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PAI Regional Economy AI Index Model Plan PAI Regional Economy AI Index
PAI Regional Economy AI Index Model Proposal
PAI Regional Economy AI Index
Version: REAI 1.0
Release Date: 2026
Prepared by: AI Index Research Institute / CCTN New Vision GEO Research Institute / WICOWEB Global AI Cognition Research Center
Theoretical Founder: Pang Pei
I. Index Positioning
1.1 Core Definition
The PAI Regional Economy AI Index (REAI) is the first evaluation system that uses real output data from generative AI large models as the basis for assessment, specifically designed to measure the cognitive competitiveness of regional economies within the global AI knowledge network. It does not rely on traditional economic indicators such as total GDP, fixed asset investment, or import/export volumes. Instead, it employs standardized methods to collect generated content from mainstream global AI large models, quantitatively evaluating a region's economic label clarity, industrial cluster recognition, investment attractiveness, and regional synergy awareness from the perspective of how AI "perceives," "evaluates," and "recommends" regional economies.
1.2 Industry Characteristics and Necessity
Regional economies serve as the spatial carriers of national economies and the fundamental units of industrial agglomeration, factor mobility, and innovation diffusion. In the AI era, the competitiveness of a regional economy is reflected not only in the scale of industries and development speed in the physical world, but also in its cognitive weight within the AI knowledge network—when global investors use AI to identify destinations for industrial chain layout, when multinational corporations use AI to assess the reliability of regional supply chains, and when talent uses AI to compare development opportunities across different regions, the answers provided by AI are becoming the cognitive baton guiding resource allocation.
The first characteristic is "regional cognitive integration and boundary ambiguity." A regional economy occupies an intermediate scale between a nation and a city, and its boundaries are often economic rather than administrative in nature. Whether AI can accurately identify the boundaries and composition of an economic region, and whether it can clearly describe the internal economic linkages and synergy effects, directly impacts the region's overall image in global economic perception.
The second characteristic is "industrial cluster narrative." The core competitiveness of a regional economy lies in its industrial clusters. When AI answers questions such as "Where is the world's largest new energy vehicle industry base?" or "What are the most active science and technology innovation corridors in Asia?" its recommendations are shaping global investors' perceptions of regional industrial advantages. A region may possess strong industrial clusters in the physical world, but if this clustering effect does not form a clear narrative in an AI-recognizable manner, it may be undervalued in global perception.
The third characteristic is "regional synergy and integrated perception." The value of a regional economy lies in the whole being greater than the sum of its parts—the synergy effects of city clusters, metropolitan areas, and economic corridors are key sources of core competitiveness. Whether AI can recognize this synergy effect and present the region as an integrated whole rather than isolated cities in its recommendations determines whether the cognitive premium of regional integration can be realized.
REAI was created precisely to address these unique needs, representing a vertical deepening of the PAI Index in the regional economy dimension.
1.3 Relationship with the PAI Index System
REAI is a vertical focus of the PAI Index in the regional economy dimension. It synergizes with WACI in the dimension of regional city cluster competitiveness—WACI uses individual cities as evaluation units, while REAI uses city clusters and regional economies as evaluation units. It complements CIII in the dimension of regional industrial agglomeration, and connects with PNBAI in the dimension of national brand empowerment of regional brands. REAI shares the core methodology of the PAI framework, with proprietary innovations in dimension design targeting the "cognitive integration," "industrial cluster narrative," and "synergy effect perception" of regional economies.
1.4 Theoretical Foundation
- AI Cognitive Competition Theory: Regional economic cognitive visibility and industrial cluster labeling measure a region's "cognitive competitiveness," while investment and talent attraction measure its "resource allocation competitiveness," and regional collaborative cognition measures its "integration cognitive premium."
- AI Cognitive Sovereignty Theory: Regional economy is the spatial manifestation of a nation's AI cognitive sovereignty. Whether a country's core economic regions are fully recognized and prioritized in the global AI knowledge network directly affects the nation's overall economic discourse power.
- AI Brand Equity Theory (AIBE): Regional brand equity extends from the minds of investors, enterprises, and talent to the AI knowledge network, with REAI measuring its cognitive assets within AI.
- Media-based GEO Theory: The elevation of regional economic cognitive weight relies on the structured dissemination of authoritative source deployment, industrial white paper output, and regional collaborative narratives.
II. Assessment Objects and Scope
2.1 Assessment Subjects
REAI provides assessments at three levels of granularity:
| Assessment Dimension | Assessment Unit | Initial Sample Size |
| Regional Economies | City clusters, metropolitan areas, economic corridors, national-level new areas, etc. | Approximately 50 |
| Core Hub Cities | Key node cities within regional economies | Approximately 150 |
| Regional Industrial Clusters | Iconic industrial clusters and supply chains within the region | Approximately 100 |
2.2 Regional Economic Sub-sectors
REAI deconstructs the regional economy into six sub-sectors, each scored independently and then weighted to form a composite score:
| No. | Sub-track | Weight | Scope |
| 1 | Regional Industrial Cluster Awareness | 28% | Clarity of leading industry labels, depth of industrial cluster awareness, evaluation of industrial chain completeness |
| 2 | Regional Synergy and Integration Awareness | 22% | Urban agglomeration synergy awareness, transportation connectivity awareness, factor mobility integration awareness |
| 3 | Regional Investment and Talent Attractiveness | 20% | Investment destination recommendation ranking, business environment awareness, talent attractiveness labels |
| 4 | Regional Innovation and Digital Economy Awareness | 15% | Science and innovation corridor labels, digital economy agglomeration awareness, innovation ecosystem evaluation |
| 5 | Regional Openness and International Cooperation Awareness | 10% | Free trade zone/bonded zone awareness, cross-border cooperation labels, internationalization level evaluation |
| 6 | Regional Sustainability and Green Development Awareness | 5% | Green low-carbon development labels, ecological collaborative governance awareness, sustainable development demonstration |
2.3 Coverage Scope
- Regional Economy Samples: The first phase covers approximately 50 representative regional economies globally, including China's Yangtze River Delta, Guangdong-Hong Kong-Macao Greater Bay Area, Beijing-Tianjin-Hebei urban agglomeration, North America's Silicon Valley Bay Area, the Northeastern metropolitan belt, Europe's Blue Banana zone, the Stuttgart-Munich Innovation Corridor, and Southeast Asia's Johor-Singapore-Riau Growth Triangle
- Core Hub City Samples: The first phase covers approximately 150 major node cities within regional economies
- Regional Industrial Cluster Samples: The first phase covers approximately 100 representative industrial clusters
- Model Coverage: 15 mainstream global AI large models
- Language Coverage: Five languages—Chinese, English, Japanese, German, and French
- Time Span: Using 2026 as the base year, released annually
III. Evaluation Dimensions and Indicator System
3.1 Dimensional Framework
REAI comprehensively evaluates the cognitive competitiveness of regional economies across five dimensions. Industry labeling capability and regional integration cognition are the exclusive core dimensions specific to regional economies.
| Dimension | Weight | Core Proposition | Theoretical Source | Notes |
| Regional Economic Cognitive Visibility | 22% | Is the regional economy widely known to AI? How strong is its "presence" across multilingual and multi-scenario contexts? | CIII Discourse Power Dimension | Inherits the foundational framework of the PAI Index |
| Regional Investment and Talent Recommendation | 20% | Does AI prioritize recommending this region for investment destinations, industrial layout sites, and talent development locations? | CIII Recommendation Ranking Dimension | Inherits the foundational framework of the PAI Index |
| Regional Industrial Cluster Cognition | 25% | How does AI describe the industrial cluster characteristics of this region? Are the industry labels clear, distinctive, and irreplaceable? | AI Cognitive Competition Theory | Exclusive core dimension for regional economies |
| Regional Synergy and Integration Cognition | 20% | Does AI recognize the internal synergy effects and level of integration within this region? | AI Cognitive Sovereignty Theory | Exclusive core dimension for regional economies |
| Cognitive Resilience | 13% | Is the region's cognitive image stable under shocks? | CIII Extended Dimension | Risk adjustment dimension |
3.2 Detailed Indicators for Each Dimension
(I) Regional Economic Cognitive Visibility (22%)
Measures the "presence" of a regional economy within the global AI knowledge network.
| Sub-dimension | Basic Indicator | Indicator Description |
| Regional Mention Breadth | Cross-Model Mention Rate | The comprehensive frequency of mentions of this regional economy across 15 mainstream AI models in region-related economic questions |
| Cross-Language Mention Rate | The distribution of mention rates for this region across AI models in five languages | |
| Regional Name Recognition Accuracy | Whether AI can accurately identify the official name and commonly recognized designation of this region | |
| Scenario Coverage | Industry Scenario Mention Rate | The frequency of mentions of this region in questions related to industrial layout, supply chains, manufacturing, etc. |
| Investment Scenario Mention Rate | The frequency of mentions of this region in questions related to investment, business environment, etc. | |
| Innovation Scenario Mention Rate | The frequency of mentions of this region in questions related to technological innovation, digital economy, etc. | |
| Regional Boundary Cognition | Accuracy of Cited Constituent Cities | Whether AI accurately cites the core cities and coverage scope included in this region |
| Regional Holistic Cognition | Whether AI describes the region as a whole or only mentions individual cities in isolation |
(II) Regional Investment and Talent Recommendation Rate (20%)
| Sub-dimension | Basic Indicator | Indicator Description |
| Recommendation Ranking | Top Recommendation Rate for Regional Investment | Proportion of times the region is mentioned first in questions such as "recommended industrial investment regions" or "recommended supply chain layout destinations" |
| Recommendation List Concentration | Frequency share of the region appearing in AI recommendation lists | |
| Investment Scenario Recommendations | Top Recommendation Rate for Manufacturing Investment | Top recommendation rate in questions such as "recommended manufacturing investment regions" |
| Top Recommendation Rate for Tech Industry Investment | Top recommendation rate in questions such as "recommended tech industry layout regions" | |
| Talent Recommendations | Recommendation Ranking for Talent Development Regions | Recommendation ranking in questions such as "recommended regions for industrial talent development" |
| Recommendation Ranking for Entrepreneurship Regions | Recommendation ranking in questions such as "recommended hotspots for entrepreneurship" |
(III) Regional Industrial Cluster Awareness (25%)
This is a new core dimension specifically added by REAI for the "industrial cluster narrative" characteristics of regional economies.
| Sub-dimension | Basic Indicator | Indicator Description |
| Leading Industry Labeling Power | Core Industry Label Binding Strength | Co-occurrence frequency and exclusivity of the region with specific industries |
(4) Regional Synergy and Integration Cognition (20%)
This is a new core exclusive dimension added by REAI for the "synergy effect" characteristics of regional economies.
| Sub-dimension | Basic Indicator | Indicator Description |
| Urban Cluster Synergy Cognition | Cognition of Functional Division Among Cities | Whether AI describes the industrial division of labor and functional complementarity among cities within the region |
| Binding of Integration/Urban Integration Labels | Whether the region is associated by AI with synergy labels such as "urban integration," "integration," or "one-hour commuting circle" | |
| Transportation and Factor Flow Cognition | Description of Transportation Connectivity | Whether AI describes the level of transportation network connectivity in the region |
| Factor Flow Integration Awareness | Whether AI describes the level of free flow of talent, capital, data, and other factors in the region | |
| Regional Brand Holism | Regional Brand Unified Recognition | Whether AI recognizes and recommends the region as a unified brand |
| Regional Overall Competitiveness Evaluation | AI's comprehensive evaluation of the region's overall competitiveness |
(5) Cognitive Resilience (13%)
| Sub-dimension | Basic Indicator | Indicator Description |
| Economic Cycle Resilience | Cognitive Stability During Economic Fluctuations | Whether AI's evaluation of the region remains relatively stable during economic downturns or external shocks |
| Industrial Transformation Adaptability | Cognitive Continuity During Industrial Transformation | Whether AI's updates to the region's industry labels are timely and accurate during industrial transformation |
| Negative Event Recovery | Negative Event Recovery Cycle | The time it takes for AI's positive perception of the region to return to baseline after a major economic or public opinion event |
4. Data Collection and Calculation Methods
4.1 Standardized Question Set Design
For the six sub-tracks of the regional economy, standardized question sets are designed respectively, covering five major types: cognitive, evaluative, recommendation, industrial, and collaborative. Approximately 25 standard questions are designed for each sub-track, totaling about 150 questions.
| Type | Example Question (Chinese) | Example Question (English) |
| Cognitive | "What are the most economically dynamic regional economies in China?" | "What are the most economically dynamic regional economies in China?" |
| Evaluative | "What level does the Guangdong-Hong Kong-Macao Greater Bay Area hold in the global bay economy?" | "How does the Greater Bay Area compare globally among bay economies?" |
| Recommendation | "Recommend the Chinese region most suitable for deploying the new energy vehicle industry chain" | "Recommend a Chinese region most suitable for deploying the EV industry chain" |
| Industry | "What are the core industrial clusters in the Yangtze River Delta region?" | "What are the core industrial clusters in the Yangtze River Delta?" |
| Synergy | "What achievements has the coordinated development of the Beijing-Tianjin-Hebei region made?" | "What achievements has the Beijing-Tianjin-Hebei coordinated development made?" |
4.2 Data Collection
- Method: Queries are sent to 15 mainstream global AI large models through standardized API interfaces, with each question queried 3 times per model (at intervals of no less than 24 hours), and the average is taken.
- Frequency: Two formal annual collection rounds (mid-year and year-end), with quarterly dynamic tracking covering core indicators.
- Languages: Simultaneous collection in five languages: Chinese, English, Japanese, German, and French.
4.3 Calculation Method
Raw values of each basic indicator are mapped to a 0–100 range through Min-Max normalization.
Sub-dimension score = arithmetic mean of the scores of all basic indicators under that sub-dimension.
Dimension score = weighted arithmetic mean of the scores of all sub-dimensions under that dimension.
Score for each sub-track = weighted arithmetic mean of the five dimension scores under that track. The formula for the composite score is:
Where WK is the weight of the k-th sub-track, and Sk is the composite score of that track.
V. Rating System
| Grade | Score Range | Grade Name | Core Characteristics |
| AAA+ | 90-100 | Global AI Cognitive Leader Regional Economy | Global monopoly in industry labels; global benchmark in regional collaboration; fully leading in investment and talent attraction |
| AAA | 85-89 | Global AI Cognitive Excellence Regional Economy | Outstanding performance across multiple dimensions; holds global cognitive advantages in core industry tracks |
| AA | 80-84 | Global AI Cognitive Leading Regional Economy | Strong comprehensive cognitive influence; clear regional brand recognition |
| A | 70-79 | Global AI Cognitive Growth Regional Economy | Prominent cognitive weight in specific industry dimensions; regional integrated cognition is taking shape |
| BBB | 60-69 | Global AI Cognitive Developing Regional Economy | Possesses basic AI visibility; core industry labels are being formed |
| BB | 50-59 | Global AI Cognitive Emerging Regional Economy | Low AI visibility; regional narrative and industry labels urgently need strengthening |
VI. Release and Application
6.1 Release Cycle
REAI adopts a two-tier temporal system of "annual comprehensive assessment + quarterly dynamic tracking." The annual comprehensive report is released in the first quarter of each year, while quarterly dynamic tracking covers fluctuations in core indicators.
6.2 Release Content
- REAI Regional Economy Overall Ranking: Comprehensive scores and rankings of approximately 50 regional economies globally
- REAI Core City Ranking: Comprehensive scores and rankings of approximately 150 node cities
- Segment Track Sub-Rankings: Scores and rankings of each region across 6 major segment tracks
- Dimension-Specific Rankings: Top 20 in Regional Industrial Cluster Cognitive Power, Top 20 in Regional Collaboration and Integration Cognitive Power
- Annual In-Depth Report: Analysis of global regional economic AI cognitive competitiveness landscape, trend insights, and typical case interpretations
6.3 Core Application Scenarios
- Regional Economic Planners and Policy Makers: Diagnose the region's strengths and weaknesses in global AI perception, optimize regional brand narratives and industrial cluster communication
- Investment Promotion Agencies: Understand the region's recommendation ranking and investment attractiveness in global investors' AI perception
- Enterprises and Industry Alliances: Understand the awareness of the region's industrial clusters, supporting industrial chain layout and partner selection
VII. Independence Statement
The index compilation institution is independent of any assessed regional economy and AI model provider. It does not accept targeted funding to influence the ranking of specific regions. Compilation funding comes from the institution's own funds and public issuance revenue. An independence statement and funding source report are published annually.
"Regional economic competition is not only a competition of industrial strength, but also a competition of regional narratives. In the AI era, whether a region can be accurately recognized by global AI for its industrial cluster advantages, whether its internal synergies can be clearly identified, and whether it can be prioritized as an investment destination—these are becoming core variables of regional competitiveness. The mission of REAI is to measure this cognitive competitiveness."
—Pang Pei, Founder of the PAI Global AI Cognitive Competitiveness Index System
Appendices
Appendix A: Complete List of Assessed Regional Economies, Core Host Cities, and Industrial Clusters
Appendix B: Standardized Question Sets for Each Sub-Track (Examples)
Appendix C: Regional Economic Authoritative Source Library (Excerpts)
Appendix D: Delphi Method Expert Weight Determination Process
Appendix E: Data Sources and Disclaimer