Revisiting the impact of vehicle emissions and other contributors to air pollution in urban built-up areas: A dynamic spatial econometric analysis

Characterizing the distribution pattern of traffic-related air pollutants in near-road neighborhoods

In near-road neighborhoods, residents are more frequently exposed to traffic-related air pollution (TRAP), and they are increasingly aware of pollution levels. Given this consideration, this study adopted portable air pollutant sensors to conduct a mobile monitoring campaign in two near-road neighborhoods, one in an urban area and one in a suburban area of Shanghai, China. The campaign characterized spatiotemporal distributions of fine particulate matter (PM2.5) and black carbon (BC) to help identify appropriate mitigation measures in these near-road micro-environments. The study identified higher mean TRAP concentrations (up to 4.7-fold and 1.7-fold higher for PM2.5 and BC, respectively), lower spatial variability, and a stronger inter-pollutant correlation in winter compared to summer. The temporal variations of TRAP between peak hour and off-peak hour were also investigated. It was identified that district-level PM2.5 increments occurred from off-peak to peak hours, with BC concentrations attributed more to traffic emissions. In addition, the spatiotemporal distribution of TRAP inside neighborhoods revealed that PM2.5 concentrations presented great temporal variability but almost remained invariant in space, while the BC concentrations showed notable spatiotemporal variability. These findings provide valuable insights into the unique spatiotemporal distributions of TRAP in different near-road neighborhoods, highlighting the important role of hyperlocal monitoring in urban micro-environments to support tailored designing and implementing appropriate mitigation measures.

How far can air pollution affect tourism in China? Evidence from panel unconditional quantile regressions

Previous studies provide empirical evidence for the connection between air pollution and tourism. However, many of them take the nexus as a linear one. It remains unexplored whether any thresholds are required for the nexus to materialize. This study systematically investigates whether PM2.5 concentrations-an essential indicator of air pollution-affect tourism in China at various tourism development levels. We analyze 284 Chinese cities from 2008 to 2018 using the Unconditional Quantile Regression method. Our statistical results reveal that air pollution positively influences tourism (regarding tourist visits and tourism revenue) in areas with low tourism development levels. However, a complex correlation between air pollution and tourism emerges when tourism development has reached a certain level. The correlation is initially negative, then positive, and finally disappears. But, the overall correlation remains negative. The effects of the interaction between air pollution and tourism resources on tourism are inverted U-shaped, implying that tourism resources can mitigate the negative effects of air pollution on tourism only when tourism development has reached a certain level. Based on the above findings, the associated policy implications are discussed.

How does the opening of China's high-speed rail affect the spatial mismatch of haze pollution and economic growth?

A better reconciliation of haze pollution and economic growth has become the social consensus in China. The development of China's economy and air quality will be significantly impacted by its efforts to create high-speed rail (HSR). Based on panel data from 265 prefecture-level cities in China from 2003 to 2019, this paper investigates how the opening of HSR affects the spatial mismatch of haze pollution and economic growth by using the spatial mismatch index model, multi-period difference-in-differences (DID) model, and intermediary effect model. We find that the spatial mismatch in China has an overall decreasing trend. And its spatial agglomeration is dominated by low levels. Further empirical analysis shows that HSR opening can effectively restrain the spatial mismatch. Even after some robustness tests and endogenous treatment, the conclusion is still valid. In addition, population density, FDI, and industrial structure are also explicit factors affecting the spatial mismatch. Second, there is significant heterogeneity in the impact. This is reflected in the fact that HSR opening can suppress the spatial mismatch of service-oriented cities and the eastern region, while other cities and regions have no noticeable effect. Third, spatial transfer of haze pollution (STHP) and balanced development of economic growth (BEG) are two important conduction paths for the opening of HSR to affect the spatial mismatch. Specifically, HSR opening can constrain the spatial mismatch by inhibiting STHP and BEG. Based on the above findings, recommendations related to promoting a better harmony between haze pollution and economic growth are proposed.

Does the low-carbon city pilot policy work in China? A company-level analysis based on the PSM-DID model

To reduce carbon emissions and pursue sustainable economic development, China's central government formulated the low-carbon city pilot (LCCP) policy. Current studies focus primarily on the impact of the policy at the macro level (provinces and cities). So far, no study has looked at the impact of the LCCP policy on companies' environmental expenditures. Besides, as the LCCP policy is a weak-constraining central policy, it is interesting to see how it works at the company level. We employ company-level empirical data and the Propensity Score Matching - Difference in Differences (PSM-DID) method, which outperforms the traditional DID model in avoiding sample selection bias, to address the above issues. We concentrate on the second phase of the LCCP policy from 2010 to 2016, encompassing 197 listed companies in China's secondary and transportation industries. Our statistical results show that if the listed company's host city has piloted the LCCP policy, the company's environmental expenditures are reduced by 0.91 points at the 1% significance level. The above finding calls attention to the policy-implementation gap between the central and the local governments in China, which may make those weak-constraining central policies like the LCCP policy have purpose-defeating outcomes at the company level.

Driving forces and relationship between air pollution and economic growth based on EKC hypothesis and STIRPAT model: evidence from Henan Province, China

The aim of this research is to analyze the main influencing factors and relationship between atmospheric environment and economic society. Using the panel data of 18 cities in Henan Province from 2006 to 2020, this paper employed some advanced econometric estimation included entropy method, extended environmental Kuznets curve (EKC) and STIRPAT model to conduct empirical estimations. The results show that most regions in Henan Province have verified the existence of the EKC hypothesis; and the peak of air pollution level in all cities of Henan Province generally occurred in around 2014. Multiple linear Ridge regression indicated that the positive driving forces of air pollution in most cities in Henan Province are industrial structure and population size; the negative driving forces are urbanization level, technical level and greening degree. Finally, we used the grey GM (1, 1) model to predict the atmospheric environment of Henan Province in 2025, 2030, 2035 and 2040. What should pay close attention to is that air pollution levels in northeastern and central Henan Province will continue to remain high.

Spatial Effects of Urban Transport on Air Pollution in Metropolitan Municipalities of Mexico

The objective of this work was to estimate the local effects and spatial spillover effects of the number of vehicles, use of urban public transport, and population density on nitrogen oxide emissions for 405 metropolitan municipalities in Mexico in 2016. To this end, a Spatial Durbin Model was estimated. We found positive direct effects of the number of vehicles and population density and negative direct effects of the use of urban public transport. The number of vehicles in circulation had negative spillover effects on the nitrogen oxide emissions of neighboring municipalities. These results indicate that the design of public policy programs aimed at reducing air pollution in Mexico should be based on coordination across metropolitan municipalities.

Does industrial agglomeration affect the regional environment? Evidence from Chinese cities

This study examines the impact of industrial agglomeration on the regional environment using panel data of 13 prefecture-level cities in the Beijing-Tianjin-Hebei region from 1998 to 2018. The empirical results show a strong spatial autocorrelation and a strong spatial lag and spatial error effect on the environmental pollution level in the Beijing-Tianjin-Hebei region. The results further show that the manufacturing agglomeration significantly aggravates the regional pollution; service agglomeration significantly alleviates the regional environmental pollution; the synergistic agglomeration of manufacturing and service industry has a significant inverted "U" type relationship on the regional environmental pollution. More specifically, the development of synergistic agglomeration intensifies regional environmental pollution at the early stage, and after reaching the inflection point, synergistic agglomeration alleviates regional environmental pollution. The inflection point occurs when the level of synergistic agglomeration is around 2.85. At the same time, there is no significant spillover effect of synergistic agglomeration on neighboring regions after calculating the feedback effect. Study findings provide insights for promoting the strategy of "two-wheel-drive development" and alleviating the contradictory relationship between industrial development and environmental pollution.

Development of season-dependent land use regression models to estimate BC and PM1 exposure

Reliable estimation of exposure to black carbon (BC) and sub-micrometer particles (PM₁) within a city is challenging because of limited monitoring data as well as the lack of models suitable for assessing the intra-urban environment. In this study, to estimate exposure levels in the inner-city area, we developed land use regression (LUR) models for BC and PM₁ based on specially designed mobile monitoring surveys conducted in 2019 and 2020 for three seasons. The daytime and nighttime LUR models were developed separately to capture additional details on the variation in pollutants. The results of mobile monitoring indicated similar temporal variation characteristics of BC and PM₁. The mean concentrations of pollutants were higher in winter (BC: 4.72 μg/m³; PM₁: 56.97 μg/m³) than in fall (BC: 3.74 μg/m³; PM₁: 33.29 μg/m³) and summer (BC: 2.77 μg/m³; PM₁: 27.04 μg/m³). For both BC and PM₁, higher nighttime concentrations were found in winter and fall, whereas higher daytime concentrations were observed in the summer. A supervised forward stepwise regression method was used to select the predictors for the LUR models. The adjusted R² of the LUR models for BC and PM₁ ranged from 0.39 to 0.66 and 0.45 to 0.80, respectively. Traffic-related predictors were incorporated into all the models for BC. In contrast, more meteorology-related predictors were incorporated into the PM₁ models. The concentration surface based on the LUR models was mapped at a spatial resolution of 100 m, and significant seasonal and diurnal trends were observed. PM₁ was dominated by seasonal variations, whereas BC showed more spatial variation. In conclusion, the development of season-dependent diurnal LUR models based on mobile monitoring could provide a methodology for the estimation of exposure and screening of influencing factors of BC and PM₁ in typical inner-city environments, and support pollution management.

Extraction of urban built-up area based on the fusion of night-time light data and point of interest data

The accurate extraction of urban built-up areas is an important prerequisite for urban planning and construction. As a kind of data that can represent urban spatial form, night-time light data has been widely used in the extraction of urban built-up areas. As one of the geographic open-source big data, point of interest (POI) data has a high spatial coupling with night-time light data, so researchers are beginning to explore the fusion of the two data in order to achieve more accurate extraction of urban built-up areas. However, the current research methods and theoretical applications of the fusion of POI data and night-time light data are still insufficient compared with the dramatically changing urban built-up areas, which needed to be further supplemented and deepened. This study proposes a new method to fuse POI data and night-time light data. The results before and after data fusion are compared, and the accuracy of urban built-up area extracted by different data and methods is analysed. The results show that the data fusion can avoid the shortage of single data and effectively improve the extraction accuracy of urban built-up areas, which is greatly helpful to supplement the study of data fusion in urban built-up areas, and also can provide decision-making guidance for urban planning and construction.

Open and Consistent Geospatial Data on Population Density, Built-Up and Settlements to Analyse Human Presence, Societal Impact and Sustainability: A Review of GHSL Applications

This review analyses peer-reviewed scientific publications and policy documents that use built-up density, population density and settlement typology spatial grids from the Global Human Settlement Layer (GHSL) project to quantify human presence and processes for sustainability. Such open and free grids provide detailed time series spanning 1975–2015 developed with consistent approaches. Improving our knowledge of cities and settlements by measuring their size extent, as well as the societal processes occurring within settlements, is key to understanding their impact on the local, regional and global environment for addressing global sustainability and the integrity of planet Earth. The reviewed papers are grouped around five main topics: Quantifying human presence; assessing settlement growth over time; estimating societal impact, assessing natural hazard risk and impact, and generating indicators for international framework agreements and policy documents. This review calls for continuing to refine and expand the work on societal variables that, when combined with essential variables including those for climate, biodiversity and ocean, can improve our understanding of the societal impact on the biosphere and help to monitor progress towards local, regional and planetary sustainability.

How Does Local Real Estate Investment Influence Neighborhood PM2.5 Concentrations? A Spatial Econometric Analysis

Real estate investment has been an important driving force in China’s economic growth in recent years, and the relationship between real estate investment and PM2.5 concentrations has been attracting widespread attention. Based on spatial econometric modelling, this paper explores the relationships between real estate investment and PM2.5 concentrations using multi-source panel data from 30 provinces in China between 1987 and 2017. The results demonstrate that compared with static spatial panel modelling, using a dynamic spatial Durbin lag model (DSDLM) more accurately reflects the influences of real estate investment on PM2.5 concentrations in China, and that PM2.5 concentrations show significant superposition effects and spillover effects. Moreover, there is an inverted U-shaped relationship between real estate investment and PM2.5 concentrations in the Eastern and Central Regions of China. At the national level, the impacts of real estate investment on land urbanization and PM2.5 concentrations first increased and then decreased over time. The key implications of this analysis are as follows. (1) it highlights the need for a unified PM2.5 monitoring platform among Chinese regions; (2) the quality of population urbanization rather than land urbanization should be given more attention; and (3) the speed of construction of green cities and building of green transportation systems and green town systems should be increased.

Evaluating the Inequality of Medical Service Accessibility Using Smart Card Data

The measurement of medical service accessibility is typically based on driving or Euclidean distance. However, in most non-emergency cases, public transport is the travel mode used by the public to access medical services. Yet, there has been little evaluation of the public transport system-based inequality of medical service accessibility. This work uses massive real smart card data (SCD) and an improved potential model to estimate the public transport-based medical service accessibility in Beijing, China. These real SCD data are used to calculate travel costs in terms of time and distance, and medical service accessibility is estimated using an improved potential model. The spatiotemporal variations and patterns of medical service accessibility are explored, and the results show that it is unevenly spatiotemporally distributed across the study area. For example, medical service accessibility in urban areas is higher than that in suburban areas, accessibility during peak periods is higher than that during off-peak periods, and accessibility on weekends is generally higher than that on weekdays. To explore the association of medical service accessibility with socio-economic factors, the relationship between accessibility and house price is investigated via a spatial econometric analysis. The results show that, at a global level, house price is positively correlated with medical service accessibility. In particular, the medical service accessibility of a higher-priced spatial housing unit is lower than that of its neighboring spatial units, owing to the positive spatial spillover effect of house price. This work sheds new light on the inequality of medical service accessibility from the perspective of public transport, which may benefit urban policymakers and planners.