To meet the complex challenges of climate change, environmental degradation, and biodiversity loss, we need to take a more integrated approach to designing future landscapes and making land-use decisions. This research uses spatial modelling and social research to explore how our rural and urban landscapes could change, and what is needed to incentivise these changes, to provide better environmental outcomes alongside economic resilience.
We use ecological modelling and simulation to explore future scenarios at spatial scales ranging from individual farms to catchments, to Aotearoa New Zealand as a whole. By simulating hundreds of thousands of future options, the research identifies strategies that can provide multiple ecosystem services while meeting climate and freshwater objectives, enhancing biodiversity, integrating te ao Māori perspectives, and supporting the bioeconomy. For example, we use multi-criteria spatial optimisation to identify opportunities for land-use and management change to meet freshwater and sustainability targets. We also use agent-based modelling to explore how incentives and policies might bring positive changes while mitigating unforeseen negative consequences.
The research involves a crucial social research component to understand the barriers and constraints that prevent landowners from changing the way their land is used and managed. The 5-year Moving the Middle programme is addressing this research need and seeks to empower rural land managers by analysing the complex financial, market, policy, and societal pressures they face, and exploring new mechanisms to support them, such as policy interventions, financial mechanisms, change agents, and narratives.
Research in this priority area is developing tools to inform decision-making by land managers, and by local, regional, and central government. This includes technical tools such as interactive online maps to quantify the benefits of nature, system dynamic models for understanding agricultural sustainability trade-offs, and visualisations of future scenarios. Publicly available software provided through this research includes the LUMASS tool for spatial systems dynamics modelling and optimisation, and an R package of ecosystem service models. Other tools are more hands-on, such as serious games and participatory approaches for discussing complex environmental challenges.
Finally, our work aims to leverage generative artificial intelligence (AI) to provide scientific knowledge to decision-makers in an accessible and cost-effective way. This includes developing frameworks to ensure AI systems are responsible, ethical, and useful.
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