Discovering effective policies for land-use planning with neuroevolution
How areas of land are allocated for different uses, such as forests, urban areas, and agriculture, has a large effect on the terrestrial carbon balance and, therefore, climate change. Based on available historical data on land-use changes and a simulation of the associated carbon emissions and remov...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2025-01-01
|
| Series: | Environmental Data Science |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S2634460225000184/type/journal_article |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849310332817768448 |
|---|---|
| author | Daniel Young Olivier Francon Elliot Meyerson Clemens Schwingshackl Jacob Bieker Hugo Cunha Babak Hodjat Risto Miikkulainen |
| author_facet | Daniel Young Olivier Francon Elliot Meyerson Clemens Schwingshackl Jacob Bieker Hugo Cunha Babak Hodjat Risto Miikkulainen |
| author_sort | Daniel Young |
| collection | DOAJ |
| description | How areas of land are allocated for different uses, such as forests, urban areas, and agriculture, has a large effect on the terrestrial carbon balance and, therefore, climate change. Based on available historical data on land-use changes and a simulation of the associated carbon emissions and removals, a surrogate model can be learned that makes it possible to evaluate the different options available to decision-makers efficiently. An evolutionary search process can then be used to discover effective land-use policies for specific locations. Such a system was built on the Project Resilience platform and evaluated with the Land-Use Harmonization dataset LUH2 and the bookkeeping model BLUE. It generates Pareto fronts that trade off carbon impact and amount of land-use change customized to different locations, thus providing a proof-of-concept tool that is potentially useful for land-use planning. |
| format | Article |
| id | doaj-art-031ec99f14194186a60eaea658d82a6c |
| institution | Kabale University |
| issn | 2634-4602 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Environmental Data Science |
| spelling | doaj-art-031ec99f14194186a60eaea658d82a6c2025-08-20T03:53:46ZengCambridge University PressEnvironmental Data Science2634-46022025-01-01410.1017/eds.2025.18Discovering effective policies for land-use planning with neuroevolutionDaniel Young0Olivier Francon1Elliot Meyerson2Clemens Schwingshackl3Jacob Bieker4Hugo Cunha5Babak Hodjat6Risto Miikkulainen7https://orcid.org/0000-0002-0062-0037Cognizant AI Labs, San Francisco, CA, USA Department of Computer Science, The University of Texas at Austin, Austin, TX, USACognizant AI Labs, San Francisco, CA, USACognizant AI Labs, San Francisco, CA, USADepartment of Geography, Ludwig-Maximilians-Universität, Munich, GermanyOpen Climate Fix, London, UKCognizant Technology Solutions, Brussels, BelgiumCognizant AI Labs, San Francisco, CA, USACognizant AI Labs, San Francisco, CA, USA Department of Computer Science, The University of Texas at Austin, Austin, TX, USAHow areas of land are allocated for different uses, such as forests, urban areas, and agriculture, has a large effect on the terrestrial carbon balance and, therefore, climate change. Based on available historical data on land-use changes and a simulation of the associated carbon emissions and removals, a surrogate model can be learned that makes it possible to evaluate the different options available to decision-makers efficiently. An evolutionary search process can then be used to discover effective land-use policies for specific locations. Such a system was built on the Project Resilience platform and evaluated with the Land-Use Harmonization dataset LUH2 and the bookkeeping model BLUE. It generates Pareto fronts that trade off carbon impact and amount of land-use change customized to different locations, thus providing a proof-of-concept tool that is potentially useful for land-use planning.https://www.cambridge.org/core/product/identifier/S2634460225000184/type/journal_articlecarbon budgetclimate changeevolutionary optimizationland-use changeneural networkssurrogate modeling |
| spellingShingle | Daniel Young Olivier Francon Elliot Meyerson Clemens Schwingshackl Jacob Bieker Hugo Cunha Babak Hodjat Risto Miikkulainen Discovering effective policies for land-use planning with neuroevolution Environmental Data Science carbon budget climate change evolutionary optimization land-use change neural networks surrogate modeling |
| title | Discovering effective policies for land-use planning with neuroevolution |
| title_full | Discovering effective policies for land-use planning with neuroevolution |
| title_fullStr | Discovering effective policies for land-use planning with neuroevolution |
| title_full_unstemmed | Discovering effective policies for land-use planning with neuroevolution |
| title_short | Discovering effective policies for land-use planning with neuroevolution |
| title_sort | discovering effective policies for land use planning with neuroevolution |
| topic | carbon budget climate change evolutionary optimization land-use change neural networks surrogate modeling |
| url | https://www.cambridge.org/core/product/identifier/S2634460225000184/type/journal_article |
| work_keys_str_mv | AT danielyoung discoveringeffectivepoliciesforlanduseplanningwithneuroevolution AT olivierfrancon discoveringeffectivepoliciesforlanduseplanningwithneuroevolution AT elliotmeyerson discoveringeffectivepoliciesforlanduseplanningwithneuroevolution AT clemensschwingshackl discoveringeffectivepoliciesforlanduseplanningwithneuroevolution AT jacobbieker discoveringeffectivepoliciesforlanduseplanningwithneuroevolution AT hugocunha discoveringeffectivepoliciesforlanduseplanningwithneuroevolution AT babakhodjat discoveringeffectivepoliciesforlanduseplanningwithneuroevolution AT ristomiikkulainen discoveringeffectivepoliciesforlanduseplanningwithneuroevolution |