My PhD investigates how AI can help us understand both ecological and social dimensions of large-scale infrastructure, with a particular focus on utility-scale solar energy (USSE). The environmental challenge is to balance rapid UK solar deployment with biodiversity protection and social legitimacy. My aim is to generate evidence that supports wildlife-friendly design of USSE and better incorporates the values of communities.
My research examines how bats and birds adapt to utility-scale photovoltaic developments. Using passive acoustic monitoring, I am gathering large audio datasets from solar farms and surrounding landscapes. AI-based classifiers let me process and analyse these data at scale, revealing fine-scale species responses and changes in community composition with distance from arrays that would not be possible otherwise. I collaborate with private landowners and industry stakeholders to access sites and share site-level findings, supporting evidence-based habitat management and informing participation in environmental stewardship initiatives.
The second strand of my research aims to develop monitoring methods for hard-to-detect nocturnal species. I am creating AI-assisted computer-vision software to detect and track bats in night-vision video recorded within solar farms. This tool enables efficient monitoring of interactions with panels, helping assess whether behaviour is being impacts and, if so, how targeted interventions might mitigate it.
The third strand of my research concerns community values towards USSE. Using both human analysts and large language models for textual analysis, I will examine public consultation material to reveal recurring themes in consultation responses to nationally significant infrastructure and novel approaches through which we can scale consultation processes for Nationally Significant Infrastructure Projects.