Main takeaways for the 2020 Urban Environment and Social Inclusion Index analysis.
1.Cities are not sharing environmental benefits and burdens equally. The majority of cities (95 out of 162) are burdening lower-income populations with poor air quality, exposure to urban heat, and lack of access to tree cover and public transport. While many cities perform well or above average on the UESI indicators, most cities are failing to achieve these environmental results in an equitable way, disproportionately burdening poorer populations. Most UESI cities are located in the left-hand quadrants of Figure 1, which indicates that environmental burdens are on average concentrated on less wealthy neighborhoods within these cities. This pattern suggests that performance and equity are not necessarily concurrent – better performance does not involve a more equitable environment and vice versa – highlighting the need for cities and local governments to actively address issues of distributional equity as part of their environmental and development interventions.
Figure 1. A four-quadrant plot examining relationship between environmental performance (in terms of average z-score, indicating the distance from the mean for a city’s performance on the UESI indicators) and equity (in terms of average concentration index, see the Equity and Social Inclusion issue profile for more details). Cities towards the top of the chart perform better on environmental issues, while cities towards the bottom of the chart perform more poorly. Along the horizontal axis, the farther away cities are from the center of the figure, the more unequally environmental burdens are distributed. In cities towards the right-hand side of the figure, wealthier neighborhoods are more heavily burdened, while in cities towards the left-hand side of the figure, poorer neighborhoods are more heavily burdened.
2.Cities’ environmental performance varies widely, but there is room for improvement within each city. Many are already meeting the UN-Habitat’s recommended level of tree cover per person, while no city achieves a perfect score on climate change or air quality. Figure 2 displays the top and bottom performers for different environmental indicators. On some issues, there’s a large range in terms of how cities perform. On public transport, for instance, Tokyo, Porto, and Paris lead the way, while U.S cities – including Bridgeport, Connecticut; Houston, Texas; Billings, Montana; and Wichita, Kansas – account for a large share of the 10 bottom-scoring cities. In terms of air quality, several cities in the South Pacific (Hobart, Toowoomba, and Cairns in Australia, and Wellington in New Zealand) are top performers; while developing country cities such as New Delhi and several cities in China (Tianjin, Zhenjiang, and Shanghai) dominate the bottom rung. North American cities tend to perform well on tree cover, as seen in the Canadian cities of Toronto and Vancouver and the US cities of Milwaukee, Wisconsin, and Portland, Oregon, while cities in developing countries, such as Tunis, Phnom Penh, Vientiane, and Casablanca, all of which have experienced rapid growth over the last several decades, have some of the lowest scores.
Figure 2. Top and bottom performers for four of the UESI issue categories based on average indicator scores.
3. Wealthier cities tend to have higher than average environmental performance compared to lower-income cities. When comparing the z-scores of neighborhood income with z-scores of average performance on UESI indicators, disparities between environmental outcomes and income are apparent. Many North American and European cities are found in the upper right-hand quadrant of Figure 3, indicating a better-than-average performance on UESI indicators and higher-than-average levels of income. Tel Aviv and Alexandria stand out as the only cities included in this upper right-hand quadrant that are not located in North America or Europe. The lower left-hand quadrant, where both UESI scores and income levels are on average lower, contains many cities in developing countries, such as Kinshasa, Lima, Beijing, Bangkok, Jakarta, and Buenos Aires, with a particularly strong representation of Asian cities. This plot gives further evidence on the positive relation between income levels and environmental performance, although there are some cities in Africa, such as Freetown and Bamako, that perform better than their economic development levels would suggest.
Figure 3. A four-quadrant plot examining the relationship between environmental performance (in terms of the z-score, or distance from the mean, for a neighborhood’s average performance on the UESI indicators) and a z-score of logged income across all city neighborhoods.
4. Air pollution is one of the most dangerous environmental threats to human health in cities. 86 percent of people living in the UESI’s cities, compared to 90 percent globally, are breathing unsafe air that does not meet the World Health Organization’s guideline (10 micrograms per cubic meter) for safe exposure to fine particulate pollution. Generated through combustion, often from coal or car emissions, fine particulate matter (PM 2.5) is small enough to lodge deep in human lung and blood tissue, contributing to health risks ranging from lung disease to stroke. 57 cities, including Oslo, Vancouver, Wellington, Anchorage, and Portland, demonstrate that better performance is possible: 100 percent of these cities’ neighborhoods have PM2.5 levels that fall within the World Health Organization standards.
Figure 4. Global map of PM2.5 exceedances according to the World Health Organization (WHO) targets. (Data Source: 2019 State of the Global Air Report).1Health Effects Institute. (2019). State of Global Air 2019. Special Report. Boston, MA:Health Effects Institute.
5. Nearly a quarter of UESI cities are water stressed. Twenty percent of the UESI cities with available data rely on water-stressed surface or groundwater sources. The cities with the most stressed water supply include the US city of Charlotte, North Carolina; the Indian cities of Kolkata and Chennai; Fortaleza, Brazil; Santiago, Chile; and Tel Aviv, Israel, each of which relies on water sources where almost the entire available water supply is withdrawn each year for urban, agricultural, and industry use. Urban water demand is expected to increase 80 percent by 2050,2Flörke, M., Schneider, C., & McDonald, R. I. (2018). Water competition between cities and agriculture driven by climate change and urban growth. Nature Sustainability, 1(1), 51-58. while climate change alters the timing and distribution of water availability, potentially putting far more cities’ water supply at risk.
Figure 5. The stress level of city’s main surface water or groundwater sources. (Data source: data on the distribution of urban water sources and surface water stress comes from The Nature Conservancy’s City Water Map (McDonald & TNC, 2016), utilizing the Water Gap Model (Alcamo et al., 2003); data for groundwater stress comes from McDonald et al. (2014)).3McDonald, R. I., Weber, K., Padowski, J., Flörke, M., Schneider, C., Green, P. A., … & Boucher, T. (2014). Water on an urban planet: Urbanization and the reach of urban water infrastructure. Global Environmental Change, 27, 96-105.4Alcamo, J., et al., Development and testing of the WaterGAP 2 global model of water use and availability. Hydrological Sciences Journal, 2003. 48(3): p. 317-338.5McDonald, R.I. and D. Shemie. (2014). Urban Water Blueprint: Mapping conservation solutions to the global water challenge. The Nature Conservancy: Washington, D.C.
6. Most cities are placing the burden of urban heat on the poor. The urban heat island effect – the temperature difference between an urban area and the surrounding rural area – is exacerbated in cities with lower tree cover or more built infrastructure. Adding vegetation to neighborhoods can help offset urban heat, providing shade and evaporative cooling. The construction of built-up structures makes urban heat more intense by storing and trapping heat, and replacing vegetation that could otherwise help keep urban areas cool. There are some cities that seem to be exceptions to this rule, including Tokyo, Boston, Casablanca, and Copenhagen, although they tend to be coastal cities where neighborhoods nearer to the water have a lower UHI, regardless of the vegetation cover of the neighborhood. Still, these exceptions suggest that it is possible for cities to adopt strategies to reduce urban heat, although it is critical for cities to consider their own specific contexts as they design these measures.
Figure 6. Most cities are placing the burden of the Urban Heat Island Effect on the poor. The color of the dots indicates how unevenly the urban heat island effect is distributed amongst neighborhoods within a city: darker circles indicate greater inequality amongst neighborhoods. The more negative values (shown in red) indicate a greater urban heat burden is placed on poorer city neighborhoods, while the more positive values (shown in gray) indicate that wealthier city neighborhoods bear a disproportionate share of a city’s urban heat.
Figure 7. Relationship between daytime Urban Heat Island (UHI) and surface characteristics (e.g., greenness (NDVI), built environment (NDBI), and surface reflectance (albedo)) within the UESI cities.
7.The UESI cities lost a total of 3,348.5 square kilometers of urban tree cover from 2001 to 2016 – an area more than four times the size of New York City. The cities that have experienced the greatest loss in urban tree cover from 2001 to 2016 (using a 2000 baseline) include Vientiane, Coimbra, Fortaleza, and Bengaluru. Figure 8 demonstrates how these cities experienced urban tree cover loss at different rates over this time period. For instance, Manauś and Houston lost tree cover most intensely in the early 2000’s, while Vientiane and Bangalore’s urban tree cover loss occurred more recently. These changes reflect different cities’ dynamics and growth over time. Vegetated space tends to be removed to make space for new developments and city infrastructure, especially in developing countries where cities are still increasing their footprint and building infrastructure as their populations increase.
Figure 8. The 20 UESI cities with the highest proportion of tree loss from 2000 to 2016. Lighter shades indicate that a greater proportion of tree loss occurred more recently, while darker shades indicate earlier tree loss, relative to a year 2000 baseline.
8.Less than half of UESI cities (72 cities) have average access to public transit within walking distance (1.2 kilometers or 0.75 miles, the distance an average city resident is willing to walk to a metro stop). Cities like Athens, Barcelona, Lisbon, San Francisco, or Paris, have public transportation stations that require a walking distance of less than 200 meters, while cities such as Chongqing, Dalian, Maputo or Houston require residents to walk an average distance of 5 kilometers (just over 3 miles) to reach a public transit station. Just 35 cities have public transit station accessible within 1.2 kilometers or less, no matter what neighborhood a resident is in; in most other cities, access to public transit can vary dramatically across different parts of the city.
Figure 9. Mean distance to transit stops among the 30 UESI cities with the highest average distance to public transportation.