Data from: Deforestation-induced emissions from mining energy transition minerals

Deforestation-Induced Emissions from Mining Energy Transition Minerals Authors: Yifei Quan, Jie-Sheng Tan-Soo Contact: [e-mail] | [e-mail] Software: All scripts were tested using R version 4.5.0 (November 2025) Overview This repository contains the full replication package for the study “Deforestation-induced emissions from mining energy transition minerals.” It provides R scripts, processed datasets, and instructions to reproduce the figures and tables in the accepted version of the manuscript and Supplementary Information. R scripts Scripts are numbered and should be run sequentially to replicate all data-processing and analytical steps: 00_buffer_function: Create direct and indirect buffers for each mining project 01_forest_loss: Extract and calculate cumulative forest loss 02_ghg_emissions: Estimate GHG emissions from mining-related deforestation 03_covariate_cleaning: Clean and prepare covariates for regression analysis 04_data_preparation: Combine processed data into the main analysis panel 05_mining_map: Replicate Figure 1 (global distribution of mines) 06_csdid_forest: Replicate Figure 2 (mining effects on direct forest loss) 07_hetero_country: Replicate Figure 3 (cross-country heterogeneity) 08_hetero_IPLC: Replicate Figure 4 (IPLC lands heterogeneity) 09_climate_impact: Replicate Table 1 (mineral-specific emission factor) Processed datasets The following anonymized and intermediate datasets support replication of the main results: panel_main: Main panel dataset for regression analysis (anonymized) hetero_ctry: Country-specific deforestation rate and forest mitigation potential (Figure 3) IPLC_merge: Mining–IPLC land overlap status (Figure 4a) hetero_iplc: Proportion of unacknowledged IPLC lands (Figure 4b) hetero_mineral: Mineral-specific emission estimates (Table 1) Raw data sources and availability are documented in the header section of each R script. Replication package structure The replication package is organized into three main folders, each serving a distinct function: code/ contains all R scripts used for data processing, analysis, and figure generation. Scripts are numbered sequentially to indicate the recommended order of execution. data/ stores both raw and processed datasets. Raw input data should be placed in data/raw/, while intermediate and final processed datasets (such as panel_main.RData and IPLC_merge.RData) should be placed in data/processed/. output/ contains all generated outputs from the analysis, including figures (PDF format) and tables (CSV format) that correspond to the results presented in the main text and Supplementary Information. To reproduce results, open the project folder in RStudio (or set it as the working directory) and execute the scripts in order. If you encounter any errors or problems while running the scripts, please reach out to the authors.