Canada_construction_emissions_inventory Public_Data
Bibliographic record
Abstract
Overview: This data was created using the code stored at the following GitHub. The code applies OpenIO-Canada to Canadian Supply-Use Tables to create EEIO matrices, then performs additional analyses on said matrices to obtain consumption-based accounts of GHG emissions driven by Canadian construction sectors, as well as data on the geographic flows and GDP intensity of embodied emissions in construction. File descriptions: CanCons_workbook_V2.xlsx A workbook summarizing all the analyses performed to create the figures in the paper "Developing a comprehensive account of embodied emissions within the Canadian construction sector". Contains all the data exported by the jupyter notebook stored at the GitHub link, as well as the additional analysis & formatting steps taken. Figure1_data.csv A direct export of the D matrix, along with totals. Figure2+6_data.csv Results of a contribution analysis of the final demand for construction-based Gross Fixed Capital Formation (GFCF) from all Canadian provinces and territories. Results show, for construction demand in each province, all the environmental impacts driven by each construction sub-sector. Figure2+6_data_GDP.csv Accompanies Figure2+6_data.csv, contains data from the Y matrix on the final demand for construction-based GFCF in $. Used for calculating intensities per unit GDP. Figure3_data_[construction sector].csv 3 files which contain the results of contribution analyses of 3 individual construction sectors: Roads & Highways, Communications Infrastructure, and Residential Buildings. Results show, for each province, the embodied environmental impacts associated with the inputs into these 3 sectors. Figure4_data_Baseline.csv Results of a standard contribution analysis of Construction GFCF to serve as a baseline for the following files. Similar to 2+6_data. Figure4_data_Zero[Region].csv Each file represents the result of a contribution analysis for Construction GFCF where the S matrix values for the [Region] (representing the environmental impacts caused by supply-chain steps within a region) have been zeroed out. This means that the results in these files represent a world where the [Region]'s contribution to the final impacts of all other regions have been removed. Subtracting these values from the baseline results in values representing each [Region]'s contribution to consumption-based impacts driven by construction every other region. This allows the flows of embodied emissions in construction materials to be mapped. Figure5_data.csv Results taken from Figure2+6_data on the distribution of energy (TJ) and emissions (kgCO2) across regions and construction sectors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.102 | 0.173 |
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; both teacher heads agree on what is shown here.
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".