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.
The Global Centre for Risk and Innovation (GCRI)
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.
GCRI · Established · service level R0
Conservation impact evaluation is the use of causal inference to estimate what protected areas, payments, restoration and policy changes achieved compared with what would have happened without them. Without that counterfactual, background trends such as remote land that would never have been cleared 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 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.
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.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Matching | Pairing treated sites with similar untreated sites on factors such as slope and access to estimate an effect. |
| Additionality | The extent to which an outcome would not have occurred without the intervention. |
| Payments for ecosystem services | Payments to land users conditional on managing land to provide an ecosystem service. |
Mountain ranges, deserts, wetlands, glaciers and reefs, and how remote each division is from large cities, shown for New Zealand as an example geography for this subject.
Data from the source register feeds the methods; calibration and an independent test gate the estimate and its limits; the decision stays with the competent authority.
Fig. 01Nodes carry their type, metrics and state; connectors show what feeds what; the gates are the validation steps; the timeline is one application. Design of the method's use, not a record of a run.
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 |
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.
Economic Development stewards causal inference and impact evaluation for the Consortium and reviews how it is applied across platforms.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.