Technology licensing contracts in supply chains with carbon cap-and-trade and vertical shareholding
Bibliographic record
Abstract
This study explores technology licensing in a low-carbon supply chain under cap-and-trade regulations, with an upstream firm holding partial shareholding in a downstream firm. We established a Stackelberg game to analyze four licensing strategies: free, fixed fee, royalty, and revenue-sharing. We investigate the effects of vertical shareholding and cap-and-trade regulation, as well as whether technology licensing yields a more favorable outcome compared to non-licensing and which licensing strategy proves superior. The findings reveal that when the upstream firm holds a higher share in the downstream firm, it results in increased profits for the upstream firm, the supply chain system, and consumer surplus, but decreased profit for the downstream firm. Furthermore, when carbon emission quotas are sufficiently high (low), a higher carbon trading price leads to increased (decreased) supply chain profitability, while inevitably decreasing consumer surplus. Increased carbon emission quotas consistently contribute to increased supply chain profitability, but have no impact on consumer surplus. All licensing contracts enhance the profitability of the upstream firm, the supply chain system, as well as consumer surplus, with revenue-sharing emerging as the most effective strategy. However, whether technology licensing promotes social welfare depends on factors such as the carbon emissions per unit of product and the environmental impact of each unit of carbon emission.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".