Comparing sources of uncertainty in community greenhouse gas estimation techniques

Abstract

Independent methods for estimating local greenhouse gas emissions have been developed utilizing different instrumentation, sampling, and estimation techniques. Comparing independent estimates theoretically improves understanding of emission sources. However, each method estimates emissions with varying fidelity, complicating comparisons across methods, cities, and over time. It is thus difficult for decision-makers to judge how to use novel estimation methods, particularly when the literature implies a singular method is best. We review 650 articles to define the scope and contours of estimation methods, develop and apply an uncertainty typology, and describe the strengths and weaknesses of different approaches. We identify two prominent process-based estimation techniques (summing of utility bills and theoretical modeling), three techniques that attribute observed atmospheric CO _2 to source locations (eddy covariance footprinting, dispersion models, and regression), and methods that spatiotemporally distribute aggregate emissions using source proxies. We find that ‘ground truth’ observations for process-based method validation are available only at the aggregate scale and emphasize that validation at the aggregate scale does not imply a valid underlying spatiotemporal distribution. ‘Ground truth’ observations are also available post-combustion as atmospheric CO _2 concentrations. While dispersion models can spatially and temporally estimate upwind source locations, missing validation data by source introduces unknowable uncertainty. We find that many comparisons in the literature are made across methods with unknowable uncertainty, making it infeasible to rank methods empirically. We see promise in the use of regression for source attribution owing to its controlling for confounding emissions, flexibly accommodating different source proxies, explicitly quantifying uncertainty, and growing availability of CO _2 samples for modeling. We see developing cross-walks between land use and end-use sectors as an important step to comparing process-based methods with those attributing atmospheric CO _2 to sources. We suggest pooling data streams can produce better decision support resources for cities with proper attribution of empirical fidelity.

Keywords

• greenhouse gas emissions
• uncertainty
• remote sensing
• atmospheric CO2
• regression
• cities