Evidence map›Paper›PMID 41786795›Full record

ArticleScientific data2026

Urban land use of national economic sectors in Guangdong-Hong Kong-Macao Greater Bay Area from 2015-2022.

Shiting Li, Qianyuan Huang, Meirong Su, Chao Xu, Yanmin Teng, Yuan Zhang, Qionghong Chen, Yuanchao Hu

Abstract readDataset
PubMed Full text at PMC Publisher
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

8 authors.

Shiting LiGuangdong Basic Research Center of Excellence for Ecological Security and Green Development, Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Ocean, Guangdong University of Technology, Guangzhou, 510006, P. R. China.
Qianyuan HuangSchool of Environment, Beijing Normal University, Beijing, 100875, P. R. China.
Meirong SuGuangdong Basic Research Center of Excellence for Ecological Security and Green Development, Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Ocean, Guangdong University of Technology, Guangzhou, 510006, P. R. China. sumr@gdut.edu.cn.
Chao XuKey Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Architecture and Urban Planning, Guangdong University of Technology, Guangzhou, 510060, P. R. China.
Yanmin TengCollege of Life Science, Hebei University, Baoding, 071000, P. R. China.
Yuan ZhangGuangdong Basic Research Center of Excellence for Ecological Security and Green Development, Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Ocean, Guangdong University of Technology, Guangzhou, 510006, P. R. China.
Qionghong ChenSchool of Technology for Sustainability, Guangdong Provincial Key Laboratory of Wastewater Information Analysis and Early Warning, Beijing Normal University, Zhuhai, 519087, P. R. China.
Yuanchao HuGuangdong Basic Research Center of Excellence for Ecological Security and Green Development, Key Laboratory for City Cluster Environmental Safety and Green Development of the Ministry of Education, School of Ecology, Environment and Ocean, Guangdong University of Technology, Guangzhou, 510006, P. R. China.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 52388101National Natural Science Foundation of China (National Science Foundation of China) 72122004
6 · The paper itself

Abstract

Land underpins nearly all human activities, with different economic sectors exhibiting varying degrees of dependence on land resources. Sector-specific land use analysis is essential for optimizing resource allocation, improving land-use efficiency, and promoting sustainable urban development. However, existing land-use classification data are often coarse-grained and lack the detail needed to capture land use at the level of national economic sectors. To address this limitation, this study constructs a sector-specific land use dataset covering 97 and 42 national economic sectors across 11 cities in the Guangdong-Hong Kong-Macao Greater Bay Area for the period 2015-2022. This dataset enables systematic examination of sectoral land use patterns, identification of high land-consuming and low-efficiency sectors, and provides empirical evidence to guide industrial restructuring, spatial planning, and resource-environment policy design. Moreover, the dataset can be integrated with complementary indicators (e.g., carbon emissions, water footprints) to support ecological management and low-carbon development strategies.

Indexed as

Anthropogenic EffectsCitiesSpatial AnalysisUrbanizationBaysChinaConservation of Natural ResourcesHong Kong

Identifiers

PMID41786795
PMCPMC13079856

What OpenQuestion holds

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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.