Optimizing Land-Use Policies to Reduce Carbon Emissions through Evolutionary Neuroevolution

Core Concepts

An evolutionary search process can discover effective land-use policies that balance carbon emissions reduction and minimal land-use change.

Abstract

This paper presents a system that uses machine learning techniques to optimize land-use policies for reducing carbon emissions while minimizing the amount of land-use change. The key elements are: Predictor Model: Three types of predictive models (linear regression, random forest, neural network) were trained on historical data to estimate the long-term carbon emissions (ELUC) resulting from different land-use changes. The neural network model was found to be the most accurate and able to extrapolate well to large land-use changes, making it suitable as the surrogate model. Prescriptor Model: Fully connected neural networks were evolved using the trained predictor as a surrogate, optimizing for two objectives: minimizing ELUC and minimizing the percentage of land-use change. The evolutionary search process discovered a Pareto front of prescriptor policies that represent different tradeoffs between the two objectives. Evaluation: The evolved prescriptors were compared to heuristic baselines and found to outperform them in the middle-change region, by exploiting nonlinear relationships to identify cases where large changes can make a big difference. An interactive demo was developed to allow decision-makers to explore the recommended land-use changes and their estimated impacts. The system provides a potentially useful tool for land-use planning, harnessing machine learning techniques to discover effective policies that balance carbon emissions and economic needs. Future work includes improving the predictor accuracy, incorporating uncertainty estimates, and extending the approach to consider additional objectives and preferences.

Stats

The following sentences contain key metrics or figures: Land-use changes can have a large effect on the terrestrial carbon balance, and therefore climate change. The tool is designed to answer three questions: (1) For a geographical grid cell, what changes to the land use can be made to reduce CO2 emissions? (2) What will be the long-term CO2 impact of changing land use in a particular way? (3) What are the optimal land-use choices that can be made with minimal cost and maximal effect? The BLUE model attributes carbon fluxes to land-use activities and provides spatially explicit ELUC estimates. The Land-Use Harmonization project (LUH2) provides data on fractional land-use patterns, underlying land-use transitions, and key agricultural management information. The predictor model predicts the long-term CO2 emissions (ELUC) directly caused by land-use changes. The prescriptor model suggests actions that optimize the outcomes, balancing ELUC reduction and minimal land-use change. The Evolved Prescriptors achieved a hypervolume of 20.52, outperforming the Even Heuristic (19.68) and the Perfect Heuristic (20.30). One Evolved Prescriptor recommended decreasing pasture and crops and increasing secondary forest, resulting in an 18.09 tC/ha decrease in carbon emissions with a 15.77% land-use change.

Quotes

"For a geographical grid cell, what changes to the land use can be made to reduce CO2 emissions?" "What will be the long-term CO2 impact of changing land use in a particular way?" "What are the optimal land-use choices that can be made with minimal cost and maximal effect?"

Key Insights Distilled From

Discovering Effective Policies for Land-Use Planning with Neuroevolution

by Risto Miikku... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2311.12304.pdf

Deeper Inquiries

How can the predictor model be further improved, for example through ensemble methods or by incorporating additional data sources?

To enhance the predictor model, ensemble methods can be employed to combine the strengths of multiple individual models. By creating an ensemble of models trained on specific regions, each model can specialize in predicting outcomes for that particular region, leading to improved accuracy. This approach can leverage the diversity of models to capture different patterns in the data and reduce overfitting. Additionally, ensembling different types of models, such as linear regression, random forest, and neural networks, can further enhance the predictive performance by leveraging the unique strengths of each model type. Incorporating additional data sources can also enhance the predictor model. By including more detailed information on land-use types, regional variations in carbon stocks, and environmental factors like disturbances or climate effects, the model can provide more accurate and nuanced predictions. Fine-tuning the model with region-specific data can help capture the complexities of how land-use changes impact carbon emissions in different areas. Furthermore, integrating real-time data on environmental conditions and land-use changes can ensure that the model remains up-to-date and reflective of current trends.

How could this land-use optimization system be integrated with other decision-making tools or models to provide a more comprehensive framework for sustainable land-use planning?

Integrating the land-use optimization system with other decision-making tools or models can create a more comprehensive framework for sustainable land-use planning. One approach is to combine the land-use optimization system with Geographic Information System (GIS) tools to visualize and analyze spatial data, allowing decision-makers to assess the impact of land-use changes on a geographic scale. By overlaying the optimization results on GIS maps, stakeholders can better understand the implications of different land-use policies. Furthermore, integrating the land-use optimization system with climate models can provide insights into the long-term effects of land-use decisions on carbon emissions and climate change. By linking the optimization system with climate models, decision-makers can evaluate the broader environmental implications of their land-use choices and make informed decisions that align with sustainability goals. Collaborating with stakeholders and experts in related fields, such as ecology, agriculture, and urban planning, can also enrich the decision-making process. By incorporating diverse perspectives and expertise, the land-use optimization system can consider a wider range of factors and trade-offs, leading to more holistic and sustainable land-use planning decisions. Additionally, integrating the system with policy evaluation frameworks can help assess the social, economic, and environmental impacts of proposed land-use policies, facilitating informed decision-making that balances multiple objectives and stakeholders' interests.