Evidence map›Paper›PMID 41193482›Full record

ArticleScientific data2025

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

Jinjie Sun, Rui Wang, Jiachen Wang, Shangchen Zhang, Mingyu Lei, Mengzhen Zhao, Junming Zhu, Can Wang, Wenjia Cai

Abstract readDataset
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jinjie SunDepartment 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.
Rui WangDepartment 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. ruiwang.rr@gmail.com.
Jiachen WangState Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing, 100084, China.ORCID http://orcid.org/0000-0001-9465-0511
Shangchen ZhangDepartment 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.ORCID http://orcid.org/0009-0000-4824-4977
Mingyu LeiDepartment 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.ORCID http://orcid.org/0000-0003-1364-0472
Mengzhen ZhaoSchool of Management, Beijing Institute of Technology, Beijing, China.
Junming ZhuSchool of Public Policy and Management, Tsinghua University, Beijing, 100084, China.ORCID http://orcid.org/0000-0002-6160-1323
Can WangState Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing, 100084, China.ORCID http://orcid.org/0000-0002-1136-792X
Wenjia CaiDepartment 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. wcai@tsinghua.edu.cn.ORCID http://orcid.org/0000-0002-4436-512X

Funding

National Natural Science Foundation of China (National Science Foundation of China) T2261129475
6 · The paper itself

Abstract

Cities are key drivers of economic progress and play a decisive role in global climate action. Cities' gross domestic product (GDP) data serves as a critical tool for evaluating economic progress and also offers a window into broader well-being, such as healthcare, education, and infrastructure. However, city-level GDP projections remain absent in China. This study uses the Cobb-Douglas production model to develop city-level GDP from 2020 to 2100, accounting for China's unique socio-economic conditions. The dataset is validated by comparing its results with historical data and other future GDP scenarios. We develop 27 scenarios by varying technology, fertility, and intercity interaction across three levels each, considering China's two-child/three-child policy, regional collaborative development, western development strategies, and technological advancements like AI. Among these, the Labor-Constraint Pathway and BAU Pathway closely align with the SSP1 and SSP2 scenarios, respectively. These scenarios provide a more accurate representation of future city-level GDP dynamics in China.

Identifiers

PMID41193482
PMCPMC12589460

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.