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).

Les titres et les descriptions proviennent des sources de données, en anglais.

Source: ND-GAIN climate adaptation intelligence
Lignes: 5 760
Colonnes: 21
Cadence de la source: Annuelle
Dernière actualisation: 6 oct. 2026
Thème: environment

Colonne	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).

10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.

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

6 oct. 2026Actuelle
20261006T021852Z-8ff4a8502a5c · sha256 8ff4a8502a5c…
5 760 lignes · premier instantané

Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (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)

Point d’accès API : https://datazimuts.com/v1/datasets/ndgain_adaptation_intel/ndgain_adaptation_panel

Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.

D’où viennent ces données et ce qui en a été fait. Le travail des autres apparaît sous forme de décomptes ; seuls les projets partagés sont nommés.

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Épinglé à l’instantané 20261006T021852Z-8ff4a8502a5c et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.

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/fr/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/fr/datasets/ndgain_adaptation_intel/ndgain_adaptation_panel?snapshot=20261006T021852Z-8ff4a8502a5c}},
  note         = {Snapshot 20261006T021852Z-8ff4a8502a5c, sha256 8ff4a8502a5ca47bb275127aa2a1ca35b71d87350e972f83330b52382cc06934; accessed 2026-10-06}
}

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<iframe src="https://datazimuts.com/embed/chart?dataset=ndgain_adaptation_intel%2Fndgain_adaptation_panel&lang=fr&theme=auto&snapshot=20261006T021852Z-8ff4a8502a5c&x=year&y=year&agg=avg" title="ND-GAIN climate adaptation panel by country" width="100%" height="380" style="border:0" loading="lazy"></iframe>

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