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Additional Information

Field	Value
Data last updated	6 Ekim 2023
Metadata last updated	19 Aralık 2025
Created	6 Ekim 2023
Format	CSV
License	Open Data Commons Attribution License
Data extraction page	https://ndap.niti.gov.in/dataset/7160
Data insights	By ranking and pinpointing the most vulnerable districts and states, these insights empower policymakers and decision-makers to strategically prioritize locations for climate risk mitigation. This data-driven approach not only informs adaptation planning but also guides targeted investments in key sectors such as agriculture, water resources, and healthcare. With a focus on vulnerable regions like the Himalayas and coastal areas, these insights enable the development of high-impact projects for climate-focused funds such as the Green Climate Fund and Adaptation Fund. Moreover, these findings facilitate the fulfillment of Nationally Determined Contributions, ensuring a more resilient response to climate change. By integrating this data into disaster management strategies, these insights pave the way for proactive and effective risk reduction, making a substantial difference in climate resilience efforts.
Data last updated	2022-03-16 00:00:00
Data retreival date	2022-06-12 00:00:00
Datastore active	True
Frequency	Once
Geo columns	['state_code', 'district_code']
Granularity	District
Has views	True
Id	98947c1d-e9ff-4e5a-9407-6c77c6ef851e
Idp ready	True
Lgd mapping	yes
Methodology	The methodology for vulnerability assessment is a systematic process involving the definition of scope, selection of assessment type, tier methods, sector, spatial scale, and assessment period. Indicators crucial for vulnerability evaluation are identified and quantified, with normalization techniques ensuring consistency. Indicators are then weighted and aggregated, providing a comprehensive representation of vulnerability. Through this structured approach, vulnerabilities are ranked, allowing for focused adaptation planning
No indicators	73
Package id	e4ccd3aa-beb8-436c-9ceb-db3998a10314
Position	1
Size	206,9 KiB
Sku	most-climate_vulnerability_assessment-dt-yr-aaa
State	active
States uts no	28
Url type	upload
Years covered	2.021
Methodology	The methodology for vulnerability assessment is a systematic process involving the definition of scope, selection of assessment type, tier methods, sector, spatial scale, and assessment period. Indicators crucial for vulnerability evaluation are identified and quantified, with normalization techniques ensuring consistency. Indicators are then weighted and aggregated, providing a comprehensive representation of vulnerability. Through this structured approach, vulnerabilities are ranked, allowing for focused adaptation planning
Indicators
Similar Resources
Granularity Level	District
Data Extraction Page	https://ndap.niti.gov.in/dataset/7160
Data Retreival Date	2022-06-12 00:00:00
Data Last Updated	2022-03-16 00:00:00
Sku	most-climate_vulnerability_assessment-dt-yr-aaa
Dataset Frequency	Once
Years Covered	2021.0
No of States/UT(s)	28
No of Districts
No of Tehsils/blocks
No of Gram Panchayats
Additional Information
Number of Indicators	73
Insights from the dataset	By ranking and pinpointing the most vulnerable districts and states, these insights empower policymakers and decision-makers to strategically prioritize locations for climate risk mitigation. This data-driven approach not only informs adaptation planning but also guides targeted investments in key sectors such as agriculture, water resources, and healthcare. With a focus on vulnerable regions like the Himalayas and coastal areas, these insights enable the development of high-impact projects for climate-focused funds such as the Green Climate Fund and Adaptation Fund. Moreover, these findings facilitate the fulfillment of Nationally Determined Contributions, ensuring a more resilient response to climate change. By integrating this data into disaster management strategies, these insights pave the way for proactive and effective risk reduction, making a substantial difference in climate resilience efforts.
IDP Ready	Yes
LGD Mapping	Yes
Mapping Status %
Geo Columns	['state_code', 'district_code']