Markus Droven

·       Leverage AI-powered image analysis and machine learning algorithms to process vast datasets efficiently

·       Accelerate innovation by using AI to simulate and test new reduction strategies before full-scale implementation

·       Recognize the importance of human expertise and on-the-ground verification to avoid over-reliance on AI

·       Scrutinize data quality and bias to prevent AI algorithms from perpetuating inaccuracies in emissions reporting

·       Develop a portfolio of optimization-based emissions mitigation decision support tools

·       Rank candidate emissions sources based on user-based criteria

·       Identify high-impact, high-efficiency mitigation project candidates

·       Compare competing mitigation projects quantitatively, through transparently computed project impact and efficiency scores