RASMI: Regional Assessment of buildings’ Material Intensities

A framework to estimate ranges of buildings' material intensities (kg/m²)

RASMI aims to answer the question which MI data are appropriate for my country/region of interest?

Please refer to the Data Descriptor article for full details.

🏠 What's RASMI?

The Regional Assessment of buildings’ Material Intensities (RASMI) is a dataset and accompanying method that provides comprehensive and consistent representative MI value ranges. Value ranges embody the inherent variability that exists in buildings.

RASMI consists of 3072 MI ranges for:

• [material] 8 materials (concrete, steel, bricks, wood, glass, copper, aluminum, and plastics)
• [structure type] 4 structural construction types (reinforced concrete structure, masonry structure, timber structure, and steel frame structure)
• [function type] 3 functional use types (Residential single-family, residential multifamily, and non-residential)
• [region] 32 global regions compatible with global IAM applications like the Shared Socioeconomic Pathways (SSP).

Each datapoint is a range of values that represent one of the unique combinations of these dimensions. This yields 8 x 4 x 3 x 32 = 3072 MI ranges.

For instance, the range of material intensities of concrete [material] in steel frame structures [structure type] used for multifamily housing [function type] in Japan [region] is estimated to be 160 kg/m² - 729 kg/m² (in the 20230905 version of the data).

🏥 Motivation

The construction materials used in buildings have significant environmental impacts and implications for global material flows and emissions. Material Intensity (MI) is a metric that measures the mass of construction materials per unit of floor area in a building, and is used to model buildings’ materials and assess their resource use and environmental performance. However, the availability and quality of MI data is inconsistent, incomparable, and limited, especially for regions in the Global South. The dataset is reproducible, traceable, and updatable, using synthetic data when required. It can be used for estimating historical and future material flows and emissions, assessing demolition waste and at-risk stocks, and evaluating urban mining potentials.

🏭 The data

is in MI_results\

• MI_ranges_(date).xlsx is the dataset of the estimated MI ranges. This is probably the file you're looking for.
• MI_data_(date).xlsx is the raw pools of MI used to create the MI ranges. This is mostly for reproducability.

Versioning

Versions are marked by the (date) in the filename.

🏢 Descriptions of main files in the repository

Expand to view

Folder	File	Decription
(root)	MI_estimator.py	Python 3 code to create the MI ranges
MI_results	See above
data_input_and_ml_processing\	buildings_v2.xlsx	data from the Heeren & Fishman DB
	buildings_v2-structure_type_ML.ows	classification of structure types (Orange suite file)
	buildings_v2-structure_type_ML.xlsx	output of the classification of structure types
	dims_structure.xlsx	structure and label options for the various dimensions of the data
postestimation\	various files	outputs of the postestimation code in MI_estimator.py
tests\	various folders and files	outputs of the tests of cross validation and effects of the pool size on the MI results

Refer to the Data Descriptor article for details.

📝 How to cite

Please cite both the Data Descriptor and the specific data version used:

Data Descriptor: Tomer Fishman, Alessio Mastrucci, Yoav Peled, Shoshanna Saxe, Bas van Ruijven (2023) Global ranges of building material intensities differentiated by region, structure, and function: the RASMI dataset In review

Data version: preferably use the DOI of the Zeonodo release, e.g. Refer to the release number (on the right)

Cite all versions? You can cite all versions by using the DOI 10.5281/zenodo.10124951. This DOI represents all versions, and will always resolve to the latest one.

📧 Contact

Tomer Fishman [email protected]