A Generative Artificial Intelligence Driven Decision Support System for Spatial Optimization of Terrestrial Nature-Based Solutions in Canada

The proposed research project aims to determine the optimal utilization of less productive agricultural land in Canada. The objective is to leverage the capabilities of generative artificial intelligence together with optimization techniques to decide where nature-based solutions, such as smart farming, tree planting, and natural area regeneration, should be implemented. Selecting the appropriate strategies for each location, not only contributes to climate change mitigation by absorbing greenhouse gases, but also promotes biodiversity and connects ecosystems. Canada’s goals of achieving a net-zero economy by 2050 will be furthered by the contributions of this research. Additionally, it will aid Canada in optimizing the utilization of its current land resources. This project also aligns closely with the Royal Bank of Canada’s focus on agriculture and land use in its climate strategy and is expected to create business value for them.

Faculty Supervisor:

Juan Moreno-Cruz;Yuri Leonenko

Student:

Partner:

Royal Bank of Canada

Discipline:

Earth science

Sector:

Finance and Insurance; Management of companies and enterprises

University:

University of Waterloo

Program: