Carbon Management Accounting Considerations for Corporate Carbon Reduction: The Limitations and Future of Integrating Life Cycle Assessment and Material Flow Cost Accounting
Bibliographic record
Abstract
This paper focuses on the integration of Material Flow Cost Accounting (MFCA) and Life Cycle Assessment (LCA) or Carbon Footprint (CFP), a Carbon Management Accounting (CMA) method that can incentivize firms to reduce carbon.A literature review was conducted to identify the decision-making situations and limitations that MFCA and LCA (CFP) integration models can support.Twenty-one previous literatures were collected and three types of MFCA and LCA (CFP) integration methods were identified: collaborative, product-based partial integration, and process-based partial integration.Next, the collected prior literature was analyzed based on the CMA decision-making framework proposed and some issues were identified.The common challenge of the three existing integrated models is that they can only provide short-term and past-oriented information.It is difficult to provide incentives for carbon reduction because it does not show the relationship between the physical information of carbon emissions and the cost information.It is also difficult to encourage management to make longterm decisions on green procurement, capital investment, environmentally conscious product design, etc.Another common issue is that the use of MFCA tends to focus on the carbon emissions of material losses rather than all the carbon emissions carried by materials, which may impede product development and capital investment with zero or low carbon emissions in mind.This may inhibit product development and capital investment in consideration of zero carbon and low carbon emissions.The quality of LCA (CFP) information to support internal corporate decision-making is still lower than that of MFCA.Finally, another issue is how to share the information from MFCA among supply chains to extend the process-based partially integrated model to the supply chain.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".