To explore the relationship between carbon storage and environmental factors in Populus plantations of different stand ages, and to reveal the carbon sequestration mechanisms of Populus plantations across different age classes, this study employed field surveys and laboratory analysis to investigate the distribution patterns and influencing factors of carbon storage in trunk-branch-leaf-root-soil systems of Populus plantations with different stand ages (10 y, 30 y, 40 y, 50 y) in the Luxi Yellow River floodplain. The results showed that the carbon storage in trunks, branches, and roots increased gradually with increasing stand age, while the carbon storage in leaves reached a maximum of 7.52 t·hm 2 at 40 y, followed by a gradual decrease. Soil carbon storage increased consistently with stand age. Overall, the total carbon storage of Populus plantations across different age classes exhibited a linear increasing trend with advancing standage. Correlation analysis, principal component analysis, and structural equation modeling indicated that diameter at breast height (DBH), tree height (H), tree age (AGE), and stand density (SD) were the key factors affecting carbon storage in Populus plantations. The findings of this study can provide theoretical basis and technical support for enhancing carbon sequestration and sink capacity, as well as ecological restoration of Populus plantations in the Luxi Yellow River floodplain.

Gross primary production (GPP) is a key indicator to evaluating ecosystem carbon sinks. Southwest China is characterised by diverse ecosystems and abundant forest resources and represents one of the most important carbon reservoirs in China. Therefore, a quantitative assessment of the uncertainty of existing GPP products and their influencing factors is important. This study investigates GPP uncertainties and its influencing factors based on the three-cornered hat (TCH) and XGBoost and SHAP methods. Thirteen products were examined, including six products from the light use efficiency (LUE) model, two products from the process-based (Process) model, three products from the machine learning (ML) model and two products from satellite-based direct proxies (Proxies). The results reveal the following: (1) All products show similar spatial patterns, with Process products fluctuating notably in 2010, 2011, and 2014, while others remain stable. (2) Relative uncertainty is lowest annually, increasing monthly and daily; ML products exhibit greater stability. Among them, CEDAR has the least uncertainty and strongest agreement with flux observations (r = 0.82), whereas EC-LUE shows the highest uncertainty. (3) Vegetation index, elevation and radiation are more influential than other factors. These findings aid GPP product selection and uncertainty assessment in complex terrains with sparse ground data.

Under global climate warming, the impact of extreme high temperatures on carbon exchange in paddy rice ecosystems remains unclear, yet they exert a profound influence on the carbon cycle in agricultural ecosystems. The characteristics of carbon dioxide (CO2) fluxes and their response to temperature were explored at two sites (Jurong and Jiangdu) across the lower reaches of the Yangtze River in China using open-path eddy covariance observations in 2021–2024. During the rice-growing season, considerable inter-annual spatial variability in high temperature was observed, with a higher frequency and larger intensity in Jurong relative to Jiangdu and more severe heat stress in 2022 relative to 2023. The jointing–booting stage was identified as the hotspot exposed to the highest frequency and longest duration of high temperature across multiple years. There was obvious variation in net ecosystem CO2 exchange (NEE) throughout the rice-growing season, with the cumulative values being −462.2 ± 55.2 gC·m−2 in 2021–2023 at Jurong and −362.4 ± 43.0 gC·m−2 in 2022–2024 at Jiangdu. The period from jointing to flowering was identified as the most sensitive time slice for NEE variation, with a daily average value of −6.3 ± 0.2 gC·m−2·d−1 in jointing–booting and −5.2 ± 2.2 gC·m−2·d−1 in booting–flowering at Jurong, as well as −4.0 ± 0.7 gC·m−2·d−1 in jointing–booting and −5.7 ± 1.1 gC·m−2·d−1 in booting–flowering at Jiangdu. The respective correlation coefficients were −0.59 and −0.37 between periodical NEE and mean air temperature at Jurong and Jiangdu, meaning that NEE showed a decreasing trend as temperature increased, owing to the simultaneous but heterogeneous changes in gross ecosystem CO2 exchange and ecosystem respiration. When the temperature was lower than 38 °C, the corresponding correlation coefficient reached −0.85 at Jurong and −0.52 at Jiangdu, suggesting that extreme high temperature prevented a decline in NEE. The response of NEE to temperature highlighted that NEE ceased to decrease when temperature surpassed 38 °C, implying that a critical threshold existed for limiting the carbon sink under extreme high temperature. These findings could provide insight for understanding carbon cycling in agricultural systems under an extreme climate.

Dual effects of tidal stress on carbon turnover by biochar incorporation in estuarine wetlands: Obscuring the promotion of plant CO2 fixation while magnifying carbon stabilization of sediments.

Driving mechanisms of vegetation carbon sink distribution based on explainable machine learning and evaluation of carbon sequestration in open-pit mines.

Corrigendum to “Carbon sink potential and contributions to dual carbon goals of the grain for green program in the arid regions of Northwest China” [Resources, Conservation & Recycling 220 (2025) 108355/j.resconrec.2025.108355

A coupled Hydro-Biogeochemical framework for evaluating lateral loss of soil organic carbon under land-use change at the basin scale.

Enhanced carbon-nitrogen coupling and reduced organic carbon stability in the Liaohe River estuary wetland, China, induced by human activity.

Factors affecting spatial variability in organic carbon storage and sources in an intertidal Halophila beccarii seagrass meadow of the Yifengxi Estuary, Southern China.

Meiofaunal diversity across a sharp gradient of fjord oxygenation: Insights from metabarcoding and morphology approaches in foraminifera.

Revealing the unrecognized climate burden of aquaculture systems: A global insight into greenhouse gas emissions and mitigation strategies.

As global climate change intensifies, carbon emission trading systems have become vital tools to reduce greenhouse gas emissions through market-based mechanisms. Carbon sink trading incentivizes emission reduction and fosters global cooperation. Recently, artificial intelligence (AI) has been widely applied in these systems to enhance efficiency, transparency, and intelligence in data analysis, market forecasting, and trading optimization. However, AI integration also introduces security risks such as data tampering, algorithmic manipulation, and system intrusions, threatening market stability, and fairness. To address these challenges, this paper adopts the attack tree method to systematically assess security risks in AI-driven carbon sink trading. The attack tree provides a structured framework to visualize potential attack paths and identify threat sources. By combining attack tree modeling with risk assessment theory, this study identifies key risk scenarios-data theft, system manipulation, market manipulation, and service disruption-and quantitatively evaluates their likelihood, potential impact, and overall threat level. Based on the analysis, corresponding protection strategies are proposed for each attack path, offering practical security measures for regulators, AI developers, and trading platform operators. The proposed framework enhances risk identification and management for AI systems in carbon markets, providing a scientific basis for targeted mitigation. Ultimately, this contributes to improving the security and stability of carbon trading systems and supports the advancement of global climate governance.

A theoretical framework for the methodology of carbon sink assessment in China: A literature review

Engineering concrete as a carbon sink for sustainable infrastructure

Peatlands are essential long-term carbon sinks, yet continued peat extraction for horticulture contributes to greenhouse gas emissions and ecosystem degradation. Here, we introduce artificial peat, a peat-formation-inspired material produced by selectively mimicking natural humification pathways under controlled alkaline conditions. Unlike conventional biomass conversion processes that aim for complete degradation, carbonization, or simple constituent replacement, this approach promotes controlled partial transformation of lignocellulosic biomass into artificial humic substances while preserving a stabilized fibrous framework. Batch and continuous processing routes operated under mild conditions (≤120 °C) using widely available feedstocks, including paludiculture biomass, wood residues, leaves, and agricultural by-products. Artificial humic acid yields ranged from 6.9 to 42.3 wt% in batch systems. Across both processing modes, carbohydrate fractions decreased and lignin underwent partial depolymerization followed by condensation into humified macromolecular structures, accompanied by a marked reduction of readily oxidizable organic matter. Multimodal analyses (elemental composition, Van Krevelen evolution, FTIR, microscopy/EDX, and oxidative thermogravimetry) revealed a transition toward oxygen-rich, condensed architectures with enhanced oxidative stability relative to raw biomass. The applied thermal–alkaline conditions are expected to promote hygienization and seed inactivation, while the conversion of labile biomass components into humic substances suggests improved chemical and potential biological stability. Produced within minutes rather than millennia, artificial peat combines humic functionality with preserved structural integrity, establishing a scalable and resource-efficient alternative to natural peat for sustainable growing media and carbon stabilization applications.

Agricultural land-use conversion in high-altitude cold-arid inland river basins profoundly affects soil ecosystems. This study investigates the middle and lower reaches of the Bayin River Basin (Qaidam Basin, China) at approximately 3000 m elevation. We examined a continuous, reversible gradient of land-use intensity ranging from intensively managed cultivated land and orchards to marginal farmland abandoned owing to salinisation and low fertility. Using a multi-model fusion framework combining geostatistics, random forest regression and partial least-squares path modelling, we quantified the spatial patterns of soil properties and the drivers of soil organic carbon (SOC). Compared with marginal farmland, both cultivated land and orchards showed markedly higher SOC content (10.7–41.1% increase), elevated total nitrogen (TN) and clay content, and reduced electrical conductivity and sand fraction. These changes demonstrate that abandonment of marginal farmland impairs SOC accumulation while accelerating soil degradation and salinisation. SOC and TN exhibited strong spatial autocorrelation over distances exceeding 27 km, largely controlled by broad-scale factors such as topography and climate. The Random Forest and Partial Least Squares Path Modeling consistently reveal a close synergistic variation between Total Nitrogen (TN) and Soil Organic Carbon (SOC). TN exerts a direct positive driving effect on SOC, while land use intensity positively affects SOC through an indirect pathway: “sand content drives land use → enhances vegetation cover → increases TN.” Reverse modeling has validated a similar driving effect of SOC on TN. This study offers practical pathways for the sustainable management of marginal farmland and the enhancement of carbon sinks, addressing a common issue in China and other developing countries.

From lime stabilization to CO2 sequestration: spontaneous recarbonation in municipal sewage sludge.

Abstract Hydroclimatic extremes are critical regulators of terrestrial carbon sink dynamics, yet their representation in terrestrial biosphere models remains highly uncertain. Here, we assessed uncertainties in TRENDY v12 model simulations of carbon sink responses to hydroclimatic extremes during 1980–2022 by systematically comparing model outputs across regions, event types, and biomes. Site-level evaluations reveal that the multi-model ensemble mean correctly captures the sign of net biome productivity (NBP) anomalies at approximately 60% of stations; however, while the multi-model ensemble mean generally replicates NBP variations during dry events, its performance degrades during wet events. Spatially, most regions act as anomalous carbon sinks during wet extremes, a pattern that largely reverses during dry events. Despite these general trends, substantial inter-model heterogeneity persists. Inter-model uncertainties are more pronounced under dry events between 30°S and 30°N, while other latitudes exhibit comparable or even greater spreads under wet events. Specifically, inter-model spread is more sensitive to wet anomalies in arid and semi-arid regions, but to drought-induced stress in semi-humid and humid regions. Across biomes, uncertainties are greater for grasslands, savannas, and shrublands during wet events, shifting to forests and croplands during dry events. Finally, we demonstrate that the divergent NBP responses primarily originate from uncertainties in simulating gross primary production (GPP). Our findings highlight the persistent challenges TRENDY models face in capturing ecosystem responses to hydroclimatic extremes, underscoring the urgent need to improve simulation fidelity in a rapidly changing climate.

Rising atmospheric carbon dioxide (CO₂) levels contribute significantly to global warming, with agricultural soils acting as both major sources and potential sinks for greenhouse gases (GHG). This study investigated the impact of food waste-derived compost amended with varying concentrations of coconut husk biochar on CO₂ emissions from highly weathered tropical soil. Biochar was produced via pyrolysis at 550 – 620°C and co-composted with domestic organic waste (cassava, plantain, and pineapple peels, and poultry manure) at ratios of 0%, 2%, 5%, and 10%. Six treatments, including an absolute control and inorganic NPK fertilizer, were applied to soil containers and incubated for 58 days. CO₂ emissions were quantified using sodium hydroxide (NaOH) trapping followed by hydrochloric acid (HCl) titration at intervals ranging from day 0 to day 58. Results indicated a significant reduction in emissions across all treatments after the initial incubation day. The Compost + 10% Biochar treatment consistently demonstrated the lowest CO₂ emissions (0.88 mg CO₂/day/kg on day 0), significantly outperforming the control (1.21 mg CO₂/day/kg), inorganic fertilizer, and lower biochar concentrations. By day 58, emissions across all treatments approached zero. The study concludes that amending food waste compost with 10% coconut husk biochar effectively suppresses soil CO₂ fluxes, acting as a carbon sink while enhancing soil fertility. This integrated organic management strategy offers a sustainable, climate-smart solution for reducing greenhouse gas emissions and promoting soil health in tropical agricultural systems.

China experienced substantial millennial-scale climate and anthropogenic land use changes, yet their combined impacts on land carbon dynamics remain largely unexamined. Here, we quantify spatiotemporal changes in terrestrial organic carbon over 851-2022 using a land surface model driven by reconstructed climate and land cover forcings. Simulated results show China's pre-industrial millennial land carbon dynamics aligned with global carbon stock and atmospheric CO2 fluctuations, such as the ~284 ppm peak in the 12th century linked to land use during the Medieval Climate Anomaly-warmed Song Dynasty. Notably, China's total land carbon emissions (13 ± 0.5 PgC) accounted for 22% of global land carbon emissions during 1700-1900, with Northeast and Southwest China experiencing the largest historical land carbon losses from intensive deforestation. Nevertheless, the 17.0 ± 1.7 PgC emissions during 851-1980 were fully offset by rapid carbon sinks over 1980-2022, driven by CO2 fertilization and large-scale afforestation. These findings provide insights into China's historical landcarbon dynamics, their underlying drivers, and global implications.