Determinants of Supply Chain Engagement in Carbon Management

JOURNAL OF BUSINESS ETHICS(2022)

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摘要
To fight climate change, firms must adopt effective and feasible carbon management practices that promote collaboration within supply chains. Engaging suppliers and customers on carbon management reduces vulnerability to climate-related risks and increases resilience and adaptability in supply chains. Therefore, it is important to understand the motives and preconditions for pursuing supply chain engagement from companies that actively engage with supply chain members in carbon management. In this study, a relational view is applied to operationalize the supply chain engagement concept to reflect the different levels of supplier and customer engagement. Based on a sample of 345 companies from the Carbon Disclosure Project’s supply chain program, the determinants of engagement were hypothesized and tested using multinomial and ordinal logistic estimation methods. The results indicate that companies that integrate climate change into their strategies and are involved in developing environmental public policy are driven by moral motives to engage their suppliers and customers in carbon management. All these factors make a stronger impact on supplier engagement than on customer engagement. Moreover, companies operating in greenhouse gas-intensive industries are driven by instrumental motives to engage their suppliers and customers because increasing greenhouse gas intensity positively influences engagement level. Company profitability appears to increase supplier engagement, but not customer engagement. Interestingly, operating in a country with stringent environmental regulations does not appear to influence supply chain engagement. By utilizing relational capabilities and collaboration, buyers can increase their suppliers’ engagement to disclose emissions, which ultimately will lead to better results in carbon management.
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关键词
Supply chain engagement, Carbon management, Carbon disclosure, Sustainable supply chain management, Multinomial logistic regression, Ordinal logistic regression
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