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

详细信息    查看全文

• 作者：Conor K. Gately ; Lucy R. Hutyra ; Ian Sue Wing ; Max N. Brondfield
• 刊名：Environmental Science & Technology (ES&T)
• 出版年：2013
• 出版时间：March 5, 2013
• 年：2013
• 卷：47
• 期：5
• 页码：2423-2430
• 全文大小：367K
• 年卷期：v.47,no.5(March 5, 2013)
• ISSN：1520-5851

文摘

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 脳 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^鈥?, above which increasing density correlates negatively with per-mile emissions.