Research on the economic burden of air pollution has focused primarily on its macroeconomic impact. However, as some studies have found that air pollution can lead to avoidance behavior–for example, reducing the time spent outdoors–we hypothesize that it can also influence consumer spending activity. We combine high frequency data on ozone and fine particulate pollution with daily consumer spending in brick-and-mortar retail in 129 postal codes in Spain during 2014 to estimate the association between the two. Using a linear fixed effects model, we find that a 1-standard deviation increase in ozone concentration (20.97 μg/m3) is associated with 3.9 percent decrease in consumer spending (95% CI: -0.066, -0.012; p<0.01). The association of fine particulate matter with consumer spending is, however, not statistically significant (β: 0.005; 95% CI: -0.009, 0.018; p>0.10). Further, we do not observe a sufficiently strong bounce-back in consumer spending in the day–or even the week–following higher ozone concentration. Also, we find that the relationship between ozone concentration and consumer spending is heterogeneous, with those aged below 25 and those aged 45 or above exhibiting stronger negative association. This research informs policymakers about a plausibly unaccounted cost of ambient air pollution, even at concentrations lower than the WHO air quality guideline for short-term exposure.@en

Although cities have risen to prominence as climate actors, emissions’ data scarcity has been the primary challenge to evaluating their performance. Here we develop a scalable, replicable machine learning approach for evaluating the mitigation performance for nearly all local administrative areas in Europe from 2001-2018. By combining publicly available, spatially explicit environmental and socio-economic data with self-reported emissions data from European cities, we predict annual carbon dioxide emissions to explore trends in city-scale mitigation performance. We find that European cities participating in transnational climate initiatives have likely decreased emissions since 2001, with slightly more than half likely to have achieved their 2020 emissions reduction target. Cities who report emissions data are more likely to have achieved greater reductions than those who fail to report any data. Despite its limitations, our model provides a replicable, scalable starting point for understanding city-level climate emissions mitigation performance.@en

Cities are crucial climate change actors, developing largely voluntary action plans and emission reduction targets. There is limited evidence, however, of their impact through transnational climate initiatives and little empirical support linking mitigation strategies and emissions reductions. Here, we show that 60% of more than 1,000 EU Covenant of Mayors’ cities are on track to achieve their 2020 emission reduction targets. Assessments of cities’ mitigation outcomes and the determinants of performance show that on-track cities tend to have less-ambitious targets and higher baseline emissions and are in countries with more-ambitious national climate policies and greater realized emissions reductions than cities that are not on track. Automated textual analysis and a regression model find city emissions reduction is influenced by plan-level, city-level and country-level characteristics. Greater emissions reductions are associated with plans targeting energy efficiency. These results of city-level achievement provide empirical support for the theorized subnational actors’ contribution to global climate mitigation.

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The Urban Environment and Social Inclusion Index (UESI) creates a new spatial framework to measure progress toward Sustainable Development Goal 11 (SDG-11). SDG-11 aims for cities to be both sustainable and inclusive by 2030 and conceptualizes this goal in spatially-explicit ways. Few data sources or indices, however, measure its progress in both a comprehensive (global coverage) and detailed (intra-city) manner. To address this gap, we use publicly-available datasets including detailed census data, satellite remote sensing, and crowdsourced data that provide global coverage and regular temporal resolution to develop spatially-explicit indicators to measure neighborhood-level environmental performance in 164 global cities. The UESI framework includes 10 indicators that assess air pollution, urban tree cover, public transit access, and urban heat at the neighborhood scale, and water stress and carbon dioxide emissions from fossil fuels at the city-level. We also present a new method for quantifying distributional equity to measure how evenly or unevenly cities are distributing environmental benefits and burdens across neighborhoods. We find that the majority of the UESI cities disproportionately burden lower-income communities with higher shares of environmental burdens and lower shares of environmental benefits. This finding holds true even in cities that perform highly on environmental indicators. In light of the challenging, rapidly evolving urban contexts, the UESI framework serves as a way of addressing some of the central challenges—data standardization, data gathering, and data localization—around the SDGs.@en

Climate change has been called “the defining challenge of our age” and yet the global community lacks adequate information to understand whether actions to address it are succeeding or failing to mitigate it. The emergence of technologies such as earth observation (EO) and Internet-of-Things (IoT) promises to provide new advances in data collection for monitoring climate change mitigation, particularly where traditional means of data exploration and analysis, such as government-led statistical census efforts, are costly and time consuming. In this review article, we examine the extent to which digital data technologies, such as EO (e.g., remote sensing satellites, unmanned aerial vehicles or UAVs, generally from space) and IoT (e.g., smart meters, sensors, and actuators, generally from the ground) can address existing gaps that impede efforts to evaluate progress toward global climate change mitigation. We argue that there is underexplored potential for EO and IoT to advance large-scale data generation that can be translated to improve climate change data collection. Finally, we discuss how a system employing digital data collection technologies could leverage advances in distributed ledger technologies to address concerns of transparency, privacy, and data governance.@en