Sectoral Aggregation Error in the Accounting of Energy and Emissions Embodied in Trade and Consumption

Da Zhang, Justin Caron and Niven Winchester

Journal of Industrial Ecology, 2019, vol. 23, issue 2, 402-411

Abstract: Correctly accounting for the energy and emissions embodied in consumption and trade is essential to effective climate policy design. Robust methods are needed for both policy making and research—for example, the assignment of border carbon adjustments (BCAs) and greenhouse gas emission reduction responsibilities rely on the consistency and accuracy of such estimates. This analysis investigates the potential magnitude and consequences of the error present in estimates of energy and emissions embodied in trade and consumption. To quantify the error of embodied emissions accounting, we compare the results from the disaggregated Global Trade Analysis Project (GTAP 8) data set, which contains 57 sectors to results from different levels of aggregation of this data set (3, 7, 16, and 26 sectors), using 5,000 randomly generated sectoral aggregation schemes as well as aggregations generated using several commonly applied decisions rules. We find that some commonly applied decision rules for sectoral aggregation can produce a large error. We further show that an aggregation scheme that clusters sectors according to their energy, emissions, and trade intensities (net exports over output) can minimize error in embodied energy and emissions accounting at different levels of aggregation. This sectoral aggregation scheme can be readily used in any input‐output analysis and provide useful information for computable general equilibrium modeling exercises in which sector aggregation is necessary, although our findings suggest that, when possible, the most disaggregated data available should be used.

Date: 2019
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