Charting sustainable logistics on the 21st-Century Maritime Silk Road: a DEA-based approach enhanced by risk considerations through prospect theory

The Belt and Road Initiative (BRI), launched by China in 2013, comprises the Silk Road Economic Belt and the 21st Century Maritime Silk Road (MSR). The MSR emphasizes maritime routes and port infrastructure to reshape global trade patterns, improve supply chain efficiency, and advance environmental sustainability. Sustainable logistics—eco-friendly transport practices that enhance efficiency and reduce environmental impact—is central to the MSR’s success. This study addresses a research gap by evaluating how strategic positioning influences the sustainable logistics potential of MSR countries through an integrated assessment of economic, environmental, infrastructure/logistics, governance, and innovation indicators. The research question is how to comprehensively and behaviorally evaluate MSR countries’ logistics sustainability potential under risk and uncertainty. To this end, the study develops a DEA-based model enhanced by Prospect Theory to incorporate behavioral decision-making (reference dependence, loss aversion, diminishing sensitivity) into efficiency evaluation. The goal is to provide actionable insights for policymakers and investors to promote sustainability-aligned logistics development along the MSR.

The literature on the BRI and MSR spans geo-economics, port sustainability, investment prioritization, and maritime operational risks. BRI-focused studies highlight China’s expanding influence and strategic nodes (Hu et al., 2022), port sustainability within China’s network (Wang et al., 2021), and port investment prioritization via link prediction (Zheng et al., 2022). Research on blue economy investments and coastal livelihoods in ASEAN reveals complex distributional impacts (Song and Fabinyi, 2022). MSR logistics research examines the role of port infrastructure connectivity in driving logistics performance and economic growth (Liang and Liu, 2020), supply chain risk using fuzzy and evidential reasoning (Jiang et al., 2023), and DEA-based port efficiency assessments (Huang et al., 2021). Spatio-temporal analyses document shifts in container port systems toward Asia (Zhang et al., 2023) and evolving network configurations with sustainability implications (Zhao et al., 2021). The MSR has also catalyzed logistics development in Chinese coastal regions, albeit unevenly (Xu et al., 2023). A key gap remains: a holistic sustainability readiness assessment across economic, energy/environment, infrastructure/logistics, governance, and innovation dimensions for countries along the MSR that integrates behavioral risk considerations.

The study integrates Prospect Theory with Data Envelopment Analysis (DEA) to construct a risk-aware efficiency evaluation framework. Prospect Theory (Kahneman and Tversky) introduces behavioral features—reference dependence, loss aversion, and diminishing sensitivity—modeled via parameters capturing gain-risk attitude, loss-risk attitude, and loss aversion. DEA (CCR/BCC) assesses relative efficiency of decision-making units (DMUs) with multiple inputs/outputs via weight optimization. Building on a behavioral DEA approach (Chen et al., 2019), the proposed risk-considering DEA proceeds in three steps: (1) Normalize inputs and outputs to enable fair cross-country comparison. (2) Identify positive and negative reference points for each indicator to anchor perceived gains/losses relative to best/worst observed performance. (3) Construct a behavioral objective that combines gains and losses with a trade-off parameter (varphi) reflecting the importance placed on gains versus losses, and risk-attitude/aversion parameters (e.g., gamma and delta) to model diminishing sensitivity in gains and losses. Inputs and outputs: The DMUs are nine key MSR checkpoint countries (Djibouti, Greece, India, Indonesia, Italy, Kenya, Malaysia, Sri Lanka, Vietnam). Inputs are China’s cumulative investment and construction in each country (AEI China Global Investment Tracker, 2023). Outputs comprise 29 indicators grouped into five dimensions: Economic (e.g., commercial services trade, export/import value indices, GDP, inflation, merchandise trade, net trade in goods), Energy and Environment (energy intensity, adjusted savings for CO2 damage, CO2 emissions per capita), Infrastructure and Logistics (container port traffic; costs and times for export/import border and documentary compliance; warehouse build time; logistics performance index; machinery and transport equipment share; transport services share), Governance and Stability (control of corruption, government effectiveness, political stability/absence of violence, rule of law), and Innovation (human capital index, industrial design applications, patent applications). Undesired outputs (e.g., costs, times, energy intensity, emissions, certain damage metrics) are treated so that lower values indicate better performance. Data sources include IMF, IEA, UNCTAD, World Bank (Open Data, B-READY), WGI, WIPO, UNIDO, and WTO. The efficiency of each country within each dimension is computed using the risk-aware DEA, yielding scores in [0,1], where 1 indicates the frontier.

Using the risk-considering DEA, efficiency scores by dimension (Table 9) are as follows: • Djibouti: Economic 0.783; Energy/Environment 1.000; Infrastructure/Logistics 0.578; Governance/Stability 0.576; Innovation 0.564. • Greece: Economic 0.633; Energy/Environment 0.707; Infrastructure/Logistics 1.000; Governance/Stability 0.613; Innovation 0.818. • India: Economic 0.621; Energy/Environment 0.697; Infrastructure/Logistics 0.625; Governance/Stability 0.603; Innovation 0.598. • Indonesia: Economic 0.577; Energy/Environment 0.616; Infrastructure/Logistics 0.562; Governance/Stability 0.561; Innovation 0.549. • Italy: Economic 0.635; Energy/Environment 0.610; Infrastructure/Logistics 1.000; Governance/Stability 0.561; Innovation 0.890. • Kenya: Economic 0.629; Energy/Environment 1.000; Infrastructure/Logistics 0.606; Governance/Stability 0.598; Innovation 0.593. • Malaysia: Economic 0.645; Energy/Environment 0.637; Infrastructure/Logistics 0.670; Governance/Stability 0.617; Innovation 0.617. • Sri Lanka: Economic 0.604; Energy/Environment 0.864; Infrastructure/Logistics 0.605; Governance/Stability 0.604; Innovation 0.600. • Vietnam: Economic 0.355; Energy/Environment 0.313; Infrastructure/Logistics 0.307; Governance/Stability 0.299; Innovation 0.430. Narrative insights highlighted in the paper include: - Vietnam is noted for cost-effective economic efficiency. - Indonesia shows notable achievements in sustainability and governance alignment. - Malaysia demonstrates balanced, across-the-board efficiency. - Greece and Italy are top performers in infrastructure and logistics (both 1.000), with strong innovation potential (Italy 0.890; Greece 0.818). - Djibouti and Kenya lead energy/environmental efficiency (both 1.000), with Djibouti also exhibiting relatively strong economic efficiency (0.783).

The integrated DEA–Prospect Theory framework addresses the research question by quantifying each country’s sustainable logistics readiness while incorporating behavioral risk attitudes. The results reveal differentiated strengths: European checkpoints (Greece, Italy) anchor infrastructure/logistics performance and innovation potential; African hubs (Djibouti, Kenya) lead in energy/environmental efficiency; Malaysia, India, and Sri Lanka provide balanced contributions; Indonesia aligns with sustainability and governance priorities; Vietnam is emphasized for cost-effective economic aspects. These patterns inform targeted strategies—e.g., leveraging Greece/Italy for infrastructure-driven collaborations, prioritizing environmental initiatives with Djibouti/Kenya, and fostering innovation partnerships with Italy and Greece. For policymakers and investors, the risk-aware lens supports decisions under uncertainty by recognizing how perceived gains/losses against reference benchmarks shape efficiency. The findings underscore the importance of coordinated, context-specific interventions to enhance the MSR’s overall sustainability and trade effectiveness.

The study contributes an innovative, risk-aware DEA model that embeds Prospect Theory to evaluate sustainable logistics potential across five dimensions for key MSR countries. It identifies distinct roles and strengths—such as infrastructure/logistics and innovation hubs in Europe, energy/environment leaders in Africa, and balanced or domain-specific strengths in Asia—providing actionable guidance for strategic planning along the MSR. The approach advances logistics assessment by integrating behavioral decision-making with multi-criteria efficiency analysis, offering a more comprehensive lens for sustainability-aligned development. Future research should incorporate direct behavioral data and richer qualitative factors (cultural, political, and legal contexts) to refine the model and enhance interpretability, enabling more holistic policy and investment decisions.

The model indirectly estimates psychological behavioral coefficients (risk attitudes and loss aversion), introducing subjectivity and potential bias. The analysis emphasizes efficiency metrics and does not deeply examine qualitative determinants such as cultural, political, or legal factors. Data constraints and the selected set of indicators may limit generalizability, and incorporating additional or alternative measures could alter relative efficiencies.