Aim 1 (Neighborhood-Level Household Data): Create a big data apparatus, covering the United States, that models a wide range of household characteristics at the “neighborhood” level, by (i) using machine-learning to intelligently aggregate block groups and thus create robust, synthetic microdata samples to capture coherent geographic units, “neighborhoods”, usually smaller than zip code areas; and, (ii) fuse a wide range of databases to model household characteristics at the neighborhood level, including public surveys of nutrition, health, behavioral risk factors, transportation, energy use, time use, personal consumption, and private data on luxury consumption and air travel; many of these will be confidential geo-coded data sources. This big data apparatus will enable a wide range of studies, some linked to climate change, others not. Aim 2 (High Spatial Resolution Household Carbon Footprint [HFC] Database): (1) Create a HCF database of unprecedented spatial resolution by exploiting information on consumption from the neighborhood-level big data apparatus, and combining it with data on the carbon emissions intensity of the differing forms of consumption; and (2) create a public HFC Web Portal and Database. Aim 3 (Linking Emissions and Local Harms): To link disparate conversations about root causes of climate change and unequal exposure to environmental harms, we will overlay geo-specific HCF data with two sets of data indicating localized exposure to environmental harms, by (i) creating an index of climate vulnerability based on projected increases in heat and flood risk, (ii) a broader index of current vulnerability to local pollutants, reflecting the complex, lived connections between different forms of environmental degradation, identifying an “irony gap” that contributes to metrics of environmental injustice (iii) identify sites of compound exposure to relatively high energy costs (which carbon pricing would increase) and local environmental harms.
“Whole Community Climate Mapping”, a collective, interdisciplinary project to create, analyze, and share with the public a household carbon footprint database and climate vulnerability index for the United States of unprecedented spatial resolution, along with a wide range of other social, health, and environmental indicators—all at the neighborhood level. Greenhouse gas (GHG) emissions are the root cause of climate change, one of the gravest threats facing humans in the 21st century. But we still know little about the intersection of demographic and geographic drivers of GHG emissions, and their overlap with a range of climate, health, and social vulnerabilities. Our knowledge is especially weak at neighborhood level, where cutting edge big data approaches are promising. Existing carbon footprint studies work at high levels of aggregation—using national or state-level demographics, in rare cases modeling zip-code level data. Existing footprint data’s low spatial resolution limits scholars’ ability to decompose emissions drivers, jointly study GHG emissions (causes) and environmental harms (effects), identify compound exposure to environmental costs, and assess the climate impacts of socio-spatial processes like gentrification and suburban exclusion. We still have only the roughest understanding of how much income inequality shapes emissions, and how these same patterns shape unequal vulnerabilities to climate change and other health threats. Growing public discussions of a Green New Deal show just how much interest there is in holistic narratives about climate change and inequality. “Whole Community Climate Mapping” will provide the kind of missing data (and visual tools) that could support public and policy debates on how to tackle these problems in an intersectional fashion.
The motive for the carbon foot-printing project is that we know that the consumption of goods and services is a massive, albeit often indirect, driver of global climate change. Indeed, most of the GHG emissions resulting from energy production and land use can be traced, through the life-cycles of goods and services, to final consumption by individuals. Consumption-based carbon accounting follows carbon in a way that is consistent with how the 21st century economy actually works. Unlike territorial accounting, it better captures the intersection of carbon and social inequalities. And because consumption-based accounting can be spatialized—down to the neighborhood level—we can connect in a single project data about carbon with a wealth of social, health and environmental metrics. We expect our research to shed light on a wide range of questions, for instance: How precisely do density and income interact in shaping carbon footprints? Are there urban forms that significantly reduce GHG emissions net of income? Net of density? Are there particular forms of high-carbon consumption that it makes sense to target with local, regional, or national policy? How do footprints co-relate to localized perceptions of well-being, and with classic public health indicators?