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A three-level framework for balancing the tradeoffs among the energy, water, and air-emission implications within the life-cycle shale gas supply chains
- He, Li, Chen, Yizhong, Li, Jing
- Resources, conservation, and recycling 2018 v.133 pp. 206-228
- algorithms, decision making, drilling, electricity generation, freshwater, greenhouse gas emissions, greenhouse gases, life cycle assessment, models, shale, shale gas, supply chain, wastewater treatment, water management, water resources
- Two critical challenges, namely high water resources consumption and growing greenhouse gas (GHG) emissions, are encountered across the current shale gas supply chains. This study presents a three-level modeling framework for economic and environmental life-cycle optimization of the shale gas supply chains. Life cycle analysis (LCA) approach and Stackelberg leader-follower game are integrated into the optimization framework to account for a hierarchical structure.This hierarchical framework is capable of not only addressing the sequential decision-making problem raised by decision makers at different levels (e.g., the whole-system decision maker as a leader and the environment-development decision maker as a follower), but also developing multilevel cooperative control of water management and GHG-emission mitigation. An application to the Marcellus Shale is then given to demonstrate the capabilities of the developed three-level model. An improved leader-follower-interactive solution algorithm based on satisfactory degree is presented to tackle the computational challenge of the three-level program. The overall satisfaction solution is generated for satisfying the goals of different decision makers by compromising the trade-offs among energy, water, and air-emission implications. Optimal solutions with respect to well drilling schedule, shale gas production, freshwater supply, wastewater treatment, GHG emissions, and electricity generation would be obtained. These analyses are capable of helping decision makers adjust their tolerances to make informed decisions for the supply chains. Moreover, the decision making is not kept static but improved by repeatedly communicating with both different models and sensitivity analysis. Through the communications, the robustness and objectivity of the model solutions can further be enhanced.