tariff design; policy conclusions; techno-economics; progressive electricity tariffs; behavioural science; client acceptance; energy efficiency feed-in tariffs
Mahmoodi Jasmin, Patel Martin K., Brosch Tobias (2021), Pay now, save later: Using insights from behavioural economics to commit consumers to environmental sustainability, in Journal of Environmental Psychology
, 76, 101625-101625.
Mahmoodi Jasmin, Hille Stefanie, Patel Martin K., Brosch Tobias (2021), Using rewards and penalties to promote sustainability: Who chooses incentive‐based electricity products and why?, in Journal of Consumer Behaviour
, 20(2), 381-398.
Yilmaz S., Majcen D., Heidari M., Mahmoodi J., Brosch T., Patel M.K. (2019), Analysis of the impact of energy efficiency labelling and potential changes on electricity demand reduction of white goods using a stock model: The case of Switzerland, in Applied Energy
, 239, 117-132.
Mahmoodi Jasmin, Prasanna Ashreeta, Hille Stefanie, Patel Martin K., Brosch Tobias (2018), Combining “carrot and stick” to incentivize sustainability in households, in Energy Policy
, 123, 31-40.
Prasanna A., MahmoodiJ., BroschT., PatelM. (2018), Recent experiences with tariffs for saving electricity in households, in Energy Policy
Two (sub-)datasets:1.) Households - LightingHeidari, Mahbod; Yilmaz, Selin; Patel, Martin K.;LIGHTING * When using the data please refer to: Heidari, M.; van der Lans, N.; Floret, I.; Patel, M.K.: Analysis of the Energy Efficiency Potential of Household Lighting in Switzerland Using a Stock Model. Energy and Buildings158 (2018), pp. 536-548, Special Issue "Energy Efficient LightUploaded on October 24, 2018https://zenodo.org/record/1470288#.YTWX9VUzYV0 2.) Households - White goodsYilmaz; Patel;An example implementation of the model, in the form of a Matlab code, has been made available for free download. It contains algorithms of the life-span distribution for appliances, forecasting of the stock and the corresponding electricity demand. The source code is open and may be readily adaptedUploaded on July 17, 2018https://zenodo.org/record/1313848#.YTWYHlUzYV0
The objective of this research project is to investigate whether two hitherto hardly studied electricity tariff structures - Feed-in tariffs (FIT) and Progressive tariffs (PT) - can mobilize substantial electricity savings (with a focus on households) and if so, how this is best achieved. This overall research question is answered by means of four interrelated tasks, which are primarily rooted in the disciplines of engineering (energy technologies), behavioral sciences (psychology) and economics.In the first task we conduct a literature review and interviews in order to obtain an understanding of the experience made with these novel types of tariffs in Switzerland and abroad (Subtask 1.1). In parallel, we will conduct an initial study to empirically assess and quantify the impact of different economic and psychological aspects of tariff design on tariff acceptance (Subtask 1.1); the effect of each of the six pre-selected factors listed on tariff acceptance will be quantified by means of an online survey with 200 participants. In the second task we first identify individual differences in cognitive-affective factors that may influence energy-relevant decisions and tariff acceptance (Subtask 2.1); in addition to computerized measures of important factors, this subtask includes the study of the behaviour of 100 participants in our Virtual Reality facilities. Based on the identified cognitive-affective factors we will differentiate the total population of private clients (households) into different clusters/subgroups of customers that prefer different tariff structures (Subtask 2.2). In the third task we first develop a database on current electricity use in households (type and quantity of energy services) and on bottom-up measures for saving electricity in households (Subtask 3.1); we combine this information in a bottom-up simulation model allowing to determine the technical (maximum theoretical) energy saving potential and the economic energy saving potential as a function of the tariff structure. We further refine the analysis by determining the so-called market potential of energy efficiency improvement and, as complementary approach, we estimate energy use based on elasticities (Subtask 3.2). Finally, in Task 4, we combine the key findings of Task 2 on the acceptance profiles by customer cluster with bottom-up modelling according to Task 3 in order to arrive at a consolidated estimate of the energy saving potential that can be mobilized by making use of cognitive-affective factors (“Nudging potential of energy efficiency improvement”). Based on the findings about the potential of energy savings, emission reduction and the attendant costs, concrete strategies and recommendations will be derived for energy suppliers and government bodies.