Protected area effectiveness
Protected sites are matched to unprotected sites with similar slope, remoteness and pressure, and differences in forest loss or species trends are estimated with sensitivity to hidden bias.
GCRI · Biodiversity Nexus
Conservation and restoration spend public and private money on the expectation that interventions work. Causal inference tests that expectation, estimating what protected areas, payments, restoration and policy changes achieved compared with what would have happened without them. Without that comparison, background trends are easily mistaken for success.
Causal inference and impact evaluation develop methods to determine whether and how interventions or exposures cause outcomes. They separate genuine effects from correlation. In biodiversity and nature, climate and hazards and oceans and coasts, causal inference shows which 위험 경감 measures actually work. It supports allocating resources toward interventions with demonstrated effect across connected risks. The canonical description is on STEM Commons.
Protected sites are matched to unprotected sites with similar slope, remoteness and pressure, and differences in forest loss or species trends are estimated with sensitivity to hidden bias.
Difference-in-differences and synthetic control designs compare enrolled land with comparable land outside a programme, before and after it starts.
Staggered adoption of moratoria, regulations or zero-deforestation commitments is used to estimate effects on clearing and on displacement of clearing to other areas.
모형, 그 가정, 데이터, 불확실성을 과학자나 엔지니어가 확인할 형태로 제시합니다. 구조는 발행된 방법을 따릅니다. 제시된 값은 예시이며 결과가 아닙니다.
How a satellite or sensor record becomes evidence on impact evaluation for conservation, and where each step adds uncertainty.
Datasets and records used by causal inference and impact evaluation on this platform, with the role each plays and the label its values carry. Sources are listed in 출처 등록부.
| 데이터 | 역할 | 라벨 |
|---|---|---|
| Global Forest Change and national land-cover series | Outcome measures | 관측 |
| Protected Planet boundaries and designation dates | Treatment timing and extent | 신고됨 |
| Programme enrolment records | Treatment assignment | 보고됨 |
| Terrain, access and land-use covariates | Matching and adjustment | 추정 |
Process explorer · Process
10 steps·4단계
절차
PRISMA 2020은 체계적 문헌고찰을 위한 보고 지침입니다. 단계는 의무적 순서가 아니라 그 체크리스트를 따르며, 이를 채택한 Nexus 문서는 없습니다.
보고서Outline of an externally published reporting guideline, not a Nexus process and not a record of operation. PRISMA 2020 mainly covers reviews of intervention effects. No Nexus page names it. Every programme is at service level R0.
Identification assumptions are probed through placebo tests, falsification checks and 민감도 분석. Replication across settings tests external validity.
| 점검 | 수행 방식 |
|---|---|
| Covariate balance | Treated and comparison units are compared on observed characteristics before effects are estimated |
| Placebo and pre-trend tests | Effects are tested in periods and places where none should exist |
| Sensitivity to hidden bias | Results state how strong an unobserved confounder would need to be to change the conclusion |
| Spillover and leakage | Neighbouring areas are examined for displaced pressure |
Unobserved confounding and spillovers can bias estimates. Effects found in one context may not transfer to another.
경제 개발 stewards causal inference and impact evaluation for the Consortium and reviews how it is applied across platforms.
Reports is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
개인정보, 민감한 인프라 세부 정보, 공유할 권리가 없는 자료는 절대 보내지 마십시오. 기여물은 사용 전에 검토됩니다. 기여물을 보낸다고 해서 저작자 지위나 편집 권한이 부여되지 않으며, 후원으로 조사 결과를 얻을 수 없습니다.
정보와 문의는 오늘부터 열려 있습니다. 문의에는 참조 번호가 부여되고 다음 단계가 안내됩니다. 신청, 결제, 컴퓨팅 할당, 등록은 해당 팀, 약관, 심사 역량이 갖추어졌을 때만 열립니다.
이 사이트의 지도, 데이터와 그림은 맥락과 일반 정보로 제시됩니다. 조언, 예보 또는 경보가 아니며, 정보의 한계는 면책 고지와 데이터 고지에 나와 있습니다.
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