ArticleScientific reports2026
Spatiotemporal changes and degradation early-warning of key ecosystem services in China from 2015 to 2020.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Change assessment and degradation early-warning of Key Ecosystem Services (KES) provide a vital scientific foundation for addressing ecological degradation challenges and optimizing ecological governance policies. Counties are the fundamental unit for spatial governance and ecological policy implementation in China. However, the spatiotemporal dynamics and degradation risks of KES at this scale have not been systematically assessed in the context of ecological civilization construction. Focusing on the critical stage of China’s ecological civilization construction from 2015 to 2020, this study took all counties nationwide as the basic units to systematically analyze the spatiotemporal dynamics of three KES: water conservation (WC), soil conservation (SC), and windbreak and sand fixation (WSF). Furthermore, a fact-based ecological degradation early-warning model was developed to enable the precise identification of degraded areas and their respective warning levels. The results showed that: (1) From 2015 to 2020, the three KES nationwide exhibited a trend of overall stability coupled with a slight increase. Spatially, high-value zones for WC were primarily concentrated in the Qinghai-Tibet Plateau (QTP), southern mountainous regions, and key forest zones of Northeast China. High-value zones for SC were predominantly distributed across the Loess Plateau, QTP, Yunnan-Guizhou Plateau, and southeastern hilly and mountainous regions. WSF, in contrast, were highly concentrated in the arid and semi-arid regions of Northern China and the QTP. (2) The ecological degradation early-warning results indicate that 48.96% of counties nationwide were in a “no alert” state, while the remaining counties exhibited varying degrees of functional degradation. Among these, counties under light, moderate, and severe alerts accounted for 36.97%, 12.26%, and 1.83%, respectively. Severe-alert areas were mainly distributed in the extremely arid regions of Northwest China and the karst mountains of Southwest China, largely driven by the substantial degradation of SC functions. Based on these alert types and priority regions, this study proposes tiered ecological governance policy recommendations. Our proposed early-warning framework facilitates the intuitive and efficient identification of county-level KES degradation risks. Thus, the findings offer a scientific foundation for formulating targeted strategies for ecological conservation and restoration in territorial spatial planning.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.