BIOGAIN Enabling biodiversity-positive transformation

BIOGAIN helps planners make fair, transparent decisions that meet energy and climate neutrality targets while delivering orientation towards biodiversity (net-gain). It surveys the impact of novel available digital and artificial intelligence (AI)-supported biodiversity data to enhance practical decision support. BIOGAIN is a timely project as regions face growing pressure to deliver renewable energy as well as a variety of other land uses (e.g. housing, farming, and transport) on limited land while combating climate change and the decline of biodiversity at the same time. Planners often lack recent, reliable, and comparable data on ecosystems and species as well as multi-dimensional biodiversity. To reach a climate-neutral society, we need decisions that curb and meet energy demand while prioritizing nature-positive outcomes and acknowledging natures contributions to people (e.g. clean water, flood protection, recreation). In this context, BIOGAIN brings together cutting-edge biodiversity data and models (including digital and AI- supported) to improve how impacts on species and habitats are predicted and used in spatial energy planning, Thre project makes trade-offs transparent to show how different choices affect biodiversity, energy production, and other land uses. Finally and most importantly, BIOGAIN tests how to prioritize options that lead to a biodiversity net-gain while balancing competing interests and multi-functional land use. The project focuses on actors at multiple planning levels, particularly in regional contexts where spatial energy planning remains underinformed by digital technology. Specifically, BIOGAIN will address planning for wind and solar energy infrastructure and real-world testing in simulation games with public and private partners. BIOGAIN integrates, supported by AI, up-to-date digital biodiversity data and ecosystem models to inform about the current state and examine the effectiveness of measures to minimize negative environmental impacts. A serious game lets participants explore real planning scenarios safely, learn from outcomes, and compare options side-by-side. Structured workshops, as part of a collaborative decision analysis and accompanied by a Discrete Choice Experiment, investigate how stakeholders weigh objectives based on forecasted outcomes. Five European universities collaborate from AT, DE, DK, NL and PO. In three case study regions, partners (public authorities, politicians, SMEs in the field of AI and biodiversity data, consultants, data specialists and NGOs) discuss and co-develop decision support how AI-supported biodiversity data can achieve best combined results for nature, energy and community to transform planning towards biodiversity net-gain solutions.