区块链技术对低碳供应链合作策略的影响研究

doi:10.16381/j.cnki.issn1003-207x.2023.0111

Abstract

Abstract:

Being the world’s largest carbon emitter， the rapid socio-economic development in China has resulted in the emergence of a prominent carbon emission problem. Addressing this issue， the underscored point is the necessity of enterprises’ involvement in carbon emission reduction behaviors. Collaborative carbon reduction is considered an indispensable approach for mitigating carbon emissions throughout supply chains. Enterprises undertake carbon reduction activities through this pivotal method. Nevertheless， the establishment of complete mutual trust between suppliers and manufacturers is confronted with challenges， which are accompanied by heightened cooperation costs and a market permeated with deceptive marketing practices. As a result， the identification of low-carbon products becomes a complex endeavor for consumers. A pathway to resolution is discovered through the utilization of blockchain technology， which adeptly tackles challenges pertaining to information exchange and sharing among multiple entities. The above challenges will be addressed through the enhancement of transaction trust and the introduction of innovative solutions. The evaluation of the extent and implications of blockchain technology in the cooperative emission reduction strategy is emphasized within the context of a low-carbon supply chain. The formulation of optimal strategies for the adoption of blockchain technology and the execution of emission reduction collaborations in such supply chains is being pursued. Additionally， an analysis is conducted to assess the influence of consumer preferences for low-carbon products， blockchain trust parameters， and cost factors on decision-making processes regarding emission reduction and pricing strategies within the supply chain system. Cooperation in carbon reduction and the adoption of blockchain technology are subject to the discretion of suppliers and manufacturers， influenced by their unique circumstances. Four distinct models are developed for making decisions within low-carbon supply chains. These models encompass individual decisions about emission reduction rates without utilizing blockchain technology， collective decisions about emission reduction rates without utilizing blockchain technology， individual decisions about emission reduction rates while incorporating blockchain technology， and collective decisions about emission reduction rates with the integration of blockchain technology. The resolution is attained through the application of the Stackelberg game model， where in the role of the dominant player is assumed by the supplier， while the manufacturer functions as the follower. With the integration of blockchain technology into the supply chain， a state of information symmetry is established between enterprises and consumers. This empowers consumers with in-depth insights into the attributes of low-carbon products， consequently nurturing an unwavering trust in such products. It is underscored that irrespective of blockchain adoption， collaborative carbon reduction remains an effective avenue for enhancing carbon reduction rates for both suppliers and manufacturers. However， the impact of such collaboration on profits is not universally favorable， necessitating prudent decision-making aligned with individual value objectives. Importantly， the introduction of blockchain technology notably amplifies the factor of green trust. This factor directly correlates with the enhancement of carbon reduction rates and profit margins. When blockchain technology is adopted by suppliers and manufacturers with an impact coefficient of period costs lower than a certain threshold， a higher reduction rate will be achieved compared to not adopting blockchain technology. However， when the costs associated with blockchain technology surpass the threshold， its utilization directly influences the profit margins of suppliers and manufacturers. This study’s data is derived from the investigation of real-life cases and references to relevant literature， aimed at providing theoretical foundations and decision-making references for the use of blockchain technology in low-carbon supply chains and the selection of emission reduction cooperation strategies.

Key words: low carbon supply chain, blockchain, collaborative emissions reduction, green trust, period costs

CLC Number:

F224.3

Jiayi Sun,Yangyang Lu,Chunxian Teng. A Study on the Impact of Blockchain Technology on the Cooperative Strategy of Low Carbon Supply Chain[J]. Chinese Journal of Management Science, 2025, 33(10): 293-303.

Figures/Tables 8

$N N$	$N C$
$r s N N = 16 λ β k A β 4 λ 4 - 32 λ 2 β 2 k + 64 k 2$	$r s N C = 3 λ β A 8 k - 6 λ 2 β 2$
$r m N N = - λ 2 β 2 + 8 k λ β A β 4 λ 4 - 32 β 2 k λ 2 + 64 k 2$	$r m N C = 3 λ β A 8 k - 6 λ 2 β 2$
$w N N = 32 A + 2 c s k 2 - 4 β 2 λ 2 A + 8 c s k + β 4 λ 4 c s β 4 λ 4 - 32 β 2 k λ 2 + 64 k 2$	$w N C = 2 A + 2 c s k - 3 λ 2 β 2 c s 4 k - 3 λ 2 β 2$
$p N N = 16 3 a + c m + c s k 2 - 6 a + 26 (c m + c s) β 2 λ 2 k + β 4 λ 4 c m + c s β 4 λ 4 - 32 β 2 k λ 2 + 64 k 2$	$p N C = 3 a + c m + c s k - 3 β 2 λ 2 c m + c s 4 k - 3 λ 2 β 2$
$d N N = A - 2 λ 2 β 2 + 16 k k β 4 λ 4 - 32 β 2 k λ 2 + 64 k 2$	$d N C = k A 4 k - 3 λ 2 β 2$
$Π s N N = 8 k 2 A 2 β 4 λ 4 - 32 β 2 k λ 2 + 64 k 2$	$Π s N C = k A 2 9 λ 2 β 2 - 16 k 8 4 k - 3 λ 2 β 2 2$
$Π m N N = 8 k - λ 2 β 2 3 A 2 k 2 β 4 λ 4 - 32 β 2 k λ 2 + 64 k 2 2$	$Π m N C = A 2 k 9 λ 2 β 2 - 8 k 8 4 k - 3 λ 2 β 2 2$
$Π s c N N = 40 k β 4 λ 4 - β 6 λ 6 - 704 k 2 λ 2 β 2 + 1536 k 3 A 2 k 2 β 4 λ 4 - 32 β 2 k λ 2 + 64 k 2 2$	$Π s c N C = 3 A 2 k 16 k - 12 λ 2 β 2$

$Y N$	$Y C$
$r s Y N = 16 β k 2 a + Δ 32 β 2 k - β 4 - 64 k 2$	$r s Y C = 3 β Δ 6 β 2 - 8 k$
$r m Y N = β Δ β 2 - 8 k β 4 - 32 β 2 k + 64 k 2$	$r m Y C = 3 β Δ 6 β 2 - 8 k$
$w Y N = 64 c m δ + 32 Δ k 2 + 4 β 2 Δ - 8 c s c m k + β 4 δ c s β 4 - 32 β 2 k + 64 k 2$	$w Y C = 2 Δ - 4 c s δ k - 3 β 2 δ c s 4 k - 3 β 2$
$p Y N = 16 (Δ + a) + 3 a k 2 - β 2 26 (Δ + a) + 6 a k + β 4 (Δ + a) β 4 - 32 β 2 k + 64 k 2$	$p Y C = (Δ + a) k - 3 β 2 + 3 a k 4 k - 3 β 2$
$d Y N = 2 β 2 - 16 k Δ c β 4 - 32 β 2 k + 64 k 2$	$d Y C = k Δ 3 β 2 - 4 k$
$Π s Y N = 32 β 2 k - β 4 - 64 k 2 F s - 8 Δ 2 k 2 β 4 - 32 β 2 k + 64 k 2$	$Π s Y C = 16 Δ 2 - 128 h s k 2 - 3 3 Δ 2 - 64 h s β 2 k - 72 β 4 h s 8 4 k - 3 β 2 2$
$Π m Y N = - β 6 k + 24 β 4 k 2 - 64 β 2 k 3 + 512 k 4 Δ 2 + ρ h m 2 β 4 - 32 β 2 k + 64 k 2 2$	$Π m Y C = 8 Δ 2 - 128 h m k 2 - 3 Δ 2 - 64 h m β 2 k - 72 β 4 h m 8 4 k - 3 β 2 2$
$Π s c Y N = ρ H + - β 6 k + 8 β 4 k 2 - 64 β 2 k 3 + 1536 k 4 Δ 2 2 β 4 - 32 β 2 k + 64 k 2 2$	$Π s c Y C = 3 Δ 2 - 16 H k + 12 β 2 H 16 k - 12 β 2$

参数	$w$			$p$			$d$
$λ$	0.1	0.5	0.9	0.1	0.5	0.9	0.1	0.5	0.9
$N N$	24.100	24.107	24.122	38.550	38.560	38.583	11.4501	11.453	11.461
$N C$	24.101	24.114	24.145	38.551	38.570	38.617	11.4502	11.457	11.472
$δ$	0.1	0.5	0.9	0.1	0.5	0.9	0.1	0.5	0.9
$Y N$	24.940	24.579	24.218	37.650	38.068	38.487	12.410	11.989	11.569
$Y C$	24.970	24.608	24.246	37.694	38.111	38.528	12.425	12.004	11.583

参数	$r s$			$r m$			$π s$			$π m$			$π s c$
$λ$	0.1	0.5	0.9	0.1	0.5	0.9	0.1	0.5	0.9	0.1	0.5	0.9	0.1	0.5	0.9
$N N$	0.005	0.023	0.041	0.002	0.011	0.021	262.209	262.310	262.545	131.105	131.168	131.315	393.314	393.478	393.860
$N C$	0.007	0.034	0.062	0.007	0.034	0.062	262.213	262.402	262.844	131.104	131.142	131.230	393.317	393.544	394.074
$δ$	0.1	0.5	0.9	0.1	0.5	0.9	0.1	0.5	0.9	0.1	0.5	0.9	0.1	0.5	0.9
$Y N$	0.050	0.048	0.046	0.025	0.024	0.023	294.764	274.261	254.464	138.944	128.688	118.786	433.708	402.949	373.250
$Y C$	0.075	0.072	0.070	0.075	0.072	0.070	295.197	274.664	254.839	138.820	128.573	118.678	434.017	403.237	373.518

References 35

[1]	Wang R， Liu W， Xiao L， et al. Path towards achieving of China’s 2020 carbon emission reduction target—a discussion of low-carbon energy policies at province level［J］. Energy Policy， 2011， 39（5）： 2740-2747.
[2]	Bian H， Jin F， Tong X. Carbon reduction consciousness， determinants and value of low-carbon transition： Evidence from textual and empirical analysis of Chinese listed manufacturing enterprises［J］. International Review of Economics & Finance， 2023， 88： 1301-1323.
[3]	孙晶琪，周奕全，王愿，等. 市场型环境规制交互下减污降碳协同增效的效应分析［J］. 中国环境管理， 2023， 15（2）： 48-57.
	Sun J Q， Zhou Y Q， Wang Y， et al. Research on the synergistic effect of reducting pollution and carbon emissions under the interaction of market-based environmental regulations［J］. Chinese Journal of Environmental Management， 2023， 15（2）： 48-57.
[4]	Tan C， Zeng Y， Ip W H， et al. B2C or O2O？ The strategic implications for the fresh produce supply chain based on blockchain technology［J］. Computers & Industrial Engineering， 2023， 183： 109499.
[5]	刘海鸥，何旭涛，高悦，等. 共建·共治·共享：区块链生态赋能双创空间多元联动协同发展研究［J］. 中国科技论坛， 2022（1）： 104-111.
	Liu H O， He X T， Gao Y， et al. Co-construction， co-governance and sharing： Blockchain ecological empowerment research on multiple linkage and coordinated development of innovation and entrepreneurship space［J］. Forum on Science and Technology in China， 2022（1）： 104-111.
[6]	余萍，魏守道. 研发溢出、消费者低碳偏好与供应链横向研发策略［J］.系统管理学报，2022，31（4）： 758-769.
	Yu P， Wei S D. Research and development spillover， consumer environmental awareness， and horizontal research and development strategy of supply chain［J］. Journal of Systems & Management， 2022， 31（4）： 758-769.
[7]	王婷婷，王道平. 政府补贴下供应链合作减排与低碳宣传的动态协调策略［J］. 运筹与管理， 2020， 29（8）： 52-61.
	Wang T T， Wang D P. Dynamic coordination strategy of cooperation on carbon emission reduction and low carbon propaganda in supply chain under government subsidy［J］. Operations Research and Management Science， 2020， 29（8）： 52-61.
[8]	Huang Z， Fan H. Responsibility-sharing subsidy policy for reducing diesel emissions from in-use off-road construction equipment［J］. Applied Energy， 2022， 320： 119301.
[9]	邢恩凤，史成东，闫秀霞，等. 三维交易模式下企业协同低碳减排研究［J］. 中国管理科学， 2020， 28（3）： 174-181.
	Xing E F， Shi C D， Yan X X， et al. Research on enterprise collaborative low carbon emission reduction under three-dimensional trading mode［J］. Chinese Journal of Management Science， 2020， 28（3）： 174-181.
[10]	Wang Y， Xu X， Zhu Q. Carbon emission reduction decisions of supply chain members under cap-and-trade regulations： A differential game analysis［J］. Computers & Industrial Engineering， 2021， 162： 107711.
[11]	Li W， Wang P， Cheng W， et al. Transnational remanufacturing decisions under carbon taxes and tariffs［J］. European Journal of Operational Research， 2024， 312（1）： 150-163.
[12]	Liu Z， Lang L， Hu B， et al. Emission reduction decision of agricultural supply chain considering carbon tax and investment cooperation［J］. Journal of Cleaner Production， 2021， 294： 126305.
[13]	蒋晓芬，高广阔，孙浩. 碳限额交易政策下考虑消费者偏好的外包减排策略研究［J］. 运筹与管理， 2023， 32（7）： 15-22.
	Jiang X F， Gao G K， Sun H. Research on outsourcing emission reduction strategies considering consumer preference under carbon cap-and-trade policy［J］. Operations Research and Management Science， 2023， 32（7）： 15-22.
[14]	Gong B， Zhang H， Gao Y， et al. Blockchain adoption and channel selection strategies in a competitive remanufacturing supply chain［J］. Computers & Industrial Engineering， 2023， 175： 108829.
[15]	盛守一. 基于区块链技术的供应链信息资源共享模型构建研究［J］. 情报科学， 2021， 39（7）： 162-168.
	Sheng S Y. Information sharing model construction of supply chain based on blockchain technology［J］. Information Science， 2021， 39（7）： 162-168.
[16]	Hu Z H， Dong Y J. Evolutionary game models of cooperative strategies in blockchain-enabled container transport chains［J］. Asia-Pacific Journal of Operational Research， 2022， 39（1）： 2140029.
[17]	吉清凯，张凤麟，方刚，等. 零售供应链中的区块链参与决策与产品定价博弈模型［J］. 中国管理科学， 2023， 31（3）： 102-112.
	Ji Q K， Zhang F L， Fang G， et al. Game model of blockchain adoption and product pricing in retail supply chain［J］. Chinese Journal of Management Science， 2023， 31（3）： 102-112.
[18]	林木西，张紫薇. “区块链+生产” 推动企业绿色生产——对政府之手的新思考［J］. 经济学动态， 2019（5）： 42-56.
	Lin M X， Zhang Z W. “Blockchain+Production” promotes the green production of enterprises： New thinking on the hand of governments［J］. Economic Perspectives， 2019（5）： 42-56.
[19]	林强，刘名武，王晓斐. 嵌入区块链信息传递功能的绿色供应链决策［J］. 计算机集成制造系统， 2024， 30（1）： 355-368.
	Lin Q， Liu M W， Wang X F. Green supply chain decision-making embedded with information transfer function of blockchain［J］. Computer Integrated Manufacturing Systems， 2024， 30（1）： 355-368.
[20]	Lotfi R， Kargar B， Gharehbaghi A， et al. Resource-constrained time–cost-quality-energy-environment tradeoff problem by considering blockchain technology， risk and robustness： A case study of healthcare project［J］. Environmental Science and Pollution Research， 2022， 29（42）： 63560-63576.
[21]	Kim Y， Raman R K， Kim Y S， et al. Efficient local secret sharing for distributed blockchain systems［J］. IEEE Communications Letters，2019，23（2）：282-285.
[22]	Liu S S， Hua G， Ma B J， et al. Competition between green and non-green products in the blockchain era［J］. International Journal of Production Economics， 2023， 264： 108970.
[23]	梁喜，肖金凤. 基于区块链和消费者敏感的双渠道供应链定价与渠道选择［J］. 中国管理科学， 2023， 31（5）： 29-38.
	Liang X， Xiao J F. Blockchain-based dual-channel supply chain pricing decision and online channel selection strategy［J］. Chinese Journal of Management Science， 2023， 31（5）： 29-38.
[24]	霍红，钟海岩. 农产品供应链质量安全中区块链技术投入的演化分析［J］.运筹与管理，2023，32（1）： 15-21.
	Huo H， Zhong H Y. Evolution analysis of blockchain technology investment based the quality and safety of agricultural supply chain［J］. Operations Research and Management Science， 2023， 32（1）： 15-21.
[25]	Sharma P K， Kumar N， Park J H. Blockchain technology toward green IoT： Opportunities and challenges［J］. IEEE Network， 2020， 34（4）： 263-269.
[26]	Hua W， Jiang J， Sun H， et al. A blockchain based peer-to-peer trading framework integrating energy and carbon markets［J］. Applied Energy， 2020， 279： 115539.
[27]	Khaqqi K N， Sikorski J J， Hadinoto K， et al. Incorporating seller/buyer reputation-based system in blockchain-enabled emission trading application［J］. Applied Energy， 2018， 209： 8-19.
[28]	Yan M， Shahidehpour M， Alabdulwahab A， et al. Blockchain for transacting energy and carbon allowance in networked microgrids［J］. IEEE Transactions on Smart Grid， 2021， 12（6）： 4702-4714.
[29]	李骏，宫艳峰，李康，等. 一汽集团乘用车动力总成低碳技术策略［J］.汽车工程，2010，32（7）：555-558+569.
	Li J， Gong Y F， Li K， et al. FAW’s low carbon strategies for car powertrain［J］. Automotive Engineering， 2010， 32（7）： 555-558+569.
[30]	张令荣，彭博，程春琪. 基于区块链技术的低碳供应链政府补贴策略研究［J］. 中国管理科学， 2023， 31（10）： 49-60.
	Zhang L R， Peng B， Cheng C Q. Research on government subsidy strategy of low-carbon supply chain based on block-chain technology［J］. Chinese Journal of Management Science， 2023， 31（10）： 49-60.
[31]	邱俊，杨玉香. 考虑低碳政策和权力结构的低碳供应链减排决策比较研究［J］. 计算机集成制造系统， 2024， 30（7）： 2566-2587.
	Qiu J， Yang Y X. Comparative study of low-carbon supply chain carbon emission reduction decisions considering low-carbon policies and power structures［J］. Computer Integrated Manufacturing Systems， 2024， 30（7）： 2566-2587.
[32]	林欢，马骋，孙琦，等. 企业社会责任下碳减排优化策略与协调机制研究［J］. 运筹与管理， 2021， 30（1）： 29-35.
	Lin H， Ma C， Sun Q， et al. Research on carbon reduction optimization strategy and coordination mechanism in consideration of corporate social responsibility［J］. Operations Research and Management Science， 2021， 30（1）： 29-35.
[33]	Ullah A， Ayat M， He Y， et al. An analysis of strategies for adopting blockchain technology in the after-sales service supply chain［J］. Computers & Industrial Engineering， 2023， 179： 109194.
[34]	贺勇，陈志豪，廖诺. 政府补贴方式对绿色供应链制造商减排决策的影响机制［J］. 中国管理科学， 2022， 30（6）： 87-98.
	He Y， Chen Z H， Liao N. The impact mechanism of government subsidy approach on manufacturer’s decision-making in green supply chain［J］. Chinese Journal of Management Science， 2022， 30（6）： 87-98.
[35]	姜永常，刘畅，白世贞，等. 应用区块链的生鲜农产品双渠道供应链最优决策［J］. 系统工程， 2023， 41（1）： 63-72.
	Jiang Y C， Liu C， Bai S Z， et al. Optimal decision-making in dual-channel supply chain of fresh agri-product by applying blockchain［J］. Systems Engineering， 2023， 41（1）： 63-72.

A Study on the Impact of Blockchain Technology on the Cooperative Strategy of Low Carbon Supply Chain

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