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• 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]

• Understanding the riparian zone from the watershed river-groundwater transformation. • Revealing the contributions of groundwater to landscape ecological patterns. • Proposing pathways to maintain a healthy landscape ecological pattern. In arid and semiarid areas with scarce precipitation, strong evaporation, and fragile ecosystems, rivers and groundwater interact frequently. Dynamic hydrological changes and solute transport jointly determine the evolution of the landscape in riparian zones, resulting in the formation of a landscape ecological pattern in arid areas that is unique and includes a system of rivers and lakes. In recent decades, unsustainable and mono-purpose grey infrastructure has been implemented in many arid areas, and the effect of river-groundwater interactions on the landscape ecological pattern in riparian zones has often been neglected. These processes have triggered variations in or even the reversal of landscape ecological patterns in these arid areas. This study selected the arid and semiarid basin in northwestern China as the study area. Multiple methods were used to study the evolution of the landscape ecological pattern in the riparian zones driven by typical hydrological processes from the perspective of river-groundwater transformation. Three results were obtained from the analysis: (1) The driving force of the evolution was identified, and the spatial geological heterogeneity, meteorological and hydrological conditions, and river-groundwater interactions played dominant roles in the evolution of the landscape ecological pattern in the riparian zones. (2) Human activities and climate change controlled the direction and intensity of landscape evolution. (3) Pathways for maintaining a healthy landscape ecological pattern in riparian zones and suggestions for identifying the key issues that should be studied in the future were proposed. This paper primarily contributes to a better understanding of landscape ecological patterns in the riparian zone in arid areas from the watershed river-groundwater transformation perspective, thereby promoting integrated watershed management. [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]

Cities consume large amounts of fossil fuels and this results in significant increase of global anthropogenic greenhouse gases. Using city-level carbon dioxide emission data from Shijiazhuang, a traditional heavy manufacturing city and one of the key cities in Jing-Jin-Ji urban agglomeration in Northern China, this paper employs logarithmic mean Divisia index decomposition method (LMDI) to explore the influence of energy intensity, consumption structure, industry structure, per capita Gross Domestic Product (GDP), and population on Shijiazhuang's total carbon dioxide emissions from 2005 to 2016. The LMDI method is further extended to analyze the impacts of above factors at the industry-segment level. This paper finds that the reduction of energy intensity is the major contributor of decreasing carbon dioxide emissions in Shijiazhuang, whereas the growth of per capita GDP is the main driving force for emission accretion. The dominant industrial segments contributing to increasing emissions in Shijiazhuang City from 2005 to 2016, have changed from CMD (coal mining and dressing), EPSH (electric power, steam and hot water production and supply) to SPFM (smelting and pressing of ferrous metals) and PPC (petroleum processing and coking). The CMD industrial segment shows a significant carbon reducing effort and indicates a strong decoupling status. However, the SPFM and PPC respectively show expansive negative decoupling and weak decoupling status, enlightening a change in the policy focus when Shijiazhuang formulates its emission-reduction plans in the future. This study provides important policy insights for the heavy manufacturing cities in other countries with similar industrial structure, as the industry-based carbon mitigation policies should always be adjusted in different stages of the cities' development according to the change of carbon emission driving industries. [ABSTRACT FROM AUTHOR]

Global climate change has become one of the greatest challenges to human development, the study of the dynamic evolution of carbon emissions at the building operation stage and its driving mechanism is of great significance for the reasonable control of carbon emissions, and helpful to provide more scientific and reasonable decision support for proposing an implementable carbon neutral pathway. According to the geospatial differences of carbon emissions at the building operation stage, this paper constructs a research framework on the influencing factors and driving mechanisms of carbon emissions at the building operation stage based on CV, kernel density, Geodetector and other methods. The study shows that first, that the carbon emissions at the building operation stage are unequally distributed in space in provinces of China, and the building carbon emissions vary greatly in structure among different regions. Second, different factors vary greatly in influence. Third, there are great differences in the driving mechanisms of carbon emissions over time, but factors such as urban population and refrigerator ownership are both key influencing factors that have a comprehensive influence on the spatio-temporal changes of carbon emissions. Fourth, all the factor pairs are bifactor-enhanced or non-linearly enhanced, and they can be classified into three types of high, medium, and low levels. Fifth, a carbon neutral pathway map for the building sector is proposed based on the spatial distribution of carbon emissions and its driving mechanism, to provide reference for foreign regions with similar conditions. • Calculated the carbon emission at the building operation stage in Chinese provinces. • Analyzed trend and composition of carbon emission at the building operation stage. • Analyze spatial-temporal changes of carbon emissions by GIS. • For the first time, analyzed driving factors of carbon emissions by Geodetector. • Proposed a carbon neutral pathway map for the building sector. [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]

In order to mitigate global climate change and air pollution, the Chinese government has assigned high priority to expanding low-carbon power generation in China. Recent studies have shown that wind power is one of the most promising renewable energy option in China. Although many studies have estimated the generation potential of onshore wind power, their results vary widely from 1783 TWh to 39,000 TWh. Therefore, we examine the different assumptions in these papers and identify three main factors influencing the results. The three influencing factors are: weather data set, land utilisation factor, and wind turbine configuration. For our model-based analysis, we define a reference scenario which is used to compare the results. Our analysis shows using a different weather data set increases the generation potential to roughly 35,000 TWh. This is 54% higher than the generation potential of the reference scenario. The land utilisation factor also has a large influence, ranging between −10% and −51%. The studies' assumptions and data should be subjected to careful scrutiny, as the calculated wind power potentials are widely used to develop decarbonisation strategies for the energy system. • Study results on wind power potentials in China vary from 1783 TWh to 39,000 TWh • Influencing factors: weather data set, land utilisation, and wind turbine configuration. • Model-based sensitivity analysis with nine scenario settings. • Chosen weather data set has the highest impact on the calculated results with a variation of 54%. [ABSTRACT FROM AUTHOR]