Researchers introduce new, bottom-up model for estimating on-road CO2 emissions

(Nanowerk News) A team of researchers in Boston University’s Department of Earth and Environment have developed a new, bottom-up model for measuring on-road vehicle emissions. The model will be used to more accurately assess the effects of vehicle travel and traffic congestion on Massachusetts’ greenhouse gas emissions. Their findings have been published online in the journal Environmental Science and Technology ("A Bottom up Approach to on-Road CO2 Emissions Estimates: Improved Spatial Accuracy and Applications for Regional Planning").
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.
Because on-road transportation is responsible for 28 percent of all U.S. fossil-fuel carbon dioxide (CO2) emissions, accurate measurement of such emissions is critical for effective regional planning. Previous emissions inventories have relied on spatial proxies such as population and road density to downscale national or state-level data. At local scales, this introduces errors where these proxy variables and actual emissions are weakly correlated.
The new, BU-developed model makes use of a 29-year, roadway-level dataset that permitted the construction of a time-series of emissions estimates at very high spatial resolution. The time-series estimates allowed the researchers to analyze trends in on-road emissions across both space and time and to compare their results with other inventories. Because these estimates did not rely on spatial proxies such as population or road density, the BU team was able to conduct a full panel regression of population density on vehicle emissions at the scale of local towns. The results will provide urban planners with valuable data on how the relationship between population density and emissions varies across towns and years in Massachusetts.
This new approach will support efforts to reduce on-road CO2 under the Massachusetts Global Warming Solutions Act, which calls for a reduction in statewide emissions of 25% below 1990 levels. As the study reports, on-road emissions in Massachusetts have risen by 12% since 1990, although they appear to have leveled off over the past few years.
Using their new model, the BU researchers found that, in Massachusetts, population density is positively correlated with vehicle emissions at densities less than 2,000 persons per square kilometer (km–2). However, above this level the correlation becomes negative, and emissions decline slowly until densities exceed 4,000 persons per km–2, and then more rapidly thereafter. This suggests that increasing population density is more effective at reducing emissions in areas where population density is already rather high. For lower-density towns, increasing population density is more likely to result in an increase rather than a decrease in vehicle emissions, possibly as a consequence of adding new resident-drivers to the roads, or from the indirect effect of local development attracting travelers from neighboring towns.
“These results highlight the value of using an emissions inventory with high spatial and temporal resolution,” says Conor Gately, a PhD student in Earth and Environment and study co-author. “At coarser scales, much of the spatial variation in emissions and population density is lost in the aggregation. In order for local and state governments to develop policies that will be effective in reducing carbon emissions, they first must have accurate data on where and when those emissions are occurring. Our method improves the spatial resolution of emissions estimates, and does so without conflating emissions and population density. The effect of population density on emissions is not constant – we find that it varies across different towns. This is valuable information for regional and town planners who are hoping to address rising carbon emissions from vehicle traffic in their towns.”
As a result of the finding of a highly nonlinear relationship between its bottom-up emission estimates and the spatially varying proxy variable used in prior studies, the BU study highlights the potential pitfalls of relying on linear predictors in the construction of downscaled emission inventories.
Source: Boston University College of Arts & Sciences
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