Carbon Balance and Management最新文献

Climate change and global warming are among the most significant issues that humanity is currently facing, and also among the issues that pose the greatest threats to all mankind. These issues are primarily driven by abnormal increases in greenhouse gas concentrations. Mathematical modeling serves as a powerful approach to analyze the dynamic patterns of atmospheric carbon dioxide. In this paper, we established a mathematical model with four state variables to investigate the dynamic behavior of the interaction between atmospheric carbon dioxide, GDP, forest area and human population. Relevant theories were employed to analyze the system’s boundedness and the stability of equilibrium points. The parameter values were estimated with the help of the actual data in China and numerical fitting was carried out to verify the results of the theoretical analysis. The Partial Rank Correlation Coefficient (PRCC) determines the sensitivity ofan input parameter to the output by measuring the correlation between a single input parameter and the model output. The sensitivity analysis of the compartments with respect to the model parameters was analyzed by using the PRCCand the Latin Hypercube Sampling test.The results indicate that the sensitivity of GDP-driven CO₂ emissions and GDP-governed atmospheric CO₂ concentration to the system is not significant. This implies that within the GDP-driven mitigation framework, the regulatory effect of GDP on atmospheric CO₂ concentration is relatively limited, and its significance is less pronounced than that of forests. Therefore, future relevant strategies should prioritize parameters with higher sensitivity (e.g., forestation). Apply the optimal control theory to regulate the atmospheric carbon dioxide level and provide the corresponding numerical fitting. Finally, corresponding discussions and suggestions were put forward with the help of the results of the theoretical analysis and numerical fitting.

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.

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.