Canada_construction_emissions_inventory_[Public_Data]
Notice bibliographique
Résumé
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. It 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: 1. T2_Figure2_data.csv A file containing the data represented in Figure 2. 2. T3_Figure3_data.csv A file containing the data represented in Figure 3. 3. T4_Figure4_data.csv A file containing the data represented in Figure 4. 4. T5_Figure5_data.csv A file containing the data represented in Figure 5. 5. T6_Figure6_data.csv A file containing the data represented in Figure 6. 6. T7_Figure7_data.csv A file containing the data represented in Figure 7. 7. 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. 8. RAW_data-for-analysis_figure2-7.zip A Zip file containing the raw data created by CanCons_Analysis_notebook.ipynb and used by the CanCons_workbook (above) to produce the figures in question. This includes: Figure2_RAW_data.csv A direct export of the D matrix, along with totals. Figure3_RAW_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. Figure3_RAW_data_GDP.csv Accompanies Figure3_RAW_data.csv. Contains data from the Y matrix on the final demand for construction-based GFCF in $. Used for calculating intensities per unit GDP used in Table 1. Figure4_RAW_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. Figure5+6_RAW_data_Baseline.csv Results of a standard contribution analysis of Construction GFCF to serve as a baseline for the following files. Figure5+6_RAW_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. Figure7_RAW_data.csv Subset of results from Figure3_RAW_data on the distribution of energy (TJ) and emissions (kgCO2) across regions and construction sectors.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,110 | 0,190 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».