The coal.labour.nexus is a standalone module to analyse the labour implications of IAM pathways. The module downscales pathways across regions of China and India to determine the ability of coal mining workers to find new employment. It was originally developed as an ex-post module for Imaclim-R World V2.0. It can be used with any transition scenario. Filler scenarios can be provided if pathways miss crucial variables.
The module is still in development.
Input data for the coal labour nexus is not made available here as it contains data which cannot be shared freely. Two sorts of input data is required to run the module:
- National demographic scenario data
- Subnational downscaling data
The data is processed by running coal.labour.nexus\data\process_data.sh which calls Building_age_pyramid.py and Process_waysout_data.py which treat each type of data respectively.
For default scenarios, demographic data are based on the 2015 UN age pyramid data1 and demographic projections from SSP scenario data2. The user is free to use demographic projections consistent with the analysed scenario.
Other data are stored in a Database.csv file with the following format:
| Country | Region | Type | Year | Value | Unit | Source | URL | Note |
|---|
Input scenarios must first be placed in the coal.labour.nexus\input\ folder. The module is designed to read scenario results presented following the IAMC time-series data template3. The code is designed to read each scenario from an independent csv file.
If some required variables is missing from the scenario data, it can be infilled with Imaclim-R World V2.0 results for typical scenario in coal.labour.nexus\Infilling_trajectories
The scenarios are defined and the model is ran from coal.labour.nexus\model.
The module is designed to perform different sensitivity analysis base on original ex ante scenarios (input scenarios). For a given input scenario, there can be several output scenarios. Those scenarios are defined in V2_batch.labour.sce.
For each scenario, the user must define a source (i.e. the input scenario). They can then define a number of additional parameters:
- Retirement age
- Coal labour productivity growth
- Source of downscaling data
- Structural change parameter (no structural change by default)
- How to consider competition for available job vacancies.
- Whether to create infilling trajectories
The module can be run through coal.labour.nexus.sce\model\run.sh or V2_batch.labour.sce directly. The code loops through each scenario and calls V2_nexus.labour.core.sce.
For each step, the core of the module fetches demographic and geographical downscaling keys. It then calculates non-coal trajectories.
For Imaclim-R World V2.0, these are found in Imaclim.Labour.sce by attributing labour from each of the 11 non-coal sectors to the subnational regions before reaggregating.
Coal labour is then found from coal production and productivity increase in V2_coal.productivity.sce
For each run results are saved alongside log files in a folder in coal.labour.nexus\output. A csv file is created for each scenario in a format analogous to the IAMC's.
The notebooks folder contains code to plot useful figures. Plots_main.py contains the code for figures used for the paper Regional employment vulnerability to rapid coal transition in China and India, an integrated and downscaled assessment (unpublished).
Footnotes
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United Nations, 2022. World Population Prospects 2022. ↩
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Kc, S., Lutz, W., 2017. The human core of the shared socioeconomic pathways: Population scenarios by age, sex and level of education for all countries to 2100. Glob. Environ. Change 42, 181–192. https://doi.org/10.1016/j.gloenvcha.2014.06.004 ↩
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https://www.iamconsortium.org/scientific-working-groups/data-protocols-and-management/iamc-time-series-data-template/ ↩