Joined-up land use strategies tackling climate change and biodiversity loss (MOSAIC)

点击翻译按钮获取中文摘要

Urgent and concerted action can stop and reverse unsustainable land use and the over-exploitation of land resources. This will however require rapid, simultaneous and coordinated action by a diversity of land use decision makers. MOSAIC aims therefore to achieve the following objectives: 1. To gain a comprehensive understanding of the motivations and drivers behind land use decisions (e.g. cultural factors, social identities, cognitive frameworks, market prices, legal frameworks, etc.), including their relative importance and interactions, within and between relevant governance levels, ranging from individual land managers to supra-national organisations. 2. To gain a thorough understanding of the awareness of key land use decision makers about climate change, biodiversity loss and renewable energy challenges, and their willingness to address these challenges. 3. To characterise future land use patterns based on spatial, social and economic models that integrate key insights into the motivations and drivers behind land use related decisions and the result of these decisions in causing displacement effects, i.e. indirect land use changes in other parts of the world. 4. To support policy design and implementation for climate change, renewable energy and biodiversity, by means of innovative cost-effective instruments and approaches co-created in policy labs contributing to and elaborating on MOSAIC’s research results in a transdisciplinary way. 5. To develop an interactive digital learning environment, embedded within a digital toolbox comprising proven technologies and approaches consistent with long-term European sustainability goals and strategies to support land use decision processes at governance levels ranging from local to supra-national scales. Interdisciplinary SSH research linked to state-of-the-art land use modelling will be core to this project, and will be embedded in a transdisciplinary research approach based on policy labs.