Localized_NZBC

This repository contains the code and data for the Net Zero Business Consultant tool developed within the Horizon 2020 project LOCALISED (grant agreement No 101036458).

It builds the Transition-Risk Index (TRI) for EU manufacturing at the NUTS-2 region x NACE Rev.2 sector level and reproduces all figures from the companion paper:

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

The TRI combines:

• Exposure — greenhouse gas emissions by region and sector
• Vulnerability — a composite index across five dimensions (Energy, Labour, Finance, Supply Chain, Technology)
• Risk — a non-compensatory geometric aggregation: Risk = Exposure^0.5 x Vulnerability^0.5

Quick start

Open the RStudio project (systemic-framework.Rproj) or set your working directory to this folder, then:

renv::restore()        # install exact package versions from renv.lock
targets::tar_make()    # run the full pipeline

All intermediate data, final tables, and figures are regenerated automatically.

Pipeline

The analysis pipeline is managed by targets, which tracks dependencies between steps and caches results. Only steps affected by a code or data change are re-executed.

graph LR
    A[Initial data] --> B[non_sector_data]
    A --> C[sector_data]
    B --> C
    B --> D[all_data]
    C --> D
    D --> E[data_reshaped]
    E --> F[data_normalized]
    F --> G[risk_data]
    G --> H[fig_maps<br/>Figures 3 & 4]
    G --> I[fig_radar<br/>Figures 5 & 6]

Loading

Target	Script	Description
non_sector_data	R/build_non_sector_data.R	Read non-sector Eurostat indicators, harmonise regions
sector_data	R/build_sector_data.R	Read sector-level indicators, standardise columns
all_data	R/merge_all_data.R	Merge sector and non-sector data into a single long table
data_reshaped	R/reshape_data.R	Expand to full region x sector grid, fix NUTS regroupings (HR, NL, PT)
data_normalized	R/normalize_data.R	Divide by number of enterprises, apply min-max scaling to [0.01, 0.99]
risk_data	R/aggregate_risk.R	Compute dimension scores, Vulnerability index, and TRI
fig_maps	R/plot_maps.R	Choropleth maps of Exposure, Vulnerability, and Risk (Figures 3 & 4)
fig_radar	R/plot_radar.R	Radar charts comparing highest/lowest risk regions (Figures 5 & 6)

Useful targets commands

targets::tar_make()              # build or rebuild the pipeline
targets::tar_visnetwork()        # interactive dependency graph
targets::tar_outdated()          # check which targets need rebuilding
targets::tar_read(risk_data)     # load any target into your R session

Repository structure

.
├── _targets.R                 # pipeline definition
├── renv.lock                  # locked package versions (R 4.5.2)
├── systemic-framework.Rproj   # RStudio project file
├── R/                         # pipeline functions (sourced by targets)
├── Code/                      # original standalone scripts (for reference)
│   └── Create Initial Data/   # optional: re-download from Eurostat API
├── Initial data/              # input datasets (Eurostat, imputed)
├── Derived data/              # intermediate outputs
├── Final data/                # analysis-ready tables (Risk_data.xlsx/.csv)
└── Figures/                   # publication figures (PNG, 600 dpi)

Reproducing the initial data (optional)

The Initial data/ folder is included in this repository. To re-download the raw data from the Eurostat API and reapply imputation:

1. Run the R scripts in Code/Create Initial Data/ (requires internet access).
2. Re-run targets::tar_make() to propagate any changes through the pipeline.

Requirements

• R >= 4.5.2 (recorded in renv.lock)
• All package dependencies are captured in renv.lock. Running renv::restore() installs them into a project-local library, leaving your system R installation untouched.

Key methodological choices

Choice	Detail
Geography	EU NUTS-2 regions (overseas territories excluded)
Sectors	NACE Rev.2 manufacturing subsectors
Normalisation	Directional min-max scaling to [0.01, 0.99]; exposure zeros preserved
Aggregation	Equal weights within and across dimensions
Risk rule	Geometric mean with alpha = 0.5 (non-compensatory)
Imputation	Sector-country median, then EU median fallback

Citation

If you use this code or data, please cite:

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

Acknowledgements

• Data sources: Eurostat and related sources cited in the paper.
• Methodological references: OECD composite-indicator guidance; IPCC AR6 risk framing.
• This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101036458.