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An Information-Driven Framework for Green Product Design in Circular Supply Chains

Jing Gao

International Journal of Information Systems and Supply Chain Management2026https://doi.org/10.4018/ijisscm.398930article
AJG 1ABDC C
Weight
0.50

What the paper says

As the circular economy advances, green product design must balance competitiveness and transparency—goals often in tension. This paper proposes an information-driven framework to support decision-making in circular supply chains by modeling the trade-off among profitability, environmental performance, and disclosure transparency. It presents a 3D optimization model using an entropy-weighted measurement of alternatives and ranking according to compromise solution method to construct a nine-criteria indicator system and apply the non-dominated sorting genetic algorithm II to generate Pareto-optimal solutions from empirical data from 30 manufacturers. Results show that high-transparency designs increase disclosure scores by 15.3% with only a 1.8% decline in profit margins, while significantly enhancing consumer brand preference (p < 0.05). The framework enables dynamic, data-informed strategies for sustainable product development across supply chain tiers. This study provides a practical decision-support tool for firms seeking competitive advantage through transparent design and offers regulators insights for tiered oversight.

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https://doi.org/https://doi.org/10.4018/ijisscm.398930

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@article{jing2026,
  title        = {{An Information-Driven Framework for Green Product Design in Circular Supply Chains}},
  author       = {Jing Gao},
  journal      = {International Journal of Information Systems and Supply Chain Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.4018/ijisscm.398930},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.