scenario simulations indicate that only the low-carbon pathway might enable all four economic regions to achieve emission peaks between 2030 and 2035, while medium- and high-carbon trajectories fail to achieve effective peaking before 2040. These results highlight the imperative for tailored low-carbon strategies at the regional level, incorporating industrial restructuring, enhancement of energy efficiency, promotion of ecological-based industries, and implementation of county-level monitoring and market incentives.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148454293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disentangling exogenous and endogenous memory in global vegetation productivity: dominant role and future strengthening of vegetation carryover effects.","authors":"Wenxi Tang, Shuguang Liu","doi":"10.1186/s13021-026-00481-x","DOIUrl":"https://doi.org/10.1186/s13021-026-00481-x","url":null,"abstract":"<p><p>Vegetation productivity is not only determined by current environmental conditions but also reflects the lagged influence of past climate and vegetation states. This temporal dependency, often referred to as ecological memory, arises from both antecedent climate conditions (exogenous lagged climatic effects, LCE) and prior vegetation states (endogenous vegetation growth carryover effects, VGC). However, their spatiotemporal variability, relative importance, and responses to future climate change remain poorly understood at the global scale. Here, we develop a unified analytical framework by integrating vector autoregressive model and impulse response functions to disentangle the roles of LCE and VGC in regulating global gross primary productivity (GPP) across space, time, and future climate scenarios. Both components exhibit rapid initial responses followed by gradual decay within approximately five months, yet differ markedly in magnitude, direction, and persistence. LCE show strong hemispheric asymmetry: in the Northern Hemisphere, increases in temperature exert the strongest positive effect on GPP, whereas increases in atmospheric dryness (vapor pressure deficit) produce the strongest negative effect. In contrast, in the Southern Hemisphere, increased precipitation is the dominant positive driver of GPP, while negative responses exhibit greater spatial heterogeneity and show no clear dominant controlling factor, suggesting more complex and regionally varying climatic influences. In contrast, VGC display globally consistent positive responses with substantially greater intensity (17.35 gC m^-2 month^-1 at a 1-month lag) and minimal hemispheric differences. Across all lag periods, VGC dominate productivity variability, contributing over 84% at short lags and remaining above 77% after 12 months, whereas LCE play a secondary but regionally differentiated role. Future projections further reveal a pronounced intensification of VGC under all emission scenarios, with peak responses increasing to 25-35 gC m^-2 month^-1 and strengthening toward the end of the century. These findings demonstrate that global vegetation productivity is primarily regulated by endogenous vegetation memory, while exogenous climatic effects provide shorter-lived and spatially heterogeneous modulation. The increasing strength of vegetation memory under future climate change highlights its critical role in shaping ecosystem responses, underscoring the need to explicitly incorporate both memory components into Earth system models to improve predictions of terrestrial carbon dynamics and ecosystem resilience.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148434773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microbial community changes mediate the decline in soil organic carbon mineralization with depth in tea plantations.","authors":"Shaobo Zhang, Yifeng Xie, Hongbing Zhang, Junyan Lv, Weizhen Wu, Wenyan Han, Gaodi Zhu, Qiuhong Wang, Claudien Habimana Simbi, Rongxiu Yin, Xin Li","doi":"10.1186/s13021-026-00485-7","DOIUrl":"https://doi.org/10.1186/s13021-026-00485-7","url":null,"abstract":"<p><strong>Background: </strong>Tea plantation soils serve as vital carbon (C) sinks rich in soil organic carbon (SOC), yet existing research primarily focuses on the 0-20 cm topsoil, leaving a lack of systematic study of SOC mineralization characteristics and microbial regulatory mechanisms across the full 0-100 cm soil profile. Therefore, we conducted incubation experiments with typical tea plantation soils to clarify changes in SOC mineralization, the SOC pool, the microbial community, and the functional genes (such as GH48 and cbhI) and enzyme activities related to C decomposition at five soil depths (0-20 cm, 20-40 cm, 40-60 cm, 60-80 cm, and 80-100 cm), and to elucidate their relationships, thereby revealing the mechanisms affecting SOC mineralization in different soil layers.</p><p><strong>Results: </strong>The results revealed significant declines in SOC mineralization rate (from 842 to 431 mg kg⁻¹), particulate organic carbon, water-soluble organic carbon, microbial biomass carbon, β-glucosidase (from 71.6 to 18.2 µg g^- 1 h^- 1 at the end of incubation), cellobiohydrolase (from 0.229 to 0.091 mg g^- 1 3d^- 1 at the end of incubation), GH48 and cbhI gene abundances (decreased from 9.2 × 10⁷ to 1.8 × 10⁷ and 7.6 × 10⁷ to 1.4 × 10⁶ copies g^- 1 at the end of incubation, respectively), respectively with increasing soil depth. The decrease in the SOC mineralization rate with increasing soil depth was significantly associated with declines in the labile C fraction, C-decomposition-related extracellular enzyme activity, and functional gene abundance. Additionally, increasing soil depth significantly altered the microbial community structure and composition, particularly the relative abundances of dominant taxa such as Alphaproteobacteria, Bacilli, Sordariomycetes, Tremellomycetes, Mortierellomycetes, and Dothideomycetes, thereby further influencing SOC mineralization rate.</p><p><strong>Conclusions: </strong>Our findings demonstrate that increasing soil depth significantly alters soil microbial community characteristics, particularly by reducing the abundance of C-degrading functional genes and enzyme activities, thereby lowering SOC mineralization rate and attenuating soil carbon emissions. These results underscore the importance of deep tillage during fertilization and the incorporation of pruning residue to enhance SOC sequestration in tea plantations.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148429582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative approach to analyzing and forecasting carbon dioxide emissions in ethiopia using Bayesian autoregressive integrated moving average (ARIMA) and Bayesian structural time series (BSTS) models.","authors":"Abdisalan Ahmed Osman, Dagne Tesfaye Mengistie, Daud Hussein Adawe, Rediat Takele Figa, Buzuneh Tasfa Marine","doi":"10.1186/s13021-026-00480-y","DOIUrl":"https://doi.org/10.1186/s13021-026-00480-y","url":null,"abstract":"<p><strong>Introduction: </strong>Carbon dioxide (CO₂) emissions are a major contributor to global climate change. In Ethiopia, CO₂ emissions have increased steadily in recent decades due to industrialization, rising energy consumption, expanding transportation systems, continued dependence on biomass fuels, and rapid population growth. Although Ethiopia has historically contributed only a small share to global greenhouse gas emissions, the recent upward trend poses challenges for sustainable development and environmental management. Accurate forecasting of CO₂ emissions is essential for designing effective climate mitigation strategies and evidence-based policy decisions. Therefore, this study aimed to analyze and forecast CO₂ emissions in Ethiopia using Bayesian Autoregressive Integrated Moving Average (ARIMA) and Bayesian Structural Time Series (BSTS) models.</p><p><strong>Methods: </strong>This study analyzed and forecasted carbon dioxide (CO₂) emissions in Ethiopia using an 82-year time series dataset covering the period from 1941 to 2022. Two Bayesian time series models were employed: Bayesian ARIMA and Bayesian Structural Time Series (BSTS). The Bayesian ARIMA model captured temporal dependencies through autoregressive and moving average components, whereas the BSTS model decomposed the time series into trend, seasonality, and regression components, allowing the incorporation of external predictors. Model parameters were estimated using Markov Chain Monte Carlo (MCMC) simulation techniques. Model performance was assessed using the Watanabe-Akaike Information Criterion (WAIC) and Leave-One-Out Information Criterion (LOOIC), with the model having the lowest values selected as the optimal forecasting model.</p><p><strong>Results: </strong>The findings revealed that Ethiopia's mean annual per capita CO₂ emissions from 1941 to 2022 were approximately 0.054 metric tons. Among the candidate models evaluated, the Bayesian ARIMA (0, 1, 1) model demonstrated the best fit and forecasting performance based on WAIC and LOOIC criteria. Forecast results from the selected model indicate that Ethiopia's per capita CO₂ emissions are projected to increase gradually, reaching approximately 0.167, 0.169, 0.171, 0.171, 0.175, 0.177, and 0.179 metric tons in 2024, 2025, 2026, 2027, 2028, 2029, and 2030, respectively.</p><p><strong>Conclusion: </strong>The study indicates a persistent upward trend in Ethiopia's annual per capita CO₂ emissions through 2030, which may intensify climate-related and environmental challenges. These findings underscore the need for strengthened environmental policies and sustainable energy interventions, including carbon taxation, cap-and-trade mechanisms, and the promotion of clean and energy-efficient technologies to reduce future emissions.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148395377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can better institutions drive cleaner energy in the United States? insights from wavelet quantile regression.","authors":"Qingzi Gu, Babatunde Sunday Eweade, Cathrine Banga, Mohamed Djafar Henni","doi":"10.1186/s13021-026-00483-9","DOIUrl":"https://doi.org/10.1186/s13021-026-00483-9","url":null,"abstract":"<p><p>The transition to clean energy in the United States remains insufficient despite rising environmental concerns and increasing renewable energy adoption. This study investigates whether better institutional quality can effectively drive cleaner energy outcomes by examining the impact of governance alongside key macroeconomic factors. Using quarterly data from 1990 to 2024, the study employs a wavelet quantile regression approach to capture nonlinear and time-varying dynamics across short-, medium-, and long-run horizons. The findings reveal that economic growth, foreign direct investment, and trade openness positively influence renewable energy consumption, particularly over longer time horizons. In contrast, carbon emissions exhibit a negative relationship with renewable energy adoption. Surprisingly, institutional quality shows a predominantly negative effect, suggesting that stronger institutions may reinforce existing fossil fuel-based energy structures rather than accelerate transition. These results highlight the complexity of institutional roles in energy transformation and emphasize the need for targeted regulatory reforms to support renewable energy expansion in the United States.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148387117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence for carbon emissions management: advances, challenges, and future directions across monitoring, prediction, and reduction.","authors":"Xiyue Cao, Xujiang Qin, Yanqiu Zuo, Junjie Fang, Hongqiang Wang","doi":"10.1186/s13021-026-00479-5","DOIUrl":"10.1186/s13021-026-00479-5","url":null,"abstract":"<p><p>Rising anthropogenic carbon emissions are a major driver of climate change and pose a critical challenge to global sustainable development. As a rapidly advancing technology, artificial intelligence (AI) has shown strong potential to enhance carbon emissions management. This review provides a critical and comprehensive synthesis of recent advances in AI-enabled approaches for carbon emissions monitoring, prediction, and reduction. For monitoring, it explores the integration of satellite remote sensing, sensor networks, and machine learning (ML) algorithms, which can improve multi-scale, high-resolution, and near-real-time monitoring capabilities. For prediction, it categorizes prediction models into three groups, namely deep learning (DL), ensemble learning, and statistical learning, to facilitate the selection of appropriate technical approaches based on varying data characteristics and prediction requirements. For reduction, it examines the practical effectiveness of AI in industrial process optimization, energy structure transformation, transportation scheduling and management, construction energy efficiency improvement, and carbon capture, utilization, and storage (CCUS). We further reveal core challenges and potential solutions across the data layer, model layer, and application layer in AI deployment, including data availability and quality, model generalization and interpretability, and engineering and governance barriers that hinder the translation of AI methods into real-world applications. Furthermore, future research directions are discussed to promote the development of more reliable and scalable AI methods that can better support decision-making and practical governance in carbon emissions management. Overall, distinct from previous reviews that mainly focus on single tasks, specific model types, or sectoral applications, this review represents, to our knowledge, one of the first review-level attempts to develop a policy-relevant and interdisciplinary AI framework for carbon emissions management across the full process of monitoring, prediction, and reduction. By integrating unified evaluation metrics, evidence matrices, deployment-constraint analysis, and a technology readiness level (TRL)-based assessment, this framework links methodological performance, application readiness, and governance needs. It provides an integrated methodological foundation for fine-grained emissions sensing, predictive analysis, and emissions reduction decision support, while supporting quantifiable, verifiable, and actionable carbon balance and management.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148366518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The synergistic impact of low-carbon and innovative city construction on carbon intensity.","authors":"Yan Zhang, Jiekuan Zhang","doi":"10.1186/s13021-026-00482-w","DOIUrl":"https://doi.org/10.1186/s13021-026-00482-w","url":null,"abstract":"<p><p>Against the backdrop of global climate change and sustainable development, cities, as major sources of carbon emissions, play a pivotal role in achieving carbon neutrality goals. This study examines the synergistic impact of innovative city and low-carbon city pilots on carbon intensity, using panel data from 272 Chinese cities. The findings reveal that low-carbon and innovative city construction (LCICC) significantly reduces carbon intensity, primarily through technological advances, industrial upgrading, and green finance. However, the carbon intensity reduction is only significant in central regions and resource-based cities. Conversely, LCICC significantly increases carbon intensity in cities characterized by lower administrative status and non-resource-dependent economic structures. This study not only enriches the theory of urban transformation and provides new perspectives for carbon reduction research but also offers the first empirical evidence of synergistic emission reduction effects from the simultaneous implementation of innovation and low-carbon city pilots. These findings provide a replicable analytical framework for other developing economies pursuing dual policy pathways toward carbon neutrality.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":" ","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148343557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}