Eco-Effective Policy Incentives for Extended Producer Responsibility - Experimental Evidence

Last registered on April 17, 2025

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Pre-Trial

Trial Information

General Information

Title

Eco-Effective Policy Incentives for Extended Producer Responsibility - Experimental Evidence

RCT ID

AEARCTR-0015781

Initial registration date

April 14, 2025

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

April 17, 2025, 7:20 AM EDT

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

Locations

Country

Germany

Region

Saxony

Primary Investigator

Name

Gari Walkowitz

Affiliation

TU Freiberg

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Maximilian Vincent Erdmann

PI Affiliation

TU Freiberg

Contact Investigator

Additional Trial Information

Status

In development

Start date

2025-04-22

End date

2025-06-27

Keywords

Behavior, Environment & Energy, Governance, Lab

Additional Keywords

Circular Economy, Extended Producer Responsibility, Eco-Design, Environmental Policy

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

Extended Producer Responsibility (EPR) is an environmental policy strategy that aims to make producers responsible for the waste management of their products and packaging across the entire life cycle. Within this policy, different types of "eco-modulation" are discussed as incentive strategies by governmental institutions aiming to improve the sustainability of the eco-design of firms’ products. Using a large-scale behavioral experiment, we study the effectiveness of different incentive strategies regarding the environmental outcome, the firms’ profit, and the regulators’ revenue. The experimental method enables us to systematically investigate under controlled conditions how producers behaviorally respond to potential changes in the provided incentive structure and the underlying psychological mechanisms. As part of an ex-ante policy assessment, we present recommendations on how producers can be informed to improve eco-design. We suggest that the incentive strategies under discussion and their impact on the actual environmental outcome need to be closely and carefully scrutinized.

External Link(s)

Registration Citation

Citation

Erdmann, Maximilian Vincent and Gari Walkowitz. 2025. "Eco-Effective Policy Incentives for Extended Producer Responsibility - Experimental Evidence ." AEA RCT Registry. April 17. https://doi.org/10.1257/rct.15781-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

In an online behavioral experiment, participants decide how a product should be designed. This includes two choices regarding 1) the eco-design and 2) the weight of the product.

In the baseline treatment, participants can choose between a higher monetary reward and a higher reduction of CO2 emissions. In treatments 2-5, we additionally introduce and test different types of policy interventions:
In treatment 2, lighter product designs are rewarded monetarily.
In treatment 3, a monetary bonus is granted if the best eco-design is chosen.
In treatment 4, a monetary penalty is imposed if the best eco-design is not chosen.
In treatment 5, the more sustainable the eco-design, the higher the monetary reward.

Intervention (Hidden)

Intervention Start Date

2025-04-22

Intervention End Date

2025-06-27

Primary Outcomes

Primary Outcomes (end points)

The study aims to evaluate the effectiveness of different incentive strategies in relation to the chosen eco-design, with respect to:
• the environmental impact, measured by the real reduction of CO2 emissions
• the firms’ profitability, reflected in the monetary rewards received by participants
• the regulator’s revenue, represented by the amount and structure of the fee

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

After the experiment, we will administer a questionnaire to collect additional information on the subjects’:
• personal values (PVQ, Schwartz 2012),
• economic preferences (Falk et al. 2018),
• and socio-economic background

Secondary Outcomes (explanation)

Experimental Design

The participants decide how to design a product. This includes two choices regarding 1) the eco-design level (1, most unsustainable but most profitable - 3, most sustainable but most unprofitable) and 2) the weight (1, lightest - 3, heaviest) of the product. The combination of 3 (eco-design level) x 3 (weight level) results in 9 possible product designs that participants can choose from.

The choices of the participants affect their personal monetary earnings and how much CO2 emissions are reduced. Each participant can reduce a minimum of 86.96 kg of CO2 and a maximum of 260,88‬ kg of CO2. CO2 reduction is achieved through the purchase of Gold Standard verified emission certificates.

In contrast to the baseline treatment, participants in treatments 2-5 make these choices additionally under incentives induced by policy intervention schemes.

The experiment will be conducted online.

Experimental Design Details

Randomization Method

The experiment will be conducted online. Treatment assignment is randomized on the individual level. Treatments are randomly assigned to individual subjects by cycling through the treatments in the order of which subjects participate in the experiment.

Randomization Unit

We randomize at the level of the individual:
• Treatment assignment (treatment 1 – treatment 5)

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

320 Individuals, fluent in English with management and decision-making experience from European countries.

Sample size: planned number of observations

320 Individuals, fluent in English with management and decision-making experience from European countries = 320 observations.

Sample size (or number of clusters) by treatment arms

Each treatment will include 64 participants, i.e., we will collect 64 observations per treatment.

In total, we will collect 5 x 64 = 320 observations.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

In our literature search, we did not find any comparable studies from which reference values could have been derived for the sample size calculation. Therefore, we based our estimation on standard statistical assumptions. Specifically, we assumed a medium effect size (Cohen’s d = 0.5), following the conventional classification by Cohen (1988). Using a significance level of α = 0.05 and aiming for a statistical power of 0.80, the calculation indicated that a sample size of 64 participants per treatment group would be sufficient to detect effects of this magnitude. In the absence of prior empirical data, we consider this sample size appropriate to address our research objectives while ensuring adequate statistical sensitivity.

Supporting Documents and Materials

IRB