Evidence map›Paper›PMID 41230452›Full record

ArticleGlobal challenges (Hoboken, NJ)2025

Dynamic Analysis of Stakeholders' Decision-Making in Power Battery Recycling Considering Risks.

Juan Huang, Zhe Wang, Zhenggang He, Weiwei Xu, Feng Luo

Abstract read
In one paragraph

Article in Global challenges (Hoboken, NJ), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Juan HuangSchool of Transportation and Logistics Southwest Jiaotong University Chengdu 610031 China.ORCID https://orcid.org/0000-0003-0474-4501
Zhe WangSchool of Economics and Management Sichuan Tourism University Chengdu 610100 China.
Zhenggang HeSchool of Transportation and Logistics Southwest Jiaotong University Chengdu 610031 China.ORCID https://orcid.org/0000-0002-1591-1435
Weiwei XuSchool of Economics and Management Sichuan Tourism University Chengdu 610100 China.
Feng LuoSchool of Economics and Management Sichuan Tourism University Chengdu 610100 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The management of risks in retired battery recycling chains remains a challenge, with a particular scarcity of dynamic models that can quantify risk loss and its impact on the co-evolution of stakeholders' strategies. To address this gap, a quantitative model integrating network calculus with conditional value-at-risk (CVaR) is proposed to quantify risk-induced loss. This study explores how varying risk parameters affect stakeholders' revenues, risk-induced loss, and their strategic behaviors. The results indicate that the impact of risk on government and recyclers differs based on their strategies, showing heightened sensitivity to risk under negative strategies. Moreover, the influence of risk parameters on risk-induced loss changes over time, with risk management capabilities peaking at a 64.1% contribution rate before declining. Government decision-making exhibits volatility in low-risk scenarios, leading to fluctuations in consumers' behaviors. Risks play a pivotal role in propelling sustainable development in the recycling market. While stakeholders initially lean toward negative strategies when risk-induced losses are minimal, escalating losses prompt a reassessment of such approaches, increasing the likelihood of transitioning to positive strategies. These findings provide valuable insights for enhancing risk management in power battery recycling.

Indexed as

CVaRdecision‐makingevolutionary gamepower battery recyclingrisk‐induced loss

Identifiers

PMID41230452
PMCPMC12602474

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.