Sustainable Perspectives for Smart Emission Control and Energy Security in Supply Chains: Evidence From Simulation and Game Theory

ABSTRACT Addressing CO 2 through artificial intelligence enabled technologies is an emerging priority in sustainable supply chains. This study investigates the joint adoption of Smart Emission Control Systems (SECS) and Energy Security (ES) within a tri‐tier supply chain of 300 firms. We develop an integrated framework combining game theory, simulation‐based optimization, reinforcement learning, and decision modeling to capture adaptive learning and profit‐driven behavior. Four adoption scenarios are evaluated. Results show that ES alone provides minor efficiency gains, while SECS alone significantly reduces carbon costs and improves profitability across all tiers. The combined adoption of SECS and ES yields the strongest environmental and financial performance. Profits are reported separately for manufacturers, agents, and retailers, reflecting their distinct positions within the supply chain. Overall, coordinated adoption produces the highest gains and demonstrates that emission‐reduction technologies and efficiency improvements are most effective when implemented together. The framework illustrates how multi‐tier, simultaneous adoption decisions can be supported and highlights the need for policy tools—such as subsidies and carbon pricing—to enhance the economic viability of sustainable practices.

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Publication Details

Journal
Sustainable Development
Published
2026-02-24
DOI
https://doi.org/10.1002/sd.70859
Primary Topic
Sustainable Supply Chain Management
Type
article
Field-Weighted Citation Impact
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article

Sustainable Perspectives for Smart Emission Control and Energy Security in Supply Chains: Evidence From Simulation and Game Theory

Zhenyu Qiu, Eldayag Mustafayev, Jafar Hussain, Hao Wang et al.
Sustainable Development
Sustainable Supply Chain Management
article

Sustainable Perspectives for Smart Emission Control and Energy Security in Supply Chains: Evidence From Simulation and Game Theory

Zhenyu Qiu, Eldayag Mustafayev, Jafar Hussain, Hao Wang, Benjamin Lev, Li Zhou
article en

Abstract

ABSTRACT Addressing CO 2 through artificial intelligence enabled technologies is an emerging priority in sustainable supply chains. This study investigates the joint adoption of Smart Emission Control Systems (SECS) and Energy Security (ES) within a tri‐tier supply chain of 300 firms. We develop an integrated framework combining game theory, simulation‐based optimization, reinforcement learning, and decision modeling to capture adaptive learning and profit‐driven behavior. Four adoption scenarios are evaluated. Results show that ES alone provides minor efficiency gains, while SECS alone significantly reduces carbon costs and improves profitability across all tiers. The combined adoption of SECS and ES yields the strongest environmental and financial performance. Profits are reported separately for manufacturers, agents, and retailers, reflecting their distinct positions within the supply chain. Overall, coordinated adoption produces the highest gains and demonstrates that emission‐reduction technologies and efficiency improvements are most effective when implemented together. The framework illustrates how multi‐tier, simultaneous adoption decisions can be supported and highlights the need for policy tools—such as subsidies and carbon pricing—to enhance the economic viability of sustainable practices.

Sustainable Development
Jiangxi University of Water Resources and Electric Power (CN), John F. Kennedy University (US), Harvard University Press (US), Guangdong Institute of Arts and Sciences (CN), Yan'an University (CN), Nanchang Institute of Science & Technology (CN), Drexel University (US), Xi'an Jiaotong University (CN)
Openalex Percentile: Top 33%
Sustainable Supply Chain Management
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Sustainable Perspectives for Smart Emission Control and Energy Security in Supply Chains: Evidence From Simulation and Game Theory — Zhenyu Qiu, Eldayag Mustafayev, et al. · Sustainable Development (2026) | TGRS Research Map | TGRS