Blockchain technology adoption decision and coordination contract in green supply chain
Bibliographic record
Abstract
Chronic information asymmetries in green markets erode consumer trust, creating dual impediments to sustainable consumption and supply chain efficiency. Blockchain addresses these challenges by leveraging its unique technical features. This research analyzes a two-stage green supply chain comprising a manufacturer and retailer, developing three models with key parameters: green preference levels, trust coefficients, and blockchain traceability costs. These models, which include a baseline non-blockchain model, blockchain-enabled decentralized decision-making and blockchain-integrated centralized decision-making frameworks, are designed to explore blockchain adoption strategies and coordination mechanisms. This research elucidates several critical insights: (1) The optimal level of green production investment correlates positively with consumer green preferences regardless of blockchain implementation. Blockchain incentivizes the manufacturer to boost green investments, stimulating demand, improving retailer profits and Consumer Surplus. (2) Beyond blockchain operational costs, consumer trust levels act as pivotal determinants in decisions regarding blockchain adoption. The manufacturer tends to be more inclined to adopt blockchain technology only when consumer trust levels and the fixed costs of blockchain implementation are both below specific thresholds. (3) Compared with the decentralized decision-making model, the centralized decision-making model exhibits elevated levels of green production investment, significantly higher overall supply chain profits, and an augmented Consumer Surplus. The design of a two-part tariff contract with fixed remuneration enables supply chain coordination. Within a defined threshold range, fixed remuneration can achieve Pareto improvement in profits for both manufacturer and retailer. (4) The asymmetric Nash bargaining equilibrium enables the efficient allocation of post-coordination surplus gains. This research offers theoretical support for blockchain adoption choices in green supply chains, promoting green, efficient, and sustainable supply chain development.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".