Beyond carbon: Multi-attribute market design for ecosystem services

Last registered on August 27, 2026

Pre-Trial

Trial Information

General Information

Title
Beyond carbon: Multi-attribute market design for ecosystem services
RCT ID
AEARCTR-0019204
Initial registration date
August 22, 2026

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
August 27, 2026, 12:14 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Region

Primary Investigator

Affiliation
The University of Queensland

Other Primary Investigator(s)

PI Affiliation
The University of Queensland
PI Affiliation
The University of Queensland
PI Affiliation
The University of Queensland

Additional Trial Information

Status
In development
Start date
2026-08-24
End date
2027-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study investigates the relative efficiency and performance of alternative designs for multi-attribute markets. We compare credit stacking and bundling of environmental offsets where heterogeneous landholders simultaneously supply multiple ecosystem services. Our theoretical framework demonstrates that pure bundling violates Tinbergen’s rule and generates an inefficient pooling equilibrium. Conversely, we prove that offering a menu of bundles can restore the first-best separating equilibrium. To empirically test these theoretical results, we implement a double-auction laboratory experiment across four institutional treatments: (i) independent markets (unbundled stacking), (ii) pure bundling, (iii) an optimally weighted menu of bundles, and (iv) a sub-optimally weighted menu. Across treatments, we vary production complementarity and specialisation levels (with two values per parameter) across three repeated rounds per session. We provide actionable insights, grounded in our theoretical results and behavioural data, to inform the design and implementation of emerging national and international multi-attribute environmental markets.
External Link(s)

Registration Citation

Citation
Friesen, Lana et al. 2026. "Beyond carbon: Multi-attribute market design for ecosystem services." AEA RCT Registry. August 27. https://doi.org/10.1257/rct.19204-1.0
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2026-08-24
Intervention End Date
2027-08-24

Primary Outcomes

Primary Outcomes (end points)
Total quantity of abatement (number of credits traded) for each type of credit, credit prices, and net benefits
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Quantity of credits traded and profits of each type of agent in the market (buyer/seller)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This study employs a laboratory experiment conducted in a standard university experimental economics lab, with participants recruited from a broader student population. Participants will be part of a single group of eight participants for the duration of the experiment.

The main experimental task involves subjects making decisions about how many units of multiple fictitious goods to produce or purchase. Each unit produced generates a cost, and each unit purchased has an associated value. Subjects will engage in trade of each good via a double auction market design. Subjects’ earnings will be proportional to the profits they make from trading goods. Subjects will have different roles as either buyers or sellers, which are fixed for the duration of the experiment. Each type of buyer or seller varies in its production costs or consumption values.

We will consider four between-subject institutional treatments: (i) a stacking design where each good is traded in independent markets, (ii) a pure bundling design with a single bundle containing one unit of each good, (iii) a bundling menu design with two different bundles containing different numbers of units of each good (with optimal weights), and (iv) a bundling menu design with sub-optimal weights. Within each treatment, we systematically vary producer specialisation and joint-production complementarity parameters (with two different values per parameter). Each specialisation-complementarity parameter pair is repeated for three rounds per session.

To control for subject heterogeneity in our regression analysis, participants complete a standard demographic questionnaire following the main experimental task.
Experimental Design Details
Not available
Randomization Method
Participants will be recruited via email through the SONA system. They choose between a list of available sessions, and the session is randomised to a treatment before it is initialised.
Randomization Unit
Individual
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
16 groups with 8 individuals per group
Sample size: planned number of observations
128 individuals
Sample size (or number of clusters) by treatment arms
32 individuals in the baseline (stacking) treatment
32 individuals in the single-package (pure bundling) treatment
32 individuals in the package menu (bundling menu) treatment with optimal weights
32 individuals in the package menu (bundling menu) treatment with sub-optimal weights
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
The University of Queensland BEL LNR
IRB Approval Date
2026-08-18
IRB Approval Number
2026/HE011653