AccScience Publishing / AJWEP / Online First / DOI: 10.36922/AJWEP026020010
Submit to AJWEP
Cite this article
5
Download
18
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
ORIGINAL RESEARCH ARTICLE

Energy transition and industrial supply chain security: Propagating mechanisms of carbon reduction risks in manufacturing networks

Yu He1 ,  Si Qin Shu1 ,  Zhen Zhen Chen1* ,  Jie Xin Tian2
Show Less
1 Department of Economics, College of Economics and Management, China Three Gorges University, Yichang, Hubei, China
2 Department of Economics, School of Economics and Management, China University of Geosciences, Wuhan, Hubei, China
AJWEP, 026020010 https://doi.org/10.36922/AJWEP026020010
Received: 10 January 2026 | Revised: 27 February 2026 | Accepted: 27 February 2026 | Published online: 9 April 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
Download PDF
XML
Cite
Abstract

Global carbon reduction mandates drive the energy transition but expose manufacturing supply chains to carbon reduction risks. This study employs complex network theory to construct a multi-stage industrial chain model, integrating numerical simulations to examine the transmission of carbon reduction risk under random and targeted shocks. Carbon reduction risks raise costs, trigger price increases, and induce risk transmission. Network robustness exhibited structural heterogeneity: random networks outperformed scale-free and small-world networks under random shocks, while small-world networks showed the highest resilience against targeted attacks. Targeted attacks triggered a 50% efficiency loss in scale-free networks at an early attack round, far exceeding the impact of random shocks of the same intensity. Network failure probability was inversely correlated with the strategic resilience parameter and positively correlated with the external dependency parameter: elevating the strategic resilience parameter above 0.5 and maintaining the external dependency parameter below 0.5 significantly enhanced small-world network resilience. Adjusting either parameter alone failed to mitigate cascading failures in scale-free networks under targeted attacks. For random networks, a strategic resilience parameter above 0.3 prevented efficiency from falling below the 50% threshold under random shocks. This study innovates by establishing a benchmark model that links carbon reduction risk dynamics to supply chain network structure, providing a quantitative analytical framework for policymakers to design targeted resilience strategies and for managers to optimize network robustness amid decarbonization pressures.

Graphical abstract
Keywords
Carbon reduction risk
Supply chain networks
Risk propagation
Complex networks
Cascading failures
Funding
The study was financially supported by the General Project of Hubei Provincial Social Science Fund (HBSKJJ20253229), the Project of Hubei Provincial Department of Education (24Q072, 24Y046), and the Young Scientists Fund of National Natural Science Foundation of China (72303127).
Conflict of interest
The authors reported no conflict of interest.
References
  1. Sgaravatti G, Tagliapietra S, Trasi C. Europe’s fiscal policy response to the energy crisis: lessons learned for a greener way out. Energy Efficiency. 2024;17(8):90. doi: 10.1007/s12053-024-10275-0

 

  1. Wu X, Li Z, Tang F. The effect of carbon price volatility on firm green transitions: Evidence from Chinese manufacturing listed firms. Energies. 2022;15(20):7456. doi: 10.3390/en15207456

 

  1. Liu C, Yang Y, Chen S. How does transition finance influence green innovation of high-polluting and high-energy- consuming firms? Evidence from China. Environ Sci Pollut Res. 2024;31(6):8026-8045. doi: 10.1007/s11356-023-31360-4

 

  1. Ge K, Xu H, Liu X. Green transition of urban land use: Spatial spillover effects and boundaries on urban land use efficiency. Environ Dev Sustain. 2025;27(2):4037-4054. doi: 10.1007/s10668-025-07022-5

 

  1. Shayegh S, Reissl S, Roshan E, Calcaterra M. An assessment of different transition pathways to a green global economy. Commun Earth Environ. 2023;4(1):448. doi: 10.1038/s43247-023-01109-5

 

  1. Basilico S, Grashof N. Accelerating the sustainability transition of brown regions: Unlocking the speed factor. Environ Innov Soc Transitions. 2024;51:100840. doi: 10.1016/j.eist.2024.100840

 

  1. Aydin M, Degirmenci T, Ahmed Z, Apergis N. Do the energy taxes, green technological innovation, and energy productivity enable the green energy transition in EU countries? Evidence from novel panel data estimators. Renew Energy. 2025;249:123236. doi: 10.1016/j.renene.2025.123236

 

  1. Aziz G, Sarwar S. Revisit the role of governance indicators to achieve sustainable economic growth of Saudi Arabia - pre and post implementation of 2030 Vision. Struct Change Econ Dyn. 2023;66:213-227. doi: 10.1016/j.strueco.2023.04.008

 

  1. Aziz G, Waheed R, Sarwar S, Khan MS. The Significance of Governance Indicators to Achieve Carbon Neutrality: A New Insight of Life Expectancy. Sustainability. 2022;15(1):766. doi: 10.3390/su15010766

 

  1. Meng X, Wu C. Empirical evidence on digitization enabling the transition to a green economy in China. Environ Sci Pollut Res. 2024;31(37):51790-51805. doi: 10.1007/s11356-024-34613-y

 

  1. Li Y, Yang X, Du E, et al. A review on carbon emission accounting approaches for the electricity power industry. Appl Energy. 2024;359:122681. doi: 10.1016/j.apenergy.2024.122681

 

  1. McDowall W, Geng Y, Huang B, et al. Circular economy policies in China and Europe. J Ind Ecol. 2017;21(3):651- 661. doi: 10.1111/jiec.12597

 

  1. Zhai X, An Y, Shi X, Liu X. Measurement of green transition and its driving factors: Evidence from China. J Clean Prod. 2022;335:130292. doi: 10.1016/j.jclepro.2021.130292

 

  1. Chen W, Zou W, Zhong K, Aliyeva A. Machine learning assessment under the development of green technology innovation: A perspective of energy transition. Renew Energy. 2023;214:65-73. doi: 10.1016/j.renene.2023.05.108

 

  1. Lüdeke-Freund F, Gold S, Bocken NMP. A review and typology of circular economy business model patterns. J Ind Ecol. 2019;23(1):36-61. doi: 10.1111/jiec.12763

 

  1. Tong X. The spatiotemporal evolution pattern and influential factor of regional carbon emission convergence in China. Adv Meteorol. 2020;2020:4361570. doi: 10.1155/2020/4361570

 

  1. Köveker T, Chiappinelli O, Kröger M, et al. Green premiums are a challenge and an opportunity for climate policy design. Nat Clim Chang. 2023;13(7):592-595. doi: 10.1038/s41558-023-01689-2

 

  1. Huang W, Wang Q, Li H, Fan H, Qian Y, Klemeš JJ. Review of recent progress of emission trading policy in China. J Clean Prod. 2022;349:131480. doi: 10.1016/j.jclepro.2022.131480

 

  1. Yang Y, Chi Y. Path selection for enterprises’ green transition: Green innovation and green mergers and acquisitions. J Clean Prod. 2023;412:137397. doi: 10.1016/j.jclepro.2023.137397

 

  1. Sun C, Li Z, Ma T, He R. Carbon efficiency and international specialization position: Evidence from global value chain position index of manufacture. Energy Policy. 2019;128:235- 242. doi: 10.1016/j.enpol.2018.12.058

 

  1. Quader MA, Ahmed S, Ghazilla RAR, Ahmed S, Dahari M. A comprehensive review on energy efficient CO2 breakthrough technologies for sustainable green iron and steel manufacturing. Renew Sustain Energy Rev. 2015;50:594- 614. doi: 10.1016/j.rser.2015.05.026

 

  1. Osório A. Not everything is green in the green transition: Theoretical considerations on market structure, prices and competition. J Clean Prod. 2023;427:139300. doi: 10.1016/j.jclepro.2023.139300

 

  1. Ma R, Lin Y, Lin B. Does digitalization support green transition in Chinese cities? Perspective from Metcalfe’s Law. J Clean Prod. 2023;425:138769. doi: 10.1016/j.jclepro.2023.138769

 

  1. Kjaer LL, Pigosso DCA, Niero M, Bech NM, McAloone TC. Product/Service-Systems for a circular economy: The route to decoupling economic growth from resource consumption? J Ind Ecol. 2019;23(1):22-35. doi: 10.1111/jiec.12747

 

  1. Wang K. The effect of transition finance on ESG performance: Empirical evidence from China’s high-carbon industries. Int J Low-Carbon Technol. 2025;20:1809-1817. doi: 10.1093/ijlct/ctaf113

 

  1. Wang Y, Liu J, Zhao Z, Ren J, Chen X. Research on carbon emission reduction effect of China’s regional digital trade under the “double carbon” target combination of the regulatory role of industrial agglomeration and carbon emissions trading mechanism. J Clean Prod. 2023;405:137049. doi: 10.1016/j.jclepro.2023.137049

 

  1. Shobande OA, Tiwari AK, Ogbeifun L. Quantifying the role of the energy transition in alleviating marginalisation and advancing inclusive green growth. J Environ Manage. 2025;390:126241. doi: 10.1016/j.jenvman.2025.126241

 

  1. Hielscher S, Sovacool BK. Contested smart and low-carbon energy futures: Media discourses of smart meters in the United Kingdom. J Clean Prod. 2018;195:978-990. doi: 10.1016/j.jclepro.2018.05.227

 

  1. Unruh GC. The real stranded assets of carbon lock-in. One Earth. 2019;1(4):399-401. doi: 10.1016/j.oneear.2019.11.012

 

  1. Unruh GC, Carrillo-Hermosilla J. Globalizing carbon lock-in. Energy Policy. 2006;34(10):1185-1197. doi: 10.1016/j.enpol.2004.10.013

 

  1. Li J, Li S. Energy investment, economic growth and carbon emissions in China-Empirical analysis based on spatial Durbin model. Energy Policy. 2020;140:111425. doi: 10.1016/j.enpol.2020.111425

 

  1. Tvinnereim E, Mehling M. Carbon pricing and deep decarbonisation. Energy Policy. 2018;121:185-189. doi: 10.1016/j.enpol.2018.06.020

 

  1. Zhao R, Gao J, Lyu X, et al. How does green transition of farmland use affect grain production capacity? Evidence from China. Reg Environ Change. 2025;25(4):153. doi: 10.1007/s10113-025-02483-w

 

  1. Watson M, Machado P, Da Silva A, et al. Sustainable aviation fuel technologies, costs, emissions, policies, and markets: A critical review. J Clean Prod. 2024;449:141472. doi: 10.1016/j.jclepro.2024.141472

 

  1. Galan M, Lindner R. To get the European transition to green hydrogen right, equitable partnerships with the Global South matter. Environ Sci Policy. 2025;168:104066. doi: 10.1016/j.envsci.2025.104066

 

  1. Sun W, Huang C. Predictions of carbon emission intensity based on factor analysis and an improved extreme learning machine from the perspective of carbon emission efficiency. J Clean Prod. 2022;338:130414. doi: 10.1016/j.jclepro.2022.130414

 

  1. McGill E, Er V, Penney T, et al. Evaluation of public health interventions from a complex systems perspective: A research methods review. Soc Sci Med. 2021;272:113697. doi: 10.1016/j.socscimed.2021.113697

 

  1. Abrol A, Fu Z, Salman M, et al. Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning. Nat Commun. 2021;12(1):353. doi: 10.1038/s41467-020-20655-6

 

  1. Song X, Jia X, Dong Q, Xu D. System reliability under cascading failure models with different load effect modes. Qual Reliab Eng Int. 2023;39(6):2094-2112. doi: 10.1002/qre.3304

 

  1. Swift AW. Stochastic models of cascading failures. J Appl Probab. 2008;45(4):907-921. doi: 10.1239/jap/1231340223

 

  1. Wang Y, Zhang F. Modeling and analysis of under-load- based cascading failures in supply chain networks. Nonlinear Dyn. 2018;92(3):1403-1417. doi: 10.1007/s11071-018-4135-z

 

  1. Zhao K, Zuo Z, Blackhurst JV. Modelling supply chain adaptation for disruptions: An empirically grounded complex adaptive systems approach. J Oper Manag. 2019;65(2):190-212. doi: 10.1002/joom.1009

 

  1. Bode C, Wagner SM. Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions. J Oper Manag. 2015;36(1):215-228. doi: 10.1016/j.jom.2014.12.004

 

  1. Alkahtani H, Aldhyani TH. Intrusion detection system to advance internet of things infrastructure-based deep learning algorithms. Complexity. 2021;2021:5579851. doi: 10.1155/2021/5579851

 

  1. Zhang Z, Zhu Y, Yang T, Li L, Zhu H, Wang H. Conversion of local industrial wastes into greener cement through geopolymer technology: A case study of high-magnesium nickel slag. J Clean Prod. 2017;141:463-471. doi: 10.1016/j.jclepro.2016.09.147

 

  1. Zhang T, Uratani J, Huang Y, Xu L, Griffiths S, Ding Y. Hydrogen liquefaction and storage: Recent progress and perspectives. Renew Sustain Energy Rev. 2023;176:113204. doi: 10.1016/j.rser.2023.113204

 

  1. Moosavi J, Fathollahi-Fard AM, Dulebenets MA. Supply chain disruption during the COVID-19 pandemic: Recognizing potential disruption management strategies. Int J Disaster Risk Reduct. 2022;75:102983. doi: 10.1016/j.ijdrr.2022.102983

 

  1. Pérez-Fortes M, Schöneberger JC, Boulamanti A, Tzimas E. Methanol synthesis using captured CO2 as raw material: Techno-economic and environmental assessment. Appl Energy. 2015;161:718-732. doi: 10.1016/j.apenergy.2015.07.067

 

  1. Ma X, Khan MN, Awosusi AA, Uzun B, Shamansurova Z. Heterogeneous impact of green energy innovation on energy transition in the G7 nations: an aggregated and disintegrated analysis through advanced quantile approach. Int J Sustain Dev World Ecol. 2024;31(3):264-278. doi: 10.1080/13504509.2023.2277422

 

  1. Ivanov D, Dolgui A, Sokolov B, Ivanova M. Literature review on disruption recovery in the supply chain. Int J Prod Res. 2017;55(20):6158-6174. doi: 10.1080/00207543.2017.1330572

 

  1. Liao S, Hu D, Ding L. Assessing the influence of supply chain collaboration value innovation, supply chain capability and competitive advantage in Taiwan’s networking communication industry. Int J Prod Econ. 2017;191:143-153. doi: 10.1016/j.ijpe.2017.06.001

 

  1. Tsolakis N, Goldsmith AT, Aivazidou E, Kumar M. Microalgae-based circular supply chain configurations using Industry 4.0 technologies for pharmaceuticals. J Clean Prod. 2023;395:136397. doi: 10.1016/j.jclepro.2023.136397

 

  1. Dolgui A, Gusikhin O, Ivanov D, Li X, Stecke K. A network- of-networks adaptation for cross-industry manufacturing repurposing. IISE Trans. 2024;56(6):666-682. doi: 10.1080/24725854.2023.2253881

 

  1. Pham T, Yenradee P. Optimal supply chain network design with process network and BOM under uncertainties: A case study in toothbrush industry. Comput Ind Eng. 2017;108:177-191. doi: 10.1016/j.cie.2017.04.012

 

  1. Antràs P, Gortari A. On the geography of global value chains. Econometrica. 2020;88(4):1553-1598. doi: 10.3982/ecta15362

 

  1. Makris S, Zoupas P, Chryssolouris G. Supply chain control logic for enabling adaptability under uncertainty. Int J Prod Res. 2011;49(1):121-137. doi: 10.1080/00207543.2010.508940

 

  1. Shapiro JF. Challenges of strategic supply chain planning and modeling. Comput Chem Eng. 2004;28(6-7):855-861. doi: 10.1016/j.compchemeng.2003.09.013

 

  1. Saha S, Nielsen IE, Moon I. Strategic inventory and pricing decision for substitutable products. Comput Ind Eng. 2021;160:107570. doi: 10.1016/j.cie.2021.107570

 

  1. Huang D. An algorithm to generate all d-lower boundary points for a stochastic flow network using dynamic flow constraints. Reliab Eng Syst Saf. 2024;249:110217. doi: 10.1016/j.ress.2024.110217

 

  1. Liang X, Bao D, Yang Z. State evaluation method for complex task network models. Inf Sci. 2023;653:119796. doi: 10.1016/j.ins.2023.119796

 

  1. Yang Y, Chen J. Comprehensive analysis of water carrying capacity based on wireless sensor network and image texture of feature extraction. Alex Eng J. 2022;61(4):2877-2886. doi: 10.1016/j.aej.2021.08.018

 

  1. Huang S, Hua Z, Wang P, Shi J. A novel longitudinal connectivity index to evaluate reticular river networks based on the combination of network maximum flow and resistance distance. J Environ Manage. 2024;367:122062. doi: 10.1016/j.jenvman.2024.122062

 

  1. Wang L, Cheng L, Liu Y. Uncertainty-oriented physics- informed long short-term memory (UOPI-LSTM) network framework for dynamic force identification with interval uncertainties. Expert Syst Appl. 2025;274:127067. doi: 10.1016/j.eswa.2025.127067

 

  1. Gao T, Yang J, Tang Q. A multi-source domain information fusion network for rotating machinery fault diagnosis under variable operating conditions. Inf Fusion. 2024;106:102278. doi: 10.1016/j.inffus.2024.102278

 

  1. Wang L, Yang Y, Xu L, Ren Z, Fan S, Zhang Y. A particle swarm optimization-based deep clustering algorithm for power load curve analysis. Swarm Evol Comput. 2024;89:101650. doi: 10.1016/j.swevo.2024.101650

 

  1. Cats O, Koppenol G, Warnier M. Robustness assessment of link capacity reduction for complex networks: Application for public transport systems. Reliab Eng Syst Saf. 2017;167:544-553. doi: 10.1016/j.ress.2017.07.009

 

  1. Salama M, El-Dakhakhni W, Tait M. Systemic risk mitigation strategy for power grid cascade failures using constrained spectral clustering. Int J Crit Infrastruct Prot. 2023;42:100622. doi: 10.1016/j.ijcip.2023.100622

 

  1. Zhang W, Luo Z. Research on intercity travel mode recognition and network structure characteristics based on complex network and random forest classification. Sci Rep. 2025;15(1):35339. doi: 10.1038/s41598-025-19392-x

 

  1. Liu Q, Adriaens P. Unraveling risk propagation in the copper value chain: A firm-level network analysis. Resour Conserv Recycl. 2025;222:108435. doi: 10.1016/j.resconrec.2025.108435

 

  1. Ghadge A, Er M, Ivanov D, Chaudhuri A. Visualisation of ripple effect in supply chains under long-term, simultaneous disruptions: a system dynamics approach. Int J Prod Res. 2022;60(20):6173-6186. doi: 10.1080/00207543.2021.1987547

 

  1. Yang Z, Song Z, Liu W. Risks and crisis propagation in global palladium trade network: Implications for critical resource supply chain security. J Ind Ecol. 2025;29(4):1223-1236. doi: 10.1111/jiec.70038

 

  1. Hu H, Guo S, Qin Y, Lin W. Two-stage stochastic programming model and algorithm for mitigating supply disruption risk on aircraft manufacturing supply chain network design. Comput Ind Eng. 2022;175:108880. doi: 10.1016/j.cie.2022.108880

 

  1. Huang Z, Zhou Y, Lin Y, Zhao Y. Resilience evaluation and enhancing for China’s electric vehicle supply chain in the presence of attacks: A complex network analysis approach. Comput Ind Eng. 2024;195:110416. doi: 10.1016/j.cie.2024.110416

 

  1. Zhang L, Su W, Liao S, Wang S. Enhancing energy security through multi-scale network analysis: Robustness in global crude oil shipping-trade networks. Reliab Eng Syst Saf. 2025;265:111525. doi: 10.1016/j.ress.2025.111525

 

  1. Tian W, Huang X, Shao L, Wang Z, Li Y. Assessment and evolution analysis of the global wood pulp trade network resilience based on underload cascading failure. J Clean Prod. 2025;518:145742. doi: 10.1016/j.jclepro.2025.145742

 

  1. Dash A, Sarmah S, Tiwari M, Jena SK, Glock CH. Cybersecurity investments in supply chains with two-stage risk propagation. Comput Ind Eng. 2024;197:110519. doi: 10.1016/j.cie.2024.110519

 

  1. Xiao R, Xiao T, Zhao P, Zhang M, Ma T, Qiu S. Structure and resilience changes of global liquefied natural gas shipping network during the Russia-Ukraine conflict. Ocean Coast Manag. 2024;252:107102. doi: 10.1016/j.ocecoaman.2024.107102

 

  1. Hou B, Wang X, Tang J. Identifying critical node set in supply chain network considering hybrid operational risk management. Int J Prod Econ. 2025;291:109864. doi: 10.1016/j.ijpe.2025.109864

 

  1. Vaid R, Jain K, Sahi GK, et al. Designing a resilient agriculture supply network for mitigating the disruptions. Ann Oper Res. 2025;344(1):313-343. doi: 10.1007/s10479-024-06143-w

 

  1. Lavassani KM, Boyd ZM, Movahedi B, Vasquez J. Ten-tier and multi-scale supply chain network analysis of medical equipment: random failure & intelligent attack analysis. Int J Prod Res. 2023;61(24):8468-8492. doi: 10.1080/00207543.2022.2152892

 

  1. Artime O, Grassia M, De Domenico M, et al. Robustness and resilience of complex networks. Nat Rev Phys. 2024;6(2):114- 131. doi: 10.1038/s42254-023-00676-y

 

  1. Zhao H, Bu H, Li Z. Resilience analysis and enhancement strategies of leader-driven automotive supply chain based on complex network. Int J Gen Syst. 2025;54(5):1-29. doi: 10.1080/03081079.2025.2590647

 

  1. Massari GF, Giannoccaro I. The importance of the structural pattern for the resilience of circular economy networks: A network-based approach. J Clean Prod. 2024;436:140164. doi: 10.1016/j.jclepro.2023.140164

 

  1. Wang W, Karimi F, Khalilpour K, Green D, Varvarigos M. Robustness analysis of electricity networks against failure or attack: The case of the Australian National Electricity Market (NEM). Int J Crit Infrastruct Prot. 2023;41:100600. doi: 10.1016/j.ijcip.2023.100600

 

  1. Negahban A, Smith JS. A joint analysis of production and seeding strategies for new products: an agent-based simulation approach. Ann Oper Res. 2018;268(1-2):41-62. doi: 10.1007/s10479-016-2389-8

 

  1. Fu X, Xu X, Li W. Cascading failure resilience analysis and recovery of automotive manufacturing supply chain networks considering enterprise roles. Physica A. 2024;634:129478. doi: 10.1016/j.physa.2023.129478
Next article in this issue
Share
Back to top
Asian Journal of Water, Environment and Pollution, Electronic ISSN: 1875-8568 Print ISSN: 0972-9860, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept