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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]

To qualify the risk of extreme weather events for power supply security during the long-term power system transformation process, this paper proposes a risk probability evaluation method based on probabilistic production simulation. Firstly, the internal relationship of extreme weather intensity and duration is depicted using the copula function, and the influences of extreme weather on power security are described using the guaranteed power output ability coefficient, which can provide the extreme scenario basis for probabilistic production simulation. Then, a probabilistic production simulation method is proposed, which includes a typical-year scenario and extreme weather events. Meanwhile, an index system is proposed to qualify the power security level, which applies the loss of load expectation (LOLE) and time of loss of load expectation (TOLE) under different scenarios and other indices to reveal the long-term power security trend. Finally, the long-term power supply risks for the Yunnan provincial power system are analyzed using the proposed method, validating that the proposed method is capable of characterizing the influences of extreme weather on power security. The security level of different long-term power transformation schemes is evaluated. [ABSTRACT FROM AUTHOR]

Nowadays, geopolitical, extreme weather and other emergencies have exacerbated the global energy crisis, and thus, have increased the urgency of the world's transition to sustainable energy. Sustainable energy policies play an important role in the process of sustainable energy transformation. The research on sustainable energy policy is mainly carried out through conventional qualitative and quantitative methods, in which bibliometrics and meta-analysis methods are paid attention to; however, the mining and analysis of the semantics of the relationships between policies are ignored. This paper uses knowledge graph technology to build a knowledge graph of China's sustainable energy policy by using 10,815 open official documents of sustainable energy policy issued by China from 1981 to 2022. It forms the relevant policy archive storage and details related organizations. The legal source can be traced through the graph database, where the powerful synergy can be seen, and the policy focus can be monitored. In terms of structural data, this paper uses graph algorithms to identify key policy nodes at different stages, to identify the key government departments for policy issuance and cluster policy issuance departments, and it investigates China's policy evolution in the issue of sustainable energy policies, the evolution of policy issuance departments, and the power co-evolution process between policy issuance departments. The research found that: (1) China's sustainable energy policy was initiated in environmental protection, and the relevant policies on collecting pollution charges has continued to play an important policy node. Additionally, the three versions of the Environmental Protection Law of the People's Republic of China have successively become the main legal source of other sustainable energy transformation policies. (2) The prominent feature of China's sustainable energy policy transformation has involved transforming the process where the issuance of policies came from a single department to the joint issuance of documents by multiple departments. The joint exercise of government functions and powers by multiple departments jointly promotes sustainable energy policies' implementation and play. (3) In the future, when formulating sustainable energy policies, the Chinese government should focus on the strategic and systematic aspects of the policies, so that the sustainable energy policies can meet both short-term and long-term development goals. At the same time, the synergy of various policies and measures should be fully played in implementing sustainable energy policies. The establishment of the policy knowledge graph based on publicly-open official documents can facilitate the analysis and visualization of sustainable energy policies, providing new ideas for policy research. This paper introduces the knowledge graph, graph machine learning algorithms and big data technology, which can deepen the depth and breadth of people's research on sustainable energy policy. This study will help the public policy formulation work in the future and has a positive reference value for the evaluation of the implementation effect of policy objectives. [ABSTRACT FROM AUTHOR]

The serious and frequent typhoon activities can easily cause extreme precipitation weather in the eastern coastal area of China, which is affected by land and sea differences. To explore the temporal and spatial characteristics of Precipitable Water Vapor (PWV) and rainfall during the typhoon period, the data of the conspicuous case named 'Meihua' in 2022 is adopted in analysis. In this paper, firstly, the accuracy of the PWV retrieved by ERA5 was evaluated, which met the experimental analysis requirements, compared with the conference value of the Radiosonde (RS). Secondly, the correlation between PWV, rainfall and the typhoon path were analyzed qualitatively and quantitatively, using 16 meteorological stations in the typhoon path. The results indicated that PWV reached its peak value 2–6 h than rainfall, which was an important reference for rainfall forecasting. Then, the 'Pearson correlation coefficient' method was used for the quantitative evaluation of the correlation between PWV and the distance of the 'weather station-typhoon'. The results showed that PWV had an obvious upward trend, with a decrease in the distance between the 'weather station-typhoon'. The variation in PWV is intense at a reduced distance, and can reach its peak 16 h before the arrival of the typhoon. A strong negative correlation was demonstrated, with an average value of −0.73 for the Pearson correlation coefficient. Analyzing the temporal and spatial changes of the typhoon track, PWV and rainfall, the results show that before the typhoon passes through the region, both the PWV and rainfall certainly reach their maximum. The variation trends of PWV and rainfall in the period of the typhoon are significantly consistent. The center of PWV and rainfall is mainly located on the northwest side of the typhoon center, which showed obvious asymmetry. [ABSTRACT FROM AUTHOR]

Due to the increased frequency of extreme weather events and the implementation of the China's dual-carbon target, thermal power companies have been under pressure to construct green infrastructure and to actively pursue low-carbon transformation in response to stricter environmental regulations. This research thus selects 30 listed thermal power enterprises in China as study objects and assesses their green investment efficiency in the low-carbon transition process using three-stage DEA evaluation model with environmental regulation as an exogenous variable. Based on this, a benchmark regression model is used to corroborate the relationship between environmental regulation and green investment. Simultaneously, we carry out analysis to compare the correlation between thermal power firms' green investment efficiency and their focus on green investments. The results show in terms of total efficiency that environmental regulation significantly improves the total efficiency of 80% of thermal power enterprises compared to the absence of this exogenous variable. With the addition of environmental regulation, firms' total efficiency declines gradually in general from 2018 to 2022, with the mean value of efficiency falling by 0.068. In terms of stage-specific efficiency, the efficiency of the green investment stage of the majority of firms is between 0.3 and 0.6, which is much lower than that of the operational stage and the market performance stage. In terms of sub-indicator efficiency, both green investment efficiency and social donation efficiency among thermal power enterprises show obvious polarization, with 30% of them having an efficiency of 1 and 30% less than 0.1. In terms of green investment focus, thermal power unit renovation has a more obvious role in boosting the green investment efficiency of thermal power enterprises than do wind power and photovoltaic projects. Therefore, both governmental departments and thermal power enterprises need to take active measures in order to achieve green transformation from the perspective of green investment efficiency. Through the segmentation of important projects of green investment, this paper provides a reasonable investment direction reference for the sustainable transformation of China's thermal power industry. It also provides a rich and novel theoretical basis for the Chinese government to further improve the relevant environmental protection laws and regulations of thermal power industry. [ABSTRACT FROM AUTHOR]

Given the fact that the high frequency of extreme weather events globally, in particular typhoons, has more of an influence on flood forecasting, there is a great need to further understand the impact of typhoon events on design storms. The main objectives of this paper are to examine the magnitude, occurrence, and mechanism of typhoon events in southeast coastal China and their contribution to the design storm study. We take Shanghai, which is a typical metropolitan region in the Yangtze River Delta, China, as an example. The impact of typhoons on the rainfall frequency analysis is quantitatively evaluated using stochastic storm transposition (SST)-based intensity–duration–frequency (IDF) estimates with various temporal and spatial structures under different return periods. The results show that there is significant variability in the storm magnitude within the transposition domain across different durations, highlighting the spatiotemporal heterogeneity over the coastal area. Moreover, the probability of random storm transposition exhibits an uneven distribution. The frequency of typhoon rainfall events within the transposition domain is notably high, and there is considerable variability in the structure of rainfall. Typhoon rainfall amplifies the intensity of design storms, and its contribution increases with return periods. The variability in design storms increases accordingly. Based on the advantages of SST, which retains the spatiotemporal structure of the rainfall in the generated scenarios, the overall framework provides an effective way to examine the impact of diverse characteristics of typhoon rainfall on frequency analysis and facilitate a deeper exploration of the direct impact of various types of extreme storms on the intensity, spatial, and temporal distributions of design storms amidst evolving environmental conditions over this metropolitan region. [ABSTRACT FROM AUTHOR]

Mountainous terrains are typical over southeast China, with complex and diverse topography, large terrain undulations, rich geographic features, and meteorological variations. Previous studies show that ERA5 meteorological variables are generally accurate with respect to large plains or urban agglomerations, while their applicability to mountainous areas remains inconclusive. In this paper, using high-precision measurements probed by ground-based remote sensing instruments in May–July 2023 at a typical mountainous Shanghuang site in southeast China, the vertical accuracy of the ERA5 reanalysis datasets were comparatively evaluated. Our findings depict that the horizontal wind speeds of the ERA5 reanalysis data show a good performance compared to the Doppler lidar observations. In quantitative terms, ERA5 horizontal wind speeds are about 8% higher than the observed values below a height of 400 m, while above 400 m, an increasing negative bias is observed along as altitude increases. Differing from the horizontal wind speeds, there is a large discrepancy in the vertical wind speeds between the ERA5 and the observations, with a deviation of −150% to 40%. In terms of the thermal variables, the temperature extracted from ERA5 are consistent with the measurements in the low troposphere. Nevertheless, large systematic errors occur at 2000–3000 m, and the overall presentation shows that the errors gradually increase with the increase in altitude. Concerning the relative humidity, the general trend in ERA5 is similar to that observed by the microwave radiometer, but the relative errors from 500 to 2500 m range from 40% to 100%. This study also reveals that ERA5 is poorly representative and requires further improvements during extreme weather events such as rainstorms and typhoons. In particular, the horizontal wind speeds at the middle and lower levels deviate strongly from the observations. Given the importance of atmospheric thermodynamic stratifications in terms of both environmental and climatic issues, the results expand the application of the ERA5 reanalysis datasets in the mountainous areas of southeast China. More importantly, it provides credible reference data for the meteorological predictions and climate modelings in the southeast China mountainous region. [ABSTRACT FROM AUTHOR]

Global extreme weather events such as rainstorms, floods and droughts have become increasingly frequent, posing significant threats to human beings worldwide. Green infrastructure has been implemented for decades to mitigate these issues. However, its widespread adoption in cities is still limited. A lack of sustainable funds was viewed as a great challenge for its widespread implementation. Many developed countries have practiced public participation and stormwater fee systems to mitigate these challenges. To assess the value that citizens place on the ecosystem benefits provided by urban green infrastructure is of great importance for increasing public participation and their willingness to pay. Thus, this paper aims to explore public preferences and their willingness to pay for the benefits of green infrastructure to support the construction and maintenance costs. A discrete choice experiment method was employed and five attributes were selected: reduction in run-off pollutant, degrees of ponding, plant type, planting aesthetics and the amount of payment. The Ngene1.2 software was used to generate a questionnaire, and data collected from the case cities, Xianyang and Xi'xian New Area, China, were analyzed using the mixed logit model. The results revealed that in Xianyang, the willingness to pay was not significantly associated with cognition, while in Xi'xian New Area, willingness to pay was positively related to cognition. Respondents exhibited a significant willingness to pay for green infrastructure to achieve reduced degrees of ponding in both cities, as well as improving planting aesthetics in Xi'xian New Area. Based on these findings, we conclude that government decision-makers should prioritize disseminating knowledge about green infrastructure to residents before implementing such projects in communities. The findings provide valuable insights into the potential economic value of urban green infrastructure and can guide policymakers and urban planners in making decisions regarding the design, implementation, and management of green infrastructure. This study contributes to the understanding of citizen perspectives and the economic evaluation of ecosystem services provided by urban green infrastructure. [ABSTRACT FROM AUTHOR]

Water vapor is an important part of the atmospheric system and plays a key role in the global water cycle, energy balance, extreme weather, and long-term climate research. It is of great significance to fully grasp the spatial and temporal distribution characteristics of water vapor for the study of various climate models. The traditional water vapor detection technology mainly has the problems of low spatial-temporal resolution and poor capture of fine changes. In recent decades, the rapid development of intelligent sensor technology and Walktrap algorithm has made it a reliable means to obtain and analyze the characteristics of water vapor. However, the acquisition of atmospheric water vapor requires two key parameters: station pressure (PS) and weighted average temperature (TM). Since most traditional stations are mainly used for geodetic research, few are equipped with meteorological intelligent sensors. Therefore, based on the traditional meteorological stations, this paper improves the equipment of the traditional stations and endows the traditional stations with precise and sensitive intelligent sensor equipment to obtain the regional water vapor characteristic data of El Niño and a province in the east of China, combined with the advanced Walktrap algorithm, in order to use the above equipment and technology to realize the correlation analysis of El Niño and regional water vapor characteristics. The results show that through the water vapor information calculated by 12 coastal stations, the relationship between monthly average PWV and seawater surface temperature is studied, and the correlation degree between the relevant characteristics is as high as 94.6%. [ABSTRACT FROM AUTHOR]

In recent years, urban communities in China have been continuously affected by extreme weather and emergencies, among which the rainstorm and waterlogging disasters pose a great threat to infrastructure and personnel safety. Chinese governments issue a series of waterlogging prevention and control policies, but the waterlogging prevention and mitigation of urban communities still needs to be optimized. The concept of "resilience" has unique advantages in the field of community disaster management, and building resilient communities can effectively make up for the limitations of the traditional top-down disaster management. Therefore, this paper focuses on the pre-disaster prevention and control of waterlogging in urban communities of China, following the idea of "concept analysis–influencing factor identification–evaluation indicators selection–impact mechanism analysis–resilience simulation prediction–empirical research–disaster adaptation strategy formulation". The structural equation model and BP neural network are used by investigating the existing anti-waterlogging capitals of the target community to predict the future waterlogging resilience. Based on this simulation prediction model, and combined with the incentive and restraint mechanisms, suggestions on corrective measures can be put forward before the occurrence of waterlogging. [ABSTRACT FROM AUTHOR]

Facing the realistic threat of natural environment deterioration and frequent extreme weather, improving agricultural carbon productivity has become an objective requirement for achieving the goal of double carbon and for promoting the high-quality development of agriculture. As an important path toward improving land-use efficiency and promoting agricultural technological progress, land transfer has a potential positive effect on improving agricultural output and inhibiting agricultural carbon emissions. Based on the current situation of land transfer and the characteristics of agricultural carbon productivity in China, this study used the panel data of 30 provinces, from 2006 to 2019, in China to empirically test the relationship between land transfer and agricultural carbon productivity via the spatial Durbin model. The results show that (1) land transfer has a positive effect on agricultural carbon productivity; (2) agricultural carbon productivity has a spatial correlation, and the impact of land transfer on agricultural carbon productivity has a spillover effect; and (3) there are regional differences in the impact of land transfer on agriculture carbon productivity. Based on the results of the study, this paper puts forward policy recommendations from three aspects through which to optimize land transfer and enhance agricultural carbon productivity. [ABSTRACT FROM AUTHOR]

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In recent years, with the increasing impact of extreme weather events on energy security, energy vulnerability has increased significantly, and more and more international institutions and departments have begun to incorporate resilience governance into energy security. This paper focuses on China's inter-provincial energy security assessment. Compared with existing relevant research, the significant features of our work are (i) introducing the concept of energy resilience and presenting its evolution mechanism and evaluation criteria, (ii) developing a gray relational projection model by using the level difference maximization and optimization theory, (iii) measuring the energy resilience of 30 Chinese provinces over space and time. Our results show that the spatial–temporal patterns of energy resilience in China changed significantly from 2005 to 2018. High energy resilience moved from provinces with abundant nonrenewable energy before 2010 to provinces with high energy diversity. Energy endowment is a primary condition to ensure a region's energy resilience. Renewable energy development, energy investment, economic development, and policy coordination play vital roles in ensuring regional energy resilience. Energy investment and economic development can effectively improve the energy resilience of resource-poor areas. This study's results will serve as a reference for China and contribute to expanding knowledge in this field. [ABSTRACT FROM AUTHOR]

The aggravation of extreme weather events has dramatically increased the risk of severe water shortages and seriously threatened agricultural production. The Huang-Huai-Hai region, an important agricultural production region in China, is subject to a severe water shortage and is often hit by drought. As a result, water-saving technologies (WSTs) have been implemented. It remains unclear how effectively these WSTs can reduce crop yield loss, crop yield variation, and the loss of net crop income caused by water scarcity. Therefore, this paper aimed to analyze the role of WSTs in response to drought by establishing a multi-objective expected utility function based on 988 farmers across the Huang-Huai-Hai region. Econometric analysis employing an endogenous switching regression model showed that using WSTs can significantly reduce crop yield loss and net income loss caused by drought. Adopting household-based WSTs or community-based water-saving technology generates even greater positive effects on crop yield and farmers' net income. Therefore, the government should promote farmers' adoption of more advanced WSTs by increasing subsidies and strengthening policy support. [ABSTRACT FROM AUTHOR]

The frequent occurrence of extreme weather disasters under global climate change has greatly impacted agricultural production, caused crop yield and price fluctuations, affected the increase of farmers' income, and hindered the development of the rural economy. As a typical mountainous region in North China, Shandong Province is a major peanut-planting province. In recent years, the peanut planting area in Shandong Province has remained relatively stable at around 10 million hectares (approximately 6.67 million hectares), accounting for approximately 14% of the national peanut planting area. Affected by meteorological disaster risks and consequent price risks, peanut farmers in Shandong Province are faced with double threats of output and price. This study is aiming to analyze the income risks faced by peanut planting farmers in the northern regions of China and to develop a comprehensive understanding of the factors influencing their income variability. The study may investigate various aspects especially the market price fluctuations, socioeconomic factors affecting peanut farming and the correlation of the peanut pricing and the local insurance. The current policy-based peanut insurance makes it difficult to fully disperse risks and guarantee stable production with the permission of relevant government department. Therefore, based on the peanut yield and price data of Shandong Province from 1991 to 2017 and following the logical context of "meteorological disaster - yield risk - price risk - income loss - income insurance", this paper uses the Copula method to calculate the premium rate of peanut income insurance and finds that under the guaranteed level of 70%-90%, the pure premium rate is between 1.5%-4.15%. Compared with the current cost insurance with a flat rate of 4% in Shandong Province, the income insurance rate is generally lower than the current rate. Secondly, there are regional differences in income insurance rates. In addition, at a higher level of protection, the income insurance rate is higher. [ABSTRACT FROM AUTHOR]

In recent years, extreme weather events caused by global climate change have occurred frequently, intensifying the frequency of flood disasters. For flood hazard analysis, high-quality data and a reasonable weight assignment of the relevant factors are critical. This study conducts four rainfall fusion methods, to fuse the Tropical Rainfall Measuring Mission (TRMM) 3B42 and the observations in Dazhou City, China. Then, the random forest was applied to obtain the weights of various factors to facilitate a comprehensive flood hazard analysis under four rainfall durations. The results show that (1) the linear regression performs best out of the four fusion methods, with a correlation coefficient of 0.56; (2) the Digital Elevation Model (DEM) is the most impact factor with a weight of more than 0.2; and (3) the proposed flood analysis system performs well, as 70% of historical flood points are distributed in high and sub-high hazard areas and more than 93% of historical flood points are distributed in medium hazard areas. This study identified the flood hazard grade and distribution in Dazhou City, which could provide a valuable methodology to contribute to flood hazard analysis and disaster management with satellite rainfall. Furthermore, the results of this paper are profound for future work on the high-resolution flood risk assessment and management in Dazhou City. [ABSTRACT FROM AUTHOR]

Climate change is having a significant impact on farmers and agriculture. Rising temperatures and extreme weather events, such as droughts and floods, are causing crop failures and reducing yields. This study evaluated existing publications from 2006 to 2022, using the Web of Science database, Citespace, and the Bibliometrix package in R language for a systematic analysis. A total of 426 publications were identified, written by 1449 authors from 56 countries. The results showed that China has the highest share of publications (16.4%), followed by the United States (14.3%) and Australia (6.8%), with China and Pakistan collaborating most frequently. The keyword timeline analysis from 2006 to 2022 identified 11 clusters of research topics related to farmers' climate change risk (CCRF). Cluster #1, "water conservation measures," had the longest duration, highlighting its significance. Key areas of CCRF research include the vulnerability of land-lost farmers, farmers' use of meteorological information, sources of risk, barriers to implementing conservation agriculture, farmers' attitudes towards heavy metal pollution, and the use of protection motivation theory in agricultural adaptation. In conclusion, climate change poses significant threats to farmers, impacting crop yields, soil fertility, and water availability. Farmers adopt adaptation strategies, but effectiveness varies due to resource limitations and policy gaps. The research on farmer adaptation to climate change is growing, emphasizing the need for supportive policies, resources, and knowledge-sharing to achieve sustainable agriculture and food security. [ABSTRACT FROM AUTHOR]

As a special socio-ecosystem, urban sustainability has been challenged by frequent human activities and natural disasters. Including climate into the socio-ecosystem evaluation framework, this paper constructed the climate, eco-environmental, and socio-economic evaluation index, respectively, to evaluate the core cities' socio-ecology in China's three economic circles. An improved entropy-TOPSIS method was subsequently employed to identify the contribution made by each indicator of the compound system, and a modified ternary Coupling Coordination Degree (CCD) model was developed to probe into the CCD levels among the three systems during the study. The results showed that: (1) The climate system's composite scores were characterized by inter-annual fluctuation without a time trend. However, the system's risk increased, manifesting in an increased probability of extreme weather events, especially in Shanghai. (2) The eco-environmental system witnessed an enormous stride, rising above the level of socio-economic development after 2007. Besides, the gap between the eco-environment and the socio-economy was gradually enlarged since 2014. (3) The eco-environment made the most contribution to the CCD's improvement, meaning enhancing the eco-environmental performance was of paramount significance. The findings can help the government formulate more effective measures to balance climate, eco-environment, and socio-economy to achieve sustainable urban development. [ABSTRACT FROM AUTHOR]

Dengue transmission is climate-sensitive and permissive conditions regularly cause large outbreaks in Asia-Pacific area. As climate change progresses, extreme weather events such as heatwaves and unusually high rainfall are predicted more intense and frequent, but their impacts on dengue outbreaks remain unclear so far. This paper aimed to investigate the relationship between extreme weather events (i.e., heatwaves, extremely high rainfall and extremely high humidity) and dengue outbreaks in China. We obtained daily number of locally acquired dengue cases and weather factors for Guangzhou, China, for the period 2006-2015. The definition of dengue outbreaks was based on daily number of locally acquired cases above the threshold (i.e., mean + 2SD of daily distribution of dengue cases during peaking period). Heatwave was defined as ≥2 days with temperature ≥ 95th percentile, and extreme rainfall and humidity defined as daily values ≥95th percentile during 2006-2015. A generalized additive model was used to examine the associations between extreme weather events and dengue outbreaks. Results showed that all three extreme weather events were associated with increased risk of dengue outbreaks, with a risk increase of 115-251% around 6 weeks after heatwaves, 173-258% around 6-13 weeks after extremely high rainfall, and 572-587% around 6-13 weeks after extremely high humidity. Each extreme weather event also had good capacity in predicting dengue outbreaks, with the model's sensitivity, specificity, accuracy, and area under the receiver operating characteristics curve all exceeding 86%. This study found that heatwaves, extremely high rainfall, and extremely high humidity could act as potential drivers of dengue outbreaks.

During the 2020/2021 winter season, three nationwide cold waves took place from 28 to 31 December 2020, as well as from 5 to 8 January and 14 to 17 January 2021. These cold waves resulted in extreme cold weather in northern and eastern China. In this study, the common features of these cold waves were analyzed, and the key factors contributing to cold waves were illustrated, and the performance of the CMA-GEPS numerical model was evaluated in predicting the cooling effect of the cold waves, and its predictability source was discussed. The results indicated that the cold waves were caused by synergistic effects in the mid- to high-latitude atmospheric circulation of both the upper and lower atmosphere, including polar vortex splitting, enhancement of blocking high, and increased meridional circulation anomaly in the Siberian high area. During the time of cold waves, the mid- to high-latitude atmospheric circulation was undergoing low-frequency adjustment, with the Arctic oscillation continuously weakening, while the blocking high and Siberian high gradually increased to historically high-intensity states. The outbreaks of the three cold waves occurred at the peak and declining points of the blocking high and Siberian high, respectively, acting as short- to medium-term forecast factors. The CMA-GEPS model demonstrated high forecasting ability for the cooling of the cold waves due to its ability to accurately predict the evolution of the Siberian high and blocking high prior to and after the cold wave with a long lead time. Predictability analysis suggested the strong variability of key factors (such as the Siberian high and blocking) in cold wave events may benefit the model's prediction of cold wave events. These findings contribute to the understanding of the physical mechanisms behind cold waves and the potential for improved forecasting of extreme cold weather events. [ABSTRACT FROM AUTHOR]

The intensification of global climate change has led to a widespread consensus on carbon reduction, with the power industry being the principal contributor to carbon emissions necessitating an inevitable transition of its energy structure. Concomitantly, the escalating frequency of natural disasters caused by extreme weather presents formidable challenges to both secure power supply and the low-carbon transition of the power system. To fulfill the developmental requirements of a low-carbon power system and address the carbon emission risks imposed by natural disasters, this paper proposes a bi-level model for generation expansion planning (GEP) that incorporates constraints on the carbon emission trajectory and the influence of natural disasters. The planning-level model optimizes investment costs of various generation technologies and energy storage (ES), as well as the overall operational expenses over the planning period, with an objective to minimize them. It incorporates carbon emission trajectory constraints and policy constraints, such as carbon peaking, carbon neutrality and renewable energy (RE) penetration rates, in order to optimize the planning installed capacity of power sources. The operational-level model aims to minimize typical daily operating costs while also simulating power unit outputs in routine and disaster scenarios. A case study is conducted in a disaster-prone province in southern China to analyze the power generation expansion planning and the trajectory of carbon emissions from 2020 to 2060 under different scenarios. The simulation results show that compared to thermal power, the planning scheme mainly focused on RE with ES is better suited to achieve the goal of a low-carbon transition of the power grid. Moreover, after considering natural disasters, the cost and carbon emissions of power system planning are higher, and the risk of carbon emissions increases with the severity of disasters. • A multi-time scale bi-level low-carbon GEP model is proposed. • Natural disaster scenarios and low-carbon policy constraints are considered in the GEP model. • Carbon emission trajectory of the planned power system is estimated and introduced as a constraint into the GEP model. • Evaluation indicators that comprehensively consider low-carbon performance and economic feasibility are proposed. [ABSTRACT FROM AUTHOR]

Semiarid regions play a pivotal role in global ecosystem and environmental governance. Changing environment (climate and land use variation) has led to the disruption of hydrology in semi-arid regions and ecological degradation in some area. Understanding their hydrological response to changing environments is crucial for ecological rehabilitation and optimal water resource allocation. This research, conducted in a Chinese semiarid watershed, integrates multi-field models about hydrology, meteorology and geography to investigate hydrological processes in typical hydrological years. The results indicate that the climate in this region is drying, and the likelihood of extreme weather events is increasing with global warming. Projecting changes from 2010 to 2060, 9.21% of grassland converting to 5.63% of forest and 3.58% of gully built-up land. As a result, the flood peak increases by 22.99% in typical drought years, while it decreases by 36.77% in normal years. Based on our analysis of the rainfallrunoff process in typical drought and normal years, we identify two distinct effects: the "local rainfall-runoff effect" and the "global rainfall-runoff effect", respectively. However, in typical wet years, the streamflow state is primarily controlled by intense and concentrated precipitation. This research provides insights into the effect of ecological restoration and typical climate variations on hydrological cycle in semiarid regions, thus contributing to more informed decision-making in environmental management and water resource planning. [ABSTRACT FROM AUTHOR]

With the increase of extreme weather events such as cold waves, power transmission line icing has become more and more severe, affecting the safe and stable operation of power systems. Thus, icing prediction has become crucial for power grids. In this study, we collect multi-source data including the historical observations of meteorological elements and transmission line icing in Sichuan during 2017–2019, and develop an artificial intelligence (artificial intelligence)- based integrated model to achieve icing thickness prediction according to meteorological elements. Using the Weather Research and Forecasting model and the three-dimensional variational data assimilation method, we analyze the weather conditions in Sichuan of China during the 2020 winter, and obtain the high-precision meteorological element fields that are related to icing prediction. The forecasted meteorological elements are then combined with the AI-based integrated model to predict icing conditions, assisting in the warning of transmission line icing. The results indicate that the AI-based integrated model displays superior performance on the accurate prediction of icing thickness in the test set, with only two samples having prediction errors of more than 3 mm. Data assimilation can effectively improve the forecast accuracy of meteorological elements near icing observation stations and thus enhance the accuracy of icing thickness prediction. In particular, icing thickness prediction is remarkably improved at Gaoqiao, Laolinkou and Erlangshan stations. [ABSTRACT FROM AUTHOR]

Haze events in the North China Plain (NCP) and a decline in ozone levels in Southern Coast China (SC) from 21 January to 9 February 2020 during the COVID-19 lockdown have attracted public curiosity and scholarly attention. Most previous studies focused on the impact of atmospheric chemistry processes associated with anomalous weather elements in these cases, but fewer studies quantified the impact of various weather elements within the context of a specific weather pattern. To identify the weather patterns responsible for inducing this unexpected situation and to further quantify the importance of different meteorological factors during the haze event, two approaches are employed. These approaches implemented the comparisons of observations in 2020 with climatology averaged over the years 2015–2019 with a novel structural SOM (self-organising map) model and with the prediction of the "business as usual" (hereafter referred to as BAU) emission strength by the GBM (gradient-boosting machine) model, respectively. The results reveal that the unexpected PM 2.5 pollution and O 3 decline from the climatology in NCP and SC could be effectively explained by the presence of a double-centre high-pressure system across China. Moreover, the GBM results provided a quantitative assessment of the importance of each meteorological factor in driving the predictions of PM 2.5 and O 3 under the specific weather system. These results indicate that temperature played the most crucial role in the haze event in NCP, as well as in the O 3 change in SC. This valuable information will ultimately contribute to our ability to predict air pollution under future emission scenarios and changing weather patterns that may be influenced by climate change. [ABSTRACT FROM AUTHOR]

Growing evidence indicates that extreme heat and rain may occur in succession within short time periods and cause greater impacts than individual events separated in time and space. Therefore, many studies have examined the impacts of compound hazard events on the social-ecological system at various scales. The definition of compound events is fundamental for such research. However, there are no existing studies that support the determination of time interval between individual events of a compound rainstorm and heatwave (CRH) event, which consists of two or more potentially qualifying component heatwave and rainstorm events. To address the deficiency in defining what individual events can constitute a CRH event, this study proposed a novel method to determine the maximum time interval for CRH events through the change in CRH event frequency with increasing time interval between individual events, using southern China as a case study. The results show that the threshold identified by the proposed method is reasonable. For more than 90% of the meteorological stations, the frequency of CRH events has reached a maximum when the time interval is less than or equal to the threshold. This study can aid in time interval selection, which is an important step for subsequent study of CRH events. [ABSTRACT FROM AUTHOR]

In the early stages, uncontrolled groundwater extraction led to the Hangjiahu (HJH) Plain becoming one of the areas with the most severe land subsidence in China. Since the beginning of this century, comprehensive measures have been taken to control the continuous aggravation of large land subsidence patterns in some areas; however, urban land subsidence issues, influenced by various factors, still persist and exhibit complex geographical distribution characteristics. In this study, we utilized Sentinel-1A images and the SBAS-InSAR technique to capture surface deformation over the HJH Plain in Zhejiang from 16 March 2017 to 20 January 2023. Through a comparative analysis with geological conditions, changes in surface mass loading, rainfall and groundwater, and land use types, we discussed the contributions of natural and anthropogenic factors to land subsidence. Augmented with optical remote sensing images and field investigations, we conducted a correlation analysis of the land subsidence status. The preliminary findings suggest that changes in surface mass loading and short-term heavy rainfall under extreme weather conditions can lead to periodic land subsidence changes in the region. Additionally, extensive infrastructure construction triggered by urbanization has resulted in significant and sustained land subsidence deformation. The research findings play an important guiding role in formulating scientifically effective strategies for land subsidence prevention and control, as well as urban planning and construction. [ABSTRACT FROM AUTHOR]

In the second of two companion papers, we quantify the potential ultimate benefits for public health and radiative forcing from the phase-out of coal use in China. With the baseline year of 2015, we estimate that the premature deaths and disability-adjusted life years (DALYs) in China will be reduced by 375,000 persons and 8.90 × 106 years (GEMM model), which is 41.7% and 54.5% of the total number of premature deaths and DALYs caused by PM 2.5 from anthropogenic sources, effectively improving the health of the population. The provinces with the most significant decreases in fatalities and disabilities are concentrated in large coal-consuming provinces. Regarding age distribution, the phase-out of coal use will effectively extend the life expectancy of the elderly, especially in the age group of 65–89 years. Health benefits' growth rate upsurges notably once more than half of coal is phased out. Based on the Parallel Offline Radiative Transfer (PORT) model, the global annual average radiative forcing due to changes in short-lived climate pollutants (SLCPs), including sulfate, black carbon, organic carbon, and O 3 associated with the phase-out of coal use, is 0.34 W m−2, -0.048 W m−2, 0.00064 W m−2, and 0.00055 W m−2, respectively. The complete phase-out of coal use in China also reduces the emission of long-lived climate pollutants (LLCPs) and causes negative radiative forcing. Among them, the global annual average radiative forcing of CO 2 , CH 4 , and N 2 O is −0.0073 W m−2, -5.48 × 10−5 W m−2, -1.60 × 10−5 W m−2. If the lifetimes of pollutants are considered, the total global reduction in heat uptake is −2.29 × 107 J m−2 over 100 years, dominated by the decrease of CO 2 , which will slow the rate of global warming and thus may reduce the probability of extreme weather events. This report will incentivize relevant policymakers to remove coal significantly to achieve carbon neutrality. [Display omitted] • Coal phase-out in China avoids half of premature deaths and DALYs attributed to PM 2.5 emission from anthropogenic sources. • The health benefits experiences an upsurge when coal removal exceeds 50%, non-linearly related to the coal removal. • Sulfate and CO 2 dominate positive and negative radiative forcing. Considering the lifetime, a cooling effect will occur. [ABSTRACT FROM AUTHOR]

China has the world's longest high-speed rail (HSR) network, marked by dense transportation and complex operations. However, frequent train use coupled with extreme weather conditions has led to rising subgrade issues. Existing railway defect records suffer from inconsistency, hindering direct applicability. Currently, there is a lack of a relevant dataset dedicated to HSR subgrade defects. To bridge this gap, we developed a comprehensive georeferenced dataset that encompasses defect records extracted from peer-reviewed literature published between 1999 and 2023 in China. Rigorous quality control procedures were implemented to eliminate duplicate data and ensure the accuracy of the dataset. The dataset consists of georeferenced records for eight different defects, spanning across 661 locations and categorized at various scales, ranging from provinces to townships. The most commonly reported defect types include subgrade settlement, frost damage, uplift deformation, and mud pumping. This dataset provides a comprehensive map of historical subgrade defects affecting high-speed railways in China. It could facilitate operational risk assessments and the prediction of subgrade performance. [ABSTRACT FROM AUTHOR]

Amidst the escalating impacts of global warming, the occurrence and severity of compound extreme weather events have risen significantly, presenting substantial threats to both lives and property. Existing response strategies predominantly focus on individual events, often overlooking the cumulative effects rising from their inherent complexity. To address this critical gap, we conducted a thorough examination of sequential extreme precipitation–heatwave compound events (SEPHCE) in China from 1975 to 2020, utilizing data from 1929 meteorological stations. Our investigation revealed a consistent rise in the frequency and duration of SEPHCE, with a particularly notable surge since 1993. Furthermore, shorter interval events disproportionately affected the regions of southwestern and southeast coastal China. Furthermore, SEPHCE onset times exhibited advancement, and the endings were delayed, thereby intensifying the overall trend. These findings underscore the pressing need to prioritize effective planning and adaptation strategies to mitigate the impact of these compound event, while also addressing the potential exacerbation of inequality resulting from climate change. [ABSTRACT FROM AUTHOR]

Coastal dunes record the evolutionary process of coastal dynamic geomorphological systems, and provide valuable information on climate change, sea level changes, environmental evolution. These dunes are essential for global change research. In view of the dynamic changes in coastal dunes caused by extreme weather, coastal erosion and human activities, we propose a comprehensive evaluation method for coastal dune evolution, which monitors Haiyang Ten Thousand Meters Beach of China from three perspectives: point, line, and surface. This method integrates LiDAR, GNSS, and RS technologies. The research results indicate that between 2016 and 2018, 68% of the GNSS point measurements in 22 survey profiles of coastal dunes were eroded along the foreshore. During 2011-2020, the coastline based on the RS data basically remained stable; only the estuarine district underwent significant changes, and the overall average change rate was 1.32 m/a. The overall morphological evolution of coastal dunes obtained by LiDAR in 2018 was relatively stable, but there was a weak erosion trend in the foreshores of the coastal dunes. This study is beneficial for providing precise targets for remediating and restoring eroded coastal areas in the future. Furthermore, it can provide technical support and informational information for ecological environment protection, coastal erosion prevention and environmental evolution in the Haiyang Ten Thousand Meters Beach Marine Nature Reserve. [ABSTRACT FROM AUTHOR]

In recent years, the influence of extreme weather patterns has led to an alarming increase in the frequency and severity of sub-surface forest fires in boreal forests. The Ledum palustre-Larix gmelinii forests of the Daxing'an Mountains of China have emerged as a hotspot for sub-surface fires, and terrain slope has been recognized as a pivotal factor shaping forest fire behavior. The present study was conducted to (1) study the effect of terrain slope on the smoldering temperature and spread rate using simulated smoldering experiments and (2) establish occurrence probability prediction model of the sub-surface fires' smoldering with different slopes based on the random forest model. The results showed that all the temperatures with different slopes were high, and the highest temperature was 947.91 °C. The spread rates in the horizontal direction were higher than those in the vertical direction, and the difference increased as the slope increased. The influence of slope on the peak temperature was greater than that of spread rate. The peak temperature was extremely positively correlated with the slope, horizontal distance and vertical depth. The spread rate was extremely positively correlated with the slope. The spread rate in the vertical direction was strongly positively correlated with the depth, but was strongly negatively correlated with the horizontal distance; the horizontal spread rate was opposite. The prediction equations for smoldering peak temperature and spread rate were established based on slope, horizontal distance, and vertical depth, and the model had a good fit (p < 0.01). Using random forest model, we established the occurrence prediction models for different slopes based on horizontal distance, vertical depth, and combustion time. The models had a good fit (AUC > 0.9) and high prediction accuracy (accuracy > 80%). The study proved the effect of slope on the characteristics of sub-surface fire smoldering, explained the variation in peak temperature and spread rate between different slopes, and established the occurrence prediction model based on the random forest model. The selected models had a good fit, and prediction accuracy met the requirement of the sub-surface fire prediction. [ABSTRACT FROM AUTHOR]

• Extreme temperatures lead to an increase in local fiscal pressure. • Areas with intensive agriculture and low power consumption are more sensitive. • Productivity, population mobility, and power supply are the main mechanisms. • PMC reform mitigated the fiscal revenue loss and increased fiscal expenditure. This paper investigates the impact of climate change on government fiscal pressure using local governments' fiscal data in China from 2000 to 2020. While previous studies have extensively explored the effects of climate change on individuals and economies, there has been limited research on the negative effects of climate change from a government fiscal perspective. Our study makes contributions by using county-level fiscal data in China, allowing for a detailed examination of the fiscal implications of weather extremes. Moreover, we comprehensively analyze the underlying mechanisms involving population mobility, industrial structure, and electricity consumption. The empirical results indicate that each additional day of extreme temperature in a year leads to a CNY 0.002 billion increase in the general public budget deficit, which is equivalent to 0.1093% of the local fiscal deficit. Furthermore, local governments heavily reliant on agriculture, experiencing low electricity consumption, and significant population outflows face even greater challenges. Notably, medical insurance and the "province-managing-county" reform program emerge as crucial mitigating factors against fiscal pressures. By providing a thorough assessment of climate change's fiscal impact on local governments, this research contributes to the theoretical foundation for governmental initiatives aimed at reducing carbon emissions. [ABSTRACT FROM AUTHOR]

Ozone (O3) pollution research and management in China have mainly focused on anthropogenic emissions, while the importance of natural processes is often overlooked. With the increasing frequency of extreme weather events, the role of natural processes in exacerbating O3 pollution is gaining attention. In September 2022, the Pearl River Delta (PRD) in southern China experienced an extended period (25 d) of regional O3 exceedances and high temperatures (second highest over last 2 decades) due to extreme weather conditions influenced by the subtropical high and typhoon peripheries. Employing an integrated approach involving field measurements, machine learning and numerical model simulations, we investigated the impact of weather-induced natural processes on O3 pollution by considering meteorological factors, natural emissions, chemistry pathways and atmospheric transport. The hot weather intensified the emission of biogenic volatile organic compounds (BVOCs) by ∼10 %. Isoprene and biogenic formaldehyde accounted for 47 % of the in situ O3 production, underscoring the predominant role of BVOC emissions in natural processes. The chemical pathway of isoprene contributing to O3 formation was further explored, with O3 production more attributable to the further degradation of early generation isoprene oxidation products (contributed 64.5 %) than the direct isoprene oxidation itself (contributed 35.5 %). Besides, it was found that the hot weather significantly promoted regional photochemical reactions, with meteorological factors contributing to an additional 10.8 ppb of O3 levels compared to normal conditions. Temperature was identified as the dominant meteorological factor. Furthermore, the typhoon nearing landfall significantly enhanced the cross-regional transport of O3 from northern to southern China through stratosphere–troposphere exchange (STE). The CAM-Chem model simulations revealed that the STE-induced O3 on the PRD surface could reach a maximum of ∼8 ppb, highlighting the non-negligible impact of STE. This study highlights the importance of natural processes exacerbated by extreme weather events in O3 pollution and provides valuable insights into O3 pollution control under global warming. [ABSTRACT FROM AUTHOR]

Recent studies on China's arid and semi-arid regions, particularly the Tarim River Basin (TRB), have shown an increase in the intensity and frequency of extreme weather events. This research examines the link between meteorological droughts, as measured by the Standardized Precipitation Evapotranspiration Index (SPEI), and hydrological droughts, as indicated by the Standardized Runoff Index (SRI) and the Standardized Terrestrial Water Storage Index (STI), over various time scales. Historical data indicate that SPEI drought frequency (DF) was 14.3–21.9%, with prevalent events in the northern oases. SRI DF ranged from 9.0% to 35.8%, concentrated around the Taklamakan and Kumtag Deserts, while STI DF varied between 4.4% and 32.7%, averaging 15% basin-wide. Future projections show an increased DF of SPEI in deserts and a decrease in oases; SRI DF decreased in deserts but increased in oases. STI changes were more moderate. The study also found a higher risk of drought progression from SPEI to SRI in the southwestern and northeastern oases, exceeding 50% probability, while central and eastern TRB had lower risks. The western TRB and inner Taklamakan Desert faced higher risks of SPEI to STI progression, with probabilities over 45%, in contrast to the lower risks in the eastern and central oases. The concurrence of SRI/STI with moderate to extreme SPEI droughts led to a higher probability and area of SRI/STI droughts, whereas consistent SPEI types showed a reduced induced probability and extent of SRI/STI droughts. This study enhances the understanding of drought propagation from meteorological to hydrological droughts in the TRB and contributes to the prevention of hydrological drought to a certain extent. [ABSTRACT FROM AUTHOR]

In the context of unprecedented extreme weather and climatic events, the internal structural factors of society play a decisive role in determining the extent to which human beings are affected by disasters and their ability to respond to them. In the past few decades, rapid urbanization in developing countries, such as China, has greatly increased social vulnerability. This process has generated uneven living conditions and created many vulnerable groups, including urban poverty, migrants, and socially and geographically marginalized groups. These groups face difficulties in living conditions, education, livelihood stability, and more. This study sets up indicators from a micro-perspective: three indicators of exposure, four indicators of sensitivity, and eight indicators of adaptive capacity. Based on this evaluation index system, this study conducted a social vulnerability assessment of the population in Hongshan District, Wuhan, China, through individual questionnaire surveys. K -means cluster analysis was used to determine high, medium, and low levels of social vulnerability, which were used to compare different community types and identify vulnerable groups. The results showed close interrelationships between different types of communities in terms of physical and built environments as well as varying levels of social vulnerability to disasters. The high-vulnerability group accounted for 12.9 % of the 599 samples, the medium-vulnerability group accounted for 48.4 %, and the low-vulnerability group accounted for 38.7 %. The higher-vulnerability groups exhibited characteristics such as low education, poor health, low annual income, unstable work, and insufficient social security. Quantitative understanding of the degree of dissimilarity in social vulnerability among different communities and populations is significant in reducing social vulnerability and disaster risk specifically and effectively. [ABSTRACT FROM AUTHOR]

Load forecasting is crucial for the economic and secure operation of power systems. Extreme weather events, such as extreme heat and typhoons, can lead to more significant fluctuations in power consumption, making load forecasting more difficult. At present, due to the lack of relevant public data, the research on load forecasting under extreme weather events is still blank, so it is necessary to release a large-scale load dataset containing extreme weather events. The dataset includes electricity consumption data of industrial and commercial users under extreme weather events such as typhoons and extreme heat, which are collected at 15-minute intervals. The data is collected over six years from smart meters installed at the power entry points of users in southern China. The dataset consists of electricity consumption data from 386 industrial and commercial users in 17 industries, with more than 50 million records. During the recording period, extreme weather events such as typhoons and extreme heat are marked to form a total of 5,741 event records. [ABSTRACT FROM AUTHOR]

Climate model evaluation work has made progress both in theory and practice, providing strong support for better understanding and predicting climate change. However, at the weather scale, there is relatively little assessment of climate models in terms of daily-scale climate phenomena, such as storm frequency and intensity. These weather-scale variables are of significant importance for our understanding of the impacts of climate change. In order to assess the capability of climate models to simulate weather-scale climate patterns, this study employs Self-Organizing Maps (SOMs) for weather pattern classification. By combining different evaluation metrics, varying the number of SOM types, changing the size of the study area, and altering the reference datasets, the climate models are evaluated to ensure the robustness of the assessment results. The results demonstrate that the size of the study area is positively correlated with observed differences, and there are correlations among different evaluation metrics. The highest correlation is observed between evaluation metrics in large-scale and small-scale spatial domains, while the correlation with SOM size is relatively low. This suggests that the choice of evaluation metrics has a minor impact on model ranking. Furthermore, when comparing the correlation coefficients calculated using the same evaluation metrics for different-sized regions, a significant positive correlation is observed. This indicates that variations in the size of the study area do not significantly affect model ranking. Further investigation of the relationship between model performance and different SOM sizes shows a significant positive correlation. The impact of dataset selection on model ranking is also compared, revealing high consistency. This enhances the reliability of model ranking. Taking into account the influence of evaluation metric selection, SOM size, and reanalysis data selection on model performance assessment, significant variations in model ranking are observed. Based on cumulative ranking, the top five models identified are ACCESS1-0, GISS-E2-R, GFDL-CM3, MIROC4h, and GFDL-ESM2M. In conclusion, factors such as evaluation metric selection, study area size, and SOM size should be considered when assessing model ranking. Weather pattern classification plays a crucial role in climate model evaluation, as it helps us better understand model performance in different weather systems, assess their ability to simulate extreme weather events, and improve the design and evaluation methods of model ensemble predictions. These findings are of great significance for optimizing and strengthening climate model evaluation methods and provide valuable insights for future research. [ABSTRACT FROM AUTHOR]

Since its reform and opening, the Chinese economy has witnessed unprecedented development. This long-term high-speed development has significantly impacted the environment. In the process of energy development and consumption, the environment has been severely polluted, and greenhouse emissions have increased. This has resulted in environmental imbalances such as global climate change, rising sea levels, and extreme weather. Using annual energy consumption (EC), economic growth (GDP), and carbon dioxide (CO2) emissions in China from 1981 to 2021, this study employed the maximum likelihood estimation method to estimate the parameters of the nonlinear MS-VAR model. Cointegration tests, regimes analysis methods, and impulse response function analysis methods were adopted to explore the differences or similarities in the dynamics of the three under various regimes. The research results are as following. (i) The cointegration test findings demonstrate a long-term equilibrium relationship among EC, economic growth, and CO2 emissions. (ii) Regime analyses exhibit that there are three regimes: "low-level regime," "medium-level regime," and "high-level regime." The three regimes have a mutual transfer transmission mechanism that exhibits nonlinear properties. (iii) Impulse response function analyses show that external EC and GDP shocks favorably impact the other two variables in all three regimes. EC in all three regimes is negatively impacted by external CO2 shocks. Moreover, in the "low-level regime," the relationship between CO2 and GDP has an inverted U-curve, whereas in the other two regimes, the relationship has a negative association. This study can help China formulate reasonable and effective CO2 emissions reduction and energy policies and successfully achieve the emissions reduction goal of the 14th Five Year Plan as well as dual carbon goals. [ABSTRACT FROM AUTHOR]

Spatial planning is broadly acknowledged for its pivotal role in local climate change adaptation. Nevertheless, variations persist among countries in their perceptions and practices concerning the effectiveness and feasibility of utilizing spatial planning for climate change adaptation. Chinese cities confront the challenges of global warming, rapid urbanization, and intensified extreme weather events. Climate change adaptation agreements, frameworks, and policies have predominantly emphasized the national scale, often overlooking local adaptation efforts. However, the ongoing reform and restructuring of China's territorial and spatial planning system encompasses the categorization, hierarchical management, control of comprehensive national land resource development, and conservation. This process is gradually establishing spatial planning strategies that synergize with the impacts of climate change, thereby offering an opportunity to integrate climate change adaptation objectives into spatial planning. This endeavor is supported by a series of regulatory standards and guidelines to ensure its effective implementation. The study scrutinizes the public draft of territorial and spatial master plans for 368 Chinese cities to assess the integration of climate change concerns at the local level and to identify disparities among cities. Employing the Awareness, Analysis, and Action Framework (3A Framework), which draws from prior scholarship and integrates relevant existing research, we identified and assessed 30 criteria for recognizing and appraising climate change adaptation strategies in territorial and spatial planning. The study's findings indicate that: (1) The role of territorial and spatial planning in integrating climate change issues at the city level requires enhancement, as reflected in the generally low "awareness" of climate change concerns, limited "analytical capacity" regarding climate change, and a certain foundation for "action response" to climate change adaptation; (2) Large cities, characterized by robust economies and population concentrations, generally outperform small and medium-sized cities. They exhibit a superior ability to delineate quantifiable climate adaptation indicators and measures. In conclusion, the study provides prospective recommendations concerning technical approaches, spatial governance, mechanisms, and actions to more effectively incorporate climate change adaptation objectives into local-level territorial and spatial planning. [ABSTRACT FROM AUTHOR]

This paper presents projected changes in temperature and precipitation extremes in China by the end of the twenty-first century based on the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations. The temporal changes and their spatial patterns in the Expert Team on Climate Change Detection and Indices (ETCCDI) indices under the RCP4.5 and RCP8.5 emission scenarios are analyzed. Compared to the reference period 1986-2005, substantial changes are projected in temperature and precipitation extremes under both emission scenarios. These changes include a decrease in cold extremes, an increase in warm extremes, and an intensification of precipitation extremes. The intermodel spread in the projection increases with time, with wider spread under RCP8.5 than RCP4.5 for most indices, especially at the subregional scale. The difference in the projected changes under the two RCPs begins to emerge in the 2040s. Analyses based on the mixed-effects analysis of variance (ANOVA) model indicate that by the end of the twenty-first century, at the national scale, the dominant contributor to the projection uncertainty of most temperature-based indices, and some precipitation extremes [including maximum 1-day precipitation (RX1day) and maximum 5-day precipitation (RX5day), and total extremely wet day total amount (R95p)], is the difference in emission scenarios. By the end of the twenty-first century, model uncertainty is the dominant factor at the regional scale and for the other indices. Natural variability can also play very important role. [ABSTRACT FROM AUTHOR]

China currently boasts the largest installed capacity of wind power; however, its output is unstable and highly dependent on weather variability. Despite this, the influence of extreme weather events on wind energy production at the interprovincial scale in China has not been fully characterized. This study aims at investigating the daily variations and regional differences in wind power output during heat wave (HW) and cold wave (CW) days in six regions of China. In addition, the study projects the monthly changes in HW and CW days in the coming decades by utilizing a stacking ensemble machine learning method. The projections are under a real-world warming scenario that incorporates current and long-term actions or policies. The findings of the study reveal that, for most regions, the daily cumulative wind power generation on HW days is close to that on normal days; however, there is a lower output during the daytime and a higher output at night. Furthermore, the number of HW days is projected to increase by 2.3 to 21.8 days during the periods of 2031–2040, 2041–2050, and 2051–2060 in these regions. By comparison, the daily cumulative wind power generation increases significantly on CW days, and the monthly distribution of CW days is expected to undergo notable changes in the future. These findings provide valuable insights into wind resource planning and operation under extreme weather conditions in China. [ABSTRACT FROM AUTHOR]

The Paris Agreement aims to combat climate change by reducing greenhouse gas emissions, with bioenergy identified as a potential solution. However, concerns remain about its impact on carbon stocks and the optimal timing for implementation. To address these challenges, we propose a comprehensive multi-objective optimization model for forest management that maximizes carbon sequestration and economic benefits. Our model integrates three key components: (1) a sophisticated carbon-sequestration model encompassing living plants, wood forest products, and soil and microbial carbon uptake, (2) dynamic factors such as forest fires and extreme weather events, and (3) an economic benefits model focused on wood-processing products. We optimized the forest-management strategy over ten years by leveraging the simulated annealing and Karush–Kuhn–Tucker (KKT) algorithms. Through simulations using data from China's Greater Khingan Mountains region, we explored the optimal logging plans for maximizing carbon sequestration without external factors. Our results revealed that the optimized logging plans significantly enhance carbon sequestration compared to proportionally averaged logging plans. Next, we investigated the impact of external factors on forest management, specifically wildfires and extreme weather events. Our findings demonstrate that wildfires have a more-substantial detrimental effect on the absolute value of carbon sequestration and the extent of improvement achieved through model optimization. At the same time, extreme cold primarily affects the growth rate of carbon sequestration. We employed a linear-weighting approach and the Analytic Hierarchy Process (AHP) to address the trade-offs between carbon sequestration and economic benefits to transform the multi-objective optimization function into a single objective. The results showed that the optimized harvesting schedule can lead to improved economic benefits compared to uniformly harvesting trees. Moreover, the joint optimization approach enabled us to identify optimal solutions that balance carbon sequestration and economic benefits, offering sustainable forest management strategies. Our study provides valuable quantitative insights into forest management strategies that balance carbon sequestration and economic benefits, making it highly relevant for real-world applications. [ABSTRACT FROM AUTHOR]

Extreme weather exerts a huge impact on human beings and it is of vital importance to study the regular pattern of meteorological and hydrological factors. In this paper, a selection of seven extreme indices is used to analyze the trend of precipitation extremes of 18 meteorological stations located in Zhejiang Province, east China using the Mann-Kendall test. Then the precipitation trends in the plum season (from May to July) and typhoon season (from August to October) are studied separately. The results show that the precipitation trend varies from east to west. There is a positive trend in the east and a negative one in the west. The largest part of Zhejiang Province shows a positive trend in heavy precipitation and the most significant upward trend is detected in Dinghai with 3.4 mm/year for precipitation on very wet days. Although the upward trend of extreme precipitation is not prevailing, the range of increase in specific areas is apparent, like Dinghai with 1.3 mm/year. Precipitation intensity exhibits an upward trend in most areas and a typical upward trend can be found in Dachendao, Tianmushan, and Yuhuan with 0.04, 0.02, and 0.05 mm/year respectively. Precipitation intensity in both plum and typhoon seasons has increased too, especially for the coastal stations. [ABSTRACT FROM AUTHOR]

• A novel two-stage SMILP investment decision-making framework to enhance distribution network resilience is proposed. • Multiple uncertainties introduced by typhoon, distribution line damage, and load fluctuations are taken into account. • The real-world data of typhoon "Mangkhut" in Guangdong, China is collected to model wind field uncertainty. • Intentional islanding constraint is notably added in the second stage. • The proposed model combines distribution network long-term resilience planning with short-term post-event recovery. The reliability of power supply in distribution network is vulnerable to extreme weather events such as typhoon. Pre-event preparation can effectively mitigate the deterioration of system resilience. Therefore, we propose a distribution network resilience enhancement decision-making framework which is formulated as a two-stage stochastic mixed-integer linear programming (SMILP) model. The first stage invests coordinately in four strategies, including hardening lines, installing distributed generators (DG), allocating mobile emergency generators (MEG), and deploying switches, etc. And the goal at the first stage is to minimize the investment cost of resilience enhancement strategies. The objective at the second stage is to ensure the minimum expected recourse operation cost of the comprehensive strategies for all typical scenarios. The proposed model can combine distribution network long-term resilience planning with short-term post-event recovery. Furthermore, in order to address the uncertain problems of wind field, line damage and load fluctuations under typhoon disaster, this paper proposes the following solutions. For wind field uncertainty, a detailed wind field model considering time transition, sea-land transition and extreme value distribution is established to deal with wind speed prediction. For line damage uncertainty, an improved stress-strength interference model is set up. And for the load fluctuation uncertainty, scenario generation using load random multipliers is applied to address load uncertainty. The proposed framework is tested in IEEE 33-bus distribution system using historical data from 2018 super typhoon "Mangkhut" in China and demonstrates the SMILP model can significantly reduce the post-event expected recourse operation cost and meanwhile improve the distribution network resilience. [ABSTRACT FROM AUTHOR]

The North Atlantic Oscillation (NAO) has been confirmed to be closely related to the weather and climate in many regions of the Northern Hemisphere; however, its effect and mechanism upon the formation of dust events (DEs) in China have rarely been discussed. By using the station observation dataset and multi-reanalysis datasets, it is found that the spring dust aerosols (DAs) in North China (30–40 ∘ N, 105–120 ∘ E), a non-dust source region, show high values with a strong interannual variability, and the spring DAs in North China are significantly correlated with the previous winter's NAO. According to the nine spring DEs affected significantly by the negative phase of the preceding winter's NAO in North China during 1980–2020, it is shown that before the outbreak of DEs, due to the transient eddy momentum (heat) convergence (divergence) over the DA source regions, the zonal wind speed increases in the upper-level troposphere, strengthening the zonal wind in the middle–lower levels through momentum downward transmission. Simultaneously, there is transient eddy momentum (heat) divergence (convergence) around the Ural Mountains, which is favorable for the establishment and maintenance of the Ural ridge, as well as the development of the air temperature and vorticity advections. The combined effects of temperature and vorticity advections result in the Siberian Highs and Mongolian cyclone to be established, strengthen, and move southward near the surface, guiding the cold air from high latitudes southward, and is favorable for the uplift and transmission of DAs to North China downstream. Simultaneously, the changes in upstream transient eddy flux transport can cause both energy and mass divergence in North China, resulting in diminishing winds during DEs, which would facilitate the maintenance of dust aerosols here and promote the outbreak of DEs. This study reveals the impact of transient eddy flux transport on the dusty weather anomalies modulated by the NAO negative signal in North China, which deepens the understanding of the formation mechanism of DEs in China. [ABSTRACT FROM AUTHOR]

Rice farming threatens freshwater resources, while also being increasingly vulnerable to drought due to climate change. Rice farming needs to become more sustainable and resilient to climate change by improving irrigation drainage systems. Small water bodies, used to store drainage water and supply irrigation in traditional rice farming systems have gradually been abandoned in recent decades. This has resulted in a higher water footprint (WF) associated with rice farming due to increased freshwater usage and wastewater release, also leaving rice production more vulnerable to extreme weather events. Here, we propose how protecting and reactivating small water bodies for rice irrigation and drainage can decrease rice production WF in China by 30%, save 9% of China's freshwater consumption, increase irrigation self-sufficiency from 3% to 31%, and alleviate yield loss in dry years by 2–3%. These findings show that redesigning rice irrigation drainage systems can help meet water scarcity challenges posed by climate change. Ponds played an important role in ancient rice-growing regions such as China and India. Here, the authors find that reviving small water bodies to recycle drainage water for irrigation can reduce China's rice production water footprint by 9% and alleviate 2-3% yield loss in dry years. [ABSTRACT FROM AUTHOR]

The study of litter can provide an important reference for understanding patterns of forest nutrient cycling and sustainable management. Here, we measured litterfall (leaves, branches, etc.) from a wet, evergreen, broad-leaved forest in Ailao Mountains of southwestern China on a monthly basis for 11 years (2005–2015). We measured the total biomass of litter fall as well as its components, and estimated the amount of C, N, P, K, S, Ca, and Mg in the amount of litterfall. We found that: The total litter of evergreen, broadleaved forest in Ailao Mountains from 2005 to 2015 was 7.70–9.46 t/ha, and the output of litterfall differed between years. This provides a safeguard for the soil fertility and biodiversity of the area. The total amount of litterfall and its components showed obvious seasonal variation, with most showing a bimodal pattern (peak from March to May and October to November). The majority of litterfall came from leaves, and the total amount as well as its components were correlated with meteorological factors (wind speed, temperate and precipitation) as well as extreme weather events. We found that among years, the nutrient concentration was sorted as C > Ca > N > K > Mg > S > P. The nutrient concentration in the fallen litter and the amount of nutrients returned showed a decreasing trend, but the decreasing rate was slowed through time. Nutrient cycling was influenced by meteorological factors, such as temperature, precipitation, and wind speed, but the nutrient utilization efficiency is high, the circulation capacity is strong, and the turnover time is short. Our results showed that although there was nutrient loss in this evergreen, broad-leaved forest, the presence of forest litterfall can effectively curb potential ecological problems in the area. [ABSTRACT FROM AUTHOR]

Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory. At 10:05 a.m. on 4 February 2022, the perilous rock mass Yizhuxiang in Leizu Town, Yuan'an County, Hubei Province, China collapsed on a large scale. The Yizhuxiang collapse was about 35 m in length, 52 m in height, and 29 m in maximum thickness. The volume of the collapse was 5.32 × 104 m3. It threatened the transport of the national trunk road, destroyed the branch road and some cement mixing station workshops, and caused serious economic losses. The rock mass exposed in the collapse was dolomite with developed joint fractures. Under the effects of mining and unloading, fractures occurred in the rock mass; the top of the rock mass was bent and deformed towards the free face; and tension cracks were formed on the rear edge. The safety ore pillar directly below the collapsed body was compressed and deformed under the action of gravity, thus accelerating the internal deformation of the rock mass and leading to the formation of multiple dominant joints inside the rock mass. In the winter of 2021, the weather was extreme, with heavy snowfall and low temperatures. Under the effects of freeze–thaw, the strength of the rock mass declined and the tension cracks further expanded so that the rock mass experienced an accelerated deformation and finally collapsed. Causes of the collapse include mining activity beneath the collapsed mass and heightened extreme weather. The cause of the landslide disaster in this area is, however, freeze–thaw, which deserves the attention of scholars and the vigilance of the local government. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) is a vitally important factor in atmospheric circulation. PWV is significant for forecasting extreme weather and understanding the dynamics of climate change. Comprehensively evaluating the performance of newly proposed remotely sensed water vapor products is crucial for guaranteeing their suitability for futural PWV applications. In this study, the accuracy of the recently established remotely sensed water vapor product from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite sensor on the Suomi National Polar-orbiting Partnership (SNPP) (VIIRS-PWV) platform within various regions of mainland China was evaluated via the PWV from Global Positioning System (GPS) observations. The GPS-derived PWV from 231 stations of the Crustal Movement Observation Network of China (CMONOC) from 2012 to 2018 was obtained through precise point positioning (PPP) techniques. The results showed that the mean value of the correlation coefficient (CC), the mean bias (MB), and the root-mean-square error (RMSE) between the VIIRS-PWV and the GPS-PWV were 0.92, −1.6 mm, and 4.7 mm, respectively. These values were comparable with the results of the PWV data derived from the Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) products. This indicates that the VIIRS product could provide PWV data with satisfactory accuracy for large-area scientific applications. Moreover, the MB and RMSE of the differences between the GPS-PWV and VIIRS-PWV showed obvious seasonal variations. The VIIRS-PWV generally performed better in winter (with the MB and RMSE values of 0.1 mm and 2.3 mm) than in summer (with the MB and RMSE values of −4.4 and 7.0 mm). Analysis among different regions revealed that the Central South (CS) region of China attained the largest mean RMSE value of 6.3 mm, and the North West (NW) region attained the smallest mean RMSE value of 3.8 mm. In addition, the southern region of China obtained a mean RMSE value of 5.6 mm, while that for the northern region of China was 3.9 mm. This indicates that the VIIRS-PWV has better accuracy within the northern region of China than within the southern region. [ABSTRACT FROM AUTHOR]