Carbon Balance and Management最新文献

The association between carbon emissions and construction intensive-use is still unknown. As a result, this research seeks to assess the carbon emission intensity and intensive use level of construction land in 38 districts (or counties) of Chongqing from 1997 to 2015 using data from construction land and economic and social development. Simultaneously, the spatial autocorrelation analysis approach is utilized to uncover the spatial correlation and spatial distribution characteristics between carbon emission intensity and intensive usage level of construction land in each district and county. The findings indicate that: (1) Because of the influence of complicated terrain types and differences in economic-social development, heavy carbon emissions and extremely intensive use are concentrated in the central parts of cities. The two main sites for micro carbon emissions and micro intensive use are the Three Gorges Reservoir Area in Northeast Chongqing and the Wuling Mountain Area in Southeast Chongqing. (2) The global spatial autocorrelation of carbon emissions and intensive use exhibits a trend of first increasing and then dropping, but it is a high value agglomeration overall. Local spatial autocorrelation reveals that the low-value agglomeration region is primarily found in Southeast and Northeast Chongqing, while the high-value area is primarily found in urban centre areas and urban development new areas. (3) In order to create a new land-use mode with the objective of “low-carbon and intensive use,” various regions should make use of various mechanisms to encourage the movement of people, land, industry, and other elements between regions. Technology development, planning advice, mode selection, and policy design are some of these tools.

Understanding carbon balance is crucial for assessing regional carbon budgets and formulating effective emission reduction policies. However, existing studies have primarily focused on carbon balance dynamics in a specific region, overlooking intercity linkages, making it difficult to guide carbon reduction strategies for inter-regional cooperation. Based on the carbon balance dynamics calculated from the carbon emissions and sinks of 16 core cities in the Yangtze River Delta (YRD) from 2000 to 2020, this study introduced a regional network-based framework to analyze the functional roles of cities in carbon balance, and employed Geodetector to quantify the spatial heterogeneity and interaction effects of key socio-ecological drivers. The results showed that the total carbon emissions in the YRD increased by 3.06 times, while carbon sinks only grew by 1.11 times, leading to a decline in the carbon balance index from -0.67 in 2000 to -0.87 in 2020. The carbon balance network in the YRD exhibited a "hub-driven, multi-level collaborative structure", with Shanghai, Suzhou, Wuxi, and Ningbo as core nodes, maintaining strong interconnections with other cities. During 2000–2020, the network density and correlation numbers initially increased before decreasing, indicating a relatively loose structure and significant potential for enhanced intercity cooperation. Socioeconomic factors, such as industrial activity and freight, were the dominant drivers of carbon emissions, whereas ecological factors, particularly vegetation coverage, most influenced carbon sinks. The carbon balance pattern was finally revealed in the YRD and policy suggestions were proposed for different cities according to their characteristics and their role in the network, which provides an insight for policymakers to develop coordinated low-carbon strategies in the YRD.

Riverine dissolved organic carbon (DOC) is a vital element of regional carbon cycling, yet its magnitude and influencing factors remain poorly quantified. Existing large uncertainties in the distribution, trends, and drivers of DOC compromise the accuracy of terrestrial carbon budget estimations. This study compiled 1922 DOC data points from literature on four major Chinese river basins (i.e., the Songhua River Basin, Yellow River Basin, Yangtze River Basin, and Pearl River Basin) for the period 1997–2023. The spatiotemporal patterns and driving mechanisms of DOC in these basins were quantified and systematically analyzed. Key results are as follows: [1] Spatially, DOC concentration (C_DOC) exhibited a distinct “north high, south low” pattern nationally, while DOC flux (F_DOC) displayed an inverted “south high, north low” distribution. Temporally, C_DOC in the four basins all showed a statistically significant increasing trend, with an average annual rise of 0.04 mg L⁻¹ yr⁻¹. Meanwhile, the F_DOC into the sea in the Yangtze River Basin and Yellow River Basin also exhibited a statistically significant increase, with an average annual growth of 0.05 Tg yr⁻¹ [3]. Attribution analysis indicated that the spatiotemporal distribution of C_DOC was influenced by both climatic factors and human activities, whereas that of F_DOC was controlled primarily by streamflow. The findings of this study reflect the national distribution and dynamics of DOC in major Chinese rivers, and provide a valuable framework together with details of key parameters to support future research into global riverine carbon cycle models.

High-integrity carbon offset systems require scientifically robust and spatially explicit frameworks to quantify carbon pools and fluxes across ecosystems. We present an integrative methodology that combines eddy covariance measurements, airborne and satellite remote sensing, and modeling to extrapolate near real-time carbon flux monitoring to larger areas, using the old-growth temperate forests of Chilean Patagonia as a case study. Our approach delivers high-resolution aboveground biomass carbon density (30 m) and net ecosystem exchange (NEE, 30 m—30 min) estimates using flux tower data. By integrating ground-based flux measurements with high-resolution remote sensing, the proposed methodology constrains model parameters and spatial extrapolation, thereby reducing uncertainty relative to conventional inventory-based approaches. Our approach offers a replicable framework for informing climate policy, conservation planning, and emerging nature-based finance instruments while meeting operational needs in terms of scalability, technological integration, reproducibility, and traceability.

Forest stock volume (FSV) is an important indicator for assessing the carbon sequestration potential of forests and is influenced by neighborhood environmental factors. However, most studies have disregarded the spatial neighborhood association of the modeled variables and its decay effect with increasing spatial distance in estimating the FSV. We propose an FSV estimation method that considers the neighborhood association decay (NAD) effect, that is, NAD-based FSV modelling, and constructed a framework for expressing and quantifying the NAD effect, specifically including the design of NAD models, determination of the optimal neighborhood size, and optimization of the NAD strategy. Finally, we estimated the FSV of the dominant tree species using UAV LiDAR and optical remote sensing data from Mengyin County, China, and evaluated the estimated results using field sample data. The results suggest that the proposed NAD-based model can effectively improve the accuracy of FSV estimation for each tree species (R² = 0.75 ~ 0.96) compared to the conventional pixel-based model. The analysis of the spatial distribution pattern of FSV in Mengyin County revealed high spatial heterogeneity of FSV (15.47-242.82 m³/ha), and a high potential for forest carbon sequestration was found with field surveys.

Background

Estimation of forest biomass stocks in vast and heterogeneous mountain ranges is critical in the context of climate change mitigation and remains challenging because of limited field observations and unknown relationships between variation in forest biomass and environmental heterogeneity. We addressed this challenge by using forest inventory plot observations and a novel spatial modelling approach. In the first step of our approach, we employ a rigorous clustering process to identify a homogeneous group of locations based on tree species and topoclimatic variables and predict potential forest aboveground biomass (AGB). Subsequently, in the second step, we incorporate finer-scale variables, including proxies of forest structure, disturbance likelihood, and elevation zones, to model deviations from the predicted potential AGB.

Results

Our method significantly improves forest AGB estimation in heterogeneous mountain landscapes, achieving a 25% reduction in prediction error compared to the best-performing existing model. The final forest AGB map, generated at 30 m resolution, reveals distinct spatial patterns, with the Central Himalayas emerging as a key carbon reservoir, harbouring forest patches exceeding 1000 t ha^-1. Aggregation of these predictions yielded a total forest AGB of 1982 Mt. In addition, we produced a 250 m resolution potential forest AGB map with associated prediction standard error.

Conclusion

The spatially explicit estimates of actual and potential forest biomass presented is important step towards elucidation of spatial distribution patterns of forest carbon pools and environmental controls. It also provides support for critical initiatives, including climate change mitigation strategies, monitoring forest landscape restoration, and combatting forest degradation challenges. The proposed approach, integrating both broad-scale environmental controls and fine-scale deviations, offers a robust method that is potentially applicable other mountainous regions and contributes for tracking changes in forest carbon over time, essential for REDD+ initiatives.

Amid China’s sustained medium-high speed economic growth, the extensive development model has caused severe climate deterioration and environmental pollution. The implementation of low-carbon city pilot policies provides a strategic direction for the sustainable development of agribusinesses. Using unbalanced panel data from 781 agriculture-related listed enterprises in China between 2007 and 2023, this study employs the PSM-DID model to estimate the average treatment effect and heterogeneity of low-carbon city pilot policies. In addition, a mediation model is applied to examine the mechanism through which digital transformation enhances the Environmental-Social-Governance (ESG) performance of these enterprises under policies. The empirical results indicate that: (1) low-carbon city pilot policies simultaneously improve the environmental, social, and governance performance of agribusiness enterprises, thereby enhancing their overall ESG performance. (2) The impact of policies on ESG, environmental, social, and governance performance varies across regions, with the western region showing the most significant improvement in ESG performance. (3) Digital transformation plays a significant mediating role in the effect of low-carbon city pilot policies on ESG, environmental, social, and governance performance. Based on these findings, agribusiness should leverage policy advantages to enhance sustainable development through multi-dimensional performance. The government should introduce targeted policies to help enterprises define development directions and formulate localized strategies. Furthermore, agribusinesses must strengthen digital transformation as a core driver for improving ESG performance.

In the context of carbon peaking and neutrality goals, combined with the pursuit of high-quality agricultural economic development, examining the spatial disparities and convergence of agricultural green and low-carbon transformation is critical for protecting the ecological environment and enhancing national agricultural ecological security. This study estimates agricultural carbon emissions across five dimensions: farmland use, rice cultivation, livestock production, farmland soils, and crop residue burning. Using the EBM-GML model, the study measures the agricultural green and low-carbon transformation index for 30 Chinese provinces from 2005 to 2023. The Dagum Gini coefficient, standard deviation ellipses, kernel density estimation, and spatial Durbin models are employed to analyse the spatial disparities and convergence of China’s agricultural green and low-carbon transformation. The findings reveal that the level of agricultural green and low-carbon transformation across provinces and the three major economic zones has increased over time, showing a spatial pattern of “high levels at the periphery and low levels at the centre,” with inter-zonal disparities gradually widening. Regional dynamics in this transformation vary significantly, with northern regions growing faster than southern regions. Nationally, the transformation expanded markedly over the sample period, accompanied by growing divergence within the eastern region. At both the national and major sub-regional levels, the agricultural green and low-carbon transformation shows no δ-convergence but demonstrates absolute and conditional β-convergence. This suggests that although regional divergence in the agricultural green and low-carbon transformation does not consistently decline over time, areas with higher transformation levels experience faster reductions than those with lower levels. Consequently, the gap between the two groups gradually narrows, ultimately converging toward a common steady-state level.

Carbon sequestration (CS), a key component of climate change mitigation and carbon neutrality efforts, is strongly influenced by land use/land cover change (LUCC). However, the spatiotemporal evolution of CS in response to future LUCC trajectories under urbanization remains underexplored. Therefore, this study focuses on the Yangtze River Economic Belt (YREB), a region characterized by rapid urbanization and high carbon emissions, and develops an integrated SD-PLUS-InVEST modelling framework to evaluate the impacts of LUCC under three SSP-RCP scenarios on regional CS dynamics. Results show that, cropland is projected to decline by 5%, 10%, and 3%, while forestland increases by 8%, 3%, and 5% under SSP1-2.6, SSP5-8.5, and SSP2-4.5, respectively. Correspondingly, CS shows a 12% increase under SSP1-2.6, an 8% decrease under SSP5-8.5, and a moderate enhancement under SSP2-4.5. The XGBoost-SHAP analysis quantified the impacts of key drivers, revealing that elevated temperature and population growth are strongly correlated with declines in CS, while economic growth is positively correlated with enhanced CS capacity. This research provides valuable insights into how LUCC affects CS under varying development scenarios, offering actionable guidance for formulating regional land-use policies that promote CS and contribute to climate mitigation.