Coping with climate change through land use optimization: a Gurobi Python implementation
Negative impacts of climate change anticipated for the future require the development and implementation of strategies to mitigate climate change, aimed at reducing concentrations of greenhouse gases in the atmosphere. Nowadays, it is also essential to complement this approach with climate change ad...
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| Formato: | Objeto de aprendizaje |
| Lenguaje: | Inglés |
| Publicado: |
2025
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/178444 |
| Aporte de: |
| Sumario: | Negative impacts of climate change anticipated for the future require the development and implementation of strategies to mitigate climate change, aimed at reducing concentrations of greenhouse gases in the atmosphere. Nowadays, it is also essential to complement this approach with climate change adaptation programs that focus on adjusting human and natural systems to the anticipated climate and its effects in order to alleviate or avoid damages, as well as to seize potential opportunities. In the past 150 years, land use and land use change, as human activities, were responsible for nearly one-third of total greenhouse gas emissions and thus were major contributors to global warming. But with a major shift in approach, enhancing planning and guided by sustainability, land use and land use change can play important, beneficial roles in climate change mitigation and adaptation strategies. In this Jupyter Notebook, an illustrative problem of land use planning incorporating climate change scenarios is formulated as a multi-objective linear program and solved with the well-known and powerful Gurobi solver. The Jupyter Notebook is stored in the server provided by Google Colab to run online Python code and is ready to call the size-limited free-trial license of the solver for the optimization. |
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