The opportunity-readiness paradox for timber-based climate solutions

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a comprehensive open dataset and methodology for analyzing timber construction potential across cities.

🏢実務担当者:Useful for construction and forestry companies to identify market opportunities and regulatory barriers.

🏛政策担当者:Highlights the need to align building codes with climate goals to unlock timber's potential.

This repository contains data and code to reproduce the publication  "The opportunity-readiness paradox for timber-based climate solutions" . In this repository, you may find the following files: building-codes.xlsx - Building code data per country collected in this study. cities.tar.xz - Outlines of the cities used in this study. The archive contains an ESRI Shapefile. We produced this dataset using administrative boundaries (polygons of administrative units at various levels) from GADM . Coordinates of city locations were obtained from Natural Earth in point format at 1:10m scale. code.tar.xz - R and MATLAB code to reproduce clustering analysis and plotting of Figures 2, 3, and 4. composite_indicators.m  - Constructs the seven composite readiness indicators for each city and generates individual world map figures for each dimension. clustering.m - Performs K-means clustering using the MDA initialization method (Camus et al., 2011). readiness.m - Merges cluster assignments with scenario-based carbon storage estimates and produces the opportunity vs. readiness comparison figures carbon_calculation.R - Computes wood material intensities and carbon substitution factors. Combines LCA-derived intensities (regional), RASMI material ranges (Fishman et al.), and substitution factors from the literature (Leskinen et al. 2018; Hassegawa et al. 2022). sensitivity_weighting.m - Expert-survey weighting of dimensions. k-NN.xlsx  - Per-city input and output data of the clustering analysis. Gridded population projections for 2020-2050 under SSP2 (column: pop_growth) are from  Wang et al. (2022) . Building stock typology data (column: low_rise) were obtained from the  Global Human Settlement Layer , including building surface area, heights, and non-residential zones. City-level carbon goals (column: city_pledge) were obtained from the CDP Cities Disclosure platform CDP Cities Disclosure platform and cross-validated with the  Net Zero Tracker and Hughes et al. (2018) Hughes et al. (2018) . Country-level net-zero commitments and climate accountability legislation (column: country_pledge) were compiled from Nick Zrinyi . Forestry and wood production statistics (columns: country_supply, saw_wood) were obtained from  FAOSTAT and the  UNECE Forestry Statistics Database . Additional national statistics were sourced from government portals (e.g., Government of China) and the  European Forest Institute . Employment data for the forestry sector (column: employment) are available from the  ILO ILOSTAT platform . Sawmill infrastructure data (column: sawmill) were compiled from the  Fordaq International Directory . Source data for timber building code scores (columns: timber_code, height_code, fire_code, and support_code) is in the file building-codes.xlsx .  Data on UNESCO heritage sites with timber construction (column: heritage) is in the file UNESCO.xlsx .  The share of the population living in timber-frame buildings (column: timber_frame) is from the PAGER database. Local timber supply (column: local_supply) was calculated within 200km around a city from roundwood-raster.tar.xz .  roundwood-raster.tar.xz - Spatially explicit roundwood production at approximately 1 km per pixel resolution. Each pixel represents the average total roundwood production (2011-2020) in m³/yr. The code to perform the local supply-demand match can be found here. The code we used to disaggregate national and regional roundwood production to the pixel level is available here. The data used to parameterize the disaggregation models are available in the roundwood-data.tar.xz archive. roundwood-data.tar.xz - Subnational roundwood production and forest area data collected for this study and used to produce the spatially explicit roundwood production map. timber/<country_name>.xlsx - Regional roundwood production and forest area data. timber/0_sources.xlsx - Sources of the subregional roundwood production data.  train/*/ * .csv - Data used for parametrizing the models. survey.csv - Results of the survey to assess the importance of the indicators to assess global timber readiness. scenarios.tar.xz - Results of the supply-demand match. scenarios.xlsx - Overview of the supply-demand match configuration. Lists the timber usage rate, timber construction rate, average floor area per capita and city, and share of single-family housing. i-f*.csv - Material intensities from the Regional Assessment of buildings' Material Intensities (RASMI) database . i-rbg.csv - Material intensities from Schneider et al. (2025). * .geojson - Results of the supply-demand match. Each GeoJSON feature represents the area required around a city to meet annual construction demand. File naming convention is detailed in the  scenarios.xlsx  file. The attribute table lists the annual construction demand per housing and construction type, annual population growth, and timber supply.  carbon.csv - Carbon emissions and footprint per city. UNESCO.xlsx - UNESCO heritage sites with timber construction. On the Timber Atlas dashboard, you can discover how much surrounding forest would be needed to meet your city’s annual timber demand for new residential construction. This tool is designed to spark interest and provide an overview of the theoretical potential of regional wood resources for future building needs.

• Zenodo https://zenodo.org/records/21351809first seen 2026-07-15 04:23:52