DOI: 10.5281/zenodo.17152309
title: Data for A Systemic Framework for Assessing the Risk of Decarbonization to Regional Manufacturing Activities in the European Union

description: README — Code and dataProject: LOCALISED

Work Package 7, Task 7.1

Paper: A Systemic Framework for Assessing the Risk of Decarbonization to Regional Manufacturing Activities in the European Union

What this repo does-------------------Builds the Transition‑Risk Index (TRI) for EU manufacturing at NUTS‑2 × NACE Rev.2, and reproduces the article’s Figures 3–6:• Exposure (emissions by region/sector)• Vulnerability (composite index)• Risk = Exposure ⊗ VulnerabilityOutputs include intermediate tables, the final analysis dataset, and publication figures.

Folder of interest------------------Code and data/  ├─ Code/                    # R scripts (run in order 1A → 5)  │  └─ Create Initial Data/  # scripts to (re)build Initial data/ from Eurostat API with imputation  ├─ Initial data/            # Eurostat inputs imputed for missing values  ├─ Derived data/            # intermediates  ├─ Final data/              # final analysis-ready tables  └─ Figures/                 # exported figures

Quick start-----------1) Open R (or RStudio) and set the working directory to “Code and data/Code”.   Example: setwd(".../Code and data/Code")2) Initial data/ contains the required Eurostat inputs referenced by the scripts.    To reproduce the inputs in Initial data/, run the scripts in Code/Create Initial Data/.    These scripts download the required datasets from the respective API and impute missing values; outputs are written to ../Initial data/.3) Run scripts sequentially (they use relative paths to ../Raw data, ../Derived data, etc.):   1A-non-sector-data.R  →  1B-sector-data.R  →  1C-all-data.R  →  2-reshape-data.R  →  3-normalize-data-by-n-enterpr.R  →  4-risk-aggregation.R  →  5A-results-maps.R, 5B-results-radar.R

What each script does---------------------Create Initial Data — Recreate inputs• Download source tables from the Eurostat API or the Localised DSP, apply light cleaning, and impute missing values.• Write the resulting inputs to Initial data/ for the analysis pipeline.

1A / 1B / 1C — Build the unified base• Read individual Eurostat datasets (some sectoral, some only regional).• Harmonize, aggregate, and align them into a single analysis-ready schema.• Write aggregated outputs to Derived data/ (and/or Final data/ as needed).

2 — Reshape and enrich• Reshapes the combined data and adds metadata.• Output: Derived data/2_All_data_long_READY.xlsx (all raw indicators in tidy long format, with indicator names and values).

3 — Normalize (enterprises & min–max)• Divide selected indicators by number of enterprises.• Apply min–max normalization to [0.01, 0.99].• Exposure keeps real zeros (zeros remain zero).• Write normalized tables to Derived data/ or Final data/.

4 — Aggregate indices• Vulnerability: build dimension scores (Energy, Labour, Finance, Supply Chain, Technology).  – Within each dimension: equal‑weight mean of directionally aligned, [0.01,0.99]‑scaled indicators.  – Dimension scores are re‑scaled to [0.01,0.99].• Aggregate Vulnerability: equal‑weight mean of the five dimensions.• TRI (Risk): combine Exposure (E) and Vulnerability (V) via a weighted geometric rule with α = 0.5 in the baseline.  – Policy‑intuitive properties: high E & high V → high risk; imbalances penalized (non‑compensatory). • Output: Final data/ (main analysis tables).

5A / 5B — Visualize results• 5A: maps and distribution plots for Exposure, Vulnerability, and Risk → Figures 3 & 4.• 5B: comparative/radar profiles for selected countries/regions/subsectors → Figures 5 & 6.• Outputs saved to Figures/.

Data flow (at a glance)-----------------------Initial data → (1A–1C) Aggregated base → (2) Tidy long file → (3) Normalized indicators → (4) Composite indices → (5) Figures                  |                       |                                                |                  v                       v                                                v            Derived data/       2_All_data_long_READY.xlsx                       Final data/ & Figures/

Assumptions & conventions-------------------------• Geography: EU NUTS‑2 regions; Sector: NACE Rev.2 manufacturing subsectors.• Equal weights by default where no evidence supports alternatives.• All indicators directionally aligned so that higher = greater transition difficulty.• Relative paths assume working directory = Code/.

Reproducing the article-----------------------• Optionally run the codes from the Code/Create Initial Data subfolder• Run 1A → 5B without interruption to regenerate:  – Figure 3: Exposure, Vulnerability, Risk maps (total manufacturing).  – Figure 4: Vulnerability dimensions (Energy, Labour, Finance, Supply Chain, Technology).  – Figure 5: Drivers of risk—highest vs. lowest risk regions (example: Germany & Greece).  – Figure 6: Subsector case (e.g., basic metals) by selected regions.• Final tables for the paper live in Final data/. Figures export to Figures/.

Requirements------------• R (version per your environment). • Install any missing packages listed at the top of each script (e.g., install.packages("...")).

Troubleshooting---------------• “File not found”: check that the previous script finished and wrote its outputs to the expected folder.• Paths: confirm getwd() ends with /Code so relative paths resolve to ../Raw data, ../Derived data, etc.• Reruns: optionally clear Derived data/, Final data/, and Figures/ before a clean rebuild.

Provenance & citation---------------------• Inputs: Eurostat and related sources cited in the paper and headers of the scripts.• Methods: OECD composite‑indicator guidance; IPCC AR6 risk framing (see paper references).• If you use this code, please cite the article:  A Systemic Framework for Assessing the Risk of Decarbonization to Regional Manufacturing Activities in the European Union.