Developing a comprehensive account of embodied emissions within the Canadian construction sector
Bibliographic record
Abstract
Abstract Construction activities are a major driver of greenhouse gas emissions worldwide. However, the majority of construction‐driven emissions are indirect, meaning that these emissions occur during the manufacturing and transport of construction materials. This is in contrast with direct emissions, which are directly emitted from construction machinery. These indirect impacts are represented as embodied emissions and are difficult to quantify at scale, limiting the effectiveness of climate policymaking in the building sector. This paper presents results from a comprehensive account of embodied emissions within the Canadian construction sector, at a resolution far higher than existing global accounts, as well as novel analyses of flows and intensities of embodied emissions. It has the specific goal of serving as a baseline for future analyses of decarbonization scenarios and the more general goal of highlighting the importance of a consumption‐based approach to climate policymaking in the sector. The accounts are produced via an environmentally extended input–output analysis based on Canadian supply–use tables for the year 2018, and results are presented for the 13 provinces and territories as well as 19 categories of buildings and infrastructure. Results show that demand from construction drives 13% of Canada's consumption‐based emissions, residential construction is by far the largest driver of emissions, and at 0.28 kgCO 2eq per Canadian dollar of GDP, the efficiency of Canadian construction is roughly in line with the OECD average. A disproportionate share of emissions is driven by construction in provinces that are growing fast in terms of their populations, feature significant extractive industries, and feature higher emissions intensities. The construction sectors of western provinces are highly interconnected and receive a disproportionate proportion of embodied emissions from Alberta, whose high level of emissions promises to complicate decarbonization efforts. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".