# Dynamic Analysis of Stakeholders' Decision‐Making in Power Battery Recycling Considering Risks

## TL;DR

This paper introduces a dynamic model to analyze how risks in power battery recycling affect stakeholders' decisions and strategies over time.

## Contribution

A novel quantitative model using network calculus and CVaR to assess risk-induced loss and its impact on stakeholder strategies.

## Key findings

- Risk impacts on government and recyclers vary with their strategies, showing higher sensitivity under negative strategies.
- Risk management capabilities peak at 64.1% contribution rate before declining over time.
- Escalating risk-induced losses encourage stakeholders to shift from negative to positive strategies.

## 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.

The risk‐induced loss quantification model based on conditional value‐at‐risk (CVaR) is established to elucidate the impact of various risk parameters on stakeholders' revenues and evolutionary paths, as well as identify the key parameters that affect risk‐induced loss. The findings offer insights for risk management in power battery recycling.