Spatial disaggregation of carbon dioxide emissions from road traffic based on multiple linear regression model

Detailed estimates of carbon dioxide emissions at fine spatial scales are critical to both modelers and decision makers dealing with global warming and climate change. Globally, traffic-related emissions of carbon dioxide are growing rapidly. This paper presents a new method based on a multiple linear regression model to disaggregate traffic-related CO2 emission estimates from the parish-level scale to a 1 × 1 km grid scale. Considering the allocation factors (population density, urban area, income, road density) together, we used a correlation and regression analysis to determine the relationship between these factors and traffic-related CO2 emissions, and developed the best-fit model. The method was applied to downscale the traffic-related CO2 emission values by parish (i.e. county) for the State of Louisiana into 1-km2 grid cells. In the four highest parishes in traffic-related CO2 emissions, the biggest area that has above average CO2 emissions is found in East Baton Rouge, and the smallest area with no CO2 emissions is also in East Baton Rouge, but Orleans has the most CO2 emissions per unit area. The result reveals that high CO2 emissions are concentrated in dense road network of urban areas with high population density and low CO2 emissions are distributed in rural areas with low population density, sparse road network. The proposed method can be used to identify the emission “hot spots” at fine scale and is considered more accurate and less time-consuming than the previous methods.

Research highlights
► Spatial disaggregation of traffic-related CO2 emissions from parish scale to 1-km2.
► Select allocation factors.
► Develop multiple linear regression model.
► Identify the emission “hot spots” at finer scale.