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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]

• Average start of the photosynthetic period in China was on the 123rd day of the year. • The average start of the photosynthetic period advances at a rate of 4.3 d (10 a)−1. • 64% of the region is temperature controlled, others are precipitation controlled. • Importance of temperature and precipitation determined by pre-season precipitation. • Climate change increases NPP by advancing the start of the photosynthetic period. In the context of global warming, the advancement of spring phenology in northern and temperate regions due to increased temperatures has been widely reported. Early and delayed start of the photosynthetic period (SOP) directly affects the vegetation net primary productivity (NPP). However, the interrelationship between climate change, the SOP, and the NPP is unclear. In this paper, we use the dynamics of decadal daily solar-induced chlorophyll fluorescence data to calculate the response of Chinese vegetation photosynthetic phenology to climate change and its impact on the NPP over the last 20 years. The results found that over the last 20 years, the average SOP in China was on the 123rd day of the year, and the SOP has advanced at an average rate of 4.3 d (10 a)−1, with a faster trend of SOP advancement in highland and high-altitude areas. 64% of SOP in China is controlled by temperature; 36% of the SOP in China is controlled by precipitation, and the relative importance of temperature and precipitation was reversed as the precipitation gradient decreased, with SOP dominated by temperature when pre-season precipitation ≧300 mm, and SOP dominated by precipitation when pre-season precipitation ≦300 mm. Finally, we find that climate change indirectly increases vegetation NPP by advancing SOP. Our study emphasizes the importance of precipitation on phenology. It provides a scientific basis for understanding and predicting the response of spring photosynthetic phenology to climate change and the contribution of spring phenology to carbon estimation in terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

In view of the climate emergency, it is necessary to model and forecast road transport emissions for implementing more effective climate policy. This paper fills an important research gap by quantifying the challenges, barriers and remedies of achieving road transport emission reduction and air quality improvement in developing countries. It contributes to the debate on top-down and bottom-up approaches to reconcile carbon and other transport related emissions. China and India are chosen as case studies. Using various official statistics and proxy variables, we estimate and compare the road transport carbon emissions of the two countries systematically from 2009 to 2020. Insights from detailed mode-specific estimations of the bottom-up approach are valuable for targeted policies and measures. An analysis of the major components of road transport carbon emissions points to strategies of promoting electric vehicles, reducing transport demand, accelerating modal shift to low-carbon modes, and cleaner electricity to achieve climate policy. [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]

• A first comprehensive and systematic review on the research of extreme precipitation in China. • Variation and regional characteristics of extreme precipitation under non-stationary conditions due to climate change and human activities. • Supports and basis for engineering application and further research on extreme precipitation and flood in China. Recent years have witnessed global massive property losses and casualties caused by extreme precipitation and its subsequent natural disasters, including floods and landslides. China is one of the countries deeply affected by these casualties. If the statistical characteristics and laws of extreme precipitation could be clearly grasped, then the negative impacts triggered by it may be minimized. China is a vast country and diverse in climate and terrain, hence different regions may be suitable for different analyses and research methods. Therefore, it is necessary to clarify the research progress, methods and current status of extreme precipitation across the country. This paper attempts to provide a comprehensive review of techniques and methods used in extreme precipitation research and engineering practice and their applications. The literature is reviewed focusing on seven aspects: (1) annual maxima method (AM), (2) peaks over threshold method (POT), (3) probable maximum precipitation (PMP), (4) non-stationary analysis of precipitation extremes, (5) intensity-duration-frequency curves (IDF), (6) uncertainty in extreme precipitation frequency analysis, and (7) spatial variability of extreme precipitation. Research on extreme precipitation in China is generally based or centered on the above seven aspects. The current study aims to provide ideas for further research on extreme precipitation frequency analysis and its response to climate change and human activities. [Display omitted] [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]

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]