ArticleScientific reports2025
District-level climate vulnerability assessment in Meghalaya using an IPCC AR5 framework.
Article in Scientific reports, 2025. 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
7 authors.
Funding
Abstract
Climate change poses growing risks to ecologically fragile and socio-economically vulnerable regions such as Meghalaya in the Indian Himalayan Region. This study aims to assess and compare district-level climate vulnerability in Meghalaya using an indicator-based framework aligned with the IPCC's AR5 conceptualization. The primary objective is to identify key drivers of vulnerability and provide spatially explicit insights to inform adaptation planning. The analysis uses eight indicators representing both biophysical and socio-economic dimensions, slope gradient, forest cover, crop yield variability, population density, female literacy, infant mortality, Multidimensional Poverty Index (MPI), and NREGS man-days, derived from official government sources for the period 2020-2024. Data were normalized according to their functional relationships with vulnerability, and unequal indicator weights were assigned through expert consultation. Composite Vulnerability Indices (CVI) were generated to rank districts, supported by GIS-based spatial analysis and attribution of indicator contributions. Results reveal that West Khasi Hills and East Garo Hills are the most vulnerable districts, primarily due to steep slopes, forest degradation, and high poverty levels. Conversely, East Jaintia Hills and South West Khasi Hills exhibit lower vulnerability owing to stronger adaptive capacities. The study concludes that enhancing forest conservation, livelihood diversification, and social protection mechanisms is critical for reducing vulnerability in high-risk districts. This framework offers a replicable approach for rapid vulnerability assessment in data-scarce mountainous regions.
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.