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City-level GDP estimates for China under alternative pathways from 2020 to 2100.

Jinjie Sun1, Rui Wang2, Jiachen Wang3

  • 1Department of Earth System Science, Institute for Global Change Studies, Ministry of Education Ecological Field Station for East Asian Migratory Birds, Tsinghua University, Beijing, 100084, China.

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|November 5, 2025
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Summary

This study projects China's city-level Gross Domestic Product (GDP) to 2100 using the Cobb-Douglas model. It accounts for socio-economic factors, providing crucial economic forecasts for urban development.

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Area of Science:

  • Urban Economics
  • Economic Modeling
  • Climate Change Policy

Background:

  • Cities are vital for economic growth and climate action.
  • City-level Gross Domestic Product (GDP) data is crucial for assessing economic progress and societal well-being.
  • China currently lacks city-level GDP projections, hindering informed urban planning.

Purpose of the Study:

  • To develop a robust model for projecting city-level GDP in China from 2020 to 2100.
  • To account for China's specific socio-economic and policy landscape in GDP forecasting.
  • To create multiple future scenarios for urban economic development.

Main Methods:

  • Utilized the Cobb-Douglas production model for GDP estimation.
  • Developed 27 distinct scenarios by varying key factors: technology, fertility rates, and intercity interaction.
  • Incorporated China's demographic policies (two-child/three-child policy), regional development strategies, and technological advancements (e.g., AI).

Main Results:

  • The developed dataset was validated against historical data and other future GDP projections.
  • The 'Labor-Constraint Pathway' and 'Business-As-Usual (BAU) Pathway' align with Shared Socioeconomic Pathways (SSP) 1 and 2, respectively.
  • The study provides a nuanced representation of future urban economic dynamics in China.

Conclusions:

  • The model offers a critical tool for understanding and forecasting China's urban economic future.
  • These projections are essential for effective urban planning, policy-making, and addressing climate change.
  • The scenario-based approach enhances the accuracy and applicability of future GDP predictions.