Carbon Balance and Management最新文献

{"title":"Multi-scenario simulation and prediction of carbon surplus and deficit under the background of carbon neutrality: a case study of Chang-Zhu-Tan metropolitan area in China.","authors":"Weiyi Sun, Jiaxi Liu, Xianzhao Liu, Tianhao Wang","doi":"10.1186/s13021-025-00314-3","DOIUrl":"10.1186/s13021-025-00314-3","url":null,"abstract":"<p><strong>Background: </strong>Global climate change, marked by persistent warming trends, has emerged as one of the foremost challenges confronting human society in the 21st century. Systematically promoting carbon peak and neutrality has become a critical priority for governments in China. As the most active urbanization region in the country, metropolitan areas assume a pivotal leadership and exemplary role in executing carbon peak and neutrality initiatives. Consequently, we focus our research on the Chang-Zhu-Tan Metropolitan Area (CMA). The STIRPAT and CA-Markov models are employed to forecast carbon sinks and carbon emissions under various scenarios in 2030 and 2060, respectively, to explore pathways to carbon neutrality under various conditions.</p><p><strong>Results: </strong>The findings indicate that the carbon surplus and deficit (CSD) values have consistently been negative from 2000 to 2020, signifying a persistent carbon deficit in the region, which has exhibited an upward trend. Notably, the CSD in Yuelu, Ningxiang, and Changsha experienced the most significant increases, particularly in Yuelu, where it reached - 11.22 × 10⁶ t by 2020. Depending on the combinations of scenarios, the CSD values are anticipated to range from - 130.75 × 10⁶ t to - 98.22 × 10⁶ t in 2030, and from - 63.28 × 10⁶ t to - 21.22 × 10⁶ t in 2060. Furthermore, the carbon emissions under different scenarios are projected to reach peaks in 2030, with a maximum of 66.54 × 10⁶ t in 2060.</p><p><strong>Conclusions: </strong>The prediction results of carbon neutrality in the CMA indicate that carbon emission is expected to reach peaks before 2030 across various scenarios. However, carbon emissions will significantly exceed the carbon sink capacity by 2060, and there is still a carbon emission gap of at least 2122.44 × 10⁴ t from achieving carbon neutrality, highlighting the necessity of accelerating emission reduction in the industrial and energy sectors. Consequently, the critical challenge to achieve carbon neutrality lies in the substantial reduction of carbon emissions.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"20 1","pages":"23"},"PeriodicalIF":3.9,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12275343/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144666769","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 co-benefits of digital economy and green finance: empirical evidence from China.","authors":"Yayun Ren, Xiaohang Xu, Yantuan Yu, Zhenhua Zhang","doi":"10.1186/s13021-025-00311-6","DOIUrl":"10.1186/s13021-025-00311-6","url":null,"abstract":"<p><p>Addressing the carbon co-benefits of policy tools requires simultaneous improvements in both the quantity and quality of carbon abatement to achieve long-term sustainability and equity. Driven by digital technologies and bolstered by green capital, the combination of the digital economy and green finance (DEGF) establishes an effective mechanism for attaining sustainable development goals. Treating the coordinated implementation of the National Big Data Comprehensive Pilot Zones (NBDCPZ) and Green Finance Reform and Innovation Pilot Zones (GFRIPZ) policies in China as a quasi-natural experiment, we identify the carbon co-benefits of DEGF using the Synthetic Control Method with penalized regression technique. Empirical findings show that DEGF significantly promotes simultaneous improvements in both the quantity and quality of carbon mitigation. These findings are robust across various validation tests, including time-placebo test, alternative model specification, and double machine learning algorithms. According to mechanisms analysis, improving green technological innovation and human capital level are the main channels that DEGF produces carbon co-benefits. The study provides China and other emerging economies seeking to promote sustainable development through digital-green integration with policy-relevant implications.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"20 1","pages":"22"},"PeriodicalIF":3.9,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12228155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144566908","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":"Long-term farmland abandonments remarkably increased the phytolith carbon sequestration in soil.","authors":"Linjiao Wang, Xiang Gao, Maoyin Sheng","doi":"10.1186/s13021-025-00312-5","DOIUrl":"10.1186/s13021-025-00312-5","url":null,"abstract":"<p><strong>Background: </strong>Phytolith-occluded organic carbon (PhytOC) is an important mechanism of long-term stable carbon sinks in terrestrial ecosystems. Farmland abandonment is a widespread land use change in the process of urbanization and industrialization and is still ongoing. Farmland abandonment can significantly affect soil carbon cycling. To elucidate the effects of farmland abandonment on soil PhytOC accumulation, in the present study, corn fields abandoned for 0 to 30 years ago in the mountainous areas of southern China were selected as the research objects. The change trends, influencing factors, and driving mechanisms of soil PhytOC accumulation during the abandonment process were studied.</p><p><strong>Results: </strong>The following results were obtained: (1) The range of PhytOC content and storage of the 0-15 cm soil profile for both active and abandoned corn fields was 0.39-1.49 g·kg^- 1 and 0.27-0.83 t·hm^- 2, respectively. (2) There was a notable enhancement in soil PhytOC accumulation as the duration of abandonment lengthened. In particular, after 30 years of abandonment, soil PhytOC accumulation rose significantly. (3) Abandonment noticeably altered the contents and ratios of soil nutrients of C, N, P and Si, along with key soil enzyme activities such as urease, sucrase, alkaline phosphatase, and catalase. (4) In the context of corn field abandonment, increase in soil PhytOC was primarily attributed to modifications in PhytOC inputs due to variations in surface vegetation cover. The impact of soil environment alterations resulting from abandonment on PhytOC decomposition was less pronounced.</p><p><strong>Conclusions: </strong>These findings are instrumental for accurately assessing the carbon sequestration potential of farmland abandonment and for developing regional carbon management strategies based on such practices.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"20 1","pages":"21"},"PeriodicalIF":3.9,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12231618/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144558691","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":"Decomposition of driving factors and peak prediction of carbon emissions in key cities in China.","authors":"Yuxin Zhang, Yao Zhang, Wei Chen, Yongjian Zhang, Jing Quan","doi":"10.1186/s13021-025-00310-7","DOIUrl":"10.1186/s13021-025-00310-7","url":null,"abstract":"<p><p>Urban areas are pivotal contributors to carbon emissions, and achieving carbon peaking at the urban level is crucial for meeting national carbon reduction targets. This study estimates the carbon emissions and intensity changes of 19 cities from 2000 to 2023 using urban statistical data. By employing the logarithmic mean Divisia index (LMDI) method, the driving factors of carbon emissions across these cities are analyzed. Additionally, a multi-scenario prediction approach is utilized to forecast the timing of carbon peaking and trends in carbon emission intensity under various scenarios. The findings reveal that, during the study period, carbon emissions exhibited an overall upward trend, while carbon emission intensity demonstrated a year-by-year decline. The population effect and per capita GDP effect were identified as significant drivers of urban carbon emissions during urban development. Conversely, reducing energy intensity and the carbon intensity of energy consumption can effectively curb the growth of carbon emissions. Under the low-carbon scenario, all cities are projected to achieve carbon peaking before 2030. In the baseline scenario, the vast majority of cities (89.47%) are expected to reach carbon peaking before 2030. However, under the high-carbon scenario, only 63.16% of cities are anticipated to achieve carbon peaking by the same deadline.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"20 1","pages":"20"},"PeriodicalIF":3.9,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12225531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144551603","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":"Large differences between UK black carbon emission factors.","authors":"Adam Brighty, Iain Staffell, Helen ApSimon","doi":"10.1186/s13021-025-00306-3","DOIUrl":"10.1186/s13021-025-00306-3","url":null,"abstract":"<p><strong>Introduction: </strong>Black carbon (BC) is a pollutant that illustrates strong links between climate warming and adverse health effects from air pollution. No standardised measurement technique for BC emissions has been implemented, making emissions and estimates highly uncertain. In this study, we evaluate two UK-based BC emission factor databases calculated using two distinct.</p><p><strong>Methods: </strong>the National Atmospheric Emissions Inventory (NAEI) and the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) model database from IIASA. The scope of this investigation was limited to the 1 A (Fuel Consumption) NFR code, which comprised the largest BC-emitting activities in the UK. Comparisons were made between a reference NAEI value and a range of low (e.g., highest abatement, newest technology), medium, and high GAINS emission factors. The NAEI value sat outside the GAINS BC ranges across 64% of the selected 1 A sources, most evidently within industrial combustion. By comparison, PM_2.5 and NO_x emission factors within the same databases showed less frequent disagreement, with 26% and 46%, respectively, of the GAINS sources not overlapping with the NAEI reference. A complementary BC emissions estimate, using NAEI activity data, found the highest variance in emissions to be within industrial, domestic, and agricultural combustion sources. Overall, this paper highlights the need to understand the differences behind these BC emission factors and to bring them into closer alignment.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"20 1","pages":"19"},"PeriodicalIF":3.9,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12224827/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144551604","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":"Afforestation as a mitigation strategy: countering climate-induced risk of forest carbon sink in China.","authors":"Yuan Cao, Deyu Zhong, Rong Shang, Qihua Ke, Mingxi Zhang, Di Xie, Shutong Liu, Chensong Zhao, Randongfang Wei","doi":"10.1186/s13021-025-00308-1","DOIUrl":"10.1186/s13021-025-00308-1","url":null,"abstract":"<p><strong>Background: </strong>China has made substantial efforts in afforestation since the 1970s, significantly contributing to the country's forest carbon sink. However, the future carbon sink dynamics remain uncertain due to anticipated changes in forest age structure, climate conditions, and atmospheric CO₂ concentrations. Moreover, the extent to which afforestation can enhance future carbon sequestration has not been fully quantified. This study focuses specifically on China and integrates forest growth models with Maximum Entropy (MaxEnt) models to project future carbon dynamics based on shifts in forest habitat suitability. A nature scenario is applied to evaluate potential climate-induced risks to forest carbon sequestration, while an afforestation scenario is used to assess the additional contribution from planned afforestation efforts.</p><p><strong>Results: </strong>The baseline aboveground biomass (AGB) of China's forests in 2020 is estimated at 11.59 ± 4.06 PgC. Under the nature scenario and assuming no future disturbances, the total AGB is projected to increase by 5.20-5.74 PgC by the 2050s and by 6.35-8.11 PgC by the 2070s, while carbon sequestration rates are expected to decline from 146.03 to 165.03 TgC/yr to approximately 122.98-137.80 TgC/yr. Between 11.79 and 39.60% of forests are at risk of land loss and compositional shifts in the 2070s, with the situation exacerbated under the SSP585 scenario. To mitigate climate-induced risks, the afforestation scenario proposes an additional 117.90-129.32 Mha of suitable forest area by the 2070s. Newly planted forests are projected to contribute approximately 37.42-65.60% of the carbon sequestration achieved by existing forests during the same period.</p><p><strong>Conclusions: </strong>Climate change is projected to cause significant forest loss and compositional changes across China. Although total forest carbon storage is expected to increase, the overall rate of carbon sequestration will likely decline. Afforestation emerges as a key strategy to enhance future forest carbon sinks. This study provides a spatially explicit assessment of carbon sequestration potential through afforestation and offers science-based guidance for the design of targeted forest policies in China.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"20 1","pages":"18"},"PeriodicalIF":3.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12182680/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339700","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":"Towards sustainable urban development: decoding the spatiotemporal relationship between urban spatial structure and carbon emissions.","authors":"Youzhi An, Guoping Wen, Mengsha Fan, Peng Zhao, Jin Sun, Mengyi He, Huili Bao, Yun Li, Na Li, Fengtai Zhang, Yanjun Zhang","doi":"10.1186/s13021-025-00304-5","DOIUrl":"10.1186/s13021-025-00304-5","url":null,"abstract":"<p><p>Understanding the spatiotemporal relationship between urban spatial structure and carbon emissions is essential for achieving sustainable urban development. However, the underlying mechanisms driving their complex interactions remain insufficiently explored. This study employs machine learning and multiscale geographically weighted regression (MGWR) to investigate the spatial and temporal dynamics of urban spatial structure and their impact on carbon emissions in the Yangtze River Economic Belt (YREB). The results reveal significant spatial heterogeneity, with carbon emissions highly concentrated in Shanghai, Jiangsu, and Zhejiang province, which are situated in the lower of Yangtze River Economic Belt, while other regions exhibit a general upward trend, characterized by urban expansion towards peripheral areas. Driving forces analysis highlights the varying effects of urban form attributes, including breadth, complexity and compactness, on carbon emissions. These findings offer theoretical insights into optimizing urban spatial structures and provide scientific support for policymakers to implement targeted carbon reduction strategies and promote sustainable urban transformation.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"20 1","pages":"17"},"PeriodicalIF":3.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339702","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":"Applying the greenhouse gas inventory calculation approach to predict the forest carbon sink.","authors":"Fredric Mosley, Jari Niemi, Sampo Soimakallio","doi":"10.1186/s13021-025-00307-2","DOIUrl":"10.1186/s13021-025-00307-2","url":null,"abstract":"<p><strong>Background: </strong>Finland's national Climate Act contains a target for carbon neutrality by 2035. Achieving this target not only depends on the effective implementation of emission reductions, but to a large part on the forest carbon sink. A recent publication of the Government's analysis, assessment, and research activities highlights a potential disparity in forest land greenhouse gas (GHG) balance estimates by the ex-ante scenario model used in the National Energy and Climate Plan (NECP), and the ex-post GHG inventory methodology used for creating an official record of emissions and removals. Better methodological compatibility is needed to answer a key question: How large will the forest carbon sink be in different scenarios? This study is a first attempt to show the usefulness of applying the GHG inventory calculation approach to predict the forest carbon sink.</p><p><strong>Results: </strong>In this study, we introduce a tool that can be used to estimate the GHG balance for forest land, what we call a \"synthetic inventory\", and validate it by comparing outputs against historical data reported in Finland's GHG inventory. Second, we use it to predict GHG balances in year leading up to 2035 at various roundwood and forest residue harvest rates. The tool can replicate forest GHG balances for forest land with an average annual error of 1.0 Mt CO₂, representing 4% of the average annual forest carbon sink. We estimate the forest GHG balance in 2035 to be around 3, -15, -32 Mt CO₂eq at levels of total annual drain 92, 80, 70 Mm³ respectively.</p><p><strong>Conclusions: </strong>According to our calculations the forest land net GHG balance in 2035 is approximately 12 Mt CO₂eq higher than what is presented in Finland's NECP. Conceptual differences between how GHGI methodologies and scenario models estimate living biomass gains and losses contribute to this outcome, in addition to uncertainties associated with both approaches. The tool presented here shows agreement with the National Inventory Report 2023 approach for forest land, and it can be quickly updated to fit new data.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"20 1","pages":"16"},"PeriodicalIF":3.9,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12181879/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339701","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":"Nitrogen addition enhances soil carbon and nutrient dynamics in Chinese croplands: a machine learning and nationwide synthesis.","authors":"Yu Li, Yuan Li","doi":"10.1186/s13021-025-00305-4","DOIUrl":"10.1186/s13021-025-00305-4","url":null,"abstract":"<p><p>Nitrogen (N) addition is a critical driver of soil organic carbon (SOC) sequestration and nutrient cycling in croplands. However, its spatial variability and long-term effects under diverse environmental conditions remain poorly understood. We synthesised data from 479 cropland sites across China and apply machine learning models to evaluate the impacts of N addition on SOC and key soil nutrient indicators, including total nitrogen (TN), nitrate (NO₃⁻-N), ammonium (NH₄⁺-N), the carbon-to-nitrogen ratio (C/N), and available phosphorus (AP). We further evaluated the moderating roles of climate zones, fertiliser types, and fertilisation duration. Our findings demonstrate that N addition significantly increased SOC, TN, NO₃⁻-N, NH₄⁺-N, and AP contents, whereas the C/N ratio remains unaffected. SOC sequestration was greater in arid regions, whereas nutrient accumulation was more pronounced in humid zones. Organic and integrated (organic-inorganic) fertilisers outperformed chemical ones in enhancing SOC and nutrient cycling. Long-term N input (> 10 years) markedly intensified SOC storage and nutrient accumulation. We further developed the high-resolution (5 km) national-scale dataset that predicts the spatial responses of SOC and nutrient dynamics to nitrogen addition across China. This AI-derived dataset enables automated mapping of soil carbon and nutrient functions, capturing substantial spatial heterogeneity under varying environmental conditions. These results provide critical insights for optimising nitrogen management strategies, enhancing soil carbon sink functions, and informing precision agriculture policies in China.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"20 1","pages":"15"},"PeriodicalIF":3.9,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12177997/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144324000","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":"Model error propagation in a compatible tree volume, biomass, and carbon prediction system.","authors":"James A Westfall, Philip J Radtke, David M Walker, John W Coulston","doi":"10.1186/s13021-025-00303-6","DOIUrl":"10.1186/s13021-025-00303-6","url":null,"abstract":"<p><strong>Background: </strong>Individual tree attributes such as volume, biomass and carbon mass are widely known to be highly correlated. As these attributes are typically predicted from statistical models, frameworks that provide compatible relationships among these attributes are usually preferred over approaches that provide independent predictions. However, the propagation of model error can be a concern as this compatibility often relies on predictions for one attribute providing the basis for other attributes. In this study, a compatible tree volume, biomass, and carbon prediction system was evaluated to ascertain how model prediction uncertainty propagates through the system and to examine the contribution to uncertainty in population estimates.</p><p><strong>Results: </strong>Generally, the total and merchantable stem volume predictions are used to derive associated biomass values and subsequently biomass is converted to carbon. As expected, the amount of uncertainty due to the models follows volume < biomass < carbon such that the carbon attribute is the most affected by error propagation. Biomass and associated carbon in tree branches tended to have larger model uncertainty than the stem components due to smaller sample sizes and a greater proportion of unexplained variation. In this model system, direct predictions of whole tree biomass provide the biomass basis and stem and branch components are harmonized to sum to the whole tree value. Corresponding harmonized carbon content values are obtained through application of a common carbon fraction. As such, whole tree biomass and carbon tended to have less model uncertainty than the constituent components primarily due to fewer contributing sources.</p><p><strong>Conclusions: </strong>Although a wide range of outcomes are realized across the various volume, biomass, and carbon components, increases in the standard error of the population estimate due to model uncertainty were always less than 5% and usually smaller than 3%. Thus, forest inventory data users desiring population estimates of tree volume, biomass, and carbon can expect little additional uncertainty due to the prediction model system while benefitting from the implicit compatibility among attributes.</p>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"20 1","pages":"14"},"PeriodicalIF":3.9,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12153174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144265009","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}