Dataset for "Comparing UK Construction and Built Environment Sectoral Coverage and Carbon Footprints Across Global MRIO Databases"

This dataset contains the analytical source code, processed data tables, and mapping frameworks supporting the conference paper "Comparing UK Construction and Built Environment Sectoral Coverage and Carbon Footprints Across Global MRIO Databases". The data focuses on the harmonisation and comparison of 13 global Multi-Regional Input-Output (MRIO) databases, specifically assessing their representation of UK Construction and Built Environment (CBE) sectors.

The dataset includes three primary components:

1-Sectoral and Geographical Resolution Data: Comprehensive Excel tables documenting the spatial and sectoral granularity of 13 MRIO databases (including CEDA, EXIOBASE, WIOD, Eora, and others). This includes a harmonisation framework that maps diverse original MRIO classifications into nine functional CBE categories (e.g., Mining, Wood, Cement, Services) to enable cross-database comparison.

2-Carbon Footprint Comparison Data: Calculated monetary emission factors (kgCO₂e/£) for the year 2022, covering specific UK CBE sectors. This data facilitates a direct quantitative comparison across four key databases: UK MRIO, SWC MRIO, EXIOBASE (Industry-by-Industry and Product-by-Product versions), and ADB MRIO.

3-Code: A suite of Jupyter Notebooks (Python) used to parse raw MRIO data, perform currency and unit conversions, calculate carbon footprints, and generate the study's comparative visualizations (Figure 1 and Figure 2).

Subjects:

Civil engineering and built environment

Civil Engineering Materials

Climate and climate change

Climate & Climate Change

Economics

Economics

Cite this dataset as:
Bakindi, A., Norman, J., Cooper, S., Allen, S., University of Bath, 2026. Dataset for "Comparing UK Construction and Built Environment Sectoral Coverage and Carbon Footprints Across Global MRIO Databases". Bath: University of Bath Research Data Archive. Available from: https://doi.org/10.15125/BATH-01640.

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Data

CF Comparison.xlsx
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet (41kB)
Creative Commons: Attribution-Share Alike 4.0

Datasets containing calculated Carbon Footprint intensities (kgCO2e/£) for 2022 across different databases (UK MRIO, SWC, ADB MRIO, and EXIOBASE).

MRIOs Geographical Coverage.xlsx
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet (51kB)
Creative Commons: Attribution-Share Alike 4.0

File contains the full list of spatial resolution (e.g., countries and regions) for Multi-Regional Input-Output (MRIO) databases.

MRIOs Sectors.xlsx
application/vnd.openxmlformats-officedocument.spreadsheetml.sheet (89kB)
Creative Commons: Attribution-Share Alike 4.0

File contains the full list of sectoral resolution for Multi-Regional Input-Output (MRIO) databases. It also includes a mapping of which databases contain distinct sectors for the construction and built environment sectors.

Code

EXIOBASE.ipynb
application/json (3kB)
Creative Commons: Attribution-Share Alike 4.0

Python script using the pymrio library to parse the EXIOBASE database and extract air emission multiplier matrices.

ADB MRIO.ipynb
application/json (3kB)
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Python script that processes the ADB MRIO 2022 dataset to calculate Carbon Footprint multipliers.

Figure1 - Sectoral … MRIOs.ipynb
application/json (268kB)
Creative Commons: Attribution-Share Alike 4.0

Python script that generates "Figure 1" to create a visual comparison of how different MRIO databases categorise construction and built environment sectors.

Figure 2 - … Comparison.ipynb
application/json (252kB)
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Python script that produces "Figure 2" for a scatter plot comparing carbon footprint intensities across 4 MRIO databases (UK MRIO, SWC MRIO ADB MRIO, EXIOBASE).

Creators

Abdulrahman Bakindi
University of Bath

0000-0003-2049-2324

Jonathan Norman
University of Leeds

0000-0002-4139-6828

Sam Cooper
University of Bath

0000-0002-3229-3026

Stephen Allen
University of Bath

0000-0002-0148-6466

University of Bath
Rights Holder

Documentation

Data collection method:

This dataset was created through the systematic compilation and harmonisation of 13 global Multi-Regional Input-Output (MRIO) databases accessed between December 2025 and January 2026. The methodology involved two primary stages. First, a comprehensive mapping exercise was conducted where 13 MRIO databases were reviewed for their geographical and sectoral resolution. A consistent set of inclusion and exclusion criteria, focusing on physical permanence, market dominance, and processing stage, was applied to identify sectors relevant to the Construction and Built Environment Sectors (CBE) . Second, a quantitative analysis was performed on a subset of four databases (UK MRIO, SWC MRIO, EXIOBASE, and ADB MRIO) chosen for their data availability and temporal alignment with the 2022 reference year.

Data processing and preparation activities:

Third-party MRIO datasets underwent specific transformation processes to ensure comparability. For EXIOBASE (v3.9.6), carbon footprints were calculated using the pymrio Python library; results were converted to kgCO2e using AR5 Global Warming Potential values and currency-converted from Euros to GBP using the 2022 average exchange rate. For the ADB MRIO database, which is not supported by pymrio, a custom Python script was used to extract intermediate transaction matrices and calculate multipliers, followed by currency conversion from USD to GBP. Data from UK MRIO and SWC MRIO were utilised in their pre-calculated monetary emission factor formats. All MRIO sector classifications were mapped to nine distinct functional categories (Mining, Wood, Cement, Concrete & Masonry, Glass/Ceramics, Metals, Other Minerals, Construction, Services, and Recycling) to facilitate the cross-database comparisons presented in the dataset.

Technical details and requirements:

The data analysis and generation of figures were performed using Python (Jupyter Notebooks). Replicating the analysis requires a Python environment with the following libraries: pymrio (specifically for EXIOBASE operations), pandas, numpy, and matplotlib. The dataset files are provided in standard .csv and .xlsx formats, accessible via Microsoft Excel or any open-source spreadsheet software.