The growing availability of innovative data sources — such as satellite imagery, Earth Observation (EO) products, remote-sensing techniques, and data products generated using machine learning and artificial intelligence — is opening new frontiers for environmental economics. These tools enable richer and more precise analysis of environmental processes and policies, covering themes such as air and water quality, wildfire and natural disaster impacts, land use, agricultural productivity, water resource management, and climate-related risks. This session aims to bring together contributions in applied environmental economics, with a particular focus on studies that leverage EO data, remote-sensing approaches, and advanced data-driven methods. Topics of interest include (but are not limited to):

• Environmental policy evaluation and regulation

• Pollution, climate change, and adaptation strategies

• Natural disasters, risk assessment, and resilience

• Land-use change, urban development, and conservation

• Agricultural and natural resource economics

• Water management, energy transition, and sustainability

• Integration of geospatial, EO, or remote-sensing data in economic analysis

• Machine learning and AI-based methods for generating or exploiting environmental data

• Causal inference approaches using remotely sensed or predicted environmental data

• New data sources and methods for environmental modelling