Evidence map›Paper›PMID 41663465›Full record

ArticleScientific data2026

Revealing urban residents' ecosystem service preferences in China: Evidence from a nationwide survey.

Shuyao Wu, Delong Li, Lumeng Liu, Zhonghao Zhang

Abstract readDataset
In one paragraph

Article in Scientific data, 2026. 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

4 authors.

Shuyao WuCollege of Geography and Remote Sensing, Hohai University, Nanjing, Jiangsu, 211100, China.
Delong LiInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, 100101, China. lidelong@igsnrr.ac.cn.ORCID http://orcid.org/0000-0003-1050-9794
Lumeng LiuCollege of Geography and Remote Sensing, Hohai University, Nanjing, Jiangsu, 211100, China.
Zhonghao ZhangCollege of Geography and Remote Sensing, Hohai University, Nanjing, Jiangsu, 211100, China.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 42301344Natural Science Foundation of Shandong Province (Shandong Provincial Natural Science Foundation) ZR2022QC253
6 · The paper itself

Abstract

Characterizing ecosystem services demand (ESD) is key to understanding the diverse preferences for various benefits from nature. However, direct evidence of the variations in ESDs among different groups of people and places remains limited. Here, a national-scale dataset of ESDs derived from a non-probabilistic survey of 20,075 urban residents across 31 provinces in China is presented. The dataset captures preferences for nine typical urban ecosystem services using a point-allotment experiment, where participants allocated a total of 100 importance points to prioritize ESDs. Key findings reveal significant variations in ESDs, with air purification receiving the highest average importance point (22.17), followed by recreation (15.60) and local climate regulation (13.62). This pattern of variation in ESDs is evident in 28 of 31 provinces. The dataset also includes detailed socioeconomic and environmental metadata, enabling further analyses of regional disparities and their drivers among ESDs. This resource offers exploratory insights into tailoring urban design and ecosystem management strategies to diverse societal needs, thereby advancing sustainable land use planning and ESD research.

Indexed as

Conservation of Natural ResourcesEcosystemUrban PopulationChinaHumansRecreationSurveys and Questionnaires

Identifiers

PMID41663465
PMCPMC12996412

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.