Metadata-Version: 2.4
Name: future_technology_transformation
Version: 0.1.0
Summary: Future Technology Transformation (FTT) models
License: MIT
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: openpyxl
Requires-Dist: tqdm
Requires-Dist: numba
Provides-Extra: gui
Requires-Dist: nicegui; extra == "gui"
Requires-Dist: plotly; extra == "gui"
Requires-Dist: nest_asyncio; extra == "gui"
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Requires-Dist: black; extra == "dev"
Requires-Dist: sphinx; extra == "dev"
Dynamic: license-file

# Future Technology Transformation (FTT)

## Modules
This repository contains a family of Future Technology Transformation (FTT) models. Models that are included are:

* FTT:Power (Mercure, 2012) - updated to 2022 (generation) and 2023 (prices)
* FTT:Heat (Knobloch et al, 2017) data up to 2020
* FTT:Industrial heat
* FTT:Transport (Mercure et al, 2018) - data up to 2022
* FTT:Freight - data up to 2023
* FTT:Hydrogen (*under review*)

## Theoretical background
The FTT family of models are based on [evolutionary economics](https://en.wikipedia.org/wiki/Evolutionary_economics). The uptake of new technologies typically follows an S-curve, which can be represented well with evolutionary dynamics (Mercure et al, 2012). The core equations for all of the models in the model family are coupled logistic equations of the [Lotka-Volterra family](https://en.wikipedia.org/wiki/Lotka%E2%80%93Volterra_equations), also known as the predator-prey equations. These equations are used to determine the evolution of the shares of various technologies in the models. Each model contains between ~10 to 25 technologies competing for market share. 

## FTT and macro-economic models
This repository contains the main version of FTT, written in Python. It comes as a package that can be imported into macro-economic models. A FORTRAN version of the model family is often used together with a macro-economic model as: [E3ME-FTT](https://www.e3me.com/). This model is managed by Cambridge Econometrics, and informs some of the inputs for the model. In specific, energy demand is an output from the coupled model. 

## Installation

Before you start, make sure that git is installed on your system, for instance by [installing GitHub Desktop](https://docs.github.com/en/desktop/installing-and-authenticating-to-github-desktop/installing-github-desktop)

1. Open your terminal at a location where you want to install ftt. Type the following in your terminal to download the package from GitHub:

   ```bash
   git clone https://github.com/cpmodel/FTT_StandAlone.git
   ```
2. The python package requirements are curated in the `environment.yml` file.
   Change directory to the repo, and then install the environment using:

   ```bash
   conda env create -f environment.yml
   ```
3. On Windows, you can start the frontend with `launch_frontend.bat`. If Python is not yet added to your path, [ensure you add this first](https://realpython.com/add-python-to-path/).

Alternatively, you can download ftt by clicking the green `Code` button in the top right, and selecting `Open with Github Desktop` if you have this installed. You can import the environment in Anaconda Navigator.

## Running the model
1. You can run the frontend of the model in your browser by either double clicking `open_frontend.bat` or by running `run_frontend.py`. Select the models to run and scenarios and explore the output.
2. Alternatively, you can run the model from the run_file.py script. Output is saved to a pickle file in the Output folder. Select the models and scenarios from the settings.ini file.
3. Create new scenarios by adding a new folder in the Inputs folder. Data is read in first from this folder, and missing data is read from the S0 baseline folder. 

## References
* Heat: Knobloch, F., Pollitt H., Chewpreecha U., Daioglou V. and Mercure J-F. (2018) ‘[Simulating the deep decarbonisation of residential heating for limiting global warming to 1.5°C](https://link.springer.com/article/10.1007/s12053-018-9710-0)’, Energy Efficiency **12**, Issue 2, pp 521–550.
* Power: Mercure (2012): [A global model of the power sector with induced technological change and natural resource depletion](https://www.sciencedirect.com/science/article/pii/S0301421512005356 ). Energy Policy **48**.
* Power: Nijsse et al. (2023): [The momentum of the solar energy transition ](https://doi.org/10.1038/s41467-023-41971-7). Nature Communications **14**
* Passenger transport: Mercure, J-F., Lam, A., Billington, S. and Pollitt, H. (2018) ‘[Integrated assessment modelling as a positive science: private passenger road transport policies to meet a climate target well below 2°C](https://pubmed.ncbi.nlm.nih.gov/30930506/)’, Climatic Change, November 2018, Volume 151, Issue 2, pp 109–129.
* Steel: Vercoulen, P.; Lee, S.; Han, X.; Zhang, W.; Cho, Y.; Pang, J. (2023) '[Carbon-Neutral Steel Production and Its Impact on the Economies of China, Japan, and Korea: A Simulation with E3ME-FTT:Steel](https://www.mdpi.com/1996-1073/16/11/4498). Energies **16**, 4498. https://doi.org/10.3390/en16114498 
