Evaluation and obstacle analysis of high-quality development in Yellow River Basin and Yangtze River Economic Belt, China

The study addresses how to evaluate and improve high-quality development (HQD) in China’s Yellow River Basin (YRB) and Yangtze River Economic Belt (YREB) under the country’s new development philosophy that emphasizes innovation, coordination, greenness, openness, and sharing. It situates HQD as China’s localized framework aligned with global sustainable development efforts (SDGs) and as a strategic response to issues such as environmental degradation, regional disparities, and the need for innovation-driven growth. Given the strategic importance of YRB and YREB to China’s economy and ecology, the research aims to comparatively assess HQD levels across the two basins from 2010 to 2019, identify subsystem contributions, and diagnose obstacle factors to guide coordinated regional development and policy under the "River Strategy".

Prior research on development quality moved from economic speed/efficiency metrics toward broader social and environmental dimensions. HQD has been framed as an extension of sustainable development with a five-dimensional connotation (innovation, coordination, greenness, openness, sharing). Indicator systems used in earlier studies often rely on single-dimensional proxies (e.g., TFP) or multi-dimensional sets lacking explicit alignment with the five cores of HQD. Studies have explored various HQD drivers (openness, technological innovation, environmental regulation, industrial upgrading, education), but many remain qualitative or limited in scope. For YRB and YREB, numerous works examine environmental, climatic, ecological services, and livelihood issues; HQD-focused studies exist but often analyze the basins separately and rarely perform quantitative obstacle diagnostics. The gaps identified are: a need for a unified, five-core HQD index system applied to both basins for comparative analysis, and a quantitative diagnosis of obstacle factors to inform coordinated policy across YRB and YREB.

Study area and data: The analysis covers 19 provincial-level units: YREB (Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Chongqing, Sichuan, Yunnan, Guizhou) and YRB (Qinghai, Ningxia, Gansu, Inner Mongolia, Shanxi, Shaanxi, Henan, Shandong). The time frame is 2010–2019, chosen to avoid COVID-19 distortions and to align with two contiguous five-year policy cycles. Data sources include China Statistical Yearbook, China Science and Technology Statistical Yearbook, China Energy Statistical Yearbook, and provincial yearbooks (processed in Excel; mapping via ArcGIS; plotting with Origin). Research framework: (1) Analyze HQD connotation via five subsystems: Innovative Development (ID), Coordinated Development (CD), Green Development (GD), Open Development (OD), Shared Development (SD). (2) Build an HQD evaluation index system with five subsystems, 11 elements, and 21 indicators. (3) Measure HQD levels spatio-temporally using an entropy weight TOPSIS model. (4) Diagnose obstacles via the Obstacle Degree Model and propose optimization strategies. Indicator system (examples): ID (inputs: R&D expenditure/GDP; R&D personnel per 10,000; outputs: patents per 10,000; technology market turnover/GDP). CD (urban–rural coordination: urban–rural income ratio; regional coordination: coefficient of variation of GDP per capita; urbanization rate). GD (resource consumption: energy, water per 10,000 Yuan GDP; pollution: wastewater, SO2, solid waste per 10,000 Yuan GDP; governance: investment intensity in industrial pollution control). OD (location/transport openness: aviation and rail employees per 10,000; openness degree: foreign investment/GDP; imports+exports/GDP). SD (social resources: health beds per 10,000; education investment/GDP; natural resources: PM2.5 concentration; water resources per capita). Measurement: Weights determined objectively by entropy method. Data standardized using min–max transformation with separate formulas for positive and negative indicators. The weighted normalized matrix V is constructed; ideal best V+ and worst V− identified; Euclidean distances D+ and D− computed; the closeness coefficient Tj = D−/(D+ + D−) yields HQD indices and ranks. Obstacle Degree Model: Factor contribution and deviation are used to compute each indicator’s obstacle degree (Oi), identifying dominant constraints. Classification: HQD indices categorized into low, relatively low, medium (0.40–0.60), relatively high (0.60–0.80), and high (0.80–1.00) levels for spatial patterning.

- Overall HQD levels rose from 2010 to 2019 in both basins, but remain modest: mean HQD index YRB 0.2886; YREB 0.4073. YREB outperforms YRB, yet YRB’s improvement rate is faster, narrowing the gap. - Subsystems ordering: For both basins, GD > CD > SD > ID > OD across 2010–2019. Mean subsystem indices: YRB—ID 0.1637, CD 0.4357, GD 0.5684, OD 0.1470, SD 0.2480; YREB—ID 0.2692, CD 0.6176, GD 0.8015, OD 0.1911, SD 0.5457. GD growth notable: YRB GD avg annual +6.14%; YRB OD +5.19%; YREB GD +4.08%. - Ranking patterns: Shanghai ranks first consistently (HQD ≈ 0.736–0.771, slight decline). Zhejiang and Jiangsu remain 2nd–3rd. Lower-performing provincial units include Henan, Shanxi, Inner Mongolia, Gansu, Ningxia, Qinghai. Some ranks changed markedly: Shaanxi (+6), Sichuan (+5), Anhui (+3) improved; Shandong (−3), Hubei (−4), Qinghai (−7), Yunnan (−8) declined (2010–2019). - Spatial disparities: YREB’s coefficient of variation in HQD declined from 0.5371 (2010) to 0.1775 (2019), indicating reduced polarization. Overall variation across both basins narrowed over time (mean CVs: 0.3918 in 2010; 0.2306 in 2015; 0.2109 in 2019). By 2019, most provinces reached at least medium HQD; number at medium level rose from 2 (2010) to 13 (2019). - Subsystem spatial insights: ID shows polarization—leaders (Shanghai 0.7714, Zhejiang 0.5617, Jiangsu 0.5469) with weak diffusion; in YRB, Shaanxi (0.3845) and Shandong (0.2461) lead. CD is generally closer across provinces but shows a "collapse" pattern upstream in YRB (e.g., Gansu 0.0137 mean). OD remains the weakest dimension: even leaders outside Shanghai are low (e.g., Shandong OD 0.2592; Shanghai OD 0.8704 vs Jiangsu 0.2964; Zhejiang 0.2681). GD improved broadly, yet declined 2015–2019 in Inner Mongolia (0.6119→0.4720), Qinghai (0.1794→0.1707), Ningxia (0.5757→0.4461). SD exhibits a divergence effect: economically advanced areas have lower SD (e.g., YRB: Shandong 0.0292; Henan 0.0593; YREB: Shanghai 0.0827; Jiangsu 0.1170) versus higher SD in less-developed regions (e.g., Yunnan 0.9493; Jiangxi 0.8982; Guizhou 0.8757). - Obstacle diagnosis (subsystems): OD and ID are primary obstacles. Mean obstacle degrees (%): YRB—OD 44.08, ID 31.66, GD 15.38, SD 7.97, CD 0.91; YREB—OD 49.57, ID 32.44, GD 8.38, SD 8.78, CD 0.82. Obstacles from ID and OD decreased from 2015 to 2019 (YRB ID 30.50→27.56; OD 43.21→39.21; YREB ID 33.31→30.54; OD 48.39→47.89). GD obstacles are higher and rising in YRB (notably upstream and midstream). - Obstacle diagnosis (indicators): Top obstacles, YRB—C14 aviation employees/10k (17.79%), C17 imports+exports/GDP (13.29%), C3 patents/10k (11.67%), C16 FDI/GDP (8.49%), C4 tech turnover/GDP (8.38%), C2 R&D personnel/10k (7.86%), C21 water resources per capita (7.25%), C13 industrial pollution control investment intensity (7.10%). Sum top eight: 81.82%. YREB—C14 aviation employees/10k (19.76%), C17 imports+exports/GDP (12.99%), C4 tech turnover/GDP (11.11%), C3 patents/10k (10.16%), C16 FDI/GDP (8.46%), C21 water resources per capita (7.87%), C2 R&D personnel/10k (7.64%), C15 rail employees/10k (6.46%). Sum top eight: 84.45%.

The findings confirm that YREB leads YRB in HQD but that disparities are shrinking, aligning with national goals of coordinated regional development. The overall modest HQD levels stem from historically extensive, resource-intensive growth models. Subsystem analyses indicate that openness (OD) and innovation (ID) are the most binding constraints, underscoring the critical importance of deepening external openness (e.g., trade, investment, transport connectivity) and strengthening innovation capacity to drive quality and efficiency gains. The pronounced ID polarization suggests insufficient spillovers from leading provinces; enhancing interprovincial diffusion mechanisms is essential. GD improvements show the impact of ecological protection initiatives, yet the rising GD pressure upstream in YRB reflects industrial structure, energy-intensive sectors, and potential “pollution refuge” effects. The SD divergence indicates that economic development does not automatically translate into improved well-being; developed regions may face public service shortfalls and environmental pressures (e.g., water scarcity, air pollution) that constrain SD. Collectively, the results support integrated policies that couple openness and innovation, reinforce green transformation, and tailor interventions by sub-basin position (upper/middle/lower reaches) to address heterogeneous constraints.

This study constructs a five-dimensional HQD evaluation system aligned with China’s new development philosophy, measures HQD levels across 19 provincial units in YRB and YREB for 2010–2019 using an entropy-weight TOPSIS model, and diagnoses obstacle factors via the Obstacle Degree Model. Key contributions: (1) HQD in both basins increased but remains low; YREB outperforms YRB, while the gap narrows. (2) Subsystems contribute unevenly with the pattern GD > CD > SD > ID > OD; GD’s contribution has risen notably. (3) OD and ID are the principal obstacles in both basins, though their barrier effects are diminishing; GD remains a bottleneck in YRB, especially upstream and midstream. Policy recommendations include prioritizing ecological civilization and green development, strengthening cross-basin economic linkages and industrial collaboration, advancing high-level openness through improved channels and platforms (under the Belt and Road framework), and accelerating innovation-driven strategies (e.g., integrating innovation and industrial chains). Differentiated strategies should reflect upstream, midstream, and downstream conditions in each basin, focusing on ecological protection, pollution control, and resource security. Future research should refine analyses at finer administrative scales (prefecture/county), adopt basin-based geographical units beyond administrative boundaries, and explore differentiated functional positioning to design characteristic industrial, energy, ecological governance, and open systems for both basins.

The analysis operates at a provincial (macro) scale due to data availability; results may differ at finer spatial scales (prefecture/county). The approach is administrative-unit based; future work should adopt basin-geographical perspectives to capture physical and socio-economic heterogeneity. Further, deeper investigation into differentiated functional positioning and comparative advantages is needed to design tailored industrial, energy, ecological governance, and openness systems for YRB and YREB.