Search Results (1,896)

Balancing ecological protection, carbon sinks, and development is a practical challenge in mountainous regions. Using Yunnan Province, China, as a case study, this paper develops a knowledge-guided probabilistic framework for carbon-oriented territorial zoning. The framework combines an indicator system, corridor analysis of pattern, risk and potential, knowledge-graph rule encoding, Bayesian mechanism calibration, and constrained posterior decoding on 11,853 effective planning cells. The results show a clear conservation–development gradient in the carbon sink priority surface: high-priority areas are concentrated in western and southwestern Yunnan, whereas low-priority areas cluster around major urban centers. Corridor analysis identifies a central resistance belt and several urban–rural bottlenecks, indicating that connectivity constraints are concentrated in a limited number of critical links. The final zoning assigns 35.4% of grids to integrated development, 25.9% to emergency intervention, 14.5% to long-term conservation, 13.8% to priority restoration, and 10.4% to risk control. Zone separability is generally strong, with one-versus-rest AUC values ranging from 0.777 to 0.995. Land use enrichment further supports the zoning results: integrated development contains 78.85% of built-up land and 45.93% of cropland, whereas Emergency intervention, priority restoration, and long-term conservation together contain 70.01% of forest area. Full article

Optimizing nitrogen (N) management is crucial for high-quality rice (Oryza sativa L.) production. However, how N affects grain quality at different positions within a panicle remains unclear. This study evaluated the effects of different N application regimes on the milling, appearance, eating, and nutritional quality of grains at varying panicle positions. We used a japonica cultivar Wuyunjing 31 in a controlled pot experiment with three N treatments: N32:0 (early heavy N), N16:16 (split application with late N topdressing), and N16:0 (low-N control). Results showed that late N topdressing (N16:16) significantly improved head rice yield across all grain positions, which was linked to higher storage protein accumulation (especially glutelin) and larger length-to-width ratio. Conversely, late N application deteriorated appearance quality by increasing the chalky grain rate and chalkiness. This negative effect was most pronounced in superior grains on upper and middle branches. Furthermore, the N16:16 treatment consistently decreased amylose content while increasing albumin, prolamin, and glutelin levels, demonstrating a clear trade-off between carbon (C) and N sinks. We speculated that these intra-panicle differences result from increased competition for carbon resources between starch and protein synthesis pathways. Overall, precision N management should account for spatial differences in grain development to effectively balance rice yield and quality. Full article

The mean annual change in aboveground biomass (ΔAGB) is a pivotal indicator for assessing forest carbon cycle dynamics. This study analyzed 791 independent Abies Mill. forest patches across China to elucidate their driving mechanisms by integrating abiotic, anthropogenic, and biotic factors. We employed a spatially explicit framework, including spatial error regression and structural equation modeling (SEM), to account for significant spatial autocorrelation (Moran’s I = 0.375, p < 0.001). Our results show that abiotic factors predominantly dictate ΔAGB, with soil fertility (pH and Total Nitrogen), elevation (DEM), and soil physical properties (Coarse Fragments and Thickness) explaining the majority of deterministic variance. This relatively low explanatory variance (marginal R² = 0.09) likely reflects the high environmental stochasticity inherent in alpine ecosystems. Specifically, soil fertility exerted the strongest positive influence (Std. Estimate = 0.33), while elevation and soil physical constraints were the primary limiting factors. Biotic factors (Stand Age, Height, and Tree Cover) played a subordinate role, contributing only a marginal 2% gain in explained variance (increasing marginal R² from 0.07 to 0.09). Path analysis revealed an “environmental filtering” hierarchy where abiotic factors shape stand structure, which in turn has limited impact on growth dynamics. These findings underscore that carbon management in alpine forests should prioritize habitat quality conservation over simple biotic structural manipulation. Full article

Accurate estimation of forest soil organic carbon (SOC) is considered critical for understanding terrestrial carbon cycling and supporting climate change mitigation strategies. However, the canopy block, intricate vertical structure of forests, and the constraints of single-source remote sensing data have presented considerable obstacles for estimating forest SOC. This study proposes a forest SOC estimation and uncertainty analysis (ForSOC-UA) framework to enhance forest SOC estimation and quantify its uncertainty in the natural secondary forests of northern China by integrating hyperspectral imagery (ZY-1F), synthetic aperture radar data (Sentinel-1), and environmental covariates (such as topography, vegetation, and soil indices). The performance of traditional machine learning models (RF, SVM, and CNN), geographically weighted regression (GWR), and a geographically weighted random forest (GWRF) model was compared across three different soil depths (0–5 cm, 5–10 cm, and 10–30 cm). The results showed that GWRF consistently outperformed all other models across all soil depth layers, with the highest accuracy achieved using multi-source data (R² = 0.58, RMSE = 27.49 g/kg, rRMSE = 0.31). Analysis of feature importance revealed that soil moisture, terrain characteristics, and Sentinel-1 polarization attributes were the primary predictors, while spectral derivatives in the red and near-infrared bands from ZY-1F also played a significant role for forest SOC estimation. The uncertainty analysis indicated a forest SOC estimation uncertainty of 37.2 g/kg in the 0–5 cm soil layer, with a decreasing trend as depth increased. This pattern is associated with the vertical spatial distribution of the measured forest SOC. This integrated approach effectively captures spatial heterogeneity and nonlinear relationships between feature and forest SOC, while also assessing estimation uncertainty, so providing a robust methodology for predicting forest SOC. The ForSOC-UA framework addresses the uncertainty quantification of SOC estimation at different vertical depths based on machine learning, providing methodological enhancements for the assessment of large-scale forest SOC and the monitoring of carbon sinks within forest ecosystems. Full article

Tropical forests are predicted to become carbon sources by mid-century under climate change. However, this trajectory may not be inevitable for forests under long-term protection. Using 12 years of eddy covariance flux data from a long-term protected tropical rainforest site in Xishuangbanna, China, we develop an explainable machine learning framework (SHAP + structural equation modeling) to disentangle the environmental drivers of net ecosystem exchange (NEE) and evapotranspiration (ET), and project their future trajectories under four CMIP6 climate scenarios. We find a fundamental divergence: while conventional climate models predict a sink-to-source transition by 2050–2066, our data-driven model—trained on conservation-era observations—projects a persistent carbon sink through 2100 across all the scenarios. This divergence suggests that long-term protection may buffer tropical forests against climate-driven decline, challenging the prevailing narrative of inevitable carbon loss. We further identify critical environmental thresholds—solar radiation (~200 W m⁻²) and air temperature (~25 °C)—beyond which carbon uptake efficiency declines. Our findings provide empirical support for nature-based climate solutions and highlight the need to integrate conservation legacies into Earth system models. Full article

Driven by the goal of carbon neutrality, low-carbon development in township spaces is essential for sustainable urban–rural growth. This paper employs a carbon accounting methodology, taking Fuqiushan Town in the Dongting Lake Ecological Economic Zone as a case study to develop a detailed carbon measurement inventory at the township scale. Using spatial analysis techniques, it synthesizes multi-source data—including land use, agricultural inputs, and population—to estimate emissions from key sources such as crop cultivation, livestock and poultry breeding, industrial production, and residential activities. The study also evaluates the carbon sequestration capacity of sinks such as woodlands and water bodies, enabling the spatial visualization of both carbon emissions and carbon sinks. Key findings include: (1) Fuqiushan Town exhibits a carbon emission profile characterized by “industrial activities as the primary source, supplemented by agriculture, with additional contributions from residential and transportation sectors,” while forested areas and water bodies serve as core carbon sink zones. (2) An innovative multidimensional indicator system for low-carbon development efficiency was established, consisting of the Low-Carbon Development Efficiency Index in Production, the Daily Life Carbon Responsibility Efficiency Index, and the Ecological Carbon Sink Efficiency Index, which together form a Comprehensive Efficiency Index for Low-Carbon Development. (3) Analysis reveals significant spatial coupling relationships and efficiency differentiation patterns among carbon emissions, industrial structure, energy dependence, and ecological background. Based on dominant carbon emission types, low-carbon efficiency thresholds, and spatial factor interactions, the 17 villages and one forest farm in the township are classified into five zones: “Industrial High-Carbon Transition Zone,” “Agricultural Pollution Reduction and Carbon Emission Reduction Synergy Zone,” “Ecological Low-Carbon Conservation Zone,” “Human Settlements Balanced Development Zone,” and “Ecological Core Zone.” Tailored low-carbon spatial planning strategies for material resources are proposed for each zone. These results offer quantitative support and spatially targeted insights for low-carbon spatial planning in ecologically sensitive townships, contributing to the achievement of objectives such as “carbon reduction and sink increase” and “rural revitalization.” Full article

Against the backdrop of intensified climate change and increasingly prominent imbalances in resource supply and demand, achieving multi-objective collaborative optimization of the Water-Energy-Food-Carbon (WEFC) nexus under uncertain conditions has become a pivotal task for regional sustainable development. Taking the Heihe River Basin, a typical arid inland river basin in northwest China with a complex WEFC nexus, as the research area, this study develops a multi-objective collaborative optimization model for the WEFC nexus, targeting three core goals: maximizing crop irrigation water productivity, minimizing carbon emissions, and enhancing low-carbon agricultural competitiveness. The model embeds constraints of regional water security, food security, land policy, and total water resource availability, introduces the uncertainty parameter τ to quantify fluctuations in available surface water, and adopts the ideal point method to convert the multi-objective problem into a single-objective optimization task by minimizing the Euclidean distance between feasible solutions and the ideal solution, with a case application in the oasis area of the basin’s middle reaches. Results show the model exhibits excellent stability across varying uncertainty levels: crop irrigation water productivity stabilizes around 1.5 kg/m³, low-carbon agricultural competitiveness at approximately 0.1003 kg/yuan, and spatial differences in resource allocation are evident. Linze gains the most water resources (16.47 × 10⁸ m³) due to geographical advantages, while Gaotai obtains the least (6.51 × 10⁸ m³). In terms of planting structure, vegetables dominate the sown area owing to low carbon emissions and high water use efficiency, while wheat planting is relatively limited by climate adaptability and market demand. Carbon sink analysis confirms vegetables as the primary carbon sequestration contributor in Ganzhou and Linze, offering a practical pathway for agricultural carbon reduction. These findings provide tailored theoretical and practical support for balancing food security, efficient resource utilization, low-carbon development, and ecological protection in arid and semi-arid regions, facilitating regional carbon neutrality and sustainable agricultural development. Full article

Background: Phytolith-occluded carbon (PhytOC) is highly stable and constitutes an important long-term carbon pool in agroecosystems, particularly in nutrient-poor, sandy soils. Silicon (Si) uptake by plants is strongly associated with phytolith formation, with Si accounting for up to 90% of phytolith composition. However, the role of Si fertilization in enhancing PhytOC sequestration under field conditions remains insufficiently quantified. Integrated fertilization strategies supporting sustainable development in climate-resilient agriculture can enhance biological carbon sequestration by increasing phytolith formation and phytolith-occluded carbon accumulation, thereby improving the carbon sink potential of cereal-based agroecosystems. Methods: A field experiment was conducted to assess phytolith and PhytOC accumulation in barley biomass under different fertilization regimes, including foliar silicon application using the liquid immune stimulant Optysil and compost fertilization. Phytolith content was determined separately for grain and straw, and PhytOC stocks were converted into CO₂ equivalents to estimate annual sequestration potential. Results: Barley produced substantial amounts of phytoliths, with consistently higher concentrations in straw than in grain. Phytolith content ranged from 18.46 to 21.28 mg g⁻¹ DM in grain and from 27.89 to 38.97 mg g⁻¹ DM in straw. Depending on fertilization treatment, annual carbon sequestration through PhytOC ranged from 16.86 to 55.17 kg CO₂ equivalents ha⁻¹. Foliar silicon application increased PhytOC accumulation in barley biomass by up to threefold compared with treatments without Si. Conclusions: The results demonstrate that optimizing silicon fertilization can substantially enhance carbon sequestration in cropping systems via phytolith formation and PhytOC stabilization. Given the dominant role of cereals in crop rotations and their high phytolith-producing capacity as monocotyledonous plants, Si-mediated PhytOC sequestration represents a promising pathway for strengthening soil carbon storage and contributing to climate change mitigation. Full article

Large-scale mapping of forest canopy height (FCH) is crucial for accurately understanding ecosystem succession and forest carbon sinks. Recently, three high-resolution FCH products have been released, including global forest canopy height (GFCH), NNGI_FCH_China (NNGI_FCH), and ETH_GlobalCanopyHeight (ETH_GCH). This study provides a detailed assessment of these FCH products across China from forest area, spatial consistency, and overall accuracy, with additional analyses of forest classification errors and evaluation under a unified forest mask. The assessment is conducted using forest inventory data, the China land cover dataset, and field measurement data. The results show that NNGI_FCH had the smallest relative error of 13.4% and achieved better estimates of forest area in all regions but the north and northeast regions. GFCH had the highest spatial consistency of 70.8% nationwide, followed by NNGI_FCH (69.7%), which performed slightly better than GFCH in the east and northwest regions. ETH_GCH exhibited the lowest spatial consistency of 35.6% and remained below 50% across all regions except the northeast and south regions. ETH_GCH demonstrated the highest overall accuracy across the country, with an R² and RMSE of 0.56 and 4.14 m, followed by NNGI_FCH (R² = 0.49, RMSE = 3.38 m) and GFCH (R² = 0.48, RMSE = 3.38 m). Validation results of ETH_GCH were relatively stable in different regions of China, while those of NNGI_FCH varied more but still outperformed GFCH. This study offers valuable insights by evaluating large-scale FCH products, which will be a key basis for in-depth studies on the utilization and improvement of future FCH mapping. Full article

To address the low efficiency of tetracycline (TC) ozonation caused by low ozone solubility, short aqueous half-life, and mass-transfer limitations, an ozone micro-nano bubble (O₃-MNBs) oxidation system was designed and systematically compared with conventional ozone sparging (Conv-O₃). Thus, this study assessed the bubble size distribution, zeta potential, ozone dissolution and decay behaviors in water, ·OH concentration, and TC oxidation products, elucidating the degradation pathways and underlying mechanisms enabled by O₃-MNBs. Relative to Conv-O₃, O₃-MNBs increased the steady-state dissolved ozone concentration by 2.57–4.33 times, reduced the ozone decay rate constant by 41.3%, and enhanced ·OH generation by 2.3 times. TC degradation in the O₃-MNB system exhibited a distinct two-stage kinetic behavior, following second-order kinetics in the initial period (0–30 s) and first-order kinetics thereafter (30–120 s). Accordingly, the TC removal efficiency of O₃-MNBs reached 96.25% within 120 s, which was 81.25% higher than that of Conv-O₃. Notably, TC removal under Conv-O₃ obeyed first-order kinetics throughout, with an apparent rate constant only 7.14% of that obtained with O₃-MNBs. These improvements were attributed to the sustained and efficient supply of oxidants, high dissolved ozone and ·OH radicals, promoting the conversion of TC intermediates toward low m/z small-molecule end products, with greater ring opening and skeletal fragmentation. Our findings suggest that the enhanced biodegradability results in a markedly reduced burden and environmental risk for subsequent biological or advanced treatment processes. Therefore, this study highlights the potential of O₃-MNBs to enhance ozone utilization and oxidation intensity, providing mechanistic insights and technical support for rapid pretreatment of antibiotic-containing wastewater. Full article

Under global climate change, analyzing carbon storage dynamics and their drivers is essential for understanding regional carbon sink capacity. Human activities and land-use change have substantially affected regional carbon storage. However, in China, most existing studies emphasize specific driving pathways, and integrated analyses of the combined effects of climate, natural, human, and landscape factors remain limited. This study aims at clarifying the integrated mechanisms by which multiple driving factors influence regional carbon storage. The InVEST model was used to analyze the carbon storage spatiotemporal changes. OPGD was then applied to evaluate the explanatory power of driving factors and their interactions, quantifying their contributions to carbon storage spatial patterns. Based on PLS-SEM, the direct and indirect effects of LULC, climate, natural, human, and landscape factors were quantified to elucidate the driving pathways of carbon storage. This study focuses on Shaanxi Province, which is a key ecological restoration region in the core area of the Loess Plateau. The main results are as follows: (1) From 2000 to 2020, carbon storage in Shaanxi Province showed a continuous increasing trend, rising from 2.97 × 10¹⁰ Mg C to 3.03 × 10¹⁰ Mg C. (2) LULC was identified as the most important direct and predominantly negative driving factor of carbon storage. (3) Natural factors had a strong positive influence on carbon storage, among which slope and NDVI exhibited the highest explanatory power; in contrast, climate factors showed weaker but still positive effects. (4) Human activities affected carbon storage through both direct and indirect pathways associated with LULC, with positive effects driven by landscape factors and negative effects driven by natural factors, while climate factors exhibited mixed but weak effects. Overall, carbon storage dynamics in Shaanxi Province reflect a hierarchical and path-dependent process shaped by the combined effects of natural constraints, human activities, and policy guidance through LULC pathways, providing important evidence for systematically understanding the driving structure and pathways of regional carbon storage. These findings highlight the importance of aligning land-use policies with regional biophysical constraints to enhance carbon sequestration efficiency. Full article

Increasing the carbon sink function of forests is critical for achieving carbon (C) neutrality in the context of global climate change. Past studies have focused on the estimation of forest biomass or C storage, while those on forest C sink potential remain limited. In particular, there remain few systematic investigations to define the forest C sink, to characterize the synergistic influencing factors, and to develop related quantitative analysis methods. The development of scientific C enhancement strategies requires the construction of C density-age models integrating multiple stand factors. These models allow accurate quantification of the gap (∆C) between actual and maximum C sequestration capacity. This study used permanent sample plot data to develop and validate a novel multi-model assessment approach for quantifying the C sink potential of Larix olgensis plantations in Heilongjiang Province, China, and to translate the results into precise management tools. An Average-Level Model (ALM) was established to define baseline C sequestration. Three innovative potential assessment models were then proposed: (1) the Empirical Upper Boundary Model (PLM1); (2) the Dummy Variable Model (PLM2); and (3) the Quantile Regression Model (PLM3). These models define the maximum C sequestration capacity from distinct perspectives. PLM1 (R² = 0.7910) characterized the theoretical upper limit of C sink potential (79.86 Mg·ha⁻¹), making it suitable for macro-strategic goal setting, though it is somewhat dependent on extreme data points. PLM2 (R² = 0.7943) achieved the best fit, and when combined with measurable stand conditions (site class index [SCI] > 16 m, stand density index [SDI] > 800 trees·ha⁻¹), it provides clear guidance for management practices. Although PLM3 showed a lower goodness-of-fit (R² = 0.1056), it provided reasonable parameter estimates and robust predictions, offering a reliable upper-bound reference for C sink project planning and risk control. At a stand age of 60 years (yr), the C sink enhancement potentials (“∆” C) corresponding to the three models were 15.73, 14.48, and 13.26 Mg·ha⁻¹, representing increases of 24.53%, 22.58%, and 20.68%, respectively, over the average level (64.13 Mg·ha⁻¹); the peak C sequestration rates of the models were 104.3%, 82.7%, and 60.5% higher than that of the ALM, with peak times occurring earlier at 9, 7, and 11 yr, respectively, underscoring the importance of the early management. The multi-model assessment approach developed here facilitates “precision carbon enhancement” by quantifying C sink potential across its theoretical, achievable, and robust upper-bound dimensions. This quantification provides both mechanistic insights into C sequestration processes and a critical link between theoretical understanding and practical forest management. This work holds significant value for advancing forestry C sinks in service of national strategies. Full article

Forest biomass reflects the carbon storage capacity of forest ecosystems. Although remote sensing-based biomass estimation techniques have become increasingly mature, the issue of signal saturation in optical remote sensing still requires further investigation. This study was conducted in the Picea schrenkiana forest of the Ili River Valley in the western Tianshan Mountains. By integrating multimodal data from ICESat-2 LiDAR and GF-6 optical imagery, we developed machine learning and deep learning models to achieve high-precision biomass estimation. Based on forest management inventory data, we extracted spectral and textural features from GF-6, along with canopy structure attributes derived from the four acquisition modes (day/night, strong/weak beams) of ICESat-2. After correlation-based feature selection, LightGBM, CatBoost, and TabNet models were trained and compared. The results showed that models integrating multi-source data significantly outperformed those based on a single data source. The TabNet model not only achieved high estimation accuracy but also provided clear feature importance rankings, with ICESat-2-derived canopy height percentiles and GF-6 red-edge vegetation indices contributing most significantly to the biomass estimation of Picea schrenkiana. These findings demonstrate the feasibility of synergistically utilizing domestic high-resolution satellites and multi-mode spaceborne LiDAR for forest biomass estimation in arid regions, providing an effective technical reference for accurate carbon sink monitoring of specific tree species in forest areas. Full article

With the wave of new campus construction gradually receding, the focus of green campus planning and design is shifting toward the low-carbon retrofitting of the existing built environment. University campuses often face challenges such as dispersed land use, inadequate spatial planning, disorganized road layouts, suboptimal landscape design, and low energy efficiency. Grounded in a review of current research on campus carbon emissions, this study integrates green technology indicators with planning and design approaches to establish a multi-scale, context-adaptive planning framework for carbon control, spanning five dimensions: intensive land use, spatial layout, transportation systems, landscape development, and facility integration. Employing a combined approach of bibliometric analysis and case studies, this research examines and compares typical university campuses both domestically and internationally to validate the effectiveness of the synergistic “technology-system-behavior” pathway in mitigating high-carbon lock-in. Through a systematic comparative analysis of representative low-carbon campuses, the synthesized results indicate that under optimal operational conditions, the clustered reorganization of functional zones demonstrates the potential to reduce transportation carbon emissions by approximately 25%; comprehensive retrofitting of building envelopes can decrease building energy consumption intensity by an estimated 30%; a multimodal coordinated transport system can increase the share of non-motorized travel to around 65%; establishing high carbon-sequestration plant communities can enhance carbon sink capacity by up to 30%; and smart facility integration can reduce overall campus carbon emissions by a projected range of 25–40%. It should be noted that these quantitative outcomes represent high-probability potential ranges, with actual performance subject to behavioral and operational fluctuations. This study provides theoretical support and practical pathways for achieving “near-zero carbon campuses” and underscores the important demonstrative role that higher education institutions can play in addressing climate change. Full article

Given less than five years remaining until the target year for the first phase of China’s dual carbon goals, this paper studies carbon allowance allocation with an in-depth study of Jiangsu Province due to its significant role in driving the Yangtze River Delta’s pioneering achievement of the dual carbon goals. This study considered 2017 (the intermediate target year) as the base year and incorporated socio-economic data such as population, GDP, and the urbanization rate. Then, methods including the entropy weight method, gravity model and social network analysis were applied to classify Jiangsu’s 95 counties. From a regional coordination perspective, carbon governance clusters were constructed with the Shapley value, based on which spatial heterogeneity patterns were analyzed, and a carbon quota allocation was proposed. The findings reveal that: (1) The dominant factors influencing cross-scale carbon reduction capacity at the county level are natural carbon sink capacity (indicator weight: 0.180) and urbanization rate (indicator weight: 0.145). (2) The correlation between carbon reduction factors among different districts and counties exhibits an uneven spatial pattern. And the spatial configuration exhibits a multi-tiered, network-like distribution. (3) Through conducting spatial analysis and spatial grouping, Jiangsu could be divided into 14 county-level carbon governance alliances, with the number of member counties ranging from 4 to 10 within each alliance. (4) The allocation of carbon quotas in Jiangsu exhibits a distinct descending gradient from the southern to the northern regions, which is coupled with the regional economic geography. This is exemplified by the highest quota in Jiangyin (496.46 Mt) in the south and the lowest in Lianyun (34.90 Mt) in the north. It is concluded that two carbon emission reduction pathways should be established as a priority: (a) Tongshan-Gulou (Xuzhou)-Yunlong-Quanshan-Jiawang and (b) Tianning-Jiangyin-Zhangjiagang-Changshu-Taicang-Kunshan. Full article