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Stream/Area:
-
Subject:
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Paper ID:
IJSREM68682
Volume:
10 |
11 |
Nov 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Independent Researcher, India
Independent Researcher, India
Scope 3 emissions dominate the greenhouse gas inventories of industrial firms, yet their calculation depends on supplier information that varies widely in completeness, specificity and method. Reporting standards prescribe how categories should be calculated but offer no operational mechanism for judging whether the underlying supplier data are good enough, or for deciding which suppliers to engage first. This paper develops a five-stage Supplier Data Quality and Engagement Framework that closes that gap. A Supplier Data Quality Index combines a five-dimension pedigree assessment, the share of primary activity data and an emission-factor quality grade into a single bounded score. Scores are mapped to category uncertainties, propagated to inventory level by quadrature, and converted into an Engagement Priority Index that weights each category by its emissions share, its uncertainty and the reporting firm’s contractual leverage. The framework is applied to the FY2024-25 and FY2025-26 disclosures of five large Indian industrial companies. Only one firm publishes a category-level Scope 3 breakdown; on that inventory of 19.55 Mt CO₂ the framework returns an inventory uncertainty of 45.7 per cent, concentrated almost entirely in two categories that together carry 99.1 per cent of emissions. Upgrading only those two to primary-data quality reduces inventory uncertainty to 18.2 per cent, a 60 per cent improvement from two engagement programmes rather than fifteen. A Monte Carlo analysis over the weighting scheme confirms the top three priorities are stable. The framework is reproducible from public disclosure alone.
KEYWORDS
This paper set out to convert Scope 3 supplier data quality from a qualitative concern into a quantity that supports resource allocation. The Supplier Data Quality and Engagement Framework scores each category on pedigree, primary-data share and emission-factor quality, maps the composite to an uncertainty band, propagates it to inventory level, and ranks categories by emissions share, data weakness and contractual leverage. Applied to the only category-level Scope 3 inventory published in a sample of five large Indian industrial companies, the framework returns an inventory uncertainty of 45.7 per cent and identifies two categories, carrying 99.1 per cent of emissions, whose upgrading to primary-data quality would reduce that figure to 18.2 per cent. The priority ordering is stable across five thousand weighting draws. The wider empirical finding is one of absence: no company in the sample publishes a data-quality score, an uncertainty estimate or a primary-data share for any Scope 3 category; only one publishes an absolute supplier count, and that without emissions or data-quality attributes; three of the five publish no Scope 3 category detail at all, and for a fourth the category table was not retrieved from the sections examined. Supplier engagement is extensively described in qualitative terms and nowhere connected to the reliability of the reported inventory. Three directions follow. The uncertainty mapping of (6) should be calibrated against observed variance from firms that have replaced spend-based factors with supplier-specific data, allowing the anchors to be estimated rather than assumed. The framework should be applied at supplier rather than category level once disclosure or internal data permits, which would make the leverage coefficient an observable procurement variable and would allow the marginal uncertainty reduction of (9) to be divided by the cost of the engagement programme that delivers it, the criterion a firm with a binding budget actually needs. And the regulatory implication deserves testing: requiring disclosure of primary-data share by category would cost reporters little, since they must already know it to perform the calculation, and would make Scope 3 inventories comparable in a way that totals alone can never be.
[1] World Resources Institute and World Business Council for Sustainable Development, Corporate Value Chain (Scope 3) Accounting and Reporting Standard. Geneva, Switzerland: WRI/WBCSD, 2011. [2] E. G. Hertwich and R. Wood, "The growing importance of scope 3 greenhouse gas emissions from industry," Environmental Research Letters, vol. 13, no. 10, 104013, 2018. [3] International Organization for Standardization, ISO 14064-1:2018 Greenhouse gases — Part 1: Specification with guidance at the organization level. Geneva, Switzerland: ISO, 2018. [4] Securities and Exchange Board of India, Circular SEBI/HO/CFD/CFD-SEC-2/P/CIR/2023/122 on BRSR Core — Framework for assurance and ESG disclosures for value chain, July 12, 2023. [5] Securities and Exchange Board of India, Circular SEBI/HO/CFD/CFD-PoD-1/P/CIR/2024/177 on industry standards for BRSR Core, December 20, 2024. [6] H. S. Matthews, C. T. Hendrickson, and C. L. Weber, "The importance of carbon footprint estimation boundaries," Environmental Science and Technology, vol. 42, no. 16, pp. 5839–5842, 2008. [7] J. Downie and W. Stubbs, "Evaluation of Australian companies’ scope 3 greenhouse gas emissions assessments," Journal of Cleaner Production, vol. 56, pp. 156–163, 2013. [8] C. Blanco, F. Caro, and C. J. Corbett, "The state of supply chain carbon footprinting: analysis of CDP disclosures by US firms," Journal of Cleaner Production, vol. 135, pp. 1189–1197, 2016. [9] L. Klaaßen and C. Stoll, "Harmonizing corporate carbon footprints," Nature Communications, vol. 12, 6149, 2021. [10] T. Busch, M. Johnson, and T. Pioch, "Corporate carbon performance data: quo vadis?," Journal of Industrial Ecology, vol. 26, no. 1, pp. 350–363, 2022.
Publication Date
11/5/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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