Table · dataset · 2026
Data Sheet 1_Symbiotic resilience in port governance: a tripartite dynamic assessment framework.zip
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<p>Port governance resilience refers to a system's capacity to withstand shocks and regain equilibrium as stakeholder demands shift; it has received little direct attention in sustainable port development.
Description
Existing frameworks capture operational efficiency and infrastructure capacity, yet they are static and one-dimensional, and they miss the dynamic co-evolutionary processes among governance stakeholders. We propose the Stakeholder Symbiotic Resilience Index (SSRI), a composite metric for evaluating the health of tripartite stakeholder relationships in port governance.
Drawing on stakeholder salience theory, we treat three groups (Port Authority, Port Enterprises, and Local Community) as co-evolving actors within a Lotka–Volterra framework. Methodologically, we advance the model from numerical simulation to data-driven empirical analysis using 2015–2024 data from Qingdao Port. The estimated interaction coefficients point to an asymmetric configuration: the Port Authority supports enterprises, enterprises contribute strongly to the community, and community development in turn strengthens the Authority, while Enterprise-to-Authority influence is negative.
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The calibrated system yields a comparison-layer SSRI of 0.8587, compared with a theoretical benchmark of 0.8411; the uncertainty-layer median is 0.593 (95% interval: [0.377, 0.826]). Policy optimization should focus on strengthening institutionalized channels for public participation and rebalancing regulatory intensity toward collaborative incentives. The proposed framework provides a transferable diagnostic tool that port authorities elsewhere can adapt as they move from passive governance toward proactive resilience building.</p>
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Where it is published
- DOI doi.org/10.3389/fmars.2026.1929353.s002 ↗
DOI / persistent id · from figshare com
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from figshare com
Topics
- From keywords
- Astronomy & Astrophysics · Chemistry · Computer Science & AI · Earth & Environmental Science · Economics & Finance · Engineering · Humanities · Life Sciences · Medicine & Health · Ocean & Atmospheric Science · Social Science
- Inferred from text
- International and comparative law 70% · Simulation 75%
Provenance · 1 source records, 18 field assertions
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