• Energy Research
• 11. Sustainability close

Summary Identifying free-ridership is significant to several issues relevant to program evaluation, including the calculation of net program benefits and assessments of political acceptability. Despite the potential of free-ridership to seriously undermine the economic efficiency of a program intervention, for instance to foster energy efficiency, the issue remains largely absent from contemporary environmental and energy policy discussions in Europe. One reason for this neglect is the inherent difficulty of assessing which households would have undertaken the energy conservation activity even without the program. This paper proposes a procedure to calculate the free-rider share using revealed preference data on home renovations from Germany’s residential sector.We employ a discrete-choice model to analyze the effect of grants on renovation choices, the output from which is used to assess the extent of free-ridership under a subsidy program very akin to an implemented grants program in Germany. Our empirical results suggest only very moderate energy savings induced by the program, making free-riding a problem of outstanding importance.

In this study, discrete choice models that combine different behavioural rules are estimated to study the visitors’ preferences in relation to their travel mode choices to access a national park. Using a revealed preference survey conducted on visitors of Teide National Park (Tenerife, Spain), we present a hybrid model specification—with random parameters—in which we assume that some attributes are evaluated by the individuals under conventional random utility maximization (RUM) rules, whereas others are evaluated under random regret minimization (RRM) rules. We then compare the results obtained using exclusively a conventional RUM approach to those obtained using both RUM and RRM approaches, derive monetary valuations of the different components of travel time and calculate direct elasticity measures. Our results provide useful instruments to evaluate policies that promote the use of more sustainable modes of transport in natural sites. Such policies should be considered as priorities in many national parks, where negative transport externalities such as traffic congestion, pollution, noise and accidents are causing problems that jeopardize not only the sustainability of the sites, but also the quality of the visit.

We aim to investigate the impact of per capita income and high technology product export on CO2 emissions in G20 countries (except Russia) from 1992 to 2014 via panel data analysis. We applied random coefficient panel regression method to capture the heterogeneity of the G20 countries. The findings indicate that per capita income has stronger impact on CO2 emission level in developing countries whereas high technology export has stronger impact on CO2 emission level in developed countries. We interpret this result in the way that developing countries increase their CO2 emissions through increased income and consumption whereas developed countries do so via production and export.

Fossil fuel consumption has triggered worries about energy security and climate change; this has promoted hydrogen as a viable option to aid in decarbonizing global energy systems. Hydrogen could substitute for fossil fuels in the future due to the economic, political, and environmental concerns related to energy production using fossil fuels. However, currently, the majority of hydrogen is produced using fossil fuels, particularly natural gas, which is not a renewable source of energy. It is therefore crucial to increase the efforts to produce hydrogen from renewable sources, rather from the existing fossil-based approaches. Thus, this study investigates how renewable energy can accelerate the production of hydrogen fuel in the future under three hydrogen economy-related energy regimes, including nuclear restrictions, hydrogen, and city gas blending, and in the scenarios which consider the geographic distribution of carbon reduction targets. A random effects regression model has been utilized, employing panel data from a global energy system which optimizes for cost and carbon targets. The results of this study demonstrate that an increase in renewable energy sources has the potential to significantly accelerate the growth of future hydrogen production under all the considered policy regimes. The policy implications of this paper suggest that promoting renewable energy investments in line with a fairer allocation of carbon reduction efforts will help to ensure a future hydrogen economy which engenders a sustainable, low carbon society.

This paper assesses whether and to what extent Flemish households are willing to pay for electricity generated with renewable resources. Via a choice experiment, households were offered the choice between as set of green electricity contracts, characterised by the renewables share in electricity supplied to their dwelling, the renewable generation technology (wind, biomass, PV...) and its impact on the electricity bill. A main effects conditional logit model and a main effects random parameter logit model are estimated. The estimation results show that households prefer ‘green contacts’, but renewables technologies are valued differently. The estimates are then used to assess the marginal willingness to pay by Flemish households for each of the contract attributes. From a policy perspective, the results suggest that a not too small proportion of Flemish households would be willing to switch to another power supply contract if that can be done at limited cost. Moreover, the results suggest that it would not be a good idea to focus on the deployment of one or only a few technologies. Policies resulting in a diversified portfolio of technologies are positively valued by households and will obtain broader support.

University building energy consumption is an important proportion of the total energy consumption of society. In order to work out the problem of poor practicability of the existing benchmarking management method of campus building energy consumption, this study proposes an evaluation model of campus building energy consumption benchmarking management. By analyzing several types of feature data of buildings, this study uses random forest method to determine the building features that have outstanding contributions to building energy consumption intensity and building classification, and uses the K-means method to reclassify buildings based on the building features obtained after screening, to obtain a building category that is more in line with the actual use situation and to solve the problem that the existing building classification is not in line with the reality. Compared with the original classification method, the new classification method showed significant improvement in many indexes, among which DBI decreased by 60.8% and CH increased by 3.73 times. Finally, the quart lines of buildings in the category of new buildings are calculated to obtain the low energy consumption line, medium energy consumption line and high energy consumption line of buildings, so as to improve the accuracy and practicability of energy consumption line classification.

Understanding the risk factors of e-bike collisions can improve e-bike riders’ safety awareness and help traffic professionals to develop effective countermeasures. This study investigates risk factors that significantly contribute to the severity of e-bike collisions. Two months of e-bike collision data were collected in the city of Ningbo, China. A random parameters multinomial logit regression (RP-MNL) is proposed to account for the unobserved heterogeneity across observations. A fixed parameters multinomial logit regression (FP-MNL) is estimated and compared with the RP-MNL under the Bayesian framework. The full Bayesian approach based on Markov chain Monte Carlo simulation is employed to estimate the model parameters. Both parameter estimates and odds ratio (OR) are used to interpret the impact of risk factors on the severity of e-bike collisions. The model comparison results show that RP-MNL outperforms FP-MNL, indicating that accommodating the unobserved heterogeneity across observations could improve the model fit. The model estimation results show that age, gender, e-bike behavior, license plate, bicycle type, location, and speed limit are statistically significant and associated with the severity of e-bike collisions. Furthermore, four risk factors, i.e., gender, e-bike behavior, bicycle type, and speed limit, are found to have heterogeneous effects on severity of e-bike collisions, appearing in the form of random parameters in the statistical model.

To mitigate carbon emissions studies have incorporated trade and energy as determinants of emissions in the environmental Kuznets curve model. These studies mostly focused on the overall trade without regard to goods trade that is more polluting. To this end, this study used a panel of African countries and investigate the role of goods trade and energy in generating carbon emissions. We utilized random coefficients and the generalised method of moment. Our findings confirmed the existence of the environmental Kuznets curve hypothesis. Findings further indicate that trade increases emissions and there exists evidence of non-linear nexus between trade and emissions. The composition effect increases emissions but the effect is not robust to different estimates. Energy increases emissions, and the indirect effect of trade through energy revealed no evidence that trade has allowed Africa the use of an energy-efficient technique of production which reduces carbon emissions. Findings also confirmed the existence of income and factor abundance pollution haven hypothesis. Therefore, trade and energy should be considered in emissions mitigation policy.