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Threshold-governed nonlinearities and configurational interactions of development activities on cultural landscape resilience in karst traditional villages

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Abstract

In mountainous regions, traditional villages face deepening tensions between cultural landscape conservation and economic development. Various development activities' impacts on Cultural Landscape Resilience (CLR) exhibit complex nonlinearities and uncharted configurational effects. This study examines 636 village-year observations from 106 karst traditional villages in Southwest China (2020–2025), using gradient boosting regression trees and configurational analysis. Results demonstrate threshold-governed nonlinearities in individual activities: tourism investment, infrastructure improvement, road expansion, public service facilities investment, and agricultural facilities investment emerge as high-importance predictors, following four distinct patterns, optimal-intensity saturation, dual-critical-point transition, discrete-threshold activation, and path-dependent lock-in. For combinational effects, tourism and infrastructure investments serve as primary interaction hubs, capable of forming strong interactions with multiple development activities and influencing CLR. Moderate-intensity configurations (M-M, M-L, L-M) generate positive CLR outcomes through synergistic mechanisms, whereas extreme configurations (L−L,H−L,L−H) produce antagonistic effects. These findings advance a precision governance framework grounded in dynamic intensity thresholds and evidence-based synergy strategies. © 2026 The Authors.  Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
Original languageEnglish
Article number115255
JournalEcological Indicators
Volume189
Online published28 Jul 2026
DOIs
Publication statusPublished - Aug 2026

Funding

This study was supported by the National Natural Science Foundation of China (52468005) and The Humanities and Social Sciences Research Project of Guizhou University (GDYB2025004, 2025). We gratefully acknowledge the Qiandongnan Miao and Dong Autonomous Prefectural People's Government for their collaboration and invaluable support during the research process.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 9 - Industry, Innovation, and Infrastructure
    SDG 9 Industry, Innovation, and Infrastructure

Research Keywords

  • Cultural landscape resilience
  • GBRT
  • Machine learning
  • Rural revitalization
  • Socio-ecological systems

Publisher's Copyright Statement

  • This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

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