Manufacturing Decarbonization Risk Index: A Theoretical and Empirical Framework

This project is derived from the Task 1 of the Work Package 7: End-user Solutions for Regional Businesses and Investors of the LOCALISED Project. LOCALISED is an HORIZON 2020 European-funded research project aimed at providing downscaled decarbonisation trajectories, consistent with Europe’s net-zero target.

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Project Structure

This project includes a structured workflow divided into four pipelines for processing data related to exposure, vulnerability, response, and index calculation. Below is the detailed directory and file structure.

Project/
│
├── Base_Data/                 
│   └── ... (files)       
│
│
├── Pipeline 1: EXPOSURE            # Pipeline for assessing exposure
│   ├── _run.R                      # R script for running the exposure pipeline
│   ├── _targets.R                  # Script defining targets and steps
│   ├── targets/                    # Directory for target-related files
│   │   └── ... (files)            
│   │
│   ├── Outputs/                   # Directory containing pipeline outputs
│   │   ├── Data/                  # Processed data outputs
│   │   │   └── ... (files)        
│   │   │
│   │   └── Plots/                 # Plots and visualizations
│   │       └── ... (files)        
│   │
│   └── R/                         # R-related files and scripts
│       ├── Excluded/              # Excluded or filtered-out data
│       │   └── ... (files)
│       │
│       └── ... (files)      
│   
│
├── Pipeline 2: VULNERABILITY      # Pipeline for assessing vulnerability
│   ├── ENERGY
│   │   ├── _run.R
│   │   ├── _targets.R
│   │   ├── tagets/
│   │   │   └── ... (files)
│   │   │
│   │   ├── Outputs/                   
│   │   │    └── Data/                 
│   │   │        └── ... (files)
│   │   │
│   │   └── R/                        
│   │       ├── Excluded/             
│   │       │   └── ... (files)
│   │       │
│   │       └── ... (files)
│   │
│   │     
│   ├── LABOR
│   │   ├── ... 
│   │     
│   ├── SUPPLY CHAIN
│   │   ├── ... 
│   │     
│   ├── FINANCE
│   │   ├── ... 
│   │     
│   ├── TECHNOLOGY
│   │   ├── ... 
│   │
│   └── WEIGHTING AND COMPOSITION
│       └── ...
│
│
├── Pipeline 3: RESPONSE           # Pipeline for response assessment
│   └── ... 
│
└── Pipeline 4: INDEX              # Pipeline for index calculations
    └── ... 

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Pipeline Descriptions

Pipeline 1: Exposure

This pipeline processes base data and produces cleaned data and visual outputs related to exposure metrics.

Scripting Exposure Maps

Below is the detailed workflow for scripting exposure maps using five key scripts:

1. Eurostat_Empl_Pers:

   • Downloads and filters Eurostat employment data.

2. Impute_Empl_Clean:

   • Imputes missing values using the MICE (Multiple Imputation by Chained Equations) method.

3. Empl_Shares:

   • Calculates employment shares.
   • Aggregates subsectors for analysis.

4. Eurostat_Emissions:

   • Downloads and filters emissions data.
   • Downscales emissions using employment shares calculated in the previous step.
   • Outputs the final dataset.

5. Exp_Map_RegEmis:

   • Generates exposure maps based on regional emissions data.

Workflow Diagram

Eurostat_Empl_Pers
(Download & Filter Employment Data)
    │
    └── Impute_Empl_Clean
(Impute Missing Values with MICE)
                │
                └── Empl_Shares
(Calculate Employment Shares and Aggregate Sub-sectors)
                         │
                         └── Eurostat_Emissions
           (Download, Filter, and Downscale Emissions Data)
                                    │
                                    └── Uses Empl Shares
                                    (Outputs Final Dataset)
                                                │
                                                └── Exp_Map_RegEmis
                                     (Create Regional Emissions Exposure Maps)

Inputs

• Employment data from Eurostat.
• Emissions data filtered and downscaled using employment shares.

Outputs

• Final downscaled emissions dataset.

• Regional emissions exposure maps.

• Inputs:

  • Emissions_Data_Raw: Raw emissions data.

• Outputs:

  • Exposure raster maps at various stages.
  • Final downloadable layers for visualization.

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Pipeline 2: Vulnerability

This pipeline will handle the processing of data to assess vulnerabilities. (Details to be completed as the project progresses.)

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Pipeline 3: Response

This pipeline will analyze response-related metrics. (Details to be completed.)

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Pipeline 4: Index

This pipeline will calculate indexes based on prior outputs from exposure, vulnerability, and response pipelines. (Details to be completed.)

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How to Run the Project

1. Set Up: Ensure all required dependencies (e.g., R libraries) are installed.
2. Execution:
   • Navigate to Pipeline 1 and run the run.R script to execute the exposure pipeline.
   • Outputs will be stored in the Outputs/ directory.
3. Next Steps: Complete subsequent pipelines as needed.

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This README now reflects the workflow for scripting exposure maps along with the overall project structure. Let me know if further adjustments are needed!