4.7 Article

Comparative analysis of government incentives and game structures on single and two-period green supply chain

Journal

JOURNAL OF CLEANER PRODUCTION
Volume 235, Issue -, Pages 1371-1398

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2019.06.168

Keywords

Green supply chain; Government incentives; Strategic inventory; Two-period model; Stackelberg game

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The Government organizations provide incentives to the manufacturers for adopting green technologies in different ways. The objective of this study is to generate a decision support framework in perspectives of the selection and successful implementation towards environment-friendly products by comparing profits of each member, greening level, consumer surplus and environmental improvement under two different incentive policies. Twelve analytical models are formulated and analyzed by considering the impact of two game structures, single and two-period procurement decisions on the sustainability goals of supply chain members. The objectives of government's social welfare are optimized here. A comprehensive analysis reveals that the manufacturer receives higher profits and the greening level is always at higher end in two-period procurement decision under manufacturer-Stackelberg game. The greening level and profit of the retailer are maximum in single-period procurement decision under retailer-Stackelberg game. As a result, optimal preference is highly sensitive to game structure and procurement decision. When the manufacturer sets a premeditated threshold for greening level, supply chain members receive higher profits under incentive policy on per-unit product because of lower sales price, higher consumer surplus and environmental improvement compared to government incentive policy on total investment in R&D. The greening level is maximum under incentive policy on total investment in R&D that results in higher environmental improvement. Supply chain members can compromise with their sustainable goals to receive higher profits in presence of incentives. (C) 2019 Elsevier Ltd. All rights reserved.

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