Exploring Indirect "Scope 3" Greenhouse Gas Emissions for Oil and Gas
Bibliographic record
Abstract
Abstract Objectives/Scope Stakeholder and government expectations on transparency and disclosure are steadily growing. Specifically for greenhouse gas emissions, there are greater pressures to move beyond reporting direct corporate emissions and indirect emissions from energy use, to disclosing other indirect emissions ("Scope 3") deemed to be material. These emissions are from sources such as capital goods, business travel, franchises and use of sold products. In 2011 WRI/WBCSD released the GHG Protocol Corporate Value Chain (Scope 3) Accounting and Reporting standard to help companies identify, estimate and report these emissions. Methods, Procedures, Process In 2014, IPIECA, in collaboration with API, began work with consultant Environ (now Ramboll Environ) to support companies on the topic through two activities. Firstly, by identifying sources of scope 3 emissions in the oil and gas industry, as well as evaluating their materiality, and secondly, by detailing the different estimation approaches for those wishing to report them. Results, Observations, Conclusions, In establishing the boundaries of a scope 3 inventory, companies must first determine which activities are material. Materiality determinations should be based on both qualitative criteria (such as influence, risk, stakeholders, outsourcing and sector guidance) and quantitative consideration in order to meet the needs of inventory users, including both reporting companies and external stakeholders. We examine the 15 categories of scope 3 emissions as defined by the 2011 WRI/WBCSD GHG Protocol Corporate Value Chain guidance, and provide detailed guidance on those categories deemed to be material, as well as summary guidance for those categories less likely to be material. Of the 15 scope 3 categories, two are likely to be most material. Use of sold products (Category 11) is the dominant category for companies in the fuels value chain. For petrochemical companies a number of categories appear likely to be material, particularly purchased goods and services (Category 1). IPIECA have since been considering approaches to corporate value chain accounting (Scope 3), intending to identify credible, consistent, and reliable scope 3 GHG accounting and reporting practices from oil and gas companies. This paper outlines some of the materiality, boundary, and methodological considerations relevant to emissions sources for the petroleum industry. It includes accounting and reporting principles, and criteria and guidance for identifying materiality. Novel, Additive information In addressing boundary issues, we describe different tactics for reporting depending on where in the value chain a business operates. In addition it explores how to address the materiality of category 11 Use of Sold Products and the issue unique to the fuels industry of how to consider the duplication of category 11 emissons in other categories.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".