When many of us think about carbon dioxide (CO2) emissions, we picture smokestacks from power plants or factories. But climate policy designers see emissions not only in ephemeral gases at the point of origin, but also in tangible objects at the point of sale or trade. From their vantage point, all commercial products contain “embodied emissions” that were produced in their manufacture, assembly and transport—and these CO2 emissions must be carefully accounted for at the state or national level to ensure that border-crossing climate policies such as carbon pricing measures are effective.
To save on computation time and expense in this accounting, policymakers typically aggregate embodied emissions data by sectors such as power, transportation and agriculture. In interstate and international climate policy design, this approach is commonly used in assigning emissions reduction requirements and penalties (e.g. border carbon adjustments (BCAs)) based on energy and emissions embodied in consumption and trade. Depending on which classification system is used to define sectors, however, such aggregation could introduce significant inaccuracy into the calculation of total embodied emissions at the state or national level. This inaccuracy, in turn, could lead to biased assessments of the effectiveness and cost of interstate or international carbon emissions reduction measures.
Now a study by researchers at or affiliated with the MIT Joint Program on the Science and Policy of Global Change explores the roots of this inaccuracy, the conditions that impact its magnitude, and aggregation strategies that policymakers can use to minimize it. Published in the Journal of Industrial Ecology, the study presents a pathway to more accurate and consistent estimates of embodied emissions—assessments more likely to inspire the confidence of signatory states and nations considering proposed emissions reduction policies, such as BCAs, that target embodied emissions.
To measure the inaccuracy introduced by aggregation in embodied emissions accounting, the researchers compared results from the disaggregated Global Trade Analysis Project (GTAP 8) data set (used by the Joint Program’s Economic Projection and Policy Analysis (EPPA) model) to results from different levels of aggregation of this dataset (reducing 57 sectors to 3, 7, 16 and 26). Applying 5,000 randomly generated sectoral aggregation schemes and others based on commonly-applied decision rules for sectoral aggregation, the researchers emerged with three main findings.
First, a number of commonly-applied decision rules for sectoral aggregation lead to a significant bias, or difference in total embodied emissions from the baseline amount calculated using disaggregated data. Second, to minimize this bias, the most disaggregated data available should be used whenever possible, as bias increases nonlinearly with increasing levels of aggregation. Third, when aggregation is necessary, a scheme that groups sectors based (in equal measure) on their emissions intensity (CO2 emissions per total production of sector) and trade intensity (net exports per total production of sector) will minimize bias at different levels of aggregation.
“If we want to use embodied emissions in trade and policymaking, we need much better accounting methods, says Joint Program Research Scientist Da Zhang, the study’s lead author. “Ideally we should have a dataset as disaggregated as possible, but our approach offers a viable alternative. We have not proved this is the best rule, but it is relatively robust for the dataset we used.”
Photo: A new strategy seeks to improve the accuracy and consistency of estimates of embodied CO2 emissions of commercial products, which are often transported in bulk on container ships. (Source: Mickoo737)