Keywords
Sustainable Development Goals, Ecosystem Services, Multi-objective Optimization, Agent-based Modelling, Land Use, Socio-economic Feasibility
Start Date
15-9-2020 4:00 PM
End Date
15-9-2020 4:20 PM
Abstract
The concept of multifunctional landscapes has recently gained popularity, stressing that a landscape can be managed to address multiple Sustainable Development Goals. In this context, multi-objective land-use optimization can help to efficiently explore a large number of land use/land management configurations with respect to their potential to minimize trade-offs among multiple, often competing, objectives. Here, evolutionary optimization algorithms are of particular value as they aim to identify Pareto-optimal solutions, i.e. solutions for which no objective can be further improved without compromising at least one of the other objectives. However, multi-objective land-use optimization has three major limitations: first, it generates a large number of biophysically Pareto-optimal landscape configurations, and it is non-trivial to identify the societally optimal one among them. Second, even when land-use constraints are included in the optimization procedure, the real-world feasibility of a given solution is unclear. Third, the potential pathway from the status quo towards the optimal solution is also unknown (if there is one), given that the multiple objectives include public goods which will be underprovided by land managers in the absence of appropriate incentives. The first limitation can be and has been addressed by combining optimization with preference information. In this presentation, we address the second and third limitations, which have received little attention so far. By bringing agent-based modelling into play, we suggest answers to the questions: how to determine which “optimal” solutions are also economically, institutionally and socially feasible (i.e. achievable)? And, which methods can be used to identify pathways towards feasible solutions? We demonstrate the applicability of our approach for a virtual case study where we wanted to optimize agricultural yield, water quality, and biodiversity corresponding to SDGs 2, 6, and 15, respectively.
Addressing economic, political and social feasibility of simulated biophysically optimal landscapes
The concept of multifunctional landscapes has recently gained popularity, stressing that a landscape can be managed to address multiple Sustainable Development Goals. In this context, multi-objective land-use optimization can help to efficiently explore a large number of land use/land management configurations with respect to their potential to minimize trade-offs among multiple, often competing, objectives. Here, evolutionary optimization algorithms are of particular value as they aim to identify Pareto-optimal solutions, i.e. solutions for which no objective can be further improved without compromising at least one of the other objectives. However, multi-objective land-use optimization has three major limitations: first, it generates a large number of biophysically Pareto-optimal landscape configurations, and it is non-trivial to identify the societally optimal one among them. Second, even when land-use constraints are included in the optimization procedure, the real-world feasibility of a given solution is unclear. Third, the potential pathway from the status quo towards the optimal solution is also unknown (if there is one), given that the multiple objectives include public goods which will be underprovided by land managers in the absence of appropriate incentives. The first limitation can be and has been addressed by combining optimization with preference information. In this presentation, we address the second and third limitations, which have received little attention so far. By bringing agent-based modelling into play, we suggest answers to the questions: how to determine which “optimal” solutions are also economically, institutionally and socially feasible (i.e. achievable)? And, which methods can be used to identify pathways towards feasible solutions? We demonstrate the applicability of our approach for a virtual case study where we wanted to optimize agricultural yield, water quality, and biodiversity corresponding to SDGs 2, 6, and 15, respectively.
Stream and Session
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