韧性视角下海外重大基础设施运营维护供应链风险协同管控策略研究

doi:10.15951/j.tmgcxb.26030136

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摘要随着“一带一路”战略持续深入推进，我国承建的大量海外重大基础设施进入运营维护关键期。与建设期相比，运营维护供应链风险传导路径复杂、多策略协同效应难以量化，需要拆解边际贡献与交互效应，才能支撑复杂场景下精细化决策。文章从韧性视角出发，围绕海外重大基础设施运营维护供应链特征识别核心风险因素，梳理风险管控可行策略，选取抵抗性、恢复性、适应性作为韧性衡量指标。采用因果推断与机器学习结合方法，构建“策略-风险-韧性”结构因果模型以识别风险传导的因果关系、排除混淆变量干扰，引入SHAP值与SHAP交互值以量化单一策略的边际贡献与组合策略的协同效应，结合反事实推理模拟不同策略场景下的韧性变化，形成风险协同管控策略的量化评估框架。选取中哈原油管道项目进行实证分析，结果表明：①单一策略中，应急配件前置存储对抵抗性提升效应最强(SHAP均值0.227)，本土化运维培育对适应性提升效应最强(SHAP均值11.354)；②组合策略存在显著协同效应，本土化运维培育与应急配件前置存储组合对适应性的协同效应倍率达8.55倍，较单一策略加和提升8.30 d；③不同韧性维度策略组合存在最优实施强度阈值，抵抗性维度最优阈值为60%，恢复性维度为70%。最后，基于量化结果提出覆盖事前、事中、事后全生命周期的协同管控策略建议。

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	汪欣
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关键词 ：韧性, 海外重大基础设施, 运营维护供应链, 风险协同管控, 反事实推理, 机器学习

Abstract：With the continuous advancement of the Belt and Road Initiative， numerous major overseas infrastructure projects constructed by China have entered critical operation and maintenance (O&M) phases. Compared to the construction phase， the risk transmission paths of the O&M supply chain are more complex and the synergistic effects of multiple strategies are difficult to quantify. Decomposing marginal contributions and interaction effects is therefore essential to support refined decision-making in complex scenarios. From the perspective of resilience， this study identifies core risk factors based on the characteristics of the major overseas infrastructure of O&M supply chain， summarizes feasible risk governance strategies， and adopts the resistance， recoverability and adaptability as metrics for supply chain resilience. An analytical model is established by integrating causal inference and machine learning， constructing a “strategy-risk-resilience” structural causal model to identify the causal relationships of risk transmission and eliminate the interference of confounding variables. SHAP values and SHAP interaction values are introduced to quantify the marginal contribution of individual strategies and the synergistic effects of combined strategies. Combined with counterfactual reasoning to simulate resilience variations under different strategic scenarios， a quantitative evaluation framework for collaborative risk governance strategies is proposed. An empirical analysis is conducted based on the China-Kazakhstan Crude Oil Pipeline Project as a case study. The results demonstrate that O&M risks are effectively mitigated， and resilience is significantly improved， yielding the following key insights： ① Among individual strategies， the pre-positioned storage of emergency spare parts yields the strongest enhancement effect on resistance (SHAP mean=0.227)， while localized development of O&M capability provides the strongest enhancement for adaptability (SHAP mean=11.354)； ② Combined strategies exhibit significant synergistic effects； for instance， the combination of localized O&M capability development and pre-positioned storage of emergency spare parts achieves a synergy multiplier of 8.55 times for adaptability， presenting an improvement of 8.30 days compared to the sum of individual strategies； ③ Optimal implementation intensity thresholds exist for strategy combinations across different resilience dimensions， specifically 60% for resistance and 70% for recoverability dimension. Finally， targeted suggestions are provided to improve risk governance level of the O&M supply chain of major overseas infrastructure.

Key words： resilience major overseas infrastructure O&M supply chain synergistic governance of risks counterfactual reasoning machine learning

收稿日期: 2026-03-27

PACS:

F252

基金资助:国家自然科学重点基金(72031008)

作者简介: 汪欣(1990—　)，男，博士，工程师。主要从事国际工程管理、应急供应链方面的研究；张水波(1966—　)，男，博士，教授。主要从事国际工程合同管理方面的研究；李鹏(1976—　)，男，博士，教授。主要从事运输投送组织筹划方面的研究；

引用本文:

汪欣, 张水波, 李鹏. 韧性视角下海外重大基础设施运营维护供应链风险协同管控策略研究[J]. 土木工程学报, 2026, 59(11): 147-164.
Wang Xin, Zhang Shuibo, Li Peng. On collaborative risk governance strategies for operation and maintenance supply chains of major overseas infrastructure： a resilience perspective. 土木工程学报, 2026, 59(11): 147-164.

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1 国务院新闻办公室. 共建“一带一路”：构建人类命运共同体的重大实践［R］. 北京：人民出版社， 2023.
2 高颖，傅永程，张水波. 海外重大基础设施运营管理：一个亟待研究的课题［J］. 天津大学学报：社会科学版， 2021， 23(5)： 407-413. GAO Ying， FU Yongcheng， ZHANG Shuibo. Operation management of overseas mega infrastructure： an urgent study［J］. Journal of Tianjin University： Social Sciences， 2021， 23(5)： 407-413. (in Chinese)
3 CHRISTOPHER M， PECK H. Building the resilient supply chain［J］. The International Journal of Logistics Management， 2004， 15(2)： 1-14.
4 FANG Y P， PEDRONI N， ZIO E. Resilience-based component importance measures for critical infrastructure network systems［J］. IEEE Transactions on Reliability， 2016， 65(2)： 502-512.
5 WAGNER S M， BODE C. An empirical investigation into supply chain vulnerability［J］. Journal of Purchasing and Supply Management， 2006， 12(6)： 301-312.
6 TOMLIN B. On the value of mitigation and contingency strategies for managing supply chain disruption risks［J］. Management Science， 2006， 52(5)： 639-657.
7 OUYANG M. Review on modeling and simulation of interdependent critical infrastructure systems［J］. Reliability Engineering & System Safety， 2014， 121： 43-60.
8 KLEINDORFER P R， SAAD G H. Managing disruption risks in supply chains［J］. Production and Operations Management， 2005， 14(1)： 53-68.
9 PECK H. Drivers of supply chain vulnerability： an integrated framework［J］. International Journal of Physical Distribution & Logistics Management， 2005， 35(4)： 210-232.
10 ORR R J， SCOTT W R. Institutional exceptions on global projects： a process model［J］. Journal of International Business Studies， 2008， 39(4)： 562-588.
11 IVANOV D， SOKOLOV B， DOLGUI A. The Ripple effect in supply chains： trade-off ‘efficiency-flexibility-resilience’ in disruption management［J］. International Journal of Production Research， 2014， 52(7)： 2154-2172.
12 SERRANO A， OLIVA R， KRAISELBURD S. Risk propagation through payment distortion in supply chains［J］. Journal of Operations Management， 2018， 58： 1-14.
13 LIU C， CAO J M， WU G D， et al. Interenterprise collaboration network in international construction projects： evidence from Chinese construction enterprises［J］. Journal of Management in Engineering， 2022， 38(2)： 05021018.
14 王金亭. 全球供应链风险管理与协同［M］. 北京：人民邮电出版社， 2025. WANG Jinting. Global supply chain risk management and coordination［M］. Beijing： Posts & Telecom Press， 2025. (in Chinese)
15 RAJESH R， RAVI V， RAO R V. Selection of risk mitigation strategy in electronic supply chains using grey theory and digraph-matrix approaches［J］. International Journal of Production Research， 2015， 53(1)： 238-257.
16 FLYNN B B， HUO B F， ZHAO X D. The impact of supply chain integration on performance： a contingency and configuration approach［J］. Journal of Operations Management， 2010， 28(1)： 58-71.
17 HENISZ W J. The institutional environment for infrastructure investment［J］. Industrial and Corporate Change， 2002， 11(2)： 355-389.
18 NIKOOKAR E， ALI I， STEVENSON M， et al. Necessary antecedents of supply chain resilience： the nonnegotiable influence of supply chain responsiveness and collaboration［J］. Journal of Supply Chain Management， 2025， 61(4)： 54-76.
19 ABDI M， AULAKH P S. Do country-level institutional frameworks and interfirm governance arrangements substitute or complement in international business relationships？［J］. Journal of International Business Studies， 2012， 43(5)： 477-497.
20 The World Bank. Connecting to compete 2023： Trade logistics in an uncertain global economy［R］. 7th ed. Washington， D. C.： The World Bank， 2023.
21 Asian Development Bank. Pacific economic monitor： July 2021［R］. Manila： Asian Development Bank， 2021.
22 AMBULKAR S， BLACKHURST J， GRAWE S. Firm’s resilience to supply chain disruptions： Scale development and empirical examination［J］. Journal of Operations Management， 2015， 33： 111-122.
23 MACKENZIE J P A D. The book of why： The new science of cause and effect［M］. New York： Basic Books， 2018.
24 刘钰婷，顾晶晶，周强. 基于结构因果模型的城市出行流量预测方法［J］. 计算机科学， 2025， 52(10)： 70-78. LIU Yuting， GU Jingjing， ZHOU Qiang. Urban flow prediction method based on structural causal model［J］. Computer Science， 2025， 52(10)： 70-78. (in Chinese)
25 WOOLDRIDGE J M. Introductory econometrics： A modern approach［M］. 7th ed. Stamford： Cengage， 2020.
26 CHERNOZHUKOV V， CHETVERIKOV D， DEMIRER M， et al. Double/debiased machine learning for treatment and structural parameters［J］. The Econometrics Journal， 2018， 21(1)： C1-C68.
27 CHEN T Q， GUESTRIN C. XGBoost： a scalable tree boosting system［C］//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York： ACM Press， 2016.
28 WODTKE G T， ZHOU X. Effect Decomposition in the presence of treatment-induced confounding： A regression-with-residuals approach［J］. Epidemiology， 2020， 31(3)： 369-375.
29 PREACHER K J， HAYES A F. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models［J］. Behavior Research Methods， 2008， 40(3)： 879-891.
30 温忠麟，叶宝娟. 中介效应分析：方法和模型发展［J］. 心理科学进展， 2014， 22(5)： 731-745. WEN Zhonglin， YE Baojuan. Analyses of mediating effects： the development of methods and models［J］. Advances in Psychological Science， 2014， 22(5)： 731-745. (in Chinese)
31 MUKAKA M M. Statistics corner： A guide to appropriate use of correlation coefficient in medical .research［J］. Malawi Medical Journal， 2012， 24(3)： 69-71.
32 OSTER E. Unobservable selection and coefficient stability： theory and evidence［J］. Journal of Business & Economic Statistics， 2019， 37(2)： 187-204.
33 刘擎超，王瑞海，蔡英凤，等. 基于CatBoost和SHAP的高级别自动驾驶车辆非预期停车冲突风险预测［J］. 汽车安全与节能学报， 2025， 16(1)： 170-180. LIU Qingchao， WANG Ruihai， CAI Yingfeng， et al. Unintended stopping conflict risk prediction for high-level autonomous vehicles based on CatBoost and SHAP［J］. Journal of Automotive Safety and Engergy， 2025， 16(1)： 170-180. (in Chinese)
34 FRIEDMAN J H， POPESCU B E. Predictive learning via rule ensembles［J］. Annals of Applied Statistics， 2008， 2(3)： 916-954.
35 蒋建忠. “反事实”框架与政治学因果推论［J］. 北大政治学评论， 2018(1)： 73-94. JIANG Jianzhong. Counter-factual framework and causal inference in political science［J］. Peking University Political Science Review， 2018(1)： 73-94. (in Chinese)
36 中国石油. 集团公司社会责任报告［EB/OL］. (2025-05-22)［2026-03-01］. https：//www.cnpc.com.cn/cnpc/qyshzrbg/gywm_list.shtml．
37 World Bank. Logistics Performance Index： Overall (1=low to 5=high)［EB/OL］. (2025-05-22)［2026-03-01］. https：//data.worldbank.org/indicator/LP.LPI.OVRL.XQ？spm=5176.28103460.0.0.79cd2988NE7n42&file=LP.LPI.OVRL.XQ.
38 GéRON A. Hands-on machine learning with Scikit-Learn， Keras， and TensorFlow： Concepts， tools， and techniques to build intelligent systems［M］. 2nd ed. Sebastopol： O’Reilly， 2019.
39 KüNZEL S R， SEKHON J S， BICKEL P J， et al. Metalearners for estimating heterogeneous treatment effects using machine learning［J］. Proceedings of the National Academy of Sciences of the United States of America， 2019， 116(10)： 4156-4165.
40 尹志成，李炜，黄宏星. 从中哈原油管道看境外能源项目实施策略［J］. 国际经济合作， 2017， 33(2)： 58-62.
41 薛子文，李志涛. 能源友谊构筑“一带一路”合作典范——中油国际管道公司中哈原油管道20年建设发展纪实［J］. 中国石油企业， 2024(8)： 85-87.
42 ISPAS L， MIRONEASA C， SEVERIN T L， et al. Artificial intelligence applications in risk management within integrated management systems： a review［J］. Systems， 2025， 13(11)： 967.