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With the increasing pressure of resource decline and environmental pollution faced by the green transformation of marine fisheries, marine fishery carbon sinks are an increasingly close link to national strategic interests and economic lifelines. It is, therefore, necessary to explore paths for the enhancement and development of the carbon sink capacity of marine fisheries. Based on the economic data of marine fisheries from 2002 to 2021, this paper measures the capacity and characteristics of marine fishery carbon sinks in provincial areas, applies the kernel density estimation method to depict the dynamic evolution of the distribution of absolute differences in marine fishery carbon sinks, and at the same time, identifies the factors influencing the spatial imbalance in the development of marine fishery carbon sinks as well as the spatial spillover effects. The results show the following: (1) From the total amount, the fishery carbon sink capacity of China's three marine economic circles has fluctuated and increased over the past 20 years, with obvious differences among different circles, and the marine fishery carbon sink capacity of the eastern, northern, and southern marine economic circles has risen sequentially. This trend corresponds to the economic disparities among the three circles, demonstrating a transition from economic to industrial driving effects. (2) From the spatial point of view, the fishery carbon sink capacity shows a trend of increasing year by year, concentrating in the circles, and overflowing between the circles. (3) The results of a partial derivative test further show that among the main influencing factors of marine fishery carbon sinks, capital, labor, and market openness have direct positive effects on marine fishery carbon sinks, and the direct effect of innovation is the strongest, and the significance of the indirect effect of the three circles is weaker compared to the direct effect and total effect. As a result, the carbon sink of China's marine fisheries has a large room for improvement, and in order to promote the high-quality development of China's marine fisheries, it is still necessary to accelerate the circulation of resources within the marine economic circle, accelerate the research of fishery technology, actively develop the carbon sink fishery, and enhance the specialization of aquatic product processing. [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]

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]

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]

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]

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–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]

In this paper, recent research on terrestrial ecosystem predictability using the conditional nonlinear optimal parameter perturbation (CNOP-P) method is summarized. The main findings include the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, the identification of key physical parameters that lead to large uncertainties in terrestrial ecosystem modeling and prediction, and the evaluation of the simulation ability and prediction skill of terrestrial ecosystems by reducing key physical parameter errors. The study areas included the Inner Mongolia region, north–south transect of eastern China, and Qinghai–Tibet Plateau region. The periods of the studies were from 1961 to 1970 for the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, and from 1951 to 2000 for the identification of the most sensitive combinations of physical parameters. Climatic Research Unit (CRU) data were employed. The numerical results indicate the important role of nonlinear changes in climate variability due to the occurrences of extreme events characterized by CNOP-P in the abrupt grassland ecosystem equilibrium state and formation of carbon sinks in China. Second, the most sensitive combinations of physical parameters to the uncertainties in simulations and predictions of terrestrial ecosystems identified by the CNOP-P method were more sensitive than those obtained by traditional methods (e.g., one-at-a-time (OAT) and stochastic methods). Furthermore, the improvement extent of the simulation ability and prediction skill of terrestrial ecosystems by reducing the errors of the sensitive physical parameter combinations identified by the CNOP-P method was higher than that by the traditional methods. [ABSTRACT FROM AUTHOR]

Net ecosystem productivity (NEP) plays an important role in understanding ecosystem function and the global carbon cycle. In this paper, the key parameters of the Carnegie Ames Stanford Approach (CASA) model, maximum light use efficiency (εmax), was optimized by using vegetation classification data. Then, the NEP was estimated by coupling the optimized CASA model, geostatistical model of soil respiration (GSMSR) and the soil respiration–soil heterotrophic respiration (Rs-Rh) relationship model. The ground observations from ChinaFLUX were used to verify the NEP estimation accuracy. The results showed that the R2 of the optimized CASA model increased from 0.411 to 0.774, and RMSE decreased from 21.425 gC·m−2·month−1 to 12.045 gC·m−2·month−1, indicating that optimizing CASA model by vegetation classification data was an effective method to improve the estimation accuracy of NEP. On this basis, the spatial and temporal distribution of NEP in China was analyzed. The research indicated that the monthly variation of NEP in China was a single peak curve with summer as the peak, which generally presented the pattern of southern region > northern region > Qinghai–Tibet region > northwest region. Furthermore, from 2001 to 2016, most regions of China showed a non-significant level upward trend, but main cropland (e.g., North China Plain and Northeast Plain) and some grassland (e.g., Ngari in Qinghai–Tibet Plateau and Xilin Gol League in Inner Mongolia) showed a non-significant-level downward trend. The study can deepen the understanding of the distribution of carbon sources/sinks in China, and provide a reference for regional carbon cycle research. [ABSTRACT FROM AUTHOR]

Agricultural mechanization is an important factor to improve the green total factor productivity of the planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Based on the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of the output-oriented distance function to measure the green total factor productivity of China's planting industry based on net carbon sinks, and empirically studies the impact of agricultural mechanization on the green total factor productivity in China's planting industry. The main findings of this paper are as follows: (1) Agricultural mechanization can promote the planting green total factor productivity significantly, and this basic conclusion is still robust after using instrumental variables and sub sample regression. (2) The path of agricultural mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover, while the mechanisms of operation scale expansion, factor allocation optimization and technical efficiency change are not significant. (3) With the improvement in the mechanization level, the promotion effect of mechanization on planting GTFP will become clearer. Given these findings, the paper adds considerable value to the empirical literature and provides various policy and practical implications. [ABSTRACT FROM AUTHOR]

Wetland vegetation aboveground biomass (AGB) directly indicates wetland ecosystem health and is critical for water purification, carbon cycle, and biodiversity conservation. Accurate AGB estimation is essential for the monitoring and supervision of ecosystems, especially in seasonal floodplain wetlands. This paper explored the capability of spectral and texture features from the Sentinel-2 Multispectral Instrument (MSI) for modeling grassland AGB using random forest (RF) and extreme gradient boosting (XGBoost) algorithms in Shengjin Lake wetland (a Ramsar site). We use five-fold cross-validation to verify the model effectiveness. The results indicated that the RF and XGBoost models had a robust and efficient performance (with root mean square error (RMSE) of 126.571 g·m−2 and R2 of 0.844 for RF, RMSE of 112.425 g·m−2 and R2 of 0.869 for XGBoost), and the XGBoost models, by contrast, performed better. Both traditional and red-edge vegetation indices (VIs) obtained satisfactory results of AGB estimation (RMSE = 127.936 g·m−2, RMSE = 125.879 g·m−2 in XGBoost models, respectively), with the red-edge VIs contributed more to the AGB models. Moreover, we selected eight gray-level co-occurrence matrix (GLCM) textures calculated by four processing window sizes using the mean value of four offsets, and further analyzed the results of three analysis sets. Textures derived from traditional and red-edge bands using a 7 × 7 window size performed better in biomass estimation. This finding suggested that textures derived from the traditional bands were as important as the red-edge bands. The introduction of textures moderately improved the accuracy of modeling AGB, whereas the use of textures alo ne was not satisfactory. This research demonstrated that using the Sentinel-2 MSI and the two ensemble algorithms is an effective method for long-term dynamic monitoring and assessment of grass AGB in seasonal floodplain wetlands, which can support sustainable management and carbon accounting of wetland ecosystems. [ABSTRACT FROM AUTHOR]

China has committed to ambitious targets to reduce its carbon emissions in the next decades, in order to combat climate change and improve the environment. The realization of the targets depends on the fair and effective mitigation plans of all provinces. However, with varying ecological and environmental conditions and social-economic development, it is a critical issue to quantify the provinces' efforts equally. This paper proposed a comprehensive fE index in coordinating ecology, equity and economy, by accounting for carbon emissions and sinks to characterize provincial carbon emission status in China, from 2000 to 2017, which shows a spatial pattern of "boundary high, central low". The provinces with higher fE value (>1.5) in boundary areas can be seen as "relative equality" provinces with good ecology circulation, equity and economic efficiency. The provinces with lower fE value (<0.7) in central areas around Bohai Bay are regarded as "severe inequality" provinces, and are identified as the hot-spot provinces, which have emitted more CO2 than their equity share by occupying the carbon emission space of other provinces in recent decades. These results could provide a reference for a provincial guide for carbon reduction and sustainable development of the low-carbon economy. [ABSTRACT FROM AUTHOR]

The Cretaceous Era has always been a focus of geologic and palaeoenvironmental studies. Previous researchers believed that the impact of the global carbon cycle represents significant short-term global biogeochemical fluctuations, leading to the formation of a large number of organic rich sediments in the marine environment. During the Turonian, a large number of organic-rich oil shales were deposited in the lakes of the Songliao Basin in the Qingshankou Formation. How the depositional environment affected the formation of oil shales in continental lakes and the characteristics of these oil shales remain controversial. In this paper, through sampling of Qingshankou Formation strata, various testing methods are used to provide a variety of new data to study the characteristics of oil shales and palaeoenvironment evolution history in the Songliao Basin. The research of the sediments in the Qingshankou Formation in the Fuyu oilfield, Songliao Basin, via result analysis revealed that the oil shales possess an excellent oil-generation potential with moderate-high total organic carbon (TOC) levels (0.58–9.43%), high hydrogen index (HI) values (265–959 mg hydrocarbons (HC)/g TOC), high extractable organic matter (EOM) levels (2.50–6.96 mg/g TOC) and high hydrocarbon fractions (48–89%). The sources of the organic matter were mainly zooplankton, red algae and higher plants (including marine organisms). The aqueous palaeoenvironment of the Qingshankou Formation was a saline water environment with a high sulfate concentration, which promoted an increase in nutrients and stratification of the water density in the lake basin. Oxygen consumption in the bottom water layer promoted the accumulation and burial of high-abundance organic matter, thus forming the high-quality oil shales in the Qingshankou Formation. The global carbon cycle, warm-humid palaeoclimate, dynamic local biogeochemical cycling and relative passive tectonism were the most likely reasons for the TOC increase and negative δ13Corg deviation. [ABSTRACT FROM AUTHOR]

To study the impact of the promotion of electric vehicles on carbon emissions in China, the full life carbon emissions of electric vehicles are studied on the basis of considering such factors as vehicle weight and grid mix composition, and fuel vehicles are added for comparison. In this paper, we collect data for 34 domestic electric vehicles, and linear regression analysis is used to model the relationship between vehicle weight and energy consumption. Then, a Hybrid Life Cycle Assessment method is used to establish the life cycle carbon emission calculation model for electric vehicles and fuel vehicles. Finally, the life cycle carbon emissions of electric vehicles and fuel vehicles under different electrical energy structures are discussed using scenario analysis. The results show that under the current grid mix composition in China, the carbon emissions of electric vehicles of the same vehicle weight class are 24% to 31% higher than that of fuel vehicles. As the proportion of clean energy in the grid mix composition increases, the advantages of electric vehicles to reduce carbon emissions will gradually emerge. [ABSTRACT FROM AUTHOR]