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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]

This paper uses the exploratory spatial data analysis and the geodetector method to analyze the spatial and temporal differentiation characteristics and the influencing factors of the COVID-19 (corona virus disease 2019) epidemic spread in mainland China based on the cumulative confirmed cases, average temperature, and socio-economic data. The results show that: (1) the epidemic spread rapidly from January 24 to February 20, 2020, and the distribution of the epidemic areas tended to be stable over time. The epidemic spread rate in Hubei province, in its surrounding, and in some economically developed cities was higher, while that in western part of China and in remote areas of central and eastern China was lower. (2) The global and local spatial correlation characteristics of the epidemic distribution present a positive correlation. Specifically, the global spatial correlation characteristics experienced a change process from agglomeration to decentralization. The local spatial correlation characteristics were mainly composed of the'high-high' and 'low-low' clustering types, and the situation of the contiguous layout was very significant. (3) The population inflow from Wuhan and the strength of economic connection were the main factors affecting the epidemic spread, together with the population distribution, transport accessibility, average temperature, and medical facilities, which affected the epidemic spread to varying degrees. (4) The detection factors interacted mainly through the mutual enhancement and nonlinear enhancement, and their influence on the epidemic spread rate exceeded that of single factors. Besides, each detection factor has an interval range that is conducive to the epidemic spread. Unlabelled Image • Spatial and temporal differentiation of COVID-19 epidemic spread in China and its influencing factors are analyzed. • The global and local spatial correlation characteristics of the epidemic distribution present a positive correlation. • The population inflow from Wuhan and strength of economic connection are the main factors affecting the epidemic spread. • The interaction influence of detection factors on the epidemic spread exceeds that of the single factor. • When the average temperature in winter is maintained at 11–16 °C, the epidemic spread rate is higher. [ABSTRACT FROM AUTHOR]

Quantitatively evaluating the spatial characteristics of regional e-retailing economy linkages is of great significance for clarifying the spatial organization of regional e-retailing economies, and promoting regional coordinated development. However, due to the lack of study data, it is difficult toconduct quantitative research on these regional e-retailing economic linkages. Taking advantage of emerging new data sources, the depth and breadth of related research cannow be improved. This paper considers 64 county-level economic areas in Zhejiang Province as network nodes.A revised gravity model was used to measure the intensity of the e-retailing economic linkage in 2016,based upon the e-retailing data provided by the Department of Commerce of Zhejiang Province, China. On this basis, the geographic information system (GIS) tool, a model-potential method and a social network, were used to analyze the spatial features of the e-retail economic linkages at the countylevel in Zhejiang Province. The results showed that the spatial polarization of the economic linkage pattern emerged as prominent, with the overall difference and east-west gradient difference between counties proving significant. In addition, the major linking partners of most regions were relatively singular, and a problem of vulnerability in e-retail economic development was shown.Secondly, the southwest region of Zhejiang Province was an important obstacle in the integration process of regional e-retail economy, through analyzing the connection scope of e-retailing economics. Thirdly, the central Zhejiang subgroupwas a key plate connecting east and west, which plays an importantlinking role in the development of regional equalizationwhen we analyzethe cohesive subgroup pattern. Inspired by this, we hypothesized that a microscopic analysis results of Zhejiang Province could provide some enlightenment for the balanced and integrated development of China's regional e-retailing economy. [ABSTRACT FROM AUTHOR]

Background: COVID-19 is still spreading rapidly around the world. In this context, how to accurately predict the turning point, duration and final scale of the epidemic in different countries, regions or cities is key to enabling decision makers and public health departments to formulate intervention measures and deploy resources.Methods: Based on COVID-19 surveillance data and human mobility data, this study predicts the epidemic trends of national and state regional administrative units in the United States from July 27, 2020, to January 22, 2021, by constructing a SIRD model considering the factors of "lockdown" and "riot".Results: (1) The spread of the epidemic in the USA has the characteristics of geographical proximity. (2) During the lockdown period, there was a strong correlation between the number of COVID-19 infected cases and residents' activities in recreational areas such as parks. (3) The turning point (the point of time in which active infected cases peak) of the early epidemic in the USA was predicted to occur in September. (4) Among the 10 states experiencing the most severe epidemic, New York, New Jersey, Massachusetts, Texas, Illinois, Pennsylvania and California are all predicted to meet the turning point in a concentrated period from July to September, while the turning point in Georgia is forecast to occur in December. No turning points in Florida and Arizona were foreseen for the forecast period, with the number of infected cases still set to be growing rapidly.Conclusions: The model was found accurately to predict the future trend of the epidemic and can be applied to other countries. It is worth noting that in the early stage there is no vaccine or approved pharmaceutical intervention for this disease, making the fight against the pandemic reliant on non-pharmaceutical interventions. Therefore, reducing mobility, focusing on personal protection and increasing social distance remain still the most effective measures to date. [ABSTRACT FROM AUTHOR]

Using remote sensing to study vegetation phenology faces a problem related to extraction methods. Phenology data from different methods vary greatly but there is a lack of widely recognized methods and evaluation approaches. In this study, based on the Leaf Area Index data from 2009 to 2015, we used two common fitting methods (Savitzky-Golay filter and Double Logistic function) and three phenological determining methods (Seasonal amplitude, Absolute value, and Seasonal Trend decomposition by Loess) to extract vegetation phenology in China. Using different thresholds, we obtained 18 extraction method combinations. Then the ground-based observations data from 31 phenology stations in relation to three key vegetation phenophases: Start Of growing Season (SOS), End Of growing Season (EOS) and Length Of growing Season (LOS) were used to evaluate the accuracy of these 18 method combinations. The results under five evaluation indicators showed that the suitable method combinations for SOS, EOS, and LOS were different. Compared with ground-based observations, SOS and EOS extracted by the suitable method combinations were delayed by 6.28 and 4.91 days, and the LOS was shorter. The potential difference of the suitable and unsuitable method combinations respectively reached −44.73, −35.79, and 37.38 days (for SOS, EOS, and LOS), clearly indicating the importance of selecting suitable method combination. The phenology dataset from Vegetation Index & Phenology (VIP) Lab. has also confirmed the reliability of our results. Furthermore, we explored the differences between remote sensing and ground-based phenology. Our study highlights the importance of using a suitable method combination to extract the vegetation phenology and provides a systematical assessment method for selecting a suitable method combination. [ABSTRACT FROM AUTHOR]

It has become a hot issue in realizing regional sustainable development to discuss the influencing factors and regulatory pathway of ecosystem health. However, the lack of driving mechanisms and regulatory pathways for ecosystem health limit the development of effective environmental management policies and urban planning. In this study, taking the Middle Reaches of the Yellow River (MRYR) of China as the study area, we introduced spatial weight coefficient and modified coefficient of spatial proximity effect to improve the "Vigor–Organization-Resilience-Ecosystem services" (VORS) model, and adopted the improved VORS model to dynamically assess the ecosystem health level. Then, we provide an in-depth analysis of the mechanisms of climate and human activities affecting ecosystem health. Finally, the multi-scale regulation of ecosystem health was implemented. The results showed that topography, climate, population urbanization, economic urbanization, land urbanization, agricultural activities, environmental pollution, regional policies and other factors all have a significant impact on the ecosystem health, but the direction and extent of impact had obvious spatial heterogeneity. The negative impact of potential evapotranspiration on ecosystem health is significantly higher in urban agglomerations than in other regions. Regular and compact urban forms are conducive to the health of urban ecosystems, and appropriate urban size and strict urban development boundaries are conducive to maintaining high level of ecosystem health in riverine cities. The evolution of the spatial relationship between environmental pollution and ecosystem health in the MRYR (ecologically vulnerable areas in the western region and urban agglomerations in mid-east region) verifies the Environmental Kuznets Curve (EKC). The study area was divided into three eco-management zones at regional scale and ten eco-management zones at county scale by using the spatial superposition analysis method. Based on this, the multi-scale regulation strategies of ecosystem health was proposed. This study improved the assessment model of ecosystem health, expanded the theoretical research on driving mechanism and multi-scale regulation pathways of ecosystem health. The research results can provide scientific basis for environmental management and urban planning in ecologically fragile areas. [ABSTRACT FROM AUTHOR]

• Improved the VORS model. • Proposed a conceptual framework for the impact of climate and human activities factors on ecosystem health. • Extended index system of influencing factors of ecosystem health. • The driving mechanisms of ecosystem health were deeply analyzed. Global and regional environmental changes caused by climate and human activities have profoundly affected terrestrial ecosystem health. Nevertheless, few studies have been deeply dissected the influencing mechanism of climate and human activities on ecosystem health. In this study, the Middle Reaches of the Yellow River (MRYR) was choosed as the study area, and the improved VORS model was adopted to dynamically assess the ecosystem health level in the MRYR. Then, the influence mechanism of climate and human activities on ecosystem health was deeply analyzed. Our results showed: (1) The ecosystem health level in the rapidly urbanized areas, the ecologically fragile areas in the midwest regions and the mountainous - plain transition zone has a dynamic local spatial structure and spatiotemporal interaction process. (2) Topography, land urbanization, economic urbanization, population urbanization, agricultural activities, environmental pollution, and regional policy have a significant effect on the ecosystem health. (3) In the mid-eastern urban agglomerations, the impervious surface increase and the decrease of ecological land use lead to the increased negative effects of potential evapotranspiration on the ecosystem health of urban agglomerations. Topography has a long-term inhibitory effect on urban expansion, which weakens the negative impact of urbanization on mountain ecosystem health. The negative impact of economic urbanization on the mid-eastern shows a "core-edge" structure with Xi'an metropolitan area, Luohe River Basin, Fenhe River Basin, Taiyuan metropolitan area and Zhengzhou metropolitan area as the core. (4) In terms of the interaction between influencing factors, the interaction between climate and human activities on ecosystem health shows significant differences between the east, middle and west. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the impacts of climate change and technical progress on the wheat yield per unit area from 1970 to 2014 in Henan, the largest agricultural province in China, using an autoregressive distributed lag approach. The bounded F-test for cointegration among the model variables yielded evidence of a long-run relationship among climate change, technical progress, and the wheat yield per unit area. In the long run, agricultural machinery and fertilizer use both had significantly positive impacts on the per unit area wheat yield. A 1% increase in the aggregate quantity of fertilizer use increased the wheat yield by 0.19%. Additionally, a 1% increase in machine use increased the wheat yield by 0.21%. In contrast, precipitation during the wheat growth period (from emergence to maturity, consisting of the period from last October to June) led to a decrease in the wheat yield per unit area. In the short run, the coefficient of the aggregate quantity of fertilizer used was negative. Land size had a significantly positive impact on the per unit area wheat yield in the short run. There was no significant short-run or long-run impact of temperature on the wheat yield per unit area in Henan Province. The results of our analysis suggest that climate change had a weak impact on the wheat yield, while technical progress played an important role in increasing the wheat yield per unit area. The results of this study have implications for national and local agriculture policies under climate change. To design well-targeted agriculture adaptation policies for the future and to reduce the adverse effects of climate change on the wheat yield, climate change and technical progress factors should be considered simultaneously. In addition, adaptive measures associated with technical progress should be given more attention. [ABSTRACT FROM AUTHOR]

Garlic is the major economic crop in China. Timely and accurate identification and mapping of garlic are significant for garlic yield prediction and garlic market management. Previous studies on garlic mapping were mainly based on all observations of the entire growing season, so the resulting maps have a hysteresis. Here, we determined the optimal identification strategy and the earliest identifiable phenophase for garlic based on all available Landsat 8/9 time series imagery in Google Earth Engine. Specifically, we evaluated the performance of different vegetation indices for each phenophase to determine the optimal classification metrics for garlic. Secondly, we identified garlic using random forest algorithm and classification metrics of different time series lengths. Finally, we determined the earliest identifiable phenophase of garlic and generated an early-season garlic distribution map. Garlic could be identified as early as March (bud differentiation period) with an F1 of 0.91. Our study demonstrates the differences in the performance of vegetation indices at different phenophases, and these differences provide a new idea for mapping crops. The generated early-season garlic distribution map provides timely data support for various stakeholders. [ABSTRACT FROM AUTHOR]

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]

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]

Spring vegetation phenology is closely influenced by photoperiod, and the presence of artificial light at Night (ALAN) therefore substantially impacts the phenological response of plants to climate change. How ALAN impacts spring phenology in relative to warming and what are the drivers regulate these impacts are not well understood. Here we focused on the extra-tropical terrestrial ecosystem (>30°N) of China where the highest urbanization has experienced using satellite images to extract the start of the growing season (SOS) from three independent datasets, as well as ALAN data from harmonized global nighttime light (NTL over 2001–2018. We found that ALAN caused earlier SOS both at the ecosystem level and for the major climate zones, and this advanced effect weakened at lower latitude regions and for the high-altitude ecosystems. Further, we discovered that the advanced effect of ALAN on SOS was strengthened in areas with lower chilling days and with the increased distance from the city center. We therefore derived a new model for the estimation of SOS including the effects of ALAN and the new model provided improved representation of SOS in terms of higher proportions of significant pixels between model estimates and observations, higher correlation coefficients, lower root mean square error, Akaike information criterion and higher Kling-Gupta efficiency. Our results highlight that the effects of ALAN on SOS were influenced by latitude, elevation, and winter chilling. Overall, our study sheds light on the impact of human activities on plant spring phenology and provides insights for predicting plant growth patterns under future urbanization and global climate change. • Artificial light at Night (ALAN) advanced spring SOS in temperate China. • Earlier SOS with ALAN weakened at low latitudes and high-elevation regions. • Lower winter chilling increased the effect of ALAN on SOS. • A new SOS model was derived considering ALAN. [ABSTRACT FROM AUTHOR]

During the 15th Conference of the Parties (COP 15), Parties agreed that reducing emissions from deforestation and forest degradation and enhancing 'removals of greenhouse gas emission by forests' (REDD+) in developing countries through positive incentives under the United Nations Framework Convention on Climate Change (UNFCCC) was capable of dealing with global emissions. As REDD+ seeks to lower emissions by stopping deforestation and forest degradation with an international payment tier according to baseline scenarios, opportunities for ecosystem benefits such as slowing habitat fragmentation, conservation of forest biodiversity, soil conservation may be also part of this effort. The primary objective of this study is to evaluate ecosystem-based benefits of REDD+, and to identify the relationships with carbon stock changes. To achieve this goal, high resolution satellite images are combined with Normalized Difference Vegetation Index (NDVI) to identify historical deforestation in study area of Central Kalimantan, Indonesia. The carbon emissions for the period of 2000-2005 and 2005-2009 are 2.73 × 10 t CO and 1.47 × 10 t CO respectively, showing an increasing trend in recent years. Dring 2005-2009, number of patches (NP), patch density (PD), mean shape index distribution (SHAPE_MN) increased 30.8%, 30.7% and 7.6%. Meanwhile, largest patch index (LPI), mean area (AREA_MN), area-weighted mean of shape index distribution (SHAPE_AM), neighbor distance (ENN_MN) and interspersion and juxtaposition index (IJI) decreased by 55.3%, 29.7%, 15.8%, 53.4% and 21.5% respectively. The area regarding as positive correlation between carbon emissions and soil erosion was approximately 8.9 × 10 ha corresponding to 96.0% of the changing forest. These results support the view that there are strong synergies among carbon loss, forest fragmentation and soil erosion in tropical forests. Such mechanism of REDD+ is likely to present opportunities for multiple benefits that fall outside the scope of carbon stocks. [ABSTRACT FROM AUTHOR]

Low-carbon cognition can potentially impact low-carbon behavior, playing an important role in sustainable urban development. Traditional low-carbon cognition research has paid little attention to the built environment, causing an underestimation of the influence of urban spatial planning and regulation. Therefore, we constructed an analytical framework of low-carbon cognition and integrated the built environment 5D variables with local forces to investigate the cultural, local, and personal impact of low-carbon cognition. Low-carbon cognition in terms of energy use, daily travel, and consumption for 1485 households in Zhengzhou City was empirically studied. The results show that residents' low-carbon cognition is hierarchical. Low-carbon travel cognition is highly susceptible to the external environment, while low-carbon energy use cognition is susceptible to cultural factors. There are differences in the effects of the three forces on residents' low-carbon cognition. The personal and cultural forces have a greater impact on residents' low-carbon energy use and consumption cognition, while the local force affects various low-carbon cognition of residents. The heterogeneity of the built environmental impact should be focused on. The "5D" built environment (Density, Diversity, Design, Distance to transit, and Destination accessibility) positively affects the level of low-carbon travel cognitive, but negatively affects the level of low-carbon consumption cognitive. This study comprehensively analyzes the low-carbon cognition of energy use, daily travel, and consumption, objectively measures the impact of "Regional force" on low-carbon cognition, and determines the heterogeneous effect of the external built environment on low-carbon cognition. We provide detailed policy recommendations for the construction of low-carbon cities and transformation of residents' living behaviors. • Residents' low-carbon cognition in terms of energy use, daily travel, and consumption was estimated. • An analytical framework of low-carbon distributed cognition was constructed to investigate the impact on low-carbon cognition. • The built environment significantly affect various residents' low-carbon cognition. • Mixed land use, POI density, and road network density are the focus of low-carbon cognition. [ABSTRACT FROM AUTHOR]

Air pollution in the form of PM 2.5 can destroy the residents' health and cause heavy economic losses. How to determine the control target of PM 2.5 concentration for each city according to their local conditions becomes the key to prevent and control PM 2.5 pollution. Based on a series of index data selected from the aspect of land, industry, energy, population, technology and economic factors in 2016, this study determines allocation schemes of control targets for PM 2.5 concentration in 28 cities of atmospheric pollution transmission channel in the Beijing-Tianjin-Hebei district under the IT-1 (35 μg/m³), IT-2 (25 μg/m³), IT-3 (15 μg/m³) and AQG (10 μg/m³) scenarios using various methods. The results show that: (1) The combination of the Super-slacks based measure (Super-SBM) model with undesirable outputs, Gini coefficient method, contribution coefficient method and Gini coefficient minimization model can inified evaluation indexes and increase the ability to systematize and compare the allocation schemes formulated based on different regulation principles. (2) The economic development performance level under the constraint condition of PM 2.5 pollution and the fairness of PM 2.5 emissions corresponding to different factors exhibit significant differences among all the cities. Although the allocation scheme determined based on the equity principle can improve the fairness of PM 2.5 emissions compared with the base year, the unfairness characteristic of PM 2.5 emissions do not fundamentally change. (3) Regional allocation schemes of control target for PM 2.5 concentration under the IT-1 scenario based on the efficiency principle or equity principle are feasible while the scheme determined based on the principle of efficiency and equity is not. The allocation schemes determined under the IT-2, IT-3 and AQG scenarios based on the equity principle are feasible while the schemes determined based on other principles are not. More attention should be paid at allocating the control target of PM 2.5 concentration based on the equity principle, rather than fousing other principles in the future. • The study formulates the allocation schemes of fine particulate matter (PM 2.5) concentration. • Combination of different methods can improve the comparability of allocation schemes. • The schemes under the IT-1 (Intermediate Target-1) scenario based on efficiency principle or equity principle are feasible. • The schemes under the IT-2, IT-3 and AQG (Air Quality Guideline value) scenarios based on equity principle are feasible. • More attention should be paid at formulating the allocation schemes based on the equity principle in the future. [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]

The dynamics of surface water play a crucial role in the hydrological cycle and are sensitive to climate change and anthropogenic activities, especially for the agricultural zone. As one of the most populous areas in China's river basins, the surface water in the Huai River Basin has significant impacts on agricultural plants, ecological balance, and socioeconomic development. However, it is unclear how water areas responded to climate change and anthropogenic water exploitation in the past decades. To understand the changes in water surface areas in the Huai River Basin, this study used the available 16,760 scenes Landsat TM, ETM+, and OLI images in this region from 1989 to 2017 and processed the data on the Google Earth Engine (GEE) platform. The vegetation index and water index were used to quantify the spatiotemporal variability of the surface water area changes over the years. The major results include: (1) The maximum area, the average area, and the seasonal variation of surface water in the Huai River Basin showed a downward trend in the past 29 years, and the year-long surface water areas showed a slight upward trend; (2) the surface water area was positively correlated with precipitation (p < 0.05), but was negatively correlated with the temperature and evapotranspiration; (3) the changes of the total area of water bodies were mainly determined by the 216 larger water bodies (>10 km2). Understanding the variations in water body areas and the controlling factors could support the designation and implementation of sustainable water management practices in agricultural, industrial, and domestic usages. [ABSTRACT FROM AUTHOR]

The 400 mm annual precipitation fluctuation zone (75°55′–127°6′E and 26°55′–53°6′N) is located in central and western China, which is a transition area from traditional agricultural to animal husbandry. It is extremely sensitive to climatic changes. The corresponding changes of the ecosystem, represented by vegetation, under the dual influences of climate change and human activities are important issues in the study of the regional ecological environment. Based on the Savitzky–Golay (S–G) filtering method, the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Differential Vegetation Index (NDVI) dataset (NDVI3g) was reconstructed in this paper. Sen's slope estimation, Mann–Kendall (M–K), multiple regression residual analysis, and the Hurst index were used to quantify the impacts of climate change and human activities on vegetation; in addition, the future persistence characteristics of the vegetation changes trend were analyzed. Vegetation changes in the study area had an obvious spatio-temporal heterogeneity. On an annual scale, the vegetation increased considerably, with a growth rate of 0.50%/10a. The multi-year mean value of NDVI and growth rate of cultivated land were the highest, followed by the forest land and grassland. On a seasonal scale, the vegetation cover increased most significantly in autumn, followed by spring and summer. In the southeastern and central parts of the study area, the vegetation cover increased significantly (P < 0.05), while it decreased significantly in the northeastern and southwestern parts. In summer, the NDVI value of all vegetation types (cultivated land, forest land and grassland) reached the maximum. The change rate of NDVI value for cultivated land reached the highest in autumn (1.57%/10a), forest land reached the highest in spring (1.15%/10a), and grassland reached the highest in autumn (0.49%/10a). The NDVI of cultivated land increased in all seasons, while forest land (−0.31%/10a) and grassland (−0.009%/10a) decreased in winter. Partial correlation analysis between vegetation and precipitation, temperature found that the areas with positive correlation accounted for 66.29% and 55.05% of the total area, respectively. Under the influence of climate change alone, 62.79% of the study area showed an increasing tendency, among which 46.79% showed a significant upward trend (P < 0.05). The NDVI decreased in 37.21% of the regions and decreased significantly in 14.88% of the regions (P < 0.05). Under the influence of human activities alone, the vegetation in the study area showed an upward trend in 59.61%, with a significant increase in 41.35% (P < 0.05), a downward trend in 40.39%, and a significant downward trend in 7.95% (P < 0.05). Vegetation growth is highly unstable and prone to drastic changes, depending on the environmental conditions. [ABSTRACT FROM AUTHOR]

Spatio-temporal variations in precipitation are affecting agricultural production in China in the context of climate change. Based on daily precipitation data from 63 national meteorological stations on the Huang-Huai-Hai Plain from 1963 to 2012, this paper analysed the spatio-temporal variations in precipitation in terms of precipitation days and intensity, using spatial interpolation, linear trend estimation and wavelet analysis. The results indicated that: (i) from 1963 to 2012, the number of annual precipitation days and intensity decreased gradually from the southeast to the northwest. Additionally, the distribution of the extreme precipitation index was similar to that of the annual precipitation index; (ii) the number of annual precipitation days and heavy precipitation days gradually decreased, while precipitation intensity and extreme precipitation days and extreme rainfall intensity remained relatively stable or decreased. The spatial patterns of annual variation trends were considerably different. The annual precipitation days and intensity trends are consistent with the overall trend, while that of the extreme rainfall index in some regions differs from the overall trend; (iii) the precipitation index displayed different periodic oscillations during the period, and the precipitation index values differed at different time scales. However, all the precipitation index values exhibited a 28-yr oscillation. [ABSTRACT FROM AUTHOR]

Over the period 1982–2015, temperatures have exhibited an asymmetric warming pattern diurnally, as well as seasonally across the Loess Plateau. However, very limited research has studied the implications and effects of such seasonally heterogeneous warming across the Loess Plateau. In this study, we also analyzed the time series trends and seasonal spatial patterns of the maximum (Tmax) and minimum (Tmin) temperatures and evaluated how different vegetation responded to daytime and nighttime warming in the Loess Plateau from 1982 to 2015 based on the NDVI and meteorological parameters (precipitation or temperature). We found that Tmax and Tmin significantly increased throughout the years except for Tmax in autumn, and the diurnal asymmetric warming showed some striking seasonal differences. For example, the increasing rates of Tmin in spring, summer, autumn, and winter were 0.75, 1.20, 1.88, and 1.10 times larger than that of Tmax, respectively. NDVI showed significantly positive correlation with Tmax and Tmin in spring and winter, while NDVI presented significantly positive correlation with Tmin in summer and Tmax in autumn across entire Loess Plateau. Furthermore, we also discovered diverse seasonal responses in terms of vegetation types to daytime and nighttime warming. For instance, Spring NDVI showed significantly positive partial correlations with Tmax and Tmin. In summer, grasslands and wetlands merely displayed significantly positive partial correlations with Tmin. Cultivated land presented significantly positive partial correlation between the NDVI and Tmax (Tmin) in autumn. In winter, cultivated land, forest, and grassland exhibited significantly positive partial correlation with Tmax and Tmin, while only wetland showed a significantly positive partial correlation with Tmax. Our results demonstrated responses of vegetation to climate extremes and enhance a better understanding of the seasonally different responses of vegetation under global climate change at different scale. [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]

Climate change is an urgent global issue, and the agricultural sector is one of the main contributors to greenhouse gas (GHG) emissions. In order to accurately analyze agricultural GHG emissions, this study constructed a dynamic computable general equilibrium model of Chinese agriculture. We aimed to examine the trends and structure of agricultural GHG emissions, and grain production in China. Results show that (i) China's agricultural CO 2 emissions will increase from 47.77 MtC in 2008 to 239.99 MtC in 2050. The input of agricultural materials is the primary source for this rise. (ii) Non-CO 2 GHG emissions contribute the most to agricultural GHG, which will reach 2214.55 MtC in 2050. (iii) The total agricultural GHG emissions in China will maintain an overall upward trend from 2008 to 2050, increasing from 220 MtC in 2008–2413.64 MtC in 2050 with an average annual growth rate of 5.87%. (iv) GHG emissions from grain production will reach 919.09 MtC by the year 2050. GHG emissions per grain production and emissions per area will increase to 343.81 kgC/t, and 4.46 tC/ha by the year of 2050, respectively. These findings carry several policy implications to China's agriculture low-carbon development, such as improve the agricultural materials utilization efficiency, optimize manure management and treatment, and promote organic planting technology. [ABSTRACT FROM AUTHOR]

Because canola is a major oilseed crop, accurately determining its planting areas is crucial for ensuring food security and achieving UN 2030 sustainable development goals. However, when canola is extracted using remote-sensing data, winter wheat causes serious interference because it has a similar growth cycle and spectral reflectance characteristics. This interference seriously limits the classification accuracy of canola, especially in mixed planting areas. Here, a novel canola flower index (CFI) is proposed based on the red, green, blue, and near-infrared bands of Sentinel-2 images to improve the accuracy of canola mapping, based on the finding that spectral reflectance of canola on the red and green bands is higher than that of winter wheat during the canola flowering period. To investigate the potential of the CFI for extracting canola, the IsoData, support vector machine (SVM), and random forest (RF) classification methods were used to extract canola based on Sentinel-2 raw images and CFI images. The results show that the average overall accuracy and kappa coefficient based on CFI images were 94.77% and 0.89, respectively, which were 1.05% and 0.02, respectively, higher than those of the Sentinel-2 raw images. Then we found that a threshold of 0.14 on the CFI image could accurately distinguish canola from non-canola vegetation, which provides a solution for automatic mapping of canola. The overall classification accuracy and kappa coefficient of this threshold method were 96.02% and 0.92, which were very similar to those of the SVM and RF methods. Moreover, the advantage of the threshold classification method is that it reduces the dependence on training samples and has good robustness and high classification efficiency. Overall, this study shows that CFI and Sentinel-2 images provide a solution for automatic and accurate canola extraction. [ABSTRACT FROM AUTHOR]

Sentinel-2 imagery is an unprecedented data source with high spatial, spectral and temporal resolution in addition to free access. The objective of this paper was to evaluate the potential of using Sentinel-2 data to map winter crops in the early growth stage. Analysis of three winter crop types—winter garlic, winter canola and winter wheat—was carried out in two agricultural regions of China. We analysed the spectral characteristics and vegetation index profiles of these crops in the early growth stage and other land cover types based on Sentinel-2 images. A decision tree classification model was built to distinguish the crops based on these data. The results demonstrate that winter garlic and winter wheat can be distinguished four months before harvest, while winter canola can be distinguished two months before harvest. The overall classification accuracy was 96.62% with a kappa coefficient of 0.95. Therefore, Sentinel-2 images can be used to accurately identify these winter crops in the early growth stage, making them an important data source in the field of agricultural remote sensing. [ABSTRACT FROM AUTHOR]

Estimates of regional net primary productivity (NPP) are very useful in modeling regional and global carbon cycles. This work proposed a new method to study NPP characteristics and changes in the Inner Mongolia Autonomous Region, China. To estimate NPP accurately, we integrated photosynthetically active radiation (PAR) with a light use efficiency model, derived from Moderate Resolution Imaging Spectroradiometer atmospheric and land products. Validation analyses showed that the PAR and NPP values simulated by the model matched observed data well. Annual NPP in the study area was about 0.25PgCa−1 from 2003 to 2008. In spatial distribution, NPP decreased from northeast to southwest in the Inner Mongolia Autonomous Region. NPP from May to September accounted for 84.2% of annual NPP, while that from July to August accounted for 44.3%. NPP was significantly correlated to both precipitation and temperature at monthly temporal scales. NPP also changed with the fraction of absorbed PAR. [Copyright &y& Elsevier]

Drought is one of the most complex and least-understood environmental disasters that can trigger environmental, societal, and economic problems. To accurately assess the drought conditions in the Yellow River Basin, this study reconstructed the Land Surface Temperature (LST) using the Annual Temperature Cycle (ATC) model and the Normalized Difference Vegetation Index (NDVI). The Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature-Vegetation Drought Index (TVDI), which are four typical remote sensing drought indices, were calculated. Then, the air temperature, precipitation, and soil moisture data were used to evaluate the applicability of each drought index to different land types. Finally, this study characterized the spatial and temporal patterns of drought in the Yellow River Basin from 2003 to 2019. The results show that: (1) Using the LST reconstructed by the ATC model to calculate the drought index can effectively improve the accuracy of drought monitoring. In most areas, the reconstructed TCI, VHI, and TVDI are more reliable for monitoring drought conditions than the unreconstructed VCI. (2) The four drought indices (TCI, VCI, VH, TVDI) represent the same temporal and spatial patterns throughout the study area. However, in some small areas, the temporal and spatial patterns represented by different drought indices are different. (3) In the Yellow River Basin, the drought level is highest in the northwest and lowest in the southwest and southeast. The dry conditions in the Yellow River Basin were stable from 2003 to 2019. The results in this paper provide a basis for better understanding and evaluating the drought conditions in the Yellow River Basin and can guide water resources management, agricultural production, and ecological protection of this area. [ABSTRACT FROM AUTHOR]

With the increasing population and continuation of climate change, an adequate food supply is vital to economic development and social stability. Winter crops are important crop types in China. Changes in winter crops planting areas not only have a direct impact on China's production and economy, but also potentially affects China's food security. Therefore, it is necessary to obtain information on the planting of winter crops. In this study, we use the time series data of individual pixels, calculate the temporal statistics of spectral bands and the vegetation indices of optical data based on the phenological characteristics of specific vegetation or crops and record them in the time series data, and apply decision trees and rule-based algorithms to generate annual maps of winter crops. First, we constructed a dataset combining all the available images from Landsat 7/8 and Sentinel-2A/B. Second, we generated an annual map of land cover types to obtain the cropland mask in 2019. Third, we generated a time series of a single cropland pixel, and calculated the phenological indicators for classification by extracting the differences in phenological characteristics of different crops: these phenological indicators include SOS (start of season), SDP (start date of peak), EOS (end of season), GUS (green-up speed) and GSL (growing-season length). Finally, we identified winter crops in 2019 based on their phenological characteristics. The main advantages of the phenology-based algorithm proposed in this study include: (1) Combining multiple sensor data to construct a high spatiotemporal resolution image collection. (2) By analyzing the whole growth season of winter crops, the planting area of winter crops can be extracted more accurately, and (3) the phenological indicators of different periods are extracted, which is conducive to monitoring winter crop planting information and seasonal dynamics. The results show that the algorithm constructed in this study can accurately extract the planting area of winter crops, with user, producer, overall accuracies and Kappa coefficients of 96.61%, 94.13%, 94.56% and 0.89, respectively, indicating that the phenology-based algorithm is reliable for large area crop classification. This research will provide a point of reference for crop area extraction and monitoring. [ABSTRACT FROM AUTHOR]

• Proposed a new assessment framework and expanded index system. • Developed a evaluation index classification standard. • Proposed a improved SI-MI model for assessing urban ecosystem health. • The reasons affecting the UEH levels of different cities were analyzed. • Recommendations were proposed for the development and management of UEH. With the rapid economic development and continuous growth of population in rapid urbanized area, the health status of urban ecosystem is undergoing profound changes, which will have a negative impact on the sustainable development. In order to systematically evaluate the health status of urban ecosystem in rapid urbanized area of central China, this paper puts forward a new assessment framework of structure-function-process-human health-development based on a deep understanding of the concept and general characteristics of urban ecosystem health, and establishes an expanded evaluation index system according to this evaluation framework. Then, on the basis of determining the index classification criteria in detail, the improved SI-MI model was used to evaluate the health status of urban ecosystem. As a case study, the framework and method were applied to the urban agglomeration in the middle reaches of the Yangtze river, a region of the rapid urbanized in China. The results showed that, the health level of most cities in the study area has been improved from 2005 to 2015. From the perspective of spatial pattern, the health level of the study area shows obvious spatial heterogeneity, and provincial capital cities and some eco-cities have better health status. It is worth noting that the function and development layer are the key parts that restrict the overall health level of urban ecosystem in the urban agglomeration in the middle reaches of the Yangtze River. After classifying all cities, we found the factors influencing the health level of different types of cities, and proposed the management suggestions for different types of cities. [ABSTRACT FROM AUTHOR]

As the land use issue, caused by urban shrinkage in China, is becoming more and more prominent, research on urban shrinkage and expansion has become particularly challenging and urgent. Based on the points of interest (POI) data, this paper redefines the scope, quantity, and area of natural cities by using threshold methods, which accurately identify the shrinkage and expansion of cities in the Yellow River affected area using night light data in 2013 and 2018. The results show that: (1) there are 3130 natural cities (48,118.75 km2) in the Yellow River affected area, including 604 shrinking cities (8407.50 km2) and 2165 expanding cities (32,972.75 km2). (2) The spatial distributions of shrinking and expanding cities are quite different. The shrinking cities are mainly located in the upper Yellow River affected area, except for the administrative cities of Lanzhou and Yinchuan; the expanding cities are mainly distributed in the middle and lower Yellow River affected area, and the administrative cities of Lanzhou and Yinchuan. (3) Shrinking and expanding cities are typically smaller cities. The research results provide a quick data supported approach for regional urban planning and land use management, for when regional and central governments formulate the outlines of urban development monitoring and regional planning. [ABSTRACT FROM AUTHOR]

Garlic and winter wheat are major economic and grain crops in China, and their boundaries have increased substantially in recent decades. Updated and accurate garlic and winter wheat maps are critical for assessing their impacts on society and the environment. Remote sensing imagery can be used to monitor spatial and temporal changes in croplands such as winter wheat and maize. However, to our knowledge, few studies are focusing on garlic area mapping. Here, we proposed a method for coupling active and passive satellite imagery for the identification of both garlic and winter wheat in Northern China. First, we used passive satellite imagery (Sentinel-2 and Landsat-8 images) to extract winter crops (garlic and winter wheat) with high accuracy. Second, we applied active satellite imagery (Sentinel-1 images) to distinguish garlic from winter wheat. Third, we generated a map of the garlic and winter wheat by coupling the above two classification results. For the evaluation of classification, the overall accuracy was 95.97%, with a kappa coefficient of 0.94 by eighteen validation quadrats (3 km by 3 km). The user's and producer's accuracies of garlic are 95.83% and 95.85%, respectively; and for the winter wheat, these two accuracies are 97.20% and 97.45%, respectively. This study provides a practical exploration of targeted crop identification in mixed planting areas using multisource remote sensing data. [ABSTRACT FROM AUTHOR]

In our review of the research on PGS (park green space) spatial equity, little consideration has been given to the impacts of small spatial scales and measurement accuracy on spatial equity assessments. Based on the web map API open data platform (including location and navigation data, etc.), this study established a fine-scaled evaluation framework of spatial equity from the aspects of sensitivity to measurement and the urban micro-perspective. The framework was integrated within the KD2SFCA (kernel density two-step floating catchment area) method, which planners use to accurately locate areas of under or over supply of urban public facilities. Taking Zhengzhou as a case study, we identified 2494 residential areas and 106 PGSs to describe the spatial equity of residents accessing different PGS levels by four travel modes. The results showed that there were significant differences in spatial equity between different PGS levels. Of the three PGS levels, the overall spatial equity for district PGS was the highest, community PGS was second, and municipal PGS was the lowest. We also found that different travel modes had important impacts on PGS spatial equity. One interesting phenomenon was that when residents chose to walk to a higher level PGS, the percentages of the population with "no supply" increased; when residents chose bicycle and private car modes to reach higher level PGS, the percentages of the population with "weak supply" and "over supply" increased. In addition, it is worth noting that community PGS was originally established to meet the needs of residents for short-distance entertainment. However, we found that almost all residents in the study area were in the extreme states of "no supply" or "over supply" when they reached community PGS by walking. These results provide a scientific basis for more reasonable and equitable allocation of urban PGS and the optimization of transportation facilities. This work can also provide a more fine-scaled research perspective and improve the scientific evaluation system for studying urban infrastructure spatial equity. [ABSTRACT FROM AUTHOR]

Accurately quantifying spatiotemporal changes in surface water is essential for water resources management, nevertheless, the dynamics of Poyang Lake surface water areas with high spatiotemporal resolution, as well as its responses to climate change, still face considerable uncertainties. Using the time series of Sentinel-1 images with 6- or 12-day intervals, the Sentinel-1 water index (SWI), and SWI-based water extraction model (SWIM) from 2015 to 2020 were used to document and study the short-term characteristics of southwest Poyang Lake surface water. The results showed that the overall accuracy of surface water area was satisfactory with an average of 91.92%, and the surface water area ranged from 129.06 km2 on 2 March 2017 to 1042.57 km2 on 17 July 2016, with significant intra- and inter-month variability. Within the 6-day interval, the maximum change of lake area was 233.42 km2 (i.e., increasing from 474.70 km2 up to 708.12 km2). We found that the correlation coefficient between the water area and the 45-day accumulated precipitation reached to 0.75 (p < 0.001). Moreover, a prediction model was built to predict the water area based on climate records. These results highlight the significance of high spatiotemporal resolution mapping for surface water in the erratic southwest Poyang Lake under a changing climate. The automated water extraction algorithm proposed in this study has potential applications in delineating surface water dynamics at broad geographic scales. [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]