Assessing Inequity in Green Space Exposure toward a "15-Minute City" in Zhengzhou, China: Using Deep Learning and Urban Big Data

Exploring the provision, efficiency and improvements of urban green spaces on accessibility at the metropolitan scale

Accurately assessing urban green space (UGS) accessibility and proposing effective and specific proposals for UGS provision improvement accordingly is vital to urban development. Taking the metropolitan of Shanghai, China as the study site, this study first assessed its UGS provision by improved multiple step floating catchment area methods and multiple indexes, including UGS accessibility, theoretical capacity, potential demand, and traffic supply. Second, it investigated the impacts on citywide UGS accessibility justice for each UGS by comparing Gini index differences of citywide UGS accessibility between the conditions when exist and non-exist for each UGS. Third, it used ternary plots to explore the influence mechanism of the factors of UGS theoretical capacity, potential demand, and traffic supply on accessibility, and introduced an RGB color triangle to spatially and simultaneously display the effects of these three factors on accessibility for each UGS in Shanghai. Fourth, it assessed and classified the UGS provision efficiency for accessibility according to the relationships among the theoretical capacity, potential demand, and traffic supply factors by 3D scatter plot. Fifth, it proposed specific types and priorities of requirement for UGS improvement according to its impact on citywide UGS accessibility justice and the effect of its theoretical capacity factor on UGS accessibility. The findings showed that UGS accessibility and its three factors in Shanghai were correlative and had a spatial clustering trend in central city areas. The majority of UGSs showed positive impact on citywide accessibility justice, which went up with the values of accessibility and the three factors. Most UGSs were dominated by theoretical capacity power. The UGS provision efficiency was relatively good for most UGSs, which had relatively well-matched conditions and demands. The improvement requirements for UGSs on accessibility investigated in this study will improve UGS provision.

How can environmental degradation and income disparities influence national health: an eye bird view on China's provinces

Growing socio-economic disparity is a global issue that could disturb community health. Numerous case studies have examined the health influences of income disparities as well as the patterns that implicate those disparities. Therefore, this study attempts to examine the core determinants of mortality rate, which are environmental degradation, green energy, health expenditures, and technology (ICT) for the 25 provinces of China over the period of 2005-2020. This study uses a series of estimators to investigate the preferred objectives in which CS-ARDL and common correlated effect mean group (CCE-MG). Estimated results show the significant contribution of environmental deterioration and income inequality to the mortality rate. Furthermore, health expenditures, ICT, and green energy significantly reduce the mortality rate. Similarly, the moderate effect of income inequality on health expenditure, green energy, and ICT significantly reduces the mortality rate in selected provinces of China. More interestingly, the current study suggests policy implications to reduce the rising trend of mortality rate.

Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms

Due to extreme weather phenomena, precipitation-induced flooding has become a frequent, widespread, and destructive natural disaster. Risk assessments of flooding have thus become a popular area of research. In this study, we studied the severe precipitation-induced flooding that occurred in Zhengzhou, Henan Province, China, in July 2021. We identified 16 basic indicators, and the random forest algorithm was used to determine the contribution of each indicator to the Zhengzhou flood. We then optimised the selected indicators and introduced the XGBoost algorithm to construct a risk index assessment model of precipitation-induced flooding. Our results identified four primary indicators for precipitation-induced flooding in the study area: total rainfall for three consecutive days, extreme daily rainfall, vegetation cover, and the river system. The Zhengzhou storm and flood risk evaluation model was constructed from 12 indicators: elevation, slope, water system index, extreme daily rainfall, total rainfall for three consecutive days, night-time light brightness, land-use type, proportion of arable land area, gross regional product, proportion of elderly population, vegetation cover, and medical rescue capacity. After streamlining the bottom four indicators in terms of contribution rate, it had the best performance, with an accuracy rate reaching 91.3%. Very high-risk and high-risk areas accounted for 11.46% and 27.50% of the total area of Zhengzhou, respectively, and their distribution was more significantly influenced by the extent of heavy rainfall, direction of river systems, and land types; the medium-risk area was the largest, accounting for 33.96% of the total area; the second-lowest-risk and low-risk areas together accounted for 27.09%. The areas with the highest risk of heavy rainfall and flooding in Zhengzhou were in the Erqi, Guanchenghui, Jinshui, Zhongyuan, and Huizi Districts and the western part of Xinmi City; these areas should be given priority attention during disaster monitoring and early warning and risk prevention and control.