Search

Forest carbon sink efficiency refers to the efficiency of input-output indicators related to carbon sinks. This paper studies carbon sink efficiency from the perspective of resource allocation; guides the optimal allocation of resources; and selects forestry employees, forestry investment amount and afforestation area as input indicators; the forest carbon sink efficiency in China is calculated and analyzed based on a data envelopment analysis model by converting the forest volume into the forest carbon sink through the volume expansion factor method. The grey prediction model is used to estimate the change in the input indicator, and the production possibility set is constructed with the input indicator before and after the change and the current output indicator. The efficiency of the decision units before the change is calculated, and through the comparison of efficiency, the conditions of forest carbon sink increase in 15 provinces are obtained. The optimal allocation of the output indicator is calculated based on the inverse data envelopment analysis model. The largest increase in forestry carbon sink is 169,362 megatons in Guangdong, and the smallest is 619 megatons in Tianjin. Finally, some suggestions for the path of forest carbon sink increment are put forward. [ABSTRACT FROM AUTHOR]

Travertine and calcareous tufa are types of continental carbonates that form in subaerial environments near groundwater seepages, springs, and rivers. These deposits exist on all continents and can provide valuable information about past climates, environmental changes, and geological activity. A recent special issue of the journal Minerals focuses on the geochemistry of travertines and calcareous tufas, with contributions from researchers studying deposits in various locations around the world. The articles cover topics such as the formation processes, depositional environments, and diagenesis of these carbonate deposits, as well as their use as paleoclimate records and indicators of geothermal resources. The studies employ a multidisciplinary approach, combining techniques such as geochemistry, mineralogy, petrography, and isotopic analysis to gain a comprehensive understanding of these complex systems. Overall, the contributions highlight the importance of geochemical tracers in enhancing our knowledge of travertines and calcareous tufas. [Extracted from the article]

The land use, land-use change and forestry (LULUCF) sector, as a source and a sink of greenhouse gas (GHG) emissions, is critical for achieving carbon neutrality. Many academic journals have published papers on land use carbon emission or sink (LUCES), but LUCES reviews are relatively rare, which poses great challenges in accurately understanding the research progress and future prospects. This work analyzes the research characteristics, hotspots and future perspectives of LUCES research by using a bibliometric analysis (such as DDA, VOSviewer, CiteSpace software) and a review based on the data (6115 scientific papers) during 1991–2023 from the Web of Science (WoS) platform. We found that (1) over the past 33 years, it first presented a steady growth, then fluctuating growth, and finally a rapid growth trend in the yearly number of publications in LUCES research. The USA (17.31%), China (14.96%), and the UK (7.37%) occupy a dominant position in this research field. (2) The related LUCES research is interdisciplinary, which mainly cover science and technology, meteorology and atmospheric sciences, geology, and environmental sciences and ecology disciplines. (3) The research hotspot analysis on LUCES shows that these articles mostly covered the follow three aspects: ecosystem services, climate change, and carbon neutrality. (4) A review of the past LUCES literature suggests that it is mainly focused on exploring the forefront issues in terms of the definition and boundaries, evaluation method and influencing factors, etc. This work suggests that further research could explore the main scientific problems on quantification of land-based carbon neutrality, quantitative analysis of the impact mechanisms, as well as interdisciplinary research and collaborative governance needed for carbon neutrality. [ABSTRACT FROM AUTHOR]

Carbon emissions are critical to climate change, and land-use change is an essential source of growth in carbon emissions. Research on land-use carbon emissions has become one of the hotspots in academic research. To explore the research hotspots and development trends of land-use carbon emissions in the last 20 years, CiteSpace software was used to conduct a quantitative analysis of relevant literature. This paper was based on the China National Knowledge Infrastructure (CNKI) and Web of Science (WoS) database literature on land-use carbon emissions from 2001 to 2020. The scientific research cooperation network CiteSpace software, with keyword co-occurrence, clustering, and burst word detection, was used to systematically analyze the main research strengths, hotspots and frontiers and clarify the research progress. The research results are as follows: (1) the amount of literature and the depth of research on land-use carbon emissions have increased yearly. However, there is little cooperation between research institutions and scholars, and there is still a lack of large-scale and stable research teams. (2) At the research hotspot level, the English literature focuses on building models and theoretical frameworks to study the internal mechanisms and driving factors of carbon emissions and climate change. The Chinese literature focuses on achieving regional carbon emissions reductions and carbon cycle goals and optimizing a low-carbon economy, transportation and land-use structure. (3) Research frontiers and trends show that the English literature first explored carbon sequestration, organic carbon, and carbon accounting. In China, the research frontiers are gradually becoming focused on influencing factors, decoupling analysis, and the built environment. The study will strengthen the intensity and depth of global carbon emission research and provide a reference for improving global climate change, protecting ecology and balancing economic development. [ABSTRACT FROM AUTHOR]

Due to the serious problems with energy efficiency, carbon emissions, and thermal comfort of rural residences in northern China, an optimization of active and passive heating technologies for rural residences is necessary. In this paper, an optimization for rural residences in northern China is conducted with four objectives: the whole life cycle carbon emission; the annual energy consumption through heating, ventilation, and air conditioning systems; the annual cost; and thermal comfort. In addition, the optimization model with active–passive heating technology synergy is resolved by NSGA-II genetic algorithm. The active and passive design variables, including the type of air source heat pump, orientation, the type and thickness of envelope insulation, the layer of window glass, the window-to-wall area ratio, as well as sunspace parameters are preferred to obtain the optimal solution. The results indicate that the optimal solution obtained by the ideal point method gives the most outstanding performance. Compared with the prototype, the optimized carbon emissions in severe cold and cold regions decreased by 56.1% and 54.6%, respectively. The annual energy consumption decreased by 59.7% and 62.2%. Finally, the roof insulation thickness is the most sensitive design variable in Pareto-optimal solution sets. This paper offers significant guidance in the application of the optimization method of active–passive technology synergy to the energy-saving design of buildings. [ABSTRACT FROM AUTHOR]

Marine plankton communities play a vital role in global carbon and nutrient cycles. Ensuring the robustness of these intricate ecosystems is critical for sustainable environmental management. In this paper, we apply complex network analysis to assess plankton community resilience against disturbances. We construct ecological interaction networks and simulate disruptions using four attack strategies targeting vertices and edges. Our multi-indicator evaluation scheme provides a more nuanced measure of robustness compared to single metrics. Our experiments revealed varying vulnerabilities across networks. Targeted attacks caused abrupt collapse, while random failures led to gradual decline. Identifying critical species and linkages is key to guiding protection efforts. Overall, plankton networks proved fragile, especially against deliberate attacks on central hubs. In our simulations, betweenness-based attacks were the most destructive. The multi-indicator robustness coefficient achieved better performance than individual metrics, with under 3% variance. This network methodology reveals hidden topological vulnerabilities and complements traditional plankton research. The quantitative insights provided in this paper can support marine conservation and governance. [ABSTRACT FROM AUTHOR]

In the past few decades, human activities have caused the emission of large amounts of carbon dioxide, which has severely impacted the Earth's ecosystem and human health. Therefore, carbon reduction has become the focus of global attention. In this study, the Zhouyuan region of China, which is rich in ancient remains, is taken as an example. Based on the land use characteristics in 1990, 2000, 2010, and 2020, the spatial-temporal evolution of land use and carbon storage in the Zhouyuan region is simulated using four methods, including land use classification, land use transfer maps, patch-level land-use simulation (PLUS), and the integrated valuation of ecosystem services and trade-offs (InVEST) models under three scenarios, including the natural development scenario, urban development priority, and heritage conservation priority in 2030. According to the results, the carbon storage in the area in 2030 under all three scenario simulations has decreased compared with 2020, indicating that the region faces great challenges in achieving its targets of carbon peak and carbon neutrality. The paper points out four causes for the decrease in carbon storage, and five suggestions for increasing carbon storage are proposed, such as developing a carbon storage master plan, applying energy-saving technologies, establishing an ecological substitution mechanism, and so on. Through the study of carbon storage in the Zhouyuan region, this paper hopes to establish a mechanism to balance urban development, heritage conservation, and carbon sinks on the one hand, and encourage more scholars to participate in the study of carbon sinks in areas rich in ancient remains, so as to to jointly promote their healthy development on the other. [ABSTRACT FROM AUTHOR]

The land-use, land-use change and forestry (LULUCF) sector is receiving increasing attention in climate change mitigation and greenhouse gas (GHG) emission offsetting. The sector itself and measures applied to mobilize this sector in order to tackle climate change are dominant in nationally determined contributions under the Paris Agreement as well as in national strategies, as in the case of Lithuania. Lithuania has set the goal of becoming a carbon-neutral country in 2050, reducing GHGs by 80% compared to 1990 and offsetting the remaining 20% through the LULUCF sector. Therefore, this paper aims at analyzing historical land-use changes in 1990–2021, as reported for the United Nations Framework Convention on Climate Change (UNFCCC) secretariat, and LULUCF's potential to achieve climate change mitigation goals, taking into account different land-use change scenarios (business as usual, forest development, forest development + additional measures and forest land 40% + additional measures) for 2030 and 2050 in Lithuania. The scenarios are based on historical and potential future policy-based land-use changes. Projections of GHG emissions/removals for different scenarios are prepared according to the Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (2006) by the Intergovernmental Panel on Climate Change (IPCC). The results indicate that land-use changes over the period 1990–2021 remained rather stable, with some increases in forest area and grassland at the expense of cropland. The whole LULUCF sector acted as a carbon sink in most cases, forests being a key category for removal. However, reaching climate neutrality in 2050 might be challenging, as the goal to offset 20% of remaining GHG emission compared to 1990 through LULUCF would not be met in any of the scenarios analyzed, even the scenario of maximal forest-area development and additional measures. Considering the high historical GHG-removal fluctuations and the uncertainties of the sector itself, caution should be taken when relying on LULUCF's potential to reach the set goals. [ABSTRACT FROM AUTHOR]

This paper explores the application and advantages of remote sensing, machine learning, and mid-infrared spectroscopy (MIR) as a popular proximal sensing spectroscopy tool in the estimation of soil organic carbon (SOC). It underscores the practical implications and benefits of the integrated approach combining machine learning, remote sensing, and proximal sensing for SOC estimation and prediction across a range of applications, including comprehensive soil health mapping and carbon credit assessment. These advanced technologies offer a promising pathway, reducing costs and resource utilization while improving the precision of SOC estimation. We conducted a comparative analysis between MIR-predicted SOC values and laboratory-measured SOC values using 36 soil samples. The results demonstrate a strong fit (R² = 0.83), underscoring the potential of this integrated approach. While acknowledging that our analysis is based on a limited sample size, these initial findings offer promise and serve as a foundation for future research. We will be providing updates when we obtain more data. Furthermore, this paper explores the potential for commercialising these technologies in Australia, with the aim of helping farmers harness the advantages of carbon markets. Based on our study's findings, coupled with insights from the existing literature, we suggest that adopting this integrated SOC measurement approach could significantly benefit local economies, enhance farmers' ability to monitor changes in soil health, and promote sustainable agricultural practices. These outcomes align with global climate change mitigation efforts. Furthermore, our study's approach, supported by other research, offers a potential template for regions worldwide seeking similar solutions. [ABSTRACT FROM AUTHOR]

Soil and water conservation measures have good carbon sinking capacity, and the comprehensive management of small watersheds involves plant measures, engineering measures and farming measures, which profoundly affect the capacity of the three major carbon pools of soil, vegetation and water bodies, making them an ideal place to carry out the monitoring and accounting of carbon sinks in soil and water conservation. The purpose of this paper is to monitor and evaluate the carbon sinks of soil and vegetation, to provide techniques and methods for the implementation of dynamic monitoring and evaluation of carbon sinks in soil and water conservation projects, and to provide theoretical and methodological support for the participation of soil and water conservation projects in carbon trading and the study of the formulation of relevant rules. In this study, field sampling and analysis, LiDAR, remote sensing and other related parameters were used to account for the carbon storage of vegetation carbon pools and soil carbon pools in the Luodi River sub-watershed, Changting County, Fujian Province, from 2001 to 2022, and to evaluate the carbon sink capacity of the various soil and water conservation management measures in the sub-watershed. The results show that after 21 years of comprehensive management, various soil and water conservation measures in the Luodi River sub-basin have significantly enhanced the role and capacity of carbon sinks, and the sub-basin's carbon stock increased by 3.97 × 104 t, with an average annual increase of 1.89 × 103 t/a. From the perspective of the carbon pools, the carbon stocks of soil and vegetation increased by 73.73% and 346.41%, respectively, from 2001 to 2022. The total carbon sunk in the sub-watershed reached 2.90 × 104 t, of which 1.57 × 104 t was in soil carbon sinks and 1.34 × 104 t was in vegetation carbon sinks. There were differences in the ability of various measures to enhance the increment of the carbon sink, among which the Castanea mollissima and the Fertilized Pinus massoniana Forest had the most obvious increase in carbon sunk, followed by the Mixed Needleleaf and Broadleaf Forest, the Nurture and Management Pinus massoniana Forest, and the Horizontal terraces Pinus massoniana Forest, and lastly, the Closed Management Forest and the Morella rubra. Various soil and water conservation measures have obvious effects of carbon retention, carbon sequestration and sink enhancement, while Castanea mollissima and Fertilized Pinus massoniana Forest and other forests that implement land preparation and afforestation with fertilization and nourishment measures have more significant increases in carbon sink capacity, which is an effective measure to improve the benefits of soil and water conservation and increase the amount of carbon sinks. [ABSTRACT FROM AUTHOR]

Land cover and its changes over time are significant for better understanding the Earth's fundamental characteristics and processes, such as global climate change, hydrology, and the carbon cycle. A number of land cover-geoprocessing models have been proposed for land cover-data production with different spatial and temporal resolutions. With the massive growth in land cover data and the increasing demand for efficient model utilization, developing efficient and convenient land cover-geoprocessing models has become a formidable challenge. Although some model-migration methods have been proposed for handling the massive data, the intricacy of land cover-data and -heterogeneity models frequently prevent current strategies from directly meeting demand. In this paper, we propose the PAMC-LC-containerization approach to overcome the difficulties associated with moving existing land cover models in the open web environment. Based on the idea of model migration, we design a standardized model description and hierarchical encapsulation strategy for land cover models, and develop migration and deployment methods. Furthermore, we assess the viability and efficacy of the proposed approach by using coupled workflows for model migration and the introduction of visualization on the Mts-WH dataset and the Google dataset. The experimental results show that the PAMC-LC approach can simplify and streamline the model migration process, with important ramifications for increasing productivity, reusing models, and lowering additional data-transmission costs. [ABSTRACT FROM AUTHOR]

Carbon emissions and land use intensity serve as crucial indicators of land management. This paper proposes a methodological framework to elucidate the sustainability of carbon emissions in rural areas via a coordination model, scrutinizes the correlation with land use intensity, and investigates the significance of influential factors. The study focuses on village-level units within Ningde City, and finds the pronounced spatial heterogeneity characterizing the distribution of carbon emissions across different villages: (1) Villages exhibiting high levels of carbon emissions are predominantly concentrated in the southeast region, whereas those with low carbon emission levels are primarily clustered in the northwest region. The majority of the villages serve as net carbon emission sources. (2) Spatial disparities exist in the impact of land use intensity on economic benefits from carbon sources, ecological benefits from carbon sinks, and carbon emission sustainability. (3) Significant variations exist in the influence of factors affecting land use intensity, economic benefits of carbon sources, ecological benefits of carbon sinks, and the sustainability of carbon emissions in rural areas. These findings could guide governments in implementing distinct land use control policies and provide a framework for assessing carbon emission sustainability within land management strategies. [ABSTRACT FROM AUTHOR]

As a world-class urban agglomeration, the Yangtze River Delta urban agglomeration is significant for China's carbon neutrality strategy when calculating the carbon budget and dividing carbon compensation zones. This paper focused on 129 county-level cities in the core area of the Yangtze River Delta urban agglomeration, calculating the carbon budget for 2000–2020 using night-time light data and analyzing the evolution of spatiotemporal patterns. On this basis, a carbon compensation zoning model was constructed using the K-means algorithm; 129 cities were divided into different carbon compensation zones after combining this model with the main functional zones. The results showed that (1) the carbon emissions increased, with overall stabilization after 2012. The distribution of high carbon emission areas evolved from a "core-periphery" pattern into a "Z-shaped" pattern. Ecological carbon sinks showed a continuous decline, with the southern sinks performing better than the northern ones. (2) The carbon budget of the study area showed a deficit, gradually widening and exhibiting an unbalanced spatial distribution characterized by a "high in the south and low in the north" pattern. (3) Eleven types of carbon compensation zones were designated after overlaying the main functional zones. Low-carbon development suggestions were proposed for each zone type. [ABSTRACT FROM AUTHOR]

Eucalyptus plantation forests in southern China provide not only the economic value of producing timber, but also the ecological value service of absorbing carbon dioxide and releasing oxygen. Based on the theory of spatial colonial modeling, this paper proposes a new method for 3D reconstruction of tree terrestrial LiDAR point clouds for determining the aboveground carbon stock of eucalyptus monocotyledons, which consists of the main steps of tree branch and trunk separation, skeleton extraction and optimization, 3D reconstruction, and carbon stock calculation. The main trunk and branches of the tree point clouds are separated using a layer-by-layer judgment and clustering method, which avoids errors in judgment caused by sagging branches. By optimizing and adjusting the skeleton to remove small redundant branches, the near-parallel branches belonging to the same tree branch are fused. The missing parts of the skeleton point clouds were complemented using the cardinal curve interpolation algorithm, and finally a real 3D structural model was generated based on the complemented and smoothed tree skeleton expansion. The bidirectional Hausdoff distance, average Hausdoff distance, and F distance were used as evaluation indexes, which were reduced by 0.7453 m, 0.0028 m, and 0.0011 m, respectively, and the improved spatial colonization algorithm enhanced the accuracy of the reconstructed tree 3D structural model. To verify the accuracy of our method to determine the carbon stock and its related parameters, we cut down 41 eucalyptus trees and destructively sampled the measurement data as reference values. The R2 of the linear fit between the reconstructed single-tree aboveground carbon stock estimates and the reference values was 0.96 with a CV(RMSE) of 16.23%, the R2 of the linear fit between the trunk volume estimates and the reference values was 0.94 with a CV(RMSE) of 19.00%, and the R2 of the linear fit between the branch volume estimates and the reference values was 0.95 with a CV(RMSE) of 38.84%. In this paper, a new method for reconstructing eucalyptus carbon stocks based on TLS point clouds is proposed, which can provide decision support for forest management and administration, forest carbon sink trading, and emission reduction policy formulation. [ABSTRACT FROM AUTHOR]

Simple Summary: In this paper, the differences in carbon sequestration microbial communities in different wetland types and their main influencing factors were investigated. It was found that the alpha diversity of cbbM carbon-sequestering microorganisms was consistent with the change trend in the total carbon content. Acidithiobacillus was used as a biomarker in lakeside wetlands, and Thiothrix and Thiodictyon were used as biomarkers in marsh wetlands. The diversity of cbbM carbon-fixing microorganisms was primarily influenced by the total nitrogen content, while the community structure was significantly affected by the soil total carbon content. The increase in soil temperature and humidity was conducive to the carbon-sequestering process of Thiomicrospira, Thiomonas, Polaromonas and Acidithiobacillus. The changes in wetland types seriously affected the characteristics of cbbM carbon sequestration in microbial communities, and a warm and humid climate may be conducive to wetland carbon sequestration. Carbon-sequestering microorganisms play an important role in the carbon cycle of wetland ecosystems. However, the response mechanism of carbon-sequestering microbial communities to wetland type changes and their relationship with soil carbon remain unclear. To explore these differences and identify the main influencing factors, this study selected marsh wetlands, river wetlands and lakeside wetlands around Qinghai Lake as research subjects. High-throughput sequencing was employed to analyze the functional gene cbbM of carbon-sequestering microorganisms. The results revealed that the alpha diversity of cbbM carbon-sequestering microorganisms mirrored the trend in total carbon content, with the highest diversity observed in marsh wetlands and the lowest in lakeside wetlands. The dominant bacterial phylum was Proteobacteria, with prevalent genera including Thiothrix, Acidithiobacillus, and Thiodictyon. Acidithiobacillus served as a biomarker in lakeside wetlands, while two other genera were indicative of marsh wetlands. The hierarchical partitioning analysis indicated that the diversity of cbbM carbon-fixing microorganisms was primarily influenced by the total nitrogen content, while the community structure was significantly affected by the soil total carbon content. Moreover, an increased soil temperature and humidity were found to favor the carbon fixation processes of Thiomicrospira, Thiomonas, Polaromonas, and Acidithiobacillus. In summary, changes in wetland types seriously affected the characteristics of cbbM carbon sequestration in microbial communities, and a warm and humid climate may be conducive to wetland carbon sequestration. [ABSTRACT FROM AUTHOR]

Peatlands are major natural carbon pool in terrestrial ecosystems globally and are essential to a variety of fields, including global ecology, hydrology, and ecosystem services. Under the context of climate change, the management and conservation of peatlands has become a topic of international concern. Nevertheless, few studies have yet systematized the overall international dynamics of existing peatland research. In this study, based on an approach integrating bibliometrics and a literature review, we systematically analyzed peatland research from a literature perspective. Alongside traditional bibliometric analyses (e.g., number of publications, research impact, and hot areas), recent top keywords in peatland research were found, including 'oil palm', 'tropical peatland', 'permafrost', and so on. Furthermore, six hot topics of peatland research were identified: (1) peatland development and the impacts and degradations, (2) the history of peatland development and factors of formation, (3) chemical element contaminants in peatlands, (4) tropical peatlands, (5) peat adsorption and its humic acids, and (6) the influence of peatland conservation on the ecosystem. In addition, this review found that the adverse consequences of peatland degradation in the context of climate change merit greater attention, that peatland-mapping techniques suitable for all regions are lacking, that a unified global assessment of carbon stocks in peatlands urgently needs to be established, spanning all countries, and that a reliable system for assessing peatland-ecosystem services needs to be implemented expeditiously. In this study, we argued that enhanced integration in research will bridge knowledge gaps and facilitate the systematic synthesis of peatlands as complex systems, which is an imperative need. [ABSTRACT FROM AUTHOR]

Vijitharan et al. [[22]] use remote sensing technologies and enhanced vegetation index threshold values to track land cover change, especially of forest land in the Vavuniya District of Sri Lanka, and they assess the subnational forest reference emission level. Land use has become a highly attractive research topic for understanding human-environment interactions in the Anthropocene [[1]]. This issue includes 15 peer-reviewed papers by scholars from developing countries that have faced, and continue to face, intensive human-land conflicts, clearly demonstrating that unbalanced interconnections between land use change and human activities are a global challenge. Third, some papers [[23], [25], [27]] analyze specific cases of land use or land management and the influencing factors behind those decisions, policies, and actions, with the goal to improve land use performance regionally. [Extracted from the article]

Anthropogenic land cover change is one of the primary sources of increasing carbon emissions and affects the potential of terrestrial ecosystems to store carbon and act as carbon sinks. As a necessary means to reduce land expansion, land-use intensification significantly impacts greenhouse gas emission reduction and the low-carbon transition of the economy. This paper constructs a framework for the relationship between intensive land use (ILU) and low carbon transition (LCT), considering direct and spatially driven effects. First, this paper constructs a multidimensional indicator to measure intensive land use and documents the spatial pattern of intensive land use levels in China. Second, this paper assesses the spatial driving effect of intensive land use on China's economic low-carbon transition. Based on data from 283 Chinese cities from 2006–2019 and using a spatial Durbin model, the study provides empirical evidence that intensive land use can significantly promote low-carbon transition in neighboring and economically linked cities (especially in eastern cities, large and medium-sized cities, and veteran economic circles). Tests introducing exogenous policy shocks further confirm the robustness of the findings. In addition, industrial structure transformation and technology spillovers are identified as the dual mechanism channels of intensive land use for low-carbon transition in China, and the spatial driving effect on neighboring cities attenuating with geographic distance is also confirmed. [ABSTRACT FROM AUTHOR]

Compared to more developed countries, the use of land resources is less efficient in China. China's vast forest land area gives it a rich and underutilized carbon sink. This is an important way for China to achieve the goals of "carbon peaking" and "carbon neutrality", which is of great significance to China's sustainable development. In the past 20 years, China has designed a series of policies to serve the development of forestry carbon sinks, namely the forestry carbon sink mechanism (FCSM). However, the questions of which policy is the most important, and what is the socio-economic value it generates, have not been fully investigated. Accordingly, this paper studied 30 provincial-level regions in China from 2005 to 2020 using the difference-in-differences (DID) model. The conclusions show that: (1) the FCSM does increase the socio-economic value of land resources, thus improving the sustainability of land resources; (2) the FCSM helps to increase forest coverage, forest stock volume and the forest coverage rate, which increases the social value of land resources from the greening path; (3) the FCSM helps to increase the gross forestry product, which increases the economic value of land resources through the path of increasing production value. [ABSTRACT FROM AUTHOR]

Land-use–cover change (LUCC)/vegetation cover plays a critical role in Earth system science and is a reflection of human activities and environmental changes. LUCC will affect the structure and function of ecosystems and a series of other terrestrial surface processes, such as energy exchange, water circulation, biogeochemical circulation, and vegetation productivity. Therefore, accurate LUCC mapping and vegetation cover monitoring are the bases for simulating the global carbon and hydrological cycles, studying the interactions of the land surface and climate, and assessing land degradation. Based on field GPS surveys and UAV data, with cloud-free and snow/glacier algorithms and the SVM classifier to train and model alpine grassland, the alpine grassland and LUCC were extracted by using Landsat-8 OLI satellite images in Sanjiangyuan National Park in this paper. The latest datasets of vegetation types with 30 m × 30 m spatial resolution in the three parks were prepared and formed. The classification results show that the SVM classifier could better distinguish the major land-use types, and the overall classification accuracy was very high. However, in the alpine grassland subcategories, the classification accuracies of the four typical grasslands were relatively low, especially between desert steppes and alpine meadows, and desert steppes and alpine steppes. It manifests the limitations of Landsat-8 multispectral remote sensing imageries in finer-resolution grassland classifications of high-altitude alpine mountains. The method can be utilized for other multispectral satellite imageries with the same band matching, such as Landsat 7, Landsat 9, Sentinel-2, etc. The method described in this paper can rapidly and efficiently process annual alpine grassland maps of the source areas of the Yellow River, the Yangtze River, and the Lancang River. It can provide timely and high-spatial-resolution datasets for supporting scientific decisions for the sustainable management of Sanjiangyuan National Park. [ABSTRACT FROM AUTHOR]

The local land use carbon budget (LUCB) balance is an important factor in achieving regional carbon neutrality. As the basic unit of China's economic development and social governance, the county level is an important part of the realization of the "double carbon" goal. This paper focuses on 1069 county units within the Yangtze River Economic Belt (YREB). It utilizes data on land use, nighttime light, energy consumption, and social and economic factors to construct carbon emission models. The spatiotemporal characteristics of LUCB in these county units are analyzed using standard deviational ellipse (SDE) and spatial autocorrelation methods. Additionally, a zoning study is conducted by examining the economic contribution coefficient (ECC) of carbon emissions, the ecological support coefficient (ESC), and their coupling relationship. The results show that (1) the total land use carbon emissions (LUCE) increased significantly during the research period, and the total carbon sink was relatively stable. (2) The LUCB is spatially high in the east and low in the west, with the center of gravity moving to the southwest as a whole. (3) The LUCB shows positive spatial autocorrelation and has significant spatial agglomeration characteristics, which are mainly high–high and low–low regional agglomeration types. (4) The ECC is high in the east and low in the west, the ESC is high in the west and low in the east, and the coordination and coupling degrees of the two are low. (5) According to the ECC and ESC, the county unit is divided into a low-carbon conservation area, an economic development area, a carbon sink development area, and a comprehensive optimization area. This study is helpful in promoting the sustainable development of carbon neutrality and low carbon in the YREB. [ABSTRACT FROM AUTHOR]

Globally, forest ecosystems, especially subtropical forests, play a central role in biogeochemical cycles and climate regulation, demonstrating their irreplaceable function. The subtropical region of China, characterized by its unique forest ecosystem, complex terrain, climate heterogeneity, diverse vegetation types, and frequent human activities, underscores the importance of the in-depth study of its net primary productivity (NPP). This paper employs the eddy covariance–light use efficiency (EC-LUE) model to quantitatively estimate the gross primary productivity (GPP) of this region from 2001 to 2018, followed by an estimation of the actual net primary productivity (ANPP) using the carbon use efficiency (CUE). The results showed that over these 18 years, the annual average ANPP was 677.17 gC m−2 a−1, exhibiting an overall increasing trend, particularly in mountainous areas, reserves, and the cultivated lands of the northeastern plains, whereas a significant decrease was observed around the urban agglomerations on the southeast coast. Furthermore, the Thornthwaite memorial model was applied to calculate the potential net primary productivity (PNPP), and diverse scenarios were set to quantitatively evaluate the impact of climate change and human activities on the vegetation productivity in the study area. It was found that in areas where the ANPP increased, both human activities and climate change jointly influenced ANPP dynamics; in areas with a decreased ANPP, the impact of human activities was particularly significant. Additionally, the heterogeneous distribution of ANPP across different altitudinal gradients and the driving effects of various climatic factors were analyzed. Finally, a partial correlation analysis was used to examine the relationships between the temperature, precipitation, and ANPP. This study indicated that temperature and precipitation have a substantial impact on the growth and distribution of vegetation in the region, yet the extent of this influence shows considerable variation among different areas. This provides a robust scientific basis for further research and understanding of the carbon dynamics of subtropical forest ecosystems and their role in the global carbon cycle. [ABSTRACT FROM AUTHOR]

Forest stock volume is the main factor to evaluate forest carbon sink level. At present, the combination of multi-source remote sensing and non-parametric models has been widely used in FSV estimation. However, the biodiversity of natural forests is complex, and the response of the spatial information of remote sensing images to FSV is significantly reduced, which seriously affects the accuracy of FSV estimation. To address this challenge, this paper takes China's Baishanzu Forest Park with representative characteristics of natural forests as the research object, integrates the forest survey data, SRTM data, and Landsat 8 images of Baishanzu Forest Park, constructs a time series dataset based on survey time, and establishes an FSV estimation model based on the CNN-LSTM-Attention algorithm. The model uses the convolutional neural network to extract the spatial features of remote sensing images, uses the LSTM to capture the time-varying characteristics of FSV, captures the feature variables with a high response to FSV through the attention mechanism, and finally completes the prediction of FSV. The experimental results show that some features (e.g., texture, elevation, etc.) of the dataset based on multi-source data feature variables are more effective in FSV estimation than spectral features. Compared with the existing models such as MLR and RF, the proposed model achieved higher accuracy in the study area (R2 = 0.8463, rMSE = 26.73 m3/ha, MAE = 16.47 m3/ha). [ABSTRACT FROM AUTHOR]

The Journal of Marine Science & Engineering has published a special issue titled "Technological Oceanography," which focuses on the use of technology to better understand the ocean. The issue includes original papers on topics such as automated tethered profilers for hydrophysical and bio-optical measurements, a multisampler tool for mesopelagic nets, the use of traditional sailboats as oceanographic platforms, laboratory studies on turbulent mass exchange, the fine structure of vertical density distribution in the Black Sea, the contribution of the Vendée Globe race to improved ocean surface information, an efficient method for nested high-resolution ocean modeling, a projection method for estimating error covariance matrices in ocean modeling, a spectral method for two-dimensional ocean acoustic propagation, a vector wavenumber integration model for underwater acoustic propagation, and a compression and storage method for high-resolution sound speed profile data. The authors express their gratitude to the contributors and reviewers of the special issue. [Extracted from the article]

Climate change is a global threat that is better understood through global research spanning many regions, countries, and life-supporting ecosystems. This review focused on the bibliometric analysis of the distribution of a global research compendium on phytoplankton, an aquatic community that accounts for half of the carbon fixation in the global carbon cycle. A total of 716 primary research papers published in the immediate past decade (2012–2022) were reviewed. The articles were sampled from Web of Science and described field and laboratory experiments quantifying the impact of global climate change on phytoplankton from different climate zones (tropical, subtropical, temperate, subpolar, and polar) and ecosystems (freshwater, brackish, and marine). Analyses of these studies suggest that the bulk of the global research effort (80%) focused on high-latitude areas, and only a few (17%) were carried out in the tropics, largely led by four countries (Australia, Brazil, India, and Saudi Arabia), while Africa's contribution was minuscule (<1%). In terms of ecosystems, the majority (76%) of the research was in marine waters, irrespective of the climate zone. Analyses of these studies also highlighted widespread disparities in phytoplankton research in tropical aquatic ecosystems, particularly in Africa. Strategic investment in terms of targeted funding, institutional networks, and partnerships between the global north and global south are necessary to increase phytoplankton research across different ecosystems in the tropics. [ABSTRACT FROM AUTHOR]

Simple Summary: In this paper, the differences in carbon sequestration microbial communities in different wetland types and their main influencing factors are investigated. It was found that Proteobacteria was the dominant group of carbon sequestration microorganisms in the marsh wetland and river source wetland, and Cyanobacteria was the dominant group of carbon sequestration microorganisms in the lakeside wetland. The α diversity, relative abundance of Proteobacteria and total carbon content of the marsh wetland were the highest, followed by the river source wetland and lakeside wetland. The soil temperature and moisture and the total carbon content were the most important factors affecting the structures of carbon-sequestering microbial communities. Compared with the riverhead and lakeside wetlands, the marsh wetland has the highest carbon storage. 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. [ABSTRACT FROM AUTHOR]

This paper discusses and reviews the main real gas effects on the thermodynamic performance of closed Brayton cycles. Cycles with carbon dioxide as working fluids are taken as a reference and a comparison of the thermodynamic cycle efficiencies that are made with other possible working fluids (pure fluids and fluid mixtures). We fixed the reduced operating conditions, in optimal conditions, so that all working fluids had the same thermodynamic global performances. Therefore, the choice of the working fluid becomes important for adapting the cycle to the different technological requirements. The positive effects of the real gas properties in supercritical cycles were approximately maximal at reduced minimum cycle temperatures of about 1.01 to 1.05, with maximum to minimum cycle temperatures of about 2.2. The use of mixtures furthers widens the application of the field of closed Brayton cycles, thereby allowing a continuous variation in the critical temperature of the resulting working fluid and, in some cases, also making it possible to take the condensation with a significant further increase in the thermodynamic cycle efficiency. The paper also demonstrates the thermodynamic convenience of resorting to mixtures of carbon dioxide and inert gases. Extensive measurements of vapour–liquid equilibria and analysis of the thermal stability and material compatibility are essential for a practical and full use of the real gas Brayton cycles. [ABSTRACT FROM AUTHOR]

Forests are critical to mitigating global climate change and regulating climate through their role in the global carbon and water cycles. Accurate monitoring of forest cover is, therefore, essential. Image segmentation networks based on convolutional neural networks have shown significant advantages in remote sensing image analysis with the development of deep learning. However, deep learning networks typically require a large amount of manual ground truth labels for training, and existing widely used image segmentation networks struggle to extract details from large-scale high resolution satellite imagery. Improving the accuracy of forest image segmentation remains a challenge. To reduce the cost of manual labelling, this paper proposed a data augmentation method that expands the training data by modifying the spatial distribution of forest remote sensing images. In addition, to improve the ability of the network to extract multi-scale detailed features and the feature information from the NIR band of satellite images, we proposed a high-resolution forest remote sensing image segmentation network by fusing multi-scale features based on double input. The experimental results using the Sanjiangyuan plateau forest dataset show that our method achieves an IoU of 90.19%, which outperforms prevalent image segmentation networks. These results demonstrate that the proposed approaches can extract forests from remote sensing images more effectively and accurately. [ABSTRACT FROM AUTHOR]

Urbanization profoundly impacts the global carbon cycle and climate change. Many studies have shown that both urban vitality and urban carbon emissions are deeply affected by spatial planning and city structure. However, the specific relationship between urban vitality and urban carbon emissions is rarely studied. An index system of urban vitality was established from four aspects: social, economic, cultural, and environmental. After analyzing the spatial distribution characteristics of urban vitality combined with spatial syntax and the TOPSIS model, this paper further investigated the influence of urban vitality-building factors on the distribution of urban carbon emissions based on the Geodetector method. The research results show that: (1) Xuzhou shows obvious spatial differences in urban vitality, mainly decreasing from the center to the surrounding areas, with a small vitality center in the northeast. (2) The impact of different dimensions of vitality on urban carbon emissions is apparently different. (3) Facilities' aggregation has the weakest explanatory power for urban carbon emissions, while the NDVI has the highest explanatory power. This study helps to clarify the spatial correlation and influence mechanism between urban vitality and urban carbon emissions. Finally, some suggestions are proposed to construct low-carbon and high-vitality cities. [ABSTRACT FROM AUTHOR]

Accurate measurement of agricultural total factor productivity (AGTFP) is crucial to measure the level of sustainable agricultural development, and agricultural carbon sink is an important element to leverage the development of green transformation. Few studies have incorporated agricultural carbon sink into the measurement framework of AGTFP, and the evolutionary dynamics and related spatial effects of Chinese AGTFP from the perspective of carbon sinks are unclear. On this basis, the paper used a provincial-level agricultural panel data set of China from 2000 to 2019 to measure the provincial indicators of agricultural carbon sinks, CO2 emissions and agricultural non-point source pollution. Then, we incorporated these environmental factors into the measurement framework of AGTFP and used the SBM-DEA model to calculate the Chinese AGTFP from the perspective of carbon sinks. We further analyzed the spatial and temporal divergence and convergence of AGTFP in China using Moran'I and spatial econometric models. We found that after measuring AGTFP, including agricultural carbon sinks, 28 out of 30 Chinese provinces showed an increased trend, but the development gap between regions was obvious. The spatial econometric model showed a significantly positive spatial correlation between the AGTFP of each province and did not have absolute α-convergence and absolute β-convergence characteristics. After adding the control variables of resource endowment of each province, it showed conditional β-convergence characteristics, and the spatial spillover effect of China's AGTFP was increasing. Finally, the paper proposed policy recommendations for the sustainable and coordinated development of China's agricultural regions in response to the research findings. [ABSTRACT FROM AUTHOR]

Under the background of double carbon, it is important to study forest pests and diseases to improve forest carbon sink. In this paper, we establish a delayed model associated with the larvae and adults of Saperda populnea, susceptible poplars, and infected poplars. First, we analyze the existence and stability of the equilibrium of the model. Second, we study the existence of Hopf bifurcation near the equilibrium and obtain the normal form of Hopf bifurcation by the multiple time scales method. Then, we analyze the direction and stability of Hopf bifurcating periodic solutions. Third, we analyze and conjecture some parameter values based on official data, and carry out numerical simulations to verify our results. Finally, we give some suggestions on the prevention and control of Saperda populnea. [ABSTRACT FROM AUTHOR]

The organic carbon pool is a crucial component of the ocean carbon cycle. The study of organic carbon distribution and interannual variability in the land-sea interface can contribute to understanding the global ocean carbon cycle and ecological effects in the context of the Anthropocene and help achieve the Sustainable Development Goals (SDGs). At present, there has been a certain amount of research on the source and flux of carbon in the ocean carbon cycle, but the prediction of marine carbon is still in its infancy. In this paper, a CNN-LSTM deep learning model that takes into account spatio-temporal features was used to make a 5-year mid-long-term rolling prediction of particulate organic carbon (POC) and yellow matter (CDOM) using MODIS Level 2 semimonthly synthetic data from the official website of NASA from January 2002 to June 2020. The model uses chlorophyll-a data to adjust the parameters. The results showed that the model could also be applied to the mid-long-term rolling prediction of POC and CDOM. The model was capable of accurately predicting POC and CDOM over periods of three and two years, respectively (R > 0.5). Meanwhile, the 5-year trends of the predicted and actual values were verified using the least squares method and the Mann-Kendall trend test. The results showed that the predicted and actual values of sea surface POC and CDOM in 2015–2020 showed an overall upward trend. The surface-level POC and CDOM in the ocean are considered to be related to primary production. The mid-long-term prediction of surface seawater organic carbon in the southern South China Sea helps humans explore the regional characteristics of organic carbon in the coral reef waters of the South China Sea and study the changing trend of surface seawater organic carbon. [ABSTRACT FROM AUTHOR]

The SSS (sea surface salinity) is an important factor affecting global climate changes, sea dynamic environments, global water cycles, marine ecological environments, and ocean carbon cycles. Satellite remote sensing is a practical way to observe SSS from space, and the key to retrieving SSS satellite products is to establish an accurate sea surface brightness temperature forward model. However, the calculation results of different forward models, which are composed of different relative permittivity models and SSR (sea surface roughness) brightness temperature increment models, are different, and the impact of this calculation difference has exceeded the accuracy requirement of the SSS inversion, and the existing SSR brightness temperature increment models, which primarily include empirical models and theoretical models, cannot match all the relative permittivity models. In order to address this problem, this paper proposes a universal DNN (deep neural network) model architecture and corresponding training scheme, and provides different SSR brightness temperature increment models for different relative permittivity models utilizing DNN based on offshore experiment data, and compares them with the existing models. The results show that the DNN models perform significantly better than the existing models, and that their calculation accuracy is close to the detection accuracy of a radiometer. Therefore, this study effectively solves the problem of SSR brightness temperature correction under different relative permittivity models, and provides a theoretical support for high-precision SSS inversion research. [ABSTRACT FROM AUTHOR]

Climate change has become a threat to humanity, and achieving carbon neutrality is a worldwide objective. Forests are significant natural carbon sinks; forest ecosystems are one of the effective ways to mitigate climate change, and professional management may effectively contribute to achieving carbon neutrality goals. Using the bibliometrix R-package in R and CiteSpace for bibliometric analysis of research areas from general statistics and knowledge base perspectives, this study systematically reviewed the status, evolution, and research hotspots of forest management in the context of carbon neutrality based on 6112 papers published in this research area between 2002 and 2022. The results revealed: (1) The research on forest management in the context of carbon neutrality has rapidly developed with a high level of attention between 2002–2022. Furthermore, this field of research has become a well-established discipline. (2) Throughout the research history, there were five main research hotspots, which were the function of forest carbon sinks, scientific and rational forest management, forest ecosystem services, assessment of forest carbon sinks, and forest management models. (3) Potential future research avenues include the development of a new model of forest management in the context of carbon neutrality. [ABSTRACT FROM AUTHOR]

In the context of carbon peaking and carbon neutrality ("double carbon"), it is urgent to clarify the effect of marine spatial planning (MSP) on carbon sink increases and emission reductions, since such planning acts as a spatial governance tool for the earth's largest carbon pool. In this paper, a linkage model between marine spatial functional zones and carbon distribution is established. To explore the relationship between marine spatial functional zones and carbon, the study analyzed the carbon increase or reduction role of sea-use activities in each zone and considered the carbon sequestration function of the marine ecosystem itself. A marine spatial pattern of "Two Spaces and Four Carbon Areas" is proposed to present the linkage. A carbon distribution pattern in marine space is delimited using the linkage model and the current MSP in the case study of the city of Tangshan, Hebei, China. Some measures have been taken or planned to be taken in Tangshan to improve the carbon sink function of the ecosystem and the marine space. The supporting role of MSP in achieving the "double carbon" goal is studied, and the paths and suggestions for integrating the "double carbon" goal into MSP are explored. [ABSTRACT FROM AUTHOR]

China's largest high-latitude permafrost distribution zone is in Northeast China. With the intensification of global warming and engineering construction, the carbon stored in permafrost will gradually thaw and be released in the form of methane gas. However, research on the changes in methane concentration and emission sources in this area is still unclear. In this paper, the AIRS (Atmospheric Infrared Sounder) data carried by the Aqua satellite were used to analyze the distribution and change trends in the overall methane concentration in the near-surface troposphere in Northeast China from 2003 to 2022. These data, combined with national meteorological and on-site monitoring data, were used to study the methane emission characteristics and sources in the permafrost area in Northeast China. The results show that the methane concentration in the near-surface troposphere of Northeast China is mainly concentrated in the permafrost area of the Da and Xiao Xing'an Mountains. From 2003 to 2022, the methane concentration in the near-surface troposphere of the permafrost area in Northeast China showed a rapid growth trend, with an average linear trend growth rate of 4.787 ppbv/a. In addition, the methane concentration in the near-surface troposphere of the permafrost area shows a significant bimodal seasonal variation pattern. The first peak appears in summer (June–August), with its maximum value appearing in August, and the second peak appears in winter (December–February), with its maximum value appearing in December. Combined with ground surface methane concentration monitoring, it was found that the maximum annual ground surface methane concentration in degraded permafrost areas occurred in spring, causing the maximum average growth rate in methane concentration, also in spring, in the near-surface troposphere of permafrost areas in Northeast China (with an average value of 6.05 ppbv/a). The growth rate of methane concentration in the southern permafrost degradation zone is higher than that in the northern permafrost stable zone. In addition, with the degradation of permafrost, the geological methane stored deep underground (methane hydrate, coal seam, etc., mainly derived from the accumulation of ancient microbial origin) in the frozen layer will become an important source of near-surface troposphere methane in the permafrost degradation area. Due to the influence of high-permeability channels after permafrost degradation, the release rate of methane gas in spring is faster than predicted, and the growth rate of methane concentration in the near-surface troposphere of permafrost areas can be increased by more than twice. These conclusions can provide a data supplement for the study of the carbon cycle in permafrost areas in Northeast China. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) change has greatly altered ecosystem carbon storage and exerted an enormous impact on terrestrial carbon cycling. Characterizing its impact on ecosystem carbon storage is critical to balance regional carbon budgets and make land use decisions. However, due to the availability of LULC data and the strong variability in LULC change, uncertainty remains high in quantifying the effect of LULC change on the historical and future carbon stock. Based on four historical LULC maps and one future LULC projection, this study combined the Land Use and Carbon Scenario Simulator (LUCAS) with a process-based CENTURY model to evaluate the historical and future LULC change and its impact on grassland carbon storage from 1991 to 2050 in northern China. Results showed that grassland experienced a drastic decrease of 16.10 × 103 km2 before 2005, while agriculture and barren land increased by 16.91 × 103 km2 and 3.73 × 103 km2, respectively. After that, grassland was projected to increase, agriculture kept steady, and barren land decreased. LULC change has resulted in enormous total ecosystem carbon loss, mainly in agro-pasture areas; the maximum 8.54% of carbon loss happened in 2000, which was primarily attributed to agriculture to grassland, forest to grassland, grassland to agriculture, and grassland to barren. Before 2000, the grassland net biome productivity was projected to be −15.54 Tg C/yr and −2.69 Tg C/yr with and without LULC change. After 2001, the LULC change showed a positive impact on the grassland carbon balance, and the region was projected to be a carbon sink. Ecological projects have made a significant contribution to grassland carbon storage. The paper provides a framework to account for the effects of LULC change on ecosystem carbon and highlights the importance of improving grassland management in balancing the grassland carbon budget, which is helpful to understand the regional carbon budget and better inform local land use strategies. [ABSTRACT FROM AUTHOR]

Global climate change caused by greenhouse gas emissions has a direct impact on the sustainable development of human society and has gained widespread concern globally. Biological carbon sequestration measures, mainly forests, are conducive to improving the ecological carbon sink capacity and play an important role in mitigating global climate change. Therefore, assessing the efficiency of forest carbon storage (FCS) is key. In view of the lack of evaluation methods for forest carbon storage efficiency (FCSE) on a large spatial scale and long time series, a method based on Data Envelopment Analysis (DEA) was proposed in this paper. The FCS was calculated using the forest storage expansion method, and the FCSE was calculated using the DEA-Malmquist index to solve the dynamic balance between regional forestry economic input and carbon storage output efficiency. The FCSE in Chinese provinces was analyzed. The results showed that from 1999 to 2018, China's FCS increased annually, from 7558.34 million tons to 9476.73 million tons, and the spatial distribution pattern of total FCS was always different among regions. The average TFP value of FCSE was 1.089, which proves that the FCSE in China has been on the rise in the past 20 years, but the efficiency growth differed between provinces and was affected by different factors such as technical efficiency and technological progress. [ABSTRACT FROM AUTHOR]

Forests cover approximately one-third of the Earth's land surface and constitute the core region of the carbon cycle on Earth. The paramount importance and multi-purpose applications of forest monitoring have gained widespread recognition over recent decades. Polarimetric synthetic aperture radar interferometry (PolInSAR) has been demonstrated as a promising technique to retrieve the forest height over large areas with a limited cost. This paper presents an overview of forest height inversion (FHI) techniques based on PolInSAR data. Firstly, we introduce the basic theories of PolInSAR and FHI procedures. Next, we review the established data-based algorithms for single-baseline data and describe innovative techniques related to multi-baseline data. Then, the model-based algorithms are also introduced with their corresponding forest scattering models under multiple data acquisition modes. Subsequently, a case study is presented to demonstrate the applicable scenarios and advantages of different algorithms. Model-based algorithms can provide accurate results when the scene and forest properties are well understood and the model assumptions are valid. Data-based algorithms, on the other hand, can handle complex scattering scenarios and are generally more robust to uncertainties in the input parameters. Finally, the prospect of forest height inversion was analyzed. It is our hope that this review will provide guidelines to future researchers to enhance further FHI algorithmic developments. [ABSTRACT FROM AUTHOR]

Grassland gross primary productivity (GPP) is an important part of global terrestrial carbon flux, and its accurate simulation and future prediction play an important role in understanding the ecosystem carbon cycle. Machine learning has potential in large-scale GPP prediction, but its application accuracy and impact factors still need further research. This paper takes the Mongolian Plateau as the research area. Six machine learning methods (multilayer perception, random forest, Adaboost, gradient boosting decision tree, XGBoost, LightGBM) were trained using remote sensing data (MODIS GPP) and 14 impact factor data and carried out the prediction of grassland GPP. Then, using flux observation data (positions of flux stations) and remote sensing data (positions of non-flux stations) as reference data, detailed accuracy evaluation and comprehensive trade-offs are carried out on the results, and key factors affecting prediction performance are further explored. The results show that: (1) The prediction results of the six methods are highly consistent with the change tendency of the reference data, demonstrating the applicability of machine learning in GPP prediction. (2) LightGBM has the best overall performance, with small absolute error (mean absolute error less than 1.3), low degree of deviation (root mean square error less than 3.2), strong model reliability (relative percentage difference more than 5.9), and a high degree of fit with reference data (regression determination coefficient more than 0.97), and the prediction results are closest to the reference data (mean bias is only −0.034). (3) Enhanced vegetation index, normalized difference vegetation index, precipitation, land use/land cover, maximum air temperature, potential evapotranspiration, and evapotranspiration are significantly higher than other factors as determining factors, and the total contribution ratio to the prediction accuracy exceeds 95%. They are the main factors influencing GPP prediction. This study can provide a reference for the application of machine learning in GPP prediction and also support the research of large-scale GPP prediction. [ABSTRACT FROM AUTHOR]

Clarifying the pattern of the urban greenhouse gas (GHG) budget and its influencing factors is the basis of promoting urban low-carbon development. This paper takes Beijing and Shenzhen—the capital city and the most rapidly developing city in China, respectively—as case studies, comprehensively accounts their GHG budgets from 2005 to 2020, and investigates and compares the factors affecting their GHG budgets. The total GHG emissions in Beijing were lowest in 2005 (160.3 TgCO2 equivalents) and peaked at 227.7 TgCO2 equivalents in 2019, and then decreased to 209.1 TgCO2 equivalents in 2020. Meanwhile, the total GHG emissions in Shenzhen gradually increased from 36.0 TgCO2 equivalents in 2005 to 121.4 TgCO2 equivalents in 2019, and then decreased to 119.1 TgCO2 equivalents in 2020. The energy activity sector was the greatest contributor to GHG emissions in this period, accounting for 82.5% and 76.0% of the total GHG emissions in Beijing and Shenzhen, respectively. The carbon sink of the ecosystems of these two cities could absorb only small parts of their emissions, and the neutralization rates of sinks ranged from 1.7% to 2.3% in Beijing and from 0.3% to 1.5% in Shenzhen. The enhancement of population, economic product, and consumption increased the greenhouse gas emissions in both cities. A 1% increase in population size, per capita GD (gross domestic product), and residential consumption level would increase total GHG emissions by 0.181%, 0.019%, and 0.030% in Beijing, respectively. The corresponding increases in Shenzhen would be 0.180%, 0.243%, and 0.172%, respectively. The household size had opposite effects on the two cities, i.e., a 1% increase in household size would increase GHG emissions by 0.487% in Shenzhen but reduce them by 2.083% in Beijing. Each 1% increase in secondary industry and energy intensity would reduce GHG emissions by 0.553% and 0.110% in Shenzhen, respectively, which are more significant reductions than those in Beijing. [ABSTRACT FROM AUTHOR]

This paper focuses on the sustainable exploration of building systems, which combines ecological concepts and low-carbon designs for a comprehensive sustainability assessment investigation. The study employed the Life Cycle Assessment (LCA)-Emergy and Life Cycle Assessment (LCA)-Carbon emission methods to discuss a range of topics, including the main contributing factors, sustainability index verification, sensitivity analysis, and potential improvement measures. From an ecological sustainability perspective, the results indicate that the building operation stage plays a critical role, accounting for approximately 45% of the entire emergy in the building commercial complex. The sustainable index (ESI) is 0.354, which is below the standard of 1. Moreover, the building operation stage also significantly contributes to carbon emissions, particularly in the 50th anniversary of operation. Based on these findings, the study recommends two potential strategies to improve the ecological state and low-carbon design which involve the use of renewable energy and carbon sink improvement, respectively. [ABSTRACT FROM AUTHOR]

As the largest ecological carbon sequestration systems on the Earth, forests play a significant role in reducing carbon dioxide, and countries around the world are actively expanding their forest areas. However, China's carbon emissions and forest area have shown an upward trend, which has seriously hindered the implementation of forestry carbon sequestration projects. This paper analyzed the temporal variation, spatial distribution, and deviation degree of the forest area and carbon emissions in China from 2004 to 2020 by using a decoupling model and a coordination model. Firstly, according to the decoupling model, the national carbon emissions and forest area are negatively decoupled. At the provincial level, Beijing, Shanghai, Jiangsu, Guizhou, Yunnan, and Gansu have weak decoupling. Expansive link areas include Shanxi, Henan, Hubei, Ningxia, and Xinjiang. The other 19 provinces show expansive negative decoupling. Secondly, according to the coordination model, national carbon emissions are coordinated to the forest area. Zhejiang, Fujian, Jiangxi, and Guangdong are basically coordinated provinces. More coordinated provinces include Ningxia. The other 25 provinces are coordinated provinces. Finally, according to the comprehensive measurement model, Inner Mongolia, Qinghai, Shaanxi, Hainan, Jilin, Anhui, Liaoning, and Heilongjiang are high-quality expansive negative decoupling provinces. Chongqing, Hunan, Tianjin, Shandong, Hebei, and Guangxi are moderate to strong expansive negative decoupling provinces. This study not only provides a new perspective for analyzing forest carbon sinks, but also provides theoretical guidance for enhancing the natural carbon sink capacity, helping to achieve global carbon peak and carbon neutrality goals. [ABSTRACT FROM AUTHOR]

Accurate estimation of forest height over a large area is beneficial to reduce the uncertainty of forest carbon sink estimation, which is of great significance to the terrestrial carbon cycle, global climate change, forest resource management, and forest-related scientific research. Forest height inversion using polarimetric interferometry synthetic aperture radar (PolInSAR) data through Random volume over ground (RVoG) models has demonstrated great potential for large-area forest height mapping. However, the wavelength and baseline length used for the PolInSAR data acquisition plays an important role during the forest height estimation procedure. In this paper, X–, C–, L–, and P–band PolInSAR datasets with four different baseline lengths were simulated and applied to explore the effects of wavelength and baseline length on forest height inversion using RVoG models. Hierarchical Bayesian models developed with a likelihood function of RVoG model were developed for estimated results uncertainty quantification and decrease. Then a similar procedure was applied in the L– and P–band airborne PolInSAR datasets with three different baselines for each band. The results showed that (1) Wavelength showed obvious effects on forest height inversion results with the RVoG model. For the simulated PolInSAR datasets, the L– and P–bands performed better than the X– and C–bands. The best performance was obtained at the P–band with a baseline combination of 10 × 4 m with an absolute error of 0.05 m and an accuracy of 97%. For the airborne PolInSAR datasets, an L–band with the longest baseline of 24 m in this study showed the best performance with R2 = 0.64, RMSE = 3.32 m, and Acc. = 77.78%. (2) It is crucial to select suitable baseline lengths to obtain accurate forest height estimation results. In the four baseline combinations of simulated PolInSAR datasets, the baseline combination of 10 × 4 m both at the L– and P–bands performed best than other baseline combinations. While for the airborne PolInSAR datasets, the longest baseline in three different baselines obtained the highest accuracy at both L– and P–bands. (3) Bayesian framework is useful for estimation results uncertainty quantification and decrease. The uncertainties related to wavelength and baseline length. The uncertainties were reduced obviously at longer wavelengths and suitable baselines. [ABSTRACT FROM AUTHOR]

The karst carbon sink caused by rock outcrops results in enrichment of the bicarbonate in soil, affecting the physiological process of plants in an all-round way. Water is the basis of plant growth and metabolic activities. In heterogeneous rock outcrop habitats, the impact of bicarbonate enrichment on the intracellular water metabolism of plant leaf is still unclear, which needs to be revealed. In this paper, the Lonicera japonica and Parthenocissus quinquefolia plants were selected as experimental materials, and electrophysiological indices were used to study their water holding, transfer and use efficiency under three simulated rock outcrop habitats, i.e., rock/soil ratio as 1, 1/4 and 0. By synchronously determining and analyzing the leaf water content, photosynthetic and chlorophyll fluorescence parameters, the response characteristics of water metabolism within leaf cells to the heterogeneous rock outcrop habitats were revealed. The results showed that the soil bicarbonate content in rock outcrop habitats increased with increasing rock/soil ratio. Under the treatment of a higher concentration of bicarbonate, the leaf intra- and intercellular water acquisition and transfer efficiency as well as the photosynthetic utilization capacity of P. quinquefolia decreased, the leaf water content was lower, and those plants had low bicarbonate utilization efficiency, which greatly weakened their drought resistance. However, the Lonicera japonica had a high bicarbonate use capacity when facing the enrichment of bicarbonate within cells, the above-mentioned capacity could significantly improve the water status of the leaves, and the water content and intracellular water-holding capacity of plant leaves in large rock outcrop habitats were significantly better than in non-rock outcrop habitats. In addition, the higher intracellular water-holding capacity was likely to maintain the stability of the intra- and intercellular water environment, thus ensuring the full development of its photosynthetic metabolic capacity, and the stable intracellular water-use efficiency also made itself more vigorous under karstic drought. Taken together, the results suggested that the water metabolic traits of Lonicera japonica made it more adaptable to karst environments. [ABSTRACT FROM AUTHOR]

Phytoplankton have a crucial role in the conversion of greenhouse gas sources and sinks in natural water bodies, such as lakes, rivers, and oceans. In response to environmental changes, phytoplankton adapt by altering their carbon utilization strategies, which affect carbon fixation rates and carbon fluxes at the water–air interface. This paper classifies and summarizes the main carbon utilization strategies of phytoplankton in terms of carbon acquisition, carbon metabolism, and carbon emission. Their carbon acquisition strategy determines their carbon uptake rate, while their carbon metabolism strategy affects their carbon fixation potential. Moreover, their carbon emission strategy determines the final net carbon fixation. A systematic study of phytoplankton carbon utilization strategies is important for the development of phytoplankton-based wastewater treatment technologies, understanding of algal greenhouse gas fixation, and assessment of greenhouse gas sources and sinks in natural water bodies. This article provides a comprehensive understanding of the ecological role of phytoplankton in natural water bodies and offers valuable references for related research. Furthermore, our research sheds light on the carbon metabolism and emission processes of phytoplankton. By analyzing the carbon metabolism and emission of phytoplankton under different carbon utilization strategies, we can more accurately evaluate the impact of phytoplankton on the carbon cycle in natural water bodies, which can contribute to environmental protection and sustainable development. [ABSTRACT FROM AUTHOR]

The soils of cold regions store up to 60% of all organic carbon on the planet. As a result of climate change, this organic matter can be biodegraded by microorganisms and thereby make an additional contribution to carbon balance. Nowadays, there are fragmentary data on organic C stocks in high-latitude soils and single works on the analysis of the quality of buried organic matter. This paper presents the first data on the molecular weight distributions of humic acids (HAs) extracted from soils and sediments in Yedoma. Molecular weight distributions of HAs preparations were obtained on an AKTAbasic 10 UPS chromatographic system (Amersham Biosciences, Sweden) using a SuperdexTM 200 10/300 GL column (with cross-linked dextran gel, fractionation range for globular proteins 10–600 kDa). As a result of the study, it was found that the buried soil horizons are characterized by the highest content of low molecular weight fraction (with molecular mass (Mr) 1.4–1.9 kDa and molar fraction in the range of 54.3–67.1%). The high molecular weight fraction is concentrated mainly in the superficial horizons and decreases with depth (with Mr 299–346 kDa and molar fraction in the range of 3.4–9.8%). In the Yedoma sediments, the maximum content of the medium-molecular fraction is observed (with Mr 24.6 kDa and 39.6% of the molar fraction), which may indicate a low rate of organic matter transformation in the permafrost. The data obtained serve as a database of analysis in terms of modeling the global carbon cycle in the cold regions of the planet. [ABSTRACT FROM AUTHOR]

Both forest management—especially forest tending—and afforestation help to enhance the carbon sequestration of forest vegetation. However, with limited resources, appropriate measures need to be selected to increase the vegetation carbon sinks based on regional endowments. This study aimed to assess the differences in the effects and costs of afforestation and forest tending on vegetation carbon sequestration and to help select suitable afforestation and forest tending areas. In this paper, we used panel fixed effects models to analyze the effects of afforestation and forest tending on vegetation carbon sequestration and conducted a regional heterogeneity analysis to identify suitable afforestation and tending areas. Our results show that the vegetation carbon sequestration capacity of forest tending is 4.48 times higher than that of afforestation, and there is obvious spatial heterogeneity in the effects of afforestation and forest tending on vegetation carbon sequestration. Specifically, the marginal contribution of afforestation is higher than that of tending in northwest and southwest China, whereas the marginal contribution of tending is greater in other regions. Additionally, the afforestation cost for vegetation carbon sequestration is 44.44 times higher than that of tending. Therefore, the management of existing forests must be enhanced, especially in northeastern, southern, and northern China. Similar to the northwest and southwest regions of China, there is still a need to emphasize the use of suitable space for afforestation. [ABSTRACT FROM AUTHOR]

Global warming is a worldwide concern, with buildings generating more than 40% of the annual global CO2 emissions. A commonly accepted system of global standards for zero-carbon buildings and communities has not yet been established. In this research, the development history, hotspots, and trends at the urban scale with theoretical and data support are summarized based on the Web of Science (WoS) Core Collection. The review contains works from 1997 to 2022. A total of 19,014 papers were collected, with an overall increasing trend in the number of articles with specific keywords. The scope of the research is broad, covering the environment, sciences, ecology, chemistry, material science, physics, meteorology, atmospheric sciences, and so on. The hotspots in the low-carbon community (LCC) and zero-carbon community (ZCC) involve a wide range of disciplines, and collaborative research between related disciplines should be strengthened to propose practical solutions for the development of zero-carbon cities. The establishment of the ZCC mainly focuses on a zero-carbon-emission construction and zero-carbon operations. This research found approaches such as the choices of building construction and material, a waste recycling system, a regenerating energy system, transportation, and an examination of the community composition to realize the ZCC. In the literature it is presented that the difficulties in the construction of the ZCC are due to the lack of research in practice, operation, and subsequent maintenance. Moreover, other scholars can deepen the research on the hotspots of ZCC. [ABSTRACT FROM AUTHOR]

Several global leaf area index (LAI) products were generated using neural networks, but the training dataset for the neural networks was sensor specific, and the construction of the training dataset was time consuming. In this paper, an unsupervised domain adaptation-based method was proposed to estimate LAI from the Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance dataset based on a training dataset constructed from the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance dataset. A transfer component analysis (TCA) algorithm was first utilized to map the MODIS and VIIRS surface reflectance into the same subspace to reduce the distribution discrepancies between the MODIS and VIIRS surface reflectance. Then, the embedded data obtained from MODIS surface reflectance dataset, along with the LAI values produced by fusing the MODIS and the Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) products, were employed to train general regression neural networks (GRNNs). Finally, for retrieving the LAI values, the embedded data acquired from the VIIRS surface reflectance dataset was input into the trained GRNNs. For multiple field sites with different biome types, we used this developed method to retrieve LAI values based on the VIIRS surface reflectance dataset. The results indicate that, based on the training dataset built from MODIS surface reflectance dataset, the domain adaptation-based retrieval method can effectively estimate LAI values from VIIRS surface reflectance dataset. By comparison with the VIIRS and MODIS LAI products, the retrieved LAI values with TCA are more consistent with the reference LAI values acquired from high-resolution remote sensing images. The coefficient of determination (R2) and root mean square error (RMSE) of the retrieved LAI values with TCA at all selected sites are 0.88 and 0.68, respectively. Furthermore, the accuracy of the retrieved LAI values with TCA is higher than the retrieved LAI values without TCA with the R2 0.81 and the RMSE 0.79. [ABSTRACT FROM AUTHOR]