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doi: 10.3934/jimo.2022032
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Fresh agricultural products supply chain coordination considering consumers' dual preferences under carbon cap-and-trade mechanism

1. 

School of Management, Jiangsu University, Zhenjiang 212013, China

2. 

Jiangsu Modern Logistics Research Base of Yangzhou University, Yangzhou University, Yangzhou 225009, China

*Corresponding author: Guanxin Yao

Received  December 2021 Revised  January 2022 Early access March 2022

In order to achieve the goal of "carbon neutral" development, under the conditions of carbon quota trading policy, consumers' preference for fresh agricultural products and low-carbon preference, this paper discusses the equilibrium decision-making problem of a two-level fresh agricultural products supply chain. And the cost sharing contract and the two-part pricing contract are introduced to coordinate the supply chain. The results show that: the fresh-keeping and low-carbon efforts made by fresh produce suppliers are related to consumers' preferences, fresh-keeping costs, low-carbon costs, fresh-keeping carbon emission factors, and carbon reduction efficiency; under certain conditions, both the cost sharing contract and the two-part pricing contract can coordinate the supply chain of fresh agricultural products; given certain costs and carbon reduction efficiency, which contract retailers choose depends on consumers' consumption preferences and the size of the supplier's initial carbon emissions; when both contracts are valid, it is more advantageous for the retailer to choose two-part pricing contract; the higher the carbon trading price, the better the incentive effect on suppliers' fresh-keeping efforts and low-carbon efforts.

Citation: Yang Yang, Guanxin Yao. Fresh agricultural products supply chain coordination considering consumers' dual preferences under carbon cap-and-trade mechanism. Journal of Industrial and Management Optimization, doi: 10.3934/jimo.2022032
References:
[1]

Aghighi. A, Goli. A, Malmir. B and Tirkolaee. EB, The stochastic location-routing-inventory problem of perishable products with reneging and balking, Journal of Ambient Intelligence and Humanized Computing, (2021). doi: 10.1007/s12652-021-03524-y.

[2]

Chunling Bao and Shibin Zhang, Route optimization of cold chain logistics in joint distribution: with consideration of carbon emission, Industrial Engineering and Management, 23(5) (2018), 95-107. 

[3]

Qingguo BaiYeming GongMingzhou Jin and Xiaohao Xu, Effects of carbon emission reduction on supply chain coordination with vendor-managed deteriorating product inventory, International Journal of Production Economics, 208 (2019), 83-99. 

[4]

Jing ChenTingting HuYan Han and Zhiping Du, Research on low-carbon coordination of dual-channel supply chain based on revenue sharing, Statistics and Decision, 550(10) (2020), 176-180. 

[5]

Xiaoning Cao, Yongming Wang, Fanghong Xue and Xiaobing Liu, Coordination strategies for dual-channel supply chain of fresh agricultural products considering the fresh-keeping effort of supplier, Chinese Journal of Management Science, (2020). doi: 10.16381/j.cnki.issn1003-207x.2017.0489.

[6]

Xiaoqiang CaiJian ChenYongbo Xiao and Xiaolin Xu, Optimization and coordination of fresh product supply chains with freshness-keeping effort, Production and Operations Management, 19(3) (2020), 261-278. 

[7]

E OzceylanN Demirel and C Cetinkaya, A closed-loop supply chain network design for automotive industry in Turkey, Computers and Industrial Engineering, 113 (2018), 727-745. 

[8]

Junhua GuoHuang Zhang and Bangyi Li, Supply chain pricing and coordination under cap-and-trade system and subsidy mechanism, Science and Technology Management Research, 1 (2020), 204-214. 

[9]

Yan He, Study on the construction of cold chain logistics system of fresh agricultural products based on green consumption, Agricultural Economics, 12 (2018), 126-128. 

[10]

I MoonYJ Jeong and S Saha, Investment and coordination decisions in a supply chain of fresh agricultural products, Operational Research, 20(4) (2020), 2307-2331. 

[11]

IE NielsenS Majumder and S Saha, Exploring the intervention of intermediary in a green supply chain, Journal of Cleaner Production, 233 (2019), 1525-1544. 

[12]

Molin LiuBin Dan and Songxuan Ma, Optimal strategies and coordination of fresh e-commerce supply chain considering freshness-keeping effort and value-added service, Chinese Journal of Management Science, 28(8) (2020), 76-88. 

[13]

Xueli Ma, Ying Zhao, Qingguo Bai and Hongguang Bo, Optimal strategies and coordination of three-echelon cold chain of fresh products considering freshness-keeping and carbon abatement, Chinese Journal of Management Science, (2021), https://kns.cnki.net/kcms/detail/11.2835.g3.20210926.1435.004.html.

[14]

R AccorsiA Gallo and R Manzini, A climate driven decision-support model for the distribution of perishable products, Journal of Cleaner Production, 165 (2017), 917-929. 

[15]

S Saha and SK Goyal, Supply chain coordination contracts with inventory level and retail price dependent demand, International Journal of Production Economics, 161 (2015), 140-152. 

[16]

T Paksoy and E Ozceylan, Environmentally conscious optimization of supply chain networks, Journal of The Operational Research Society, 65(6) (2014), 855-872. 

[17]

Daoping WangMengying Zhu and Tingting Wang, Research on the cost sharing contract of fresh-product supply chain's fresh-keeping efforts, Industrial Engineering and Management, 25(2) (2020), 36-43. 

[18]

Kexi Wang and Anna Dai, On the influencing factors of online shopping intentions of green fresh agricultural products based on logit model, Journal of Hunan University of Science and Technology (Social Science Edition), 20(2) (2017), 87-93. 

[19]

Xunping WangJie DongTao Han and Junhu Ruan, The optimization of cold chain delivery routes considering carbon emission and temporal-spatial distance, Journal of System Engineering, 34(4) (2019), 556-565. 

[20]

Chunming XuDaozhi Zhao and Jie Min, Optimal inventory models for deteriorating items with stock dependent demand under carbon emissions reduction, System Engineering-Theory and Practice, 38(6) (2018), 1512-1524. 

[21]

Xiaoping XuWei ZhangPing He and Xiaoyan Xu, Production and pricing problems in make-to-order supply chain with cap-and-trade regulation, Omega, 66 (2017), 248-257. 

[22]

Huixiao Yang and Wenbo Chen, Retailer-driven Carbon emission abatement with consumer environmental awareness and carbon tax: revenue-sharing versus costing-sharing, Omega, 78 (2018), 179-191. 

[23]

Yakun YuanJiumei Chen and Bin Dan, Research on the efficiency and its convergence of cold-chain logistics considering carbon constriction: taking fresh agricultural products as an example, Science and Technology Management Research, 14 (2020), 254-260. 

[24]

Z Basiri and J Heydari, A mathematical model for green supply chain coordination with substitutable products, Journal of Cleaner Production, 145 (2017), 232-249. 

[25]

Dongmei ZhangGuohong ShiGuanxin YaoJing Xu and Panqian Dai, Decisions of ordering and preservation for fresh agricultural products under carbon emission constraints, Industrial Engineering and Management, 25(1) (2020), 145-151. 

[26]

Qingming Zou, Liqing Hu and Tingjun Zou, Pricing and coordination of a supply chain with fairness concerns under carbon cap-and-trade mechanism, Chinese Journal of Management Science, (2020). doi: 10.16381/j.cnki.issn1003-207x.2020.0550.

[27]

Xiaoyu ZhangYing MaJia Ma and Jining Zhang, Metropolitan resident's cognition and willingness of payment for low-carbon agricultural products-Empirical analysis on low-carbon vegetables in Shanghai, Acta Agricultural Shanghai, 35(3) (2019), 116-122. 

[28]

Xiao Zhang and Shiyang An, Dual-channel supply chain coordination of fresh products considering the retailer's fairness concern under fresh-keeping cost sharing, Industrial Engineering and Management, (2020), http://kns.cnki.net/kcms/detail/31.1738.T.20200408.1132.005.html.

[29]

Yanju ZhouFengying HuZhenglong Zhou and Xiongwei Zhou, Impact of optimal carbon tax rate on supply chain structure and social welfare, System Engineering-Theory and Practice, 37(4) (2017), 886-900. 

[30]

Yuting ZhengJianbin LiZhiyuan Chen and Mangmang Ming, Optimal decisions of cold chain distributor under uncertain in demand, Journal of Management Sciences in China, 22(1) (2019), 94-106. 

[31]

Yanju ZhouFengying Hu and Zhenglong Zhou, Study on joint contract coordination to promote green product demand under the retailer-dominance, Journal of Industrial Engineering, 34(2) (2020), 194-204. 

show all references

References:
[1]

Aghighi. A, Goli. A, Malmir. B and Tirkolaee. EB, The stochastic location-routing-inventory problem of perishable products with reneging and balking, Journal of Ambient Intelligence and Humanized Computing, (2021). doi: 10.1007/s12652-021-03524-y.

[2]

Chunling Bao and Shibin Zhang, Route optimization of cold chain logistics in joint distribution: with consideration of carbon emission, Industrial Engineering and Management, 23(5) (2018), 95-107. 

[3]

Qingguo BaiYeming GongMingzhou Jin and Xiaohao Xu, Effects of carbon emission reduction on supply chain coordination with vendor-managed deteriorating product inventory, International Journal of Production Economics, 208 (2019), 83-99. 

[4]

Jing ChenTingting HuYan Han and Zhiping Du, Research on low-carbon coordination of dual-channel supply chain based on revenue sharing, Statistics and Decision, 550(10) (2020), 176-180. 

[5]

Xiaoning Cao, Yongming Wang, Fanghong Xue and Xiaobing Liu, Coordination strategies for dual-channel supply chain of fresh agricultural products considering the fresh-keeping effort of supplier, Chinese Journal of Management Science, (2020). doi: 10.16381/j.cnki.issn1003-207x.2017.0489.

[6]

Xiaoqiang CaiJian ChenYongbo Xiao and Xiaolin Xu, Optimization and coordination of fresh product supply chains with freshness-keeping effort, Production and Operations Management, 19(3) (2020), 261-278. 

[7]

E OzceylanN Demirel and C Cetinkaya, A closed-loop supply chain network design for automotive industry in Turkey, Computers and Industrial Engineering, 113 (2018), 727-745. 

[8]

Junhua GuoHuang Zhang and Bangyi Li, Supply chain pricing and coordination under cap-and-trade system and subsidy mechanism, Science and Technology Management Research, 1 (2020), 204-214. 

[9]

Yan He, Study on the construction of cold chain logistics system of fresh agricultural products based on green consumption, Agricultural Economics, 12 (2018), 126-128. 

[10]

I MoonYJ Jeong and S Saha, Investment and coordination decisions in a supply chain of fresh agricultural products, Operational Research, 20(4) (2020), 2307-2331. 

[11]

IE NielsenS Majumder and S Saha, Exploring the intervention of intermediary in a green supply chain, Journal of Cleaner Production, 233 (2019), 1525-1544. 

[12]

Molin LiuBin Dan and Songxuan Ma, Optimal strategies and coordination of fresh e-commerce supply chain considering freshness-keeping effort and value-added service, Chinese Journal of Management Science, 28(8) (2020), 76-88. 

[13]

Xueli Ma, Ying Zhao, Qingguo Bai and Hongguang Bo, Optimal strategies and coordination of three-echelon cold chain of fresh products considering freshness-keeping and carbon abatement, Chinese Journal of Management Science, (2021), https://kns.cnki.net/kcms/detail/11.2835.g3.20210926.1435.004.html.

[14]

R AccorsiA Gallo and R Manzini, A climate driven decision-support model for the distribution of perishable products, Journal of Cleaner Production, 165 (2017), 917-929. 

[15]

S Saha and SK Goyal, Supply chain coordination contracts with inventory level and retail price dependent demand, International Journal of Production Economics, 161 (2015), 140-152. 

[16]

T Paksoy and E Ozceylan, Environmentally conscious optimization of supply chain networks, Journal of The Operational Research Society, 65(6) (2014), 855-872. 

[17]

Daoping WangMengying Zhu and Tingting Wang, Research on the cost sharing contract of fresh-product supply chain's fresh-keeping efforts, Industrial Engineering and Management, 25(2) (2020), 36-43. 

[18]

Kexi Wang and Anna Dai, On the influencing factors of online shopping intentions of green fresh agricultural products based on logit model, Journal of Hunan University of Science and Technology (Social Science Edition), 20(2) (2017), 87-93. 

[19]

Xunping WangJie DongTao Han and Junhu Ruan, The optimization of cold chain delivery routes considering carbon emission and temporal-spatial distance, Journal of System Engineering, 34(4) (2019), 556-565. 

[20]

Chunming XuDaozhi Zhao and Jie Min, Optimal inventory models for deteriorating items with stock dependent demand under carbon emissions reduction, System Engineering-Theory and Practice, 38(6) (2018), 1512-1524. 

[21]

Xiaoping XuWei ZhangPing He and Xiaoyan Xu, Production and pricing problems in make-to-order supply chain with cap-and-trade regulation, Omega, 66 (2017), 248-257. 

[22]

Huixiao Yang and Wenbo Chen, Retailer-driven Carbon emission abatement with consumer environmental awareness and carbon tax: revenue-sharing versus costing-sharing, Omega, 78 (2018), 179-191. 

[23]

Yakun YuanJiumei Chen and Bin Dan, Research on the efficiency and its convergence of cold-chain logistics considering carbon constriction: taking fresh agricultural products as an example, Science and Technology Management Research, 14 (2020), 254-260. 

[24]

Z Basiri and J Heydari, A mathematical model for green supply chain coordination with substitutable products, Journal of Cleaner Production, 145 (2017), 232-249. 

[25]

Dongmei ZhangGuohong ShiGuanxin YaoJing Xu and Panqian Dai, Decisions of ordering and preservation for fresh agricultural products under carbon emission constraints, Industrial Engineering and Management, 25(1) (2020), 145-151. 

[26]

Qingming Zou, Liqing Hu and Tingjun Zou, Pricing and coordination of a supply chain with fairness concerns under carbon cap-and-trade mechanism, Chinese Journal of Management Science, (2020). doi: 10.16381/j.cnki.issn1003-207x.2020.0550.

[27]

Xiaoyu ZhangYing MaJia Ma and Jining Zhang, Metropolitan resident's cognition and willingness of payment for low-carbon agricultural products-Empirical analysis on low-carbon vegetables in Shanghai, Acta Agricultural Shanghai, 35(3) (2019), 116-122. 

[28]

Xiao Zhang and Shiyang An, Dual-channel supply chain coordination of fresh products considering the retailer's fairness concern under fresh-keeping cost sharing, Industrial Engineering and Management, (2020), http://kns.cnki.net/kcms/detail/31.1738.T.20200408.1132.005.html.

[29]

Yanju ZhouFengying HuZhenglong Zhou and Xiongwei Zhou, Impact of optimal carbon tax rate on supply chain structure and social welfare, System Engineering-Theory and Practice, 37(4) (2017), 886-900. 

[30]

Yuting ZhengJianbin LiZhiyuan Chen and Mangmang Ming, Optimal decisions of cold chain distributor under uncertain in demand, Journal of Management Sciences in China, 22(1) (2019), 94-106. 

[31]

Yanju ZhouFengying Hu and Zhenglong Zhou, Study on joint contract coordination to promote green product demand under the retailer-dominance, Journal of Industrial Engineering, 34(2) (2020), 194-204. 

Figure 1.  Influence of $ k $ on decision variables and supply chain revenue
Figure 2.  Influence of $ m $ on decision variables and supply chain revenue
Figure 3.  Influence of $ \mu,\gamma $ on decision variables and supply chain revenue
Figure 4.  Influence of $ n,h $ on decision variables and supply chain revenue
Figure 5.  Influence of $ \beta $ on decision variables and supply chain revenue
Figure 6.  Influence of $ e_0 $ on decision variables and supply chain revenue
Figure 7.  Influence of $ E $ on decision variables and supply chain revenue
Table 1.  Related symbols used in the model
Notations Description Notations Description
$ c $ unit production cost of products $ k $ sensitivity coefficient of freshness
$ e_0 $ initial carbon emissions $ m $ sensitivity coefficient of low carbon
$ E $ total carbon quota $ 〖\pi_Z〗^i $ revenue of supply chain under different modes
$ \beta $ carbon trading price $ 〖\pi_s〗^i $ revenue of the retailer under different modes
$ \mu $ fresh-keeping effort cost coefficient $ 〖\pi_g〗^i $ revenue of the supplier under different modes
$ D $ market demand $ w $ unit wholesale price of products
$ \gamma $ carbon emission coefficient of fresh-keeping superscript 1 optimal decision under centralized mode
$ h $ carbon emission reduction efficiency superscript 2 optimal decision in decentralized mode
$ n $ low-carbon effort cost coefficient superscript 3 optimal decision-making in cost-sharing mode
$ d $ market potential demand superscript 4 optimal decision under two-part contract mode
$ p $ unit sales price of products superscript * optimal solution of income
Decision variables Description Decision variables Description
$ I $ low carbon efforts $ \alpha $ fresh-keeping efforts
Notations Description Notations Description
$ c $ unit production cost of products $ k $ sensitivity coefficient of freshness
$ e_0 $ initial carbon emissions $ m $ sensitivity coefficient of low carbon
$ E $ total carbon quota $ 〖\pi_Z〗^i $ revenue of supply chain under different modes
$ \beta $ carbon trading price $ 〖\pi_s〗^i $ revenue of the retailer under different modes
$ \mu $ fresh-keeping effort cost coefficient $ 〖\pi_g〗^i $ revenue of the supplier under different modes
$ D $ market demand $ w $ unit wholesale price of products
$ \gamma $ carbon emission coefficient of fresh-keeping superscript 1 optimal decision under centralized mode
$ h $ carbon emission reduction efficiency superscript 2 optimal decision in decentralized mode
$ n $ low-carbon effort cost coefficient superscript 3 optimal decision-making in cost-sharing mode
$ d $ market potential demand superscript 4 optimal decision under two-part contract mode
$ p $ unit sales price of products superscript * optimal solution of income
Decision variables Description Decision variables Description
$ I $ low carbon efforts $ \alpha $ fresh-keeping efforts
Table 2.  effective region of two contracts
$ \beta(e_0-E) $ effective area of the cost-sharing contract effective area of the two-part pricing contract common region
$ 600 $ $ M\in(1.85AB,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ \emptyset $
$ 400 $ $ M\in(1.7AB,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ \emptyset $
$ 200 $ $ M\in(1.4AB,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ \emptyset $
$ 100 $ $ M\in(1.05AB,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ \emptyset $
$ 0 $ $ M\in(0,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ M\in(0.5AB,0.763AB) $
$ -100 $ $ M\in(0,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ M\in(0.5AB,0.763AB) $
$ -200 $ $ M\in(0,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ M\in(0.5AB,0.763AB) $
$ -400 $ $ M\in(0,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ M\in(0.5AB,0.763AB) $
$ -600 $ $ M\in(0,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ M\in(0.5AB,0.763AB) $
$ \beta(e_0-E) $ effective area of the cost-sharing contract effective area of the two-part pricing contract common region
$ 600 $ $ M\in(1.85AB,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ \emptyset $
$ 400 $ $ M\in(1.7AB,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ \emptyset $
$ 200 $ $ M\in(1.4AB,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ \emptyset $
$ 100 $ $ M\in(1.05AB,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ \emptyset $
$ 0 $ $ M\in(0,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ M\in(0.5AB,0.763AB) $
$ -100 $ $ M\in(0,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ M\in(0.5AB,0.763AB) $
$ -200 $ $ M\in(0,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ M\in(0.5AB,0.763AB) $
$ -400 $ $ M\in(0,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ M\in(0.5AB,0.763AB) $
$ -600 $ $ M\in(0,2.667AB) $ $ M\in(0.5AB,0.763AB) $ $ M\in(0.5AB,0.763AB) $
Table 3.  Optimal decisions and benefits under four different modes
variables Centralized decision-making Decentralized decision-making Cost sharing Two-part pricing
$ \alpha $ $ 11.721 $ $ 4.893 $ $ 5.335 $ $ 5.253 $
$ I $ $ 9.767 $ $ 4.078 $ $ 4.445 $ $ 4.378 $
$ p $ $ 37.302 $ $ 44.670 $ $ 45.168 $ $ 39.288 $
$ w $ $ - $ $ 32.466 $ $ 32.779 $ $ 21.133 $
$ \pi_g $ $ - $ $ 563.786 $ $ 566.800 $ $ 563.786 $
$ \pi_s $ $ - $ $ 299.293 $ $ 303.383 $ $ 474.497 $
$ \pi_Z $ $ 1230.698 $ $ 863.079 $ $ 870.183 $ $ 1038.283 $
variables Centralized decision-making Decentralized decision-making Cost sharing Two-part pricing
$ \alpha $ $ 11.721 $ $ 4.893 $ $ 5.335 $ $ 5.253 $
$ I $ $ 9.767 $ $ 4.078 $ $ 4.445 $ $ 4.378 $
$ p $ $ 37.302 $ $ 44.670 $ $ 45.168 $ $ 39.288 $
$ w $ $ - $ $ 32.466 $ $ 32.779 $ $ 21.133 $
$ \pi_g $ $ - $ $ 563.786 $ $ 566.800 $ $ 563.786 $
$ \pi_s $ $ - $ $ 299.293 $ $ 303.383 $ $ 474.497 $
$ \pi_Z $ $ 1230.698 $ $ 863.079 $ $ 870.183 $ $ 1038.283 $
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