Open Access
Issue
RAIRO-Oper. Res.
Volume 58, Number 5, September-October 2024
Page(s) 4499 - 4520
DOI https://doi.org/10.1051/ro/2024185
Published online 21 October 2024
  • X. Han, M. Khouja and X. Liu, A dynamic model considering consumer green awareness and environmental subsidy. Int. J. Prod. Econ. 260 (2023) 108840. [CrossRef] [Google Scholar]
  • E. Sane-Zerang, J. Razmi and A.A. Taleizadeh, Coordination in a closed-loop supply chain under asymmetric and symmetric information with sales effort-dependent demand. J. Bus. Econ. 90 (2020) 303–334. [Google Scholar]
  • V. Babich and G. Hilary, Om forum – distributed ledgers and operations: What operations management researchers should know about blockchain technology. Manuf. Serv. Oper. Manage. 22 (2020) 223–240. [CrossRef] [Google Scholar]
  • R. Luo, L. Zhou, Y. Song and T. Fan, Evaluating the impact of carbon tax policy on manufacturing and remanufacturing decisions in a closed-loop supply chain. Int. J. Prod. Econ. 245 (2022) 108408. [CrossRef] [Google Scholar]
  • Y. Lu, The blockchain: state-of-the-art and research challenges. J. Ind. Inf. Integr. 15 (2019) 80–90. [Google Scholar]
  • C.G. Schmidt and S.M. Wagner, Blockchain and supply chain relations: a transaction cost theory perspective. J. Purchasing Supply Manage. 25 (2019) 100552. [CrossRef] [Google Scholar]
  • S.F. Cheng, G. De Franco, H. Jiang and P. Lin, Riding the blockchain mania: public firms’ speculative 8-k disclosures. Manage. Sci. 65 (2019) 5901–5913. [CrossRef] [Google Scholar]
  • S. Saberi, M. Kouhizadeh, J. Sarkis and L. Shen, Blockchain technology and its relationships to sustainable supply chain management. Int. J. Prod. Res. 57 (2019) 2117–2135. [CrossRef] [Google Scholar]
  • Y. Zhou and X. Ye, Differential game model of joint emission reduction strategies and contract design in a dual-channel supply chain. J. Clean. Prod. 190 (2018) 592–607. [CrossRef] [Google Scholar]
  • X. Xia, J. Ruan, Z. Juan, Y. Shi, X. Wang and F.T.S. Chan, Upstream-downstream joint carbon reduction strategies based on low-carbon promotion. Int. J. Environ. Res. Publ. Health 15 (2018) 1351. [CrossRef] [Google Scholar]
  • Q. Wang, D. Zhao and L. He, Contracting emission reduction for supply chains considering market low-carbon preference. J. Clean. Prod. 120 (2016) 72–84. [CrossRef] [Google Scholar]
  • Y. Zu, L. Chen and Y. Fan, Research on low-carbon strategies in supply chain with environmental regulations based on differential game. J. Clean. Prod. 177 (2018) 527–546. [CrossRef] [Google Scholar]
  • X. Ma, W. Ho, P. Ji and S. Talluri, Coordinated pricing analysis with the carbon tax scheme in a supply chain. Decis. Sci. 49 (2018) 863–900. [Google Scholar]
  • J. Xue, R. Gong, L. Zhao, X. Ji and Y. Xu, A green supply-chain decision model for energy-saving products that accounts for government subsidies. Sustainability 11 (2019) 2209. [CrossRef] [Google Scholar]
  • J.-S. Lim and Y.-G. Kim, Combining carbon tax and R&D subsidy for climate change mitigation. Energy Econ. 34 (2012) S496–S502. [CrossRef] [Google Scholar]
  • C. Che, Y. Chen, X. Zhang and Z. Zhang, The impact of different government subsidy methods on low-carbon emission reduction strategies in dual-channel supply chain. Complexity 2021 (2021) 6668243. [CrossRef] [Google Scholar]
  • G.I. Galinato and J.K. Yoder, An integrated tax-subsidy policy for carbon emission reduction. Res. Energy Econ. 32 (2010) 310–326. [CrossRef] [Google Scholar]
  • D.P. Brown, A. Eckert and H. Eckert, Carbon pricing with an output subsidy under imperfect competition: the case of Alberta’s restructured electricity market. Res. Energy Econ. 52 (2018) 102–123. [CrossRef] [Google Scholar]
  • T.-H. Kim, R. Goyat, M.K. Rai, G. Kumar, W.J. Buchanan, R. Saha and R. Thomas, A novel trust evaluation process for secure localization using a decentralized blockchain in wireless sensor networks. IEEE Access 7 (2019) 184133–184144. [CrossRef] [Google Scholar]
  • W. Liang, Y. Fan, K.-C. Li, D. Zhang and J.-L. Gaudiot, Secure data storage and recovery in industrial blockchain network environments. IEEE Trans. Ind. Inf. 16 (2020) 6543–6552. [CrossRef] [Google Scholar]
  • T. Morstyn and M.D. McCulloch, Multiclass energy management for peer-to-peer energy trading driven by prosumer preferences. IEEE Trans. Power Syst. 34 (2018) 4005–4014. [Google Scholar]
  • E. Funk, J. Riddell, F. Ankel and D. Cabrera, Blockchain technology: a data framework to improve validity, trust, and accountability of information exchange in health professions education. Acad. Med. 93 (2018) 1791–1794. [CrossRef] [PubMed] [Google Scholar]
  • R. Yuan, Y.-B. Xia, H.-B. Chen, B.-Y. Zang and J. Xie, Shadoweth: private smart contract on public blockchain. J. Comput. Sci. Technol. 33 (2018) 542–556. [CrossRef] [Google Scholar]
  • G.M. Hastig and M.M.S. Sodhi, Blockchain for supply chain traceability: business requirements and critical success factors. Prod. Oper. Manage. 29 (2020) 935–954. [CrossRef] [Google Scholar]
  • T. AlSkaif, J.L. Crespo-Vazquez, M. Sekuloski, G. van Leeuwen and J.P.S. Catalão, Blockchain-based fully peer-to-peer energy trading strategies for residential energy systems. IEEE Trans. Ind. Inf. 18 (2021) 231–241. [Google Scholar]
  • Y. Chen, S. Ding, Z. Xu, H. Zheng and S. Yang, Blockchain-based medical records secure storage and medical service framework. J. Med. Syst. 43 (2019) 1–9. [CrossRef] [Google Scholar]
  • M.S. Asif, H. Lau, D. Nakandala, Y. Fan and H. Hurriyet, Case study research of green life cycle model for the evaluation and reduction of scope 3 emissions in food supply chains. Corporate Soc. Respons. Environ. Manage. 29 (2022) 1050–1066. [CrossRef] [Google Scholar]
  • N. Kshetri, 1 blockchain’s roles in meeting key supply chain management objectives. Int. J. Inf. Manage. 39 (2018) 80–89. [CrossRef] [Google Scholar]
  • S. Yadav and S.P. Singh, Blockchain critical success factors for sustainable supply chain. Res. Conserv. Recycling 152 (2020) 104505. [CrossRef] [Google Scholar]
  • S. Tönnissen and F. Teuteberg, Analysing the impact of blockchain-technology for operations and supply chain management: an explanatory model drawn from multiple case studies. Int. J. Inf. Manage. 52 (2020) 101953. [CrossRef] [Google Scholar]
  • R. Azzi, R.K. Chamoun and M. Sokhn, The power of a blockchain-based supply chain. Comput. Ind. Eng. 135 (2019) 582–592. [CrossRef] [Google Scholar]
  • S.Y. Sheng, Information sharing model construction of supply chain based on blockchain technology. Inf. Sci. 39 (2021) 162–168. [CrossRef] [Google Scholar]
  • Z.-P. Fan, X.-Y. Wu and B.-B. Cao, Considering the traceability awareness of consumers: Should the supply chain adopt the blockchain technology? Ann. Oper. Res. (2022) 1–24. [Google Scholar]
  • L. Liu and F.T. Li, Investment decision and coordination of blockchain technology in fresh supply chain considering retailers’ risk aversion. J. Ind. Eng. Eng. Manage. 36 (2022) 159–171. [Google Scholar]
  • F. Longo, L. Nicoletti, A. Padovano, G. d’Atri and M. Forte, Blockchain-enabled supply chain: an experimental study. Comput. Ind. Eng. 136 (2019) 57–69. [CrossRef] [Google Scholar]
  • V.K. Manupati, T. Schoenherr, M. Ramkumar, S.M. Wagner, S.K. Pabba and R.J.I.J. Inder Raj Singh, A blockchain-based approach for a multi-echelon sustainable supply chain. Int. J. Prod. Res. 58 (2020) 2222–2241. [CrossRef] [Google Scholar]
  • R. Cole, M. Stevenson and J. Aitken, Blockchain technology: implications for operations and supply chain management. Supply Chain Manage. Int. J. 24 (2019) 469–483. [CrossRef] [Google Scholar]
  • X. Liang and J. Xiao, Blockchain-based dual-channel supply chain pricing decision and online channel selection strategy. Chin. J. Manage. Sci. 31 (2023) 29–38. [Google Scholar]
  • Y. Yan and J. Zhang, A study on supply chain subject with a risk-aversion retailer based on block chain technology. Ind. Eng. Manage. 23 (2018) 33–42. [Google Scholar]
  • J. Li, L. Yi and Y. Xiao, Research on information sharing mechanism of emission reduction in supply chain based on blockchain under information asymmetry. Chin. J. Manage. Sci. 29 (2021) 131–139. [Google Scholar]
  • J.A. Poyago-Theotoky, The organization of R&D and environmental policy. J. Econ. Behav. Org. 62 (2007) 63–75. [CrossRef] [Google Scholar]
  • L. Zhang, J. Wang and B. Peng, Research on emission reduction decision under the trading path of carbon quota within and outside the supply chain. Chin. J. Manage. Sci. 28 (2020) 145–154. [Google Scholar]
  • L. Zhang, B. Peng and C. Cheng, Research on government subsidy strategy of low-carbon supply chain based on block-chain technology. Chin. J. Manage. Sci. 9 (2021) 1–13. [Google Scholar]
  • L. Xu, Z. Di and J. Chen, Evolutionary game of inland shipping pollution control under government co-supervision. Marine Pollut. Bull. 171 (2021) 112730. [CrossRef] [Google Scholar]
  • N. Golinucci, F. Tonini, M.V. Rocco and E. Colombo, Towards BITCO2, an individual consumption-based carbon emission reduction mechanism. Energy Policy 183 (2023) 113851. [CrossRef] [Google Scholar]
  • C. Groening, J. Sarkis and Q. Zhu, Green marketing consumer-level theory review: a compendium of applied theories and further research directions. J. Clean. Prod. 172 (2018) 1848–1866. [CrossRef] [Google Scholar]

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