• Energy Research

The paper has two main goals: to draw a summary picture of the progress made towards transport decarbonisation in Europe, and to identify future developments concerning the 2020–2030 decade. The analysis is based on the 4th and 5th reports prepared by the Member States under the obligation Renewable Energy Directive (2009/28/EC) and on the National Energy and Climate Plans (NECPs) for the 2020–2030 decade, paying specific attention to the use of renewables in the transport sector. We find that the Member States rely on two strategies: increasing the production and use of biofuels, especially those produced by advanced materials, and supporting the diffusion of electric vehicles. Performing a scenario analysis capturing the planned policies and goals indicated in the NECPs, we estimate that the biofuel strategy can deliver a GHG reduction of up to 19 MtCO2eq (−3.6%), while the electrification strategy can deliver a GHG reduction up to 45 MtCO2eq (−8.3%). Jointly used, the GHG reduction could reach up to 64 MtCO2eq (−11.9%).

The paper has two main goals: to draw a summary picture of the progress made towards transport decarbonisation in Europe, and to identify future developments concerning the 2020–2030 decade. The analysis is based on the 4th and 5th reports prepared by the Member States under the obligation Renewable Energy Directive (2009/28/EC) and on the National Energy and Climate Plans (NECPs) for the 2020–2030 decade, paying specific attention to the use of renewables in the transport sector. We find that the Member States rely on two strategies: increasing the production and use of biofuels, especially those produced by advanced materials, and supporting the diffusion of electric vehicles. Performing a scenario analysis capturing the planned policies and goals indicated in the NECPs, we estimate that the biofuel strategy can deliver a GHG reduction of up to 19 MtCO2eq (−3.6%), while the electrification strategy can deliver a GHG reduction up to 45 MtCO2eq (−8.3%). Jointly used, the GHG reduction could reach up to 64 MtCO2eq (−11.9%).

We present the results of a stated preference study undertaken in Italy in 2017 on individuals’ preferences between an electric car (EC) and a petrol car, with the purpose of assessing the impact of the latent variable EC knowledge on purchasing decisions. We estimate a multinomial, a mixed and two hybrid mixed logit models, with the interaction between EC knowledge, car attributes and additional exogenous covariates. We use three measurement equations to estimate the self-assessed car knowledge, assessed EC knowledge and EC driving experience. We report three main findings. First, the inclusion of EC knowledge improves our capability to explain car choice. Second, the degree of EC knowledge does not change the negative perception respondents have, ceteris paribus, on ECs. Third, the level of EC knowledge influences the importance placed on the attributes of the choice model. Specifically, a higher level of EC knowledge is associated with a lower concern with fast charging station density. Our results are useful for car manufacturers who wish to improve their marketing strategies through tailored advertising efforts, and for policy makers who wish to implement educational campaigns as a means to foster EC uptake.

We present the results of a stated preference study undertaken in Italy in 2017 on individuals’ preferences between an electric car (EC) and a petrol car, with the purpose of assessing the impact of the latent variable EC knowledge on purchasing decisions. We estimate a multinomial, a mixed and two hybrid mixed logit models, with the interaction between EC knowledge, car attributes and additional exogenous covariates. We use three measurement equations to estimate the self-assessed car knowledge, assessed EC knowledge and EC driving experience. We report three main findings. First, the inclusion of EC knowledge improves our capability to explain car choice. Second, the degree of EC knowledge does not change the negative perception respondents have, ceteris paribus, on ECs. Third, the level of EC knowledge influences the importance placed on the attributes of the choice model. Specifically, a higher level of EC knowledge is associated with a lower concern with fast charging station density. Our results are useful for car manufacturers who wish to improve their marketing strategies through tailored advertising efforts, and for policy makers who wish to implement educational campaigns as a means to foster EC uptake.

The paper reports the results of a stated preference study, carried out in Italy in 2017, on consumers' preferences between an electric car (EC) and a petrol car. The focus is on the role of driving range. We find that the linear specification leads to lower willingness to pay (WTP) estimate for the driving range than the logarithmic, quadratic and EC-specific ones. The estimation of a mixed logit model leads to a coefficient of the EC-specific range attribute six times larger than the coefficient of the non-EC one. The jointly statistically significant covariates explaining the heterogeneity of the coefficient of the EC-specific driving range attribute are gender, number of cars owned by the family, and knowledge of cars. The implied WTP varies from 37 to 106 V/km, depending on the socio-economic characteristics of the respondent. Simulative analysis shows that very relevant increases in the probability of buying an electric car (ranging from 28% to 68%) over a petrol one require jointly improvements in the fast charging network, driving range and financial incentives.

The paper reports the results of a stated preference study, carried out in Italy in 2017, on consumers' preferences between an electric car (EC) and a petrol car. The focus is on the role of driving range. We find that the linear specification leads to lower willingness to pay (WTP) estimate for the driving range than the logarithmic, quadratic and EC-specific ones. The estimation of a mixed logit model leads to a coefficient of the EC-specific range attribute six times larger than the coefficient of the non-EC one. The jointly statistically significant covariates explaining the heterogeneity of the coefficient of the EC-specific driving range attribute are gender, number of cars owned by the family, and knowledge of cars. The implied WTP varies from 37 to 106 V/km, depending on the socio-economic characteristics of the respondent. Simulative analysis shows that very relevant increases in the probability of buying an electric car (ranging from 28% to 68%) over a petrol one require jointly improvements in the fast charging network, driving range and financial incentives.

Italy has a very low level of uptake of electric cars (ECs), equal to 0.6% for the year 2019, despite significant efforts put in place by policy makers to stimulate their use. This paper investigates the barriers to wider EC diffusion via a survey administered in 2019 to a representative sample (N = 870) of the Italian population. We discuss and rank the barriers, aggregate them via principal component analysis (PCA) on the basis of the polychoric correlation matrix, perform a cluster analysis and analyse the socio-economic determinants of the respondents. The findings of this paper suggest a series of improvements that could be made by various actors. To overcome the financial barriers to EC uptake, Italian policy makers should reinforce incentives at the national and local levels and car manufacturers should bring to the market cheaper ECs belonging to the A and B segments. The charging-related barriers require investments on fast charging stations along the main toll highways and a new regulation regarding multi-house dwellings. Finally, reliable and complete information is needed to enhance knowledge about the technological and environmental pros and cons of ECs.

Italy has a very low level of uptake of electric cars (ECs), equal to 0.6% for the year 2019, despite significant efforts put in place by policy makers to stimulate their use. This paper investigates the barriers to wider EC diffusion via a survey administered in 2019 to a representative sample (N = 870) of the Italian population. We discuss and rank the barriers, aggregate them via principal component analysis (PCA) on the basis of the polychoric correlation matrix, perform a cluster analysis and analyse the socio-economic determinants of the respondents. The findings of this paper suggest a series of improvements that could be made by various actors. To overcome the financial barriers to EC uptake, Italian policy makers should reinforce incentives at the national and local levels and car manufacturers should bring to the market cheaper ECs belonging to the A and B segments. The charging-related barriers require investments on fast charging stations along the main toll highways and a new regulation regarding multi-house dwellings. Finally, reliable and complete information is needed to enhance knowledge about the technological and environmental pros and cons of ECs.