A large number of articles have documented that as population density of cities increases, car use declines and public transit use rises. These articles had a significant impact of promoting high-density compact urban development to mitigate traffic congestion. Another approach followed by other researchers used the urban scaling model to indicate that traffic congestion increases as population size of cities increases, thus generating a possible contradictory result. Therefore, this study examines the role of both density and population size on traffic congestion in 164 global cities by the use of Stochastic Impacts by Regression on Population, Affluence and Technology model. We divide 164 cities into the two subgroups of 66 low density cities and 98 high density cities for analysis. The findings from the subgroups analysis indicated a clear-cut difference on the critical role of density in low-density cities and the exclusive role of population size in high-density cities. Furthermore, using threshold regression model, 164 cities are divided into the two regions of large and small population cities to determine population scale advantage of traffic congestion. Our findings highlight the importance of including analysis of subgroups based on density and/or population size in future studies of traffic congestion.

Do larger cities still suffer from higher crime rates? The scaling relationship between the number of crimes and the population size for the maximum of 758 cities with more than 50,000 inhabitants in the United States from 1999 to 2014 was analyzed. For the total group of cities, the relationship is superlinear for both violent and property crimes. However, for the subgroups of the top 12, top 24, and top 50 largest cities, the relationship changes to sublinear for both violent and property crimes. Results from the panel data analysis are in support of these findings. Along with population size, income per capita and population density also influence the outcome of crime counts. Implications from these findings will be discussed.

Despite numerous studies on multiple socio-economic factors influencing urban PM_2.5 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 PM_2.5 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.

Many countries in the world have been experiencing widely varying rates of change in their carbon intensity (CI) of economic output. The dynamic trend of CI in this research is measured by the progress ratio (PR) from an experience curve (EC) involving 127 countries during the period of 1980–2011. The overall average PR of 88.8% estimated for the total group of 127 indicates a decreasing trend of carbon intensity. This means that each doubling of the cumulative CO₂ emission by this group has reduced carbon intensity by 11.2%. While a majority of 83 countries experienced a decreasing trend with an average PR of 73.1%, the remaining 44 countries have experienced an increasing trend with an average PR of 114.5%. When two different types of EC, classical and kinked, were applied, 73 countries displayed a kinked slope with an average PR of 73.4%, and 54 countries displayed a classical slope with an average PR of 104.2%. Examination of the type of trend and slope of EC suggests the chance of a major improvement of the future CI in the following order: (1) the 35 countries with a classical slope and an increasing trend of CIs; (2) the nine countries with a kinked slope and an increasing trend of CIs; (3) the 19 countries with a classical slope and a decreasing trend of CIs; and (4) the 64 countries with a kinked slope and a decreasing trend of CIs. Further implications from these findings are discussed.

Despite the fact that fine particulate matter (PM_2.5) causes serious health issues, few studies have investigated the level and annual rate of PM_2.5 change across a large number of countries. For a better understanding of the global trend of PM_2.5, this study classified 190 countries into groups showing different trends of PM_2.5 change during the 2000–2014 period by estimating the progress ratio (PR) from the experience curve (EC), with PM_2.5 exposure (PME)–the population-weighted average annual concentration of PM_2.5 to which a person is exposed—as the dependent variable and the cumulative energy consumption as the independent variable. The results showed a wide variation of PRs across countries: While the average PR for 190 countries was 96.5%, indicating only a moderate decreasing PME trend of 3.5% for each doubling of the cumulative energy consumption, a majority of 118 countries experienced a decreasing trend of PME with an average PR of 88.1%, and the remaining 72 countries displayed an increasing trend with an average PR of 110.4%. When two different types of EC, classical and kinked, were applied, the chances of possible improvement in the future PME could be suggested in the descending order as follows: (1) the 60 countries with an increasing classical slope; (2) the 12 countries with an increasing kinked slope; (3) the 75 countries with a decreasing classical slope; and (4) the 43 countries with a decreasing kinked slope. The reason is that both increasing classical and kinked slopes are more likely to be replaced by decreasing kinked slopes, while decreasing classical and kinked slopes are less likely to change in the future. Population size seems to play a role: A majority of 52%, or 38 out of the 72 countries with an increasing slope, had a population size of bigger than 10 million inhabitants. Many of these countries came from SSA, EAP, and LAC regions. By identifying different patterns of past trends based on the analysis of PME for individual countries, this study suggests a possible change of the future slope for different groups of countries.

Despite numerous studies on energy productivity and efficiency, only a few focus on the electricity intensity (EI) of economic output. As these studies largely examine the declining trend in EI, the increasing and/or fluctuating trends in EI have not been studied. We analyze EI trends by estimating the progress ratios from experience curves of 91 countries from 1991 to 2011. The results reveal wide variation in progress ratios, ranging from 53% to 135%, with an average of 101.5%. Furthermore, more than half of the 91 countries displayed a kinked slope, indicating the fluctuating rate of change in EI. The rate of population growth seems to be related to the increasing EI trends. A clear understanding of the relative performance of each country in terms of the progress ratio and the pattern of EI trends would be useful for the country’s policymakers to develop strategic options for the future.