Productivity co-benefits of Energy Saving Technology

Last registered on December 07, 2023


Trial Information

General Information

Productivity co-benefits of Energy Saving Technology
Initial registration date
September 18, 2023

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
September 20, 2023, 11:20 AM EDT

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

Last updated
December 07, 2023, 11:05 PM EST

Last updated is the most recent time when changes to the trial's registration were published.


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Primary Investigator

Good Business Lab, Madras School of Economics

Other Primary Investigator(s)

PI Affiliation
Good Business Lab
PI Affiliation
Good Business Lab
PI Affiliation
University of California San Diego
PI Affiliation
University of Michigan

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
India is host to 63 million Micro, Small and Medium scale Enterprises (MSMEs), contributing to a large share of employment, industrial output as well as high volume of emissions per unit of output. Therefore, adoption of energy efficient (EE) technologies by MSMEs is crucial in improving not only their competitiveness through cost reduction but also worker wellbeing and productivity through improvements in the work environment. Enterprise owners most often do not internalize the benefits of the latter; like productivity gains due to reduction in exposure of workers to heat, pollution etc. Lack of information, financing options, skilled technical personnel, poor management practices etc. are some of the important barriers faced by MSMEs. Our proposed work seeks to address information gaps in the adoption of EE technologies which are cost-effective as well as have productivity co-benefits through impacts on the workforce. This is vital for reducing fossil fuels energy consumption, greenhouse gas emission and co-pollutants in air and their harmful effects on the workforce.

External Link(s)

Registration Citation

Adhvaryu, Achyuta et al. 2023. "Productivity co-benefits of Energy Saving Technology." AEA RCT Registry. December 07.
Sponsors & Partners

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Experimental Details


Low adoption of energy-efficient (EE) technologies, particularly among small and medium-sized enterprises (MSMEs), is attributed to information gaps and market failures. Studies show that providing information on economic benefits and payback periods significantly influences EE technology adoption, while factors like actual energy use and environmental benefits have less impact. To address this, we plan to develop a Return on Investment (ROI) calculator that estimates total efficiency gains for firms, including direct benefits like fuel savings and indirect benefits like improved worker health. We aim to test the impact of this calculator on the adoption of EE technologies compared to traditional peer-based information in a pilot randomized controlled trial (RCT).
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
1.Whether firms have relevant information EE technologies, their costs, benefits, network of local service providers, technical know-how and their impact on workforce well-being
2.Attitudes towards energy management and energy-efficient technologies among firms
3.Whether there was actual take-up of EE technologies among the firm
4.Monthly consumption of solid fuels like wood, coal etc. and monthly consumption of electricity
5.Perceived improvements in work environment like temperature reduction
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The RCT aims to help textile MSMEs adopt energy-efficient technologies by randomly assigning firms to three groups:
T1: Firms receive information about EE technology benefits from peers.
T2: Firms receive the same peer information as T1, along with an ROI Calculator and brief training.
C: Control group receives no information or ROI calculator
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Firm level randomisation
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
100 textile firms
Sample size (or number of clusters) by treatment arms
33 firms control, 33 firms receive information on the benefits of EE technologies and 33 firms firms receive the peer information along with an ROI Calculator and brief training.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Good Business Lab Foundation Institutional Review Board
IRB Approval Date
IRB Approval Number
GBL 31072023
Analysis Plan

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