Dataset for "Timely deployment of best in class technologies to enable development and decarbonise construction"

This dataset contains the data used to plot the figures in the work "Timely deployment of best in class technologies to enable development and decarbonise construction". 

Each associated file corresponds to an individual figure. The full methodological descriptions are found in the Methods and Supplementary Methods of the associated research article.  A description of the relevant associated data files are below:

Fig1a_data.csv        Approximate production of materials in 2019 and associated CO2 emissions, for all production (Fig.1a). Data are presented in terms of mass produced (Gt), volume produced (billion m3), and CO2 emissions (Gt) from production. Use phase CO2 emissions or uptake (i.e., fluxes) and end-of-life fluxes are not represented, Scope 2 emissions occurring from production are included for steel, aluminium and cement, but Scope 3 CO2 emissions are excluded. Additionally, carbon-uptake by biomass during cultivation is not reflected. "Wood" products include all roundwood; asphalt concrete includes bitumen binder and aggregates; "Cement-based materials" includes all Portland cement-based products (i.e., including concrete, mortar, cement-based blocks, pavers, and tiles, etc.). Data sources and modelling assumptions for the data presented in this figure are presented in the Methods section, and numerical values for the charts are provided in Supplementary Table 1.  
Fig1b_data.csv        Approximate production of materials in 2019 and associated CO2 emissions, for use in construction (Fig.1b). Data are presented in terms of mass produced (Gt), volume produced (billion m3), and CO2 emissions (Gt) from production. Use phase CO2 emissions or uptake (i.e., fluxes) and end-of-life fluxes are not represented, Scope 2 emissions occurring from production are included for steel, aluminium and cement, but Scope 3 CO2 emissions are excluded. Additionally, carbon-uptake by biomass during cultivation is not reflected. "Wood" products include all roundwood; asphalt concrete includes bitumen binder and aggregates; "Cement-based materials" includes all Portland cement-based products (i.e., including concrete, mortar, cement-based blocks, pavers, and tiles, etc.). Data sources and modelling assumptions for the data presented in this figure are presented in the Methods section, and numerical values for the charts are provided in Supplementary Table 1.  
Fig2_data.csv        Historical data for the consumption of the main materials used in construction (cement-based materials, steel and timber) (Fig.2). "WORLD" is the sum of all countries. "WORLDMINCHINA" is the sum of all countries except China. Annual cement production is in units of kt; annual clinker production is in units of kt; annual steel production is in units of kt; annual timber production is in units of 10s of m3; GDP is in units of US Dollars (2023 PPP$); population is in units of number of persons.  
Fig3a_data.csv        Comparison of estimates by top-down and bottom-up approaches to project annual consumption of structural construction materials in 2050 (fig.3a). Units are Gt/year. 
Fig3b_data.csv        Distribution of projected 2019-2050 cement-based materials demand by wealth intervals of different regions (Fig.3b). The values for individual world regions can be found in Supplementary Table 9 of the associated research article. Units are Gt. 
Fig3c_data.csv        Cumulative distribution of projected 2019-2050 cement-based materials demand by wealth intervals of different regions (Fig.3c). The values for individual world regions can be found in Supplementary Table 9 of the associated research article. Units are Gt. 
Fig4_data.ods         Figures were generated from the tables in the .ods sheet, and exported as SVG and composited (Fig.4)
Fig5_cementscript.R     Fig.5b was generated using the .R script for cement. The variable "efactor" was adjusted to the efficiency value of the paper for every case.
Fig5_steelscript.R     Fig.5d was generated using the .R script for cement. The variable "efactor" was adjusted to the efficiency value of the paper for every case.