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Record W4386245777 · doi:10.1002/ieam.4816

Why process-level Scope 3 accounting is needed for delivering supply chain greenhouse gas emission reduction

2023· editorial· en· W4386245777 on OpenAlexaff
Mia Emborg, Shannon M. Lloyd, Stig Irving Olsen

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2023
Typeeditorial
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsScope (computer science)Greenhouse gasBusinessSupply chainEuropean unionDirectiveSustainabilityAccountingFinanceEnvironmental economicsEconomicsEconomic policyMarketing

Abstract

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Until recently, companies have mainly focused on the reporting and reduction of direct emissions from their own operations (Scope 1) and those generated from purchased electricity (Scope 2). However, the need to accelerate reductions of greenhouse gas (GHG) emissions in response to the climate crisis has led to an increased focus on those emissions generated from corporations' supply chains—Scope 3 emissions. Taking responsibility for Scope 3 emissions has been encouraged for more than a decade, yet this has not been accompanied by significant supply chain emission reductions (Patchell, 2018). Introducing process-level GHG emission accounting into the existing Scope 3 reporting framework offers a promising means for identifying activities that drive supply chain emissions, ensuring that reduction efforts can be prioritized within organizations, rather than having the responsibility cascade down the supply chain. Scope 3 emissions account for as much as three-quarters of total emissions (CDP, 2023) and are reportedly more than 11 times higher, on average, than direct operating emissions (CDP, 2021). Recognizing the importance of reducing these emissions, governments, investors, and other key stakeholders are increasingly requiring Scope 3 disclosure and target setting. Soon, the European Union (EU) Corporate Sustainability Reporting Directive will require large European companies and third-country companies with significant business volume in the EU to disclose Scope 3 emissions (the European Parliament and the Council of the European Union, 2022). The United States Securities and Exchange Commission (SEC) proposed climate disclosure rule would require disclosure of Scope 3 emissions when deemed material to investors or when the company has Scope 3 emissions targets. Introducing process-level GHG emission accounting into the existing Scope 3 reporting framework offers a promising means for identifying activities that drive supply chain emissions. The GHG Protocol Scope 3 Standard, which provides a framework for estimating, reporting, and tracking emission performance, is the only internationally accepted method for Scope 3 emission reporting today (Callahan et al., 2021). While the Scope 3 Standard takes a life cycle perspective to identify supply chain emission sources, it does not require a full life cycle assessment (LCA) of supply chains (Callahan et al., 2021). Evaluation of cradle-to-gate emissions is required for some supply chain categories (e.g., purchased goods and services, capital goods, fuel- and energy-related activities) but is optional for others (e.g., transportation and distribution, generated waste, business travel, and leased assets) (Callahan et al., 2021). When companies include cradle-to-gate supply chain emissions, they rely on emission factors (representative values designed to relate the quantity of a pollutant released per unit of an associated activity) derived using LCA or environmentally extended input–output models (spend methods) based upon industry averages rather than the reporting companies' specific supply chains (Barrow et al., 2013). This approach is considerably (and purposely) less rigid and time-consuming than the LCA methodology. However, it provides fewer actionable results. Life cycle assessment is more actionable than current supply chain accounting approaches because it uses process-level analyses rather than deriving emissions according to a checklist of value chain activities. In LCA, processes are evaluated according to a reference flow, that is, the amount of output required to derive a specified amount of function from the product system being assessed (Hauschild et al., 2018). This approach provides insights into which supply chain activities are driving emissions, and from this, companies can then determine where they are able to influence emission reductions. Assessing emissions for the first five Scope 3 categories (purchased goods and services; capital goods; fuel and energy-related activities; transportation; and waste) according to the operations and equipment integral to delivering the reporting company's services or products. For example, for a company's fleet of cars, this includes emissions associated with manufacturing, transporting, maintaining, and repairing cars and producing fuel consumed by the cars. Companies may still use industry average emission factors to estimate emissions, but these estimates will be transparently linked to the equipment or operations controlled by the company. Evaluating the reporting companies' level of influence for each process. A tool such as an action prioritization matrix could be adapted to assess companies' level of influence, according to the level of process control and cost of implementation. The results could be evaluated alongside the size of potential emission reduction to determine the overall reduction potential, based on feasibility, to prioritize actions. Based upon the proposed modifications, we suggest that reporting companies should (at a minimum) assume responsibility for activities they control and can change. This approach is grounded in process-level LCA but can be integrated into the current Scope 3 accounting framework. Similar considerations should be encouraged for downstream emissions linked to reporting companies' operations and activities. These recommendations do not discount companies' responsibilities for reducing emissions from sources for which they have little control. For these, other strategies will be needed (e.g., mechanisms for enabling collective action to reduce overall supply chain emissions). Rather, these recommendations are an important step in changing the mindset that Scope 3 emissions belong to third parties and ought to be mitigated at those sources by those third parties. Mia Emborg: Writing—original draft; writing—review and editing. Shannon Lloyd: Writing—review and editing. Stig Olsen: Writing—review and editing.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.387
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.275
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2023
Admission routes1
Has abstractyes

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