Search

• Net carbon sink efficiency is used to measure carbon neutral performance. • A spatial spillover effect existed between urbanization and net carbon sink efficiency across counties. • Being surrounded by highly urbanized units resulted in positive effects on a unit's net carbon sink efficiency. • The impact of socioeconomic factors on net carbon sink efficiency is heterogeneous. Reducing carbon emissions and improving vegetation carbon sequestration (VCS) have long been recognized as the key ways to address the aim of carbon neutrality, requiring global effort and typical regional experiences. This paper develops an assessment framework for measuring the net carbon sink efficiency (NCSE) based on the concept of net-zero carbon sink to depict both carbon emission and VCS simultaneously. This paper primarily runs a spatial autocorrelation analysis to explore spatial characteristics of urbanization and NCSE of Yangtze River Delta urban agglomeration (YRD) in China from 2000 to 2020, followed by employs a spatial econometric model to investigate the spatial spillover effect of urbanization on NCSE. The results show that NCSE in YRD exhibits clear spatial disparities, descending from the southern part dominated by mountainous hills to the flatter northern region. The spatial evolutionary trend of urbanization and NCSE has a significant positive correlation, manifested as H-H and L-L agglomeration, covering most counties in southeast coastal areas and northern plains, respectively. The NCSE can improve even during the continued urbanization in YRD. Specifically, for every 1% increase in urbanization, NCSE will increase by 0.164%, of which 0.118% in direct and 0.046% in indirect growth, respectively. In addition, GDP per capita, industrial structure, technological progress, road area and direct foreign investment are benefit for NCSE. In contrast, population density, environmental regulation, and built-up area tend to decline NCSE. These findings may help to formulate the urban sustainable development planning and differentiated carbon neutrality policies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Mangrove species of multiple natural reserves was implemented fine classifications. • Two adaptive stacking algorithms achieve classification accuracies of 90.4%-96.2%. • ACE and AMO stackings outperform traditional stacking in classifying mangrove species. • MPViT has excellent mangrove species classification performance. • Interpretation of the sensitivity of remote sensing features to identify mangrove species. Mangrove is the highest productive ecosystems in the global coastal zone, which has high blue carbon sink function and carbon neutrality potential. Fine species classification is essential for mangrove conservation and sustainable development, and has attracted much attention in recent years using ensemble learning and multi-dimensional data. However, the current mangrove species classification based on traditional stacking ensemble learning still faces challenges due to the correlation between base classifiers, differences in meta -classifier capabilities, the subjectivity of parameter tuning, and data redundancy. To address these issues, this paper utilized unmanned aerial vehicle (UAV) multispectral images of three mangrove nature reserves in Beibu Gulf, south China, to examine the classification and generalization ability of our proposed Adaptive Stacking Ensemble Learning (ASEL) algorithm for different mangrove species. We also aim to verify the feasibility of the Multi-Path Vision Transformer for Dense Prediction (MPViT) algorithm for mangrove species mapping, and compare its performance with the ASEL algorithm for mangrove species classification. Finally, we used the SHapley Additive Explanations (SHAP) method to measure the contribution of feature variables to the model, exploring the sensitivity of different image features to mangrove species mapping. This study highlights that: (1) The two ASEL algorithms achieved high accuracy classification of mangrove species with the overall classification accuracy ranging from 79.8% to 96.2%. The ACE-Stacking and AOM-Stacking algorithms performed better classification ability than the traditional stacking algorithm, with the mean overall classification accuracy increasing from 0.9% to 3.3%. The McNemar test further indicated that the differences in classification results derived from three algorithms were significant at the 95% confidence interval, demonstrating the better classification and generalization ability of the ASEL algorithm. (2) The MPViT algorithm achieved better classification accuracy in the three reserves, with an overall accuracy of 95.5%-97.3%, which was 0.6%-5.4% higher than the two ASEL algorithms. The average accuracy of identifying mangrove species was over 95.3% in all reserves, demonstrating the desirable mangrove classification performance of MPViT algorithm. (3) The identification accuracies (F1 scores) of mangrove species in different reserves were ranged from 0.848 to 0.984. Cyperus malaccensi had the highest identification accuracy. (4) The SHAP method interpreted the great contribution of digital surface model and variable atmospherically resistant index features on mangrove species classification. The red band and ratio vegetation index was sensitive to Aegiceras corniculatum and to Cyperus malaccensi. [ABSTRACT FROM AUTHOR]

• An ecosystem process model-based NEP was predicted under varying climate scenarios. • Alpine grasslands were projected to play an important part in carbon sink contributions in Qinghai Province in the 21st century. • An earlier peak and faster decreasing NEP projected for a warmer climate scenario (RCP 8.5). • A more stable carbon sink was projected under a less warm, stable precipitation scenario (RCP 4.5). The alpine grasslands of the Qinghai-Tibetan Plateau play an important role in multiple ecosystem functions, all of which are key in regulating regional climate influences and providing pristine headwaters for millions of people downstream from this basin. Alpine grasslands act as a carbon sink, storing carbon dioxide and keeping heat-trapping greenhouse gases out of the atmosphere, but it is not clear how this will change in a warming climate in the future. In this paper, the net ecosystem productivity (NEP) of alpine grasslands in Qinghai province, on the Qinghai-Tibetan Plateau, was predicted for the period from 2010 to 2099. Trends and stability were analyzed under two climate scenarios, Representative Concentration Pathway 4.5 (RCP4.5) and 8.5 (RCP8.5) representing the lower and higher emission scenarios for greenhouse gases. The results suggest that grasslands will continue to contribute as a carbon sink, with a positive NEP through this century. Almost the same magnitude (38 Tg C a-1, 1 T g = 1012 g) of contribution was projected under both scenarios. Grasslands are projected to be the major contributor to NEP in Qinghai province, with more than 89% of NEP in the future. The carbon sink function will increase over more than 69% of the grasslands and peak around 2069 (RCP4.5) or 2066 (RCP8.5). Then the carbon sink function will begin to decrease and it will decrease more quickly and become more variable under the RCP8.5 than the RCP4.5. The impacts of temperature and precipitation changes were analyzed and NEP was found to be more sensitive to temperature than precipitation change. The trend of increasing contribution to NEP is driven by a warming climate, while the stability of NEP is mainly influenced by the precipitation, which results in an upward trend before the peak and a decline due to stresses from limits in available water in a continued warming climate. [ABSTRACT FROM AUTHOR]

[Display omitted] • Landscape of blue carbon research from the perspective of bibliometrics. • Blue carbon ecosystem was the main research area, but macroalgae deserve attention. • Artificial intervention and methods are potential hotspots in the future research. • Statistics, co-occurrence analysis and thematic evolution are main used methods. Blue carbon sequestration is a nature-based solution to environmental problems caused by climate change and has a significant positive impact on the planet and human well-being. This paper bibliometrically and systematically analyzed 2,613 studies found in the Web of Science Core Collection (WoSCC) from 2003 to 2021 to explore the hotspots and trends of blue carbon research using the Bibliometrix package in R studio and VOSviewer software. The results based on network co-occurrence analysis indicated that mangroves, seagrasses, and salt marshes were the major blue carbon ecosystems (BCEs), and mangrove carbon sinks were the most popular central research area. Furthermore, phytoplankton and microalgae have essential roles in the marine carbon cycle and bioenergy utilization, as revealed by the high proportion of published literature, while the carbon transport mechanisms of seagrass and macroalgae was understudied. Thematic evolution analysis revealed that remote sensing was the most commonly used technology as an emerging research field for ecological monitoring and ecological investigation in blue carbon research in recent years. In addition, increasing carbon sinks by artificial approaches, fishery carbon sinks, and carbon sequestration by macroalgae have been potential hotspots for blue carbon research development. The findings from this study shed light on the trends of blue carbon in the past eighteen years, current research points in the field, and areas that need more research investment going forward. [ABSTRACT FROM AUTHOR]

• The carbon emission coefficients method and net ecosystem productivity (NEP) were used to evaluate urban carbon emissions and green space carbon sinks, respectively. • A multi-objective programming model (MOP) was constructed to derive the optimal solution of the amount of urban green space based on the carbon balance theory. • Combined with ventilation resistance and species migration resistance assessment, priority distribution areas of green space in 2035 were identified. • The PLUS model was introduced to optimize the spatial pattern of urban green space. • Optimized green space network stability and carbon sinks will be enhanced in 2035. Cities experience the most intensive level of human activity. As a result, more than 60 % of global CO 2 emissions come from urban areas. Urban green space has the dual ecological benefits of increasing carbon sinks and reducing carbon emissions. Creating green space is essential to promoting the development of a low-carbon cycle in a city. Therefore, exploring the quantitative structure and spatial pattern optimization of urban green space from the perspective of carbon balance can effectively improve the total carbon sink of a city. Based on the carbon balance theory, this paper first evaluates the carbon offsetting capability (COC) of urban green space in Beijing in 2020. Then, CO 2 emissions is predicted, COC improvement targets are established, and the quantity of standard green space is calculated under these targets in 2035. A multi-objective programming model (MOP) is constructed to derive the optimal solution to determine the amount of standard green space needed to meet the constraints of urban development planning and maximize the carbon sink. A circuit model is used to identify the priority distribution area of green space in 2035, and the Patch-generating Land Use Simulation (PLUS) model is used to simulate the spatial pattern optimization results. The results show that: (1) CO 2 emissions in Beijing caused by human activities in 2020 totaled about 240.12 million tons, the net absorption of CO 2 of green space was about 8.99 million tons, and the COC was about 3.74 %; (2) in 2035, Beijing's CO 2 emissions will be about 265.40 million tons. Under 5 %, 10 %, 15 %, 20 %, and 25 % COC improvement gradients, the demand for standard green space will be 12,204.80 km2, 12,763.80 km2, 13,353.85 km2, 13,943.90 km2, and 14,533.96 km2, respectively; (3) the results of the Multi-Objective Programming (MOP) model show that the optimal COC in 2035 is 4.19 %, and the standard green space quantity is 13,012.24 km2; (4) In 2035, Beijing's urban green space will be characterized by a network structure of circular radiation. The optimized COC is 4.37 %, and the standard green space quantity is 13,577.86 km2, which is largely consistent with the MOP model's predicted result. This study can provide theoretical and methodological support for urban green space planning, and expand nature-based solutions for the low-carbon development in cities. [ABSTRACT FROM AUTHOR]

[Display omitted] • Visualization and quantitative analysis of the global ecological carbon sinks and sources. • Enhance the prediction of carbon sequestration dynamics under climate change. • Climate change potentially affecting the carbon-climate feedback. • Research content is becoming clearer and more systematic. • Keyword clustering the classification of the research into six relatively independent subtopics. Climate change is extensively affecting the global ecosystem, especially the ecosystem carbon sequestration and sequestration potential, which are issues of global concern. The proposed concept of "carbon neutrality" in 2020 has brought ecological carbon sequestration to the forefront. Therefore, the research on climate change and ecosystem carbon sequestration need to be systematically reviewed, summarized, and examined. Based on the Web of Science database, this paper analyzed 4005 articles (1992–2022 year) after rigorous screening via a bibliometrics analysis and then presented the research trends and future research focus of ecological carbon sequestration. The following conclusions are drawn: (1) the research over the last 30 years has steadily improved, and the annual number of publications has increased in a cubic polynomial fashion (R 2 = 0.9937), with 87.57 % of the total publications appearing after 2009. Global Change Biology and Science of the Total Environment are the most influential journals in the field; (2) participation in such research is becoming increasingly common with expanding research areas covering Eurasia, America, Oceania, and Africa. The United States and China are the most productive and influential countries; (3) the diversity of this research is increasing, and the research content is becoming more explicit and systematic. Most research focuses on climate change, carbon sequestration, management, land-use changes, and nitrogen and soil organic carbon; (4) although traditional ecological evaluation techniques were essential early on, remote sensing and modeling have become the primary methods for assessment; (5) keyword clustering allows the classification of the research into six relatively independent subtopics: climate change and terrestrial ecosystems, sequestration and organic-carbon, ecosystem services, nitrogen and carbon cycling, biomass and forest, and blue carbon, which provides a reference for further research on ecological carbon sequestration and carbon neutrality. [ABSTRACT FROM AUTHOR]

• Both GPP and GPP max in Chinese grasslands showed increases at different space–time scales. • Trend analysis and breakpoint tests were used for the first time to monitor GPP change simultaneously in Chinese grasslands. • GPP max × CUP is a robust indicator to explain annual-scale variation in GPP. At the ecosystem level, Gross Primary Productivity (GPP) was defined as the organic compounds formed by plants that absorb atmospheric CO 2 through photosynthesis and sequester carbon in plant bodies. How to accurately assess the spatiotemporal evolution of ecosystem carbon indicators has become a critical issue to be addressed. Chinese grasslands are located in a typical arid and semi-arid climate zone and are sensitive to global changes, which will inevitably have serious impacts on the function and structure of Chinese grasslands. Based on this, our paper takes Chinese grassland ecosystems as the research area, and used multi-source GPP dataset from terrestrial ecosystem model simulations and remote sensing satellite observations to quantitatively analyze the spatiotemporal evolution patterns of GPP in Chinese grasslands over the past 40 years. We combined trend analysis and breakpoint test, and then to analyze the mechanism components of GPP space–time variability. The main results are as follows: (1) The model and remote sensing estimated GPP results showed that more than 80% of Chinese grasslands show a significant increasing trend over the past 40 years, with growth rates ranging from 0.68 to 3.13 g C/m−2 year−1. (2) GPP max also shows that more than 80% of Chinese grasslands are growing rapidly, with an overall growth rate of more than 0.1 g C/m−2 year−1 in each region. The overall long-term trends and interannual variability of multi-source GPP and GPP max are generally consistent, yet vegetation dynamics in local areas are still uncertain. (3) The breakpoint test showed that 'monotonically increasing' was the largest breakpoint type of GPP in Chinese grasslands (33.09%), and the direction of change of GPP in Chinese grasslands before and after the breakpoint was also increasing. (4) GPP max × CUP explained 91% of the temporal variability of annual-scale GPP in Chinese grasslands from a mechanistic view, and we found that the peak photosynthetic growth and the length of the phenological period synergistically controlled the interannual variability of GPP in Chinese grasslands. Under the scenario of rapid global change, our study accurately assessed the long-term dynamics of GPP and its mechanism-driven of grassland ecosystems in China, which is helpful for estimating and predicting the carbon budget of grassland ecosystems in China, and has important guiding significance for policy formulation to mitigate climate change. [ABSTRACT FROM AUTHOR]

• The net primary productivity (NPP) significantly increased in marshes of study area. • Summer daytime and nighttime temperatures had an asymmetric effect on marsh NPP. • Increased precipitation in summer and autumn can enhance the NPP of marsh vegetation. The western Songnen Plain has a large area of marshes that are able to modulate the regional carbon cycle. Net primary productivity (NPP) is an important ecological indicator of carbon storage within the marsh ecosystems. Although climate change could have significantly changed marsh vegetation NPP during the past decades, the spatiotemporal variation in marsh vegetation NPP and how climate change affects marsh vegetation NPP remain unclear in the western Songnen Plain. In this paper, we assessed the spatiotemporal variation in the NPP of marshes at the western Songnen Plain and evaluated its response to climate change from 2000 to 2020. The NPP of marshes over the western Songnen Plain increased significantly at a rate of 5.69 g C/m2/a during 2000 to 2020, with an average value of about 273.62 g C/m2/a. The largest increase of the NPP of marshes was mainly distributed in the northwest part of the Songnen Plain. Correlation analysis results showed that the increased precipitation in summer and autumn could enhance the NPP of marsh vegetation. By contrast, the increase of mean temperature in summer reduced the NPP of marshes. In addition, we first found the asymmetric impacts of summer minimum temperature (T min) and maximum temperature (T max) on marsh vegetation NPP. Notably, the increase in summer T max could significantly reduce NPP, while the increase in T min could increase NPP. Under the background of global asymmetric warming of daytime and nighttime temperatures, more attention should be paid to the different effects of T max and T min on marsh NPP in the western Sonegnen Plain. The findings of this study can provide theoretical guidance for ecological protection and the adaptive management of marsh vegetation at the western Songnen Plain. [ABSTRACT FROM AUTHOR]

[Display omitted] • The response of ecosystem WUE to soil moisture drought was quantitatively examined. • WUE increased significantly in most vegetated areas of the world during 1982–2018. • Drought had approximately 4-month lagged effect on WUE. • Drought-resilient ecosystems were concentrated in the Northern Hemisphere. Water use efficiency (WUE) is an ecological indicator reflecting the link between carbon and water cycles in terrestrial ecosystems, which is often affected by drought disturbance. However, knowledge about the influences of soil moisture drought on WUE is still lacking. Therefore, this paper aims to quantify the lagged effect and impact of soil moisture drought on terrestrial ecosystem WUE from 1982 to 2018 using ERA5 and Global Land Surface Satellite (GLASS) datasets. Drought conditions are described by the soil moisture anomaly percentage index (SMAPI). The lagged effect of drought on WUE is measured by the month with the maximum significant correlation between SMAPI and WUE. The impact of drought on WUE is estimated through the relative change of WUE during drought and non-drought periods. The results showed that: (1) Drought had an approximately 4-month lagged effect on WUE, which was observed in 70.87% of the global vegetated areas. The lagged effect of drought on WUE was a short period (1–4 months) for shrubland and sparse vegetation, middle and long periods (5–12 months) for forest. (2) When drought occurred, WUE decreased by 36.95% in the Tibetan Plateau and 24.93% in West Africa, while WUE in North Europe, Alaska/N.W. Canada, and West Asia increased by 14.64%, 8.83%, and 8.53%, respectively. The negative and positive impacts of drought on WUE in each vegetation type were commensurate. Our results provide useful information for understanding the response of ecosystem carbon and water cycles to drought.. [ABSTRACT FROM AUTHOR]

• The specific contribution of GUAs to the SDGs has been analysed. • Green urban areas contribute directly to the achievement of SDG11, SDG15 and SDG13. • Equity in sustainable development has been assessed at a sub-neighbourhood level. • Equity requires an adequate location, size, quantity and quality of GUA. The Agenda for Sustainable Development 2030 of United Nations is made up of the 17 Sustainable Development Goals (SDGs) that humanity will have to meet by 2030. In achieving the SDGs, green urban areas (GUA) play a fundamental role at the local level as they provide recreational and bioclimatic regulatory functions and act as a carbon sink, as well. Specifically, the GUAs contribute directly to three SDGs: SDG 11 Sustainable cities and communities, SDG 13 Climate Action and SDG 15 Life on land. This paper evaluates direct contribution of GUA to this SDGs with high spatial resolution in the case study of the city of Valencia (Spain). The evaluation carried out has made it possible to make a diagnosis of the quantity and accessibility of GUA at sub-neighbourhood level. The results for SDG 11 show that only 9.23% of the population do not have desirable access to GUA and 2.73% live in areas without easy walking distance access to GUA. On the other hand, the evaluation of SDG 15 shows that each inhabitant has at their disposal 10 m2 of GUA, below the average of cities of more than 250,000 inhabitants in Spain. The high spatial resolution of the evaluation has also made it possible to identify the city areas with the worst access to GUA and the least amount of GUA per inhabitant. In consequence, the results allow determining zones with high potential to improve. Additionally, the quantification of the CO 2 fixed by the GUA carried out for the evaluation of SDG 13, shows that the fixed carbon is equivalent to 0.04% of total gross GHG emissions of the city and is 36% higher than the total GHG emissions of the annual fuel consumption of the total fleet in the city. Finally, the monitoring of the indicators applied allows evaluating the evolution of the GUA to improve the sustainable development of the city. [ABSTRACT FROM AUTHOR]