Abstract
Despite numerous studies on multiple socio-economic factors influencing urban PM2.5 pollution in China, only a few comparable studies have focused on developed countries. We analyzed the impact of three major socio-economic factors (i.e., income per capita, population density, and population size of a city) on PM2.5 concentrations for 254 cities from six developed countries. We used the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model with three separate data sets covering the period of 2001 to 2013. Each data set of 254 cities were further categorized into five subgroups of cities ranked by variable levels of income, density, and population. The results from the multivariate panel regression revealed a wide variation of coefficients. The most consistent results came from the six income coefficients, all of which met the statistical test of significance. All income coefficients except one carried negative signs, supporting the applicability of the environmental Kuznet curve. In contrast, the five density coefficients produced statistically significant positive signs, supporting the results from previous studies. However, we discovered an interesting U-shaped distribution of density coefficients across the six subgroups of cities, which may be unique to developed countries with urban pollution. The results from the population coefficients were not conclusive, which is similar to the results of previous studies. Implications from the results of this study for urban and national policy makers are discussed.
Highlights
Heavy fine particulate (PM2.5 ) pollution has increased and become a high risk to public health in densely populated urban areas in many countries
The results show that economic development measured by city GDP per capita and population density are the two most influential factors analyzed by these five studies, followed by traffic intensity analyzed by four articles, and industrial structure and energy or electricity intensity examined by three articles
The impact of income measured by city GDP per capita on PM2.5 pollution for the full sample of 254 cities was highest, in that a 1% increase in income generated a −0.074 reduction of PM2.5 concentrations
Summary
Heavy fine particulate (PM2.5 ) pollution has increased and become a high risk to public health in densely populated urban areas in many countries. The results show that economic development measured by city GDP per capita and population density are the two most influential factors analyzed by these five studies, followed by traffic intensity analyzed by four articles, and industrial structure and energy or electricity intensity examined by three articles. Other factors such central heating, trade openness, and foreign direct investment were analyzed in one article each. Carozzi and Roth [13] found a positive and statistically significant population density coefficient of +0.13 for PM2.5 concentrations They found that doubling the density would increase the average PM2.5 pollution roughly 10% across 933 U.S cities. For cities in India and Africa, they discovered a U-shaped trend for PM2.5 concentrations as urban population increased
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