Technology Adoption under Emissions Taxes and Permit Markets with Price Collars

Last registered on March 06, 2024
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Pre-Trial

Trial Information

General Information

Title
Technology Adoption under Emissions Taxes and Permit Markets with Price Collars
RCT ID
AEARCTR-0013060
Initial registration date
February 19, 2024

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
March 06, 2024, 2:38 PM EST

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

Locations

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Primary Investigator

Name
Timothy Cason
Affiliation
Purdue University
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Other Primary Investigator(s)

PI Name
John Stranlund
PI Affiliation
University of Massachusetts-Amherst
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PI Name
Frans de Vries
PI Affiliation
University of Aberdeen
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Additional Trial Information

Status
In development
Start date
2024-02-21
End date
2024-09-30
Keywords
Behavior, Environment & Energy, Lab
Additional Keywords
Emissions trading, Carbon taxes, Price controls, Auctions
JEL code(s)
C90, D44, H23
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This lab experiment investigates how price controls in emissions permit markets affect incentives for the adoption of new technology that lowers abatement costs. A theoretical model shows that the investment incentives depend on (i) the specific placement of the price collar (defined by price ceilings and floors) and (ii) the size (width) of the collar. The experimental setting includes uncertainty in emissions and abatement costs, and heterogeneous firms. Firms are more likely to adopt the new technology when the collar raises the expected permit price relative to a no-price-control baseline, especially when the high collar is narrow. Conversely, adoption incentives are weaker when the collar lowers the expected permit price, especially when the low collar is narrow. Prices are determined using a uniform price, sealed bid auction. In the narrow collar treatments, the experiment implements the narrowest range possible—a fixed price equivalent to an emissions tax.
External Link(s)

Registration Citation

Citation
Cason, Timothy, Frans de Vries and John Stranlund. 2024. "Technology Adoption under Emissions Taxes and Permit Markets with Price Collars." AEA RCT Registry. March 06. https://doi.org/10.1257/rct.13060-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
Interventions include 5 different treatments, all featuring different types of price controls: A no-control baseline, a high collar raising the expected permit price, a low collar lowering the expected permit price, and two variants of emission taxes (high and low) with a collar width of zero.
Intervention Start Date
2024-02-21
Intervention End Date
2024-09-30

Primary Outcomes

Primary Outcomes (end points)
Investment frequency
Investment likelihood for different firm types, as high and low cost types are predicted to invest in different treatments
Auction (clearing) prices, which depend in equilibrium on investment decisions and random abatement cost shocks.
Note: Treatment comparisons will be made after dropping the initial adjustment periods, as is common in market experiments.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Total emissions
Convergence speed of investments towards equilibrium levels.
Convergence speed of auction prices towards equilibrium levels.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This is a laboratory experiment, conducted in a standard university experimental economics lab, recruiting students broadly across the university. Subjects will be randomly assigned to treatments, and then randomly assigned to heterogenous firm types (8 firms per market) that remain constant throughout the experiment.
Permits are auctioned each period, and firms can reduce their abatement costs by purchasing more permits in the auction. They submit bid schedules, specifying different per-unit prices for various quantities of permits. Bid prices are constrained by the price controls implemented for each treatment. Firms can also incur a fixed (binary) investment cost to implement a new technology that lowers their entire abatement cost schedule. The permit price is determined with a uniform price (last accepted bid) rule. Subjects receive feedback at the end of every period, indicating their incurred abatement costs, permit expenditures, earnings, and permit price. They make investment decisions and auction bids across 20 periods of stationary repetition, following an initial practice period.
Experimental Design Details
Not available
Randomization Method
Subjects will be recruited by email using ORSEE. They choose between a list of available sessions, and the session is randomized to a treatment before it is initialized.
Randomization Unit
Individual
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
30 groups of 8 subjects
Sample size: planned number of observations
240 individuals
Sample size (or number of clusters) by treatment arms
48 individuals (6 groups) in each of the 5 treatments.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Main outcome is the investment frequency, which in equilibrium ranges from 0, 2, 4,6 and 8 firms across the 5 treatments. This implies an nominal equilibrium (predicted) effect size of 2. The standard deviation in average investment across 8-subject groups in an earlier related study by Cason, Stranlund and de Vries (2023, JAERE) ranged between 0.5 and 0.6 across treatments. We assume a standard deviation of 1.0 to be conservative, leading to a normalized effect size of 2.0 across pairwise adjacent treatments. These assumptions lead to a required sample size of 6 sessions per treatment for one-tailed tests that can detect treatment differences in the main investment outcome at 90 percent power with 0.05 significance, for both t-tests and nonparametric Wilcoxon-Mann-Whitney tests (G*Power 3.1.9.4). One-tailed tests are justified due to theoretically-based directional hypotheses of investment levels across treatments. Variance may be elevated due to the lack of price controls in the baseline treatment; additional groups may be scheduled for this treatment if preliminary data indicate variance that is greater than what is assumed here.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Purdue University Institutional Review Board
IRB Approval Date
2022-03-01
IRB Approval Number
1802020293
Analysis Plan