Align pathways with master

.github/workflows/main.yml

@@ -13,10 +13,6 @@ jobs:
         with:
           args: "./"
 
-      - name: Run isort
-        uses: jamescurtin/isort-action@master
-        with:
-          configuration: --profile black
 
       - name: Check for modified files
         id: git-check
@@ -43,6 +39,13 @@ jobs:
     steps:
       - uses: actions/checkout@v2
 
+      - name: Install system dependencies for pyenv
+        run: |
+          sudo apt-get update
+          sudo apt-get install build-essential zlib1g-dev libffi-dev libssl-dev \
+                               libbz2-dev libreadline-dev libsqlite3-dev liblzma-dev \
+                               libncurses-dev tk-dev
+
       - name: Install python version
         uses: gabrielfalcao/pyenv-action@v13
         with:
@@ -88,6 +91,7 @@ jobs:
           pip install
           build
           --user
+          --break-system-packages
 
       - name: Build a binary wheel and a source tarball
         run: >-

MANIFEST.in

@@ -31,18 +31,28 @@ include premise/data/metals/*.csv
 include premise/data/metals/*.yml
 include premise/data/metals/*.yaml
 include premise/data/transport/*.yaml
+include premise/data/transport/*.csv
+include premise/data/transport/bus/*.csv
+include premise/data/transport/car/*.csv
+include premise/data/transport/truck/*.csv
+include premise/data/transport/two-wheeler/*.csv
+include premise/data/battery/*.yaml
+include premise/data/battery/*.csv
 include premise/data/GAINS_emission_factors/*.csv
 include premise/data/GAINS_emission_factors/*.yaml
 include premise/data/GAINS_emission_factors/iam_data/CLE/*.csv
 include premise/data/GAINS_emission_factors/iam_data/MFR/*.csv
 include premise/data/geomap/*.yml
 include premise/data/geomap/*.yaml
 include premise/data/renewables/*.csv
+include premise/data/renewables/*.yaml
 include premise/data/direct_air_capture/*.yaml
 include premise/data/transport/car/*.csv
 include premise/data/transport/bus/*.csv
 include premise/data/transport/truck/*.csv
 include premise/data/transport/two-wheeler/*.csv
+include premise/data/battery/*.csv
+include premise/data/battery/*.yaml
 include premise/data/utils/*.csv
 include premise/data/utils/*.yml
 include premise/data/utils/*.yaml

README.md

@@ -34,6 +34,30 @@ but also in other LCA software, such as SimaPro, OpenLCA, or directly in Python.
 The tool is described in the following scientific publication: [Sacchi et al, 2022](https://doi.org/10.1016/j.rser.2022.112311).
 If this tool helps you in your research, please consider citing this publication.
 
+Also, use the following references to cite the scenarios used with the tool:
+
+- REMIND scenarios: Baumstark et al. REMIND2.1: transformation and innovation dynamics of the energy-economic system within climate and sustainability limits, Geoscientific Model Development, 2021.
+- IMAGE scenarios: Stehfest, Elke, et al. Integrated assessment of global environmental change with IMAGE 3.0: Model description and policy applications. Netherlands Environmental Assessment Agency (PBL), 2014.
+- TIAM-UCL scenarios: []()
+
+Models
+------
+
+The tool currently supports the following IAMs:
+
+| Model    | Description |
+|----------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| REMIND   | REMIND (Regionalized Model of Investment and Development) is an integrated assessment model that combines macroeconomic growth, energy system, and climate policy analysis. It is designed to analyze long-term energy transition pathways, accounting for technological, economic, and environmental factors. REMIND simulates how regions invest in different technologies and energy resources to balance economic growth and climate targets, while considering factors like energy efficiency, emissions, and resource availability. The model is particularly strong in its detailed representation of energy markets and macroeconomic interactions across regions, making it valuable for global climate policy assessments. |
+| IMAGE    | IMAGE (Integrated Model to Assess the Global Environment) is a comprehensive IAM developed to explore the interactions between human development, energy consumption, and environmental systems over the long term. It focuses on assessing how land use, food systems, energy systems, and climate change interact under different policy scenarios. The model integrates biophysical processes, such as land-use change and greenhouse gas emissions, with socio-economic drivers like population growth and economic development. IMAGE is commonly used for analyzing sustainable development strategies, climate impacts, biodiversity loss, and exploring mitigation and adaptation options. |
+| TIAM-UCL | TIAM-UCL (TIMES Integrated Assessment Model by University College London) is a global energy system model based on the TIMES (The Integrated MARKAL-EFOM System) framework, developed to evaluate long-term decarbonization pathways for global energy systems. It provides detailed insights into energy technology options, resource availability, and emission reduction strategies under various climate policy scenarios. The model focuses on the trade-offs and synergies between energy security, economic costs, and environmental outcomes. TIAM-UCL is frequently used to analyze scenarios consistent with the Paris Agreement and examine technological innovation's role in mitigating climate change globally. |
+
+
+
+What's new in 2.1.4?
+====================
+
+- Integration of TIAM-UCL scenarios
+
 
 What's new in 2.1.1?
 ====================

docs/consequential.rst

@@ -3,8 +3,8 @@ Consequential modelling
 
 The premise module allows users to import and adjust
 the consequential system model of the ecoinvent database
-v3.8 and 3.9, with a focus on electricity and fuel markets.
-This work is based on a publication with available
+v.3.8, v.3.9 and v.3.10, with a focus on electricity and fuel markets.
+This work is based on a publication available
 at https://doi.org/10.1016/j.rser.2023.113830
 
 If you use this module, please cite the publication:
@@ -15,16 +15,14 @@ Renewable and Sustainable Energy Reviews,
 Volume 188, 2023, doi: 10.1016/j.rser.2023.113830
 
 Currently, the identification of marginal supplying
-technologies is limited
-to the electricity and fuel sectors.
+technologies is limited to the electricity and fuel sectors.
 
 Some technologies are excluded from the marginal markets
 due to constraints on their feedstock availability.
 This typically applies to waste-to-energy (e.g., waste-based CHP)
 or waste-to-fuel (e.g., residue-based biofuel) plants.
 For steel markets, only the BF-BOF route is considered.
 
-
 Some imported inventories cannot be
 directly linked to the ecoinvent consequential database.
 To address this, a mapping file is provided under