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 リスク削減 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.
モデル、その仮定、データ、不確実性を、科学者やエンジニアが確認する形で示します。構造は公表された手法に従っています。描かれた値は例示であり、知見ではありません。
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 出典の登録簿.
| データ | 役割 | ラベル |
|---|---|---|
| Global Forest Change and national land-cover series | Outcome measures | 観測 |
| Protected Planet boundaries and designation dates | Treatment timing and extent | 申告済み |
| Programme enrolment records | Treatment assignment | 報告値 |
| Terrain, access and land-use covariates | Matching and adjustment | 推定 |
Process explorer · Process
10 steps·4 段階
プロセス
PRISMA 2020 はシステマティック・レビューのための報告ガイドラインです。ステップは義務づけられた順序ではなくそのチェックリストに従っており、これを採用している Nexus の文書はありません。
報告書Outline 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 感度分析. Replication across settings tests external validity.
| 確認 | 実施方法 |
|---|---|
| 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.
経済開発 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.
個人データ、重要インフラの機微な詳細、共有する権利のないものは決して送らないでください。寄稿は利用前にレビューされます。送付によって著作者としての権利や編集上の権利が生じることはなく、スポンサーシップで調査結果を買うことは決してできません。
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