Optimizing Daily Factors by Comparing Intuitive and Framework-Based Decisions’ Environmental Impact

Household decisions around energy use and carbon emissions are complex, being influenced by environmental impact, cost, convenience, and individual behavior. This literature review examines three pillars of decision support - Multi‐Criteria Decision Analysis (MCDA) frameworks, behavioral economics models, and AI‐driven tools - to identify how they can be integrated into a seamless user experience to reduce environmental impact. First, it surveys the current statistics of residential impact and focuses on certain areas of high impact. Next, it explores the behavioral barriers, such as heuristics, temporal discounting, and habit formation, which impede widespread adoption of optimized choices. Finally, it ascertains the worth of emerging AI assistants capable of selecting MCDA methods, defining criteria, and assigning weights based on user inputs, location, and demographics to then feed into an MCDA calculator.