Climate change pressures have elevated carbon footprint reduction to a strategic priority in sustainable supply chain management, particularly within high-emission industries such as mining, cement, and agriculture. This study develops a blockchain-enabled analytical framework that integrates emissions traceability, smart contract automation, and network-level optimization. Grounded in socio-technical systems theory, it operationalizes two novel quantitative indicators—the Transparency Impact Factor (TIF) and the Smart Contract Optimization Ratio (SCOR)—to evaluate blockchain’s measurable contribution to decarbonization. The study combines a structured literature synthesis with large-scale computational analysis across 1016 NAICS-classified U.S. industries to identify margin-intensive emission hotspots where blockchain interventions can yield substantial reductions. Using Monte Carlo simulations and real-time emissions datasets, the research establishes a reproducible modeling architecture for emission-centric supply chain optimization. The findings advance theory by demonstrating how blockchain-enabled transparency and automation can produce verifiable sustainability outcomes, while also offering practical value by improving the reliability of Environmental, Social, and Governance (ESG) reporting. This strengthens organizations’ competitive positioning through enhanced stakeholder trust, data-driven decarbonization strategies, and access to ESG-oriented investment capital. Addressing four research questions on emissions monitoring, intervention modeling, smart contract–based optimization, and sustainability reporting transformation, the study highlights blockchain’s scalable potential in supporting system-wide decarbonization across supply chains.
Tunca et al. (2026) studied this question.