Aligning Rural Industrial Integration With the Sustainable Development Goals

A Diagnostic Framework and Policy Pathways

Datos Bibliográficos

ID12308563
AutoresYuelong Su (0000-0003-3122-7972, College of Land Management Huazhong Agricultural University Wuhan China, autor de correspondencia), Zhengkun Yang (The College of Urban & Environmental Sciences Central China Normal University Wuhan China), Qingyang Bai (College of Land Management Huazhong Agricultural University Wuhan China), Shumiao Shu (0000-0003-4836-0286, Tuojiang River Basin High‐Quality Development Research Center Neijiang Normal University Neijiang China, autor de correspondencia), Hao Li (0000-0001-5192-5458, The College of Urban & Environmental Sciences Central China Normal University Wuhan China)
Año2026
Fecha de publicación2026-01-21
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaSustainable Development (JOURNAL)
Identificadores de la revistaISSN: 0968-0802 • E-ISSN: 1099-1719
EditorialWiley (PUBLISHER • GB)
DOI10.1002/sd.70636
OpenAlexW7125109132
IdiomaEN
Referencias citadas76

Rural industrial integration (RII) is promoted to advance the United Nations Sustainable Development Goals (SDGs), yet the extent and mechanisms of its synergies and trade‐offs with sustainability remain unclear. To address this gap, we develop a diagnostic framework that treats RII and SDGs as independent systems and quantifies their coupling coordination degree (CCD). For 17 prefectures in Hubei, China, from 2010 to 2023, composite indices are derived using a projection pursuit model optimized by a differential evolution algorithm (DE‐PPM). Key drivers of the spatiotemporal variation in CCD are identified through an Optimal‐parameter Geodetector (OPGD) and XGBoost, and policy pathways to 2035 are explored with multi‐scenario simulations. Empirical results provide clarity: while both systems show steady development, their coordination remains at a low‐to‐moderate level, with a provincial mean CCD of approximately 0.546 and a persistent spatial pattern characterized by stronger coordination in the west, weaker in the east, and a noticeable dip in central regions. Driver analysis highlights the economic base, production conditions, and natural endowments as core determinants. Scenario projections indicate that CCD will reach 0.654 by 2035 under business as usual (BAU), 0.705 under the enhanced coordination scenario (ECS) that strengthens education and technology investment, and 0.738 under the ecological priority scenario (EPS). The framework and evidence collectively demonstrate that prioritizing ecological protection and enhancing systemic coordination are effective strategies for transitioning from moderate to advanced coordination levels, offering practical guidance for environmental managers and regional planners committed to fostering sustainable rural development

Core (optical fiber · Differential (mechanical device · Empirical evidence · Empirical research · Industrial Symbiosis · Production (economics · Sustainability · Sustainable development · Environmental Impact and Sustainability · Sustainable Industrial Ecology · Water Resources and Sustainability