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integrated-lca-master

Data, code, and package used in the publication: Charalambous et al., 2024. Integrating emerging technologies deployed at scale within prospective life cycle assessment, Sustainable Production and Consumption

https://github.com/margotcha/integrated-lca-master

Science Score: 57.0%

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    CITATION.cff file
    Found CITATION.cff file
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    Found codemeta.json file
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    .zenodo.json file
    Found .zenodo.json file
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    DOI references
    Found 2 DOI reference(s) in README
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    Scientific vocabulary similarity
    Low similarity (9.7%) to scientific vocabulary

Keywords

fuels lifecycleassessment prospective technosphere
Last synced: 6 months ago · JSON representation ·

Repository

Data, code, and package used in the publication: Charalambous et al., 2024. Integrating emerging technologies deployed at scale within prospective life cycle assessment, Sustainable Production and Consumption

Basic Info
  • Host: GitHub
  • Owner: MargotCha
  • License: bsd-3-clause
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 52.9 MB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 1
  • Releases: 0
Topics
fuels lifecycleassessment prospective technosphere
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

Integrating emerging technologies deployed at scale within prospective life-cycle assessments

--- ### 📖 Description The repository contains data files and tailored notebooks and codes to create the LCI database and reproduce the results presented in the article. DOI: https://doi.org/10.1016/j.spc.2024.08.016
*Charalambous et al.*, 2024. Integrating emerging technologies deployed at scale within prospective life-cycle assessments.

📂 Repository Structure

```sh └── Integrated-LCA-master/ ├── .gitignore ├── LICENSE ├── Notebooks/ │ └── Setting up/ │ └── 01-Setup non-integrated LCA.ipynb │ └── 02-Setup integrated LCA.ipynb │ └── Calculations/ │ └── 01-Non-integrated LCA calculation.ipynb │ └── 02-Integrated LCA calculations.ipynb │ └── Fetching info/ │ └── 01-Diesel market regional share.py │ └── 02-Diesel market share.py │ └── 03-Synthetic diesel market share.py │ └── Plotting/ │ └── 01-Main-manuscript.ipynb │ └── 02-Supplementary.ipynb │ └── Examples/ │ └── examplenotebook.ipynb ├── Data/ │ └── LCIA/
├── IntLCA/ │ ├── _init_.py │ ├── IntLCA.py │ └── utils/ ├── README.md ├── environment.yml ├── graphicalabstract.png

```

⚙️ Documentation

📍 The Data folder includes:

  • The LCIA folder→ Three excel files that are used for creating or updating the LCIA method.

📍 The Notebooks folder includes: - Setting up folder → Notebooks to create the databases - Calculations folder → Notebooks to calculate LCA impacts - Examples folder → Notebook that shows how to perform integrated LCA with matrices - Plotting folder → Notebooks to plot the LCA impacts - Fetcing info folder → Notebooks to fetch information from the environmental databases

📍 The IntLCA folder → Includes a package created to perform integrated LCA. The file includes utils folder with all the modules required.

🔧 Installation

To install the IntLCA package use pypi:

sh pip install IntLCA-dev

🚀 Usage

To ensure the replication of the results presented in the article, it is highly recommended starting a new environment.

1. Set Up the Environment

Using Anaconda, build the environment using environment.yml:

bash conda env create -f environment.yml Details on how to use the package are provided in the corresponding notebooks. Reach out if you encounter issues!

Owner

  • Name: Margarita Athanasia Charalambous
  • Login: MargotCha
  • Kind: user
  • Location: Zurich
  • Company: ETH Zurich

PhD student

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  Charalambous M. A. (2024). Integrating emerging technologies deployed at scale within prospective life cycle assessments. Sustainable Production and Consumption.
message: Please cite this dataset using these metadata.
type: dataset
authors:
  - given-names: Margarita
    family-names: Charalambous
    email: margarita.charalambous@chem.ethz.ch
    affiliation: >-
        Department of Chemistry and Bioscience, ETH Zurich, Vladimir-Prelog-Weg 1, 8093 Zurich, Switzerland
repository-code: >-
  https://github.com/MargotCha/Integrated-LCA-master.git
abstract: >-
    Climate policies will strongly affect future supply chains in ways that can be predicted using integrated assessment models (IAMs). The outcomes of IAMs are now being used to conduct prospective life cycle assessments (pLCA) where the background data reflects expected future changes in the economy. However, the technological representation of emerging technologies is often limited in IAMs, which cover a reduced number of routes, thus offering limited insights into their role in future scenarios. This study addresses this gap by integrating emerging technologies omitted in IAMs into future markets, providing a more robust foundation for pLCAs. Diesel, widely used in transportation, heating, and power systems, has established itself as an integral part of the world's infrastructure. Hence, to illustrate our approach, here we analyze the future environmental impacts of heavy-duty trucks fueled with synthetic Fischer-Tropsch e-diesel, incorporating our technology in the diesel market of the background system, through an integrated LCA approach. The standard non-integrated LCA would analyze these technologies in the foreground, assuming that the background is given. In contrast, our integrated LCA, which is particularly suited for cases where technologies in the foreground are deployed at scale, makes both systems consistent with each other. Our findings reveal mismatches in climate impacts depending on the climate pathway and technology of up to 35 % between the integrated and non-integrated approaches, which increase over time, particularly from 2020 to 2050, and are more pronounced when assessing highly carbon-negative or carbon-positive technologies. Overall, we stress the importance of having consistent foreground and background systems for performing more meaningful and accurate LCAs. Moreover, we provide detailed guidelines on implementing such integrated analysis in current software packages, aiming to enhance the reliability of pLCAs for emerging technologies.
    ```

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Dependencies

setup.py pypi
  • brightway2 ==2.4.3
  • ipython ==8.12.3
  • jupyterlab *
  • premise >=1.5.1
environment.yml conda
  • asttokens 2.2.1
  • backcall 0.2.0
  • backports 1.0
  • backports.functools_lru_cache 1.6.4
  • bzip2 1.0.8
  • ca-certificates 2023.5.7
  • colorama 0.4.6
  • comm 0.1.3
  • debugpy 1.6.7
  • decorator 5.1.1
  • executing 1.2.0
  • importlib-metadata 6.6.0
  • importlib_metadata 6.6.0
  • ipykernel 6.23.1
  • ipython 8.14.0
  • jedi 0.18.2
  • jupyter_client 8.2.0
  • jupyter_core 5.3.0
  • libffi 3.4.2
  • libsodium 1.0.18
  • libsqlite 3.42.0
  • libzlib 1.2.13
  • matplotlib-inline 0.1.6
  • nest-asyncio 1.5.6
  • openssl 3.1.1
  • packaging 23.1
  • parso 0.8.3
  • pickleshare 0.7.5
  • pip 23.1.2
  • platformdirs 3.5.1
  • prompt-toolkit 3.0.38
  • prompt_toolkit 3.0.38
  • psutil 5.9.5
  • pure_eval 0.2.2
  • pygments 2.15.1
  • python 3.9.16
  • python-dateutil 2.8.2
  • python_abi 3.9
  • pyzmq 25.1.0
  • setuptools 67.7.2
  • six 1.16.0
  • stack_data 0.6.2
  • tk 8.6.12
  • tornado 6.3.2
  • traitlets 5.9.0
  • typing-extensions 4.6.3
  • typing_extensions 4.6.3
  • ucrt 10.0.22621.0
  • vc 14.3
  • vc14_runtime 14.34.31931
  • vs2015_runtime 14.34.31931
  • wcwidth 0.2.6
  • wheel 0.40.0
  • xz 5.2.6
  • zeromq 4.3.4
  • zipp 3.15.0