1 Introduction

Energy has a key role in helping the economy, sustainability and the promotion of economic growth, meeting rising energy demand, and simultaneously considering the carbon emission mitigation goal, posing huge pressure on policymakers [23]. In this vein, a sustainability transition requires policymakers to collect adequate information about the environmental context and especially about the socio-economic system. Miller and Senadeera [24] argue householders can have an important role in this transition, for instance adopting energy efficiency and saving behaviours, or accepting low-carbon technologies. The complexity surrounding individual perception is not new to literature; in his work, Boudet [7] highlighted the elements that most influence this aspect, considering four categories: technology, people, place, and process. In the literature, it has been shown that public perception concerning energy issues affects energy future conditions [7] and public attitudes concerning energy sources impact energy consumption and policy choices, and this can influence decisions on energy consumption. Therefore, if consumers have a misperception about the energy mix, it is difficult to achieve the objective of a sustainable energy system. We can easily understand that asymmetric information may generate an “energy efficiency gap” when the optimal social level of energy investment is not achieved [3]. The debate is focused on what mix of energy sources would be adequate to build a sustainable energy system. The main difficulties concern the fact that it is not feasible to make rational choices among energy sources and that individual preferences are not homogeneously distributed because they change across different populations. Therefore, if consumers have a misperception about the energy mix, it is hard to achieve the objective of a sustainable energy system.

Conventionally, misperception captures the scope and magnitude of the distorted reality that individuals adopt and hold [14]. The concept of misperception has been applied in several contexts, such as environmental quality and climate change impact [29], technology [27], electric-drive vehicles [4], unemployment [9] and immigration [1], but the existent literature concerning energy misperception is limited. For instance, Lee et al. [22] find that “habits” are one of the main causes of inefficient energy consumption and that feedback may be a solution to reduce energy consumption and mitigate consumers’ misperception concerning their effectiveness level of energy use. Concerning energy efficiency, Allcott [2] demonstrates that misperception about gasoline prices and relative energy costs could increase or decrease energy efficiency. Public perception has been considered in different fields in the energy context, for instance in the public acceptance related to energy technologies, energy sources or energy policy. Indeed, according to the literature [11, 21, 33], public perception has an important role because a higher level of benefits perception increases public acceptance, and in addition, energy acceptance is strongly affected by individual perception; this affects public support for expanding sources. Moreover, Stoutenboroughetal et al. (2017) in their work found that people who think that the risks associated with nuclear waste storage are high have low support for nuclear energy. This issue has always been addressed by considering energy sources separately; for instance, Devine-Wright [12] investigated the public perception of wind energy, and other papers Ntanoset al. [26], Kim et al. [18] examined public perception of renewable resources only. Spence et al. [31] studied public opinion related to different energy sources asking how favourable or unfavourable their overall opinions or impressions of the several energy sources for producing electricity currently. The novelty of our work is that we analyse public perception towards different energy sources jointly, asking how much of the electricity used in their country they perceive is generated from different renewable and non-renewable energy sources. Further, another originality is that we take into account particular age groups, thus Generation Z and Millennials. Indeed, an important issue about the level of misperception in the energy market is its level in the “Next Generation”. The literature emphasizes the importance of studying Generation Z (GenZ) and Millennials as categories of interest because they are the “future consumers”, and their consumption behaviour can be linked with sustainable concerns and intertemporal consumer choices. Kymäläinen et al. [20] argue that, since future consumers are at the centre of social acceptance, we should investigate their attitudes from the business perspective to build sustainable choices. Moreover, their consumer behaviour and lifestyle choices are different from the previous generations. Nowadays, Generation Z has developed a great interest in sustainability. This change is generating an effect on general life aspects like changes in products and services, and investments made by institutions and industry.

This paper aims to investigate the presence of energy misperception, and specifically its level in the Next Generation groups (i.e., Generation Z and Millennials) to cover the gap in the economic literature. Moreover, this is the first work that analyses the energy misperception for specific categories, thus Generation Z and Millennials [10].

We expected the younger ones will overstate the share of energy sources that they think are used in the production of electricity in Italy, and overall, we found the younger have more misperception than less young. One of the possible explanations could be that younger individuals are more negatively affected by media and social media, for instance about oil spills and emissions, but also because GenZ is a generation for which ethics are very important. We have also seen that younger have more misperception of renewable energy sources. According to Mosdor’s survey, over 80% of Generation Z think that institutions should invest more in renewable energy resources; this aspect could affect individual perception.

This paper is structured as follows: Sect. 2 presents the method and data in which we describe the experimental design, Sect. 3 describes the empirical approach, Sect. 4 focuses on results, and finally, in Sect. 5, we explain the conclusion and policy implications.

2 Method and data

2.1 Method

We collected 288 complete observations through an online survey. The Google form was published on the Instagram page of “ale.conomista” which has more than 53,000 followers. The aim was to gather a sample composed of individuals from 18 years old to 24 years old, to capture the “Generation Z” perception, individuals from 25 years old to 40 to analyse the “Millennial” energy perception, and individuals belonging to Generation X category (from 41 years old).Footnote 1 Fox et al. [13] found that online platforms are comfortable for the younger populations, as they are familiar with an online social network, and we think this aspect may help us to receive reliable answers.

In the questionnaire, participants were asked to answer 30 questions to find evidence about individual attitudes, such as sensitivity to climate change, environmental issues, propensity for social issues and to help people. We have also five socio-demographic questions. Since we wanted to capture the energy misperception about the national electricity mix for different energy sources (e.g. Biomass, Coal, Hydroelectric power, Natural Gas, Nuclear power, Solar power, Wind power) we asked how much of the electricity used in their country they perceive is generated by each different energy sources; with respect the amount of each energy source that they perceive being produced, respondents are asked to provide their perceptions following a four option shown in the Table 1.Footnote 2 To reach the goal of our analysis, we need to compare the actual value with the perceived value. In addition, we selected several explanatory variables to try to capture possible determinants of energy misperception. We focused on a potential misperception about the national electricity mix, thus the division of electrical production among different energy sources. Therefore, we analysed how individual perceptions about the share of each energy source in the electricity mix deviate from the real values. To build a measure of misperception, we created a dummy variable for each energy source; specifically, we fixed 0 to state a “Well-perception” (when the answer is equal to the real value) and 1 to indicate “Misperception”. Table 2 shows the percentage of electricity production in Italy in 2020, according to “Our World in Data”. “Our World in Data” is a scientific publication site where it is possible to find data that shows how conditions in the world change and why this happens. In Table 3, it has shown the percentage of misperception for each energy source, taken from individual answers. From this table, we can argue that the level of energy misperception is significant, except for Nuclear power, probably because Italy does not have nuclear power plants and does not use these technologies.

Table 1 Questions about individual’s perception
Full size table
Table 2 Transformation of real value into quartile
Full size table
Table 3 Percentage of misperception for each energy sources
Full size table

In Table 4 we can see the observation number and percentage among different generation groups. Generation Z is not representative of the Country but it can show a preliminary picture of the different levels of misperception among them, taking into account the other two categories.

Table 4 Descriptive statistic for GenZ, Millennials and GenX
Full size table

To explain the possible determinants of different misperceptions among “Next generation” groups, we considered different variables; such variables are related to the individual status of the respondents, their views of energy policy, as well as their attitudes towards some everyday energy use and climate issues. Most of the variables are expressed in the Likert Scale (for instance, 1 = a very large amount, 2 = a large amount, 3 = a medium amount, 4 = a small amount and 5 = none at all).

3 Empirical approach

To reach our research goal, we proceed as follows: firstly, to reduce the dimensionality of possible explanatory variables we conducted a factor analysis for all variables, excluding socio-demographic variables, and to each factor, we gave an economic interpretation. Secondly, we analysed the correlation between misperceptions and factors previously determined in the factor analysis, among misperceptions and, finally, between Next Generation and misperceptions. Thirdly, for each energy source, we ran different Linear Probability Models (LPM); at the beginning, with only socio-demographic variables, and then with the factors found in the previous step and the socio-demographic variables jointly. In addition, we considered other LPMs taking into account the two age groups separately. The last two steps concern a Logit model to capture over-perception and the perception of Natural gas sources. Finally, we used an Ordered logit model to investigate the bias intensity for each energy source.

In the next subsections, we will explain the different analyses applied.

3.1 Factor analysis

We first provided a Factor analysis to reduce the dimensionality of our analysis. This technique allows us to show that there is a statistical and interpretative association among our covariates. Generally, the obtained factors are not correlated and they can explain almost the same information embodied in the original variable; for this reason, Factor Analysis can help us to avoid multicollinearity problems. After running a factor test, we verified the feasibility of factor analysis through the Kaiser–Meyer–Olkin test (KMO),Footnote 3 and we considered 12 factors,Footnote 4 each of them embodying variables that capture similar determinants of misperception. In the appendix, we have Tables 11 and 12 which explain our factors, and we can notice that our Factor analysis is efficient because we have coherence in the obtained factors.

3.2 Correlation

We first estimated a correlation between each misperception and the factors obtained in the previous step, to consider only significant factors in the analysis. Moreover, we checked the correlation between all misperceptions of different energy sources. Table 5 shows the correlation with factors and Table 6 the correlation among misperceptions.

Table 5 Correlation with factors
Full size table
Table 6 Correlation among mispreception
Full size table

The robustness of these correlations will be checked in the linear probability model. An interesting aspect could be the study of correlation among misperceptions of different energy sources (Table 6), to try to capture how individuals consider these sources similar to each other. We have a positive correlation between biomass and all energy sources, and this leads us to understand that individuals do not have a good perception of biomass. For coal and natural gas, we have a positive correlation with nuclear; we can notice the peculiar position of nuclear power as a source that, despite its zero CO2 emissions, it is perceived as similar to climate-altering sources individuals tend to misperceive the relative contribution of different energy sources to tackle greenhouse gas emission issue; probably, for this reason, they evaluate nuclear similar to natural gas in this context. This is in line with findings by Bickerstaff et al. [5] who studied the “nuclear resurgence” (people tend to consider it negatively due to the eventual consequences). Finally, we can conclude with the correlation between misperceptions and both Generation Z and Millennials (Table 7). Overall, for the Generation Z category, we have a positive correlation with all energy misperceptions, except for coal which is not statistically significant. For the other category, we have an overall negative correlation with energy misperception except for hydroelectric power and natural gas (they are not statistically significant). From this analysis, we may aspect that, overall “Generation Z” has more misperceptions than “Millennial”; we will test it with a linear probability model.

Table 7 Correlation with GenZ and Millennial
Full size table

3.3 Linear probability model

We estimated different Linear probability models (LPM) for each energy source to detect if the sample of interest, Generation Z and Millennials, has a misperception, and which group has more misperception than the other. In the Appendix, we display the complete Linear probability models for each energy source, with and without all factors considered. This technique models the response probability as a function linear in parameters, and we use that because our dependent variable (all energy misperception) is binary. One of the drawbacks of the Linear Probability model is the heteroskedasticity problem that we solved using robust standard errors.

We first run an LPM considering the results for our two dummy variables of interest, Generation Z and Millennial, and not considering the third Generation X in our sample. However, for clarity and transparency, we also show the results according to the logit model in the Appendix (Table 15). Table 8 illustrates the results for both Generation Z and Millennials, and we may observe that the younger (Generation Z) have more misperception for renewable resources than less younger, except for Hydroelectric power. Moreover, we find Millennials have more misperception of Coal than Generation Z; for Natural Gas, the less young have less misperception than younger; in the latter case, the coefficients for GenZ and Millennials are not statistically significant.

Table 8 LPM
Full size table

We then run a second LPM considering separately the two age groups to identify what are the determinants of the misperception for both Generation Z and Millennials and to avoid a problem of heterogeneity between the two groups. In the Appendix, we show all regression tables.Footnote 5 The main results are that for Generation Z the determinants of misperception for non-renewable sources (Coal and Natural gas) are Factor 2 (Care about the environment and Climate Change, with a negative sign), Factor 3 (Trust and satisfaction, positive sign), Factor 7 (Opinion on climate change, negative sign) and Factor 9 (Individual feelings, positive sign), while for Millennial the main determinants are Factor 1 (Worried about energy issues, positive sign), Factor 2 (Care about the environment and Climate Change, negative sign), Factor 4 (Sensitive for social issues, negative sign), Factor 5 (Discrimination, negative sign), Factor 6 (Worried about being dependent on others and fossil fuel, positive sign), Factor 10 (Patriotism, positive sign) and Factor 12 (Politic/social well-being, negative sign).

Otherwise, the misperception of renewable sources for Generation Z depends on several factors, such as Factor 1 (worried about energy issues, positive sign), Factor 5 (Discrimination, negative sign), Factor 6 (Worried about being dependent on others and fossil fuel, negative sign), Factor 7 (Opinion on climate change, positive sign), Factor 9 (Individual feelings, positive sign), Factor 10 (Patriotism, positive sign) and Factor 12 (Politic/social well-being, negative sign). As concerned Millennials, the main determinants are Factor 1 (worried about energy issues, positive sign), Factor 4 (Sensitive about social issues, positive sign) and Factor 11 (Worried about Climate Change (ways for reducing climate change), positive sign).

3.3.1 Change in the predicted probabilities

We estimated the change in the predicted probabilities for Generation Z and Millennials (Table 9), holding the other variables at their mean. We do see that the changes in the predicted probabilities are quite consistent. We also found that there is coherence in terms of significativity, according to the coefficients estimated in the LPM.

Table 9 Change in PP
Full size table

3.4 Logit model

As we have seen in the previous Table 1, the Natural Gas source is the only one that is in the second quartile (25–50%) and for this reason, we can investigate if Generation Z and Millennials have an underperception or an over-perception of this energy sources. For this purpose, we used a Logit model regression to capture the direction of bias for both groups; the main results are shown in Table 10 below.Footnote 6 In this particular case, we decided to use a Logit because it is a more accurate model when using a binary dependent variable. Previously, we used an LMP because we were more interested in the interpretation of the coefficient, and using LPM helps us in this purpose. In this case, we are interested not in the magnitude of the coefficient but only in its direction.

Table 10 Logit model to capture bias direction for natural gas
Full size table

According to the table, we can notice that the youngest people (GenZ) tend to overestimate the production level from Natural Gas sources. We may conclude that not only GenerationZ show a higher percentage of energy misperception concerning the older, but younger individuals over-estimate the electricity production level, and not under-estimate it.

3.5 Ordered logit

Finally, since our dependent variable is defined as a categorical ordinal variable, for our last analysis we considered an Ordered logit regression, to identify the bias intensity for each different energy source, using the same previous covariates. In this case, our dependent variables are structured considering the distance from the real value (Well-perception) of each energy source and the different levels of misperceptions. We tested the parallel assumption with the Brant test and overall, we accept the null hypothesis with a p-value greater than 0.05; therefore, we can use the Ordered logit model. The main results are displayed in Table 23 in the Appendix.Footnote 7 Overall, we found that the level of bias can be identified in the range between 25 and 50% for both Generation Z and Millennials, except for the Coal source concerning Millennials; in this case, the intensity of bias is a little higher. An interesting result regards Nuclear power. We can notice that very few individuals have a strong misperception of Nuclear, specifically the older ones. Moreover, the intensity of bias for each level is not very consistent; this finding could be linked with the current situation in Italy. Italy closed its nuclear power plants in 1990, after the Chornobyl accident. The reluctance towards nuclear energy in Italy was confirmed when in 2008 Italian government tried to propose a new nuclear program in the Country, but this proposal, which it was established arrangements to generate 25% of the country’s electricity from nuclear power by 2030, was rejected in a referendum in June 2011; therefore, we could aspect these results.

We can conclude that overall Generation Z shows a higher probability of over-perceiving energy sources in the electricity energy mix in Italy. Otherwise, we can notice that the intensity of this bias for Generation Z is similar Millennials.

4 Results

In this paper, the main goal is to investigate the energy misperception of the energy electricity mix in Italy, among “Next Generation”. Specifically, we focused on two different groups: the younger ones Generation Z, and Millennials. We considered questions from the European Social Survey, a multi-country survey administered annually in 30 different European countries, between 2002 and 2018, and we conducted an online experiment through the Instagram platform. In the questionnaire administered, the subjects were asked to indicate their perception about how they perceive energy share among four different options: (1) 0–25%, (2) 25–50%, (3) 50–75%, (4) 75–100%, and in addition, answered other several questions, to capture individual characteristics and attitudes, such as the propensity for environmental problem, attitude for helping people, their trust in general, trust in politics and people, and socio-demographic questions. The actual electricity mix is measured through “Our World in Data”, and we considered the 2020 production in Italy.

To provide an answer to our research question, first, we ran a factor analysis to reduce the dimensionality of the analysis, and we estimated the correlation between misperception for each energy source, misperception and the obtained factors, and finally the correlation between each energy sources misperception and both groups Generation Z and Millennial. After these steps, we implemented a linear probability model to compare both groups Generation Z and Millennials and to determine which group has more misperception. Then, we considered another linear probability analysis to try to capture the possible determinants of misperception. For the natural gas source, because it is the only one that has a real value greater than 0–25%, we were able to implement a logit model to look into which group has more under-perception and over-perception; finally, we ran an Ordered logit to identify the measure of bias for both Generation Z and Millennial.

From the correlation analysis, we have argued that overall, people do not have a good perception of different energy sources; in fact, we can easily observe that individuals believe that these energy sources are similar to each other, and particularly that Generation Z has more misperception than Millennials. This is in line with the results by Bollani et al. [6] who find Millennial students are sensitive to sustainability concerns (e.g., they believe in labelling and certification systems as communication tools to reduce environmental impact). In light of this, our estimate results lend support to our hypothesis that the younger have more misperception than the older, specifically in the Linear Probability Model we found that not only do both categories have energy misperception, but the Generation Z group has more misperception for renewable resources than non-renewable, except for Hydroelectric power.

As concerns the possible determinants of misperception we found that regarding non-renewable resources (e.g., Coal and Natural Gas), people who are concerned about protecting the environment and who feel responsible for environmental conditions, therefore those who have a greater propensity against climate change, have less misperception on non-renewable resources. Pidgeon et al. [28] in their analysis found that the perception of energy sources is related to climate policy goals. In their survey, respondents chose the use of renewable technologies as the best solution to climate change; therefore, people who have fewer attitudes and concerns on climate and environmental issues should have more misperception of non-renewable resources. Regarding factor 3 (trust and satisfaction), it seems that individual who trusts politicians and policy, in general, shows a misperception of non-renewable resources. This could mean that the “Next generation” groups think that the policymakers do not follow pro-environmental practices. This aspect is relevant; indeed, according to Kulin et al. (2020) trust strongly affects climate policy preferences. Further, as suggested by Harring [16], people perceive environmental policy instruments as less effective due to the high level of corruption in the public sector, and this suggests that well-functioning institutions are crucial for environmental policy support.

Moreover, individuals who are more sensitive to equity issues, are more inclined to be fair and prudent about energy transition, and therefore people may over-perceive non-renewable resources. This is in line with the findings of Islar et al. [17] and the concept of Energy Justice (Sovacool et al., 2015) that argue decisions on energy processions may have social implications (for instance, some consequences could affect workers’ employed status).

Concerning renewable resources, our findings show that individuals who are worried about energy issues, such as “security supplies”, tend to misperceive some energy sources, like nuclear power. Pidgeon et al. [28] affirmed that traditional sources are perceived as more suitable to guarantee the security of supply, and this can be interpreted as more inclined attitudes of individuals to prefer local sources, or to consider and support banned sources (e.g., nuclear in Italy). In addition, we found that individuals who are more sensitive to equity issues are more inclined to be fair, and that have more propensity to the foreigner acceptance, tend to have a misperception of renewable resources.

Another determinant of misperception may be political orientation, which in our case is captured by attachment to country and EU (factor 10), which can affect the perception and judgment of different energy sources; specifically, in our study, we argued that people that are attached to country and EU have more misperception, both for renewable and non-renewable resources. Finally, we should highlight how Nuclear energy source is not perceived as renewable sources; indeed, in our results, we have seen that people who feel responsible for reducing climate change, have a misperception of nuclear energy. Therefore, individuals can misperceive the relative contribution of energy sources to greenhouse gas emissions, indeed nuclear tends to be considered, for instance, similar to natural gas, because people perceive negative environmental consequences of nuclear power (e.g. explosions of nuclear power plants, radioactive waste.)

Another important step is represented by the identification of over-perception and under-perception of natural gas energy source, for both Generation Z and Millennials. We concluded that Generation Z tends to have more over-perception of natural gas and, on the other hand, Millennials have more under-perception of natural gas than Generation Z.

Finally, with the Ordered logit model, we have seen that, although the response percentage is different for each percentage range, the bias intensity is quite similar for both groups analysed.

5 Conclusion and policy implications

Based on empirical findings, our work points out that the Next generation in Italy does not have a real perception of the actual electricity energy mix, and this could be a hurdle which might not allow the necessary energy and sustainable transition. Young people between 17 and 25 years old usually move away from their households, and this inevitably increases their responsibility, developing opportunities for sustainable consumption habits [20], and therefore investigating their energy consumption habits and possible obstacles to sustainable transition is crucial. In light of this, to increase awareness in the Generation Z category, it may be useful to promote awareness-raising campaigns that focus on the possibility of having good job opportunities in the energy sector. This can be a good strategy because this generation has experienced an economic crisis, unlike the Millennials, and they strongly aspire to future stability. In addition, also information campaigns could be a helpful tool to increase individual awareness for those who have little knowledge, or for those who do not have any kind of knowledge about energy issues. in the literature, this is consistent with several studies about consumers’ perceptions that highlight the presence of low levels of knowledge and awareness; this is crucial because increasing awareness could lead the individual to solve environmental and energy-consumption problems.

In the proposed online experiment, the considered sample is not heterogeneous because we did not want to consider different population groups. For this reason, it might be notable to detect how different skilled individuals could respond to our investigation. An interesting extension could be comparing the perception of our sample with the “Experts” category, such as academic professors, researchers, assistants, associate lecturers, and so on. Therefore, the first step could be to expand our current sample on “Next Generation”, and then collect a new sample for the “Experts” category; in this way, not only we will be able to compare”Next Generation” with “Experts”, but we may consider other “age-category” such as “Generation X”, thus people between 41 and 56 years old, and “Boomers” those between 57 and 66 years old. Finally, it might be interesting to extend our research to other EU countries, to improve our findings and investigate how energy perception among different European countries changes.

In addition, this work analyses the act of individual attitudes but our results are limited, due to our main purpose; however, it is crucial to emphasize the importance of applying an approach that considers human and behavioural dimensions of the individual, to better understand the psychological factors that most influence energy behaviour and, consequently, a sustainable energy transition.