Evidence map›Paper›PMID 41996445›Full record

ArticlePloS one2026

Supply chain carbon reduction considering consumer skepticism and blockchain technology under the cap-and-trade policy.

Peng-Peng Yuan, Qingsong Wang

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Peng-Peng YuanSchool of Digital Economics and Management, Wuxi University, Wuxi, China.
Qingsong WangSchool of Digital Economics and Management, Suzhou City University, Suzhou, China.ORCID https://orcid.org/0009-0002-4868-2146

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the context of low-carbon development, both firms and consumers can contribute to environmental protection. Given that consumers are often skeptical of corporate claims regarding product carbon reductions, this study explores how manufacturers can leverage blockchain technology to invest in carbon abatement under carbon cap-and-trade policies. Then, this study develops three game models within a supply chain involving a manufacturer and a socially responsible retailer to explore firms' abatement and operational strategies. The model design is based on existing literature as well as real-world corporate practices, cap-and-trade policies, and blockchain transparency mechanisms. The study finds that unit abatement level is negatively correlated with consumer skepticism and positively correlated with the retailer's corporate social responsibility. When the unit application cost of blockchain is below a certain threshold, manufacturers should adopt blockchain technology to mitigate consumer skepticism, thereby increasing product abatement levels and improving profits. Moreover, this threshold increases with market size and the degree of consumer skepticism. Retailers' socially responsible behavior can enhance both the manufacturer's unit abatement level and profitability, while stronger consumer preferences for low-carbon products or higher carbon prices can further motivate retailers to assume social responsibility. When the cost-sharing ratio is at a low level, both firms can achieve a win-win outcome in terms of profitability and environmental performance. The optimal cost-sharing ratio is positively related to the level of consumer low-carbon preference and the level of social responsibility. These findings provide theoretical insights and practical guidance for promoting credible carbon reduction strategies in supply chains.

Indexed as

BlockchainCarbonCommerceConsumer BehaviorHumansCarbon

Identifiers

PMID41996445
PMCPMC13089761

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.