A new multi-criteria decision making approach based on wins in league to avoid rank reversal: A case study on prioritizing environmental deterioration strategies in arid urban areas

In this study, based on the multi-source nature and humanities data of 270 Chinese cities from 2007 to2018, the spatio-temporal evolution characteristics of SO2 emissions are revealed by using Moran’s I, a hot spot analysis, kernel density, and standard deviation ellipse models. The spatial scale heterogeneity of influencing factors is explored by using the multiscale geographically weighted regression model to make the regression results more accurate and reliable. The results show that (1) SO2 emissions showed spatial clustering characteristics during the study period, decreased by 85.12% through pollution governance, and exhibited spatial heterogeneity of differentiation. (2) The spatial distribution direction of SO2 emissions’ standard deviation ellipse in cities was “northeast–southwest”. The gravity center of the SO2 emissions shifted to the northeast, from Zhumadian City to Zhoukou City in Henan Province. The results of hot spots showed a polarization trend of “clustering hot spots in the north and dispersing cold spots in the south”. (3) The MGWR model is more accurate than the OLS and classical GWR regressions. The different spatial bandwidths have a different effect on the identification of influencing factors. There were several main influencing factors on urban SO2 emissions: the regional innovation and entrepreneurship level, government intervention, and urban precipitation; important factors: population intensity, financial development, and foreign direct investment; secondary factors: industrial structure upgrading and road construction. Based on the above conclusions, this paper explores the spatial heterogeneity of urban SO2 emissions and their influencing factors, and provides empirical evidence and reference for the precise management of SO2 emission reduction in “one city, one policy”.

Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO2 Emissions in Chinese Cities: Fresh Evidence from MGWR

Research and application of an evolutionary deep learning model based on improved grey wolf optimization algorithm and DBN-ELM for AQI prediction

Quantitative simulation and verification of urbanization and eco-environment coupling coil in Beijing-Tianjin-Hebei urban agglomeration, China

With the development of the economy and the accumulation of social wealth, urban residents have begun to give more attention to quality of life than to material needs. Consequently, environmental factors that affect human health, such as air quality, have become a new focus when traveling. A travel scheme with relatively low pollutant exposure to travelers can not only improve their health and satisfy their goals but also benefit social stability and sustained progress. However, low spatiotemporal resolution and coarse spatial details of the distribution of PM2.5 (particles with an aerodynamic diameter of 2.5 μm or less) educe the success rate of short-distance healthy travel route planning. This paper proposes a short-distance healthy route planning approach that is based on PM2.5 retrieval with high spatiotemporal resolution and a dynamic Dijkstra algorithm. First, fine spatial resolution images, meteorological data, and socioeconomic data are used to retrieve the spatial distribution of PM2.5 concentration in hourly intervals via a back-propagation neural network (BPNN). Second, a PM2.5 concentration value is obtained for each road section, and the harm degree to the human body is calculated as the weight of each road section. Then, the healthiest route is obtained based on the Dijkstra algorithm. Finally, the route planning effectiveness is verified by comparing the PM2.5 potential dose descending rate between the healthy route and the shortest route. The results show that the coefficient of determination (R2) of the PM2.5 retrieval approach that is based on multisource data and BPNN is 0.85, which can ensure the accuracy of the PM2.5 data at the street level. On this basis, the potential dose reduction rate of the healthy route can reach up to 20%, which proves that our approach can perform well. It can effectively improve the safety of travel and alleviate the anxiety that is caused by air pollution. In addition, it provides an easy implementation strategy for software for health management. • A low-cost short-distance healthy route planning method can meet the needs of people for short-distance healthy travel. • Healthy routes updating in real time can help people avoid air pollution and maximally protect their health. • This paper explores factors that influence the accuracy of the short-distance healthy route. 

A short-distance healthy route planning approach

Linking urbanization and air quality together: A review and a perspective on the future sustainable urban development

PM2.5 pollution has emerged as a global human health risk. The best measure of its impact is a population’s PM2.5 exposure (PPM2.5E), an index that simultaneously considers PM2.5 concentrations and population spatial density. The spatiotemporal variation of PPM2.5E over the Beijing-Tianjin-Hebei (BTH) region, which is the national capital region of China, was investigated using a Bayesian space-time model, and the influence patterns of the anthropic and geographical factors were identified using the GeoDetector model and Pearson correlation analysis. The spatial pattern of PPM2.5E maintained a stable structure over the BTH region’s distinct terrain, which has been described as “high in the northwest, low in the southeast”. The spatial difference of PPM2.5E intensified annually. An overall increase of 6.192 (95% CI 6.186, 6.203) ×103 μg/m3 ∙ persons/km2 per year occurred over the BTH region from 1998 to 2017. The evolution of PPM2.5E in the region can be described as “high value, high increase” and “low value, low increase”, since human activities related to gross domestic product (GDP) and energy consumption (EC) were the main factors in its occurrence. GDP had the strongest explanatory power of 76% (P < 0.01), followed by EC and elevation (EL), which accounted for 61% (P < 0.01) and 40% (P < 0.01), respectively. There were four factors, proportion of secondary industry (PSI), normalized differential vegetation index (NDVI), relief amplitude (RA), and EL, associated negatively with PPM2.5E and four factors, GDP, EC, annual precipitation (AP), and annual average temperature (AAT), associated positively with PPM2.5E. Remarkably, the interaction of GDP and NDVI, which was 90%, had the greatest explanatory power for PPM2.5E ′ s diffusion and impact on the BTH region.

Spatiotemporal variation and determinants of population's PM2.5 exposure risk in China, 1998-2017: a case study of the Beijing-Tianjin-Hebei region.

Green development is necessary for China to carry out high-quality economic development. As an important institutional arrangement in the vertical government structure, fiscal decentralization supports regional green development. Local government environmental preferences indicate local environmental protection awareness and affect the process of regional green development to a certain extent. Based on the review of relevant theories and literature, this study conducts an empirical analysis based on Chinese provincial panel data from 2007 to 2019 using a two-way fixed effects model and a panel threshold model. Both revenue decentralization and expenditure decentralization have a U-shaped nonlinear relationship with the green development efficiency calculated by the superefficiency SBM model, which includes undesirable output. Neither factor has a positive effect on green development efficiency at this stage, while local government environmental preferences are positively connected to green development efficiency. Furthermore, a moderating effect is observed in the relationship between fiscal decentralization and green development efficiency. Under the same level of revenue decentralization or expenditure decentralization, the stronger the environmental preferences of the local authority are, the higher the green development efficiency. This moderating effect is more significant in coastal areas than inland areas. Further research reveals a threshold for the moderating effect of local government environmental preferences on fiscal decentralization on green development. When the environmental preferences of local government are below the threshold, both revenue decentralization and expenditure decentralization significantly inhibit the improvement in green development efficiency. After the threshold is passed, the negative effects of both on green development efficiency are markedly curtailed. Then, the government performance appraisal system should be further optimized, fiscal decentralization reform should be strengthened, local financial investment in environmental protection should be expanded, and synergistic regional development should be promoted. China’s green development should be promoted to an advanced stage.

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A Study on the Impact of Fiscal Decentralization on Green Development from the Perspective of Government Environmental Preferences

The economic linkages between cities within the Yangtze River Economic Belt city cluster are important for precisely implementing the Yangtze River Economic Belt strategy. Accordingly, this study used the revised gravity model, social network method, and spatial Durbin model to analyze the economic linkage strength, spatial-temporal differentiation, and driving factors of the three major city cluster in the Yangtze River Economic Belt from 2006–2020. The results confirm that the Chengdu-Chongqing urban agglomeration has formed a network structure with Chengdu and Chongqing as the two poles; the urban agglomeration in the middle reaches of the Yangtze River has formed a point-axis development pattern of Wuhan, Changsha and Nanchang. The Yangtze River Delta urban agglomeration formed a multi-center radiation and sub-network development pattern. Additionally, the characteristics of network structure significantly affected the flow of factors, the level of economic development was the basis for strengthening communication and cooperation between cities. Meanwhile, the advanced industrial structures played an important role in promoting industrial linkages, and human capital exerts an obvious spatial spillover effect. Therefore, these three major urban agglomerations should implement differentiated development strategies and enhance the synergy of their linkage networks, thus promoting high-quality development.

Xiaoyan Zhang

Papers

Linking urbanization and air quality together: A review and a perspective on the future sustainable urban development

Spatiotemporal variation and determinants of population's PM2.5 exposure risk in China, 1998-2017: a case study of the Beijing-Tianjin-Hebei region.

A Study on the Impact of Fiscal Decentralization on Green Development from the Perspective of Government Environmental Preferences

Understanding the structure and determinants of economic linkage network: The case of three major city clusters in Yangtze River Economic belt