Country-year panel of the ND-GAIN Country Index (2026 release, University of Notre Dame): headline 0-100 climate adaptation index plus 0-1 vulnerability and readiness components and nine sector scores (water, food, health, ecosystems, habitat, infrastructure vulnerability; economic, governance, social readiness), 192 countries 1995-2024, with per-year ranks, year-on-year changes and fixed adaptation tiers. Keyless Notre Dame download; joins on country_code with the catalog's other global panels. Fills the catalog's climate-adaptation gap (existing climate coverage has no composite adaptation/vulnerability measure).

Source: ND-GAIN climate adaptation intelligence
Rows: 5,760
Columns: 21
Source cadence: Yearly
Last refreshed: Oct 6, 2026
Theme: environment

Column	Type	Description
country_code	string	ISO 3166-1 alpha-3 country code (file column 'ISO3'; all 192 entries are clean ISO3, no aggregates).
country	string	Country name from the catalog's canonical ISO3 mapper.
year	integer	Reference year (file year columns 1995-2024).
gain_index	float	Headline ND-GAIN Index, 0-100 (100 = best adaptation position: least vulnerable and most ready). (unit: index points)
vulnerability	float	Vulnerability component, 0-1 (lower = less vulnerable to climate disruption; mean of exposure, sensitivity and adaptive-capacity components). (unit: score)
readiness	float	Readiness component, 0-1 (higher = more ready to leverage investment for adaptation; mean of economic, governance and social readiness). (unit: score)
vuln_water	float	Water vulnerability sector score, 0-1. Higher = more vulnerable (vuln_) or more ready (ready_). (unit: score)
vuln_food	float	Food vulnerability sector score, 0-1. Higher = more vulnerable (vuln_) or more ready (ready_). (unit: score)
vuln_health	float	Health vulnerability sector score, 0-1. Higher = more vulnerable (vuln_) or more ready (ready_). (unit: score)
vuln_ecosystems	float	Ecosystem services vulnerability sector score, 0-1. Higher = more vulnerable (vuln_) or more ready (ready_). (unit: score)
vuln_habitat	float	Human habitat vulnerability sector score, 0-1. Higher = more vulnerable (vuln_) or more ready (ready_). (unit: score)
vuln_infrastructure	float	Infrastructure vulnerability sector score, 0-1. Higher = more vulnerable (vuln_) or more ready (ready_). (unit: score)
ready_economic	float	Economic readiness sector score, 0-1. Higher = more vulnerable (vuln_) or more ready (ready_). (unit: score)
ready_governance	float	Governance readiness sector score, 0-1. Higher = more vulnerable (vuln_) or more ready (ready_). (unit: score)
ready_social	float	Social readiness sector score, 0-1. Higher = more vulnerable (vuln_) or more ready (ready_). (unit: score)
gain_rank	float	Per-year cross-country rank on the headline index (1 = best adaptation position; null when gain_index is null). (unit: rank)
vulnerability_rank	float	Per-year cross-country rank on vulnerability (1 = most vulnerable; null when vulnerability is null). (unit: rank)
readiness_rank	float	Per-year cross-country rank on readiness (1 = most ready; null when readiness is null). (unit: rank)
gain_yoy_pp	float	Year-on-year change of the headline index in points (null for each country's first year). (unit: index points)
adaptation_tier	string	Fixed headline cuts: t1 >= 60 (adaptation leaders), t2 >= 50, t3 >= 40, t4 < 40 (most at-risk); null when gain_index is null.
row_hash	string	Deterministic 12-hex row identity hash (country_code\|year).

First 10 sample rows — a preview, not the complete dataset.

country_code	country	year	gain_index	vulnerability	readiness	vuln_water	vuln_food	vuln_health	vuln_ecosystems	vuln_habitat	vuln_infrastructure	ready_economic	ready_governance	ready_social	gain_rank	vulnerability_rank	readiness_rank	gain_yoy_pp	adaptation_tier	row_hash
AFG	Afghanistan	1,995	29.789	0.648	0.244	0.655	0.672	0.778	0.539	0.596	—	0.35	0.067	0.314	186	5	180	—	t4	ab61a7e0e8e0
AFG	Afghanistan	1,996	29.781	0.648	0.244	0.654	0.672	0.778	0.54	0.598	—	0.35	0.067	0.314	186	5	180	-0.008	t4	64645b46a4df
AFG	Afghanistan	1,997	29.818	0.648	0.245	0.652	0.672	0.778	0.541	0.599	—	0.35	0.07	0.314	186	5	179	0.037	t4	1bdec963d52a
AFG	Afghanistan	1,998	29.947	0.647	0.246	0.651	0.664	0.778	0.541	0.6	—	0.35	0.073	0.314	185	6	179	0.128	t4	81b4cb57f871
AFG	Afghanistan	1,999	30.011	0.645	0.246	0.651	0.655	0.778	0.541	0.602	—	0.35	0.073	0.314	186	6	180	0.064	t4	61dce9de9a85
AFG	Afghanistan	2,000	30.079	0.644	0.246	0.651	0.647	0.778	0.541	0.603	—	0.35	0.072	0.314	187	6	180	0.068	t4	451e5542f3f9
AFG	Afghanistan	2,001	30.467	0.642	0.251	0.651	0.637	0.777	0.541	0.604	—	0.35	0.09	0.314	186	7	177	0.388	t4	f764e51f74ea
AFG	Afghanistan	2,002	30.87	0.64	0.257	0.65	0.628	0.776	0.541	0.605	—	0.35	0.108	0.314	186	8	176	0.403	t4	5af63b961982
AFG	Afghanistan	2,003	31.535	0.63	0.261	0.649	0.62	0.735	0.541	0.605	—	0.35	0.118	0.314	181	10	176	0.666	t4	77f01a56255a
AFG	Afghanistan	2,004	32.116	0.632	0.274	0.647	0.612	0.752	0.541	0.605	—	0.35	0.157	0.314	182	9	170	0.581	t4	07f3ecd34d09

Oct 6, 2026Current
20261006T021852Z-8ff4a8502a5c · sha256 8ff4a8502a5c…
5,760 rows · first snapshot

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/ndgain_adaptation_intel/ndgain_adaptation_panel" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/ndgain_adaptation_intel/ndgain_adaptation_panel").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

API endpoint: https://datazimuts.com/v1/datasets/ndgain_adaptation_intel/ndgain_adaptation_panel

Tip: fetch /llms.txt for the full machine-readable catalog.

Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.

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Pinned to snapshot 20261006T021852Z-8ff4a8502a5c and its content hash, so readers get exactly the data you used.

APA

ND-GAIN climate adaptation intelligence. (2026). ND-GAIN climate adaptation panel by country [Data set, snapshot 20261006T021852Z-8ff4a8502a5c, sha256 8ff4a8502a5c]. Datazimuts. Retrieved 2026-10-06, from https://datazimuts.com/en/datasets/ndgain_adaptation_intel/ndgain_adaptation_panel?snapshot=20261006T021852Z-8ff4a8502a5c

BibTeX

@misc{dz_ndgain_adaptation_intel_ndgain_adaptatio_8ff4a850,
  title        = {{ND-GAIN climate adaptation panel by country}},
  author       = {{ND-GAIN climate adaptation intelligence}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/ndgain_adaptation_intel/ndgain_adaptation_panel?snapshot=20261006T021852Z-8ff4a8502a5c}},
  note         = {Snapshot 20261006T021852Z-8ff4a8502a5c, sha256 8ff4a8502a5ca47bb275127aa2a1ca35b71d87350e972f83330b52382cc06934; accessed 2026-10-06}
}

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ND-GAIN climate adaptation panel by country

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