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With the increase in global temperature, the global change situation dominated by climate warming is becoming more and more serious. Climate change will cause differences in the suitable areas of species in different periods. Ginkgo biloba L., a rare and endangered wild plant protected at the national level in China, is the oldest relict plant in the world. Because of severe climate change, only China's wild Ginkgo biloba has been preserved, yet China's wild Ginkgo biloba population is facing extinction risk. Ginkgo biloba has rich ornamental value, application value, economic value, medicinal value and ecological value. Not only can it produce economic and ecological benefits, but it can also produce huge social benefits. Based on the data of Ginkgo biloba sample distribution, bioclimatic variables and soil variables, this paper uses the MaxEnt model to simulate Ginkgo biloba suitable area under current and future different climate scenarios, and analyzes the changes in the potential suitable area of Ginkgo biloba in the future through ArcGIS 10.6. The results are as follows: (1) the results simulated by the MaxEnt model are AUC > 0.9, showing that the simulation results have a high accuracy; (2) the min temperature of the coldest month, precipitation of the wettest month, elevation, and temperature seasonality are the main environmental variables affecting the change in the Ginkgo biloba suitable area; (3) under future climate scenarios, the suitable area of Ginkgo biloba is predicted to expand in the future, covering most of the south and some northeast regions, and moderate temperature and precipitation changes under climate change are conducive for the growth of Ginkgo biloba; and (4) in the future, the distribution center of the suitable area will move to the northeast. According to the conclusions in this paper, it is expected to provide theoretical reference for cultivation and management, sustainable utilization and solution of ecological environment problems of Ginkgo biloba. [ABSTRACT FROM AUTHOR]

On 20 July 2021, an extreme rainstorm battered Zhengzhou in China's Henan Province, killing 302 people, including 14 individuals who drowned in a subway tunnel and 6 who drowned in a road tunnel. As the global climate warms, extreme weather events similar to the Zhengzhou flood will become more frequent, with increasingly catastrophic consequences for society. Taking a case study-based approach by focusing on the record-breaking Zhengzhou flood, this paper examines the governance capacity of inland cities in North China for managing extreme precipitation and flooding events from the perspective of the flood risk management process. Based on in-depth case analysis, our paper hypothesizes that inland cities in North China still have low risk perceptions of extreme weather events, which was manifested in insufficient pre-disaster preparation and prevention, poor risk communication, and slow emergency response. Accordingly, it is recommended that inland cities update their risk perceptions of extreme rainfall and flooding events, which are no longer low-probability, high-impact "black swans", but turning into high-probability, high-impact "gray rhinos." In particular, cities must make sufficient preparation for extreme weather events by revising contingency plans and strengthening their implementation, improving risk communication of meteorological warnings, and synchronizing emergency response with meteorological warnings. [ABSTRACT FROM AUTHOR]

This paper makes a significant contribution to understanding the logic of deforestation in Northern Myanmar and connects global trends and regional political economy with local environmental changes. Methodologically, through a combination of remote sensing GIS analysis, for which we use a newly available Myanmar Forest Change dataset produced by TerraPulse and the Smithsonian Conservation Biology Institute, as well as on-the-ground field research observations and interviews with farmers, this paper examines how the expansion of maize plantations in the northern part of Myanmar has implications for deforestation in the region. It argues that a combination of global commodity price shock around 2011-2012 plus easy market access to China generated strong incentives for local farmers to increase the cultivation of maize. The paper contributes to how we understand the environmental impacts of Chinese demands for agricultural products in Southeast Asia. [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]

China is one of the countries that are most severely affected by floods worldwide. Due to the geographical and climatic environment, floods frequently occur in China. Rapid socioeconomic growth and urban sprawl in the past decades have significantly changed both exposure and vulnerability dimensions of flood risk in China. In response to high risks of flood, the Chinese government has adopted a series of effective measures, such as the "Spongy City" Program and building many large dams and reservoirs, and some measures have achieved significant results. However, there is still a lack of studies with an integrated view on analyzing the causes, socioeconomic impact trends, and disaster risk reduction (DRR) measures of flood risk in China in the past decades. Accordingly, this paper aims to fill in the gap and provides some new insights into China's contributions in DRR over the period of 1950–2019. Our results show that annual flood-induced fatalities and socioeconomic vulnerability to floods have significantly decreased in China, owing to a range of structural and non-structural measures. Nevertheless, China still faces the complex coupling effects of climate change and urbanization, and thus threats from extreme floods. In addition, China needs to further improve its flood risk management system. [ABSTRACT FROM AUTHOR]

High-quality carbon reduction policies play a crucial role in tackling global climate change and reducing carbon dioxide emissions. China, as the world's largest emitter of carbon dioxide, has committed to peaking its carbon emission by 2030. This study focuses on the evaluation of 12 carbon reduction policies implemented by the Chinese government. A quantitative evaluation index system for carbon reduction policies was designed. Next, the policy modeling consistency (PMC) index method was utilized to assess the quality of these policies. The findings are as follows: Firstly, the average PMC index value of the 12 carbon reduction policies is 6.75, indicating a good performance overall. Secondly, the carbon reduction policies established by the Chinese government are generally effective; among the twelve policies analyzed, one policy received a perfect evaluation grade, four policies were graded as excellent, six policies were graded as good, and one policy received a bad evaluation grade. Thirdly, the indicators Z3, Z4, Z5, and Z9 in the evaluation index system for carbon reduction policies scored relatively low, suggesting that there are some deficiencies in terms of policy timeliness, issuing institutions, policy tools, and policy evaluation within the 12 policies. Fourthly, this study presents a PMC curved surface diagram to visually illustrate the strengths and weaknesses of the carbon reduction policies. Finally, based on the research findings, recommendations are provided to enhance the quality of carbon reduction policies. [ABSTRACT FROM AUTHOR]

Low-carbon city transformation is an important action area of China's 14th Five-Year Plan (2021–2025) that aims to reach peak CO2 emissions by 2030 and achieve carbon neutrality before 2060. Responding to global climate change is not only a national responsibility but also an individual responsibility and very much depends on societal participation and acceptance. While many scholars argue that public participation would be critical to low-carbon cities developing in China, there is apparently a lack of sufficient research on the level of public participation. This systematic review aims to summarize the current related research about public awareness on low-carbon city transformation in China, and learn about the challenges and barriers of public attitude and behaviour towards the low-carbon lifestyle. This study reveals that: (1) although most scholars discussed the conception and policies of low-carbon cities, research methods, the theoretical foundation and the number of cities targeted for research are limited; (2) a public's attention to low-carbon cities mainly focus on a low-carbon life, and there is a clear gap between low-carbon awareness and behaviour; and (3) although scholars had different opinions about the factors that affect low-carbon behaviour, most of them agree that education and government policy have an influence on the populations' low-carbon behaviour in China. [ABSTRACT FROM AUTHOR]

Net precipitation (NP) is the primary source of soil water essential for the functioning of vegetated ecosystems. By quantifying NP as the difference between gross precipitation and canopy interception evaporation, this study examined the dynamics of NP in China from 2001 to 2020 and the contribution of environmental factors to NP variations was investigated. The findings revealed a multiyear mean NP of 674.62 mm, showcasing a 2.93 mm/yr increase. The spatiotemporal variations in NP were mainly attributed to a remarkable increase in precipitation rather than canopy interception. Notably, climate (temperature, wind speed, surface solar radiation downward and vapor pressure deficit) and vegetation factors (leaf area index and net primary productivity) played a dominant role in NP in 61.53% and 15.39% of China, respectively. The dominant factors contributing to NP changes were vapor pressure deficit (mean contribution rate: −43.68%), temperature (mean contribution rate: 11.69%), and leaf area index (mean contribution rate: 2.13%). The vapor pressure deficit negatively exerts a negative influence on the southern and eastern regions. Temperature and leaf area index have the greatest effect on the northeastern and southwestern regions, respectively. The results provide valuable insights into the pivotal role of climatic and vegetation factors in ecohydrological cycles. [ABSTRACT FROM AUTHOR]

This study presents the environmental mapping of the Chilika Lake coastal lagoon, India, using satellite images Landsat 8-9 OLI/TIRS processed using machine learning (ML) methods. The largest brackish water coastal lagoon in Asia, Chilika Lake, is a wetland of international importance included in the Ramsar site due to its rich biodiversity, productivity, and precious habitat for migrating birds and rare species. The vulnerable ecosystems of the Chilika Lagoon are subject to climate effects (monsoon effects) and anthropogenic activities (overexploitation through fishing and pollution by microplastics). Such environmental pressure results in the eutrophication of the lake, coastal erosion, fluctuations in size, and changes in land cover types in the surrounding landscapes. The habitat monitoring of the coastal lagoons is complex and difficult to implement with conventional Geographic Information System (GIS) methods. In particular, landscape variability, patch fragmentation, and landscape dynamics play a crucial role in environmental dynamics along the eastern coasts of the Bay of Bengal, which is strongly affected by the Indian monsoon system, which controls the precipitation pattern and ecosystem structure. To improve methods of environmental monitoring of coastal areas, this study employs the methods of ML and Artificial Neural Networks (ANNs), which present a powerful tool for computer vision, image classification, and analysis of Earth Observation (EO) data. Multispectral satellite data were processed by several ML image classification methods, including Random Forest (RF), Support Vector Machine (SVM), and the ANN-based MultiLayer Perceptron (MLP) Classifier. The results are compared and discussed. The ANN-based approach outperformed the other methods in terms of accuracy and precision of mapping. Ten land cover classes around the Chilika coastal lagoon were identified via spatio-temporal variations in land cover types from 2019 until 2024. This study provides ML-based maps implemented using Geographic Resources Analysis Support System (GRASS) GIS image analysis software and aims to support ML-based mapping approach of environmental processes over the Chilika Lake coastal lagoon, India. [ABSTRACT FROM AUTHOR]

Continued urban expansion (UE) has long been regarded as a huge challenge for climate change mitigation. However, much less is known about how UE affects carbon emissions (CEs), especially in the urban agglomerations of the Yellow River Basin (UAYRB), China. In this regard, this study introduced kernel density analysis, the Gini coefficient, and Markov chains to reveal the UE patterns and carbon emissions intensity (CEI) in the UAYRB at the county level, and explored the spatial heterogeneity of the impact of UE on CEI with the geographically and temporally weighted regression model. The results show that both CEI and UE in the UAYRB showed a steady growing trend during the study period. The kernel density of CEI and UE revealed that CEI in the UAYRB was weakening, while the UE rate continuously slowed down. The Gini coefficients of both CEI and UE in the UAYRB region were at high levels, indicating obvious spatial imbalance. The Markov transfer probability matrix for CEI with a time span of five years showed that CEI growth will still occur over the next five years, while that of UE was more obvious. Meanwhile, counties with a regression coefficient of UE on CEI higher than 0 covered the majority, and the distribution pattern remained quite stable. The regression coefficients of different urban landscape metrics on CEI in the UAYRB varied greatly; except for the landscape shape index, the regression coefficients of the aggregation index, interspersion and juxtaposition index, and patch density overall remained positive. These findings can advance the policy enlightenment of the high-quality development of the Yellow River Basin. [ABSTRACT FROM AUTHOR]

In recent years, global climate change and human alterations to land use have led to a decrease in ecosystem services, making ecosystems more vulnerable. However, unlike the well-established risk assessment frameworks used in natural disaster research, the concept of ecological risks arising from changes in land use is still in its early stages, with its nuances and assessment methodologies yet to be clearly defined. This study proposes a new framework for assessing ecological risks resulting from changes in land use in the Tarim River Basin. The framework employs a coupled PLUS and Invest model to evaluate the ecological risks of land use change under three development scenarios projected for the Tarim River Basin in Xinjiang by 2035. The findings indicate that: (1) Between 2000 and 2020, the predominant land use types in the Tarim River Basin in Xinjiang were primarily unused land, followed by grassland and cropland. Conversely, grassland, water, and construction land were relatively less prevalent. During this period, the area of unused land and cultivated land increased, while grassland, forest land, and water exhibited a declining trend. Moving forward, under the three scenarios from 2020 to 2035, land use changes in the study area are characterized by the expansion of cropland and unused land, coupled with a significant decrease in grassland area, while other land categories demonstrate minor fluctuations. (2) From 2020 to 2035, across various scenarios, the total ecosystem service within the study area demonstrates an overall increasing trend in both the northern and southern marginal zones. Specifically, under the baseline scenario, the total amount of ecosystem services in the study area decreased by 15.247% compared to 2020. Similarly, under the economic development scenario, this decrease amounted to 13.358% compared to 2020. Conversely, under the ecological protection scenario, the decrease reached 19.852% compared to 2020. (3) The structure of ecological risk levels from 2020 to 2035, across multiple scenarios, demonstrates a consistent pattern, characterized by a predominant proportion of moderate risk. Conversely, other risk levels occupy relatively smaller proportions of the area. [ABSTRACT FROM AUTHOR]

The intensification of the regional water cycle resulting from climate change, coupled with the influence of human activities, has brought about alterations in the frequency, scale, and intensity of droughts. In this study, based on hydrological big data and the standardized runoff drought index (SRI), the multi-scale spatiotemporal evolution of hydrological drought in China from 1948 to 2014 was analyzed using the run-length theory and gravity center model. Meanwhile, the hydrological drought hazard index was constructed to analyze the distribution of the hazard levels of drought in China. The results showed that, during 1948~2014, there was an opposite spatial distribution between the average intensity and the average coverage–duration–frequency of drought in the Yellow River Basin, Haihe River Basin and southeastern river basins. The drought situation in most river basins in China has shown an aggravating trend, among which the southeastern river basins, Haihe River Basin, Songliao River Basin and Pearl River Basin have generally shown an aggravating trend. The drought situation in China was severe in the 1950s and 1960s, gradually reduced in the 1970s, 1980s, and 1990s, and the drought situation was the mildest in the 1990s. After entering the 21st century, the drought situation began to worsen sharply. Meanwhile, from 1948 to 2014, the hazard level of drought generally presented a pattern of high in the west and north, and low in the east and south. The hazard levels of drought in the northwest and northeast were generally higher, and those in the southwest and southeast regions were generally lower. In general, the hazard levels of drought were relatively high in most areas of China. [ABSTRACT FROM AUTHOR]

Climate policymaking in China has gone through three major phases over the course of three decades. This paper applies the concept of policy learning to an analysis of what the Chinese government has learned about climate policymaking during this period. Our analysis shows that this thirty-year history of climate policymaking can be viewed as a conceptual learning process because the Chinese government has continuously adjusted the problem definition of climate change, redefined climate policy goals, and updated climate strategies. First, the Chinese leadership has redefined climate change from a scientific and diplomatic problem, to a developmental and strategic issue, and finally to an overarching grand strategy guiding national development in the next forty years. Second, the Chinese government has fundamentally redefined its climate policy goal from avoiding climate action so as to foster economic development to reinvigorating economic development through climate action. Third, the Chinese government has updated its climate change strategy from solely emphasizing climate mitigation to a more balanced consideration of mitigation and adaptation, and from state-dominant governance processes to more diversified governance processes that involve the participation of the corporate sector and a greater a role of the law. [ABSTRACT FROM AUTHOR]

Different regions exhibit significant differences in the characteristics of extreme precipitation, and the differentiation of such characteristics is not reported for all regions. To distinguish the characteristics of extreme precipitation in different regions in China, we extracted the distribution characteristics of extreme precipitation from daily precipitation data at 649 stations in China, 1961–2017, spatially grouped them by cluster analysis, and, finally, evaluated the effectiveness of zoning by a homogeneity test. Results showed that China can be divided into 33–35 extreme precipitation zones. Our map exhibits better homogeneity than the conventional climate map and other methods reported in the literature and better characterizes the regional distribution characteristics of extreme precipitation. It is noted that long–duration extreme precipitation has a more significant regional distribution consistency than short–duration extreme precipitation. Further, the western and northern regions of China are more prone to sudden, high–intensity extreme precipitation events, whereas the southeastern region is more vulnerable to frequent, high–intensity extreme precipitation events. [ABSTRACT FROM AUTHOR]

Achieving Sustainable Development Goal 7 (SDG 7) and SDG 13 together requires a holistic and integrated approach to simultaneously address the challenges of clean energy and climate action. In order to find integrated policy strategies, this study offers a comparative analysis using the case of China and the world regarding energy access, energy intensity, clean cooking, renewable energy, global warming gases, and investment in energy by the private sector to advance SDGs 7 and 13, applying a principal component regression (PCR) and forecasting models for the period 1990 to 2021. Overall, these findings indicate that China is making significant progress towards meeting the goals of the Paris Agreement. This progress is evident in the notable variations observed in key variables such as access to clean cooking solutions, private sector investments in energy, renewable energy generation, and enhanced energy efficiency. In contrast, the global landscape exhibits only minimal fluctuations in these aspects within its framework. The PCR proves that all the components are significant regarding China, whereas, for the world, seven components are significant out of eight. Furthermore, the global temperature projection indicates that the world is nearing the 1-degree Celsius threshold, with the current temperature standing at 0.558 degrees Celsius. This suggests that the goal of limiting global warming to 1.5 degrees Celsius by 2030 remains attainable. Notably, China's projected average temperature for 2030 is 7.2 degrees Celsius, marking a 12% decrease from the 2021 temperature level. This trajectory aligns with China's commitment to achieving the 1.5-degree Celsius target by 2030. This study makes a valuable contribution to the field of energy transition, offering insights into the path to maintaining global warming at 1.5 degrees Celsius as stipulated by the Paris Agreement by 2030. [ABSTRACT FROM AUTHOR]

China's forest ecosystem plays a crucial role in carbon sequestration, serving as a cornerstone in China's journey toward achieving carbon neutrality by 2060. Yet, previous research primarily emphasized climate change's influence on forest carbon sequestration, neglecting tree species' suitable area changes. This study combinates the Lund–Potsdam–Jena model (LPJ) and the maximum entropy model (MaxENT) to reveal the coupling impacts of climate and tree species' suitable area changes on forest aboveground biomass carbon (ABC) in China. Key findings include the following: (1) China's forests are distributed unevenly, with the northeastern (temperate coniferous broad-leaved mixed forest, TCBMF), southwestern, and southeastern regions (subtropical evergreen broad-leaved forest, SEBF) as primary hubs. Notably, forest ABC rates in TCBMF exhibited a worrisome decline, whereas those in SEBF showed an increasing trend from 1993 to 2012 based on satellite observation and LPJ simulation. (2) Under different future scenarios, the forest ABC in TCBMF is projected to decline steadily from 2015 to 2060, with the SSP5-8.5 scenario recording the greatest decline (−4.6 Mg/ha/10a). Conversely, the forest ABC in SEBF is expected to increase under all scenarios (2015–2060), peaking at 1.3 Mg/ha/10a in SSP5-8.5. (3) Changes in forest ABC are highly attributed to climate and changes in tree species' highly suitable area. By 2060, the suitable area for Larix gmelinii in TCBMF will significantly reduce to a peak of 65.71 × 104 km2 under SSP5-8.5, while Schima superba Gardner & Champ and Camphora officinarum in SEBF will expand to peaks of 94.07 × 104 km2 and 104.22 × 104 km2, respectively. The geographic detector's results indicated that the climate and tree species' suitable area changes showed bi-variate and nonlinear enhanced effects on forest ABC change. These findings would offer effective strategies for achieving carbon neutrality. [ABSTRACT FROM AUTHOR]

Understanding how drought is impacted by both natural and human influences is crucial to the sustainable utilization and protection of water resources. We established a drought severity index (DSI) based on the terrestrial water storage anomaly (TWSA) derived from the GRACE satellite to detect drought characteristics and trends over ten major river basins in China from 2002 to 2017. The influence of natural factors (terrestrial water storage components, precipitation, evapotranspiration, runoff, NDVI, and teleconnection factors (ENSO, PDO, NAO, and AO)) and a human factor (LULC) on drought were investigated and quantified from the perspective of water storage components based on the Theil–Sen trend and Mann–Kendall test method, the perspective of climate change based on cross wavelet transforms, and the perspective of water balance based on Random Forest. The results indicated that (1) almost all humid and arid basins experienced major drought periods during 2002–2006 and 2014–2017, respectively. The southern IRB and central YZRB regions exhibited notable declines in DSI trends, while the majority of the HLRB, IRB, LRB, YRB, HRB, and SWRB experienced significant increases in DSI trends; (2) abnormal groundwater decreases were the main cause of drought triggered by insufficient terrestrial water storage in most basins; (3) ENSO was the strongest teleconnection factor in most humid basins, and NAO, PDO, and AO were the strongest teleconnection factors in the arid basins and PRB. Most significant resonance cycles lasted 12–64 months in 2005–2014; and (4) the influence of an anthropogenic driver (LULC) has become as important as, or more important than, natural factors (runoff and teleconnection factors) on hydrological drought. [ABSTRACT FROM AUTHOR]

Much attention has been given to the change rule of a single extreme event, and there are few reports on comprehensive characteristics of multiple extreme events in a certain region. Based on the analyzes of annual frequency of extreme high temperature, extreme low temperature, extreme drought, extreme precipitation, and extreme typhoon events in China from 1961 to 2020, a multi extreme events composite risk grade index (MXCI) was constructed and applied to the comprehensive characteristics analyzes of multiple extreme events in China. The results show that the high value areas of MXCI were mainly located in southeast China and southwest China. Over the past 60 years, the MXCI has shown a decreasing trend in western China and most of southeastern China, and an increasing trend in the middle zone from southwest China to northeast China. Through comparative analysis, MXCI can objectively reflect the comprehensive characteristics of multiple extreme climate events in a region, which is helpful to understand regional extreme climate characteristics and effectively cope with extreme climate risks. [ABSTRACT FROM AUTHOR]

Climate change has dramatic impacts on the growth and the geographical distribution of tea (Camellia sinensis L.). Assessing the potential distribution of tea will help decision makers to formulate appropriate adaptation measures to use the altered climatic resources and avoid the damage from climate hazards. The objective in this study is to model the current and future distribution of tea species based on the four SSPs scenarios using the MaxEnt model in China. For the modeling procedure, tea growth records in 410 sites and 9 climate variables were used in this paper. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the model. The AUC value was over 0.9 in this study, showing the excellent simulation result of the model. In relation to the current distribution, areas of 82.01 × 104 km2 (8.51% of total land area in China), 115.97 × 104 km2 (12.03% of total land area in China), and 67.14 × 104 km2 (6.97% of total land area in China) were recognized as Marginal, Medium, and Optimal climate suitable habitats for tea over China. Compared to the current distribution, most of the Optimal suitability areas in southeast China would be lost in four scenarios. The area of Marginal and Medium suitable habitats would expand in SSP370 and SSP585, especially in 2041–2061 and 2081–2100. The suitable area of tea would expand northwards and westwards, suggesting that additional new suitable habitats could be created for tea production with the future climate change, especially in Shandong, Henan, Guizhou, and Yunnan Provinces. This research would provide vital scientific understanding for policy making on tea production, tea garden site chosen and adopyion of adaptation methods in the future. [ABSTRACT FROM AUTHOR]

Whether or not large-scale vegetation restoration will lead to a decrease in regional terrestrial water storage is a controversial topic. This study employed the Geodetector model, in conjunction with observed and satellite hydro-meteorological data, to detect the changes in terrestrial water storage anomaly (TWSA) and to identify the contributions of climate change and vegetation greening across China during the years 1982–2019. The results revealed that: (1) during the period of 1982–2019, TWSA showed a downward trend in about two thirds of the country, with significant declines in North China, southeast Tibet, and northwest Xinjiang, and an upward trend in the remaining third of the country, with significant increases mainly in the Qaidam Basin, the Yangtze River, and the Songhua River; (2) the positive correlation between normalized vegetation index (NDVI) and TWSA accounts for 48.64% of the total vegetation area across China. In addition, the response of vegetation greenness lags behind the TWSA and precipitation, and the lag time was shorter in arid and semi-arid regions dominated by grasslands, and longer in relatively humid regions dominated by forests and savannas; (3) furthermore, TWSAs decreased with the increase in NDVI and evapotranspiration (ET) in arid and semi-arid areas, and increased with the rise in NDVI and ET in the humid regions. The Geodetector model was used to detect the effects of climate, vegetation, and human factors on TWSA. It is worth mentioning that NDVI, precipitation, and ET were some of the main factors affecting TWSA. Therefore, it is essential to implement rational ecological engineering to mitigate climate change's negative effects and maintain water resources' sustainability in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

China is currently recognized as the leading global energy consumer and CO2 emitter. A significant amount of carbon emissions can be attributed to urban public buildings. Establishing an equitable and efficient carbon emission allocation mechanism is a crucial step to meeting the ambitious targets in China's 2030 carbon peak plan. In this study, we estimate the total amount of CO2 emissions from urban public buildings by 2030 and propose a preliminary scheme of carbon quota assignment for each province. By means of applying the zero-sum gains data envelopment analysis (ZSG-DEA) model, the carbon emission quotas allocation of urban public buildings in China's 30 provinces is proposed, and the corresponding pressure to reduce provincial carbon emissions is analyzed. The results indicate that Qinghai has the lowest carbon emission rate (0.01%) for urban public buildings, while Guangdong has the highest (9.06%). Among the provinces, Jiangsu, Jiangxi, and Tianjin face the least pressure in reducing carbon emissions from urban public buildings. On the other hand, Hebei, Beijing, and Anhui are under great pressure to decrease carbon emissions. Notably, Hebei is predicted to have the highest emission reduction requirement of 95.66 million tons. In terms of pressures on carbon emissions reduction for urban public buildings, Jiangsu, Jiangxi, and Tianjin exhibit the least pressure. Hebei, Beijing, and Anhui are facing intense pressure to decrease carbon emissions. These findings offer policymakers valuable insights into developing a fair and efficient carbon allowance allocation strategy, while also contributing to China's efforts to mitigate carbon emissions and combat climate change. [ABSTRACT FROM AUTHOR]

Under the threat of global climate change, China has proposed a dual carbon goal of peak carbon and carbon neutrality. As the vital carrier for territorial spatial planning, production–living–ecological (PLE) spaces drive carbon emissions and are important to the dual carbon goals. In this study, carbon emissions and sinks of PLE spaces in cities in Heilongjiang Province from 2005 to 2020 were calculated and spatial–temporal changes were analyzed. The carbon emission structure was analyzed in segmentation sectors. The land use changes and socioeconomic factors on carbon emissions were analyzed, and emission reduction strategies were implemented. The results show the following: (1) Carbon emissions from production and living spaces increased yearly. Carbon sinks were smaller than emissions, but capacity was stable. (2) Higher-emission cities were concentrated in southwest Heilongjiang, and carbon emission differences between regions gradually increased. (3) Among carbon emission sectors, agricultural and household made up smaller proportions, while animal husbandry, industrial, transportation, and traffic travel contributed most. Carbon emission structures were transformed by adjusting urban development and industrial structure. (4) For most cities, industrial space was the main emission space, but agricultural production and urban–rural living spaces dominated in some cities. (5) GDP, urbanization rate, and area of city paved roads suppressed emissions in cities with decreased carbon emission grades. The industrial structure and coal consumption inhibited emissions in cities with maintaining and increasing carbon emissions grades. [ABSTRACT FROM AUTHOR]

Despite the fact that gross primary productivity (GPP) and water use efficiency (WUE) have been widely used as indicators to evaluate the water-carbon cycle, uncertainties exist in the patterns of GPP and WUE responses to climate variability along different aridity gradients. In this study, the aridity index was used to divide China into four arid-humid zones. The spatiotemporal variability of multiple vegetation types GPP and WUE in response to climate change under different arid-humid zones were investigated based on remote sensing data. The results indicated that the increasing trend of WUE in the four arid-humid zones of China was less pronounced than GPP from 2001 to 2021. The GPP value decreased gradually from the humid to the arid zone, and the WUE value in the arid zone was slightly higher than in the semi-arid zone. The GPP of all vegetation types in China showed a tendency to increase, while shrubland and wetland WUE tended to decrease. The major vegetation types (e.g., forest, cropland and grassland) in each aridity gradient contributed to the changes in local GPP and WUE. However, in individual arid-humid zones, wetland and shrubland also exhibited high GPP and WUE values that were not inferior to forest and cropland. Temperature and precipitation were the main climatic factors responsible for the increase in vegetation GPP in different aridity gradients, with a higher positive correlation for temperature than precipitation. WUE showed a distinct positive and negative correlation with the thermal factors (temperature and net radiation) and the moisture factors (precipitation and relative humidity); this pattern was more pronounced in the humid and semi-humid zones. Net radiation and precipitation may be the main climatic factors causing a slight upward trend in WUE across the arid-humid zones, while the decrease in shrubland and wetland WUE may be related to relative humidity and precipitation. [ABSTRACT FROM AUTHOR]

With climate change and rising sea levels, the residents and assets in low-elevation coastal zones (LECZ) are at increasing risk. The application of high-resolution gridded population datasets in recent years has highlighted the threats faced by people living in LECZ. However, the potential exposure of gross domestic product (GDP) within LECZ remains unknown, due to the absence of refined GDP datasets and corresponding analyzes for coastal regions. The climate-related risks faced by LECZ may still be underestimated. In this study, we estimated the potential exposure of GDP in the LECZ across China by overlying DEM with new gridded GDP datasets generated by random forest models. The results show that 24.02% and 22.7% of China's total GDP were located in the LECZ in 2010 and 2019, respectively, while the area of the LECZ only accounted for 1.91% of China's territory. Significant variability appears in the spatial-temporal pattern and the volume of GDP across sectors, which impedes disaster prevention and mitigation efforts within administrative regions. Interannual comparisons reveal a rapid increase in GDP within the LECZ, but a decline in its share of the country. Policy reasons may have driven the slow shift of China's economy to regions far from the LECZ. [ABSTRACT FROM AUTHOR]

In efforts to improve regional ecosystem service functions, coordinate land development and ecological conservation, and establish a reference for optimizing land resource allocation and policy formulation to cope with climate change, it is critical to investigate the spatial distribution of land use/cover change (LUCC) and ecosystem services (ESs) under future climate change. This study proposes a framework based on the Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP), integrating the patch-generating land use simulation (PLUS) model and the integrated valuation of ecosystem services and tradeoffs (InVEST) model to analyze the spatial agglomeration of ESs, to analyze the importance of each driving factors. The results of the study show as follows: (1) the combination of CMIP6 and PLUS models can effectively simulate land use with an overall accuracy of 0.9379. (2) In spatial correlation, ESs show good clustering in all three future scenarios, with similar distribution of cold hotspots in the SSP126 and SSP245 scenarios. Hotspots are more dispersed and cold spots are shifted to the west in the SSP585 scenario. (3) GDP is an important factor in carbon storage and habitat quality, and precipitation has a greater impact on soil retention and water production. Overall, ESs can be increased by appropriately controlling population and economic development, balancing economic development and ecological protection, promoting energy transition, maintaining ecological hotspot areas, and improving cold spot areas. [ABSTRACT FROM AUTHOR]

The overall goal of this study was to examine the impacts of climate change on the mean and variance of rice yields in China by using historical climate and crop data. An econometric model was established to estimate Just–Pope stochastic production functions and identify the potential impacts of climate change on the mean and variance of rice yields by type, keeping other factors constant. Based on the estimated production functions, the contribution rate of climatic factors to rice yield was then assessed by conducting the growth accounting of yields over the past 30 years. The results showed that both the mean rice yield and the yield variability were influenced by changes in the mean climate conditions and climatic variance. In the future, the impacts of climate change on rice yields will depend on local regions' present climatic conditions. The results have implications for improving the adaptation capacity of rice production. [ABSTRACT FROM AUTHOR]

The El Niño Southe58rn Oscillation (ENSO) is a typical oscillation affecting climate change, and its stable periodicity, long-lasting effect, and predictable characteristics have become important indicators for regional climate prediction. In this study, we analyze the Standardized Precipitation Evapotranspiration Index (SPEI), the Niño3.4 index, the Southern Oscillation Index (SOI), and the Multivariate ENSO Index (MEI). Additionally, we explore the spatial and temporal distribution of the correlation coefficients between ENSO and SPEI and the time lag between ENSO events of varying intensities and droughts. The results reveal that the use of Nino3.4, MEI, and SOI produces differences in the occurrence time, end time, and intensity of ENSO events. Nino3.4 and MEI produce similar results for identifying ENSO events, and the Nino3.4 index accurately identifies and describes ENSO events with higher reliability. In China, the drought-sensitive areas vulnerable to ENSO events include southern China, the Jiangnan region, the middle and lower reaches of the Yangtze River, and the arid and semi-arid areas of northwestern China. Droughts in these areas correlate significantly with meteorological drought, and time-series correlations between ENSO events and droughts are significantly stronger in regions close to the ocean. Drought occurrence lags ENSO events: when using the Niño3.4 index to identify ENSO, droughts lag the strongest and weakest El Niño events by 0–12 months. However, when using the MEI as a criterion for ENSO, droughts lag the strongest and weakest El Niño events by 0–7 months. The time lag between the strongest ENSO event and drought is shorter than that for the weakest ENSO event, and droughts have a wider impact. The results of this study can provide a climate-change-compatible basis for drought monitoring and prediction. [ABSTRACT FROM AUTHOR]

Simple Summary: The deciduous shrub Comptonia is a monotypic genus of Myricaceae, which currently is distributed only in eastern North America, with a smaller range than that in other periods of the Cenozoic. By analyzing the macroscopic and microscopic characteristics of the leaves, we describe a new species of Comptonia (i.e., Comptonia hirsuta) from the Hannuoba Formation in Zhuozi, Inner Mongolia, North China. The co-occurring fruits were also studied based on their morphological characteristics, which were assigned to Comptonia tymensis. Variation in the distribution range of Comptonia indicates the influence of global cooling on the expansion of this plant. Furthermore, the Bering Land Bridge played an important role in the migration from North America to East Asia. The Thulean route may have provided an opportunity for plant exchange between western Europe and eastern North America. Moreover, the reason for the disappearance of Comptonia from China according to the analysis of the changes in both the global climate and the distribution of Comptonia fossils is also discussed. It is suggested that the climatic changes after the late Miocene and the progenitive pattern of Comptonia together caused the disappearance of Comptonia in China. Comptonia (Myricaceae) is well known as a monotypic genus living only in eastern North America; however, fossils show that the genus occurred extensively in the Northern Hemisphere during the Cenozoic. We observed dozens of Comptonia leaf fossils from the early Miocene in Zhuozi, China. The leaf architecture characteristics and epidermal features of the fossil specimens are described in detail here for the first time, and they were assigned to a new species: Comptonia hirsuta. The fruit fossils collected simultaneously from the same layer were assigned to Comptonia tymensis. The global fossil records indicate that the spatial distribution range of Comptonia reached its peak in both the Eocene and Miocene as two warm periods and then gradually decreased in the Oligocene, as well as after the late Miocene, because of the cooling global climate. Furthermore, the Comptonia taxon in East Asia may have migrated from North America via the Bering route in the late Paleocene or Eocene. Plant exchange between western Europe and eastern North America possibly occurred during the Eocene via the Thulean route. Phytogeographic variation in the Comptonia fossils from China also indicates that the reason for the disappearance of Comptonia from China may not only be due to the prolonged cooling and drying after the late Miocene, but also due to its progenitive pattern. [ABSTRACT FROM AUTHOR]

Regional climate is complicated and unpredictable in the context of global climate change. Farmers on the Loess Plateau, who rely on agriculture and natural resources for subsistence, are one of the groups feeling the early effects of climate change. Their vulnerability is determined by their degree of connection with the natural environment. Frequent droughts on the Loess Plateau have severely challenged farmers' livelihoods, although some actions have been taken to adapt to these changes. To enable farmers to find sustainable livelihood strategies in challenging natural conditions, we established a research framework to link livelihood vulnerability and resilience and applied it to Jiaxian County, a specific research area in the Loess Plateau of China. To validate previous research, we studied the fluctuation trends of farmers' livelihood vulnerability and livelihood resilience in the past 30 years and the interrelationships between these two trends and their influencing factors. The results are as follows: since 1990, livelihood vulnerability has been polarized; however, moderate vulnerability has always been dominant. Livelihood resilience shows a trend of continuous enhancement. The relationship between livelihood vulnerability and resilience is complex, and the direction of change between the two can be both similar and different. The topography, arable land conditions, soil quality, and irrigation conditions in different areas impact vulnerability and resilience, and the degree of impact is different in different periods. Farmers' livelihood strategies depend on their cognitive decision making and livelihood assets, which are critical vulnerability and resilience factors. Most farmers in the study area have undergone significant livelihood strategy changes, while some maintain their original livelihood strategies. These findings provide policy implications for reducing vulnerability, enhancing resilience, and helping smallholder farmers find sustainable livelihood strategies to avoid poverty traps. [ABSTRACT FROM AUTHOR]

Vegetation canopy interception (Ic) of precipitation is a considerable component of the global hydrological cycles. Although the measurement and modeling of canopy interception have been explored worldwide at the individual, stand or ecosystem scale, it is still unclear how to recognize this process at the regional or global scales within the context of global climate change. In this study, a revised Gash model was employed to estimate canopy interception based on remote sensing and meteorological data. The spatial and temporal variations in Ic were investigated and the main environmental factors were explored in China for the 2000–2018 period. The results showed that the revised Gash model performed well in modeling canopy interception at the regional scale compared with the PML_V2 dataset product and the in-situ measurements. The average annual Ic in China from 2000 to 2018 was 166.55 mm, with a significant decreasing spatial pattern from the Southeastern to the Northwestern regions. The ratio of canopy interception to precipitation (Ir) displayed a similar spatial pattern, with an average value of 22.30%. At the temporal scale, the mean annual Ic significantly increased at a rate of 1.79 mm yr−1 (p < 0.01) during the study period, and the increasing trend was more pronounced during the 2000–2009 period, at a rate of 3.34 mm yr−1 (p < 0.01). In most vegetation types, except for the deciduous broad-leaved forest and temperate desert, canopy interception showed a significant increasing trend (p < 0.01). Precipitation, temperature, and the normalized differential vegetation index (NDVI) were considered to be the main factors affecting the variations of Ic in China during the last two decades, with specific dominant factors varying in different areas. Specifically, precipitation was considered to control the variations of Ic in the Northwestern regions, temperature mainly influenced the Southern regions, and the NDVI was identified as the main factor in regions where significant ecological conservation projects are established, such as the Loess Plateau. Our findings are expected to not only contribute to the understanding of regional ecohydrological cycle but also provide valuable insights into the methodology of interception modeling at the regional and global scales. [ABSTRACT FROM AUTHOR]

In recent years, China has suffered from frequent extreme precipitation events, and predicting their future trends has become an essential part of the current research on this issue. Because of the inevitable uncertainties associated with individual models for climate prediction, this study uses a machine learning approach to integrate and fit multiple models. The results show that the use of several evaluation metrics provides better results than the traditional ensemble median method. The correlation coefficients with the actual observations were found to improve from about 0.8 to 0.9, while the correlation coefficients of the precipitation amount (PRCPTOT), very heavy precipitation days (R20mm), and extreme precipitation intensity (SDII95) reached 0.95. Based on this, the precipitation simulations of moderate forced scenario for sharing socio-economic path (SSP2-4.5) from 27 coupled models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used to explore potential changes in future extreme precipitation events in China and to calculate the distribution and trends of the PRCPTOT, extreme precipitation amount (R95pTOT), maximum consecutive 5-day precipitation (Rx5day), precipitation intensity (SDII), SDII95, and R20mm for the early 21st century (2023–2050), mid-21st century (2051–2075), and late 21st century (2076–2100), respectively. The results showed that the most significant increase in extreme precipitation indices is expected to occur by the end of the century, with the R95pTOT, Rx5day, and SDII95 increasing by 13.73%, 9.43%, and 9.34%, respectively, from the base period. The remaining three precipitation indexes, the PRCPTOT, SDII, and R20mm, also showed increases of 8.77%, 6.84%, and 4.02%, respectively. Additionally, there were apparent differences in the spatial variation of extreme precipitation. There were significant increasing trends of extreme precipitation indexes in central China and northeast China in the three periods, among which the total annual precipitation showed an increasing trend in central and northern China and a decreasing trend in western and south China. An increasing trend of annual precipitation intensity was found to be mainly concentrated in central China and south China, and the annual precipitation frequency showed a larger increasing trend at the beginning of this century. The annual precipitation frequency showed an increasing trend in the early part of this century. In general, all the indices showed an overall increasing trend in the future period, with the PRCPTOT, Rx5day, and SDII95 showing the most significant overall increasing trends. [ABSTRACT FROM AUTHOR]

Based on the homogenized daily data of 2255 meteorological stations during the past 60 years from 1961 to 2020, the potential evapotranspiration was calculated using the revised FAO56 Penman–Monteith model, and then the annual AI (aridity index, the ratio of annual potential evapotranspiration to annual precipitation) was employed to analyze the dry-wet climate change in China. The GCM models' prediction data was used to analyze the possible trends of dry-wet climate in China by the end of this century. The results showed that in the past 60 years, the climate in China was getting wetter, especially in the western regions of China, including Xinjiang, western Qinghai, Gansu, western Inner Mongolia, and northwestern Tibet. In the last 10 years, China's climate has become more humid. Compared with the 1960s, the total area of aridity has decreased by about 650,000 square kilometers. The changes of different climate zones have regional and periodical characteristics. There was a tendency to get wet periods in all four seasons, especially in summer. Analysis of GCM model projection data shows that by the end of this century, the climate in China would have a general trend of becoming drier. The drier regions are mainly located in the central and eastern parts of China, while the western regions of China continue to maintain the wetting trends. In the case of high emissions, the trends of drying in the central and eastern and wetting in the west are more significant than in the case of medium emission. [ABSTRACT FROM AUTHOR]

Ecosystem services are the basis of human survival and development and play an irreplaceable role in maintaining the dynamic balance of the earth's life support system and environment. This study evaluated the annual forest ecosystem service values (ESVs) and their spatial evolution characteristics from 2001 to 2020 in China and revealed the impact of climatic factors as well. The equivalent factor method was applied to calculate ESVs in combination with net primary productivity (NPP) calculated by the CASA model. The water conservation results based on the InVEST model and equivalent factor method were compared to test the reliability of the method. From 2001 to 2020, the annual forest ESVs ranged from RMB 9.17 trillion to 10.81 trillion, with an average of RMB 9.99 trillion in China. The forest ESVs increased from the northwest to the southeast regions of China with the lowest values of less than RMB 3 million per square kilometer and relatively high values of more than RMB 9 million per square kilometer. In the past 20 years, the forest ESVs have shown a significant increasing trend, especially in the Northeast Forest Region and Southeast Forest Region. The ESVs have decreased only in very few forest areas such as Cuona and Motuo counties on the southern edge of Tibet Province in Southwest China and Pingtung and Kaohsiung counties in southern Taiwan. The mean annual forest ESV was necessarily higher in the recent 10 years (2011–2020, RMB 10.43 trillion) than in the previous 10 years (2001–2010, RMB 9.55 trillion), while the spatial growth rate was usually less than 20%. The annual forest ESVs were significantly correlated with temperature, precipitation and evapotranspiration but not with sunshine hours and relative humidity across the 20 years. In most areas (>60%), the positive impact of various climatic factors was dominant, and the area positively affected by precipitation was the largest (76%). The mean annual forest water conservation from 2001 to 2020 was RMB 1.46 trillion based on the InVEST model and was RMB 1.77 trillion based on the equivalent factor method. Overall, the results obtained by the two methods are roughly equal and mostly spatially matched. This study has some guiding significance for utilizing resources rationally, strengthening ecological environment protection and improving adaptability to climate change. [ABSTRACT FROM AUTHOR]

Cropland abandonment occurs frequently in many countries and regions around the world, particularly in those with poor environmental conditions, such as mountainous regions. In Chongqing county, China, over 76% of the total area is mountainous. Due to the lack of reliable remote sensing monitoring and identification methods, the spatial and temporal distribution of abandoned cropland areas and its underlying causes are poorly understood. Thus, the extent of cropland abandonment in Chongqing, since 2001, was estimated using land use trajectories. The following results were obtained: (1) the cropland abandonment rate was 12.2-15.4% from 2001 to 2020, with an average of 13.3%; (2) hotspots of abandoned cropland were concentrated in the north and southeast. Cropland abandonment was clustered in the northern, southeastern, and southwestern areas; (3) socio-economic factors (including gross domestic product density, population density, and road density) had a greater impact on the spatial distribution of abandoned cropland than environmental factors. Based on the results, the government should strive to reduce production costs associated with poor agricultural infrastructure, sporadic cropland, and higher labor costs by providing grain subsidies, undertaking cropland consolidation, encouraging land transfer, and improving agricultural infrastructure. [ABSTRACT FROM AUTHOR]

Climate is an essential component in the sustainability of tourism cities. Coastal tourism cities face unprecedented challenges under a changing climate. The complexity of the tourism–climate interface predicates the need for tools that can assess the weather and climate accurately. Tourism climate indices have been widely developed to evaluate the temporal and spatial distribution of climate resources, but these indices are not entirely applicable to coastal cities facing air pollution. This study developed a Coastal Tourism Climate Index (CTCI) to assess the tourism climate suitability of Chinese coastal cities. The CTCI was developed to include five variables: thermal comfort, sunshine, precipitation, wind, and air quality. This index was applied and verified in the case of nine coastal tourism cities in China compared to the Holiday Climate Index (HCI: Beach). According to the results, the CTCI is more suitable for coastal tourism climate assessment in China. Finally, corresponding countermeasures are put forward for the balanced and sustainable development of Chinese coastal tourism cities. This study takes the lead in applying big data to the development and validation of tourism climate indices. These findings provide novel insights for the tourism climate assessment of coastal destinations facing air pollution. [ABSTRACT FROM AUTHOR]

Climate change is transforming winter environmental conditions rapidly. Shifts in snow regimes and freeze/thaw cycles that are unique to the harsh winter season can strongly influence ecological processes and biodiversity patterns of mammals and birds. However, the role of the winter environment in structuring a species richness pattern is generally downplayed, especially in temperate regions. Here we developed a suite of winter habitat indices at 500 m spatial resolution by fusing MODIS snow products and NASA MEaSUREs daily freeze/thaw records from passive microwave sensors and tested how these indices could improve the explanation of species richness patterns across China. We found that the winter habitat indices provided unique and mutually complementary environmental information compared to the commonly used Dynamic Habitat Indices (DHIs). Winter habitat indices significantly increased the explanatory power for species richness of all mammal and bird groups. Particularly, winter habitat indices contributed more to the explanation of bird species than mammals. Regarding the independent contribution, winter season length made the largest contributions to the explained variance of winter birds (30%), resident birds (27%), and mammals (18%), while the frequency of snow-free frozen ground contributed the most to the explanation of species richness for summer birds (23%). Our research provides new insights into the interpretation of broad-scale species diversity, which has great implications for biodiversity assessment and conservation. [ABSTRACT FROM AUTHOR]

Remote sensing vegetation index data contain important information about the effects of ozone pollution, climate change and other factors on vegetation growth. However, the absence of long-term observational data on surface ozone pollution and neglected air pollution-induced effects on vegetation growth have made it difficult to conduct in-depth studies on the long-term, large-scale ozone pollution effects on vegetation health. In this study, a multiple linear regression model was developed, based on normalized difference vegetation index (NDVI) data, ozone mass mixing ratio (OMR) data at 1000 hPa, and temperature (T), precipitation (P) and surface net radiation (SSR) data during 1982–2020 to quantitatively assess the impact of ozone pollution and climate change on vegetation growth in China on growing season. The OMR data showed an increasing trend in 99.9% of regions in China over the last 39 years, and both NDVI values showed increasing trends on a spatial basis with different ozone pollution levels. Additionally, the significant correlations between NDVI and OMR, temperature and SSR indicate that vegetation activity is closely related to ozone pollution and climate change. Ozone pollution affected 12.5% of NDVI, and climate change affected 26.7% of NDVI. Furthermore, the effects from ozone pollution and climate change on forest, shrub, grass and crop vegetation were evaluated. Notably, the impact of ozone pollution on vegetation growth was 0.47 times that of climate change, indicating that the impact of ozone pollution on vegetation growth cannot be ignored. This study not only deepens the understanding of the effects of ozone pollution and climate change on vegetation growth but also provides a research framework for the large-scale monitoring of air pollution on vegetation health using remote sensing vegetation data. [ABSTRACT FROM AUTHOR]

The intensification of global climate change leads to frequent mountain torrents, landslides, debris flows and other disasters, which seriously threaten the safety of residents' lives and property. However, few studies have compared and analyzed the livelihood vulnerability and adaptation strategies of farmers in different disaster-threatened areas under the background of climate change. Based on survey data of 327 households in the areas threatened by mountain floods, landslides and debris flow in Sichuan Province, this study analyzed the characteristics of livelihood vulnerability and adaptation strategies of households in the areas threatened by different disaster types and constructed multinomial logistic regression models to explore their correlations. The findings show that: (1) The livelihood vulnerability indices of farmers in different hazard types showed different characteristics. Among them, the livelihood vulnerability index of farmers in landslide-threatened zones is the highest, followed by the livelihood vulnerability index of farmers in debris-flow-threatened zones, and finally the livelihood vulnerability index of farmers in flash flood threat zones. At the same time, all three natural hazards show a trend of higher vulnerability in the sensitivity dimension than in the exposure and livelihood resilience dimensions. (2) The nonfarming livelihood strategy is the main livelihood strategy for farmers in different disaster-type-threatened areas. At the same time, the vulnerability of farmers choosing the nonfarming livelihood strategy is much higher than that of farmers choosing the part-time livelihood strategy and pure farming livelihood strategy, and the vulnerability of sensitivity dimension is higher than that of the exposure dimension and livelihood resilience dimension. (3) For farmers in landslide- and debris-flow-threatened areas, livelihood resilience is an important factor affecting their livelihood strategy. There was a positive correlation between livelihood resilience and farmers' choice of pure agricultural livelihood strategies in these two natural-disaster-threatened areas. This study deepens our understanding of the characteristics and relationships of farmers' livelihood vulnerability and adaptation strategies under different disaster types in the context of climate change, and then provides the reference basis for the formulation of livelihood-adaptive capacity promotion-related policy. [ABSTRACT FROM AUTHOR]

The spatiotemporal evolution of vegetation and its influencing factors can be used to explore the relationships among vegetation, climate change, and human activities, which are of great importance for guiding scientific management of regional ecological environments. In recent years, remote sensing technology has been widely used in dynamic monitoring of vegetation. In this study, the normalized difference vegetation index (NDVI) and standardized precipitation–evapotranspiration index (SPEI) from 1998 to 2017 were used to study the spatiotemporal variation of NDVI in China. The influences of climate change and human activities on NDVI variation were investigated based on the Mann–Kendall test, correlation analysis, and other methods. The results show that the growth rate of NDVI in China was 0.003 year−1. Regions with improved and degraded vegetation accounted for 71.02% and 22.97% of the national territorial area, respectively. The SPEI decreased in 60.08% of the area and exhibited an insignificant drought trend overall. Human activities affected the vegetation cover in the directions of both destruction and restoration. As the elevation and slope increased, the correlation between NDVI and SPEI gradually increased, whereas the impact of human activities on vegetation decreased. Further studies should focus on vegetation changes in the Continental Basin, Southwest Rivers, and Liaohe River Basin. [ABSTRACT FROM AUTHOR]