China's regional economy is showing a more pronounced geographic "K-shaped" divergence

Overview

This report focuses on the geographic "K-shaped" divergence within China's economy. Nomura believes that the gains from AI-driven growth have not spread evenly, but have instead accrued mainly to a small number of "smart" cities. These cities are showing stronger performance in GDP growth and fixed asset investment, while growth pressures are increasing in the rest of the country. Based on this, the report judges that Beijing may step up policy support and accelerate fiscal reform in 2H 2026, but because the economy is diverging rather than experiencing a broad-based loss of momentum, the rationale for comprehensive, aggressive stimulus is constrained.

Core views

The core view is that China's economy is experiencing clearer regional divergence: a small number of cities with advantages in AI industries, talent, capital, and digital infrastructure are growing faster, forming the upward branch; the rest of the regions are slowing, forming the downward branch. The seven "smart" cities together contributed 20% of national growth in 1H 2026, the highest level in at least two decades. Meanwhile, other regions account for about 83% of national GDP, but their growth rate fell from 4.9% to 4.5%, showing that overall growth pressure still comes mainly from broad areas.

Analysis framework

The report uses a regional comparison framework, comparing a small number of "smart" cities with the rest of the country, focusing on real GDP growth, contribution to national growth, and fixed asset investment performance. By comparing the divergence among the national aggregate, the seven key cities, and other regions, it assesses whether AI-related activity is changing the regional growth structure and further derives policy implications.

Methodology notes

• Macro regional comparisonGeographic K-shaped divergence analysis

  Split the growth paths of different regions within an economy into an upward branch and a downward branch.

  The report treats the small number of "smart" cities benefiting from AI-related economic activity as the upward branch, and the rest of the country as the downward or pressured branch, using differences in GDP and fixed asset investment to measure the degree of divergence.

• Policy extrapolationGrowth pressure and fiscal reform constraint analysis

  Infer central policy and local fiscal system adjustment pressures from uneven regional growth.

  The report argues that widening geographic inequality will force Beijing to accelerate fiscal reform to strengthen local government tax bases; however, resilience in a small number of cities reduces the need for broad-based aggressive stimulus.

Asset mapping & comparison

Structured mapping from thesis to named assets (strengths, weaknesses, peers, risks).

• China macro assets

  Directly related

  Strengths

  AI-related growth and investment resilience in a small number of "smart" cities support structural opportunities.

  Weaknesses

  Slower growth in the rest of the country weighs on the slope of aggregate recovery.

  Comparison

  Compared with aggregate national GDP, a regional breakdown better reveals growth quality and policy constraints.

  Risks

  If AI diffusion falls short of expectations or other regions slow further, nationwide growth pressure may rise.

• Local government and fiscal-related assets

  Indirectly related

  Strengths

  Fiscal reform may improve local governments' long-term tax base and debt-servicing capacity.

  Weaknesses

  Widening regional inequality suggests that fiscal pressure in some localities may persist.

  Comparison

  The fiscal fundamental gap between strong cities and weak regions may continue to widen.

  Risks

  If reform progresses more slowly than expected or short-term fiscal pressure worsens, local credit risk pricing may be affected.

• AI-related industry chains and urban clusters

  Thematically related

  Strengths

  Fixed asset investment in cities such as Beijing and Shanghai outperformed the national level, showing support from AI-related activity.

  Weaknesses

  The benefits to growth are concentrated and may not fully offset the drag from the rest of the country.

  Comparison

  Compared with traditional investment-driven regions, AI-related cities have stronger growth resilience.

  Risks

  Policy regulation, the capital expenditure cycle, or technology diffusion bottlenecks may weaken sustainability.

Key data

• China real GDP growth4.7% year over year in 1H 2026; 5.0% in 2025Growth slowed at the national level.
• Weighted average GDP growth of the seven "smart" cities5.6% in 1H 2026; 5.4% in 2025A small number of key cities accelerated against the trend, and all seven cities posted higher growth.
• Contribution of the seven "smart" cities to national growth20% in 1H 2026The highest level in at least two decades.
• GDP share of the rest of the countryAbout 83%These regions still make up the main body of China's economy.
• Real GDP growth of the rest of the country4.5% in 1H 2026; 4.9% in 2025Regions outside the small number of "smart" cities slowed markedly.
• National fixed asset investment growth-5.7% year over year in 1H 2026Fixed asset investment contracted significantly at the national level.
• Beijing fixed asset investment growth3.0% year over year in 1H 2026Maintained positive growth despite the nationwide contraction in fixed asset investment.
• Shanghai fixed asset investment growth6.8% year over year in 1H 2026Reflects the possible regional investment resilience brought by AI-related economic activity.

Impact & implications

The investment and macro implications are that China's marginal growth momentum may become increasingly concentrated in a small number of AI-related cities and industrial clusters, while traditional regions face greater fiscal and growth pressure. This structure increases policy complexity: on the one hand, pressured regions and local public finance need support; on the other hand, it does not necessarily justify nationwide aggressive stimulus. For asset allocation, the focus should be on divergence across regions, industries, and local fiscal credit, rather than only on national aggregate indicators.

Risks

• Growth in a small number of cities may fail to offset the broad slowdown in the rest of the country.
• Widening regional inequality may intensify local fiscal pressure and policy coordination difficulties.
• If policy support is insufficient, downside growth risks may rise in 2H 2026.
• If policy relies excessively on a small number of high-growth cities, it may deepen resource and income gaps across regions.
• The sustainability of AI-related investment and growth still requires further verification.

What to watch

• Whether Beijing steps up growth-stabilization policy support in 2H 2026.
• Whether fiscal reform accelerates and whether local government tax bases improve materially.
• Whether GDP and fixed asset investment in the seven "smart" cities continue to outperform the national level.
• Whether GDP growth in the rest of the country continues to decline.
• Whether AI-related economic activity spreads from core cities such as Beijing and Shanghai to more regions.