The field of environmental risk assessment and renewable energy planning is moving towards the development of high-resolution spatio-temporal datasets and advanced machine learning techniques to improve predictive models and forecasting accuracy. This shift is driven by the need for more accurate and reliable data to inform decision-making and strategic planning in areas such as wildfire risk assessment, carbon intensity forecasting, and renewable energy operation. Notable papers in this area include:

• IberFire, which introduces a spatio-temporal dataset for wildfire risk assessment in Spain and provides a reproducible methodology for constructing similar datasets.
• EnsembleCI, which presents an adaptive ensemble learning-based approach for carbon intensity forecasting that surpasses existing methods in terms of accuracy and regional adaptability.
• A data-driven approach to high-resolution ensemble weather forecasting that supports efficient and reliable renewable energy planning and operation.