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Henan Province is the birthplace of Chinese civilization and one of the earliest human settlements, which means that the area has an important national cultural heritage. Traditional villages are an important facet of this cultural heritage, and studying their spatiotemporal characteristics in different periods has important theoretical and practical significance for the sustainable development and protection of cultural heritage in the region. This paper takes the traditional villages of the Yellow River basin in Henan Province that were formed before 1919 as the research object. Information on the ancient river was obtained through a literature search as well as via field research, and the important tributaries of the Yellow River in different periods were mapped using ArcGIS 10.0 software. The nearest neighbor index, kernel density estimation, standard deviation ellipse and other methods were adopted to analyze the spatiotemporal characteristics of the traditional villages. The factors that influenced the evolution of traditional villages were explored in depth by combining changes in the course of the river and water conservancy projects. The results show that the formation of traditional villages along the Yellow River in Henan Province has experienced a historical track of growth, contraction, growth and prosperity and stability. The traditional villages along the Yellow River in Henan Province generally show a clustered pattern, forming a dense concentration of traditional villages in the middle reaches of the Yellow River. The center of gravity shows a migration trend from southeast to northwest. In terms of influencing factors, the spatiotemporal evolution of the relationship between villages and their distance to water is closely related to climatic fluctuations, changes in channel, water conservancy projects and social and cultural factors. This paper deepens our understanding of the relationship between traditional village evolution and watersheds by improving the consistency between village spatial distribution and historical geography and provides a useful theoretical reference for the sustainable development of China's traditional villages. [ABSTRACT FROM AUTHOR]

The influences of human activity on regional climate over China have been widely reported and drawn great attention from both the scientific community and governments. This paper reviews the evidence of the anthropogenic influence on regional climate over China from the perspectives of surface air temperature (SAT), precipitation, droughts, and surface wind speed, based on studies published since 2018. The reviewed evidence indicates that human activities, including greenhouse gas and anthropogenic aerosol emissions, land use and cover change, urbanization, and anthropogenic heat release, have contributed to changes in the SAT trend and the likelihood of regional record-breaking extreme high/low temperature events over China. The anthropogenically forced SAT signal can be detected back to the 1870s in the southeastern Tibetan Plateau region. Although the anthropogenic signal of summer precipitation over China is detectable and anthropogenic forcing has contributed to an increased likelihood of regional record-breaking heavy/low precipitation events, the anthropogenic precipitation signal over China is relatively obscure. Moreover, human activities have also contributed to a decline in surface wind speed, weakening of monsoon precipitation, and an increase in the frequency of droughts and compound extreme climate/weather events over China in recent decades. This review can serve as a reference both for further understanding the causes of regional climate changes over China and for sound decision-making on regional climate mitigation and adaptation. Additionally, a few key or challenging scientific issues associated with the human influence on regional climate changes are discussed in the context of future research. [ABSTRACT FROM AUTHOR]

Carbon neutrality is an important policy in the current global response to climate change and has been widely recognized by various industries. In the process of promoting carbon neutrality, new energy plays a pivotal role. In this study, the definition and connotation of new energy and its role and specific operation in the energy transition of carbon neutrality are firstly explained. Promoting new energy development requires significant green and low-carbon investments. Taking China as an example, this paper analyzes the opportunities brought by the carbon neutral process to the field of green finance and analyzes the main features and development trends of green finance in China at present. Then this paper proposes policy recommendations to strengthen the development of green finance in China in terms of improving the green financial policy system, enhancing the supply capacity of green financial services, and optimizing the supporting environment for green financial development. Finally, this paper analyzes the measures and experiences of the United States in promoting low-carbon development and proposes countermeasures for China's low-carbon development on the basis of the five major relationships that need attention in China's carbon-neutral process. That is, strengthen the top-level design and improve the regulatory policy system; optimize the energy structure and increase the proportion of clean energy; optimize the industrial structure and reduce energy consumption in key industries; build a complete low-carbon technology system and promote low-carbon technology research and development and demonstration applications, and encourage local conditions to explore low-carbon development paths. The development of green finance can contribute to the advancement of new energy technologies, thus contributing to the achievement of carbon neutrality goals. [ABSTRACT FROM AUTHOR]

Climate change has a significant impact on agriculture so it is necessary to adapt to climate change to promote agricultural transformation and sustainable development. The key to achieving such a goal is to promote the development of climate smart agriculture (CSA) against the background of climate change. The paper first uses official statistical data to evaluate the effect of climate smart agricultural technology project, and then uses the statistical method of questionnaire survey to further investigate and statistically analyze the implementation effect of climate smart agricultural technology project in the pilot area. Further, semi-structured interview method is employed to conduct more in-depth investigation and analysis on some main topics on the basis of preliminary investigation and statistical analysis. The interviews focus on the environmental and economic effects of project practice, the difficulties of smallholder farmers' technology extension, and the farmers' willingness to participate. The study indicates that to promote climate smart agriculture technologies is an effective way to solve agricultural problem in practice under the background of climate change, and can achieve at least three benefits. The first is to reduce production costs, which can be reduced by 10% to 15% in terms of fertilizers and pesticides alone. The second is to improve productivity, and preliminary results show that food production can be increased by more than 5%. Finally, it has carbon sequestration capabilities and can significantly reduce greenhouse gas (GHG) emissions. Therefore, the promotion of climate smart agriculture (CSA) technologies helps to solve the problem of agricultural transformation and sustainable development. To this end, it is necessary to drive the transformation and optimization of regional agricultural ecological environment and production system, and promote the innovation and extension of CSA technology. In addition, some soft conditions (e.g., talent, system and technology, etc.) and hard conditions (e.g., modern agricultural supply chain, agricultural big data and the Internet of Things, etc.), must be created, too. [ABSTRACT FROM AUTHOR]

This paper matched the data for all non-financial listed firms in China from 1990 to 2017 with weather data and used temperature and precipitation to represent climate change. Using daily temperature and daily precipitation data, we constructed temperature bins and precipitation bins. This study presents a potential first empirical analysis that applies the risk perception theory to comprehensively examine the impacts of daily temperature and daily precipitation on firms' debt financing costs. Compared to a day with a suitable temperature, a day with temperatures above 30 °C increases firms' debt financing costs by 0.2411%. Compared to a sunny day, a day with torrential rain increases firms' financing costs by 0.2697%. Approximately $122.13 million and $20.49 million in extra costs were incurred across all firms in 2017. We conducted a series of heterogeneity tests. We found that the negative impacts of climate change on debt financing costs are greater for firms that investor considers more vulnerable. Finally, we found two possible mechanisms. First, from the firm's subjective initiative perspective, firms that respond proactively during seasons of high temperatures and heavy rains can reduce the negative impacts. Second, adverse weather caused by climate change may also increase firms' debt financing costs by blocking travel. These results enrich the literature on firms' debt financing costs and contribute to the understanding of climate change's impact on firms. [Display omitted] • An extra high-temperature day increases firms' debt financing cost by 0.2411%. • An extra heavy-rain day increases firms' debt financing costs by 0.2697%. • The negative impacts will be greater for those firms that investor considers more vulnerable. • Firms' response proactively can reduce the negative impacts. • Climate change also may increase firms' debt financing costs by blocking travel. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new repeatable batch processing method is developed for regional SUHI assessment. • A framework is proposed to explain the formation of SUHI effect. • The impacts of most driving factors on SUHI effect are spatial heterogeneous. • More attention should be paid to small cities. The urban heat island (UHI) effect has become a hot topic worldwide because of the double stress of global climate change and rapid urbanization. However, the deficiencies of existing methods hinder the monitoring and assessment of the surface urban heat island (SUHI) effect at the regional scale. This paper presented a new method to assess SUHI intensity, and then applied it in China. The results indicated that: (1) compared with other methods, this new method has some merits like higher accuracy, fewer operational steps, and higher portability. It enables accurate, semi-automated assessment of SUHI intensity at the regional scale. (2) SUHI effect exists in 81.02% of the cities in China. These cities were mainly concentrated in southeastern China, which presented the significant characteristic of spatial agglomeration. (3) Urban-rural differences in relative humidity, vegetation coverage, evapotranspiration, waterbody coverage, and the number of industrial enterprises are the main driving factors for the SUHI effect in China. Furthermore, there are significant interactions among these driving factors. (4) The spatial heterogeneity of the influence directions and intensities of many driving factors is obvious. Therefore, traditional regression models should be utilized more cautiously because they cannot capture the spatial heterogeneity of variables. (5) The hypothesis of "the larger the city size, the higher the SUHI intensity" is not valid, so more attention should be paid to small cities in the future. [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]

• 170 rainfall-related excavation failure cases in China were collected for analyses. • 10 conceptual models were developed for illustration of the excavation failures. • Soil conditions and technical factors involved in the failures were summarized. Over the last 30 years, intense rainfall events due to abnormal climate change have brought about numerous excavation failures in the urban areas of China. These heavy rainfall-related engineering failures, characterized by failure or collapse of retaining structures and/or retained slopes, have caused large-scale damages to urban environment as well as injuries and casualties. Thus, a summary of such failures along with corresponding statistical patterns and contributing factors is essential, which can help practitioners further understand the risk of excavation failures associated with heavy rainfall and then could develop or adopt appropriate measures for prevention of future failures. Based on these considerations, this paper reviewed 170 excavation failure cases occurring during 1994–2018 in China, using a method of descriptive statistics. The review mainly focused on their statistical failure patterns (temporal distributions of failures, types of failures and types of failed soil strata, etc.) and contributory factors (e.g., heavy rainfall influences and technical factors). It was disclosed that the failures occurred in those deep excavations in sandy soils during summer more frequently. In general, the failures can be categorized into six basic types, which are mainly governed by 11 kinds of adverse influences of heavy rainfall (e.g., seepage erosion under high hydraulic gradient, intense runoff scouring and water gushing impact from broken storm sewers nearby, etc.) and 16 technical factors (e.g., ignorance or improper protection of adjacent preexisting drainage structures, deficiencies in drainage or dewatering system, and poor construction quality of temporary retaining structures, etc.). The findings and lessons learned from this review are practically useful for designers and contractors to reduce the incidence of future excavation failures in the context of global climate change. [ABSTRACT FROM AUTHOR]