The DOI matches its Crossref record.
Scope 3 Emissions: Measurement and Management
Abstract
Given that climate change is one of the biggest risks facing the real economy as well as the financial industry, there is an urgent need to measure and manage this risk. One way to measure this risk is the carbon exposure of products and their corresponding supply chains. While the process of measuring and managing Scope 1 and Scope 2 emissions is well established, the same cannot be said of Scope 3 emissions. This article first provides an overview of frameworks for measuring and managing Scope 3 emissions in an ideal world, where product-level marginal emission factors are known with certainty. It then lays out the issues surrounding the measurement and management of uncertain Scope 3 emissions. It also discusses the progress made so far on these issues, provides recommendations, and lays out a research agenda for future work.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“While calculating Scope 1 and 2 emissions is relatively easy, a plethora of challenges crop up in estimating Scope 3 emissions that are embodied in the supply chain.”
From Carbon Leakage, Consumption, and Trade (2022) · cites this paper as [69]
In the citing paper
“Therefore, carbon footprinting also provides a metric of the transition risk that companies are facing in the supply chains (69).”
From Carbon Leakage, Consumption, and Trade (2022) · cites this paper as [69]
Citations
Cites
Still being checked.
Cited by
Sorted by citations
Carbon Leakage, Consumption, and Trade
Grubb, M., Jordan, N. D., Hertwich, E., Neuhoff, K., Das, K., Bandyopadhyay, K. R., van Asselt, H., Sato, M., Wang, R., Pizer, W. A., et al. (2022) · Annual Review of Environment and Resources · doi:10.1146/annurev-environ-120820-053625 · Cited by 210
Scope 3 emissions: Data quality and machine learning prediction accuracy
Nguyen, Q., Diaz‐Rainey, I., Kitto, A., McNeil, B. I., Pittman, N. A., & Zhang, R. (2023) · PLOS Climate · doi:10.1371/journal.pclm.0000208 · Cited by 54
Role of supplier engagement to reduce Scope 3 emissions in circular supply chains
Butt, A. S., Alghababsheh, M., Sindhwani, R., & Gwalani, H. (2024) · Business Strategy and the Environment · doi:10.1002/bse.3994 · Cited by 44
Estimating scope 3 greenhouse gas emissions through the shareholder network of publicly traded firms
Mejía, C., & Kajikawa, Y. (2024) · Sustainability Science · doi:10.1007/s11625-023-01460-8 · Cited by 23
Category-specific benchmarking of Scope 3 emissions for corporate clusters
Buchenau, N., Oetzel, J., & Hechelmann, R. (2024) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.115019 · Cited by 16
Metadata
- Authors
- Shrimali, G.
- Affiliations
- Stanford University (US)
- Corresponding author
- Shrimali, Gireesh
- ORCID
- Shrimali, Gireesh
- Venue
- The Journal of Impact and ESG Investing
- Publisher
- With Intelligence LLC
- Published
- 2022-06-02
- Volume
- 3
- Issue
- 1
- Pages
- 31-54
- ISSN
- 2693-1974 (ISSN-L)
- 2693-1982
- OpenAlex
- W4281737348
- Cited by
- 33
- References
- 0
- Citations by year
- 2026: 9 · 2025: 7 · 2024: 10 · 2023: 5 · 2022: 2
- FWCI
- 1.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Journal of Impact and ESG Investing (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Environmental Engineering › Environmental Impact and Sustainability
- Social Sciences › Business, Management and Accounting › Strategy and Management › Sustainable Supply Chain Management
- Social Sciences › Economics, Econometrics and Finance › Finance › Sustainable Finance and Green Bonds
- Keywords
- Scope 3 emissions, supply chain emissions, climate risk management, emission measurement, emission uncertainty, marginal emission factors, climate risk disclosure
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
