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Unlocking Green Frontiers: Dynamic QCA of MLP-Driven Transformation in Resource-Based Cities of the Yellow River Basin in China

Yong Jiang¹²*, Chuanwang Liu¹², Junli Zhang¹², Xinyi Li¹²

Advanced Management Research / 2026,8(1): 1-27 / 2026-05-12 22 16

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• Information:

  1. School of Economics and Management, China University of Geosciences, Beijing, Beijing;
  2. MOE Social Science Laboratory of Mineral Resources Security Governance, China University of Geosciences, Beijing, Beijing

• Keywords:
  Multi-level perspective (MLP); Yellow River Basin; Resource-based cities; Green total factor productivity; Dynamic fsQCA
• Abstract: Resource-based cities (RBCs) in China’s Yellow River Basin have long underpinned national energy and raw-material security, yet their heavy reliance on extraction and primary processing has generated persistent vulnerabilities, including resource depletion, ecological degradation, and rigid single-industry structures. As environmental constraints tighten and traditional factor advantages weaken, these cities face the imperative of green transformation — a systemic transition encompassing economic restructuring, social adaptation, environmental governance, and technological upgrading. This study addresses the heterogeneity of transformation outcomes by integrating the multi-level perspective (MLP) with dynamic fuzzy-set qualitative comparative analysis (dynamic fsQCA). Drawing on panel data for 38 RBCs in the Yellow River Basin spanning 2007-2023, we construct a three-tier analytical framework that assigns internet penetration (A) and patent grants (B) to the micro-niche layer; industrial structure upgrading (C), inward FDI intensity (D), and government R&D expenditure share (E) to the meso-regime layer; and per-capita GDP (F) and environmental regulation intensity (G) to the macro-landscape layer. Analyzing two policy-period cross-sections (2007-2015 and 2016- 2023), we identify sufficient configurational pathways toward high green total factor productivity (GTFP), assess temporal evolution using between-group and within-group calibration-adjusted consistency distances, and examine path heterogeneity across four city typologies — mature, growing, declining, and regenerating. Three core findings emerge. First, no single condition constitutes a necessary antecedent for GTFP improvement, and this conclusion is robust across both time periods and city types, with all between-group adjusted distances below the 0.2 significance threshold. Second, three configurations are identified in Phase 1 (overall consistency = 0.861; coverage = 0.748) and four in Phase 2 (consistency = 0.879; coverage = 0.812); the most salient structural shift is the elevation of industrial structure upgrading from a peripheral to a core condition, with its prevalence among high-GTFP cities rising from 31.2% to 82.8%. Third, city typologies show differentiated coverage patterns across the identified configurations, reflecting their varying degrees of alignment with specific transition pathways. Collectively, the evidence reveals a system-wide transition from macro resource-and-institution-driven transformation toward micro-meso co-driven transformation, providing configurational foundations for designing adaptive, city-type-differentiated green transition policies in resource-dependent regions.
• DOI: 10.35534/fm.0801001 (registering DOI)
• Cite: Jiang, Y., Liu, C. W., Zhang, J. L. & Li, X. Y. (2026). Unlocking Green Frontiers: Dynamic QCA of MLP-Driven Transformation in Resource-Based Cities of the Yellow River Basin in China. Advanced Management Research, 8(1), 1-27.