China's ongoing rural to urban transformation benefits the population but is not evenly spread

The study investigates how China’s rapid rural-to-urban transformation, spanning land, population, and industrial dimensions, has unfolded unevenly across regions and how it affects urban–rural well-being disparities. Prior assessments often used single-factor proxies (e.g., urban population share or urban construction land) or relied on coarse administrative statistics, limiting long-term, fine-scale analysis. The authors aim to quantify integrated rural–urban transformation degree (RUTD) and the coupling among the three tracks (CD) at prefectural and provincial levels from 1990 to 2020, identify typical transformation patterns, trace their evolution, and evaluate how these patterns relate to urban–rural well-being inequalities (DWBI). This work addresses crucial policy questions regarding whether integrated and coupled urbanization can mitigate urban–rural disparities in China.

Past research largely measured urbanization via single proxies (urban population share, urban land share, or non-agricultural GDP share), which cannot fully represent the multifaceted, interlinked flows of land, labor, and capital. Statistical inconsistencies and changing administrative boundaries further constrained long-term, detailed analyses, causing most national studies to stay at provincial scales. Decoupling among the three urbanization tracks is common worldwide and linked to social and environmental issues (e.g., slums from rapid population urbanization without infrastructure in Africa; environmental degradation and unemployment in parts of Latin America; ghost cities in China due to land-led urbanization). Remote sensing advances have enabled global mapping of impervious surfaces and human footprints, offering consistent, long-term spatial data to infer population and industrial urbanization and delineate transformation patterns. Debates persist on whether China’s urbanization widens or narrows urban–rural gaps; emerging evidence suggests urbanization can reduce income inequality, underscoring the need to analyze how well-being inequities respond to transformation patterns.

Study scope and periods: Prefectural- and provincial-level analyses for China in 1990, 2000, 2010, and 2020. Data: Urban/rural settlement masks from GHSL GHS_SMOD R2022A (1 km, 1990/2000/2010/2020); gridded population GHS_POP R2022A (1 km); construction land from CNLUCC (30 m) aggregated; gridded real GDP (1 km, 1992–2019) with 1992 used as proxy for 1990 and 2019 for 2020. Integrated rural–urban transformation degree (RUTD): Defined using an urbanization cube. Three component rates were computed per region: population urbanization (PU = share of resident population in urban cells), land urbanization (LU = share of construction land in urban cells), and industry urbanization (EU = share of GDP in urban cells). RUTD synthesizes these three metrics via cube-derived distances/projections to represent the integrated transformation level. Coupling degree (CD): Based on the angular deviation between the observed state and the perfectly coupled trajectory in the urbanization cube; scaled to [0,1], with higher values indicating stronger coupling among the three tracks. Pattern recognition: Five indicators (PU, LU, EU, RUTD, CD) were jointly clustered using a self-organizing map (SOM) in R (Kohonen package). A sensitivity analysis over candidate cluster sizes identified an optimal six-cluster solution representing six typical rural–urban transformation patterns (RUTPs). Well-being and inequality: Objective well-being via Human Development Index (HDI) proxies at provincial scale: per capita health care expenditure (PHCE) for health, mean years of schooling (MYS) for education, and per capita disposable income (PDI) for living standards; geometric aggregation to HDI for urban and rural areas. Subjective well-being index (SWBI) from Chinese General Social Survey (CGSS) self-rated happiness: 2003 as proxy for 2000, 2018 as proxy for 2020. An integrated well-being index (WBI) was computed as the geometric mean of HDI and SWBI for urban and rural areas; DWBI is the ratio of urban to rural WBI. Statistical analysis: Spatiotemporal mapping of RUTD and CD at prefectural and provincial levels; trajectory analysis of RUTP transitions (1990–2020); quadratic regressions assessing nonlinear relationships between normalized RUTD and DWBI, and normalized CD and DWBI, over 2000, 2010, and 2020.

• Spatial gradients: Both RUTD and CD are consistently higher east of the Hu Line than to the west, indicating more advanced and coordinated transformation in eastern China.
• RUTD trends: Mean RUTD increased from 0.596 (1990) to 0.643 (2000), 0.669 (2010), and 0.675 (2020). The share of prefectures with increasing RUTD declined over time, and the change rate decelerated from 0.047 per decade (1990–2000) to 0.026 (2000–2010) and 0.006 (2010–2020). Regions with the largest increases were predominantly in eastern/central China with initially low RUTD.
• CD trends: Mean CD rose from 0.797 (1990) to 0.827 (2000), 0.856 (2010), and 0.884 (2020), indicating progressively stronger coupling of the three tracks. Over 90% of prefectures increased in CD across all sub-periods; however, the fraction with decreasing CD grew from 2.87% (1990–2000) to 6.57% (2010–2020), mainly in northwest China. The CD increase rate slightly decelerated from 0.030 to 0.028 per decade.
• Six RUTP clusters: C1 (least desirable; low across indicators; lagging industry urbanization; mainly southern Qinghai, Tibet, western Sichuan); C2 (primary RUTP; higher population than land/industry urbanization; mainly northwest); C3 (socioeconomic centers in relatively underdeveloped NW/NE; population inflow not matched by land/industry); C4 (like C3 but land urbanization synchronized with population; mainly southwest); C5 (provincial capitals and areas around national urban agglomerations in central/west; high population/land, relatively lower industry urbanization); C6 (national-level urban agglomerations; highest values across indicators; most desirable).
• Pattern transitions (1990–2020): 57.02% of prefectures changed RUTP; 42.98% remained unchanged (mostly in west/north/northeast). Overall, 53.58% improved, 3.44% degraded, and 42.98% unchanged. Most common transitions: C5→C6 (80 prefectures, mainly national-level urban agglomerations) and C4→C5 (36, in Guangxi–Guangdong–Jiangxi border, Yunnan–Guizhou–Sichuan border, and south-central Shanxi).
• Urban–rural well-being: Quadratic relation between normalized RUTD and normalized DWBI shows a turning point: below it, higher RUTD associates with larger DWBI (e.g., Xinjiang, Qinghai, Gansu, Jilin, Heilongjiang); above it, higher RUTD associates with smaller DWBI (more developed provinces). From 2000 to 2020, points shifted rightward and provinces left of the turning point decreased from five to three, indicating weakening positive and strengthening negative correlations as urbanization advances. Normalized CD and DWBI show a significant nonlinear negative correlation (p < 0.1); as CD rises, DWBI declines, with increasing R² over time. Provinces with more advanced RUTPs tend to have smaller urban–rural well-being inequities.

The study demonstrates that integrated and coupled rural–urban transformation correlates with reduced urban–rural well-being inequalities. Eastern China’s higher RUTD and CD reflect historical policy prioritization and industrial agglomeration, while western regions lag and show more decoupling. The six-pattern typology clarifies where imbalances arise (e.g., population outpacing land/industry or industry lagging) and where policy should focus. Nonlinear relationships indicate that at early stages, rising urbanization may initially widen well-being gaps, but once transformation passes a threshold and becomes better coupled, further urbanization mitigates disparities. Policy initiatives since 2000 (balanced regional development and coordinated urban–rural strategies) have expanded high-RUTD areas inland and improved coupling, contributing to narrowing DWBI; however, challenges persist, notably widespread lagging industry urbanization (even in C6), and many western prefectures remaining in primary patterns. The conceptual mechanism highlights how mismatches among the three tracks can exacerbate well-being gaps, while integrated, coupled transformation supports harmonized urban–rural development.

This work produces the first national, long-term maps of integrated rural–urban transformation degree and coupling at prefectural/provincial scales using earth observation, identifies six typical transformation patterns, and tracks their evolution (1990–2020). China’s RUTD and CD have generally risen, with stronger coupling and a shift from coastal agglomeration toward inland equilibrium. Over half of prefectures improved their transformation pattern, though many western areas remained unchanged. Nonlinear analyses show that advanced and better-coupled transformation aligns with smaller urban–rural well-being inequities, suggesting that continued coordinated urbanization can further close gaps. The study contributes a big-earth-data-driven framework for monitoring RUTP and a mechanism relating RUTP to well-being disparities, informing rural revitalization and socially sustainable urbanization. Future work should refine transformation metrics to capture increasingly complex urban–rural relations and continue addressing lagging industry urbanization, especially in western regions.

• Measurement simplification: The integrated RUTD indicator, although widely used, is constructed for a simplified urban–rural system and may not capture future complexities of urban–rural relations.
• Data approximations: Gridded GDP for 1990 and 2020 were proxied by 1992 and 2019, respectively; PHCE for 2000 and 2020 by 2001 and 2019; SWBI for 2000 and 2020 by CGSS 2003 and 2018. These substitutions may introduce uncertainty.
• Spatial/administrative issues: Potential inconsistencies due to changing administrative boundaries and aggregation from multiple gridded products.
• Residual decoupling: Some northwest prefectures show declining CD in recent periods, limiting generalizability of nationwide trends.