The Impact of Financial Support to SMEs to Improve Energy Efficiency: A Pan-European RCT

Last registered on January 10, 2025

Pre-Trial

Trial Information

General Information

Title
The Impact of Financial Support to SMEs to Improve Energy Efficiency: A Pan-European RCT
RCT ID
AEARCTR-0015124
Initial registration date
January 09, 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
January 10, 2025, 2:13 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

Affiliation
World Bank

Other Primary Investigator(s)

PI Affiliation
World Bank
PI Affiliation
MIT
PI Affiliation
World Bank
PI Affiliation
World Bank
PI Affiliation
IFC

Additional Trial Information

Status
On going
Start date
2024-07-21
End date
2026-12-31
Secondary IDs
DG GROW, EISMEA
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study investigates whether financial support can improve energy efficiency among small and medium enterprises (SMEs) across 32 eligible European countries. Using a Randomized Controlled Trial (RCT), we assess the impact of providing €10,000 grants to 900 SMEs to implement energy-saving solutions alongside sustainability advisory services offered by the Enterprise Europe Network (EEN). Both treated and comparison firms receive standard EEN advisory services, but only the treated firms gain access to financial support, supporting (co-)investments in energy-efficient technologies, consultancy, and training for their employees. The study is set to also examine heterogeneous effects by firm type and industry sector, exploring the mechanisms driving these outcomes. By addressing financial constraints, this research contributes to understanding pathways for Europe’s transition to a sustainable, low-carbon economy.
External Link(s)

Registration Citation

Citation
Avdeenko, Alexandra et al. 2025. "The Impact of Financial Support to SMEs to Improve Energy Efficiency: A Pan-European RCT." AEA RCT Registry. January 10. https://doi.org/10.1257/rct.15124-1.0
Sponsors & Partners

Sponsors

Partner

Experimental Details

Interventions

Intervention(s)
The EENergy project, funded by the EU’s Single Market Program (SMP), supports small and medium enterprises (SMEs) in improving energy efficiency and sustainability, contributing to the EU’s goal of achieving climate neutrality by 2050. Implemented by the Enterprise Europe Network (EEN), the program offers advisory services to 1,800 SMEs, with 900 of them receiving up to €10,000 in direct financial support. This funding is earmarked for investments in renewable technologies, energy efficiency consultancy, and employee training. Eligible SMEs must collaborate with EEN’s sustainability advisors to create tailored action plans targeting reduction in energy consumption.
Intervention Start Date
2024-07-21
Intervention End Date
2026-03-31

Primary Outcomes

Primary Outcomes (end points)
energy efficiency, energy resilience, business performance
Primary Outcomes (explanation)
Defined/ measured as:
A) Self-reported survey outcomes at the firm-level (over 2-3 follow-up waves):
1) Energy use as measured by electricity bills
2) Non-electricity energy use (e.g. fuel usage)
3) Investment in renewable energy (binary for any, expenditure, generation capacity, percent of energy mix that is renewable)
4) Energy intensity (expenditure per $ of output), energy expenditure
5i) Worker training for energy efficiency (EE) (binary for any, expenditure)
6) Consulting for EE (binary for any, expenditure)
7) Firm TFP, size, profits
8) Number of workers with EE as part of their job (binary for any, number of workers, salary of these workers)
9) Expect to invest in EE in next 6 months (and if yes, on which categories)

B) Energy bills (expenditure, intensity per $ of output/sales)
C) Business performance as per admin data such as Orbis (capital investment, 7 above)
D) Advisor survey for prices, effort (hours and quality of service provided) (using specification 3 above)
E) Energy audits

Secondary Outcomes

Secondary Outcomes (end points)
Further related business and advisor-level outcomes
Secondary Outcomes (explanation)
A) Self-reported survey outcomes at the firm-level:
1) Capacity utilization (percent), total capacity
2) Non-EE capital investment (any, expenditure) and rental (any, rental rates)
3) Total energy efficiency (EE) related capital investment (any, expenditure) and rental (any, rental rates)
4) Product mix and emissions intensity of product mix
B) Fuel bills (expenditure, intensity per $ of output/sales)

Experimental Design

Experimental Design
The design is a two-stage randomized trial, implemented over two calls (one between Feb-May 2024, one between Dec-Feb 2025). In the first stage, firms are assigned to treatment and control (max 900 T, 900 C). As a result of the first call for funding, 1,406 firms were randomized, with 707 firms allocated to the Cash Group (50.28%) and 699 firms to the Standard Group (49.72%). The second call randomization is planned for March 2025. Details on the second-stage randomization are available in the PAP.
Experimental Design Details
Not available
Randomization Method
Following the collection of application (baseline) data, a lottery was employed. Explored will be the comparison between SMEs that receive direct financial support of up to EUR 10k and those that “only” receive standard EEN advisory services. The randomization for the EENergy project was conducted after the eligibility checks were completed and ineligible firms were removed. The allocation of firms that applied for the first EENErgy call into the two groups — the Cash Group and the Standard Beneficiary Group — was performed on June 21st, 2024, using STATA (randtreat command).

Specifications:
1. Treatment vs control
2. Test for spillovers: compare control firms amongst high-intensity advisors with other control firms
3. Test for spillovers amongst treated firms: compare treated firms amongst high-intensity advisors with other treated firms
4. Advisor elasticity of effort, prices: regress prices, effort of advisors on high intensity dummy
Pool across calls with calls and strata dummies if specification comparable. PDS lasso to improve precision using baseline controls.
Correct for differential attrition using weighted regressions and check for differential attrition by baseline characteristics.

Randomization Unit
Main: Firm
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Randomization is at the firm level, but we considered 62 strata of EEN advisory service providers in call 1. Additionally, we plan to control for the fact randomization occurred after two separate calls.
Sample size: planned number of observations
1,800 SMEs
Sample size (or number of clusters) by treatment arms
Planned: 900 treatment vs. 900 control (in two calls).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Using the indicator "share of electricity produced with own renewables (e.g., solar panels)", a sample size of 1,800 firms (900 in each group), and assuming a significance level of 0.05, and a delta (expected change) of 10 percentage points between control and treatment groups (from about 21% to 31%), the resulting power for detecting an increase in the renewable indicator is 0.998.
IRB

Institutional Review Boards (IRBs)

IRB Name
HMLUS
IRB Approval Date
2024-04-24
IRB Approval Number
2507