Do US metropolitan core counties have lower scope 1 and 2 CO ₂ emissions than less urbanized counties?

Abstract

Recent sustainability research has focused on urban systems given their high share of environmental impacts and potential for centralized impact mitigation. Recent research emphasizes descriptive statistics from place-based case studies to argue for policy action. This limits the potential for general insights and decision support. Here, we implement generalized linear and multiple linear regression analyses to obtain more robust insights on the relationship between urbanization and greenhouse gas (GHG) emissions in the US We used consistently derived county-level scope 1 and scope 2 GHG inventories for our response variable while predictor variables included dummy-coded variables for county geographic type (central, outlying, and nonmetropolitan), median household income, population density, and climate indices (heating degree days (HDD) and cooling degree days (CDD)). We find that there is not enough statistical evidence indicating per capita scope 1 and 2 emissions differ by geographic type, ceteris paribus. These results are robust for different assumed electricity emissions factors. We do find statistically significant differences in per capita emissions by sector for different county types, with transportation and residential emissions highest in nonmetropolitan (rural) counties, transportation emissions lowest in central counties, and commercial sector emissions highest in central counties. These results indicate the importance of regional land use and transportation dynamics when planning local emissions mitigation measures.

The implications of scope and boundary choice on the establishment and success of metropolitan greenhouse gas reduction targets in the United States

“…For reference, US 2016 per capita emissions were roughly 20 metric tons CO 2 e per person (US Census Bureau, 2018b, US Environmental Protection Agency EPA 2018b). The average per capita emissions we found are comparable to the average Scope 1 per capita emissions that Tamayao et al (2014) found for central and outlying counties across US MSAs. Thus, from the perspective of production-based emissions, there appears to be a relationship between the centrality of a county within an MSA and its per capita GHG emissions.…”

“…The second part of our analysis focuses on the implications of cities confining their inventories and planning to the city limits. Expanding on the central, outlying, and rural levels of aggregation employed by Tamayao et al (2014), we establish three different geographic categories for evaluating a given metropolitan area: (1) the urban 'core' county/counties, (2) the 'central' counties surrounding the urban core, and (3) the 'outlying' counties of the MSA. For our analysis, we classify the urban 'core' as the county (or counties) that house(s) the primary city of an MSA.…”

“…However, examples of regional GHG inventories and policies have emerged from the San Francisco Bay Area Air Quality Management District (BAAQMD) and the Sacramento Area Council of Governments (SACOG), and may serve as an indication of a more wide-spread shift toward a regional focus moving forward (BAAQMD 2015, SACOG 2015. The implications of geographic boundary choices on metropolitan-level emissions assessment and planning are highlighted by the fact that central (urban) counties appear to have lower per capita transportation emissions and higher per capita commercial sector emissions compared to nonmetropolitan (rural) counties (Tamayao et al 2014). The expansion to regional emission planning and management is further supported by the fact that a majority of communities within metropolitan areas do not have an established GHG reduction target.…”

“…Thus, from the perspective of production-based emissions, there appears to be a relationship between the centrality of a county within an MSA and its per capita GHG emissions. However, the fact that Tamayao et al (2014) found that scope 1 and 2 emissions do not statistically significantly differ by geographic type, further highlights the importance of clearly and consistently stating scope and boundary choices when estimating local-level GHG emissions (e.g. Scope 1 versus Scope 1+Scope 2+Scope 3; production-based estimates versus consumptionbased estimates).…”

An integrated approach for estimating greenhouse gas emissions from 100 U.S. metropolitan areas

“…However, under a productionbased approach, a majority of the electricity sector emissions for that metropolitan area would be attributed to outlying/rural counties in Illinois, Indiana, and Michigan (where the electricity generation facilities are likely located) (see for example Weber et al 2010). A regression-based approach to relate the amount of electricity consumed in a county to the amount of electricity produced in a county has also exhibited a fairly large range of uncertainty (Tamayao et al 2014). By comparing our emission estimates to self-reported estimates produced by cities, our analysis allows for additional comparison between consumption-based and production-based estimates and places any disparities in the context of the overall emissions profile of a metropolitan area.…”