Innovative payments for environmental services to curb deforestation in the Brazilian Amazon

Last registered on September 08, 2021

Pre-Trial

Trial Information

General Information

Title
Innovative payments for environmental services to curb deforestation in the Brazilian Amazon
RCT ID
AEARCTR-0008193
Initial registration date
September 06, 2021
Last updated
September 08, 2021, 4:15 PM EDT

Locations

There are documents in this trial unavailable to the public. Use the button below to request access to this information.

Request Information

Primary Investigator

Affiliation
French National Research Institute for Agriculture, Food and Environment (INRAE)

Other Primary Investigator(s)

PI Affiliation
French National Research Institute for Agriculture, Food and Environment (INRAE)

Additional Trial Information

Status
In development
Start date
2021-09-12
End date
2022-11-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Curbing deforestation in developing countries may be a cost-effective way to reduce carbon emissions and address climate change. But how to prevent the deforestation and degradation of forest lands in areas where landowners depend, for their livelihood, on slash-and-burn agriculture and extensive cattle ranching, two primary drivers of deforestation? In Brazil, the implementation of command-and-control measures, the expansion of protected areas, and interventions in the soy and beef supply chains, such as the Soy Moratorium established in 2006, have significantly curbed deforestation in the Amazon between 2005 and 2013. Despite this overall improvement, deforestation rates have continued at 5,000-7,000 km2 per year since 2009 and have increased again thereafter. Today, it is often argued that new mechanisms targeting small landowners are required to achieve further reductions in deforestation in the Amazon. Payment for Ecosystem Services (PES) contract may be an option but we need more evidence about which PES contracts work on the ground. In particular, we need to know which programs are most effective, in which context, and whether they work equally for all targeted participants. The objective of this experiment is to test the impact of different contracts of payments for environmental services (PES) on the deforestation decision of the Brazilian Amazonian cattle breeders. The experiment is based on the implementation of a pilot conservation program, in which participants are randomly offered a PES in exchange for not cutting their forest for an entire year. We randomly offer two types of contracts, which differ in their specifications, one being more flexible than the other. We evaluate whether one contract outperform the other, looking at forest loss, as measured by satellite imagery using SAR Sentinel-1 images. In each arm of the RCT, PES contracts is offered to participants following a Becker-DeGroot-Marschak (BDM) procedure. This design has many advantages: it is incentive compatible, since revealing the true price remains the best strategy for the participant; it allows to infer the causal effect of compensation payments on forest cover; and it allows estimating heterogeneous treatment effects.
External Link(s)

Registration Citation

Citation
Demarchi Dias, Gabriela and Julie Subervie. 2021. "Innovative payments for environmental services to curb deforestation in the Brazilian Amazon." AEA RCT Registry. September 08. https://doi.org/10.1257/rct.8193-1.0
Experimental Details

Interventions

Intervention(s)
The objective of this experiment is to test the impact of different contracts of payments for environmental services (PES) on the deforestation decision of the Brazilian Amazonian cattle breeders. The experiment is based on the implementation of a pilot conservation program, in which participants will be randomly offered a PES in exchange for not cutting their forest for an entire year. The treatment is the PES.
Intervention Start Date
2021-09-12
Intervention End Date
2022-09-30

Primary Outcomes

Primary Outcomes (end points)
The main indicator of deforestation is the loss of forest cover as measured by satellite imagery. Deforestation monitoring is carried out using SAR Sentinel-1 images, processed by the so-called CuSum algorithm developed by Ygorra et al. (2021).
Primary Outcomes (explanation)
The Sentinel-1 synthetic aperture radar (SAR) time series facilitates regular monitoring of forest cover, degradation and deforestation. Not being so sensitive to atmospheric conditions, radar remote sensing is the favourite tool concerning tropical domain mapping. Its high resolution (10 m) allows the identification of low intensity deforestation and selective logging. The CuSum algorithm detects simple changes in the backscattered signal.

Secondary Outcomes

Secondary Outcomes (end points)
Other outcomes include (i) the abandonment of the contract (by simple termination) and (ii) cost-effectiveness measured as the ratio of the area cut and the payment paid.
Secondary Outcomes (explanation)
Contract A is less flexible than contract B, it should theoretically lead to a relatively higher abandonment rate. The objective of the experiment is not only to determine which contract makes it possible to save the largest area in the forest. It is also expected to say which contract makes it possible to spend the least money per hectare of forest saved.

Experimental Design

Experimental Design
We use a randomized control trial (RCT) to compare the relative efficiency of contracts A and B. In each arm of the RCT, PES contracts is offered to participants following a Becker-DeGroot-Marschak (BDM) procedure.
Each volunteer participant is asked to sign an FPIC form specifying that they understand that their participation in the pilot program does not guarantee that they will receive a conditional payment at the end, as the pilot includes two phases of randomization.
In the first phase of randomization (lottery #1), the volunteers are randomly assigned to the arms of the trial, which include one control group and two treatment groups. People in the treatment groups are offered to participate in a second lottery, which will designate individuals who will receive a conditional payment. People in the control group are not offered such an opportunity and will be left with a gift worth 5 euros in compensation for the time given to the investigators.
In the second phase of randomization (lottery #2), people assigned to the treatment groups are asked to reveal the price they are willing to accept not to cut down their forest. They then participate in a (BDM) lottery which determines whether they are offered a conditional payment contract for the next twelve months.
Experimental Design Details
Not available
Randomization Method
Random assignment to the arms of the trial (lottery #1) is performed using computer software. Lottery #2 consists of drawing a ball at random in a ballot box following a BDM procedure.
Randomization Unit
Individual farmer randomization
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clusters.
Sample size: planned number of observations
Sample size is 600 farms.
Sample size (or number of clusters) by treatment arms
Sample size by treatment arm is 200.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The minimum detectable effect size for the main outcome (forest loss) is smaller than 0.3 hectare.
IRB

Institutional Review Boards (IRBs)

IRB Name
CCERP
IRB Approval Date
2021-07-21
IRB Approval Number
N/A