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Low-carbon travel of urban residents is an important issue of public concern for sustainable development of global cities under climate change. The carbon emissions generated by school trips occupy a significant proportion of the carbon emissions of people's travel and are increasing year by year. In this paper, we review the progress in research on carbon emissions generated by school trips, the socio-spatial differences in school trips, and the influence mechanisms and optimization simulations of carbon emissions from school trips. Two major dilemmas are observed in the human-environment relationship of low-carbon school trips. First, the dramatic reconstruction of social space in cities and the imbalanced development of public service supply in the rapid urbanization process have led to the socio-spatial heterogeneity of the complex environment of school trips, thus making it difficult to form an effective model of governance of urban space that is conducive to low-carbon school trips. Second, the interaction mechanisms related to school trips remain unclear due to the dynamic and multi-scale nature of the geographic environment, which hinders a clear description of the effects of the built environment on low-carbon school trips and the in-depth simulation of the optimization path. Therefore, in future research, researchers should focus on multi-paradigm crossover and fusion, carry out prospective exploration on the human-environment interactions associated with low-carbon school trips, enrich the research methods of human-environment relationship, discuss the mechanisms of the complete built environment on low-carbon school trips, simulate the spatial nested structure and refine the governance model to meet the needs for low-carbon school trips. [ABSTRACT FROM AUTHOR]

Effective strategies, policies and measures for carbon emission reduction need to be developed and implemented according to good understanding of both local conditions and spatial differentiation mechanism of energy consumption associated with human activities at high resolution. In the study, we first collected statistical yearbooks, high resolution remotely sensed imageries, and 3895 usable questionnaires for the urban areas of Kaifeng; then measured the carbon emissions from household energy consumption, using the accounting method provided in the IPCC GHG Inventory Guidelines; and finally applied both exploratory and explanatory statistical methods to characterize the spatial pattern of carbon emissions at high resolution, identify key influencing factors, and gain better understanding of the spatial differentiation mechanism of urban residential carbon emissions. Our study reached the following conclusions: (1) Central heating facilities with controllable flow are important for carbon emissions reduction, but its spatial distribution shows unfairness; (2) Spatial clusters of high carbon emission areas were primarily located in the outer suburbs of the city, validated to some extent the hypothesis that urban sprawl has a driving effect on the increasing urban residential carbon emissions; (3) Factors like size of residential area, family structure, life style, personal preference and behavior rather than household income have significant impacts on household carbon emissions, implying that effective control of residential areas, promotion of family life and low-carbon lifestyle, and effective guidance of proper behaviors and preferences will play a crucial role in reducing urban residential carbon emissions; and (4) Most of the identified influencing factors exhibit clear and specific spatial patterns and gradients of impact, implying that measures for urban residential carbon emission reduction should be adapted to location conditions. The study has generated a set of concrete evidences and improved understandings of the spatially differentiated mechanisms upon which the formation and deployment of any effective strategies, policies and measures for reducing urban residential carbon emissions should be based. • Central heating facilities with controllable flow are important for carbon emissions reduction, but its spatial distribution shows unfairness. • Carbon emissions from electricity consumption account for 75.28% of the total. • Income is not a significant influencing factor, but building area and behavior preference are. • Specific emissions reduction measures for high emissions groups are necessary. • Urban sprawl has a driving effect on the increase of carbon emissions. [ABSTRACT FROM AUTHOR]

By introducing an improved potential model and using the Internet map navigation service and GIS spatial analysis technology platform, this paper studies the spatial accessibility and equity of the medical treatment of residential buildings in the main urban area of Zhengzhou. The findings show that, overall, the spatial accessibility of medical treatment in the study area extends in the northeast and southwest directions, and presents a zonal spreading trend. There exist certain differences and imbalances in the medical facilities and services in each ring, reflect in a gradual deterioration from the first ring to the third ring. According to residents' demand for medical treatment and the distribution of medical resources, the medical spaces themselves can be roughly divided into three regions: "high enjoyment type" (ample resources and sparse population), "general type" (average amount of resources and concentrated population), and "lagging type" (few resources and sparse population). With regard to equity, and based on the Gini coefficient and Lorenz curve, the number of beds in medical facilities was found to be positively correlated with the population, and the total supply of resources to be balanced with population demand, whereas medical resources were seen to be scarce in the outskirts of Zhengzhou and far from the central area. The location entropy index suggests that accessibility to medical facilities in most residential areas is below the average level. While the total amount of hospital resources across the population was found to be relatively equitable, much room for improvement can be said to remain in terms of residents' accessibility when they arrive at hospitals for medical treatment. These research results provide new insights into equity evaluations of urban resource accessibility, as well as a scientific basis for the Chinese government to plan the distribution of medical resources. • Real-time travel big data drawn from an Internet map application were used to calculate travel and transportation costs. • Spatial pattern of residential population at the residential building level was estimated in order to reflect actual demand. • Equity in the access of the residential areas in the study area to medical facilities is evaluated from the perspective of different spatial scales (middle-level and micro-level). • Zhengzhou could roughly be divided into three regions: "high enjoyment type", "general type", and "lagging type". • The lag of public service facilities in the process of urban expansion needs to be taken seriously by the government. [ABSTRACT FROM AUTHOR]

Global warming intensifies the water cycle and regional water condition differences by changing the surface environment. The ecological system in environmental sensitive areas of China (ESAC) is stressed by water resources, and the ecological effect of dry–wet evolution is even more profound. However, the applicability of different potential evapotranspiration (ET 0) assessment methods and the response mechanism of ET 0 to climate change remain unknown. In this study, we evaluated the reliability of ET TH , ET Harg , and ET PM derived from Thornthwaite (TH), Hargreaves (Harg), and Penman–Monteith (PM) formulas using the monthly average air temperature (Tavg), maximum air temperature (Tmax), minimum air temperature, sunshine duration (SSD), wind speed (WIN), and relative humidity (RHU) from 1961 to 2018. Further, non-parametric trend analysis, correlation analysis, multiple regression analysis, wavelet analysis, and seasonal and trend decomposition using Loess were used to analyze the spatial–temporal evolution of ET 0 and the moisture index (MI) and its relationship with climate change in ESAC from 1961 to 2018. The results are as follows: (1) The P-M formula has excellent applicability in the research area. Under the background of average temperature rising at 0.035 °C yr−1, ET PM decreases with a rate of −0.59 mm yr−1, which indicates the "pan evaporation paradox" phenomenon. Using 1993 as the node, ET PM changed from a significant decrease to an increase. (2) WIN, SSD, Tavg, and Tmax significantly promoted changes in ET PM , and RHU and precipitation (Pr) were mainly reduced. The determinant factor of ET PM change was Tavg from 1961 to 2018. (3) MI change was mainly influenced by Pr, SSD, and ET PM. Pr promoted the change, whereas SSD and ET PM reduced it. The linear combination of ET PM and SSD could explain the change in MI. Our findings verified the applicability of PM in areas with severe interannual precipitation fluctuations, revealing the evolution of dry–wet pattern changes and their driving mechanisms in ESAC, which can deepen the cognition of the law of long-term land surface water cycle and effectively guide the formulation and implementation of regional drought and flood disaster response measures. • This research evaluated the applicability of Thornthwaite, Hargreaves, and Penman–Monteith method in ESAC. • ET PM is positive with WIN, SSD, Tavg, and Tmax, and negative with RHU and Pr. • ET PM variation indicating an obvious "pan evaporation paradox". • Moisture index is mainly influenced by Pr, SSD, and ET PM. [ABSTRACT FROM AUTHOR]

The explosive growth of ride-hailing as a new mode of transportation worldwide has attracted great attention from both academic and planning communities. Ride-hailing services have induced profound changes in residential travel patterns. However, the mechanisms and spatio-temporal roles of the influence the built environment imposes on ride-hailing services remain unclear. Based on ride-hailing GPS data and Web map big data, in this study, spatio-temporal entropy indicators of ride-hailing travel behavior and fine-scaled built environment indicators are constructed. Based on these, global and local regression models are developed to explore the spatio-temporal impact mechanisms of various built environments and socio-economic factors on ride-hailing travel in Chengdu, China. A strong correlation is found between various types of built environment and the spatio-temporal entropy of ride-hailing travel. Specifically, whether working on weekdays or weekends, building density and education facilities impose a significant inhibitory effect on ride-hailing spatio-temporal entropy, while mixed land use, house prices, catering, corporate businesses, lifestyle facilities, tourist attractions, and distance to transit significantly increase ride-hailing spatio-temporal entropy. In addition, the uncertain geographic context and the differences in residents' activities on different days of the week cause significant spatio-temporal heterogeneity in the impact the built environment has on ride-hailing spatio-temporal entropy. Noteworthy, mixed land use exerts the strongest promoting effect on ride-hailing and a positive effect on ride-hailing pick-ups and drop-offs in the whole region. The intensity of this effect tends to decay gradually from the urban center to the periphery. Furthermore, ride-hailing can both substitute for and complement public transport. More interestingly, the study also found an "olive-shaped" social structure between the socio-economic level and the demand for ride-hailing. This study thus not only enriches the theory of spatial behavior within the context of the new era, the results also help planners to reduce car travel dependence by optimizing the built environment. This holds important practical significance for sustainable urban development. • Constructed a spatial-behavioral analysis framework based on multi-source spatio-temporal big data. • Proposing a spatio-temporal entropy model that can evaluate of the regularity of human mobility within an activity period. • Use GWR models to investigate the relation between built environment and ride-hailing travel in different temporal contexts. • There is significant spatio-temporal heterogeneity in the effects of various built environments on ride-hailing O Ds travel. [ABSTRACT FROM AUTHOR]

Vegetation in arid and semi-arid regions is extremely sensitive to climate change and human activities, and when disturbed, it is prone to irreversible degradation. However, the mechanisms of how natural and anthropogenic factors drive vegetation variations remain unclear for environmentally sensitive and vulnerable areas. To understand the relationships between vegetation variation, climate change, human activities, relief degree of the land surface, and El Niño-Southern Oscillation (ENSO), trend analysis, partial correlation analysis, residual trend (RESTREND), and wavelet coherence approach were utilized. Normalized difference vegetation index (NDVI), meteorological data, and ENSO were used as data sources to determine the spatial response of vegetation growth to climate change and human activity from 1982 to 2018. This study defined the 400 mm annual precipitation fluctuation zone in China (APFZC) for the first time, involving 15 provinces, accounting for 27.37% of China. Vegetation activity was mainly driven by precipitation, temperature, sunshine duration, and wind speed. Precipitation and temperature promoted vegetation activity, while the wind speed inhibited it. Moreover, vegetation variation exerts an obvious topographic effect and is deeply affected by ENSO. Under the influence of climate change from 1982 to 2018, the vegetation became greener in the Loess Plateau, North China Plain, Qinghai, and the border regions of Inner Mongolia and Liaoning; however, in the northeastern part of Inner Mongolia and the western part of Heilongjiang, vegetation was browning. In 1982–1998 and 1999–2018, the contribution rates of climate change to vegetation variation were 50.36% and 42.89%, respectively, and human activities were 49.64% and 57.11%, respectively. This information is crucial for alleviating climate change and for the ecological restoration of vegetation, especially in environmentally sensitive and vulnerable areas. [ABSTRACT FROM AUTHOR]