MétaCan
Menu
Back to cohort
Record W4393526392 · doi:10.5281/zenodo.8205912

Canada_construction_emissions_inventory_[Public_Data]

2024· dataset· en· W4393526392 on OpenAlexaffabout
Leopold Wambersie, Claudiane Ouellet‐Plamondon

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2024
Typedataset
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsEmission inventoryEnvironmental scienceGeographyMeteorology

Abstract

fetched live from OpenAlex

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. 9. IO_system.zip A Zip file containing CSVs of all the matrices which represent the EEIO system. These include the A, Y, S, FY matrices, as well as matrices which were used in the development of the IO system (Z, F, U, V, g, q), and the matrices representing the results of the analysis and the characterization of impacts (C, D, E). For more information on these matrices please see the methodology section of the paper "Developing a comprehensive account of embodied emissions within the Canadian construction sector". The .zip includes the following matrices: A.csv - Technology matrix (normalized Z matrix). C.csv - Characterization matrix. D.csv - Matrix of total characterized environmental impacts associated with each category of final demand. E.csv - Matrix of total environmental impacts associated with each category of final demand. F.csv - Matrix of direct impacts associated with the production of each product category. FY.csv - Matrix of direct impacts associated with final consumption. L.csv - Leontief inverse. S.csv - Matrix of direct impacts associated with the production of each product category normalized by dollar of input. U.csv - Use matrix for intermediates (value of products used by industries in 2018 Canadian Dollars). V.csv - Supply matrix for intermediates (products supplied by industries in Can$). Y.csv - Matrix of spending by final demand categories. Z.csv - Input-Output table for Canada. g.csv - Diagonalized vector representing total industry output in Can$. q.csv - Diagonalized vector of total product output (also known as x vector). Canadian regional abbreviations: 'CA-AB' - Alberta 'CA-BC' - British Columbia 'CA-MB' - Manitoba 'CA-NB' - New Brunswick 'CA-NL' - Newfoundland and Labrador 'CA-NS' - Nova Scotia 'CA-NT' - Northwest Territories 'CA-NU' - Nunavut 'CA-ON' - Ontario 'CA-PE' - Prince Edward Island 'CA-QC' - Quebec 'CA-SK' - Saskatchewan 'CA-YT' - Yukon Country codes for international trade: AT AustriaBE BelgiumBG BulgariaCY CyprusCZ Czech RepublicDE GermanyDK DenmarkEE EstoniaES SpainFI FinlandFR FranceGR GreeceHR CroatiaHU HungaryIE IrelandIT ItalyLT LithuaniaLU LuxembourgLV LatviaMT MaltaNL NetherlandsPL PolandPT PortugalRO RomaniaSE SwedenSI SloveniaSK Slovak RepublicGB United KingdomUS United StatesJP JapanCN ChinaCA CanadaKR South KoreaBR BrazilIN IndiaMX MexicoRU Russian FederationAU AustraliaCH SwitzerlandTR TurkeyTW TaiwanNO NorwayID IndonesiaZA South AfricaWA RoW Asia and PacificWL RoW AmericaWE RoW EuropeWF RoW AfricaWM RoW Middle East

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0020.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.2570.217

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.

Opus teacher head0.020
GPT teacher head0.230
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicSmart Materials for ConstructionFrench-language works237,207