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
Impact evaluation for conservation
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.
01Register
Impact evaluation for conservation on the record.
Method recordSTEM method
- MethodCausal inference and impact evaluation
- StewardEconomic Development
- Applied inBiodiversity Nexus
- Data4 inputs
- Checks4 validation steps
- Canonical pageSTEM Commons
02Application
What causal inference and impact evaluation does here.
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 risk reduction measures actually work. It supports allocating resources toward interventions with demonstrated effect across connected risks. The canonical description is on the STEM Commons.
Restoration and payment programme evaluation
Difference-in-differences and synthetic control designs compare enrolled land with comparable land outside a programme, before and after it starts.
Policy and supply-chain commitments
Staggered adoption of moratoria, regulations or zero-deforestation commitments is used to estimate effects on clearing and on displacement of clearing to other areas.
03Methods and evidence
How impact evaluation for conservation is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
Earth observation chain
How a satellite or sensor record becomes evidence on impact evaluation for conservation, and where each step adds uncertainty.
- 01 · Acquireorbit, sensor and revisitUncertainty: gaps from cloud and orbit
- 02 · Calibrateradiometric and geometricUncertainty: sensor drift
- 03 · Correctatmosphere and terrainUncertainty: aerosol and slope assumptions
- 04 · Retrieve or classifythe variable or the classUncertainty: model and training error
- 05 · Validateagainst ground truthUncertainty: sample design and size
- 06 · Publishwith error bars and lineageUncertainty: stated, never hidden
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
04Data
What the method consumes.
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 the source register.
| Data | Role | Label |
|---|---|---|
| Global Forest Change and national land-cover series | Outcome measures | OBSERVED |
| Protected Planet boundaries and designation dates | Treatment timing and extent | DECLARED |
| Programme enrolment records | Treatment assignment | REPORTED |
| Terrain, access and land-use covariates | Matching and adjustment | ESTIMATED |
05Process
How the process runs.
Process
Impact evaluation for conservation: PRISMA 2020 review reporting
PRISMA 2020 is a reporting guideline for systematic reviews; the steps follow its checklist, not a mandated sequence, and no Nexus document adopts it.
01 Plan
- 01Registration and protocolRecord · The review teamThe guideline asks for registration details, access to the protocol and any amendments.
- 02Eligibility criteriaRecord · The review teamThe guideline asks for the inclusion and exclusion criteria and how studies were grouped.
02 Find and select
- 03Search: sources and strategiesStep · The review teamThe guideline asks for the information sources and the full search strategies. It does not fix which sources are used.
- 04Selection of studiesStep · The review teamThe guideline asks how studies were judged eligible, including how many reviewers screened. It does not fix the number of reviewers.
- 05Flow diagramRecordThe flow diagram shows the number of records found, the number of studies included and the reasons for exclusion.
03 Analyse
- 06Data collectionStep · The review teamThe guideline asks how data were collected from reports and which data items were sought.
- 07Risk of biasStep · The review teamThe guideline asks for the methods and tools used to assess risk of bias.
- 08SynthesisModel · The review teamThe guideline asks for the effect measures and the synthesis methods. It does not prescribe meta-analysis.
- 09Certainty of evidenceStep · The review teamThe guideline asks for the methods used to assess certainty, and reports the result per outcome. It does not name a grading scale.
04 Report
- 10Review reportRecordThe guideline helps authors report why the review was done, what they did and what they found.
ReportsOutline 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.
06Validation
How results are checked.
Identification assumptions are probed through placebo tests, falsification checks and sensitivity analysis. Replication across settings tests external validity.
| Check | How it is done |
|---|---|
| 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.
07Connections
Topics that rely on this method.
08Stewardship
Who maintains the method.
Economic Development stewards causal inference and impact evaluation for the Consortium and reviews how it is applied across platforms.
09Connected
Where this page leads across the Consortium.
10Questions
Questions and answers.
What are the limits of causal inference and impact evaluation in this field?
Unobserved confounding and spillovers can bias estimates. Effects found in one context may not transfer to another.
How are results checked?
Identification assumptions are probed through placebo tests, falsification checks and sensitivity analysis. Replication across settings tests external validity.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
ReportsContribute
Contribute evidence from this page.
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.
GCRIBiodiversity NexusGlobal Sector Risk and ResilienceObservatory Signal
- EmailGateway GW-GCRISend it from this pageEvidence, a correction, a case, a method, a review, a translation or profile input. It arrives tagged with this page, so the editor who owns it receives it.
- ZenodoOpen depositDeposit it and submit to the communityPublish your own record on Zenodo with your ORCID iD, then submit it to the Reports community there. Curators review it; the DOI stays yours.
- ReportsGCRISee every way to contributeThe contribution types, how each is reviewed, what credit you receive, the licence and what is never accepted.
Never send personal data, sensitive infrastructure detail or anything you have no right to share. A contribution is reviewed before use; sending it confers no authorship or editorial rights, and sponsorship never buys a finding.
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