Search Results (607)

Soil organic carbon (SOC) pool plays an extremely important role in regulating the global carbon (C) cycle and climate change. Atmospheric nitrogen (N) and phosphorus (P) deposition caused by human activities has significant impacts on soil C sequestration potential of terrestrial ecosystem. To investigate the effects of N and P deposition on soil C sequestration and C-N coupling relationship in broad-leaved evergreen forests, a 6-year field nutrient regulation experiment was implemented in subtropical Castanopsis sclerophylla forests with four different N and P additions: N addition (100 kg N·hm⁻²·year⁻¹), N + P (100 kg N·hm⁻²·year⁻¹ + 50 kg P·hm⁻²·year⁻¹), P addition (50 kg P·hm⁻²·year⁻¹), and CK (0 kg N·hm⁻²·year⁻¹). The changes in the C and N contents and stable isotope distributions (δ¹³C and δ¹⁵N) of different soil organic fractions were examined. The results showed that the SOC and total nitrogen (STN) (p > 0.05) increased with N addition, while SOC significantly decreased with P addition (p < 0.05), and N + P treatment has low effect on SOC, STN (p > 0.05). By density grouping, it was found that N addition significantly increased light fraction C and N (LFOC, LFN), significantly decreased the light fraction C to N ratio (LFOC/N) (p < 0.05), and increased heavy fraction C and N (HFOC, HFN) accumulation and light fraction to total organic C ratio (LFOC/SOC, p > 0.05). Contrary to N addition, P addition was detrimental to the accumulation of LFOC, LFN and reduced LFOC/SOC. It was found that different reactive oxidized carbon (ROC) increased under N addition but ROC/SOC did not change, while N + P and P treatments increased ROC/SOC, resulting in a decrease in SOC chemical stability. Stable isotope analysis showed that N addition promoted the accumulation of new soil organic matter, whereas P addition enhanced the transformation and utilization of C and N from pre-existing organic matter. Additionally, N addition indirectly increased LFOC by significantly decreasing pH; significantly contributed to LFOC and ROC by increasing STN accumulation promoted by NO₃⁻-N and NH₄⁺-N; and decreased light fraction δ¹³C by significantly increasing dissolved organic C (p < 0.05). P addition had directly significant negative effect on LFOC and SOC (p < 0.05). In conclusion, six-year N deposition enhances soil C and N sequestration while the P enrichment reduces the content of soil C, N fractions and stability in Castanopsis sclerophylla forests. The results provide a scientific basis for predicting the soil C sink function of evergreen broad-leaved forest ecosystem under the background of future climate change. Full article

While boreal forests constitute 30% of the Earth’s forested area and are responsible for 20% of the global carbon sink, there is considerable concern about their sustainability. This paper focuses on the role of elevated CO₂, examining whether wood volume in these forests has responded to increased CO₂ over the last 60 years. To accomplish this, we use a rich set of wood volume measurement data from the Province of Alberta, Canada, and deploy quasi-experimental techniques to determine the effect of elevated CO₂. While the few experimental studies that have examined boreal forests have found almost no effect of elevated CO₂, our results indicate that a 1.0% increase in lifetime exposure to CO₂ leads to a 1.1% increase in aboveground wood volume in these boreal forests. This study showcases the value of research designs that use natural settings to better account for the effects of prolonged exposure to elevated CO₂. Our results should enable improved delineation of the drivers of historical changes in wood volume and carbon storage in boreal forests. In addition, when combined with other studies, these results will likely aid policymakers in designing management or policy approaches that will enhance the sustainability of forests in boreal regions. Full article

Prolonged inundations are altering coastal forest ecosystems of the southeastern US, causing extensive tree die-offs and the development of ghost forests. This hydrological stressor also alters carbon fluxes, threatening the stability of coastal carbon sinks. This study was conducted to investigate the interactions between hydrological drivers and ecosystem responses by analyzing daily eddy covariance flux data from a wetland forest in North Carolina, USA, spanning 2009–2019. We analyzed temporal patterns of net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (RE) under both flooded and non-flooded conditions and evaluated their relationships with observed tree mortality. Generalized Additive Modeling (GAM) revealed that groundwater table depth (GWT), leaf area index (LAI), NEE, and net radiation (Rn) were key predictors of mortality transitions (R² = 0.98). Elevated GWT induces root anoxia; declining LAI reduces productivity; elevated NEE signals physiological breakdown; and higher Rn may amplify evapotranspiration stress. Receiver Operating Characteristic (ROC) analysis revealed critical early warning thresholds for tree mortality: GWT = 2.23 cm, LAI = 2.99, NEE = 1.27 g C m⁻² d⁻¹, and Rn = 167.54 W m⁻². These values offer a basis for forecasting forest mortality risk and guiding early warning systems. Our findings highlight the dominant role of hydrological variability in ecosystem degradation and offer a threshold-based framework for early detection of mortality risks. This approach provides insights into managing coastal forest resilience amid accelerating sea level rise. Full article

Ecosystem services—the benefits humans derive from nature—are foundational to environmental sustainability and economic well-being, with carbon sequestration and storage standing out as critical regulating services in the fight against climate change. This study presents a comprehensive financial valuation of the carbon sequestration, storage and product substitution ecosystem services provided by the Hungarian forest-based sector. Using a multi-scenario framework, four complementary valuation concepts are assessed: total carbon storage (biomass, soil, and harvested wood products), annual net sequestration, emissions avoided through material and energy substitution, and marketable carbon value under voluntary carbon market (VCM) and EU Carbon Removal Certification Framework (CRCF) mechanisms. Data sources include the National Forestry Database, the Hungarian Greenhouse Gas Inventory, and national estimates on substitution effects and soil carbon stocks. The total carbon stock of Hungarian forests is estimated at 1289 million tons of CO₂ eq, corresponding to a theoretical climate liability value of over EUR 64 billion. Annual sequestration is valued at approximately 380 million EUR/year, while avoided emissions contribute an additional 453 million EUR/year in mitigation benefits. A comparative analysis of two mutually exclusive crediting strategies—improved forest management projects (IFMs) avoiding final harvesting versus long-term carbon storage through the use of harvested wood products—reveals that intensified harvesting for durable wood use offers higher revenue potential (up to 90 million EUR/year) than non-harvesting IFM scenarios. These findings highlight the dual role of forests as both carbon sinks and sources of climate-smart materials and call for policy frameworks that integrate substitution benefits and long-term storage opportunities in support of effective climate and bioeconomy strategies. Full article

Mangrove forests are widely recognized for their effectiveness as carbon sinks and serve as critical ecosystems for mitigating the effects of climate change. Current research lacks comprehensive, large-scale carbon storage datasets for wetland ecosystems, particularly across Mexico and other understudied regions worldwide. Therefore, the objective of this study was to develop a high spatial resolution map of carbon stocks, encompassing both aboveground and belowground components, within the Marismas Nacionales system, which is the largest mangrove complex in northeastern Pacific Mexico. Our approach integrates primary field data collected during 2023–2024 and incorporates some historical plot measurements (2011–present) to enhance spatial coverage. These were combined with contemporary remote sensing data, including Sentinel-1, Sentinel-2, and LiDAR, analyzed using Random Forest algorithms. Our spatial models achieved strong predictive accuracy (R² = 0.94–0.95), effectively resolving fine-scale variations driven by canopy structure, hydrologic regime, and spectral heterogeneity. The application of Local Indicators of Spatial Association (LISA) revealed the presence of carbon “hotspots,” which encompass 33% of the total area but contribute to 46% of the overall carbon stocks, amounting to 21.5 Tg C. Notably, elevated concentrations of carbon stocks are observed in the central regions, including the Agua Brava Lagoon and at the southern portion of the study area, where pristine mangrove stands thrive. Also, our analysis reveals that 74.6% of these carbon hotspots fall within existing protected areas, demonstrating relatively effective—though incomplete—conservation coverage across the Marismas Nacionales wetlands. We further identified important cold spots and ecotones that represent priority areas for rehabilitation and adaptive management. These findings establish a transferable framework for enhancing national carbon accounting while advancing nature-based solutions that support both climate mitigation and adaptation goals. Full article

The Qinghai–Tibetan Plateau (QTP), often referred to as the “Third Pole” of the world, harbors alpine grassland ecosystems that play an essential role as global carbon sinks, helping to mitigate the pace of climate change. Nonetheless, alterations in natural environmental conditions coupled with escalating human activities have disrupted the seasonal growth cycles of grasslands, thereby intensifying degradation processes. To date, the key drivers and lifecycle dynamics of Grassland Depletion across the QTP remain contentious, limiting our comprehension of its ecological repercussions and regulatory mechanisms. This study comprehensively investigates grassland degradation on the Qinghai–Tibetan Plateau, analyzing its drivers and changes in ecological suitability during the growing season. By integrating natural factors (e.g., precipitation and temperature) and anthropogenic influences (e.g., population density and grazing intensity), it examines observational data from over 160 monitoring stations collected between the 1980s and 2020. The findings reveal three distinct phases of grassland degradation: an acute degradation phase in 1990 (GDI, Grassland Degradation Index = 2.53), a partial recovery phase from 1996 to 2005 (GDI < 2.0) during which the proportion of degraded grassland decreased from 71.85% in 1990 to 51.22% in 2005, and a renewed intensification of degradation after 2006 (GDI > 2.0), with degraded grassland areas reaching 56.39% by 2020. Among the influencing variables, precipitation emerged as the most significant driver, interacting closely with anthropogenic factors such as grazing practices and population distribution. Specifically, the combined impacts of precipitation with population density, grazing pressure, and elevation were particularly notable, yielding interaction q-values of 0.796, 0.767, and 0.752, respectively. Our findings reveal that while grasslands exhibit superior carbon sink potential relative to forests, their productivity and ecological functionality are undergoing considerable declines due to the compounded effects of multiple interacting factors. Consequently, the spatial distribution of ecologically suitable zones has contracted significantly, with the remaining high-suitability regions concentrating in the “twin-star” zones of Baingoin and Zanda grasslands, areas recognized as focal points for future ecosystem preservation. Furthermore, the effects of climate change and intensifying anthropogenic activity have driven the reduction in highly suitable grassland areas, shrinking from 41,232 km² in 1990 to 24,485 km² by 2020, with projections indicating a further decrease to only 2844 km² by 2060. This study sheds light on the intricate mechanisms behind Grassland Depletion, providing essential guidance for conservation efforts and ecological restoration on the QTP. Moreover, it offers theoretical underpinnings to support China’s carbon neutrality and peak carbon emission goals. Full article

Accurate estimation of forest carbon stock is critical for understanding ecosystem carbon dynamics and informing climate mitigation strategies. This study presents a deep learning framework that integrates Sentinel-2 multispectral imagery with an enhanced residual neural network for estimating aboveground forest carbon stock in the Liuchong River Basin, Bijie City, Guizhou Province, China. The proposed model incorporates multiscale residual blocks and channel attention mechanisms to improve spatial feature extraction and spectral dependency modeling. A dataset of 150 ground inventory plots was employed for supervised training and validation. Comparative experiments with Random Forest, Gradient Boosting Decision Trees (GBDT), and Vision Transformer (ViT) demonstrate that the enhanced ResNet achieves the best performance, with a root mean square error (RMSE) of 23.02 Mg/ha and a coefficient of determination ($R^2$) of 0.773 on the test set. Spatial mapping results further reveal that the model effectively captures fine-scale carbon stock variations across mountainous forested landscapes. These findings underscore the potential of combining multispectral remote sensing and advanced neural architectures for scalable, high-resolution forest carbon estimation in complex terrain. Full article

The transition zone between the Qinghai–Tibet and Loess Plateaus is an important ecological functional area and carbon (C) reservoir in China. Studying the main drivers of C density changes in forest ecosystems is crucial to enhance the C sink potential of those ecosystems in ecologically fragile regions. In this study, four stand types at different succession stages in the transition zone of Xinglong Mountain were selected as the study objective. The C densities of the ecosystem, vegetation, plant debris, and soil of each stand type were estimated, and the related driving factors were quantified. The results showed that the forest ecosystem C density continuously increased significantly with natural succession (381.23 Mg/hm² to 466.88 Mg/hm²), indicating that the ecosystem has a high potential for C sequestration with progressive forest succession. The increase in ecosystem C density was mainly contributed to by the vegetation C density, which was jointly affected by the vegetation characteristics (C sink, mean diameter at breast height, mean tree height), litter C/N (nitrogen), and surface soil C/N, with factors explaining 95.1% of the variation in vegetation C density, while the net effect of vegetation characteristics was the strongest (13.9%). Overall, this study provides a new insight for understanding the C cycle mechanism in ecologically fragile areas and further improves the theoretical framework for understanding the C sink function of forest ecosystems. Full article

Research on forest carbon sinks is crucial for mitigating global climate change and achieving carbon peaking and neutrality. However, studies at the county level remain relatively limited. This study utilized multi-source remote sensing data and the Carnegie–Ames-Stanford Approach (CASA) and soil respiration models to estimate the forest net ecosystem productivity (NEP) in Taoyuan County from 2000 to 2023. The spatiotemporal differentiation was analyzed using seasonal Mann–Kendall tests, Theil–Sen slope estimation, and standard deviation ellipses. The forest NEP for 2035 was predicted under multiple climate scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5) by applying a discrete coupling of the Patch-generating Land Use Simulation (PLUS) model, incorporating territorial spatial planning policy, and using the CASA model. The results indicated that the Taoyuan County forest NEP exhibited a fluctuating upward trend from 2000 to 2023, with higher (lower) values in the west/south (east/north). Under future warming and humidification, the overall forest NEP in Taoyuan County was projected to decrease by 2035, with predicted NEP values across scenarios ranking as SSP5-8.5 > SSP1-2.6 > SSP2-4.5. The findings offer practical insights for improving local forest management, optimizing forest configuration, and guiding county-level “dual-carbon” policies under future climate and land use change, thereby contributing to ecological sustainability. Full article

Wetlands are critical components of the global carbon cycle, yet their carbon sink dynamics under hydrological fluctuations remain insufficiently understood. This study employed the Carnegie-Ames-Stanford Approach (CASA) model to estimate the net ecosystem productivity (NEP) of the Dongting Lake wetland and explored the spatiotemporal dynamics and driving mechanisms of carbon sinks from 2000 to 2022, utilizing the Theil-Sen median trend, Mann-Kendall test, and attribution based on the differentiating equation (ADE). Results showed that (1) the annual mean spatial NEP was 50.24 g C/m²/a, which first increased and then decreased, with an overall trend of −1.5 g C/m²/a. The carbon sink was strongest in spring, declined in summer, and shifted to a carbon source in autumn and winter. (2) Climate variability and human activities contributed +2.17 and −3.73 g C/m²/a to NEP, respectively. Human activities were the primary driver of carbon sink degradation (74.30%), whereas climate change mainly promoted carbon sequestration (25.70%). However, from 2000–2011 to 2011–2022, climate change shifted from enhancing to limiting carbon sequestration, mainly due to the transition from water storage and lake reclamation to ecological restoration policies and intensified climate anomalies. (3) NEP was negatively correlated with precipitation and water level. Land use adjustments, such as forest expansion and conversion of cropland and reed to sedge, alongside maintaining growing season water levels between 24.06~26.44 m, are recommended to sustain and enhance wetland carbon sinks. Despite inherent uncertainties in model parameterization and the lack of sufficient in situ flux validation, these findings could provide valuable scientific insights for wetland carbon management and policy-making. Full article

Forests are a key terrestrial carbon sink, storing carbon in biomass, the forest floor, and the mineral soil (SOC). Since Pinus radiata D. Don is the most widely planted forest species in Chile, it is important to understand how environmental and soil factors influence these carbon pools. Our objective was to evaluate the effects of climate and site variables on carbon stocks in adult radiata pine plantations across contrasting water and nutrient conditions. Three 1000 m² plots were installed at 20 sites with sandy, granitic, recent ash, and metamorphic soils, which were selected along a productivity gradient. Biomass carbon stocks were estimated using allometric equations, and carbon stocks in the forest floor and mineral soil (up to 1 m deep) were assessed. SOC varied significantly, from 139.9 Mg ha⁻¹ in sandy soils to 382.4 Mg ha⁻¹ in metamorphic soils. Total carbon stocks (TCS) per site ranged from 331.0 Mg ha⁻¹ in sandy soils to 552.9 Mg ha⁻¹ in metamorphic soils. Across all soil types, the forest floor held the lowest carbon stock. Correlation analyses and linear models revealed that variables related to soil water availability, nitrogen content, precipitation, and stand productivity positively increased SOC and TCS stocks. In contrast, temperature, evapotranspiration, and sand content had a negative effect. The developed models will allow more accurate estimation estimates of C stocks at SOC and in the total stand. Full article

The development of a forestry carbon sink project is an important way to achieve carbon neutrality and carbon reduction, and the collective forest carbon sink project is an important part of China’s forestry carbon sink project. As the main management entity of collective forests, whether farmers are willing to produce forestry carbon sinks is directly related to the implementation effect of the project. In this paper, a partial equilibrium model of farmers’ forestry production behavior was established based on production function and utility function, and the path to enhance farmers’ willingness to produce forestry carbon sink through forestry carbon sink projects was analyzed in combination with forest ecological management theory. In terms of empirical analysis, the PSM-DID econometric model was established based on the survey data of LY in Zhejiang Province, China, and the following conclusions were drawn: (1) With the receipt of revenues from forestry carbon sequestration projects and partial cost-sharing by the government, farmers’ participation in forestry carbon sink projects can save investment in forest land management. (2) The saved forestry production costs and forestry carbon sink project subsidies can make up for the loss of farmers’ timber income, so that the net income of forestry will not be significantly reduced. (3) The forestry production factors saved by farmers can be transferred to non-agricultural sectors and increase non-agricultural net income, so that the net income of rural households participating in forestry carbon sink projects will increase. The forestry carbon sink project can improve the utility level of farmers and increase the willingness of farmers to produce forestry carbon sinks by delivering income to farmers and saving forestry production factors. This study demonstrates that a well-designed forestry carbon sink compensation mechanism, combined with an optimized allocation of production factors, can effectively enhance farmers’ willingness to participate. This insight is also applicable to countries or regions that rely on small-scale forestry operations. Full article

China’s accelerated urbanization has instigated construction land expansion and ecological land attrition, aggravating the carbon emission disequilibrium. Notably, the “land carbon emission elasticity coefficient” in urban agglomerations far exceeds international benchmarks, underscoring the contradiction between spatial expansion and low-carbon goals. Existing research predominantly centers on single-spatial-type or static-model analyses, lacking cross-scale mechanism exploration, policy heterogeneity consideration, and differentiated carbon metabolism assessment across functional spaces. This study takes Xiong’an New Area as a case, delineating the spatiotemporal evolution of land use and carbon emissions during 2017–2023. Construction land expanded by 26.8%, propelling an 11-fold escalation in carbon emissions, while emission intensity decreased by 11.4% due to energy efficiency improvements and renewable energy adoption. Cultivated land reduction (31.8%) caused a 73.4% decline in agricultural emissions, and ecological land network restructuring (65.3% forest expansion and wetland restoration) significantly enhanced carbon sequestration. This research validates a governance paradigm prioritizing “structural optimization” over “scale expansion”—synergizing construction land intensification with ecological restoration to decelerate emission growth and strengthen carbon sink systems. Full article

Forest land plays a vital role as a terrestrial carbon sink. Urbanization, particularly the conversion of forest land into agricultural and construction areas, has significantly affected the carbon sink capacity of forests. The protection of carbon sinks in forest land has become a critical issue in advancing the dual carbon strategy. Taking Liushahe Town as a case study, this study develops an integrated framework of analysis and response strategies, which encompass “land use change prediction, forest land carbon sink evaluation, and multi-objective optimization”. The purpose is to identify an optimal forest planning scheme that balances carbon sink capacity and biodiversity. The results indicate that: (1) Land use change substantially affects the extent of forest land in Liushahe Town, in which the area exhibits an initial increase followed by a decrease, and is projected to shrink to 89.88% of its 2021 level by 2041. (2) There are significant disparities in carbon sink performance among various forest land plots. The strategic elimination of inefficient plots and preservation of those with high carbon sink potential are key to enhancing the resilience of forest land to disturbances. (3) Multi-objective optimization planning schemes effectively reconcile carbon sinks and biodiversity, and enhance the synergistic effects of forest ecosystem services. Overall, this research provides practical guidance and methodological support for the protection of carbon sinks in forest land within township-scale spatial planning. Full article

Accurate estimation of soil organic carbon (SOC) content is crucial for assessing terrestrial ecosystem carbon stocks. Although traditional methods offer relatively high estimation accuracy, they are limited by poor timeliness and high costs. Combining measured data, remote sensing technology, and machine learning (ML) algorithms enables rapid, efficient, and accurate large-scale prediction. However, single ML models often face issues like high feature variable redundancy and weak generalization ability. Integrated models can effectively overcome these problems. This study focuses on the Weigan–Kuqa River oasis (Wei-Ku Oasis), a typical arid oasis in northwest China. It integrates Sentinel-2A multispectral imagery, a digital elevation model, ERA5 meteorological reanalysis data, soil attribute, and land use (LU) data to estimate SOC. The Boruta algorithm, Lasso regression, and its combination methods were used to screen feature variables, constructing a multidimensional feature space. Ensemble models like Random Forest (RF), Gradient Boosting Machine (GBM), and the Stacking model are built. Results show that the Stacking model, constructed by combining the screened variable sets, exhibited optimal prediction accuracy (test set R² = 0.61, RMSE = 2.17 g∙kg⁻¹, RPD = 1.61), which reduced the prediction error by 9% compared to single model prediction. Difference Vegetation Index (DVI), Bare Soil Evapotranspiration (BSE), and type of land use (TLU) have a substantial multidimensional synergistic influence on the spatial differentiation pattern of the SOC. The implementation of TLU has been demonstrated to exert a substantial influence on the model’s estimation performance, as evidenced by an augmentation of 24% in the R² of the test set. The integration of Boruta–Lasso combination screening and Stacking has been shown to facilitate the construction of a high-precision SOC content estimation model. This model has the capacity to provide technical support for precision fertilization in oasis regions in arid zones and the management of regional carbon sinks. Full article