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In this article, we investigate the heterogeneity in the responsiveness of Swiss household electricity demand to changes in prices and income. We focus on segments of consumers with different intensities of electricity consumption by using a panel quantile regression approach. This estimation strategy is applied to a rich micro-level longitudinal d...
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Context 1
... exclude households who do not report electricity consumption from a bill and those who report an annual consumption below 200 kWh or above 30 000 kWh. 7 The final panel data set is a sample of 3856 observations corresponding to a total of 1483 households for which data is available for at least two waves of the SHEDS. Table 1 displays descriptive statistics for this dataset. ...Context 2
... shown in Table 1, the annual residential electricity use is characterized by substantial variation. The average values are slightly lower in comparison to official energy statistics from the Swiss Federal Office of Energy, which reports an average household annual residential electricity consumption of 5096 kWh in 2015-5311 kWh for 2018 (OFEN, 2019). ...Similar publications
Electricity load forecasting plays an indispensable role in the electric power systems. However, its characteristics of uncertainty and complexity are hard to handle. This paper proposes a probabilistic load forecasting approach named QRCNN-E. Specifically, the deep convolutional neural network is applied to model the non-linear relationship with t...
Citations
... Aggregated data, on the other hand, tends to show lower price elasticity, as it averages the consumption patterns of both price-sensitive and less price-sensitive households across a broader region (Franco et al., 2023) This difference in elasticity is attributed to the aggregation of diverse household behaviors in aggregated data, which masks the heterogeneity observed in disaggregated data (Oberfield and Raval, 2021). The use of disaggregated data allows for a more nuanced understanding of consumer behavior, revealing significant variations in price sensitivity across different household segments (Tilov et al., 2020;Zhou and Teng, 2013). In contrast, aggregated data provides a more generalized view, which can be useful for broad economic analysis but may overlook specific behavioral patterns (Chetty et al., 2013). ...
This systematic review investigates disparities in research coverage and assesses the implications of income and price elasticities on household electricity demand. By analyzing studies across various regions and income groups, it aims to uncover imbalances in research distribution and understand their impact on energy policy development. The review employs a systematic approach, analyzing peer-reviewed and gray literature from Scopus, Web of Science, and Google Scholar databases from 1980 to 2023. It addresses four research questions: regional and income group classifications, differences in income elasticities, variations in price elasticities, and the accuracy and variability of household electricity demand elasticity estimates between aggregated and dis-aggregated data studies. The review reveals significant imbalances, with the Middle East, North Africa, and Sub-Saharan Africa underrepresented, while Europe and Central Asia exhibit robust research. Consistent inelasticity in electricity demand is observed across contexts and timeframes. High-income countries dominate this research, leading to a lack of empirical studies in low-income regions, hindering tailored interventions. Understanding short-and long-run elasticities is crucial for effective demand forecasting and policy design. This review uniquely compares income and price elasticities from aggregated and disaggregated data studies, highlighting regional research disparities and their implications for energy policy. It emphasizes the need for inclusive, context-specific investigations to foster sustainable development and equitable energy access globally.
... The heterogeneity among cities by economic output and resource allocation efficiency may lead to significant differences in the policy's impact on total factor energy efficiency and the relationship may be non-linear. With reference to Tilov et al. [56], this study uses a panel quantile regression model to estimate the marginal effect of air-quality ecological compensation under various total factor energy efficiencies. The quantile regression results are shown in Figure 3. ...
The impact of air-quality ecological compensation on total factor energy efficiency can help a country to achieve high-quality economic development with the goals of peak carbon emissions and carbon neutrality, and to explore a win–win path for the economy and the environment. This study investigates the impact of air-quality ecological compensation (AQEC) on total factor energy efficiency using the time-varying difference-in-difference model with a sample of 282 Chinese cities from 2004 to 2022. The results show AQEC significantly increases total factor energy efficiency by 1.71% in the pilot areas. This result remains robust after undergoing PSM-DID, considering only the first three pilot batches, an instrumental variable approach, the exclusion of other policies interference, and dual machine learning. The mechanisms analysis indicates that AQEC enhances total factor energy efficiency by promoting industrial structure advancement and green technology innovation. Furthermore, it is noteworthy that heterogeneity exists in the effect of AQEC on total factor energy efficiency, particularly in cities with an old industrial base and small cities. Overall, this study refines the causal relationship between air-quality ecological compensation and total factor energy efficiency, providing empirical evidence and policy insights for China and other countries to enhance energy efficiency and promote urban ecological civilization.
... To the authors' knowledge, no paper in the domestic water demand literature has used QR to address the problem of individual unobserved heterogeneity. In contrast, studies of other utility sectors such as electricity have used this methodology to estimate demand and deal with unobserved heterogeneity (Schleich et al. 2013;Huang 2015;Hancevic and Navajas 2015;Silva et al. 2017;Chindarkar and Goyal 2019;Frondel et al. 2019;Uhr et al. 2019;Kostakis 2020;Tilov et al. 2020). ...
... This method enables assessment of different effects of the independent variables on the entire distribution of the dependent variable. In this study, QR was used to answer the question of whether an explanatory variable has a varying impact across conditional quantiles of consumption (Tilov et al. 2020). ...
... QR also makes it possible to account for unobserved differences related to consumption intensity between households (Tilov et al. 2020) and to capture heterogeneity in the coefficients across quantiles of a consumption distribution and across households. QR gives more flexibility in the estimation of a factor effect on consumption level because it enables estimation of a range of conditional quantile functions. ...
This paper examines the problem of unobserved heterogeneity in urban water demand. It uses a panel quantile regression (QR) approach to focus on segments of consumers with different levels of water consumption. This estimation strategy is applied to a rich set of panel microdata capturing the consumption of water for 4,023 households in Valencia (Spain) between the years 2009 and 2011. To capture heterogeneity in a city’s residential household water consumption, a QR approach is applied to the specified water demand model, enabling analysis for different quantiles (levels) of consumption. The QR shows the behaviour of the parameters for different consumption levels. It enables differentiation of consumer reactions to different independent variables at each quantile of the distribution of the dependent variable. The results provide strong evidence of unobserved heterogeneity at different levels. This approach is useful in that it can lead to better-informed tariff design by providing an understanding of heterogeneity in price elasticities.
... The most frequently used determinants of persistent consumption are behavioral control and contextual determinants. Even though the price elasticity of drinking water and energy usage is debated [35][36][37][38], the behavioral control that comes with household income is found to be significantly correlated with higher usage in the long term in several studies [39][40][41][42][43][44][45]. This might also be due to little efficiency behavior by occupying bigger dwellings [43,[45][46][47] and owning and using more energy-using devices [39,45,46,48]. ...
... Even though the price elasticity of drinking water and energy usage is debated [35][36][37][38], the behavioral control that comes with household income is found to be significantly correlated with higher usage in the long term in several studies [39][40][41][42][43][44][45]. This might also be due to little efficiency behavior by occupying bigger dwellings [43,[45][46][47] and owning and using more energy-using devices [39,45,46,48]. Moreover, household size also contributes to higher water and energy usage [41,43,46,47,49]. ...
... Even though the price elasticity of drinking water and energy usage is debated [35][36][37][38], the behavioral control that comes with household income is found to be significantly correlated with higher usage in the long term in several studies [39][40][41][42][43][44][45]. This might also be due to little efficiency behavior by occupying bigger dwellings [43,[45][46][47] and owning and using more energy-using devices [39,45,46,48]. Moreover, household size also contributes to higher water and energy usage [41,43,46,47,49]. ...
Over the last decades, drinking water and energy use have increased exponentially. To preserve ecosystems in the long term, a change in behavior is necessary on all levels of society including on the household level. This paper presents an integrated review of the determinants of long-term drinking water and energy conservation behavior of households. We identified forty-nine relevant studies discussing long-term conservation behavior in the context of drinking water and energy use. Long-term conservation behavior was measured as either persistent behavior, maintaining behavioral change, or intentions to maintain behavior, each with specific determinants. We found four key factors for long-term conservation behavior: consumption feedback, household characteristics, effort, and motives for conservation behavior. For future studies, we suggest follow-up questionnaires or interviews to measure the persistence of behavior and differentiate between curtailment and efficiency behavior. Worthwhile avenues for future research on long-term conservation behavior are household-tailored feedback mechanisms and the interaction between contextual factors and effort-based choices.
... Shi et al. (2020) developed a LASSO regression model to identify the main drivers of direct or indirect HCEs. Belaid and Garcia (2016) conducted an empirical study on the main factors that motivate energy saving behavior in (Besagni and Borgarello, 2019) and Quantile regression model (Tilov et al., 2020) are frequently used to explain the effect of factors such as social-economic and household characteristics on HEC. ...
Households are an important sector in carrying out human development activities, accounting for more than 30% of the total global energy consumption. The continued growth of household energy consumption (HEC) and carbon emissions is threatening economic and environmental sustainability. This review focuses on the research in the field of HEC and conducts a bibliometric analysis of research articles from the Web of Science Core Collection since 2000. The results show that: 1) HEC research has undergone rapid development since 2014, and interdisciplinary fusion and collaborative research have become dominant trends. 2) Keyword co-occurrence analysis clearly identifies the current urgent themes, including energy demand and its determinants, environmental impact factors and assessments, and energy-saving technologies and emission reduction measures. 3) The analysis of citations reveals that economic models, such as input-output models and life cycle assessment, are frequently employed in the field of HEC. Based on a summary of household energy-saving and emissions reduction work, this paper critically discusses the limitations of existing measures such as smart home technology, sustainable energy systems, and behavioral interventions. The main directions for promoting household energy-saving development in the future are identified: including improving the security and customer engagement of smart home technology, focusing on the availability and stability of sustainable energy, and paying more attention to low-income and aging households in behavioral intervention measures. One of the important obstacles facing research is how to reduce energy management efficiency and usage costs through technology and policy.
... As shown in Table 2, there exists a disparity in the assessment of how urban form and population density influence residential energy consumption among researchers, a phenomenon stemming from various underlying causes. [8,9,11,[14][15][16][17]19,[81][82][83][84][85][86] It does not affect the amount of REC [10,23,[71][72][73][74] The role of density on REC The role of density publications Positive correlation [34,35,40,54,67,75] Negative correlation [8,9,28,[30][31][32][33][36][37][38][39][41][42][43]46,47,[49][50][51][52]56,57,60,64,66,68,[76][77][78] Not disclosed (Not. Sig) [29,45,48,53,55,59,61,62,65,[69][70][71][72][73][74]82] Note. ...
... They ascribe the increase in energy demand to the penetration of air-conditioning and high-consumption appliances [12]. In contrast, other studies argue that there is a positive correlation between the age of the building and REC [8,9,30,35,46,48,51,53,56,60,61,71,73,74] which means energy demand would go up by increasing the dwelling age. They interpret this observed result by emphasizing improving insulation [91] and energy efficiency [35] in recent buildings, thus lowering the total energy demand. ...
... Interestingly, they ascribe it to the use of more energy-intensive appliances by the average person in a rural household and argue that necessities are more energy-intensive than luxuries [36]. Furthermore, this phenomenon can be attributed to sprawl development, as well as substantial disparities in housing types and population density between urban areas and suburban or rural regions [28,31,37,46,49,50,52,76,77]. In contrast, Tso & Guan divulge that if a house were in an urban area rather than a rural area, the expected average household energy consumption would be increased by 61.722 kWh/year [35]. ...
The first step for reducing residential energy use is identifying the effective variables in this sector. This review paper extracts 10 urban form variables and discusses their correlations, interpretations, and frequencies alongside methodologies used to quantify their impacts. This review also identifies the parameters that cause mixed effects of density on residential energy use in different studies. Multinomial logistic regression is used to quantify the odds of obtaining a positive or non-significant association rather than a negative correlation. The model can predict the impact of density on residential energy consumption in almost 80% of the cases based on the identified parameters, namely the number of indicators considered in the model as the proxy of density, type of energy, unit of measurement, methodology, data reliability, published year, relevancy, geographical location of case studies and their climate classifications. The result shows that while density correlates negatively with residential energy use in cold climates, its impact could be considered positive in temperate regions.
... Given that the electricity production at day d is usually highly influenced by preceding hydrometeorological conditions, our ML models include moving-averages for the discharge over the previous weeks (i.e. 7, 15, and 30 days). We believe that using only discharge to reconstruct HP is a reasonable choice and sufficient to reconstruct HP because (i) the actual discharge (Q) is the dominant driver of HP (Schaefli et al 2019) and (ii) potential temperature effects on HP demand (Tilov et al 2020) are indirectly also included in Q, which strongly depends on temperature. In addition, the day of the year was included as a predictor, as a proxy for human factors that influence HP (e.g. ...
low-carbon power system, being a renewable energy source that can complement solar and wind power, which are highly intermittent. However, hydropower is itself dependent on local weather conditions and climate variability. Moreover, extreme climate conditions, such as hot-dry compound events, can have a major impact on hydropower production (HP). Here, we examine the impacts of hot-dry conditions on HP under current and future climate scenarios in Switzerland, a country where hydropower provides the biggest share ~60\%) of renewable electricity. Overall, our results point out that the impacts of hot-dry conditions on HP are case-specific. We found that hot-dry compound conditions during the warmer months negatively impact HP in power plants with little or no water storage capacity (run-of-river schemes). On the contrary, schemes with large, seasonal accumulation lakes and significant glacier resources benefit from hot-dry conditions in summer even in the future, which is an important result for Alpine hydropower.
... In this regard, a policy shift from purely technically oriented efficiency programs toward a mix of technological and behavioral change campaigns could represent a valuable effective strategy. In line with other recent contributions, we also believe that targeting heterogeneous groups of households to observe their different capacity for changing behavior may additionally contribute to this purpose (Tilov et al. 2020;Podgornik et al. 2016). Idealistically, successful energy-optimization policies shall be able to achieve significant curtails in domestic energy consumption over time and for different income groups. ...
Despite the increasing relevance of urban energy programs targeting energy savings in the residential sector, studies exploring rebounds in domestic electricity demand at the urban scale remain limited. The latter occur when increased housing energy efficiency translating into a decrease in the energy price does not lead to a decrease in residential demand for energy usage. This paper provides the first attempt to derive magnitudes of the direct rebound effects for residential electricity demand utilizing district-level data from the 146 districts of the French city of Nice for the year 2016. For the analysis, we employ both non-spatial and spatial specifications, by which we control simultaneously for both spatial dependence and spatial heterogeneity. From our findings, higher-energy efficiency districts do not register necessarily lower magnitudes of rebound effects compared to lower-energy efficiency districts. On the contrary, the districts of Nice endowed with the most efficient energy-saving technologies denote among the highest rebound effects (around 55%) for energy efficiency. At the same time, the relationship between the rebound effect and household income remains blurry.
... However, households also showed the characteristic of moving from one cluster to another depending on the season. Therefore, residential patterns regarding similar groups are subject to changing patterns that confirms the need for a detailed and fragmented investigation of residential consumption profiles, both between and within chosen socio-techno-economic groups, since electricity profiles are heterogeneous [15]. Similarly, [16] clusters residential consumption of 5566 households in London to investigate consumption behavior. ...
... For specific hours or timeframes, quantile regression is a further option, as applied among others by [15,22,25]. ...
Residential electrification of transport and heat is changing consumption and its characteristics significantly. Previous studies have demonstrated the impact of socio-techno-economic determinants on residential consumption. However, they fail to capture the distributional characteristics of such consumer groups, which impact network planning and flexibility assessment. Using actual residential electricity consumption profile data for 720,000 households in Denmark, we demonstrate that heat pumps are more likely to influence aggregated peak consumption than electric vehicles. At the same time, other socio-economic factors, such as occupancy, dwelling area and income, show little impact. Comparing the extrapolation of a comprehensive rollout of heat pumps or electric vehicles indicates that the most common consumer category deploying heat pumps has 14% more maximum consumption during peak load hours, 46% more average consumption and twice the higher median compared to households owning an electric vehicle. Electric vehicle show already flexibility with coincidence factors that ranges between 5 and 15% with a maximum of 17% whereas heat pumps are mostly baseload. The detailed and holistic outcomes of this study support flexibility assessment and grid planning in future studies but also the operation of flexible technologies.
... Another, possibly one of the most important, factor of energy expenditures is the saving attitude or spending habits of households. Since, it is obvious that heterogeneity among energy consumers that is based on frugality habits may influence the effectiveness of energy policies applied through tax and price policies (Tilov et al. 2020). Studies in the literature argued that motivation to save money on energy bills is an important determinant of energy consumption (Chen et al. 2017;Delmas et al. 2013;Kranz and Picot 2012;Tilov et al. 2020). ...
... Since, it is obvious that heterogeneity among energy consumers that is based on frugality habits may influence the effectiveness of energy policies applied through tax and price policies (Tilov et al. 2020). Studies in the literature argued that motivation to save money on energy bills is an important determinant of energy consumption (Chen et al. 2017;Delmas et al. 2013;Kranz and Picot 2012;Tilov et al. 2020). In our study, it is also found that households that have relatively more money-saving habits consume significantly less energy. ...
Since the energy expenditure of households is an essential component of the total demand for energy, making the energy consumption in dwellings sustainable is vital to manage financial, environmental, and political risks related to energy import and consumption because controlled and sustainable energy expenditure, as one of the most prominent issues in today’s world, depends on a consensus of common sense regarding all the parties in the sector including households. This matter has rendered an investigation into the factors affecting the energy expenditure of households valuable and constituted the motivation of this study. The aim of the study is to determine the factors that affect the entire energy expenditure of households in Turkey, including heating, electricity, kitchen needs, and fuel needs for transportation, and to determine the quantities of these effects. In this regard, household budget research surveys conducted by the Turkish Statistical Institute were used in the study. The study concluded that socioeconomic indicators, particularly the age, marital status, educational background, and income of household responsible people and the technical characteristics of houses, have an effect on the energy expenditure of households. The results of the study provide valuable information to policy makers and decision makers in order to minimize the risks originating from energy import and expenditure by revealing the factors affecting the energy expenditure of households in Turkey.
