How a Low-Carbon Economy Affects Decision-Making and Profit Development in Large Corporations: Case Studies for Unilever and Maersk
Bibliographic record
Abstract
In the context of global warming, the "low-carbon economy" based on low energy usance and low pollution has become a global debating issue.Developed countries from Europe and the United States boost the "low-carbon revolution" in a vigorous manner with high energy efficiency and low emission as the core, focus on the development of "low-carbon technology", and make major adjustments to industry, energy and other policies, technology and trade.As the country vigorously promotes a low-carbon economy, it will inevitably affect the financial market where large companies and even multinational companies are located.The existing academic literature is still lacking in research in this area.We aim to show how a low-carbon economy will affect the development of financial markets and large companies, whether it is a good idea to develop a low-carbon economy from the perspective of large companies, and to provide concrete low-carbon solutions for large companies.Here we select two representative companies: Unilever and Maersk for descriptive analysis and case analysis.We found that a low-carbon economy is positively correlated with the profits of large companies, that is, a low-carbon economy can indeed bring sustainable growth in profits for large companies.Our research can inform other businesses seeking to implement a low-carbon economy, while also helping governments formulate effective energy policy.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".