Balancing Cost and Environmental Impact: A Linear Programming Approach to Sustainable Shopping

Abstract

A linear programming model is devised for consumer purchasing decisions minimizing weighted cost and environmental impact using real-world data.  Minor shifts towards environmental preferences can greatly reduce impact with minimal cost increase. Spending slightly more on the bundle than the cheapest option can cut environmental impact by a third. Conversely, less than 10% compromise in impact yields over 15% cost savings. Consumers can find efficient midpoints—cost-effective and environmentally sustainable options—through strategic decisions. Additionally, a synthetic dataset, modeling different societal dispositions through Beta distributions of cost-environment orientation parameters, simulates societal attitudes, showing that a 10% reduction in environmental impact is possible with a 23% higher economic burden, while 60% of this reduction can be achieved with only 3.1% increase in cost when maintaining a balanced societal disposition.  This demonstrates the potential of optimization-based strategic purchasing decisions to achieve significant economic and environmental efficiencies when accompanied by increased environmental awareness.