Terrestrial carbon sinks in China and around the world and their contribution to carbon neutrality

Spatio-temporal variations in carbon sources, sinks and footprints of cropland ecosystems in the Middle and Lower Yangtze River Plain of China, 2013-2022

Cropland ecosystems, which are most affected by human activities, are dual carriers of carbon sources and sinks. It has significant implications for the achievement of the "two-carbon" objective. The Middle and Lower Yangtze River Plain (MLYRP) is the principal grain-producing area of China, which is a great agricultural country. The development of green agriculture in this plain is of vital importance. Nevertheless, there is a lack of attention to the dynamics of the carbon footprints of cropland. Hence, this study was conducted with the help of carbon emission coefficient method. It investigated the spatio-temporal variations of carbon sources, sinks and carbon footprints of cropland ecosystems in this plain from 2013 to 2022. The findings suggest that (1) Carbon uptake was fluctuating up during the study period. Carbon uptake was higher in paddy and wheat. (2) Carbon emissions were declining year by year. Fertilizer and irrigated agriculture produced more carbon emissions. The top four for both indicators were Anhui, Jiangsu, Hubei and Hunan provinces. (3) The carbon footprint declined in fluctuations. This indicator ranked the top four in Hubei, Anhui, Zhejiang and Jiangsu provinces. The spatial distribution pattern of the above three indicators was more in the north and less in the south. (4) Cropland ecosystems exhibited carbon sinks. There were relatively large carbon eco-surplus and high carbon eco-efficiency. Nevertheless, the carbon ecological surplus was decreasing in fluctuation. Consequently, MLYRP should keep popularizing new technologies such as green manure crops and precision agriculture.

Rise in wetland carbon uptake linked to increased potential evapotranspiration

Precisely assessing wetland net ecosystem productivity (NEP) is important for accurately evaluating global carbon budgets. However, constrained by the quality of observational data and insufficient understanding of driving mechanisms, assessments of China's wetland NEP still have considerable uncertainties. Therefore, this study assessed continuous observations from 30 eddy covariance flux towers across various wetland types in China and applied the random forest (RF) model to simulate the spatiotemporal dynamics of China's wetland NEP. The results showed that from 1982 to 2020, China's wetlands represented a net C-CO2 sink overall, with an average NEP of 21.61 ± 0.04 mg C m-2 h-1 and annual net C-CO2 absorption of 56.23 Tg C. Riverine and coastal wetlands had the highest NEP, while freshwater marshes had the lowest. From 1982 to 2020, the wetland NEP in China exhibited a significant increasing trend. Further analysis indicated that potential evapotranspiration (PET) is the main driving factor behind the significant increase in NEP in China's wetlands, with a clear threshold effect: NEP rises with PET up to a certain point (e.g., 160 mm), after which it declines. This study accurately quantified the spatiotemporal dynamics of China's wetland NEP and revealed the critical impact of PET on NEP, thus providing a new perspective for performing wetland carbon cycle research and formulating climate change mitigation strategies.

Cereal-legume intercropping stimulates straw decomposition and promotes soil organic carbon stability

Increasing carbon (C) sequestration and stability in agricultural soils is a key strategy to mitigate climate change towards C neutrality. Crop diversification is an initiative to increase C sequestration in fields, but it is unclear how legume-based crop diversification impacts the functional components of soil organic carbon (SOC) in dryland, including the formation and transformation of particulate organic carbon (POC) and mineral-associated organic carbon (MAOC). We investigated the decomposition of straw residues, the fate of photosynthesized C, as well as the formation of MAOC and POC fractions using an in situ13C labeling technique in the soybean-wheat intercropping, soybean-maize intercropping and their respective monocropping systems, with and without cover crops. After 4-year treatments, the total SOC content in bulk soil remained unchanged, while MAOC content increased significantly by 5.6% with intercropping. Moreover, the in situ13C labeling results confirmed that more photosynthesized C was transferred to MAOC, and less was retained in the POC fraction. Intercropping significantly increased total soil N and mineral N content by 15.3% and 13.4%, respectively, and decreased soil and microbial C/N ratio by 11.3% and 17.4%, respectively. This outcome, therefore, relieved microbial N limitation and accelerated straw residue decomposition. Accordingly, the potential of MAOC formation was strengthened for better SOC persistence. Our study suggests that legume-based crop diversification can effectively enrich N and support POC transformation to MAOC, accordingly contributing to the persistent SOC pool and thus potentially achieving C neutrality under climate change in dryland agroecosystems.

Managing Ammonia for Multiple Benefits Based on Verified High-Resolution Emission Inventory in China

Atmospheric ammonia (NH3) has multiple impacts on the environment, climate change, and human health. China is the largest emitter of NH3 globally, with the dynamic inventory of NH3 emissions remaining uncertain. Here, we use the second national agricultural pollution source censuses, integrated satellite data, 15N isotope source apportionment, and multiple models to better understand those key features of NH3 emissions and its environmental impacts in China. Our results show that the total NH3 emissions were estimated to be 11.2 ± 1.1 million tonnes in 2020, with three emission peaks in April, June, and October, primarily driven by agricultural sources, which contributed 74% of the total emissions. Furthermore, employing a series of quantitative analyses, we estimated the contribution of NH3 emissions to ecosystem impacts. The NH3 emissions have contributed approximately 22% to secondary PM2.5 formation and a 16.6% increase in nitrogen loading of surface waters, while ammonium deposition led to a decrease in soil pH by 0.0032 units and an increase in the terrestrial carbon sink by 44.6 million tonnes in 2020. Reducing agricultural NH3 emissions in China would contribute to the mitigation of air and water pollution challenges, saving damage costs estimated at around 22 billion US dollars due to avoided human and ecosystem health impacts.

Assessing the ecological effects of the World's Largest Forestry Eco-engineering: Three-North Protective Forest Program within the initially scheduled range from 1978 to 2022

China's Three-North Protective Forest Program (TNP) is the world's most ambitious afforestation project (ongoing from 1978 to 2050), which aims to increase forest coverage through afforestation and reforestation, protect agriculture, reduce soil erosion, and control desertification. Although TNP has been ongoing for 45 years, its rationales and effects remain uncertain. Here, we conducted a range-wide assessment of TNP by analyzing data from >10,000 scenes of satellite images and >50,000 field survey plots. The TNP range and definitions of shelterbelts, arboreal forests, and shrublands were changed during the study period, but we used the initial TNP range (4.07 million km2) and the definitions in 1978 for keeping the consistency, comparability, and comprehensiveness. The TNP increased forest coverage from 5.05% in 1978 to 9.69% in 2022, with arboreal forests, shrublands, and shelterbelts increasing by 42.5%, 184.4%, and 53.6%, respectively. However, only 40.1% of the 471,113 km2 afforested area was established between 1978 and 2022. The well-established shelterbelts improved crop yield by 4.3%-9.5%, but only 10.2% of all the farmlands in TNP regions (TNR) were protected. The total area of soil erosion due to hydraulic forces was reduced by 447,363 km2, with 61% of this reduction attributed to TNP. TNP contributed to the reduction of desertification by 15%, largely due to the low rate of afforestation success and the largely decreased grasslands. The total carbon sequestration from TNP was 1.96 Pg C. Moreover, water storage in TNR showed a decreasing trend, but the contribution rate of TNP was only 7.8%. Our results illustrate that forestry eco-engineering projects are feasible in the management and restoration of arid and semi-arid degraded lands, but attention must be paid to fully considering the ecological carrying capacity of water resources, matching the species to sites, strengthening the post-afforestation management, as well as keeping the balances between composite ecosystems.

Estimating forest aboveground carbon sink based on landsat time series and its response to climate change

Accurately estimating forest carbon sink and exploring their climate-driven mechanisms are critical to achieving carbon neutrality and sustainable development. Fewer studies have used machine learning-based dynamic models to estimate forest carbon sink. The climate-driven mechanisms in Shangri-La have yet to be explored. In this study, a genetic algorithm (GA) was used to optimize the parameters of random forest (RF) to establish dynamic models to estimate the carbon sink intensity (CSI) of Pinus densata in Shangri-La and analyze the combined effects of multi-climatic factors on CSI. We found that (1) GA can effectively improve the estimation accuracy of RF, the R2 can be improved by up to 34.8%, and the optimal GA-RF model R2 is 0.83. (2) The CSI of Pinus densata in Shangri-La was 0.45-0.72 t C·hm- 2 from 1987 to 2017. (3) Precipitation has the most significant effect on CSI. The combined weak drive of precipitation, temperature, and surface solar radiation on CSI was the most dominant drive for Pinus densata CSI. These results indicate that dynamic models can be used for large-scale long-term estimation of carbon sink in highland forest, providing a feasible method. Clarifying the driving mechanism will provide a scientific basis for forest resource management.

Interannual variations in grassland carbon fluxes and attribution of influencing factors in Qilian Mountains, China

Clarifying the driving factors of grassland carbon sequestration is essential for understanding its role in the regional carbon balance. However, there is a lack of studies on the upscaling of carbon flux in the Qilian Mountains (QLMs) and the driving factors of its interannual variation (IAV). Based on long-term eddy covariance observations in the QLMs, this study estimated the net ecosystem CO2 exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (ER) of the QLMs grassland using four machine learning methods (random forest regression (RF), extremely randomized tree regression (ETR), support vector regression (SVR), and extreme gradient boosting (XGBoost)) to obtain the optimal estimation model. Subsequently, the spatiotemporal variations of GPP, ER, and NEE in the QLMs grasslands were conducted in a comprehensive analysis. The factors influencing the IAV of carbon flux, the contribution of monthly NEE to NEE IAV, and the contribution of different grassland types of NEE to NEE IAV were explored. Our findings revealed that the accuracy and resolution of the grassland carbon flux estimated by the RF method in this study are higher than those of global products. The grassland exhibited a weak carbon sink from 2000 to 2022, with an average NEE of -26.46 ± 6.80 g Cm-2 yr-1, and it acted as a carbon sink from May to September. The spatial distribution pattern of carbon sequestration was "low in the northwest and high in the southeast". LAI was the key driving factors of IAV for GPP and ER, while NEE IAV was primarily influenced by precipitation and temperature. Climate and vegetation factors primarily regulated NEE IAV by affecting the GPP and ER of plants, and NEE IAV was primarily driven by GPP. Furthermore, NEE in alpine meadows and alpine steppes dominated the NEE IAV of the entire grassland, and summer NEE contributed the most to the NEE IAV. The results will help us to better understand the carbon cycling mechanism in grassland ecosystems and provide new data support and a theoretical foundation for regional carbon cycling research.

Advanced precipitation peak offsets middle growing-season drought in impacting grassland C sink

Redistribution of precipitation across seasons is a widespread phenomenon affecting dryland ecosystems globally. However, the impacts of shifting seasonal precipitation patterns on carbon (C) cycling and sequestration in dryland ecosystems remain poorly understood. In this study, we conducted a 10-yr (2013-2022) field manipulative experiment that altered the timing of growing-season precipitation peaks in a semi-arid grassland. We found that the delayed precipitation peak suppressed plant growth and thus reduced gross ecosystem productivity, ecosystem respiration, and net ecosystem productivity due to middle growing-season water stress. Surprisingly, shifting more precipitation to the early growing season can advance plant development, increase the dominance of drought-tolerant forbs, and thus compensate for the negative impacts of middle growing-season water stress on ecosystem C cycling, leading to a neutral change in grassland C sink. Our findings indicate that greater precipitation and plant development in spring could act as a crucial mechanism, maintaining plant growth and stabilizing ecosystem C sink. This underscores the urgent need to incorporate precipitation seasonality into Earth system models, which is crucial for improving projections of terrestrial C cycling and sequestration under future climate change scenarios.

Mapping surface soil organic carbon density of cultivated land using machine learning in Zhengzhou

Research on soil organic carbon (SOC) is crucial for improving soil carbon sinks and achieving the "double-carbon" goal. This study introduces ten auxiliary variables based on the data from a 2021 land quality survey in Zhengzhou and a multi-objective regional geochemical survey. It uses geostatistical ordinary kriging (OK) interpolation, as well as classical machine learning (ML) models, including random forest (RF) and support vector machine (SVM), to map soil organic carbon density (SOCD) in the topsoil layer (0 - 20 cm) of cultivated land. It partitions the sampling data to assess the generalization capability of the machine learning models, with Zhongmu County designated as an independent test set (dataset2) and the remaining data as the training set (dataset1). The three models are trained using dataset1, and the trained machine learning models are directly applied to dataset2 to evaluate and compare their generalization performance. The distribution of SOCD and SOCS in soils of various types and textures is analyzed using the optimal interpolation method. The results indicated that: (1) The average SOC densities predicted by OK interpolation, RF, and SVM are 3.70, 3.74, and 3.63 kg/m2, with test set precisions (R2) of 0.34, 0.60, and 0.81, respectively. (2) ML achieves a significantly higher predictive precision than traditional OK interpolation. The RF model's precision is 0.21 higher than the SVM model and more precise in estimating carbon stock. (3) When applied to the dataset2, the RF model exhibited superior generalization capabilities (R2 = 0.52, MSE = 0.32) over the SVM model (R2 = 0.32, MSE = 0.45). (4) The spatial distribution of surface SOCD in the study area exhibits a decreasing gradient from west to east and from south to north. The total carbon stock in the study area is estimated at approximately 10.76 × 106t. (5) The integration of soil attribute variables, climatic variables, remote sensing data, and machine learning techniques holds significant promise for the high-precision and high-quality mapping of soil organic carbon density (SOCD) in agricultural soils.

Assessment of the potential for carbon sink enhancement in the overlapping ecological project areas of China

Ecological engineering can significantly improve ecosystem carbon sequestration. However, few studies have projected the carbon sink trends in regions where ecological engineering projects overlap and have not considered the different climate change conditions and land use scenarios. Using the ensemble empirical mode decomposition method and machine learning algorithms (enhanced boosted regression trees), the aims of this study to elucidate the stability of carbon sinks and their driving mechanisms in areas where ecological projects overlap and to predict the potential enhancement in carbon sinks under varying climate and human activity scenarios. The findings revealed that: (1) The carbon sinks clearly and steadily increased in regions where five ecological projects were implemented from 1982 to 2019. In contrast, the carbon sinks did not significantly increase in regions with two or three ecological projects. (2) As the number of ecological projects increased, the impact of human activities on the carbon sinks gradually decreased. In eastern China, rapid economic development and significant interference from human activities hindered the growth of carbon sinks. In contrast, in western China, the warming and humidification trend of the climate, large-scale afforestation, and other ecological projects have significantly improved carbon sinks. (3) The regions with five overlapping ecological projects exhibited the greatest enhancement and stability of carbon sinks under different scenarios. Compared with the SSP585 scenario, under the SSP126 scenario, the carbon sinks increased, and their stability was greater. Achieving carbon neutrality requires major ecological projects to account for the limitations imposed by climatic conditions. Instead of isolated projects or the implementation of single restoration measures, a comprehensive approach that uses the synergistic effects of combined ecological strategies is recommended.

Contributions of ecological restoration policies to China's land carbon balance

Unleashing the land sector's potential for climate mitigation requires purpose-driven changes in land management. However, contributions of past management changes to the current global and regional carbon cycles remain unclear. Here, we use vegetation modelling to reveal how a portfolio of ecological restoration policies has impacted China's terrestrial carbon balance through developing counterfactual 'no-policy' scenarios. Pursuing conventional policies and assuming no changes in climate or atmospheric carbon dioxide (CO2) since 1980 would have led China's land sector to be a carbon source of 0.11 Pg C yr-1 for 2001-2020, in stark contrast to a sink of 175.9 Tg C yr-1 in reality. About 72.7% of this difference can be attributed to land management changes, including afforestation and reforestation (49.0%), reduced wood extraction (21.8%), fire prevention and suppression (1.6%) and grassland grazing exclusion (0.3%). The remaining 27.3% come from changes in atmospheric CO2 (42.2%) and climate (-14.9%). Our results underscore the potential of active land management in achieving 'carbon-neutrality' in China.

Increased straw return promoted soil organic carbon accumulation in China's croplands over the past 40 years

Quantifying changes in soil organic carbon (SOC) stocks within croplands across a broad spatiotemporal scale in response to anthropogenic and environmental factors offers valuable insights for sustainable agriculture aimed to improve soil health. Using a validated and widely used soil carbon model RothC, we simulated the SOC dynamics across intensive croplands in China that support ∼22 % of the global population using only 7 % of the global cropland area. The modelling results demonstrate that the optimized RothC effectively captures SOC dynamics measured across 29 long-term field trials during 40 years. Between 1980 and 2020, the average SOC at the top 30 cm in croplands increased from 40 Mg C ha-1 to 49 Mg C ha-1, resulting in a national carbon sequestration of 1100 Tg C, with an average carbon sequestration rate of 27 Tg C yr-1. The annual increase rate of SOC (relative to the SOC stock of the previous year), starting at <0.2 % yr-1 in the 1980s, reached around 0.4 % yr-1 in the 1990s and further rose to about 0.8 % yr-1 in the 2000s and 2010s. Notably, the eastern and southern regions, comprising about 40 % of the croplands, contributed about two-thirds of the national SOC gain. In northeast China, SOC slightly decreased from 58 Mg C ha-1 in 1980 to 57 Mg C ha-1 in 2020, resulting in a total decline of 28 Tg C. Increased organic C inputs, particularly from the straw return, was the crucial factor in SOC increase. Future strategies should focus on region-specific optimization of straw management. Specifically, in northeast China, increasing the proportion of straw returned to fields can prevent further SOC decline. In regions with SOC increase, such as the eastern and southern regions, diversified straw utilization (e.g., bioenergy production), could further mitigate greenhouse gas emissions.

Substantial nitrogen abatement accompanying decarbonization suppresses terrestrial carbon sinks in China

China faces challenges in reaching its carbon neutrality goal by the year 2060 to meet the Paris Agreement and improving air quality simultaneously. Dramatic nitrogen emission reductions will be brought by this ambitious target, yet their impact on the natural ecosystem is not clear. Here, by combining two atmospheric chemistry models and two process-based terrestrial ecosystem models constrained using nationwide measurements, we show that atmospheric nitrogen deposition in China's terrestrial land will decrease by 44-57% following two emission control scenarios including one aiming at carbon neutrality. They consequently result in a pronounced shrinkage in terrestrial net ecosystem production, by 11-20% depending on models and emission scenarios. Our results indicate that the nitrogen emission reductions accompanying decarbonization would undermine natural carbon sinks and in turn set back progress toward carbon neutrality. This unintended impact calls for great concern about the trade-offs between nitrogen management and carbon neutrality.

Permafrost carbon cycle and its dynamics on the Tibetan Plateau

Our knowledge on permafrost carbon (C) cycle is crucial for understanding its feedback to climate warming and developing nature-based solutions for mitigating climate change. To understand the characteristics of permafrost C cycle on the Tibetan Plateau, the largest alpine permafrost region around the world, we summarized recent advances including the stocks and fluxes of permafrost C and their responses to thawing, and depicted permafrost C dynamics within this century. We find that this alpine permafrost region stores approximately 14.1 Pg (1 Pg=1015 g) of soil organic C (SOC) in the top 3 m. Both substantial gaseous emissions and lateral C transport occur across this permafrost region. Moreover, the mobilization of frozen C is expedited by permafrost thaw, especially by the formation of thermokarst landscapes, which could release significant amounts of C into the atmosphere and surrounding water bodies. This alpine permafrost region nevertheless remains an important C sink, and its capacity to sequester C will continue to increase by 2100. For future perspectives, we would suggest developing long-term in situ observation networks of C stocks and fluxes with improved temporal and spatial coverage, and exploring the mechanisms underlying the response of ecosystem C cycle to permafrost thaw. In addition, it is essential to improve the projection of permafrost C dynamics through in-depth model-data fusion on the Tibetan Plateau.

Landsat-based spatiotemporal estimation of subtropical forest aboveground carbon storage using machine learning algorithms with hyperparameter tuning

Introduction

The aboveground carbon storage (AGC) in forests serves as a crucial metric for evaluating both the composition of the forest ecosystem and the quality of the forest. It also plays a significant role in assessing the quality of regional ecosystems. However, current technical limitations introduce a degree of uncertainty in estimating forest AGC at a regional scale. Despite these challenges, remote sensing technology provides an accurate means of monitoring forest AGC. Furthermore, the implementation of machine learning algorithms can enhance the precision of AGC estimates. Lishui City, with its rich forest resources and an approximate forest coverage rate of 80%, serves as a representative example of the typical subtropical forest distribution in Zhejiang Province.

Methods

Therefore, this study uses Landsat remote sensing images, employing backpropagation neural network (BPNN), random forest (RF), and categorical boosting (CatBoost) to model the forest AGC of Lishui City, selecting the best model to estimate and analyze its forest AGC spatiotemporal dynamics over the past 30 years (1989-2019).

Results

The study shows that: (1) The texture information calculated based on 9×9 and 11×11 windows is an important variable in constructing the remote sensing estimation model of the forest AGC in Lishui City; (2) All three machine learning techniques are capable of estimating forest AGC in Lishui City with high precision. Notably, the CatBoost algorithm outperforms the others in terms of accuracy, achieving a model training accuracy and testing accuracy R2 of 0.95 and 0.83, and RMSE of 2.98 Mg C ha-1 and 4.93 Mg C ha-1, respectively. (3) Spatially, the central and southwestern regions of Lishui City exhibit high levels of forest AGC, whereas the eastern and northeastern regions display comparatively lower levels. Over time, there has been a consistent increase in the total forest AGC in Lishui City over the past three decades, escalating from 1.36×107 Mg C in 1989 to 6.16×107 Mg C in 2019.

Discussion

This study provided a set of effective hyperparameters and model of machine learning suitable for subtropical forests and a reference data for improving carbon sequestration capacity of subtropical forests in Lishui City.

Bacteria-Like Gaiella Accelerate Soil Carbon Loss by Decomposing Organic Matter of Grazing Soils in Alpine Meadows on the Qinghai-Tibet Plateau

The alpine meadows of the Qinghai-Tibet Plateau have significant potential for storing soil carbon, which is important to global carbon sequestration. Grazing is a major threat to its potential for carbon sequestration. However, grazing poses a major threat to this potential by speeding up the breakdown of organic matter in the soil and releasing carbon, which may further lead to positive carbon-climate change feedback and threaten ecological security. Therefore, in order to accurately explore the driving mechanism and regulatory factors of soil organic matter decomposition in grazing alpine meadows on the Qinghai-Tibet Plateau, we took the grazing sample plots of typical alpine meadows as the research object and set up grazing intensities of different life cycles, aiming to explore the relationship and main regulatory factors of grazing on soil organic matter decomposition and soil microorganisms. The results show the following: (1) soil microorganisms, especially Acidobacteria and Acidobacteria, drove the decomposition of organic matter in the soil, thereby accelerating the release of soil carbon, which was not conducive to soil carbon sequestration in grassland; (2) the grazing triggering effect formed a positive feedback with soil microbial carbon release, accelerating the decomposition of organic matter and soil carbon loss; and (3) the grazing ban and light grazing were more conducive to slowing down soil organic matter decomposition and increasing soil carbon sequestration.

Vegetation redistribution is predicted to intensify soil organic carbon loss under future climate changes on the Tibetan Plateau

Vegetation redistribution may bring unexpected climate-soil carbon cycling in terrestrial biomes. However, whether and how vegetation redistribution alters the soil carbon pool under climate change is still poorly understood on the Tibetan Plateau. Here, we applied the G-Range model to simulate the cover of herbs, shrubs and trees, net primary productivity (NPP) and soil organic carbon density (SOCD) at the depth of 60 cm on Tibetan Plateau for the individual years 2020 and 2060, using climate projection for Representative Concentration Pathways (RCP) 4.5 and RCP8.5 scenarios with the RegCM4.6 model system. Vegetation redistribution was defined as the transitions in bare ground, herbs, shrubs and trees between 2020 and 2060, with approximately 57.9 % (RCP4.5) and 59 % (RCP8.5) of the area will redistribute vegetation over the whole Tibetan Plateau. The vegetation cover will increase by about 2.4 % (RCP4.5) and 1.9 % (RCP8.5), while the NPP and SOCD will decrease by about -14.3 g C m-2 yr-1 and -907 g C m-2 (RCP4.5), and -1.8 g C m-2 yr-1and -920 g C m-2 (RCP8.5). Shrubs and trees will expand in the east, and herbs will expand in the northwest part of the Plateau. These areas are projected to be hotspots with greater SOCD reduction in response to future climate change, and will include lower net plant carbon input due to the negative NPP. Our study indicates that the SOC pool will become a carbon source under increased air temperature and rainfall on the Tibetan Plateau by 2060, especially for the area with vegetation redistribution. These results revealed the potential risk of vegetation redistribution under climate change in alpine ecosystems, indicating the policymakers need to pay attention on the vegetation redistribution to mitigate the soil carbon emission and achieve the goal of carbon neutrality on the Tibetan Plateau.

Terrestrial vegetation carbon sink analysis and driving mechanism identification in the Qinghai-Tibet Plateau

The estimation of terrestrial carbon sinks in the Qinghai-Tibet Plateau (QTP) still faces significant uncertainties, and the spatiotemporal dynamics of terrestrial carbon sinks along altitudinal gradients remain unexplored. Moreover, the driving mechanisms of terrestrial carbon sinks at the watershed scale in the QTP continue to be lacking. To address these research gaps, based on multi-source remote sensing data and meteorological data, this study calculated the Net Ecosystem Productivity (NEP) in the QTP from 2000 to 2020 using the Modis NPP-soil respiration model. Through the coefficient of variation (CV), the Mann-Kendall test (MK), and the spatial autocorrelation methods, the spatial distribution pattern and spatiotemporal trends of NEP were investigated. Employing a pixel accumulation method, the variation of NEP along altitudinal gradients was explored. Grey relation analysis, Pearson correlation analysis, and Geographical detector (GD) were used to investigate the driving mechanisms of NEP at the watershed scale. Results showed that: (1) the terrestrial ecosystem in the QTP served as a carbon sink, which produced a total of 2.04 Pg C from 2000 to 2020, and the multi-year average of total carbon sinks was 96.92 Tg C; (2) the spatial distribution of NEP shows a decreasing change from southeast to northwest, and the clustering characteristic of NEP is significant at the watershed scale; (3) the elevation of 4507 m we proposed is likely to be a key threshold for biophysical processes of the terrestrial ecosystems in the QTP; (4) the fluctuation and change trend of carbon sources and carbon sinks show significant differences between the East and West; (5) at the watershed scale, precipitation and temperature play a dominant role in the variation of NEP, while the impact of human activities on NEP variation is weak. Our study aims to address the existing knowledge gaps and provide valuable insights into the management of terrestrial carbon sinks in QTP.

Urbanization promotes carbon storage or not? The evidence during the rapid process of China

China's commitment to attaining carbon neutrality by 2060 has galvanized research into carbon sequestration, a critical approach for mitigating climate change. Despite the rapid urbanization observed since the turn of the millennium, a comprehensive analysis of how urbanization influences urban carbon storage throughout China remains elusive. Our investigation delves into the nuanced effects of urbanization on carbon storage, dissecting both the direct and indirect influences by considering urban-suburban gradients and varying degrees of urban intensity. We particularly scrutinize the roles of climatic and anthropogenic factors in mediating the indirect effects of urbanization on carbon storage. Our findings reveal that urbanization in China has precipitated a direct reduction in carbon storage by approximately 13.89 Tg of carbon (Tg C). Remarkably, urban sprawl has led to a diminution of vegetation carbon storage by 8.65 Tg C and a decrease in soil carbon storage by 5.24 Tg C, the latter resulting from the sequestration of impervious surfaces and the elimination of organic matter inputs following vegetation removal. Meanwhile, carbon storage in urban greenspaces has exhibited an increase of 6.90 Tg C and offsetting 49.70% of the carbon loss induced by direct urbanization effects. However, the indirect effects of urbanization predominantly diminish carbon storage in urban greenspaces by an average of 5.40%. The degree of urban vegetation management emerges as a pivotal factor influencing the indirect effects of urbanization on carbon storage. To bolster urban carbon storage, curbing urban sprawl and augmenting urban green spaces are imperative strategies. Insights from this study are instrumental in steering sustainable urban planning and advancing towards the goal of carbon neutrality.

Analyzing temporal and spatial forest carbon storage using Google Plus Code: a case study of Zijin Mountain National Forest Park, China

BACKGROUND

It is always a challenging job to compare forest resources as there is not a standardized spatial unit with location information. Google Plus Code, the newest alphanumeric geocoding system, uses 20 specifically selected letters and numbers to assign a unique global ID to every cell at different levels of a hierarchical grid system which is established based on latitude and longitude. It can be used as a standardized, unique global geospatial unit to segment, locate, quantitate, evaluate, and compare natural resources with area, boundary, and location information embedded.

RESULTS

For this proof-of-concept case study, forest inventory data from 1987, 2002, and 2019 for the Zijin Mountain National Forest Park in Jiangsu Province, China was analyzed based on Google Plus Code grid/cell. This enabled the quantification of carbon storage at each cell allowing for the comparison of estimated carbon storage at same or different locations over time.

CONCLUSIONS

This methodology is used to quantify the impacts of changing forest conditions and forest management activities on carbon storage with high spatial accuracy through the 32-year study period. Furthermore, this technique could be used for providing technical support and validation of carbon credit quantification and management.

Nitrogen deposition mediates more stochastic processes in structuring plant community than soil microbial community in the Eurasian steppe

Anthropogenic environmental changes may affect community assembly through mediating both deterministic (e.g., competitive exclusion and environmental filtering) and stochastic processes (e.g., birth/death and dispersal/colonization). It is traditionally thought that environmental changes have a larger mediation effect on stochastic processes in structuring soil microbial community than aboveground plant community; however, this hypothesis remains largely untested. Here we report an unexpected pattern that nitrogen (N) deposition has a larger mediation effect on stochastic processes in structuring plant community than soil microbial community (those <2 mm in diameter, including archaea, bacteria, fungi, and protists) in the Eurasian steppe. We performed a ten-year nitrogen deposition experiment in a semiarid grassland ecosystem in Inner Mongolia, manipulating nine rates (0-50 g N m-2 per year) at two frequencies (nitrogen added twice or 12 times per year) under two grassland management strategies (fencing or mowing). We separated the compositional variation of plant and soil microbial communities caused by each treatment into the deterministic and stochastic components with a recently-developed method. As nitrogen addition rate increased, the relative importance of stochastic component of plant community first increased and then decreased, while that of soil microbial community first decreased and then increased. On the whole, the relative importance of stochastic component was significantly larger in plant community (0.552±0.035; mean±standard error) than in microbial community (0.427±0.035). Consistently, the proportion of compositional variation explained by the deterministic soil and community indices was smaller for plant community (0.172-0.186) than microbial community (0.240-0.767). Meanwhile, as nitrogen addition rate increased, the linkage between plant and microbial community composition first became weaker and then became stronger. The larger stochasticity in plant community relative to microbial community assembly suggested that more stochastic strategies (e.g., seeds addition) should be adopted to maintain above- than below-ground biodiversity under the pressure of nitrogen deposition.

The trajectory of carbon emissions and terrestrial carbon sinks at the provincial level in China

Global greenhouse gas emission, major factor driving climate change, has been increasing since nineteenth century. STIRPAT and CEVSA models were performed to estimate the carbon emission peaks and terrestrial ecosystem carbon sinks at the provincial level in China, respectively. Utilizing the growth characteristics and the peak time criteria for the period 1997-2019, the patterns of energy consumption and CO2 emissions from 30 Chinese provinces are categorized into four groups: (i) one-stage increase (5 provinces), (ii) two-stage increase (10 provinces), (iii) maximum around 2013 (13 provinces), and (iv) maximum around 2017 (2 provinces). According to the STIRPAT model, the anticipated time of peak CO2 emissions for Beijing from the third group is ~ 2025 in both business-as-usual and high-speed scenarios. For Xinjiang Uygur autonomous region from the first group and Zhejiang province from the second group, the expected peak time is 2025 to 2030. Shaanxi province from the fourth group is likely to reach carbon emission peak before 2030. The inventory-based estimate of China's terrestrial carbon sink is ~ 266.2 Tg C/a during the period 1982-2015, offsetting 18.3% of contemporary CO2 emissions. The province-level CO2 emissions, peak emissions and terrestrial carbon sinks estimates presented here are significant for those concerned with carbon neutrality.

Conservation Agriculture Impacts on Economic Profitability and Environmental Performance of Agroecosystems

The rationale of this study originates from the primary sector's multiple roles in the global warming issue. Agriculture is reported among the main causes of anthropogenic global warming. At the same time, it is profoundly impacted by climate change and concurrently holds potential as a solution through the sequestration of soil organic carbon (SOC) facilitated by Conservation Agriculture (CA). However, the findings in the literature are controversial on the SOC sequestration capacity and the profitability of CA implementation. Considering the new and old objectives of the sector, this paper tackles the assessment of the actual capabilities of CA to be a viable strategy to pursue the social good of climate change mitigation and concurrently be profitable for farmers. The economic profitability and environmental performance of CA are assessed analysing data from a field experiment in Northern Italy (European temperate area) and identifying the best management practice by means of a data envelopment analysis.

Seasonal variation of net ecosystem carbon exchange and gross primary production over a Loess Plateau semi-arid grassland of northwest China

Grassland ecosystems store approximately one-third of the global terrestrial carbon stocks, which play a crucial role in regulating the carbon cycle on regional and global scales, but the current scientific understanding of the variation in net carbon dioxide exchange (NEE) on grassland ecosystems is still limited. Based on the eddy covariance technique, this study investigated the seasonal variation of ecosystem respiration (Reco) and gross primary production (GPP) from 2018 to 2020 in a semi-arid grassland on the Loess Plateau in northwest China. The results indicated that the annual cumulative average NEE value was - 0.778 kg C/m2, the growing season cumulative value accounted for approximately 83.81%, which suggested that the semiarid grassland showed a notable soil carbon sink. The correlation analysis revealed that soil temperature (Ts) (RReco = 0.71, RGPP = 0.61) and soil water content (SWC) (RReco = 0.47, RGPP = 0.44) were the two main driving factors in modulating the variation of daily average GPP and Reco (P < 0.01). Therefore, the monthly average of GPP and Reco increased with the increase in Ts (RGPP = 0.716, P < 0.01; RReco = 0.586, P < 0.05), resulting in an increase in the carbon sequestration capacity of the grass ecosystem. This study also showed that soil moisture has a promoting effect on the response of Reco and GPP to Ts, and the correlation among GPP, Reco, and Ts was much stronger under wet conditions. For instance, the coefficient of determination of Reco and GPP with Ts under wet conditions in 2018 increased by 0.248 and 0.286, respectively, compared to those under droughty conditions. Additionally, the temperature sensitivity of Reco (Q10) increased by 46.13% compared to dry conditions. In addition, carbon exchange models should consider the synergistic effect of Ts and SWC as one of the main driving factors for theoretical interpretation or modeling. Under the potential scenario of future global warming and the frequent extreme weather events, our findings have important implications for predicting future CO2 exchange and establishing an optimal ecological model of carbon flux exchange.

The soil microbial necromass carbon and the carbon pool stability drive a stronge priming effect following vegetation restoration

The priming effect stands as a critical factor influencing the balance of soil organic carbon (SOC). Following vegetation restoration, the carbon (C) pool stability in Platycladus orientalis forests (PO) varies, and the priming effect resulting from exogenous C addition also differs significantly. Here, we selected PO with restoration ages of 10, 15, and 30 years in the rocky mountainous area in northern China and conducted measurements of soil properties, microbial communities, microbial necromass C (MNC), SOC fractions, and the priming effect characteristics to explore the main influencing factors of the priming effect, especially the microbiological mechanisms. Our results showed that the ratio of mineral-associated organic C to particulate organic C increased. The characteristics of the priming effect showed the same pattern, and there was a significant positive correlation between the C pool stability and the priming effect. The diversity of the fungal communities increased with increasing vegetation restoration age, and the content and proportion of fungal necromass C (FNC) also increased synchronously, reaching the maximum value in the soil of PO that had been restored for 30 years. In addition, the soil water content and total nitrogen indirectly affected the priming effect by influencing the microbial communities. In summary, the results suggested that vegetation restoration can enhance the C pool stability by promoting an increase in soil FNC, thereby producing a positive priming effect. This can help deepen our understanding of the SOC mineralization changes induced by fresh C input following vegetation restoration and provides a theoretical basis for better explaining the C cycle between soil and atmosphere under the vegetation restoration models in the future.

Reduced Carbon Dioxide by Overexpressing EPSPS Transgene in Arabidopsis and Rice: Implications in Carbon Neutrality through Genetically Engineered Plants

With the increasing challenges of climate change caused by global warming, the effective reduction of carbon dioxide (CO2) becomes an urgent environmental issue for the sustainable development of human society. Previous reports indicated increased biomass in genetically engineered (GE) Arabidopsis and rice overexpressing the 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) gene, suggesting the possibility of consuming more carbon by GE plants. However, whether overexpressing the EPSPS gene in GE plants consumes more CO2 remains a question. To address this question, we measured expression of the EPSPS gene, intercellular CO2 concentration, photosynthetic ratios, and gene expression (RNA-seq and RT-qPCR) in GE (overexpression) and non-GE (normal expression) Arabidopsis and rice plants. Results showed substantially increased EPSPS expression accompanied with CO2 consumption in the GE Arabidopsis and rice plants. Furthermore, overexpressing the EPSPS gene affected carbon-fixation related biological pathways. We also confirmed significant upregulation of four key carbon-fixation associated genes, in addition to increased photosynthetic ratios, in all GE plants. Our finding of significantly enhanced carbon fixation in GE plants overexpressing the EPSPS transgene provides a novel strategy to reduce global CO2 for carbon neutrality by genetic engineering of plant species, in addition to increased plant production by enhanced photosynthesis.

Soil organic carbon changes in China's croplands: A newly estimation based on DNDC model

Soil Organic Carbon (SOC) in cropland represents a significant facet of the terrestrial ecosystem's carbon reservoirs, playing a pivotal role in global climate change mitigation efforts. Within the specific context of China, cropland SOC not only extends its implications beyond environmental impact but also serves as a critical factor in ensuring the stability and security of the nation's food supply. However, there is an ongoing argument about the changes in SOC and their spatial and temporal distribution patterns within China's croplands. In this study, we constructed a new county-level DNDC database for 2020, building upon 2003 research that quantified SOC stock in China's cropland using the DNDC model. Our aim was to assess the SOC storage and temporal changes of China's cropland in 2020 using same methodology to enhance estimation accuracy. The simulation results of the validated DNDC model revealed that the average SOC storage of China's croplands (0-30 cm) in 2020 was 6.02 Pg C, with the Northeast region contributing 23 % (1.37 Pg C). The SOC density in China varied from 18.55 to 152.57 t C ha-1, averaging at 49.65 t C ha-1. In 2020, China's cropland transitioned from a net loss of SOC in 2003 to a carbon sink, with cropland SOC density and SOC storage increased by 18.2 % and 21.6 % respectively. Notably, despite experiencing a loss of SOC compared to 2003, the Northeast region had the highest average SOC density in China. This study highlights that despite the increase in SOC density and storage in China's croplands over the last 17 years, there remains substantial potential for carbon sequestration given the current spatial distribution of SOC density's significant heterogeneity within China. The findings of this study offer data support for China's strategy to achieve food security and carbon neutrality.

Maximum potential of vegetation carbon sink in Chinese forests

Forest vegetation is essential in sequestering carbon dioxide (CO2) from the atmosphere and mediating global warming. The carbon (C) sink potential of forest vegetation in different provinces is vital for policymakers to develop C-neutral technical routes and regional priorities in China; however, the mechanism remains unclear. In this study, we compiled the public data on forest vegetation biomass or storage along forest succession series between 2003 and 2022 and obtained the spatial variation of the maximum C storage(BCmax) of forest vegetation using classic logistic equation and nonlinear fitting. Furthermore, the C sink potential (∆Cpot) of the Chinese forest vegetation was calculated based on the differences between the BCmax and intensive field-investigated data in the 2010s. The results showed that the BCmax in the Chinese forest vegetation was approximately 19.03 Pg. The BCmax in southwest and northeast China were higher than those in other regions. The ∆Cpot was estimated as 8.83 Pg. Moreover, 1 km × 1 km spatial raster data for ∆Cpot were produced using the spatial raster calculation. Similarly, the per capita ∆Cpot of regions with low economic development (southwest, central, and southern Chinese provinces) were five to ten times higher than those of regions with a higher economic level. The ∆Cpot correlated negatively with gross domestic product (GDP)across all Chinese provinces. Our findings provide new insights into the ∆Cpot of the Chinese forest vegetation under natural restoration and emphasize that some differences in financial and political support among different provinces facilitate achieving a large ∆Cpot for C neutrality.

Ecosystem carbon storage considering combined environmental and land-use changes in the future and pathways to carbon neutrality in developed regions

Assessing the carbon storage capacity of terrestrial ecosystems is crucial for land management and carbon reduction policymaking. There is still a knowledge gap regarding how ecosystem carbon storage will be impacted by combined environmental and land-use factors and their spatial-temporal changes, especially in developed regions where urbanization has slowed down. This study investigated how developed regions in subtropical and tropical areas might increase carbon storage and achieve carbon neutrality, using Guangdong Province in South China as an example. Based on the sustainable development assumption, three land-management scenarios were developed and simulated for 2020-2060 using the Patch-generating Land Use Simulation model. Without considering disturbance and natural losses, carbon storage was estimated by net ecosystem productivity (NEP)-the difference between net primary productivity (NPP) and heterotrophic respiration (HR). NPP was predicted using an artificial neural network model trained by historical NPP data and 16 environmental and land-use variables. HR was predicted using soil respiration models from previous research. Based on the balance between carbon storage and emissions, we predicted the allowable fossil fuel consumption to achieve net-zero CO2 emissions in 2060. The results show that Guangdong's total carbon storage changes from 73.7 MtC in 2020 to 70.6-74.8 MtC in 2060 under different scenarios. Nonlinear relationships exist between the carbon stored and the areas of different land-use types. Topography, temperatures, and land-use configurations jointly lead to significantly varied carbon storage between croplands and between forests in space and time. Protecting and regenerating forests in subtropical areas and forest edges is more effective than afforestation in lowland tropical areas for storing carbon. Net-zero CO2 emissions rely more on reducing emissions than land management. To achieve this, the proportion of fossil energy in total energy consumption should be lowered from 75.5 % in 2020 to ~25 % in 2060.

Dynamic Response of the cbbL Carbon Sequestration Microbial Community to Wetland Type in Qinghai Lake

The soil carbon storage in the Qinghai-Tibet Plateau wetlands is affected by microbiota and wetland types, but the response mechanisms of carbon sequestration microorganisms on the Qinghai-Tibet Plateau to different wetland types are still poorly described. To explore the differences in carbon sequestration microbial communities in different wetlands and the main influencing factors, this study took a marsh wetland, river source wetland and lakeside wetland of Qinghai Lake as the research objects and used high-throughput sequencing to study the functional gene, cbbL, of carbon sequestration microorganisms. The results showed that the dominant bacterial group of carbon sequestration microorganisms in marsh and river source wetlands was Proteobacteria, and the dominant bacterial group in the lakeside wetland was Cyanobacteria. The alpha diversity, relative abundance of Proteobacteria and total carbon content were the highest in the marsh wetland, followed by the river source wetland, and they were the lowest in the lakeside wetland. In addition, the physical and chemical characteristics of the three wetland types were significantly different, and the soil temperature and moisture and total carbon content were the most important factors affecting the community structures of carbon-sequestering microorganisms. There was little difference in the total nitrogen contents between the marsh wetland and river source wetland. However, the total nitrogen content was also an important factor affecting the diversity of the carbon sequestration microbial community. In summary, the wetland type significantly affects the process of soil carbon sequestration. Compared with the riverhead and lakeside wetlands, the marsh wetland has the highest carbon storage.

Ongoing CO2 monitoring verify CO2 emissions and sinks in China during 2018-2021

Accurate estimating CO2 emissions and sinks is crucial in achieving carbon neutrality in China. However, CO2 emissions from bottom-up inventories are uncertain at regional scales and lack independent verification from atmospheric perspectives. Here we integrated 39 high-precision CO2 stations in China to top-down invert emission-sink variations at 45 km × 45 km and achieved a full range of inventories verification. The results show that China's CO2 emissions are 15% higher than those of five bottom-up inventories, to an annual total of 3.40 Pg C a-1 for 2018-2021. After deducting human and livestock respiration, the annual CO2 emissions were 3.13 Pg C a-1 (11.48 Pg CO2 a-1). The annual increase in emissions slowed from 3.7% in 2019 to 1.1% in 2020 and resumed growth to 4.0% in 2021, consistent with observed CO2 growth rates in China. China's land CO2 sink, deducting farmland sinks and lateral fluxes, was 0.57 Pg C a-1 (2.09 Pg CO2 a-1) for 2018-2021 (higher than most global inverse models), accounting for ∼16.9% of anthropogenic CO2 emissions. The land sink in China decreased by -19.3% in 2019 due to a weak El Niño event and increased by 3.2% in 2020 and 13.7% in 2021. It is worth noting that inverse CO2 emissions and sinks in western China still face large uncertainty due to limited CO2 monitoring. Overall, our top-down estimates demonstrate spatiotemporal variations in CO2 emissions and sinks from atmospheric perspectives and highlight challenges for different provinces in achieving 2060 carbon neutrality with verified estimates.

Spatial difference of carbon budget and carbon balance zoning based on land use change: a case study of Henan Province, China

Land use change is one of the key reasons for the rise in global carbon emissions. Incorporating practical methods for carbon governance into the major strategic decisions of countries around the world is important for controlling carbon emissions. This study aims to carry out a regional land use carbon budget assessment and build a carbon balance zoning optimization framework. As a result, China will be better able to implement low-carbon strategies and reach carbon peaking and carbon neutrality. Using the data of land use and energy consumption for Henan Province from 2000 to 2020, a carbon budget assessment system was constructed. According to the analysis of the geographical distribution of carbon budget, an evaluation system was developed and a carbon balance partition was established from the natural, economic, ecological and resource structure. A regionally differentiated development strategy was proposed. The findings revealed that: (1) Land use carbon emissions of Henan Province reflected a significant increasing trend, while the variation in carbon absorption of land use was stable. Carbon emissions increased by 87,120.25×104 t in 2020 compared to 2000, but the carbon absorption remained at approximately 1735×104 t over the years and there was an overall state of carbon deficit. (2) The geographical distribution of carbon emissions in Henan Province was characterized by higher in the central part and lower in the surroundings, and the distribution of carbon absorption was higher in the west and lower in the east. The distribution pattern was closely related to the level of land use and the structure of energy consumption. (3) From the carbon balance analysis, the 158 counties in Henan Province were divided into four carbon balance functional areas, namely the carbon sink functional area, low-carbon development area, carbon intensity control area, and high-carbon optimization area. Different optimized development strategies were proposed for each functional area.

Spatial variations and mechanisms for the stability of water use efficiency in China

A clearer understanding of the stability of water use efficiency (WUE) and its driving factors contributes to improving water use efficiency and strengthening water resource management. However, the stability of WUE is unclear. Based on the EEMD method, this study analyses the spatial variations and mechanisms for the stability of WUE in China, especially in the National Forest Protection Project (NFPP) areas. It is found that the stable WUE was dominated by non-significant trends and increasing trends in China, accounting for 33.59% and 34.19%, respectively. The non-significant trend of stable WUE was mainly located in the Three-North shelterbelt program area, and the increasing trend of stable WUE was in Huaihe and Taihu, Taihang Mountains, and Pearl River shelterbelt program areas. Precipitation and soil moisture promoted the stable WUE in these project areas. The unstable WUE was dominated by positive reversals or negative reversals of WUE trends. The positive reversals of unstable WUE were mainly located in the Yellow River shelterbelt program areas, which was promoted by temperature and radiation, while the negative reversals of unstable WUE were mainly distributed in the Yangtze River and Liaohe shelterbelt program areas, which were mainly induced by saturation water vapor pressure difference (VPD). Our results highlight that some ecological restoration programs need to be improved to cope with the negative climate impact on the stability of WUE.

The fusion of multiple scale data indicates that the carbon sink function of the Qinghai-Tibet Plateau is substantial

BACKGROUND

The Qinghai-Tibet Plateau is the "sensitive area" of climate change, and also the "driver" and "amplifier" of global change. The response and feedback of its carbon dynamics to climate change will significantly affect the content of greenhouse gases in the atmosphere. However, due to the unique geographical environment characteristics of the Qinghai-Tibet Plateau, there is still much controversy about its carbon source and sink estimation results. This study designed a new algorithm based on machine learning to improve the accuracy of carbon source and sink estimation by integrating multiple scale carbon input (net primary productivity, NPP) and output (soil heterotrophic respiration, Rh) information from remote sensing and ground observations. Then, we compared spatial patterns of NPP and Rh derived from the fusion of multiple scale data with other widely used products and tried to quantify the differences and uncertainties of carbon sink simulation at a regional scale.

RESULTS

Our results indicate that although global warming has potentially increased the Rh of the Qinghai-Tibet Plateau, it will also increase its NPP, and its current performance is a net carbon sink area (carbon sink amount is 22.3 Tg C/year). Comparative analysis with other data products shows that CASA, GLOPEM, and MODIS products based on remote sensing underestimate the carbon input of the Qinghai-Tibet Plateau (30-70%), which is the main reason for the severe underestimation of the carbon sink level of the Qinghai-Tibet Plateau (even considered as a carbon source).

CONCLUSIONS

The estimation of the carbon sink in the Qinghai-Tibet Plateau is of great significance for ensuring its ecological barrier function. It can deepen the community's understanding of the response to climate change in sensitive areas of the plateau. This study can provide an essential basis for assessing the uncertainty of carbon sources and sinks in the Qinghai-Tibet Plateau, and also provide a scientific reference for helping China achieve "carbon neutrality" by 2060.

Digital mapping of soil organic carbon density in China using an ensemble model

The soil organic carbon stock (SOCS) is considered as one of the largest carbon reservoirs in terrestrial ecosystems, and small changes in soil can cause significant changes in atmospheric CO2 concentration. Understanding organic carbon accumulation in soils is crucial if China is to meet its dual carbon target. In this study, the soil organic carbon density (SOCD) in China was digitally mapped using an ensemble machine learning (ML) model. First, based on SOCD data obtained at depths of 0-20 cm from 4356 sampling points (15 environmental covariates), we compared the performance of four ML models, namely random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and artificial neural network (ANN) models, in terms of coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values. Then, we ensembled four models using Voting Regressor and the principle of stacking. The results showed that ensemble model (EM) accuracy was high (RMSE = 1.29, R2 = 0.85, MAE = 0.81), so that it could be a good choice for future research. Finally, the EM was used to predict the spatial distribution of SOCD in China, which ranged from 0.63 to 13.79 kg C/m2 (average = 4.09 (±1.90) kg C/m2). The SOC storage amount in surface soil (0-20 cm) was 39.40 Pg C. This study developed a novel, ensemble ML model for SOC prediction, and improved our understanding of the spatial distribution of SOC in China.

Comparison of plant diversity-carbon storage relationships along altitudinal gradients in temperate forests and shrublands

Understanding the mechanisms underlying the relationship between biodiversity and ecosystem function (BEF) is critical for the implementation of productive and resilient ecosystem management. However, the differences in BEF relationships along altitudinal gradients between forests and shrublands are poorly understood, impeding the ability to manage terrestrial ecosystems and promote their carbon sinks. Using data from 37962 trees of 115 temperate forest and 134 shrubland plots of Taihang Mountains Priority Reserve, we analyzed the effects of species diversity, structural diversity, climate factors and soil moisture on carbon storage along altitudinal gradients in temperate forests and shrublands. We found that: (1) Structural diversity, rather than species diversity, mainly promoted carbon storage in forests. While species diversity had greater positive effect on carbon storage in shrublands. (2) Mean annual temperature (MAT) had a direct negative effect on forest carbon storage, and indirectly affected forest carbon storage by inhibiting structural diversity. In contrast, MAT promoted shrubland carbon storage directly and indirectly through the positive mediating effect of species diversity. (3) Increasing altitudinal gradients enhanced the structural diversity-carbon relationship in forests, but weakened the species diversity-carbon relationship in shrublands. Niche and architectural complementarity and different life strategies of forests and shrubs mainly explain these findings. These differential characteristics are critical for our comprehensive understanding of the BEF relationship and could help guide the differentiated management of forests and shrublands in reaction to environmental changes.

Carbon Storage in Different Compartments in Eucalyptus Stands and Native Cerrado Vegetation

This study evaluated Carbon (C) storage in different compartments in eucalyptus stands and native Cerrado vegetation. To determine C above ground, an inventory was carried out in the areas where diameter at breast height (DBH), diameter at base height (Db), and total tree height (H) were measured. In the stands, the rigorous cubage was made by the direct method, and in the native vegetation, it was determined by the indirect method through an allometric equation. Roots were collected by direct method using circular monoliths to a depth of 60 cm and determined by the volume of the cylinder. Samples were collected up to 100 cm deep to estimate C stock in the soil. All samples collected directly had C determined using the CHNS elemental analyzer. Gas samples were collected using a manually closed chamber, and the gas concentration was determined by gas chromatography. The results indicate high C storage in the studied areas > 183.99 Mg ha^-1, could contribute to CO₂ mitigation > 674.17 Mg ha^-1. In addition to low emissions (<1 kg ha^-1 yr^-1) for the three evaluated areas, with no statistical difference in relation to the Global Warming Potential. Concerning the native cerrado vegetation conversion, the "4-year-old eucalyptus stand" seemed to restore the original soil carbon stocks in the first-meter depth, regardless of some losses that might have occurred right after establishment. Conversely, a significant loss of carbon in the soil was observed due to the alternative setting, where similar natural land was converted into agriculture, mostly soybean, and then, years later, turned into the "6-year-old eucalyptus stand" (28.43 Mg ha^-1). Under this study, these mixed series of C baselines in landscape transitions have reflected on unlike C dynamics outcomes, whereas at the bottom line, total C stocks were higher in the younger forest (4-year-old stand). Therefore, our finding indicates that we should be thoughtful regarding upscaling carbon emissions and sequestration from small-scale measurements to regional scales.

A carbon-neutrality-capactiy index for evaluating carbon sink contributions

The accurate determination of the carbon-neutrality capacity (CNC) of a region is crucial for developing policies related to emissions and climate change. However, a systematic diagnostic method for determining the CNC that considers the rock chemical weathering carbon sink (RCS) is lacking. Moreover, it is challenging but indispensable to establish a fast and practical index model to determine the CNC. Here, we selected Guizhou as the study area, used the methods for different types of carbon sinks, and constructed a CNC index (CNCI) model. We found that: (1) the carbonate rock chemical weathering carbon sink flux was 30.3 t CO₂ km^-2 yr^-1. Guizhou accounted for 1.8% of the land area and contributed 5.4% of the carbonate chemical weathering carbon sink; (2) the silicate rock chemical weathering carbon sink and its flux were 1.44 × 10³ t CO₂ and 2.43 t CO₂ km^-2 yr^-1, respectively; (3) the vegetation-soil ecosystem carbon sink and its flux were 1.37 × 10⁸ t CO₂ and 831.70 t CO₂ km^-2 yr^-1, respectively; (4) the carbon emissions (CEs) were 280 Tg CO₂, about 2.8% of the total for China; and (5) the total carbon sinks in Guizhou were 160 Tg CO₂, with a CNCI of 57%, which is 4.8 times of China and 2.1 times of the world. In summary, we conducted a systematic diagnosis of the CNC considering the RCS and established a CNCI model. The results of this study have a strong implication and significance for national and global CNC determination and gap analysis.

Plant diversity drives soil carbon sequestration: evidence from 150 years of vegetation restoration in the temperate zone

Large-scale afforestation is considered a natural way to address climate challenges (e.g., the greenhouse effect). However, there is a paucity of evidence linking plant diversity to soil carbon sequestration pathways during long-term natural restoration of temperate vegetation. In particular, the carbon sequestration mechanisms and functions of woody plants require further study. Therefore, we conducted a comparative study of plant diversity and soil carbon sequestration characteristics during 150 years of natural vegetation restoration in the temperate zone to provide a comprehensive assessment of the effects of long-term natural vegetation restoration processes on soil organic carbon stocks. The results suggested positive effects of woody plant diversity on carbon sequestration. In addition, fine root biomass and deadfall accumulation were significantly positively correlated with soil organic carbon stocks, and carbon was stored in large grain size aggregates (1-5 mm). Meanwhile, the diversity of Fabaceae and Rosaceae was observed to be important for soil organic carbon accumulation, and the carbon sequestration function of shrubs should not be neglected during vegetation restoration. Finally, we identified three plants that showed high potential for carbon sequestration: Lespedeza bicolor, Sophora davidii, and Cotoneaster multiflorus, which should be considered for inclusion in the construction of local artificial vegetation. Among them, L. bicolor is probably the best choice.

Learning ensembles of process-based models for high accurately evaluating the one-hundred-year carbon sink potential of China's forest ecosystem

China's forests play a vital role in the global carbon cycle through the absorption of atmospheric CO₂ to mitigate climate change caused by the increase of anthropogenic CO₂. It is essential to evaluate the carbon sink potential (CSP) of China's forest ecosystem. Combining NDVI, field-investigated, and vegetation and soil carbon density data modeled by process-based models, we developed the state-of-the-art learning ensembles model of process-based models (the multi-model random forest ensemble (MMRFE) model) to evaluate the carbon stocks of China's forest ecosystem in historical (1982-2021) and future (2022-2081, without NDVI-driven data) periods. Meanwhile, we proposed a new carbon sink index (CSindex) to scientifically and accurately evaluate carbon sink status and identify carbon sink intensity zones, reducing the probability of random misjudgments as a carbon sink. The new MMRFE models showed good simulation results in simulating forest vegetation and soil carbon density in China (significant positive correlation with the observed values, r = 0.94, P < 0.001). The modeled results show that a cumulative increase of 1.33 Pg C in historical carbon stocks of forest ecosystem is equivalent to 48.62 Bt CO₂, which is approximately 52.03% of the cumulative increased CO₂ emissions in China from 1959 to 2018. In the next 60 years, China's forest ecosystem will absorb annually 1.69 (RCP45 scenario) to 1.85 (RCP85 scenario) Bt CO₂. Compared with the carbon stock in the historical period, the cumulative absorption of CO₂ by China's forest ecosystem in 2032-2036, 2062-2066, and 2077-2081 are approximately 11.25-39.68, 110.66-121.49 and 101.31-111.11 Bt CO_2, respectively. In historical and future periods, the medium and strong carbon sink intensity regions identified by the historical CSindex covered 65% of the total forest area, cumulative absorbing approximately 31.60 and 65.83-72.22 Bt CO₂, respectively. In the future, China's forest ecosystem has a large CSP with a non-continuous increasing trend. However, the CSP should not be underestimated. Notably, the medium carbon sink intensity region should be the priority for natural carbon sequestration action. This study not only provides an important methodological basis for accurately estimating the future CSP of forest ecosystem but also provides important decision support for future forest ecosystem carbon sequestration action.

Modeling forest carbon sink trading with carbon credit using stochastic differential game

Forest carbon sinks integrated into the carbon trading is an important way to solve the funding shortage of carbon emission reduction and is a major measure to achieve carbon sequestration and thereby carbon neutrality. We propose a carbon credit trading mechanism based on forest carbon sinks and build an optimal carbon credit trading model on forest companies and manufacturers. The scale of carbon sink forest and trading price is determined by stochastic differential game method. Reasonable emission reduction responsibilities are set to achieve coordination between forest carbon sequestration and economic development. The results show that farsighted forest company is more likely to participate in carbon credit trade when the total amount of forest carbon sequestration is small, while shortsighted forest company is more likely to participate in carbon credit trade when the total amount of forest carbon sequestration is large. Low emission reduction responsibility in carbon credit trade is conductive to increasing forest carbon sequestration, and improving the enthusiasm of shortsighted forest company to participate in the trade, which reflects the superiority of the proposed carbon credit trading mechanism. This implies that the forest company should undertake low emission reduction responsibilities to achieve a win-win situation for economic and environmental benefits.

Desert Abiotic Carbon Sequestration Weakening by Precipitation

Desert carbon sequestration plays an active role in promoting carbon neutralization. However, the current understanding of the effect of hydrothermal interactions and soil properties on desert carbon sequestration after precipitation remains unclear. Based on the experiment in the hinterland of the Taklimakan Desert, we found that the heavy precipitation will accelerate the weakening of abiotic carbon sequestration in deserts under the background of global warming and intensified water cycle. The high soil moisture can significantly stimulate sand to release CO₂ at an incredible speed by rapidly increasing microbial activity and organic matter diffusion. At this time, the CO₂ flux in the shifting sand was synergistically affected by soil temperature and soil moisture. As far as soil properties are concerned, with less organic carbon substrate and stronger soil alkalinity, the carbon sequestration of shifting sand is gradually highlighted and strengthened at low temperature. On the contrary, the carbon sequestration of shifting sand is gradually weakened. Our study provides a new way to assess the contribution of desert to the global carbon cycle and improve the accuracy and scope of application.

Impacts of vegetation restoration on water resources and carbon sequestration in the mountainous area of Haihe River basin, China

The mountainous region of the Haihe River basin (MHRB) plays an important role in the water resource supply of its nearby mega-cities, including Beijing and Tianjin, and large areas of cropland. With the implementation of afforestation projects in recent decades, vegetation and carbon (C) uptake have greatly increased in the MHRB. In addition, the annual runoff has significantly declined, threatening regional water security. The trade-off relationship between water yield and C uptake in the MHRB remains unknown. This study employed a biogeochemical model (Biome-BGC) to simulate the natural vegetation dynamics and gross primary productivity (GPP) during 1982-2019 driven by climate forcing. A distributed hydrological model (geomorphology-based hydrological model, GBHM) was adopted to assess the impact of vegetation restoration on the hydrological processes. The results indicated that the leaf area index in the MHRB increased significantly (P < 0.01) during 1982-2019, which led to evapotranspiration increase and runoff (R) reduction. Under the influence of vegetation restoration, both the GPP and the water use efficiency (WUE) increased significantly in the MHRB during 2000-2019, however, the improvement of WUE decreased with the aridity index increasing. Our results showed that vegetation restoration can improve C sequestration efficiency in the MHRB and that the trade-off between water yield and C sequestration should be considered in planning ecological projects to achieve C neutrality.

Ecological engineering induced carbon sinks shifting from decreasing to increasing during 1981-2019 in China

Substantial evidence shows that most of China's terrestrial ecosystems are important carbon sinks. However, the nonlinear trend of the carbon sinks and their nonlinear response to driving factors are unclear. Taking the net ecosystem productivity (NEP) as a proxy for the ecosystem carbon sink, the nonlinear relationships between the monotonically increasing trends and decreasing to increasing shifts in the carbon sink to climate change and ecological engineering were investigated based on ensemble empirical mode decomposition (EEMD) and machine learning algorithm (boosted regression tree model, BRT). The results suggest that 16.75 % of the carbon sinks in China experienced a monotonic increase. Additionally, 20.55 % of the carbon sinks shifted from decreasing to increasing trends, primarily after 1995, and these carbon sinks were located in the key ecological engineering areas, such as the middle reaches of the Yellow River shelterbelt program area, the Liaohe shelterbelt program area, the Grain to Green program area, and the Three-North Forest shelterbelt program area. Moreover, carbon sinks exhibited strong spatial autocorrelation with low-low clustering in the north and high-high clustering in the south. The increase in CO₂ (slope of CO₂ < 1.8 g/m²/s/y) and solar radiation (slope of radiation >1 w/m²/y) promoted the monotonic increase in the carbon sinks in the center of China. The increase in the areas of forest and grassland shifted the carbon sink trend from decreasing to increasing in the key ecological engineering program areas, and economic development reversed the carbon sink reduction in the Pearl River shelterbelt program area. These findings highlight the positive effect of ecological engineering on carbon sinks and provide adaptation strategies and guidance for China to achieve the "carbon neutrality" target.

How does heterogeneous environmental regulation affect net carbon emissions: Spatial and threshold analysis for China

The Chinese government has made great efforts in air pollutant reduction and carried out strict regulation policies. Since numerous air pollutants and CO₂ tend to have the same root, source, and process, recent studies argue that environmental regulation may also contribute to reducing carbon emissions. To investigate how various types of environmental regulations affect carbon emissions reduction, this paper constructs the spatial Durbin model and panel threshold model based on provincial panel data in China during 2003-2019. The main findings are as follows: First, China's net carbon emissions show a decreasing trend from east to west, displaying significant spatial agglomeration characteristics. Then, formal and informal environmental regulations have inverted U-shaped impacts on net carbon emissions. The "green paradox" and "reverse emission reduction" effects come into play at different stages. Finally, the threshold model reveals that with the improvement of regional technological innovation levels, the carbon-reducing effect of environmental regulation will increasingly come to the fore. These research findings are conducive to providing theoretical guidance for government to formulate and implement environmental regulation policies rationally.

Assessment of Multiple Ecosystem Services and Ecological Security Pattern in Shanxi Province, China

The ecological security pattern construction could effectively regulate ecological processes and ensure ecological functions, then rationally allocate natural resources and green infrastructure, and, finally, realize ecological security. In view of serious soil erosion, accelerated land desertification, soil pollution and habitat degradation in Shanxi Province, the spatial distribution of six key ecosystem services, including water conservation (WC), soil conservation (SC), sand fixation (SF), carbon storage (CS), net primary productivity (NPP) and habitat quality (HQ), was analyzed by using multiple models. The comprehensive ability of multiple ecosystem services in different regions was quantified by calculating multiple ecosystem services landscape index (MESLI). Combined with ecosystem services hotspots, the ecological security pattern of Shanxi Province was constructed by using the minimum cumulative resistance model. The results showed that the spatial differences in ecosystem services in Shanxi Province were obvious, which was low in the seven major basins and Fen River valley, and high in the mountains (especially Taihang and Lvliang Mountains) for WC, SC, CS, NPP and HQ, while high SF was only distributed in the northern Shanxi. The MESLI showed that the ability to provide multiple ecosystem services simultaneously was low in Shanxi Province, with the medium and low grade MESLI regions accounting for 58.61%, and only 18.07% for the high grade MESLI regions. The important protected areas and ecological sources of the ecological security pattern were concentrated in the Lvliang and Taihang Mountains, which were consistent with the key areas of ecosystem services. The ecological corridors illustrated network distribution with ecological sources as the center, the low-, medium- and high-level buffers accounted for 26.34%, 17.03% and 16.35%, respectively. The results will provide important implications for economic transformation, high-quality development and ecological sustainable development in resource-based regions worldwide.

The grassland carbon cycle: Mechanisms, responses to global changes, and potential contribution to carbon neutrality

Grassland is one of the largest terrestrial biomes, providing critical ecosystem services such as food production, biodiversity conservation, and climate change mitigation. Global climate change and land-use intensification have been causing grassland degradation and desertification worldwide. As one of the primary medium for ecosystem energy flow and biogeochemical cycling, grassland carbon (C) cycling is the most fundamental process for maintaining ecosystem services. In this review, we first summarize recent advances in our understanding of the mechanisms underpinning spatial and temporal patterns of the grassland C cycle, discuss the importance of grasslands in regulating inter- and intra-annual variations in global C fluxes, and explore the previously unappreciated complexity in abiotic processes controlling the grassland C balance, including soil inorganic C accumulation, photochemical and thermal degradation, and wind erosion. We also discuss how climate and land-use changes could alter the grassland C balance by modifying the water budget, nutrient cycling and additional plant and soil processes. Further, we examine why and how increasing aridity and improper land use may induce significant losses in grassland C stocks. Finally, we identify several priorities for future grassland C research, including improving understanding of abiotic processes in the grassland C cycle, strengthening monitoring of grassland C dynamics by integrating ground inventory, flux monitoring, and modern remote sensing techniques, and selecting appropriate plant species combinations with suitable traits and strong resistance to climate fluctuations, which would help design sustainable grassland restoration strategies in a changing climate.

Habitats generated by the restoration of coal mining subsidence land differentially alter the content and composition of soil organic carbon

The content and composition of soil organic carbon (SOC) can characterize soil carbon storage capacity, which varies significantly between habitats. Ecological restoration in coal mining subsidence land forms a variety of habitats, which are ideal to study the effects of habitats on SOC storage capacity. Based on the analysis of the content and composition of SOC in three habitats (farmland, wetland and lakeside grassland) generated by different restoration time of the farmland which was destroyed by coal mining subsidence, we found that farmland had the highest SOC storage capacity among the three habitats. Both dissolved organic carbon (DOC) and heavy fraction organic carbon (HFOC) exhibited higher concentrations in the farmland (20.29 mg/kg, 6.96 mg/g) than in the wetland (19.62 mg/kg, 2.47 mg/g) or lakeside grassland (5.68 mg/kg, 2.31 mg/g), and the concentrations increased significantly over time, owing to the higher content of nitrogen in the farmland. The wetland and lakeside grassland needed more time than the farmland to recover the SOC storage capacity. The findings illustrate that the SOC storage capacity of farmland destroyed by coal mining subsidence could be restored through ecological restoration and indicate that the recovery rate depends on the reconstructed habitat types, among which farmland shows great advantages mainly due to the nitrogen addition.

The spatial and temporal distribution of China’s forest carbon

Introduction

China’s forests have sequestrated a significant amount of carbon over the past two decades. However, it is not clear whether China’s forests will be able to continue to have as much carbon sequestration potential capacity in the future.

Methods

In order to research China’s forest carbon storage and carbon sequestration potential capacities at spatial and temporal scales, we built a digital forest model for each province of China using the data from The China Forest Resources Report (2014– 2018) and calculated the carbon storage capacity and sequestration potential capacity of each province with the current management practices without considering natural successions.

Results

The results showed that the current forest carbon storage is 10.0 Pg C, and the carbon sequestration potential in the next 40 years (from year 2019 to 2058) will be 5.04 Pg C. Since immature forests account for the majority of current forests, the carbon sequestration capacity of the forest was also high (0.202 Pg C year−1). However, the forest carbon storage reached the maximum with the increase of stand maturity. At this time, if scenarios such as afforestation and reforestation, human and natural disturbances, and natural succession are not considered, the carbon sequestration capacity of forests will continue to decrease. After 90 years, all stands will develop into mature and over-mature forests, and the forest carbon sequestration capacity is 0.008 Pg year−1; and the carbon sequestration rate is ~4% of what it is nowadays. The change trend of forest carbon in each province is consistent with that of the country. In addition, considering the large forest coverage area in China, the differences in tree species and growing conditions, the forest carbon storage and carbon sequestration capacities among provinces were different. The growth rate of carbon density in high-latitude provinces (such as Heilongjiang, Jilin, and Inner Mongolia) was lower than that in the south (Guangdong, Guangxi, or Hunan), but the forest carbon potential was higher.

Discussion

Planning and implementing targeted forest management strategies is the key to increasing forest carbon storage and extending the service time of forest carbon sinks in provinces. In order to reach the national carbon neutrality goals, we recommend that each province have an informative strategic forest management plan.

The urgent need to develop a new grassland map in China: based on the consistency and accuracy of ten land cover products

Grasslands are the most dominant terrestrial ecosystem in China, but few national grassland maps have been generated. The grassland resource map produced in the 1980s is widely used as background data, but it has not been updated for almost 40 years. Therefore, a reliable map depicting the current spatial distribution of grasslands across the country is urgently needed. In this study, we evaluated the grassland consistency and accuracy of ten land cover datasets (GLC2000, GlobCover, CCI-LC, MCD12Q1, CLUD, GlobeLand30, GLC-FCS30, CGLS-LC100, CLCD, and FROM-GLC) for 2000, 2010, and 2020 based on extensive fieldwork. We concluded that the area of these ten grassland products ranges from 107.80×10⁴ to 332.46×10⁴ km², with CLCD and MCD12Q1 having the highest area consistency. The spatial and sample consistency is highest in the regions of east-central Inner Mongolia, the Qinghai-Tibet Plateau and northern Xinjiang, while the distribution of southern grasslands is scattered and differs considerably among the ten products. MCD12Q1 is significantly more accurate than the other nine products, with an overall accuracy (OA) reaching 77.51% and a kappa coefficient of 0.51; CLCD is slightly less accurate than MCD12Q1 (OA=73.02%, kappa coefficient=0.45) and is more conducive to the fine monitoring and management of grassland because of its 30-meter resolution. The highest accuracy of grassland was found in the Inner Mongolia-Ningxia region and Qinghai-Tibet Plateau, while the accuracy was worst in the southeastern region. In the future grassland mapping, cartographers should improve the accuracy of the grassland distribution in South China and regions where grassland is confused with forest, cropland and bare land. We specify the availability of valuable data in existing land cover datasets for China's grasslands and call for researchers and the government to actively produce a new generation of grassland maps.