# README: Labour Projections for Energy Transition ## Project Overview This code repository implements all workflows and scripts for a comprehensive labour demand and income projection study using multi-region input-output (MRIO) analysis, econometric modeling, and scenario-based forecasting. ---## Ⅰ. Environment Setup**Part 1. Setting Environment** *Initialize R environment: load libraries, set working directories, and define global options.* ---## Ⅱ. MRIO Baseline Analysis for 2015- **Part 2:** MRIO Analysis on 2015 labor embodied in electricity final products - **Part 3:** MRIO Analysis on direct/indirect labor - **Part 4:** MRIO Analysis using updated indicators (Ms) *Objective: Construct the 2015 baseline for labor intensity, direct and indirect labor attribution across electricity value chains via MRIO methods.* ---## Ⅲ. Future Labor Intensity: Regression & Projection- **Part 5:** Compiling Deflator Dataset - **Parts 6–7:** Assembling Historical and Future (SSP) Covariates - **Parts 8–10:** Defining dependent variable, data understanding, panel data prep - **Part 11:** Sector-wise labor demand/regression modeling - **Part 12:** Future projections of labor intensity *Delivers both the empirical basis and predictive modeling underpinning scenario-based labor intensity forecasts.* ---## Ⅳ. Predicting Future Labor and Income- **Part 13:** Labour compensation level (by industry, by sector group) - **Part 14:** Integrated projections of workforce and labor income under future scenarios *Projects future labor force and income, supporting downstream economic and social analysis.* ---## Ⅴ. Uncertainty Analysis- **Part 27:** Monte Carlo simulation of scenario/model uncertainty - **Part 28:** Uncertainty due to A and L matrix variation - **Part 29:** Alternative labour productivity function impacts - **Part 21:** Empirical robot adoption patterns - **Part 22:** Structural modeling and future projections for robot penetration *Quantifies robustness and sensitivity of main results to data, modeling, and parametric uncertainty. Explores labor impacts under accelerated automation scenarios, including sectoral heterogeneity.* ---## ⅤI. Main Results**Parts 15–19: Primary Figures (Figures 1–5 of the Article)** - **Part 15:** Figure 1- **Part 16:** Figure 2- **Part 17:** Figure 3- **Part 18:** Figure 4- **Part 19:** Figure 5**Part 20: Other Figures & Tables** *Main Figures in the manuscript and additional figures and tables.* ---## VII. Structural Decomposition Analytics- **Part 23:** Decomposition of drivers underlying labor change - **Part 24:** Adjustments for revised labor datasets (fix2) *Provides diagnostic perspective on what drives labor change and data corrections for robustness.* ---## VIII. Extensions **Part 25: Hard-to-Abate Sector Analysis** *Case studies focused on high-impact, difficult-to-decarbonize sectors such as cement and steel, using the core methods.* **Part 26: Global Value Chain (GVC) Indicators** *Computes and interprets GVC indicators for cross-comparison and international context.* **Part 30: Comparison Across MRIO Tables** *Assesses result consistency and methodological robustness across different MRIO database sources or vintages.* ## Contact For questions, please contact Meng Li, Shanghai Jiao Tong University, [email removed]. --- *Last updated: March 2026*