Estimating energy demand elasticity in the European Union: a panel data analysis of gasoline prices, income, and inflation

Joana Correia Luís Mortágua Batista Estimating energy demand elasticity in the European Union: A panel data analysis of gasoline prices, income, and inflation. i Universidade do Minho Escola de Economia e Gestão Joana Correia Luís Mortágua Batista Estimating energy demand elasticity in the European Union: A panel data analysis of gasoline prices, income, and inflation. Dissertação de Mestrado Mestrado em economia Área de especialização Trabalho efetuado sob a orientação da Professora Doutora Rita Sousa Abril de 2025 ii iii iv DIREITOS DE AUTOR E CONDIÇÕES DE UTILIZAÇÃO DO TRABALHO POR TERCEIROS Este é um trabalho académico que pode ser utilizado por terceiros desde que respeitadas as regras e boas práticas internacionalmente aceites, no que concerne aos direitos de autor e direitos conexos. Assim, o presente trabalho pode ser utilizado nos termos previstos na licença abaixo indicada. Caso o utilizador necessite de permissão para poder fazer um uso do trabalho em condições não previstas no licenciamento indicado, deverá contactar o autor, através do RepositóriUM da Universidade do Minho. Licença concedida aos utilizadores deste trabalho Atribuição CC BY https://creativecommons.org/licenses/by/4.0/ v STATEMENT OF INTEGRITY I hereby declare having conducted this academic work with integrity. I confirm that I have not used plagiarism or any form of undue use of information or falsification of results along the process leading to its elaboration. I further declare that I have fully acknowledged the Code of Ethical Conduct of the University of Minho. vi Resumo Esta tese analisa como o consumo de energia na União Europeia responde, no curto prazo, a variações em variáveis económicas chave. Utilizando dados em painel de 22 países da UE entre 2001 e 2019, foi aplicado um modelo de efeitos aleatórios para estimar os efeitos dos preços da gasolina, do rendimento nacional e da inflação, na procura de energia primária. O objetivo é compreender as elasticidades a curto prazo e fornecer informações úteis para a elaboração de políticas. Os resultados mostram que a procura de energia diminui quando os preços da gasolina aumentam, sendo o efeito modesto. Isto significa que medidas baseadas apenas nos preços, como os impostos sobre os combustíveis, podem não reduzir rapidamente o consumo de energia, especialmente em setores como os transportes, onde as alternativas são limitadas. A elasticidade do rendimento também é baixa, sugerindo que o crescimento económico tem um efeito limitado sobre a procura de energia a curto prazo, o que apoia os objetivos da UE de dissociar o consumo de energia do crescimento do PIB. As conclusões sugerem que a política energética da UE deve combinar instrumentos de fixação de preços com outras medidas, tais como o investimento em infraestruturas, o apoio à inovação e a proteção específica dos grupos vulneráveis. Uma combinação de instrumentos é mais eficaz do que depender apenas de sinais de preços. Embora este estudo seja limitado pela utilização de dados a nível nacional e de um modelo estático, contribui para a nossa compreensão da forma como a procura de energia reage às condições económicas. Futuras investigações poderão basear-se neste estudo, utilizando dados mais detalhados e modelos dinâmicos. Palavras-chave: consumo de energia, elasticidade dos preços, União Europeia, preço da gasolina, fatores macroeconómicos, política energética. 4 Source: Prepared by the author with data from Our World in Data, retrieved from https://ourworldindata.org/grapher/fossil-fuel-price-index Figure 1, above, shows the price index of Brent crude oil, Dutch TTF gas and coal in Europe between 2005 and 2018. This shows significant price fluctuations, particularly around the 2008 financial crisis, after a period of recovery and a second significant drop between 2014 and 2015, caused by a shock in global oil and gas supplies, which led prices to their lowest levels in more than a decade, showing even more the dependence of European markets on fossil fuels. These events show that we need energy policies that can adapt and are based on real evidence. It's important to understand how energy demand responds to changes in variables like price, income, and unemployment to make effective ways to reduce the negative effects of future crises. Also, how countries respond to the same crisis shows how important it is to compare and consider each country's economic and social context. Figure 2 – Energy dependence in Europe (2019) Source: Prepared by the author with data from Eurostat (2024) retrieved from https://ec.europa.eu/eurostat/databrowser/view/sdg_07_50/default/table Figure 2, above, presents the variation in energy dependence between European Union countries in 2019. This demonstrates that there are significant differences in energy 5 dependence across Europe. Eastern and southern countries such as Malta, Cyprus, Greece and Hungary are more dependent on energy imports, while northern countries such as Sweden, Finland and Estonia are less dependent due to more developed energy infrastructure and domestic resources. Norway is an exception, since it is a large energy exporter. This shows that European countries are exposed to different levels of risk when energy prices rise, or when supplies are affected. It also shows that energy policies should be adapted to the situation of each country, instead of using the same approach for all countries. Although the concepts of energy price elasticity and energy intensity are well established in the literature, there are still some gaps in understanding how to best respond to how energy demand reacts to macroeconomic shocks in the short term, particularly during periods of economic transition. Most existing studies focus on long-term estimates, which tend to obscure behavioural changes that may occur more rapidly during crises or periods of instability (Labandeira et al., 2017). Understanding short-term elasticities is important to make better policy responses during economic crises, when consumer behaviour tends to change more in the short term than the long term. In addition, the impact of variables such as fuel prices, income, inflation or unemployment on energy consumption is still unclear. The results obtained are often inconclusive across countries and methodologies, and short-term elasticity estimates for European economies continued to be understudied (Gao et al., 2021). Furthermore, many empirical models use annual data, which makes it hard to see how energy demand change over time and how policies implemented over time impact it (Ajayi & Reiner, 2020). So, there is a need for a more detail and over time studies that can give more information about how energy demand changes in response to variation in economic conditions in the European Union. In the context of constant economic policy changes, it is becoming more important to understand how energy demand reacts to shocks in the economic sector. Energy consumption patterns in the EU are defined by a combination of structural situations and short-term effects, such as fluctuations in fuel prices and changes in income. Analysing the elasticities of energy demand in relation to these variations provides important information about the behavioural, economic and social dynamics that have an impact on consumption. The European Union is a case study in how to respond to demand in times of economic and geopolitical instability, as recent events such as the 2008 financial crisis, the COVID-19 pandemic and the war in Ukraine have caused significant changes in economies and the 6 energy sector. In addition, the internal diversity of the EU, where countries show significant differences in terms of fossil fuel dependence, energy efficiency and fiscal performance, highlights the need for comparative analyses that can control for these structural differences (Roupas et al., 2011). This study aims to complement the literature on energy demand by presenting recent and focused estimates of short-run price in the European Union. Moreover, while many previous studies emphasize long-term trends (Labandeira et al.,2017), this research attempts to offer a more context sensitive analysis of the EU during a period of energy and economic transition. The analysis allows for a better understanding of how energy demand responds to economic indicators during periods of structural change, a topic that remains understudied. From a policy perspective, the results contribute to the formulation of appropriate and flexible instruments, such as fuel taxes or energy subsidies, at national and European levels. Furthermore, by demonstrating how energy consumption responds to income, price, and inflation changes, this study can improve the accuracy and balance of energy and climate policies, particularly in times of present and future energy crises. The objective of this study is to estimate the short-run economic determinants of energy demand in the European Union member states during the period 2001-2019. Using a panel dataset from 22 countries, the study focuses on the response of primary energy consumption to fluctuations in fuel prices, national income, inflation, and unemployment. Fixed-effects and random-effects models are used, complemented by year dummies to control for time-specific shocks. The empirical analysis allows to estimate the short-term elasticities of primary energy consumption, meaning, it allows us to estimate how energy demand responds to price changes in different regions and over time. After the introduction, the thesis is divided into four main parts. The first part presents a review of the literature on energy demand, focusing on elasticity, macroeconomic influences and the political context of the EU. The second part describes the data sources, variables and panel data methodology used to estimate short-term elasticities. The third part presents the empirical analysis, which studies the impact of fuel prices, income, inflation and macroeconomic shocks on energy consumption. The final section summarizes the main conclusions, discusses their policy implications and presents suggestions for future research. 7 2 Literature review In the current context of a rapidly changing global energy landscape, the European Union (EU) is striving to balance environmental sustainability with economic resilience. The EU has set an ambitious target of achieving climate neutrality by 2050, alongside an interim goal of reducing greenhouse gas emissions by 55% by 2030 (European commission, 2020). These commitments have drawn increased attention to the structural and behavioral dimensions of energy consumption. At the same time, energy prices have become increasingly volatile (Shevchenko, 2023) due to various geopolitical factors, particularly the Russian invasion of Ukraine, alongside systemic disruptions stemming from the COVID-19 pandemic. These events have highlighted the EU's vulnerability to outside energy dependencies and have emphasized the urgent need to transition to more secure and sustainable energy sources (Tesfu, 2024).This transition is crucial not only for environmental goals but also for ensuring long-term energy security and stability. Energy price elasticity and energy intensity are important concepts that help us understand how energy markets work and how consumers respond to changes in energy prices and policies. Price elasticity refers to how much energy demand changes in response to shifts in cost, which is useful for evaluating the effectiveness of fiscal policies like carbon taxes or fuel subsidies (Labandeira et al., 2017) Meanwhile, energy intensity looks at the amount of energy consumed for each unit of economic output, giving us insight into economic efficiency and the potential for reducing carbon emissions in industrial sectors (Díaz et al., 2019). Considering these concepts is crucial, not just for their technical aspects but also because they have significant implications for policy-making, investment decisions, and the socioeconomic conditions within EU member states. Understanding these metrics can provide valuable guidance for developing effective strategies moving forward. This literature review looks at a wide range of studies to explore how energy price elasticity and energy intensity have changed in the EU over time. It is organized into three main parts. The first part presents topics on how energy price elasticity and energy intensity have shifted across the EU in the last ten years. The second part looks into the enery market instability and context, considering its impact in energy price elasticity. Finally, the third part looks at the various factors of the energy transition that also impact energy price elasticiy. 8 2.1 Energy price elasticity and energy intensity Energy price elasticity has been a subject of significant development over recent decades, a large amount of empirical literature focusing on the European context. The work on energy demand responsiveness starts with Houthakker's (1951) study, which laid the foundation for the subsequent theoretical models in the analysis of energy demand elasticities, focusing on price elasticity. Subsequent contributions by Taylor (1975) and Espey & Espey (2004) refined these findings, with a focus on the price elasticity of residential electricity demand. The concept of price elasticity varies significantly depending on many factors, including energy type, consumption sector, time horizon, and geographical region. Labandeira et al. (2017) examined the elasticities of energy demand. They found that average short-run price elasticities tend to be inelastic (i.e., less than one in absolute value). In the European context, short-run elasticities for petrol and diesel are proposed to be between -0.1 and -0.3, while long-run elasticities tend to be larger, suggesting greater consumer adaptability over time (Dahl, 2012). Research on long-run price elasticities in the transport sector demonstrates a higher capacity for behaviour and structure to change over time. As Small and Van Dender (2007) demonstrate, although short-term responses to fuel price changes tend to be limited, longterm effects are more significant due to improvements in fuel efficiency and shifts in behavioral patterns when it comes to travel, driven by long-term policy. In addition, Donna (2018) developed a theoretical model of urban travel demand, finding that long-run elasticities for automobile use and public transport are significantly higher than their shortrun elasticities. This finding suggests that consumers may be able to adapt more significantly to price variations over time, as long as they are supported by appropriate policy interventions, such as investments in public transit and technological alternatives. In a consistent estimation of price elasticity across the EU-28, Zeleke (2016) observed that, while the average price elasticity remains relatively low, structural reforms such as deregulation and the expansion of public transportation have led to increased responsiveness in some countries. These results suggest that price elasticity is not uniform across the region, but can be significantly influenced by national-level policy choices and infrastructure development. 9 More recently, empirical results show that price elasticity can change across EU countries. Alberini et al. (2021) estimated that the short-run price elasticities of gasoline in Germany are close to -0.25, while Baranzini and Weber (2013) found a lower elasticity of -0.09 in Switzerland. On the other hand, Fridstrøm and Østli (2021) showed that gasoline price elasticity was significantly higher in Norway, approximately at -1.08. These variations show how important the national context is, infrastructure accessibility, and fuel taxes are in defining how consumers react to changes in energy price. Further differentiation emerges when the price elasticity of fuel demand relative to car usage is disaggregated by income level. Berry and Börjesson (2022) used micro panel data to demonstrate that lower-income households exhibit greater fuel price sensitivity compared to higher-income counterparts, particularly in car usage (Berry & Börjesson, 2022). The concept of energy intensity, defined as the ratio of energy consumption to economic output, has gained renewed relevance in the EU context as a proxy for both energy efficiency and climate change mitigation (Burke & Csereklyei, 2016). Declining energy intensity is often seen as a positive indicator of decoupling economic growth from fossil energy use, although the underlying drivers are complicated. (Wing, 2008). According to Burke and Csereklyei (2016), these drivers range from improvements in energy efficiency to structural economic changes, such as the transition from manufacturing to service-based sectors. Historically, industrialized EU countries have experienced a steady decline in energy intensity since the 1970s, a trend accelerated by successive waves of technology adoption, environmental regulations, and the integration of renewable energy sources (Wing, 2008). Metcalf (2008) explored these patterns in the U.S. context, but his findings regarding the role of economic structure and policy instruments apply equally to the EU. Similarly, Oseni (2009) demonstrated that among 16 OECD countries, those with greater investments in energysaving technologies and proactive policy frameworks experienced more significant declines in energy intensity. Zhang (2023) highlighted how the pandemic temporarily disrupted energy intensity patterns in China, while Tesfu (2024) illustrated that the EU's response to the recent energy crisis involved accelerating renewable investments to reduce long-term energy intensity and exposure to fossil fuel volatility. Díaz et al. (2019) explored the relationship between economic growth, energy intensity, and the energy mix, revealing that countries emphasizing cleaner energy sources tend to exhibit stronger decoupling. 10 In conclusion, the decline in energy intensity across Europe has not been uniform. According to Gulzar and Farooq (2022), disparities in national energy mixes, industrial specialization, and policy enforcement levels create substantial variation. For instance, Eastern European countries tend to have higher intensity levels due to legacy infrastructure and greater dependence on coal-based energy. The literature review on energy price elasticity shows that consumer responses to energy prices can change between countries, and sectors in the European Union. Therefore, it is important to understand these differences to develop better energy policies as energy intensity changes. 2.2 Energy markets uncertainty Energy markets are susceptible to price volatility, which can be attributed to endogenous market mechanisms and exogenous shocks. Understanding this volatility is important, since significant and unexpected prices changes allows for an estimation of the elasticity of energy demand in the short term. Analysing how demand responds to changes in prices helps explain how consumers respond to energy price shocks, which is important for analysing the impact these shocks in different sectors. In the last twenty years, the European Union has faced several disruptions that have affected its energy systems. These include the global financial crisis in 2008, the collapse of oil prices between 2014 and 2015, the COVID-19 pandemic in 2020, and the energy crisis in 2022 that followed the invasion of Ukraine. Each of these events has posed challenges to the resilience of energy systems in the EU. They have also caused a chain reaction that impacted fuel prices, inflation levels, investment flows, and even the long-term direction of policy. (Kubinschi, Barnea, & Zlatcu, 2019). The study by Kubinschi, Barnea, and Zlatcu (2019) examined fuel price volatility across several European countries and highlighted strong interdependencies in energy pricing, suggesting the presence of systemic contagion effects within the region. These findings are in alignment with those of Shevchenko (2023), who emphasized the influence of external shocks and global market fluctuations on domestic energy price dynamics in Europe. Zhang (2023) further contributed to the field by illustrating how the pandemic amplified short-term volatility in gasoline prices, leading to erratic consumption behavior and temporary deviations from historical short run price elasticity patterns. 11 Hamilton (2009) and Ghoshray (2011) provided key information on the macroeconomic implications of oil shocks, demonstrating that increases in energy prices contribute to reduced output, higher unemployment, and inflationary pressure. Huynh (2016) used dynamic stochastic general equilibrium (DSGE) models to analyse the propagation of energy price shocks through the business cycle. Their findings indicated that such shocks influence economic performance. Energy prices also have a significant impact on inflation, especially in energy import dependent EU economies. Arpa et al. (2006) and Punzi (2019) show that oil price shocks and energy price uncertainty can rapidly affect both headline and core inflation through CPI transmission and inflation expectations, often triggering restrictive monetary policy. In fact, Southern EU countries face greater inflationary impacts due to higher energy intensity and import dependence (Uche, 20-22; Roupas et al., 2011). These vulnerabilities highlight the need for tailored policy responses. Energy policy instruments, including taxes, subsidies, and emissions trading systems, aim to modify consumption behaviors by changing price signals (Gillingham & Palmer, 2014; García-Álvarez et al., 2023). These instruments are necessary to resolve market failure, such as the external effects and differences in information, which are common in energy markets. As a result, these tools can have an impact on both short-term and long-term energy demand elasticities. By influencing the real cost of energy consumption and lead to changes in the economy, they determine how consumers and companies respond to energy price fluctuations over time. While the potential indirect effects of current policy instruments are recognised, they will not be addressed in this empirical analysis, which is mostly focused on energy prices and their direct effect on consumption behavior. To conclude, there are important considerations in the literature about the rebound effect and the limitations of price-based mechanisms to fully capture consumer behaviour (Stern, 2012; Small & Van Dender, 2007), as well as the possible progressive impacts of carbon pricing and the need for equality in policy design. However, given their difficulty, these topics should be considered in future research. 12 2.3 The EU energy transition The transition of the European Union towards a low-carbon economy is changing the conditions that affect how people use energy. Changes in energy systems, patterns of income distribution, and new challenges related to energy access all play a role in how people react to changes in prices and income. Even though this study mainly focuses on short-term factors that influence primary energy consumption, it is important to understand the wider aspects of the energy transition. This understanding helps us to better interpret consumption behaviors in a world that is continuously changing due to new economic and technological developments. The European Union’s transition to a low-carbon energy system has been marked by the accelerated deployment of renewable energy sources (RES) such as wind, solar, and bioenergy (Tesfu, 2024). There has been significant progress across the European Union, but this progress has also led to new challenges related to network stability and investment uncertainty, which require adjustments to changes in the economy and the market conditions. This expansion, driven by environmental and geopolitical imperatives, including the desire to reduce dependence on imported fossil fuels, has required substantial investments in system flexibility, energy storage, and smart grids (Bynoe & Moonsammy, 2023). Despite these advancements, the transition remains uneven, influenced by national differences in institutional strength, regulatory frameworks, and innovation capacity (Strielkowski et al., 2024; Nam & Jin, 2021). Furthermore, the ability of different countries to substitute fossil fuels for renewables varies according to structural factors, including capital-labor-energy substitution ratios and the existence of targeted policy incentives such as feed-in tariffs or renewable auctions (Li et al., 2023). In addition to the technical challenges we face, the social aspect of the energy transition is becoming more important. Households with lower incomes tend to react more strongly to changes in energy prices, which raises concerns about unfair impacts if there are not enough compensatory policies in place. (Harold et al., 2017; Berry & Börjesson, 2024). The concept of energy vulnerability expands this view, considering not only economic access but also physical infrastructure and social exclusion (Bouzarovski & Petrova, 2015; Thomson et al., 2017). Although instruments like the Just Transition Fund aim to address these disparities, implementation gaps remain, particularly in countries with weaker administrative structures (Strielkowski et al., 2024). 13 To conclude, this literature review shows that technological innovation, energy system resilience, and economic and social adaptations are important for the European Union transition to a low carbon economy. An analysis of the elasticity of energy prices and income allows for a better understanding of consumer behavior, the impact of policies and economic weaknesses, and helps to define more effective policies focusing on a global economy in constant change 3 Data and analysis This section presents the data and approach used to estimate the response of energy demand to the main macroeconomic variables and fuel prices in EU countries. Based on the work of Labandeira et al. (2017), Berry and Börjesson (2024) and Zeleke (2016), the analysis was designed to estimate the response of energy demand to changes in fuel prices and energy policies. 3.1 Data Data and sources This study uses a panel dataset covering 22 European Union Member States over the period 2001 to 2019. The sample selection was based on the availability and consistency of key indicators across countries and years, aiming to ensure comparability and reliable econometric estimation. Some countries have been excluded from the dataset due to a lack of data for the variable used. These countries are Bulgaria, Croatia, Cyprus, Estonia, Latvia, Lithuania, Malta, Romania and Slovenia. They were removed to ensure that the final data set was complete and that data could be compared across countries and years in order to obtain valid results for econometric analysis. The dataset includes annual observations for each country, resulting in 418 observations in total. The choice of the 2001–2019 period allows for the capture of major macroeconomic and energy-related events, including the 2008 global financial crisis, the 20 Table 2, below, gives the descriptive statistics for the main variables. There is a large variation across the sample. For example, GDP per capita varies from about 3,000 USD in the poorest countries to more than 100,000 USD in the richest. Gasoline prices range from under 0.60 USD/liter to over 2.60 USD/liter. Inflation and unemployment also vary significantly, showing how the EU countries were affected differently by crises like the 2008 recession and the sovereign debt crisis.EU Table 2 – Descriptive statistics of the variables Variable Unit Obs Mean Std. dev. Min Max GDP $ 418 1.33e+12 2.87e+12 6.45e+09 1.63e+13 GDPpercapita $/pc 418 36,015 23,661 3,169 112,418 Income $ 418 5.07e+11 5.21e+11 8.71e+09 2.99e+12 Inflation % 418 2.59 4.10 -1.14 54.40 HICP index N 418 93.88 15.46 31.80 234.44 Unemployment % 418 8.89 6.15 1.87 37.32 Gasoline price $/l 418 1.54 0.42 0.60 2.62 Diesel price $/l 418 1.42 0.41 0.53 2.40 Fossil fuel consumption %/total energy 418 75.66 18.98 28.46 99.32 Primary energy consumption Gj/pc 418 174.16 78.15 43.99 436.26 These initial observations show the importance of using econometric models that control for country and year effects. They also suggest that elasticities will not be the same in all countries, which supports the use of panel data methods that allow for this kind of difference. 3.2 Econometric model This study uses a panel data structure to estimate short-run energy demand elasticities across European Union Member States. Panel data allows the observation of multiple countries over several periods, controlling for country-specific factors that do not vary over time, such as cultural preferences, long-standing infrastructure differences, and climatic conditions. As discussed by Wooldridge (2020), panel models offer distinct advantages over pure cross-sectional or time-series approaches, particularly by capturing unobserved heterogeneity across observational units. A pooled OLS estimation, ignoring the panel nature 21 of the data, would likely produce biased and inconsistent estimates due to the omission of relevant unobserved variables. By contrast, panel models effectively correct for this bias. The period analyzed (2001–2019) covers several important macroeconomic and political events, such as the 2008 global financial crisis, the 2011 eurozone sovereign debt crisis, and the growth period stages just before the COVID-19 pandemic. These events introduce significant cross-country and temporal variations. By exploiting both the crosssectional and time dimensions, the model captures not only static differences between countries but also the dynamic responses of energy consumption to economic shocks. The dependent variable is the natural logarithm of primary energy consumption per capita, measured in gigajoules (GJ). Using a per capita specification ensures comparability across countries of different sizes, while the log transformation facilitates elasticity interpretation and stabilizes variance. The key explanatory variables are: • The lagged natural logarithm of real retail gasoline prices (USD/liter), • The lagged natural logarithm of national income per capita (constant 2015 USD), • The lagged natural logarithm of the Harmonized Index of Consumer Prices (HICP). All monetary variables were deflated to constant 2015 US dollars to adjust for inflation. Logarithmic transformations were applied to continuous variables, following established best practices (Gujarati, 2009; Wooldridge, 2020). Inflation rates were log-transformed only when strictly positive, resulting in 33 missing observations due to deflation periods. Missing data were addressed using conservative imputation techniques, and sensitivity checks confirmed that the distribution of key variables was not significantly altered. The main explanatory variables were lagged by one period to account for adjustment delays in consumer behavior and to reduce simultaneity bias. The relationship between energy demand and its determinants was estimated using a linear panel data regression model. A log-log (double-log) specification was chosen because it allows for a direct interpretation of coefficients as elasticities (Gujarati, 2009). Two models were initially considered: a fixed effects model and a random effects model. The fixed effects model allows correlation between country-specific factors and the regressors, while the random effects model assumes no such correlation, offering more 22 efficient estimates if the assumption holds. The Hausman test was used to choose between models. The result (p-value = 1.0000) supports the random effects specification, suggesting that country-specific effects are uncorrelated with the explanatory variables. Although dynamic panel data methods such as the Arellano-Bond estimator (GMM) could be employed in contexts with strong endogeneity concerns, they were not considered necessary here. The primary interest lies in estimating contemporaneous short-run elasticities). The econometric analysis was conducted using STATA software (StataCorp, 2021), which provided the necessary tools for estimating the panel data model and performing robustness checks. 23 Thus, the final model estimated is: ln(𝐸𝑛𝑒𝑟𝑔𝑦𝐷𝑒𝑚𝑎𝑛𝑑𝑖𝑡)=β0+β1ln(𝑃𝑔𝑎𝑠𝑜𝑙𝑖𝑛𝑒𝑖,𝑡−1)+β2ln(𝐼𝑛𝑐𝑜𝑚𝑒𝑖,𝑡−1)+β3ln(𝐻𝐼𝐶𝑃𝑖,𝑡−1)+ +∑γ𝑡𝑌𝑒𝑎𝑟𝑡 2019 𝑡=2003 +𝑢𝑖+ϵ𝑖𝑡 Where: • ln(𝐸𝑛𝑒𝑟𝑔𝑦𝐷𝑒𝑚𝑎𝑛𝑑𝑖𝑡) is the natural logarithm of primary energy consumption per capita in country i and year t. • ln(𝑃𝑔𝑎𝑠𝑜𝑙𝑖𝑛𝑒𝑖,𝑡−1) is the lagged natural logarithmic of the gasoline price. • ln(𝐼𝑛𝑐𝑜𝑚𝑒𝑖,𝑡−1) is the lagged natural logarithm of national income in constant dollars, a proxy for economic activity. • ln(𝐻𝐼𝐶𝑃𝑖,𝑡−1) is the lagged natural logarithm of the Harmonized Index of Consumer Prices. • 𝑌𝑒𝑎𝑟𝑡 are year dummy variables capturing time-fixed effects from 2003 to 2019. • ui represent the unobserved countryspecific random effects. • ϵit is the idiosyncratic error term. Robust standard errors clustered at the country level were used to account for potential heteroskedasticity and autocorrelation. 4 Results and discussion 4.1 Main results This chapter presents and discusses the results obtained from the econometric estimations of energy demand elasticities in European Union Member States over the period 2001–2019. The analysis focuses on the short-run responses of primary energy consumption to fluctuations in fuel prices, income, and inflation, using a linear panel data model with random effects and robust standard errors clustered at the country level. The performance of the econometric model was evaluated using different indicators to check how well it explains the changes in energy consumption across countries and over time. 24 Overall, the results show that the model has a good fit and that the estimates are statistically reliable. First, the within R-squared of the model is around 0.65. This means that the model explains about 65% of the changes in energy consumption inside each country during the period of study. Although this is not a very high value, it is acceptable for panel data analysis that works with economic and energy variables, which are often influenced by many other factors that are not included in the model. This result indicates that the selected independent variables capture an important part of what affects energy demand in the European Union. Second, the Hausman test was performed to decide whether to use fixed effects or random effects. The result (p-value = 1.0000) showed that random effects are appropriate because the country-specific factors are not correlated with the independent variables. This makes the estimation more efficient and allows the model to use variation between countries as well as variation over time inside each country. The decision to use lagged explanatory variables also improves the model. By including past values of gasoline prices, income, and inflation, the model assumes that changes in energy consumption respond to previous economic conditions, which reduces the risk of simultaneity problems. This approach makes the short-run elasticity estimates more consistent with real-world behavior. Moreover, the inclusion of year dummies adds an important control for external shocks that could have affected all countries at the same time. These events, for example global crises or common EU policy changes, could distort the results if they were not controlled. By capturing the effects of these time-specific shocks, the model avoids bias in the estimation of the elasticities. The diagnostic tests and model corrections suggest that the estimates are robust. The results can be considered reliable to describe how primary energy demand in the EU responds to short-term changes in economic variables and fuel prices between 2001 and 2019. After validating the model, the main findings from the regression analysis were summarized in Table 4.1. The estimated coefficients for gasoline price, income, and inflation are all significant and show the expected signs based on economic theory. 25 Table 3 – Random-effects regression results for ln(Energy Demand) Predictor b SE z P>|z| ln_Pgasoline (L1) -0.095 0.042 -2.25 .025 ln_national_income (L1) 0.020 0.005 3.84 .000 ln_prices_index (L1) 0.458 0.032 14.40 .000 year 2003 -0.014 0.017 -0.83 .407 year 2004 0.007 0.018 0.42 .672 year 2005 0.006 0.021 0.29 .774 year 2006 0.007 0.025 0.29 .773 year 2007 -0.010 0.027 -0.38 .706 year 2008 -0.032 0.029 -1.11 .268 year 2009 -0.085 0.037 -2.31 .021 year 2010 -0.084 0.030 -2.85 .004 year 2011 -0.112 0.036 -3.14 .002 year 2012 -0.137 0.038 -3.64 .000 year 2013 -0.153 0.041 -3.76 .000 year 2014 -0.197 0.042 -4.71 .000 year 2015 -0.193 0.042 -4.58 .000 year 2016 -0.201 0.037 -5.48 .000 year 2017 -0.222 0.029 -7.66 .000 year 2018 -0.225 0.029 -7.64 .000 year 2019 -0.230 0.035 -6.58 .000 Constant 2.613 0.206 12.68 .000 Model fit statistics: Number of observations: 396 Number of groups: 22 (countries) R² (Within): 0.648 Wald χ²(20) = 655.51, p < .001 σᵤ = 0.434 σₑ = 0.055 ρ = 0.984 Gasoline price elasticity The estimated short-run gasoline price elasticity is -0.095, indicating that a 1% increase in the real retail price of gasoline leads, on average, to a 0.095% decrease in primary energy consumption per capita in the following year, holding other factors constant. This elasticity is statistically significant at the 5% level, as shown by the p-value of 0.025. The negative sign is consistent with economic theory and previous studies such as Labandeira et al. (2017), who found that energy demand is generally price inelastic in the short 26 run. Consumers often cannot immediately adjust their energy consumption because of habits, lack of alternatives, or the nature of energy use in transportation and heating. As a result, the short-term reaction is usually smaller than the long-term adjustment. The relatively low magnitude of the elasticity suggests that, although fuel price increases discourage energy consumption, the effect is limited in the short term. This may reflect the essential nature of energy in daily life, where individuals and companies have few immediate substitutes. It also highlights the importance of complementary policies, such as investments in public transport or improvements in energy efficiency, to strengthen the impact of price-based measures over time. Moreover, the use of lagged gasoline prices in the model suggests that behavioral adjustments do not occur immediately after price changes, but rather with some delay, which is realistic given the time needed to adapt consumption patterns. Income elasticity The estimated short-run income elasticity is 0.020, positive and highly significant (pvalue = 0.000). This implies that a 1% increase in income per capita leads, on average, to a 0.020% increase in energy consumption per capita. The result is consistent with the theory that energy demand increases with economic growth. As incomes rise, households may purchase more energy-consuming goods and services, and industrial production may expand, both leading to higher overall energy use. However, the relatively low value of the income elasticity compared to many previous works (such as Labandeira et al., 2017; Gao et al., 2021) suggests a weaker link between income and energy demand in the EU during the study period. This could reflect saturation effects, greater energy efficiency, or structural economic changes, such as a shift towards services and less energy-intensive activities. It is also important to note that even if the effect is modest, the positive income-energy link still poses challenges for energy and climate policy. As economies grow, energy consumption tends to rise unless significant efforts are made to improve energy efficiency or to change consumption patterns. Therefore, achieving the EU’s climate goals will require not only decoupling emissions from energy, but also decoupling energy consumption from economic growth itself. 27 Inflation elasticity The coefficient for the lagged Harmonized Index of Consumer Prices (HICP) is estimated at 0.458 and is statistically significant at the 1% level (p-value = 0.000). This indicates that higher inflation is associated with a notable increase in primary energy consumption per capita. One possible interpretation is that inflationary pressures may affect relative prices, shifting consumption patterns in ways that increase energy use. Alternatively, during periods of inflation, energy may be treated as a basic necessity, resulting in relatively inelastic consumption patterns. The relatively high magnitude of the inflation elasticity compared to previous expectations suggests that inflation dynamics can have a substantial impact on energy consumption, making it an important factor to consider in energy demand forecasting and policy design. Given the strength of this relationship, further research would be useful to better understand the channels through which inflation influences energy demand, particularly across different sectors and income groups. All main explanatory variables are statistically significant, and their signs are aligned with theoretical expectations. These results indicate that macroeconomic conditions are crucial determinants of energy consumption patterns across the European Union, while shortrun fuel price responsiveness remains limited. Policies that aim to manage energy demand must therefore integrate both market-based instruments and broader macroeconomic considerations. The estimated elasticities are consistent with the previous findings in the literature review. For instance. Labandeira et al. (2017) find that short run price gasoline price elasticity, are typically between -0.1 and -0.3, which is close to the value obtained in this study (-0.095). Regarding income elasticity the results obtained was 0.020, which is lower than the values presented by Gao et al. (2021), where estimates are between 0.2 and 0.5. This difference could suggest a smaller relationship between income growth and energy consumption within the European Union during this period in analyse, possibly due to economic changes and improvements in energy efficiency. 28 Year Fixed Effects In addition to the main explanatory variables, the regression model includes year dummy variables to capture the effects of time-specific shocks that were common across all countries during the period under analysis. The inclusion of year fixed effects is important because it controls for unobserved events that could simultaneously influence energy consumption in all Member States, such as financial crises, oil price shocks, or major policy changes at the EU level. The estimated coefficients for the year dummies provide interesting insights into the historical evolution of energy demand. The coefficients for the years from 2003 to 2008 are small in magnitude and statistically insignificant. This suggests that, during this earlier period, there were no strong common shocks affecting energy consumption across the European Union, or that national-level factors dominated the variation in energy demand. However, starting from 2009, the year dummy coefficients become negative and statistically significant, particularly from 2009 onwards. For example, the coefficient for 2009 is negative and significant at the 5% level (p-value = 0.021), and the coefficients for the years 2010 to 2019 are increasingly negative and highly statistically significant (p-value < 0.01 for most years). This pattern coincides with the impacts of the global financial crisis, the eurozone sovereign debt crisis, and the subsequent period of slow recovery and austerity policies across the EU. The most pronounced negative effects occur between 2012 and 2019, suggesting a persistent and strong downward pressure on energy demand during this period. Factors such as reduced economic activity, tighter public and private budgets, improvements in energy efficiency, technological change, and structural shifts toward less energy-intensive sectors may have contributed to this sustained reduction in energy consumption. Overall, the pattern of year fixed effects confirms that energy consumption in the EU is influenced not only by prices and income, but also by broader macroeconomic and political conditions. By explicitly controlling for these time-specific shocks, the model ensures that the estimated elasticities for gasoline prices, income, and inflation are not biased by external events that simultaneously affected all countries. 29 4.2 Policy analyses Alignment with EU Policies The short-run elasticities that we estimated in this study give helpful insights that match with the European Union’s energy and climate policy frameworks until 2019, especially the European Green Deal and the Fit-for-55 Package. Even though our empirical analysis mainly looks at the effects of price, income, and inflation, the findings are useful to understand important challenges that EU policies aim to address. First, the finding of low short-run price elasticity reinforces the EU’s approach of combining price signals with regulatory and investment measures. Since energy consumption does not respond strongly to price increases in the short term, policies relying solely on carbon pricing or taxes are unlikely to achieve rapid demand reductions. The European Green Deal reflects this by emphasizing investments in clean energy infrastructure, energy efficiency, and sustainable mobility options, to gradually enable behavioral shifts. Second, the positive, but very modest, income elasticity observed supports the EU’s strategy of decoupling economic growth from energy consumption. Although the empirical evidence suggests that economic expansion still leads to slight increases in energy demand, this underlines the relevance of improving energy efficiency and encouraging low-carbon innovation, as promoted by EU initiatives. Third, the limited but significant impact of inflation on energy demand highlights the importance of consumer protection measures. EU policies, such as the Governance Regulation and the Electricity Directive, already required Member States to monitor and mitigate energy poverty. The results of this study, showing consumers’ limited ability to adjust energy use during price increases, reinforce the necessity of targeted support for vulnerable groups. Finally, the period covered by the analysis, which includes the global financial crisis, shows the importance of integrating economic resilience into energy policy design. Instruments like the Just Transition Mechanism demonstrate the EU’s recognition that energy and climate goals must be pursued with attention to broader economic and social stability. 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