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This paper investigates the impact of Chinese firms' environmental, social, and governance (ESG) performance on their financial constraint and financing activities. We find a negative association between firms' ESG performance and their financial constraint driven by the Chinese government's commitment to tackling climate change. Compared with state‐owned enterprises (SOEs), non‐SOEs have alleviated their financial constraint through both equity and debt issuance, thanks to the stock price appreciation and green credit. High‐pollution firms benefit from both equity and debt issuance, while low‐pollution firms mainly finance through equity issuance. Our findings demonstrate the leading role of the Chinese government in its domestic capital markets. [ABSTRACT FROM AUTHOR]

• The extent of flooding in China is greater than in any other country. • Nearly 66% of China's landmass is submerged by flooding. • Financial toll of flooding accounts for approximately 1.42% of the annual GDP. • Various forms of climatic change contribute to flooding. The extent of flooding in China is more significant than in any other country. Our research reveals that approximately 66 % of China's landmass is submerged by flooding, affecting about 50 % of the population. Furthermore, the financial toll of flooding now accounts for approximately 1.42 % of the annual gross domestic product (GDP), which is almost 40 times higher than the corresponding figure for the United States. We have observed that Zhengzhou city in Henan province, which faced a devastating flood in 2021, received a significant amount of rainfall, specifically a total of 552.5 mm within a 24-hour period. The floods in Henan province in 2021 caused considerable damage, including impacting nearly 15 million people, resulting in almost 400 deaths, damaging over 10,000 square kilometers of agricultural land, causing almost $19 billion in economic losses, and leading to the collapse of over 35,000 households and damage to various properties. In a similar manner, the significant flooding that occurred in southern China in 2020 impacted approximately 7.1 million individuals across eight provinces and resulted in 54 fatalities, the collapse of 6,700 houses, and incurred a direct economic loss of US$3.33 billion. We found that rainstorms have significantly increased to 10 % in the last 60 years in China. In this paper, we delved into exploring the existing published articles, reports, and government and authoritative legal texts to analyze the causes and impacts of flooding in China's flood-prone regions and potential mitigation strategies to reduce the repercussions of distressing flood events. We believe this study will help policymakers by providing new insights while formulating policy to reduce the loss caused by high-level flooding. [ABSTRACT FROM AUTHOR]

• Analyzed residents' thermal demand characteristics under the global climate change. • A new thermal comfort evaluation model of traditional dwellings was established. • The new thermal neutral temperature and comfort range of residents were proposed. • Different measures to improve the old and new types of dwellings were put forward. Global climate change has led to increased differences in the indoor thermal environment of traditional dwellings and residents' thermal adaptability, but quantitative research in this area needs to be updated and improved. In this paper, taking the old and new types of traditional dwellings of Guangfu Ancient City in Hebei province, China as the example, we first investigated the current situation of indoor thermal environment and thermal comfort characteristics of residents by means of subjective questionnaire survey and objective data measurement. The measured data showed that the average indoor temperature of old-type traditional dwellings in winter was far lower than the thermal comfort range of human body, and it was also higher than the relevant standard in summer. Secondly, we established a new thermal comfort evaluation model of traditional dwellings and proposed a new thermal neutral temperature of residents: old-type traditional dwellings (18.1 °C in winter and 27.1 °C in summer), new-type traditional dwellings (19.8 °C in winter and 25.7 °C in summer). Although the measured data showed that the thermal environment quality of the old-type traditional dwellings was poor, the residents had strong thermal adaptability. Finally, we put forward the different living performance improvement strategies of old and new types of traditional dwellings: The old-type traditional dwellings should focus on strengthening the thermal performance of the envelope structure, while the new-type traditional dwellings should solve the problem of low indoor humidity in winter. This research improves the evaluation system of human thermal comfort of dwellings in cold region of China, and provides a good case reference. [ABSTRACT FROM AUTHOR]

With the continuous development of industry, the massive emission of carbon dioxide (CO2) has become a global challenge that cannot be ignored due to its nature as the primary driver of global climate change and environmental crisis. Carbon neutrality is proposed as a global initiative to mitigate climate change. As a developing industrial country, China provides about one-third of global carbon emissions and has set a carbon-neutral goal by 2060. To achieve this goal, continuous efforts across the country are required. In this review, Zhejiang Province, one of the most developed regions in China, is set as a model for analysis. We first summarize the current situation of carbon emission in Zhejiang Province. Then, corresponding sustainable approaches such as ecological and industrial solutions for the reduction of future carbon emissions are introduced for Zhejiang Province. We also provide a direction for the realization of carbon neutrality, focusing on the most promising solutions for Zhejiang Province. [ABSTRACT FROM AUTHOR]

Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R2) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change. [ABSTRACT FROM AUTHOR]

Managing urban water in many cities becomes more challenging under impacts of climate change and urbanization. Low impact development (LID) measures have been increasingly recognized and applied thanks to their capability of tackling multi‐dimensional water problems associated with both quantity and quality issues. Yet, in China limited attention has been paid to guide the planning strategy of LIDs in the preliminary stage, which often suffers from inadequate and imprecise data and information. In this study, an approach integrating a GIS‐based spatial evaluation model, a fuzzy comprehensive evaluation module and a multi‐objective optimization algorithm, is proposed to prioritize the spatial distribution of LIDs (i.e., type, placement, and extent) at the subcatchment scale in a case study in Southern China. By evaluating the trade‐offs among runoff reduction, pollution control, and economic investment, the synergies, and combination strategies of four types of LIDs are explored. Knowledge gained from this study is necessary and beneficial for water planners and practitioners to improve LID planning efficiency under constrained conditions. [ABSTRACT FROM AUTHOR]

Universities, the important locations for scientific research and education, have the responsibility to lead ecological civilization and low carbon transition. Ecological footprint evaluation (EFE) is usually used to measure sustainability of campuses. Although it can provide guidance and reference for overall campus planning, it lacks effective significance for individual behavior, especially when the reduction of carbon emissions is the aim. On the other hand a possible solution can be represented by machine learning. It can identify the key factors that will influence individual's overall carbon emissions caused by students' daily behavior, it can be used to find effective ways to reduce individual carbon emissions. This paper applied EFE and machine learning to comprehensively evaluate campus sustainability and students' carbon emissions. Huazhong University of Science and Technology (HUST), a "University in the Forest", was used as a study case in China. Even if HUST is endowned with a forest coverage of 72%, here we showed that its Ecological Footprint Index was −12.52, indicating strong unsustainability. This is mainly due to the high energy and food consumption, caused by the large population living in the campus and the lacking of energy saving measures. The per capita ecological footprint was relatively high, compared with other universities in the world, which meant more efforts needed to be done on ecological sustainability. Low carbon emission is a key feature for a sustainable campus. Based on the questionnaire survey delivered to 486 students who live in the campus, their daily active data were collected in terms of students' personal clothing, food, housing, consumption and transportation. And their associated carbon emissions were calculated based on emission intensities of Chinese population. Based on 486 detailed datasets, machine learning was then used to identify the key daily behavior to influence students' total carbon emission. Results showed that making behavior changes in air conditioning, food and electric bicycle were the most effective ways to reduce carbon emissions. Finally, while effective suggestions were proposed based on qualitative and quantitative evaluations, it is concluded that it is imperative for universities in China to formulate effective low-carbon policies, to achieve sustainable development and to confront global climate change. • A review of main initiatives of sustainable campus construction worldwide. • An evaluation combining campus ecological footprint and individual carbon footprint. • The ecological footprint of "China Forest University" reflects unsustainable. • Machine learning identifies internal driving force of campus carbon emissions. • Targeted guidelines on campus low-carbon construction are provided. [ABSTRACT FROM AUTHOR]

More than half of the total population in China are living in cities. Especially, the people in highly developed and spatially integrated city clusters, i.e., urban agglomerations (UAs), are facing increasing human‐perceived heat stress that describes the combined effects of hot temperature, high humidity, and lowered surface wind speed. By analyzing multiple indicators over 20 major UAs across China, we demonstrate that summer heat stress has been significantly intensifying in nearly all UAs during 1971–2014. This intensification is more profound in northern than southern regions and is especially stronger in more urbanized and densely populated areas (e.g., Beijing‐Tianjin‐Hebei and the Yangtze River Delta). Based on a dynamic classification of weather stations using time‐varying land use/land cover maps, we find that urban core areas exhibit distinctly stronger increasing heat stress trends than their surrounding rural areas. On average, urbanization contributes to approximately one‐quarter of the total increase in mean heat stress over urban core areas of UAs and nearly half of the total increase in extreme heat stress events. The urbanization effect is also dependent on the geographical region within China. Urbanization tends to have stronger intensifying effects on heat stress in UAs with higher population density in low‐altitude areas, while it has a relatively weaker intensifying and even weakening effect in some arid and high‐altitude regions. Moreover, as various heat stress metrics may yield different estimations of long‐term trend and urbanization contribution, the particular choice of heat stress indicator is of critical importance for investigations on this subject matter. Plain Language Summary: More and more people are living in highly developed and spatially integrated city clusters (known as urban agglomerations, UAs) and facing increasing heat risks in a warming climate. Human‐perceived heat stress describes the combined effects of hot temperature, high humidity, and lowered surface wind speed, posing severe threats to human society and the natural environment. However, how heat stress changes in UAs of fast‐urbanizing China and to what degree local urbanization contributes to these changes remains unclear. Here, we investigate the changes of heat stress over the largest 20 UAs across China by examining eight different heat stress indicators and quantify the relative contribution of local urbanization to these changes. We show that all indicators of heat stress increase in nearly all UAs of China, especially in more urbanized and populated UAs. It is estimated that, on average, local urbanization contributes to around one‐quarter of the total increase in mean heat stress in the urban core areas of UA and nearly half of the increase in extreme heat stress events. We also find that urbanization tends to have a stronger warming effect on wet and low‐altitude regions and relative weaker warming or event cooling effects on arid and high‐altitude areas. Key Points: All indicators of heat stress increase in nearly all major urban agglomerations (UAs), especially in more urbanized and populated UAsOn average, local urbanization contributes to ≈1/4 (1/2) of the total increase in mean (extreme) heat stress in the urban core areas of UAsUrbanization tends to have a strong warming (cooling or weak warming) effect on heat stress in wet (arid) areas and low (high) altitudes [ABSTRACT FROM AUTHOR]