American Institute of Mathematical Sciences

Advanced
doi: 10.3934/jimo.2020175
Online First

Online First articles are published articles within a journal that have not yet been assigned to a formal issue. This means they do not yet have a volume number, issue number, or page numbers assigned to them, however, they can still be found and cited using their DOI (Digital Object Identifier). Online First publication benefits the research community by making new scientific discoveries known as quickly as possible.

Readers can access Online First articles via the “Online First” tab for the selected journal.

Electricity supply chain coordination with carbon abatement technology investment under the benchmarking mechanism

Wei Chen 1, , Yongkai Ma 1,, and  Weihao Hu 2,

1. 

School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, 611731, China
2. 

School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

* Corresponding author: Yongkai Ma

Received  July 2020 Revised  October 2020 Early access December 2020

The introduction of the benchmarking mechanism into the electricity industry has influenced whether utility firms choose to invest in carbon abatement technology. This study presents an electricity supply chain that includes a utility firm as the leader and a retailer as the follower to decide on the electricity price and carbon abatement technology investment. The study discusses the impact of the benchmarking mechanism on the decision-making of the electricity supply chain enterprises. The main conclusions are as follows: (1) Investing in carbon abatement technology increased electricity demand, customer surplus, and profits of the electricity supply chain enterprises. (2) Carbon abatement technology investment and profits of the supply chain enterprises increased with the unit carbon quota. (3) A revenue-sharing and cost-sharing contract could be used to coordinate the electricity supply chain.

Keywords: Benchmarking mechanism, carbon abatement technology investment, electricity supply chain.
Mathematics Subject Classification: Primary: 91A12, 90B06; Secondary: 90B50.
Citation: Wei Chen, Yongkai Ma, Weihao Hu. Electricity supply chain coordination with carbon abatement technology investment under the benchmarking mechanism. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2020175
References:
[1]

A. Abdi, A. Abdi, N. Akbarpour, A. S. Amiri and M. Hajiaghaei-Keshteli, Innovative approaches to design and address green supply chain network with simultaneous pick-up and split delivery, Journal of Cleaner Production, 250 (2020), 119437. doi: 10.1016/j.jclepro.2019.119437.  Google Scholar

[2]

M. H. Albadi and E. F. Elsaadany, A summary of demand response in electricity markets, Electric Power Systems Research, 78 (2008), 1989-1996.  doi: 10.1016/j.epsr.2008.04.002.  Google Scholar

[3]

S. A. H. S. Amiri, A. Zahedi, M. Kazemi, J. Soroor and M. Hajiaghaei-Keshteli, Determination of the Optimal Sales Level of Perishable Goods in a Two-echelon Supply Chain Network, Computers & Industrial Engineering, 137 (2019), 106156. doi: 10.1016/j.cie.2019.106156.  Google Scholar

[4]

A. Bhaumik, S. K. Roy and G. W. Weber, Multi-objective linguistic-neutrosophic matrix game and its applications to tourism management, Journal of Dynamics & Games, (2020). doi: 10.3934/jdg.2020031.  Google Scholar

[5]

J. Bushnell, Oligopoly equilibria in electricity contract markets, Journal of Regulatory Economics, 32 (2007), 225-245.  doi: 10.1007/s11149-007-9031-2.  Google Scholar

[6]

CEC (China Electricity Council), How to participate in the national carbon market?, http://www.sohu.com/a/211438692_418320. Accessed Dec, (2017). Google Scholar

[7]

CEN (China Energy Network), Electric power industry will become the main force of national carbon market, http://www.ocn.com.cn/touzi/chanjing/201712/dvqon20094629.shtml. Accessed Dec, (2017). Google Scholar

[8]

W. Chen, J. Chen and Y. K. Ma, Renewable energy investment and carbon emissions under cap-and-trade mechanisms, Journal of Cleaner Production, 278 (2021). Google Scholar

[9]

Y. Chen, S. T. Ng and M. U. Hossain, Approach to establish carbon emission benchmarking for construction materials, Carbon Management, 9 (2018), 587-604.  doi: 10.1080/17583004.2018.1522094.  Google Scholar

[10]

M. Dolmatova, D. Kozlovskiy, O. Khrustaleva andT. Sultanova, A. Vasin, Market parameters dependent indices for competition evaluation in electricity market, Electric Power Systems Research, 190 (2021), 106762. doi: 10.1016/j.epsr.2020.106762.  Google Scholar

[11]

S. K. Das and S. K. Roy, Effect of variable carbon emission in a multi-objective transportation-p-facility location problem under neutrosophic environment, Computers & Industrial Engineering, 132 (2019), 311-324.  doi: 10.1016/j.cie.2019.04.037.  Google Scholar

[12]

B. T. Erfan, M. Abbas, D. Zahra, S. Mehdi and W. Gerhard-Wilhelm, A novel hybrid method using fuzzy decision making and multi-objective programming for sustainable-reliable supplier selection in two-echelon supply chain design, Journal of Cleaner Production, 250 (2020), 119517, 19pp. doi: 10.1016/j.jclepro.2019.119517.  Google Scholar

[13]

D. Ghosh and J. Shah, A comparative analysis of greening policies across supply chain structures, International Journal of Production Economics, 135 (2012), 568-583.  doi: 10.1016/j.ijpe.2011.05.027.  Google Scholar

[14]

A. Goli, E. B. Tirkolaee, B. Malmir, G. B. Bian and A. K. Sangaiah, A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand, Computing, 101 (2019), 499-529.  doi: 10.1007/s00607-018-00692-2.  Google Scholar

[15]

M. Hajiaghaeikeshteli, S. M. Sajadifar and R. Haji, Determination of the economical policy of a three-echelon inventory system with (R, Q) ordering policy and information sharing, The International Journal of Advanced Manufacturing Technology, 55 (2011), 831-841.   Google Scholar

[16]

M. Hajiaghaeikeshteli and S. M. Sajadifar, Deriving the cost function for a class of three-echelon inventory system with N-retailers and one-for-one ordering policy, The International Journal of Advanced Manufacturing Technology, 50 (2010), 343-351.  doi: 10.1007/s00170-009-2486-9.  Google Scholar

[17]

E. Kropat, G. W. Weber and E. B. Tirkolaee, Foundations of semialgebraic gene-environment networks, Journal of Dynamics & Games, 7 (2020), 253-268.  doi: 10.3934/jdg.2020018.  Google Scholar

[18]

C. Koo, H. Kim and T. Hong, Framework for the analysis of the low-carbon scenario 2020 to achieve the national carbon Emissions reduction target: Focused on educational facilities, Energy Policy, 73 (2014), 356-367.  doi: 10.1016/j.enpol.2014.05.009.  Google Scholar

[19]

M. S. Kiran, E. Ozceylan, M. Gunduz and T. Paksoy, Swarm intelligence approaches to estimate electricity energy demand in Turkey, Knowledge Based Systems, (2012), 93–103. doi: 10.1016/j.knosys.2012.06.009.  Google Scholar

[20]

S. Khalilpourazari, S. Soltanzadeh, G. W. Weber and S. K. Roy, Designing an efficient blood supply chain network in crisis: Neural learning, optimization and case study, Annals of Operations Research, 289 (2020), 123-152.  doi: 10.1007/s10479-019-03437-2.  Google Scholar

[21]

S. Khalilpourazari and S. H. Pasandideh, Modeling and optimization of multi-item multi-constrained EOQ model for growing items, Knowledge Based Systems, (2019), 150–162. doi: 10.1016/j.knosys.2018.10.032.  Google Scholar

[22]

W. Krabs and S. W. Pickl, Analysis, Controllability and Optimization of Time-Discrete Systems and Dynamical Games, Lecture Notes in Economics and Mathematical Systems, 2003. doi: 10.1007/978-3-642-18973-9.  Google Scholar

[23]

A. Kumar, V. Jain and S. Kumar, A comprehensive environment friendly approach for supplier selection, Omega, 42 (2014), 109-123.  doi: 10.1016/j.omega.2013.04.003.  Google Scholar

[24]

K.-H. Lee, Strategy equilibrium in stackelberg model with transmission congestion in electricity market, Journal of Electrical Engineering & Technology, 9 (2014), 90-97.  doi: 10.5370/JEET.2014.9.1.090.  Google Scholar

[25]

Z. Liao, X. Zhu and J. Shi, Case study on initial allocation of Shanghai carbon emission trading based on Shapley value, Journal of Cleaner Production, 103 (2015), 338-344.  doi: 10.1016/j.jclepro.2014.06.045.  Google Scholar

[26]

D. Lozovanu and Pi ckl S, Discrete control and algorithms for solving antagonistic dynamic games on networks, Optimization, 58 (2009), 665-683.  doi: 10.1080/02331930902819253.  Google Scholar

[27]

X. Liu, Sub-sector Carbon dioxide emissions estimation that come from primal energy consumption in china in 2011, Science & Technology for Development, (2011). doi: 10.11842/chips.2011.01.002.  Google Scholar

[28]

Z. Liu, T. D. Anderson and J. M. Cruz, Consumer environmental awareness and competition in two-stage supply chains, European Journal of Operational Research, 218 (2012), 602-613.  doi: 10.1016/j.ejor.2011.11.027.  Google Scholar

[29]

A. Mostafaeipour, M. Qolipour, M. Rezaei and E. Babaee-Tirkolaee, Investigation of off-grid photovoltaic systems for a reverse osmosis desalination system: A case study, Desalination, (2019), 91–103. doi: 10.1016/j.desal.2018.03.007.  Google Scholar

[30]

T. Paksoya, E. Ozceylana and G. Weberb, A multi objective model for optimization of a green supply chain network, AIP Conference Proceedings, 1239 (2010), 311-320.  doi: 10.1063/1.3459765.  Google Scholar

[31]

M. Pervin, S. K. Roy and G. Weber, Analysis of inventory control model with shortage under time-dependent demand and time-varying holding cost including stochastic deterioration, Annals of Operations Research, 260 (2018), 437-460.  doi: 10.1007/s10479-016-2355-5.  Google Scholar

[32]

T. Paksoy, T. Bektas and E. Ozceylan, Operational and environmental performance measures in a multi-product closed-loop supply chain, Transportation Research Part E-logistics and Transportation Review, 47 (2011), 532-546.  doi: 10.1016/j.tre.2010.12.001.  Google Scholar

[33]

S. Swami and J. Shah, Channel Coordination in Green Supply Chain Management, Journal of the Operational Research Society, 64 (2013), 336-351.  doi: 10.1057/jors.2012.44.  Google Scholar

[34]

J. Taheri-Tolgari, A. Mirzazadeh and F. Jolai, An inventory model for imperfect items under inflationary conditions with considering inspection errors, Computers & Mathematics with Applications, 63 (2012), 1007-1019.  doi: 10.1016/j.camwa.2011.09.050.  Google Scholar

[35]

E. B. Tirkolaee, A. Goli, M. Hematian, A. Kumar and H. Tao, Multi-objective multi-mode resource constrained project scheduling problem using Pareto-based algorithms, Computing, 101 (2019), 547-570.  doi: 10.1007/s00607-018-00693-1.  Google Scholar

[36]

A. Vasin, P. Kartunova and G. W. Weber, Models for capacity and electricity market design, Central European Journal of Operations Research, 21 (2013), 651-661.  doi: 10.1007/s10100-012-0259-2.  Google Scholar

[37]

A. Vasin, Game-theoretic Study of Electricity Market Mechanisms, Procedia Computer Science, 31 (2014), 124-132.  doi: 10.1016/j.procs.2014.05.252.  Google Scholar

[38]

Q. Wang, D. Zhao and L. He, Contracting emission reduction for supply chains considering market low-carbon preference, Journal of Cleaner Production, 120 (2016), 72-84.  doi: 10.1016/j.jclepro.2015.11.049.  Google Scholar

[39]

X. Xu, P. He, H. Xu and Q. P. Zhang, Supply chain coordination with green technology under cap-and-trade regulation, International Journal of Production Economics, 183 (2017), 433-442. doi: 10.1016/j.ijpe.2016.08.029.  Google Scholar

[40]

A. Yildizbasi, A. Calik, T. Paksoy, R. Z. Farahani and G Weber, Multi-level optimization of an automotive closed-loop supply chain network with interactive fuzzy programming approaches, Technological and Economic Development of Economy, 24 (2018), 1004-1028.  doi: 10.3846/20294913.2016.1253044.  Google Scholar

[41]

N. Zhang and Y. Choi, Total-factor carbon emission performance of fossil fuel electricity plants in China: A metafrontier non-radial Malmquist index analysis, Energy Economics, 40 (2013), 549-559.  doi: 10.1016/j.eneco.2013.08.012.  Google Scholar

[42]

Y.-J. Zhang, A.-D. Wang and W. Tan, The impact of China's carbon allowance allocation rules on the product prices and emission reduction behaviors of ETS-covered enterprises, Energy Policy, 86 (2015), 176-185.  doi: 10.1016/j.enpol.2015.07.004.  Google Scholar

show all references

References:
[1]

A. Abdi, A. Abdi, N. Akbarpour, A. S. Amiri and M. Hajiaghaei-Keshteli, Innovative approaches to design and address green supply chain network with simultaneous pick-up and split delivery, Journal of Cleaner Production, 250 (2020), 119437. doi: 10.1016/j.jclepro.2019.119437.  Google Scholar

[2]

M. H. Albadi and E. F. Elsaadany, A summary of demand response in electricity markets, Electric Power Systems Research, 78 (2008), 1989-1996.  doi: 10.1016/j.epsr.2008.04.002.  Google Scholar

[3]

S. A. H. S. Amiri, A. Zahedi, M. Kazemi, J. Soroor and M. Hajiaghaei-Keshteli, Determination of the Optimal Sales Level of Perishable Goods in a Two-echelon Supply Chain Network, Computers & Industrial Engineering, 137 (2019), 106156. doi: 10.1016/j.cie.2019.106156.  Google Scholar

[4]

A. Bhaumik, S. K. Roy and G. W. Weber, Multi-objective linguistic-neutrosophic matrix game and its applications to tourism management, Journal of Dynamics & Games, (2020). doi: 10.3934/jdg.2020031.  Google Scholar

[5]

J. Bushnell, Oligopoly equilibria in electricity contract markets, Journal of Regulatory Economics, 32 (2007), 225-245.  doi: 10.1007/s11149-007-9031-2.  Google Scholar

[6]

CEC (China Electricity Council), How to participate in the national carbon market?, http://www.sohu.com/a/211438692_418320. Accessed Dec, (2017). Google Scholar

[7]

CEN (China Energy Network), Electric power industry will become the main force of national carbon market, http://www.ocn.com.cn/touzi/chanjing/201712/dvqon20094629.shtml. Accessed Dec, (2017). Google Scholar

[8]

W. Chen, J. Chen and Y. K. Ma, Renewable energy investment and carbon emissions under cap-and-trade mechanisms, Journal of Cleaner Production, 278 (2021). Google Scholar

[9]

Y. Chen, S. T. Ng and M. U. Hossain, Approach to establish carbon emission benchmarking for construction materials, Carbon Management, 9 (2018), 587-604.  doi: 10.1080/17583004.2018.1522094.  Google Scholar

[10]

M. Dolmatova, D. Kozlovskiy, O. Khrustaleva andT. Sultanova, A. Vasin, Market parameters dependent indices for competition evaluation in electricity market, Electric Power Systems Research, 190 (2021), 106762. doi: 10.1016/j.epsr.2020.106762.  Google Scholar

[11]

S. K. Das and S. K. Roy, Effect of variable carbon emission in a multi-objective transportation-p-facility location problem under neutrosophic environment, Computers & Industrial Engineering, 132 (2019), 311-324.  doi: 10.1016/j.cie.2019.04.037.  Google Scholar

[12]

B. T. Erfan, M. Abbas, D. Zahra, S. Mehdi and W. Gerhard-Wilhelm, A novel hybrid method using fuzzy decision making and multi-objective programming for sustainable-reliable supplier selection in two-echelon supply chain design, Journal of Cleaner Production, 250 (2020), 119517, 19pp. doi: 10.1016/j.jclepro.2019.119517.  Google Scholar

[13]

D. Ghosh and J. Shah, A comparative analysis of greening policies across supply chain structures, International Journal of Production Economics, 135 (2012), 568-583.  doi: 10.1016/j.ijpe.2011.05.027.  Google Scholar

[14]

A. Goli, E. B. Tirkolaee, B. Malmir, G. B. Bian and A. K. Sangaiah, A multi-objective invasive weed optimization algorithm for robust aggregate production planning under uncertain seasonal demand, Computing, 101 (2019), 499-529.  doi: 10.1007/s00607-018-00692-2.  Google Scholar

[15]

M. Hajiaghaeikeshteli, S. M. Sajadifar and R. Haji, Determination of the economical policy of a three-echelon inventory system with (R, Q) ordering policy and information sharing, The International Journal of Advanced Manufacturing Technology, 55 (2011), 831-841.   Google Scholar

[16]

M. Hajiaghaeikeshteli and S. M. Sajadifar, Deriving the cost function for a class of three-echelon inventory system with N-retailers and one-for-one ordering policy, The International Journal of Advanced Manufacturing Technology, 50 (2010), 343-351.  doi: 10.1007/s00170-009-2486-9.  Google Scholar

[17]

E. Kropat, G. W. Weber and E. B. Tirkolaee, Foundations of semialgebraic gene-environment networks, Journal of Dynamics & Games, 7 (2020), 253-268.  doi: 10.3934/jdg.2020018.  Google Scholar

[18]

C. Koo, H. Kim and T. Hong, Framework for the analysis of the low-carbon scenario 2020 to achieve the national carbon Emissions reduction target: Focused on educational facilities, Energy Policy, 73 (2014), 356-367.  doi: 10.1016/j.enpol.2014.05.009.  Google Scholar

[19]

M. S. Kiran, E. Ozceylan, M. Gunduz and T. Paksoy, Swarm intelligence approaches to estimate electricity energy demand in Turkey, Knowledge Based Systems, (2012), 93–103. doi: 10.1016/j.knosys.2012.06.009.  Google Scholar

[20]

S. Khalilpourazari, S. Soltanzadeh, G. W. Weber and S. K. Roy, Designing an efficient blood supply chain network in crisis: Neural learning, optimization and case study, Annals of Operations Research, 289 (2020), 123-152.  doi: 10.1007/s10479-019-03437-2.  Google Scholar

[21]

S. Khalilpourazari and S. H. Pasandideh, Modeling and optimization of multi-item multi-constrained EOQ model for growing items, Knowledge Based Systems, (2019), 150–162. doi: 10.1016/j.knosys.2018.10.032.  Google Scholar

[22]

W. Krabs and S. W. Pickl, Analysis, Controllability and Optimization of Time-Discrete Systems and Dynamical Games, Lecture Notes in Economics and Mathematical Systems, 2003. doi: 10.1007/978-3-642-18973-9.  Google Scholar

[23]

A. Kumar, V. Jain and S. Kumar, A comprehensive environment friendly approach for supplier selection, Omega, 42 (2014), 109-123.  doi: 10.1016/j.omega.2013.04.003.  Google Scholar

[24]

K.-H. Lee, Strategy equilibrium in stackelberg model with transmission congestion in electricity market, Journal of Electrical Engineering & Technology, 9 (2014), 90-97.  doi: 10.5370/JEET.2014.9.1.090.  Google Scholar

[25]

Z. Liao, X. Zhu and J. Shi, Case study on initial allocation of Shanghai carbon emission trading based on Shapley value, Journal of Cleaner Production, 103 (2015), 338-344.  doi: 10.1016/j.jclepro.2014.06.045.  Google Scholar

[26]

D. Lozovanu and Pi ckl S, Discrete control and algorithms for solving antagonistic dynamic games on networks, Optimization, 58 (2009), 665-683.  doi: 10.1080/02331930902819253.  Google Scholar

[27]

X. Liu, Sub-sector Carbon dioxide emissions estimation that come from primal energy consumption in china in 2011, Science & Technology for Development, (2011). doi: 10.11842/chips.2011.01.002.  Google Scholar

[28]

Z. Liu, T. D. Anderson and J. M. Cruz, Consumer environmental awareness and competition in two-stage supply chains, European Journal of Operational Research, 218 (2012), 602-613.  doi: 10.1016/j.ejor.2011.11.027.  Google Scholar

[29]

A. Mostafaeipour, M. Qolipour, M. Rezaei and E. Babaee-Tirkolaee, Investigation of off-grid photovoltaic systems for a reverse osmosis desalination system: A case study, Desalination, (2019), 91–103. doi: 10.1016/j.desal.2018.03.007.  Google Scholar

[30]

T. Paksoya, E. Ozceylana and G. Weberb, A multi objective model for optimization of a green supply chain network, AIP Conference Proceedings, 1239 (2010), 311-320.  doi: 10.1063/1.3459765.  Google Scholar

[31]

M. Pervin, S. K. Roy and G. Weber, Analysis of inventory control model with shortage under time-dependent demand and time-varying holding cost including stochastic deterioration, Annals of Operations Research, 260 (2018), 437-460.  doi: 10.1007/s10479-016-2355-5.  Google Scholar

[32]

T. Paksoy, T. Bektas and E. Ozceylan, Operational and environmental performance measures in a multi-product closed-loop supply chain, Transportation Research Part E-logistics and Transportation Review, 47 (2011), 532-546.  doi: 10.1016/j.tre.2010.12.001.  Google Scholar

[33]

S. Swami and J. Shah, Channel Coordination in Green Supply Chain Management, Journal of the Operational Research Society, 64 (2013), 336-351.  doi: 10.1057/jors.2012.44.  Google Scholar

[34]

J. Taheri-Tolgari, A. Mirzazadeh and F. Jolai, An inventory model for imperfect items under inflationary conditions with considering inspection errors, Computers & Mathematics with Applications, 63 (2012), 1007-1019.  doi: 10.1016/j.camwa.2011.09.050.  Google Scholar

[35]

E. B. Tirkolaee, A. Goli, M. Hematian, A. Kumar and H. Tao, Multi-objective multi-mode resource constrained project scheduling problem using Pareto-based algorithms, Computing, 101 (2019), 547-570.  doi: 10.1007/s00607-018-00693-1.  Google Scholar

[36]

A. Vasin, P. Kartunova and G. W. Weber, Models for capacity and electricity market design, Central European Journal of Operations Research, 21 (2013), 651-661.  doi: 10.1007/s10100-012-0259-2.  Google Scholar

[37]

A. Vasin, Game-theoretic Study of Electricity Market Mechanisms, Procedia Computer Science, 31 (2014), 124-132.  doi: 10.1016/j.procs.2014.05.252.  Google Scholar

[38]

Q. Wang, D. Zhao and L. He, Contracting emission reduction for supply chains considering market low-carbon preference, Journal of Cleaner Production, 120 (2016), 72-84.  doi: 10.1016/j.jclepro.2015.11.049.  Google Scholar

[39]

X. Xu, P. He, H. Xu and Q. P. Zhang, Supply chain coordination with green technology under cap-and-trade regulation, International Journal of Production Economics, 183 (2017), 433-442. doi: 10.1016/j.ijpe.2016.08.029.  Google Scholar

[40]

A. Yildizbasi, A. Calik, T. Paksoy, R. Z. Farahani and G Weber, Multi-level optimization of an automotive closed-loop supply chain network with interactive fuzzy programming approaches, Technological and Economic Development of Economy, 24 (2018), 1004-1028.  doi: 10.3846/20294913.2016.1253044.  Google Scholar

[41]

N. Zhang and Y. Choi, Total-factor carbon emission performance of fossil fuel electricity plants in China: A metafrontier non-radial Malmquist index analysis, Energy Economics, 40 (2013), 549-559.  doi: 10.1016/j.eneco.2013.08.012.  Google Scholar

[42]

Y.-J. Zhang, A.-D. Wang and W. Tan, The impact of China's carbon allowance allocation rules on the product prices and emission reduction behaviors of ETS-covered enterprises, Energy Policy, 86 (2015), 176-185.  doi: 10.1016/j.enpol.2015.07.004.  Google Scholar

Figure 1.  Electricity supply chain with two strategies
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 2.  Electricity price $ p $ as $ e_0 $
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 3.  Electricity demand $ q $ as $ e_0 $
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 4.  Carbon abatement technology investment $ \xi $ as $ e_0 $
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 5.  The supply chain enterprises' profits as $ e_0 $
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 6.  Price sensitivity
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 7.  Carbon abatement technology sensitivity
Figure Options

Download full-size image

Download as PowerPoint slide

Table 1.  Notation and description of parameters
Notation Description
$ q $ Electricity demand
$ a $ Potential electricity demand
$ b $ Sensitivity coefficient of electricity price
$ c $ Cost coefficient of electricity production
$ k $ Cost coefficient of carbon abatement technology investment
$ e_0 $ Unit carbon emission-free quota under the BM
$ e $ Initial unit carbon emission
$ t $ Carbon price
$ \pi _R $ Profit of the electricity retailer
$ \pi _U $ $ \pi _S $ Profit of the utility firm Profit of the supply chain
Decision Variables
$ p $ Electricity price
$ w $ Electricity wholesale price
$ \xi $ Carbon abatement technology investment
Table Options

Download as excel

Notation Description
$ q $ Electricity demand
$ a $ Potential electricity demand
$ b $ Sensitivity coefficient of electricity price
$ c $ Cost coefficient of electricity production
$ k $ Cost coefficient of carbon abatement technology investment
$ e_0 $ Unit carbon emission-free quota under the BM
$ e $ Initial unit carbon emission
$ t $ Carbon price
$ \pi _R $ Profit of the electricity retailer
$ \pi _U $ $ \pi _S $ Profit of the utility firm Profit of the supply chain
Decision Variables
$ p $ Electricity price
$ w $ Electricity wholesale price
$ \xi $ Carbon abatement technology investment
[1]

Feimin Zhong, Wei Zeng, Zhongbao Zhou. Mechanism design in a supply chain with ambiguity in private information. Journal of Industrial & Management Optimization, 2020, 16 (1) : 261-287. doi: 10.3934/jimo.2018151
[2]

Wei Chen, Fuying Jing, Li Zhong. Coordination strategy for a dual-channel electricity supply chain with sustainability. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021139
[3]

Tinggui Chen, Yanhui Jiang. Research on operating mechanism for creative products supply chain based on game theory. Discrete & Continuous Dynamical Systems - S, 2015, 8 (6) : 1103-1112. doi: 10.3934/dcdss.2015.8.1103
[4]

Xiaoli Yang, Jin Liang, Bei Hu. Minimization of carbon abatement cost: Modeling, analysis and simulation. Discrete & Continuous Dynamical Systems - B, 2017, 22 (7) : 2939-2969. doi: 10.3934/dcdsb.2017158
[5]

Qingguo Bai, Fanwen Meng. Impact of risk aversion on two-echelon supply chain systems with carbon emission reduction constraints. Journal of Industrial & Management Optimization, 2020, 16 (4) : 1943-1965. doi: 10.3934/jimo.2019037
[6]

Dayi He, Xiaoling Chen, Qi Huang. Influences of carbon emission abatement on firms' production policy based on newsboy model. Journal of Industrial & Management Optimization, 2017, 13 (1) : 251-265. doi: 10.3934/jimo.2016015
[7]

Shuhua Zhang, Junying Zhao, Ming Yan, Xinyu Wang. Modeling and computation of mean field game with compound carbon abatement mechanisms. Journal of Industrial & Management Optimization, 2021, 17 (6) : 3333-3347. doi: 10.3934/jimo.2020121
[8]

Jing Shi, Tiaojun Xiao. Service investment and consumer returns policy in a vendor-managed inventory supply chain. Journal of Industrial & Management Optimization, 2015, 11 (2) : 439-459. doi: 10.3934/jimo.2015.11.439
[9]

Shunfu Jin, Wuyi Yue, Chao Meng, Zsolt Saffer. A novel active DRX mechanism in LTE technology and its performance evaluation. Journal of Industrial & Management Optimization, 2015, 11 (3) : 849-866. doi: 10.3934/jimo.2015.11.849
[10]

Jianxiong Zhang, Zhenyu Bai, Wansheng Tang. Optimal pricing policy for deteriorating items with preservation technology investment. Journal of Industrial & Management Optimization, 2014, 10 (4) : 1261-1277. doi: 10.3934/jimo.2014.10.1261
[11]

Juliang Zhang, Jian Chen. Information sharing in a make-to-stock supply chain. Journal of Industrial & Management Optimization, 2014, 10 (4) : 1169-1189. doi: 10.3934/jimo.2014.10.1169
[12]

Honglin Yang, Jiawu Peng. Coordinating a supply chain with demand information updating. Journal of Industrial & Management Optimization, 2020  doi: 10.3934/jimo.2020181
[13]

Yeong-Cheng Liou, Siegfried Schaible, Jen-Chih Yao. Supply chain inventory management via a Stackelberg equilibrium. Journal of Industrial & Management Optimization, 2006, 2 (1) : 81-94. doi: 10.3934/jimo.2006.2.81
[14]

Juliang Zhang. Coordination of supply chain with buyer's promotion. Journal of Industrial & Management Optimization, 2007, 3 (4) : 715-726. doi: 10.3934/jimo.2007.3.715
[15]

Liping Zhang. A nonlinear complementarity model for supply chain network equilibrium. Journal of Industrial & Management Optimization, 2007, 3 (4) : 727-737. doi: 10.3934/jimo.2007.3.727
[16]

Na Song, Ximin Huang, Yue Xie, Wai-Ki Ching, Tak-Kuen Siu. Impact of reorder option in supply chain coordination. Journal of Industrial & Management Optimization, 2017, 13 (1) : 449-475. doi: 10.3934/jimo.2016026
[17]

Joseph Geunes, Panos M. Pardalos. Introduction to the Special Issue on Supply Chain Optimization. Journal of Industrial & Management Optimization, 2007, 3 (1) : i-ii. doi: 10.3934/jimo.2007.3.1i
[18]

Jia Shu, Jie Sun. Designing the distribution network for an integrated supply chain. Journal of Industrial & Management Optimization, 2006, 2 (3) : 339-349. doi: 10.3934/jimo.2006.2.339
[19]

Jun Pei, Panos M. Pardalos, Xinbao Liu, Wenjuan Fan, Shanlin Yang, Ling Wang. Coordination of production and transportation in supply chain scheduling. Journal of Industrial & Management Optimization, 2015, 11 (2) : 399-419. doi: 10.3934/jimo.2015.11.399
[20]

Chandan Mahato, Gour Chandra Mahata. Optimal replenishment, pricing and preservation technology investment policies for non-instantaneous deteriorating items under two-level trade credit policy. Journal of Industrial & Management Optimization, 2021  doi: 10.3934/jimo.2021123

2020 Impact Factor: 1.801

Tools

Article outline

Figures and Tables

[Back to Top]

Copyright © 2021 American Institute of Mathematical Sciences