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

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.

Authors

Institutions

Publication Details

Journal
Scientific Data
Published
2026-02-09
DOI
https://doi.org/10.1038/s41597-026-06689-3
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Zhonghao Zhang, Delong Li, Shuyao Wu, L. Liu
Scientific Data
Land Use and Ecosystem Services
article

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

Zhonghao Zhang, Delong Li, Shuyao Wu, L. Liu
article en

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.

Scientific DataVol. 13(1)
Hohai University (CN), Institute of Geographic Sciences and Natural Resources Research (CN)
National Natural Science Foundation of China, Natural Science Foundation of Shandong Province
Openalex Percentile: Top 73%
Land Use and Ecosystem Services
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Revealing urban residents’ ecosystem service preferences in China: Evidence from a nationwide survey — Zhonghao Zhang, Delong Li, et al. · Scientific Data (2026) | TGRS Research Map | TGRS