ArticleScientific data2025
Global land system maps at 1 km resolution for 1.5 °C climate.
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 2 papers.
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
2 citing papers in PubMed.
- 1 km HILDA + based land cover/use map time series of China under 1.5 °C climate of this century.Scientific data · 2025Article
- Global land system maps at 1 km resolution for 1.5 °C climate.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Climate pledges are a key pathway for achieving temperature control but also exert profound cascading effects on the global Earth system. Evaluating such cascading effects often requires land change forecast products. However, current forecast products are all based on shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs). We proposed a comprehensive approach to generating 2100 land system maps under the 1.5 °C climate scenario and the baseline scenario by harmonizing the Global Change Analysis Model (GCAM), Globeland30, and the Land-N2N model. The maps exhibit a spatial resolution of 1 km and consist of 30 land system types, reflecting local high, medium, and low densities of land cover types. Additionally, we evaluated the Land-N2N using the kappa coefficient and figure of merit (FoM). The average kappa coefficient and FoM values across all the basins were 83.14% and 8.48%, respectively, demonstrating the reliability of the Land-N2N model in simulating land system changes. The dataset provides finer resolution quantitative support for global climate change mitigation and essential data for related research.
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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.