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doi: 10.3934/jimo.2022049
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Study on government subsidy in a two-level supply chain of direct-fired biomass power generation based on contract coordination

Kun Fan 1,, , Wenjin Mao 1, , Hua Qu 1, , Xinning Li 2, and  Meng Wang 3,

1. 

School of Economics and Management Beijing Forestry University, Beijing, 100091, China
2. 

Neusoft Education Technology Group, Dalian Neusoft University of Information, Dalian, 116023, China
3. 

School of Economics and Management, Zhengzhou University of Light Industry, Zhengzhou, 450001, China

*Corresponding author: Kun Fan

Received  August 2021 Revised  January 2022 Early access April 2022

Fund Project: The research is supported by Ministry of Education Humanities and Social Sciences Foundation of China (No.21YJA630012)and Beijing Municipal Social Science Foundation (No.16GLC059)

Biomass power generation is helpful to build a clean, low-carbon, and green energy system, but the shortage of raw materials supply severely restricts the development of the biomass power generation industry in China. To solve problems of supply chain disharmony and low efficiency of government subsidy caused by stochastic factors in biomass power supply chain, this paper studies the decision-making of government subsidy on biomass power generation supply chain. First, a two-level supply chain model under stochastic supply and stochastic output environment is built. The two-level supply chain consists of farmers and the biomass power plant. Second, to achieve the coordination of the two-level supply chain through the calculation of the model, it sets up the combined contract model based on surplus compensation, shortage penalty, and revenue sharing. Then, the validity of the contract is demonstrated by the data obtained from the field survey. Finally, depending on the model and contract coordination results of the supply chain, the impacts of government subsidies on two members' decision-making are analyzed, and the changes of members' profits and chain's profits are discussed. Therefore, the government subsidy strategy for biomass direct-fired power generation is proposed.

Keywords: Biomass direct combustion power generation, contract coordination, government subsidy.
Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.
Citation: Kun Fan, Wenjin Mao, Hua Qu, Xinning Li, Meng Wang. Study on government subsidy in a two-level supply chain of direct-fired biomass power generation based on contract coordination. Journal of Industrial and Management Optimization, doi: 10.3934/jimo.2022049
References:
[1]

L. Ari, A. Antti, K. Jyrki-Pekko, P. Marjo and S. Lauri, Improving the financial performance of solid forest fuel supply using a simple moisture and dry matter loss simulation and optimization, Biomass and Bioenergy, 116 (2018), 72-79. 

[2]

G. P. Cachon, Supply chain coordination with contracts, Handbooks in Operations Research and Management Science, 11 (2003), 227-339.  doi: 10.1016/S0927-0507(03)11006-7.

[3]

R. Fan, L. Dong, E. Policy and and N. France, The dynamic analysis and simulation of government subsidy strategies in low-carbon diffusion considering the behavior of heterogeneous agents, Energy Policy, 117 (2018), 252-262. 

[4]

A. Ghadge, S. Dani, R. Ojha and N. Caldwell, Using risk sharing contracts for supply chain risk mitigation: A buyer-supplier power and dependence perspective, Computers and Industrial Engineering, 103 (2017), 262-270.  doi: 10.1016/j.cie.2016.11.034.

[5]

B. C. Giri, S. Bardhan and T. Maiti, Coordinating a three-layer supply chain with uncertain demand and random yield, International Journal of Production Research, 54 (2017), 1-20. 

[6]

B. C. Giri, J. K. Majhi, S. Bardhan and K. S. Chaudhuri, Coordinating a three-level supply chain with effort and price dependent stochastic demand under random yield, Ann. Oper. Res., 307 (2021), 175-206.  doi: 10.1007/s10479-021-04257-z.

[7]

A. Hafezalkotob, Competition of two green and regular supply chains under environmental protection and revenue seeking policies of government, Computers and Industrial Engineering, 82 (2015), 103-114. 

[8]

J. Heydari, K. Govindan and R. Sadeghi, Reverse supply chain coordination under stochastic remanufacturing capacity, International Journal of Production Economics, 202 (2018), 1-11. 

[9]

B. Hu and Y. Feng, Optimization and coordination of supply chain with revenue sharing contracts and service requirement under supply and demand uncertainty, International Journal of Production Economics, 183 (2017), 185-193. 

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C. Jing, Returns with wholesale-price-discount contract in a newsvendor problem, International Journal of Production Economics, 130 (2011), 104-111. 

[11]

J. Li, Y. Li and S. Zhang, Optimal expansion timing decisions in multi-stage PPP projects involving dedicated asset and government subsidies, J. Ind. Manag. Optim., 16 (2020), 2065-2086.  doi: 10.3934/jimo.2019043.

[12]

Y. Li and Y. Wei, Research on green supply chain coordination considering stochastic demand and blockchain doncepts, Systems Engineering, (2021).

[13]

C. H. Lim and H. L. Lam, Biomass supply chain optimisation via novel biomass element life cycle Analysis (BELCA), Applied Energy, 161 (2016), 733-745. 

[14]

Q. Lin, Q. Zhao and B. Lev, Influenza vaccine supply chain coordination under uncertain supply and demand, European J. Oper. Res., 297 (2022), 930-948.  doi: 10.1016/j.ejor.2021.05.025.

[15]

F. Morandi and H. Ostergard, Sustainability assessment of two chains of biomass supply from field to bioenergy, Dtu Sustain Conference, (2014).

[16]

B. A. Pasternack, Optimal pricing and return policies for perishable commodities, Marketing Science, (2008).

[17]

F. Salimi and B. Vandani, Designing a bio-fuel network considering links reliability and risk-pooling effect in bio-refineries, Reliability Engineering and System Safety, 174 (2018), 96-107. 

[18]

J. J. Spengler, Vertical integration and antitrust policy, Journal of Political Economy, 58 (1950), 347-352. 

[19]

C. Wang, W. Wang and R. Huang, Supply chain enterprise operations and government carbon tax decisions considering carbon emissions, Journal of Cleaner Production, 152 (2017), 271-280. 

[20]

G. Xie, Cooperative strategies for sustainability in a decentralized supply chain with competing suppliers, Journal of Cleaner Production, 113 (2016), 807-821. 

[21]

J. Yan and G. H. Zheng, Coordination contracts for supply chain considering supply disruption and loss aversion, Journal of Railway Science and Engineering, 17 (2020), 9. 

[22]

H. Yang and W. Chen, Retailer-driven carbon emission abatement with consumer environmental awareness and carbon tax: Revenue-sharing versus Cost-sharing, Omega, 78 (2018), 179-191. 

[23]

H. Yang, Y. Sun and Y. Gong, The agricultural supply chain dontracts under random yield, Systems Engineering, (2021).

[24]

S. Yang, J. Yang and L. Abdel-Malek, Sourcing with random yields and stochastic demand: A newsvendor approach, Computers & Operations Research, 34 (2007), 3682-3690. 

[25]

F. Ye, Y. Li, Q. Lin and Y. Zhan, Modeling of China's cassava-based bioethanol supply chain operation and coordination, Energy, 120 (2017), 217-228. 

[26]

S. Zhao and Q. Zhu, Remanufacturing supply chain coordination under the stochastic remanufacturability rate and the random demand, Ann. Oper. Res., 257 (2017), 661-695.  doi: 10.1007/s10479-015-2021-3.

show all references

References:
[1]

L. Ari, A. Antti, K. Jyrki-Pekko, P. Marjo and S. Lauri, Improving the financial performance of solid forest fuel supply using a simple moisture and dry matter loss simulation and optimization, Biomass and Bioenergy, 116 (2018), 72-79. 

[2]

G. P. Cachon, Supply chain coordination with contracts, Handbooks in Operations Research and Management Science, 11 (2003), 227-339.  doi: 10.1016/S0927-0507(03)11006-7.

[3]

R. Fan, L. Dong, E. Policy and and N. France, The dynamic analysis and simulation of government subsidy strategies in low-carbon diffusion considering the behavior of heterogeneous agents, Energy Policy, 117 (2018), 252-262. 

[4]

A. Ghadge, S. Dani, R. Ojha and N. Caldwell, Using risk sharing contracts for supply chain risk mitigation: A buyer-supplier power and dependence perspective, Computers and Industrial Engineering, 103 (2017), 262-270.  doi: 10.1016/j.cie.2016.11.034.

[5]

B. C. Giri, S. Bardhan and T. Maiti, Coordinating a three-layer supply chain with uncertain demand and random yield, International Journal of Production Research, 54 (2017), 1-20. 

[6]

B. C. Giri, J. K. Majhi, S. Bardhan and K. S. Chaudhuri, Coordinating a three-level supply chain with effort and price dependent stochastic demand under random yield, Ann. Oper. Res., 307 (2021), 175-206.  doi: 10.1007/s10479-021-04257-z.

[7]

A. Hafezalkotob, Competition of two green and regular supply chains under environmental protection and revenue seeking policies of government, Computers and Industrial Engineering, 82 (2015), 103-114. 

[8]

J. Heydari, K. Govindan and R. Sadeghi, Reverse supply chain coordination under stochastic remanufacturing capacity, International Journal of Production Economics, 202 (2018), 1-11. 

[9]

B. Hu and Y. Feng, Optimization and coordination of supply chain with revenue sharing contracts and service requirement under supply and demand uncertainty, International Journal of Production Economics, 183 (2017), 185-193. 

[10]

C. Jing, Returns with wholesale-price-discount contract in a newsvendor problem, International Journal of Production Economics, 130 (2011), 104-111. 

[11]

J. Li, Y. Li and S. Zhang, Optimal expansion timing decisions in multi-stage PPP projects involving dedicated asset and government subsidies, J. Ind. Manag. Optim., 16 (2020), 2065-2086.  doi: 10.3934/jimo.2019043.

[12]

Y. Li and Y. Wei, Research on green supply chain coordination considering stochastic demand and blockchain doncepts, Systems Engineering, (2021).

[13]

C. H. Lim and H. L. Lam, Biomass supply chain optimisation via novel biomass element life cycle Analysis (BELCA), Applied Energy, 161 (2016), 733-745. 

[14]

Q. Lin, Q. Zhao and B. Lev, Influenza vaccine supply chain coordination under uncertain supply and demand, European J. Oper. Res., 297 (2022), 930-948.  doi: 10.1016/j.ejor.2021.05.025.

[15]

F. Morandi and H. Ostergard, Sustainability assessment of two chains of biomass supply from field to bioenergy, Dtu Sustain Conference, (2014).

[16]

B. A. Pasternack, Optimal pricing and return policies for perishable commodities, Marketing Science, (2008).

[17]

F. Salimi and B. Vandani, Designing a bio-fuel network considering links reliability and risk-pooling effect in bio-refineries, Reliability Engineering and System Safety, 174 (2018), 96-107. 

[18]

J. J. Spengler, Vertical integration and antitrust policy, Journal of Political Economy, 58 (1950), 347-352. 

[19]

C. Wang, W. Wang and R. Huang, Supply chain enterprise operations and government carbon tax decisions considering carbon emissions, Journal of Cleaner Production, 152 (2017), 271-280. 

[20]

G. Xie, Cooperative strategies for sustainability in a decentralized supply chain with competing suppliers, Journal of Cleaner Production, 113 (2016), 807-821. 

[21]

J. Yan and G. H. Zheng, Coordination contracts for supply chain considering supply disruption and loss aversion, Journal of Railway Science and Engineering, 17 (2020), 9. 

[22]

H. Yang and W. Chen, Retailer-driven carbon emission abatement with consumer environmental awareness and carbon tax: Revenue-sharing versus Cost-sharing, Omega, 78 (2018), 179-191. 

[23]

H. Yang, Y. Sun and Y. Gong, The agricultural supply chain dontracts under random yield, Systems Engineering, (2021).

[24]

S. Yang, J. Yang and L. Abdel-Malek, Sourcing with random yields and stochastic demand: A newsvendor approach, Computers & Operations Research, 34 (2007), 3682-3690. 

[25]

F. Ye, Y. Li, Q. Lin and Y. Zhan, Modeling of China's cassava-based bioethanol supply chain operation and coordination, Energy, 120 (2017), 217-228. 

[26]

S. Zhao and Q. Zhu, Remanufacturing supply chain coordination under the stochastic remanufacturability rate and the random demand, Ann. Oper. Res., 257 (2017), 661-695.  doi: 10.1007/s10479-015-2021-3.

Figure 1.  The expected profit of whole supply chain under centralized decision-making
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Figure 2.  Expected profit of farmers and foresters and the biomass power plant respectly
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Figure 3.  Profit of the supply chain under different subsidy distribution policies
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Table 1.  Situation under different subsidy distribution policies when total subsidy value = 350
$ r_A $ $ r_B $ r b $ \lambda $ x q
0 7/18 259 55 $ 13.64\% $ 161135 173087
10 17/45 265 47 $ 11.63\% $ 164141 173020
20 11/30 271 38 $ 9.52\% $ 167091 172956
30 16/45 278 29 $ 7.32\% $ 169989 172895
40 31/90 285 20 $ 5.00\% $ 172838 172838
50 1/3 292 10 $ 2.56\% $ 175639 172783
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$ r_A $ $ r_B $ r b $ \lambda $ x q
0 7/18 259 55 $ 13.64\% $ 161135 173087
10 17/45 265 47 $ 11.63\% $ 164141 173020
20 11/30 271 38 $ 9.52\% $ 167091 172956
30 16/45 278 29 $ 7.32\% $ 169989 172895
40 31/90 285 20 $ 5.00\% $ 172838 172838
50 1/3 292 10 $ 2.56\% $ 175639 172783
Table 2.  Situation under different subsidy distributions when total subsidy value = 200
$ r_A $ $ r_B $ $ E(\pi_A) $ $ E(\pi_B) $ $ E(\pi_C) $ x q
0 2/9 2999732 18998304 21998036 155921 167486
10 19/90 2455410 18661118 21116528 158725 167310
20 1/5 1928749 18323116 20251865 161468 167135
30 17/90 1419743 17983412 19403156 164153 166959
40 8/45 928479 17641108 18569587 166783 166783
50 1/6 455139 17295277 17750416 169359 166605
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$ r_A $ $ r_B $ $ E(\pi_A) $ $ E(\pi_B) $ $ E(\pi_C) $ x q
0 2/9 2999732 18998304 21998036 155921 167486
10 19/90 2455410 18661118 21116528 158725 167310
20 1/5 1928749 18323116 20251865 161468 167135
30 17/90 1419743 17983412 19403156 164153 166959
40 8/45 928479 17641108 18569587 166783 166783
50 1/6 455139 17295277 17750416 169359 166605
Table 3.  Situation under different subsidy distribution policies
$ r_A $ $ r_B $ $ E(\pi_A) $ $ E(\pi_B) $ $ E(\pi_C) $ x q
0 0 -1407826 -8916234 -10324060 144216 154913
10 0 -1116079 -8482206 -9598285 147293 155260
20 0 -843398 -8012288 -8855686 150340 155617
30 0 -592460 -7504496 -8096956 153361 155983
40 0 -366136 -6956586 -7322722 156358 156358
50 0 -167527 -6366028 -6533555 159334 156743
0 0.057 -310685 -1967673 -2278359 148015 158994
10 0.057 -178318 -1355214 -1533532 151145 159321
20 0.057 -73484 -698095 -771579 154244 159658
30 0.057 496 6284 6780 157314 160003
40 0.057 40045 760846 800891 160357 160357
50 0.057 41286 1568873 1610159 163376 160719
0 0.157 1675358 10610602 12285960 153230 164596
10 0.157 1518248 11538685 13056933 156431 164893
20 0.157 1318612 12526818 13845431 159597 165198
30 0.157 1072002 13578691 14650692 162729 165511
40 0.157 773601 14698425 15472027 165831 165831
50 0.157 418174 15890630 16308805 168905 166159
0 0.257 3712806 23514436 27227242 157182 168841
10 0.257 3257909 24760109 28018018 160434 169112
20 0.257 2745391 26081216 28826607 163647 169390
30 0.257 2169674 27482542 29652217 166824 169675
40 0.257 1524706 28969420 30494127 169967 169967
50 0.257 803889 30547792 31351681 173080 170266
0 0.357 5783661 36629858 42413519 160282 172171
10 0.357 5025561 38194268 43219829 163572 172420
20 0.357 4194682 39849485 44044167 166820 172676
30 0.357 3284320 41601399 44885719 170031 172937
40 0.357 2287187 43456554 45743741 173205 173205
50 0.357 1195321 45422232 46617553 176346 173479
Table Options

Download as excel

$ r_A $ $ r_B $ $ E(\pi_A) $ $ E(\pi_B) $ $ E(\pi_C) $ x q
0 0 -1407826 -8916234 -10324060 144216 154913
10 0 -1116079 -8482206 -9598285 147293 155260
20 0 -843398 -8012288 -8855686 150340 155617
30 0 -592460 -7504496 -8096956 153361 155983
40 0 -366136 -6956586 -7322722 156358 156358
50 0 -167527 -6366028 -6533555 159334 156743
0 0.057 -310685 -1967673 -2278359 148015 158994
10 0.057 -178318 -1355214 -1533532 151145 159321
20 0.057 -73484 -698095 -771579 154244 159658
30 0.057 496 6284 6780 157314 160003
40 0.057 40045 760846 800891 160357 160357
50 0.057 41286 1568873 1610159 163376 160719
0 0.157 1675358 10610602 12285960 153230 164596
10 0.157 1518248 11538685 13056933 156431 164893
20 0.157 1318612 12526818 13845431 159597 165198
30 0.157 1072002 13578691 14650692 162729 165511
40 0.157 773601 14698425 15472027 165831 165831
50 0.157 418174 15890630 16308805 168905 166159
0 0.257 3712806 23514436 27227242 157182 168841
10 0.257 3257909 24760109 28018018 160434 169112
20 0.257 2745391 26081216 28826607 163647 169390
30 0.257 2169674 27482542 29652217 166824 169675
40 0.257 1524706 28969420 30494127 169967 169967
50 0.257 803889 30547792 31351681 173080 170266
0 0.357 5783661 36629858 42413519 160282 172171
10 0.357 5025561 38194268 43219829 163572 172420
20 0.357 4194682 39849485 44044167 166820 172676
30 0.357 3284320 41601399 44885719 170031 172937
40 0.357 2287187 43456554 45743741 173205 173205
50 0.357 1195321 45422232 46617553 176346 173479
Table 4.  Profit statement of biomass power plant under different subsidies when $ r_A $ = 20
$ r_B $ $ r_B+p_B $ x Q $ R(\pi_B) $ $ C(\pi_B) $ $ E(\pi_B) $ p
0 0.393 150340 155617 57988171 66000459 -8012288 -13.82%
0.007 0.4 150862 156157 59200760 66326206 -7125446 -12.04%
0.057 0.45 154244 159658 67808514 68506609 -698095 -1.03%
0.107 0.5 157120 162635 76338929 70475997 5862932 7.68%
0.157 0.55 159596 165198 84805198 72278380 12526818 14.77%
0.207 0.6 161753 167430 93218692 73947086 19271606 20.67%
0.257 0.65 163647 169390 101586998 75505782 26081216 25.67%
0.307 0.7 165324 171127 109917862 76974277 32943585 29.97%
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$ r_B $ $ r_B+p_B $ x Q $ R(\pi_B) $ $ C(\pi_B) $ $ E(\pi_B) $ p
0 0.393 150340 155617 57988171 66000459 -8012288 -13.82%
0.007 0.4 150862 156157 59200760 66326206 -7125446 -12.04%
0.057 0.45 154244 159658 67808514 68506609 -698095 -1.03%
0.107 0.5 157120 162635 76338929 70475997 5862932 7.68%
0.157 0.55 159596 165198 84805198 72278380 12526818 14.77%
0.207 0.6 161753 167430 93218692 73947086 19271606 20.67%
0.257 0.65 163647 169390 101586998 75505782 26081216 25.67%
0.307 0.7 165324 171127 109917862 76974277 32943585 29.97%
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