Industry perspectives on scope 3 emissions reduction in manufacturing: challenges, opportunities, and the role of AI

Abstract

Scope 3 emissions typically constitute the largest share of manufacturing firms’ carbon footprints, yet they remain among the most difficult to manage beyond reporting. Prior research has advanced accounting methods, digital infrastructures, and AI enabled analytics, but offers limited empirical clarity on how practitioners operationalise Scope 3 work in day to day supplier, procurement, and product related decisions. This study addresses that gap through eight semi structured interviews with senior professionals working across manufacturing value chains in multiple regions. Using a Gioia inspired analytic approach, the study develops a transparent data structure linking participant concepts to second order themes and aggregate dimensions. Findings indicate that Scope 3 progress is frequently constrained not only by data limitations, but by organisational and interorganisational frictions: (i) credibility gaps where estimates are reportable but not “decision grade” due to methodological volatility, baseline drift, and fragmented infrastructures; (ii) supplier engagement routines that remain disclosure heavy unless supported by leverage, segmentation, and capability building mechanisms; (iii) AI valued primarily for workflow acceleration under hybrid human oversight, yet bounded by input quality, governance, and adoption constraints; and (iv) action bottlenecks in procurement where trade-offs require explicit decision rules and accountability loops. Building on these insights, the paper proposes a Scope 3 Alignment Framework showing how credible data, relational supplier models, scalable workflows, and procurement actionability must reinforce one another for reporting to translate into sustained emissions reduction.