“The impact of energy efficiency and decarbonisation policies on the European road transport sector”

Tzeiranaki, Sofia Tsemekidi; Marina, Economidou; Paolo, Bertoldi; Thiel, Christian; Fontaras, Georgios; Clementi, Enrico Luca; Franco, Camilo A.

doi:10.1016/j.tra.2023.103623

Cited by 17 publications

(21 citation statements)

References 46 publications

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“…The quantitative variable fuel consumption (L/100 km) was identified as the dependent variable. Five continuous quantitative variables were selected as independent variables: slope ( • ), engine load (%), engine speed (rpm), speed (km/h), and acceleration (m/s 2 ), and one incremental variable: gear (1)(2)(3)(4)(5)(6). Then, the basic descriptive statistics of the selected variables were calculated and the significance of their impact on the modeled dependent variable was assessed (Tables 3 and 4).…”

Section: Multiple Regression Modelmentioning

confidence: 99%

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Modeling Vehicle Fuel Consumption Using a Low-Cost OBD-II Interface

Rykała,

Grzelak,

Rykała

et al. 2023

Energies

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As a result of ever-growing energy demands, motor vehicles are among the largest contributors to overall energy consumption. This has led researchers to focus on fuel consumption, which has important implications for the environment, the economy, and geopolitical stability. This article presents a comprehensive analysis of various fuel consumption modeling methods, with the aim of identifying parameters that significantly influence fuel consumption. The scientific novelty of this article lies in its use of low-cost technology, i.e., an OBD-II interface paired with a mobile phone, combined with modern mathematical modeling methods to create an accurate model of the fuel consumption of a vehicle. A vehicle test drive was performed, during which variations in selected parameters were recorded. Based on the obtained data, a model of the vehicle’s fuel consumption was built using three forecasting methods: a multivariate regression model, decision trees, and neural networks. The results show that the multivariate regression model obtained the lowest MSE, MAR, and MRSE coefficients, indicating that this was the best forecasting method among those tested. Sufficient forecast error results were obtained using neural networks, with increases of approximately 73%, 10%, and 131% in MSE, MAE, and MRAE, respectively, compared to regression results. The worst results were obtained with the decision tree model, with increases of approximately 163%, 21%, and 92% in MSE, MAE, and MRAE compared to the regression results.

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Section: Multiple Regression Modelmentioning

confidence: 99%

“…Among the largest contributors to overall energy consumption is motor vehicles. Increased energy consumption is stimulating interest in the field of fuel consumption due to the huge impact of this factor on the economy, environment, geopolitical stability, and national security of individual countries [1].…”

Section: Introductionmentioning

confidence: 99%

Modeling Vehicle Fuel Consumption Using a Low-Cost OBD-II Interface

Rykała,

Grzelak,

Rykała

et al. 2023

Energies

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“…The transport sector in Europe is therefore also highly vulnerable to carbon price changes. Approximately 40% of energy consumption occurs in the transportation sector of the region, and 25.4% of total carbon emissions also occur in this sector (Tzeiranaki et al 2023 ). With this degree of dependence on fossil fuels, it is to be expected that carbon price movements would be reflected in transportation costs faced by the public.…”

Section: Introductionmentioning

confidence: 99%

Carbon pricing and aggregate macroeconomic performance in the Eurozone: a contribution to the climate policy debate using the EU ETS and Macroeconomic Performance Index

Olasehinde-Williams

2024

Environ Sci Pollut Res

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This article contributes to the carbon pricing debate by providing new evidence on the aggregate macroeconomic effect of the European Union Emissions Trading Scheme (EU ETS) in the Eurozone. To this end, a novel macroeconomic performance index is created to capture the overall economic performance of the Eurozone countries. The index is a weighted aggregation of key macroeconomic variables—GDP growth rate, inflation rate, employment rate, exchange rate, and long-term inflation rate—for the 19 member countries of the group. The effect of the EU ETS on this macroeconomic performance index is then empirically examined while controlling for the effects of physical capital accumulation, human capital accumulation and regulatory quality. A panel framework covering the period 2005–2022 is set up to achieve this objective and the relationship is examined using panel method of moments quantile regression with fixed effects, as well as fixed and random effects regressions of Driscoll and Kraay. A number of important revelations are made. Firstly, the behavior of the macroeconomic performance index constructed clearly reflects the economic reality of the Eurozone, with downward spikes visible in periods corresponding with the economic crisis of 2007–2009, the Eurozone debt crisis of 2010/2011, the aftermath of the financial crisis of 2016, and periods around the outbreak of the Coronavirus (COVID-19) pandemic. Hence, a case is made for the use of the macroeconomic performance index as a superior aggregate measure of the overall economic performance in empirical research. Secondly, a statistically significant negative effect of the EU ETS on aggregate macroeconomic performance in the Eurozone is confirmed. This shows that there are significant economic costs associated with the use of carbon pricing as a means of lowering pollution. Thirdly, the findings further show that the negative impact gradually decreases (in absolute values) from lower to higher quantiles. Overall, higher carbon prices cause greater economic disruption when macroeconomic performance is relatively poor but have less damaging effect when aggregate economic performance is relatively strong. Policy recommendations based on the study findings are also provided.

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“…Spain possesses vast renewable energy potentials which places it at advantage of benefiting from the HRES scenario [12]. On the other hand, the building sector accounts for about 43% of the total final energy consumption with about two-thirds of this from the residential buildings [13]. According to the International Energy Agency (IEA), the Spanish residential sector reliance on energy from fossil fuels still accounts for more than 50% of the sectors' final energy consumption [14].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Weather Datasets for Rural Energy Communities Simulation

Vallès

Sáez

Shobo

et al. 2023

36th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 20

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Rural areas are strategic assets to ensure sustainable energy transition due to their potential to implement renewable energy production. In this context, the EU seeks to promote synergies between renewable energy deployment and rural development, through several funding programs and supporting policies that benefit both parties. Consequently, the number of rural Energy Communities is increasing, and researchers are focusing on optimizing their designs. In this regard, one critical aspect when simulating the energy demand of Energy Communities is the use of weather data which are mostly not available for specific locations. In these cases, data from nearby urban centers are typically used, if available. But these data may not be representative of the typical weather conditions of the specific rural area under study which will negatively affect such design. In this context, this paper studies the impact of using urban areas' weather data when designing Energy Communities in the nearby rural areas. Therefore, the case study of the Tarragona province in Spain is presented, driven by 43 weather stations homogeneously distributed across the region. Findings reveal higher correlation in temperature and solar irradiation between local data and province's capital data than with thirdparty data, despite climatic differences. Heating degree day analysis indicates third-party's data closer to reality. Given Spain's heating demand, accurate data is crucial for HDD, therefore, when local data is unavailable, third-party data is recommended, despite lower correlation.

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“The impact of energy efficiency and decarbonisation policies on the European road transport sector”

Cited by 17 publications

References 46 publications

Modeling Vehicle Fuel Consumption Using a Low-Cost OBD-II Interface

Modeling Vehicle Fuel Consumption Using a Low-Cost OBD-II Interface

Carbon pricing and aggregate macroeconomic performance in the Eurozone: a contribution to the climate policy debate using the EU ETS and Macroeconomic Performance Index

Evaluation of Weather Datasets for Rural Energy Communities Simulation

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