<p>Retailers today are challenged with evolving customer predilections and mounting pressure to commit to sustainable practices. The level of complexity involved in such interactions is difficult to model with existing standard forecast methodologies. In this paper we propose a novel forecasting methodology by blending uncertainty quantification by means of conformal prediction and the principles of behavioural decision making. It is based on weighted asymmetric loss functions that consider carbon emissions designed to take into account practical environmental issues and management requirements of a company. Explainable AI tool, such as Shapley-value attribution, are used to make the results understandable, so that teams from marketing, operations and sustainability can work better together. The system also includes governance, such as stress testing, audit checks, trust monitoring, and is adaptive to policy changes and market conditions. Tests with real retail data demonstrate that the model provides accurate, segment-based estimates for various consumer groups such as Eco Advocates and Sceptical Buyers. Such knowledge can contribute to better marketing, inventory, and environmental fit. Reliability is enhanced over conventional models by increasing trust from the stakeholders. It is a meld of technology, behavioral science, and transparent governance that serves the circular economy objectives. This framework provides a pragmatic and scalable approach which allows businesses to reconcile profit with sustainability, setting the stage for more robust and responsible retail systems.</p>