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Use Case 5: Investigate and exploit energy demand curves.

Discrete choice analysis snapshot
The flow of Use Case 5

Rationale: In the energy efficiency space, many utilities provide rebates to their customers that purchase energy efficient appliances. An important finding is that individuals use different discount rates for different types of goods in different contexts. In the case of domestic energy technologies, revealed discount rates were found to be clustered in the 5% to 40% range, but higher rates were applied to refrigerators and water heaters than to heating equipment and weatherization measures. Other studies have found short-term discount rates as high as 300% for air-conditioning technologies. This marked variability suggests that discount rates are influenced by many elements of the decision context, including perceived risk, framing, and social arrangements. Again, financial literacy can play here an important role in this case study in understanding the determinants of energy demand curves.

Research questions:  The present analysis seeks to examine the impact of energy related financial literacy, demographic factors, environmental literacy and behavioural intention/attitude on discount rate and willingness to pay for more efficient household appliances.
1. What impact do financial literacy, energy literacy, environmental concern have on implicit discount rates?
2. What impact do factors such as financial information (purchase price, operating cost), risk reduction, energy discounts and loans have on implicit discount rates for home appliances?
3. Does providing more salient financial information impact implicit discount rates and willingness to purchase more efficient home appliances?
4. For consumers who choose to purchase more efficient

Design and impact: Using experimental approaches, we can allow for different choices to be varied across consumers, by mapping the demand curves for energy efficient devices. Designing relative surveys, we will try to estimate possible nonlinearities of the demand curve. In these experiments, each surveyed customer will be presented with several hypothetical offers by energy suppliers and will be asked to identify the offer that he/she would choose and other attributes that consumers face in real life scenarios. These experiments have the advantage of being able to include service attributes that have not been offered in real world markets or have not varied sufficiently in markets to allow estimation. Surveys are expected to be conducted in several countries using a large number of participants for maximizing estimation of consumers’ heterogeneity. Discrete choice models would be used to assess the effectiveness of different types of attributes in influencing consumers’ preferences.

Key Performance Indicators (KPIs):   

1. Socio-demographic information.

2. Residence characteristics.

3. House appliance Factors.

4. Energy related financial literacy

5. Behavioural intention and attitude.

6. Environmental literacy.

7. Discount rate.

8. Rebound effect.


Involved Energy Actors:   PPC and CWATT

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 957117. The information contained in this website reflects only the authors’ view. EC is not responsible for any use that may be made of this information.