Carbon Balance and Management最新文献

Background

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.

Results

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.

Conclusions

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.

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.

Introduction

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.

Methods

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.

Background

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.

Results

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.

Conclusions

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.

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.

Background

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.

Results

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.

Conclusions

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.

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.

Background

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.

Results

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.

Conclusions

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.

The differences in logistics carbon emission and carbon absorption in different areas lead to potential conflicts in the green development of regional logistics. The Yangtze River Delta (YRD) in China is a critical coastal developed region for economic integration development and opening up, with logistics playing a substantial role in energy consumption and carbon emissions. Therefore, addressing the low-carbon transformation of logistics in the YRD is a matter of great concern. The framework of carbon balance accounting and prediction of logistics consist of ‘basic accounting-factor analysis-prediction simulation’ is constructed. Then, this study accounts the logistics carbon emissions (LCE) and logistics carbon capacity (LCC) in the four subregions (Shanghai, Jiangsu, Zhejiang and Anhui) from 2010 to 2021. Estimates the influencing factors of LCE through the geographically and Temporally Weighted Regression model (GTWR). Then, constructs the prediction model for the logistics carbon balance statue based on System Dynamics (SD) structure under four single-factor scenarios and two cross-factor scenarios from 2022 to 2030. Results showed that: (1) The logistics carbon deficit in the YRD is prominent. And the four sub-regions show different spatio-temporal evolution characteristics. (2) The influences of economic level and technical level on LCE are particularly obvious and also has spatio-temporal heterogeneity. (3) There is a trade-off between the pursuit of economic development and carbon emission control. S1 and S2 will continue to witness the increase of logistics carbon pollution. Under S3-S4, the effect of LCE reduction is relatively weak. S5 shows a significant carbon reduction effect, S6 could achieve a good balance between economic development and carbon emissions. (4) Promote the reform of transportation from highway to railway, ensure access to affordable and clean energy for logistic, promote the coordinated carbon reduction of regional logistics and synchronous construction of ecological and artificial carbon pool based on the conditions of developed coastal areas could be feasible paths to achieve carbon balance for YRD.

Background

Harvested wood products (HWPs) have a pivotal role in climate change mitigation, a recognition solidified in many Nationally Determined Contributions (NDCs) under the Paris Agreement. Integrating HWPs' greenhouse gas (GHG) emissions and removals into accounting requirements relies on typical decision-oriented tools known as wood product models (WPMs). The study introduces the TimberTracer (TT) framework, designed to simulate HWP carbon stock, substitution effects, and emissions from wood decay and bioenergy.

Results

Coupled with the 3D-CMCC-FEM forest growth model, TimberTracer was applied to Laricio Pine (Pinus nigra subsp. laricio) in Italy’s Bonis watershed, evaluating three forest management practices (clearcut, selective thinning, and shelterwood) and four wood-use scenarios (business as usual, increased recycling rate, extended average lifespan, and a simultaneous increase in both the recycling rate and the average lifespan) over a 140 year planning horizon, to assess the overall carbon balance of HWPs. Furthermore, this study evaluates the consequences of disregarding landfill methane emissions and relying on static substitution factors, assessing their impact on the mitigation potential of various options. This investigation, covering HWPs stock, carbon (C) emissions, and the substitution effect, revealed that selective thinning emerged as the optimal forest management scenario. In addition, a simultaneous 10% increase in both the recycling rate and half-life, under the so-called “sustainability” scenario, proved to be the optimal wood-use strategy. Finally, the analysis shows that failing to account for landfill methane emissions and the use of dynamic substitution can significantly overestimate the mitigation potential of various forest management and wood-use options, which underscores the critical importance of a comprehensive accounting in climate mitigation strategies involving HWPs.

Conclusions

Our study highlights the critical role of harvested wood products (HWPs) in climate change mitigation, as endorsed by multiple Nationally Determined Contributions (NDCs) under the Paris Agreement. Utilizing the TimberTracer framework coupled with the 3D-CMCC-FEM forest growth model, we identified selective thinning as the optimal forest management practice. Additionally, enhancing recycling rates and extending product lifespan effectively bolstered the carbon balance. Moreover, this study emphasizes the necessity of accounting for landfill methane emissions and dynamic product substitution, as failing to do so may significantly overestimate the mitigation potential of implemented projects. These findings offer actionable insights to optimize forest management strategies and advance climate change mitigation efforts.