A Bottom up Approach to on-Road CO₂ Emissions Estimates: Improved Spatial Accuracy and Applications for Regional Planning

Abstract

On-road transportation is responsible for 28% of all U.S. fossil-fuel CO2 emissions. Mapping vehicle emissions at regional scales is challenging due to data limitations. Existing emission inventories use spatial proxies such as population and road density to downscale national or state-level data. Such procedures introduce errors where the proxy variables and actual emissions are weakly correlated, and limit analysis of the relationship between emissions and demographic trends at local scales. We develop an on-road emission inventory product for Massachusetts-based on roadway-level traffic data obtained from the Highway Performance Monitoring System (HPMS). We provide annual estimates of on-road CO2 emissions at a 1 × 1 km grid scale for the years 1980 through 2008. We compared our results with on-road emissions estimates from the Emissions Database for Global Atmospheric Research (EDGAR), with the Vulcan Product, and with estimates derived from state fuel consumption statistics reported by the Federal Highway Administration (FHWA). Our model differs from FHWA estimates by less than 8.5% on average, and is within 5% of Vulcan estimates. We found that EDGAR estimates systematically exceed FHWA by an average of 22.8%. Panel regression analysis of per-mile CO2 emissions on population density at the town scale shows a statistically significant correlation that varies systematically in sign and magnitude as population density increases. Population density has a positive correlation with per-mile CO2 emissions for densities below 2000 persons km(-2), above which increasing density correlates negatively with per-mile emissions.

Cities, traffic, and CO 2 : A multidecadal assessment of trends, drivers, and scaling relationships

“…Uncertainty in the magnitude of on-road emissions at the national level is estimated to be on the order of 3-5% for developed countries (4,35), but at subnational or state scales, existing inventories disagree by as much as 40% (3), and at city scales, uncertainty can be as large as 50-100% (13). Direct quantification of the uncertainty in US on-road emissions is made impossible by the absence of independent data sources against which to compare government estimates (4).…”

“…Nationally, the on-road sector represented 28% of total fossil fuel CO 2 emissions in 2012 and is responsible for almost half of the growth in total US emissions since 1990 (2). Despite being a substantial component of US emissions, on-road CO 2 remains poorly quantified at substate and urban scales (3)(4)(5). Reducing the uncertainty of on-road CO 2 emissions at finer spatial scales is critical to understanding the determinants of motor vehicle emissions (3), constraining carbon budgets (4), and supporting greenhouse gas (GHG) emission monitoring and abatement verification (5), particularly at the scale of cities, which have emerged as hubs of climate change mitigation activity (6).…”

“…This assumption was shown to be invalid for the United States, where per capita emissions vary by an order of magnitude across states (12). Population becomes an even less reliable predictor of total CO 2 emissions at finer scales, where local patterns of concentrated point and line sources dominate over more diffuse area sources (3,13). Used alone, population may be a valid predictor for residential and commercial sector emissions, but it performs poorly when used to model emissions from power stations or the on-road sector (3,4,13).…”

“…Population becomes an even less reliable predictor of total CO 2 emissions at finer scales, where local patterns of concentrated point and line sources dominate over more diffuse area sources (3,13). Used alone, population may be a valid predictor for residential and commercial sector emissions, but it performs poorly when used to model emissions from power stations or the on-road sector (3,4,13). Recent global inventories, such as the Fossil Fuel Data Assimilation System, partially correct for this deviation by modeling power plant emissions directly as point sources, although on-road emissions are still spatially allocated using population and luminosity data (15).…”

High-resolution quantification of atmospheric CO<sub>2</sub> mixing ratios in the Greater Toronto Area, Canada

“…These factors contributed 374 to the decommissioning of TAO in January 2016. The poor performance of our model system when 375 using the EDGAR v4.2 inventory to simulate CO2 mixing ratios was also found by a study quantifying 376 on-road CO2 emissions in Massachusetts, USA (Gately et al, 2013). In this study, EDGAR emission 377 estimates were found to be significantly larger than any other inventory by as much as 9.3 million 378 tons, or >33 %.…”