Determinants Of Energy Use Research Articles

Dynamics of total and industrial energy use in Turkiye from 1991 to 2019: a case study

AbstractEnergy use is an indicator of economic growth. However, high energy intensity has two main disadvantages. First, low energy efficiency increases a country’s dependence on other countries, especially when the country lacks energy sources. Second, if the country’s energy needs are met using traditional fossil fuels, this increases its CO2 emissions and reduces its air quality. Improving energy efficiency and reducing energy intensity are essential to reach the sustainability targets. This paper investigates the determinants of energy use in Turkiye for the period 1991–2019 by taking a dual approach. First, utilizing the Tapio decoupling factor, the decoupling factor analysis is not only being done for total energy use and real GDP, but also for industrial energy use and industrial income. Second, the factors determining the country’s total energy use are also examined, followed by an investigation of the indicators of energy use in the industry sector, which is highly energy intensive. For the industrial sector, two different decomposition analyses are performed and results are compared. The refined Laspeyres index method is adopted, and for each analysis, three main factors are considered. The empirical findings demonstrate that the income effect and population effect increased Turkiye’s total energy use, whereas the energy intensity effect decreased it. The first decomposition analysis for the industrial energy use reveals partly contrasting results with the previously published articles. For the industry sector, the second analysis show that productivity and employment increased Turkiye’s sectoral energy use; however, the sector’s energy intensity reduced it. Turkiye achieved some success in terms of reducing energy intensity at the sectoral and aggregate levels; however, as the findings of the present study demonstrate, further efforts are needed to lessen the country’s energy dependence and also to achieve future environmental sustainability targets. Trends relating to the determining factors in total and sectoral energy use are also compared in this paper, and some policy implications are presented.

Built structures influence patterns of energy demand and CO2 emissions across countries

Built structures, i.e. the patterns of settlements and transport infrastructures, are known to influence per-capita energy demand and CO2 emissions at the urban level. At the national level, the role of built structures is seldom considered due to poor data availability. Instead, other potential determinants of energy demand and CO2 emissions, primarily GDP, are more frequently assessed. We present a set of national-level indicators to characterize patterns of built structures. We quantify these indicators for 113 countries and statistically analyze the results along with final energy use and territorial CO2 emissions, as well as factors commonly included in national-level analyses of determinants of energy use and emissions. We find that these indicators are about equally important for predicting energy demand and CO2 emissions as GDP and other conventional factors. The area of built-up land per capita is the most important predictor, second only to the effect of GDP.

Environmental wellbeing in the context of sustainable development: Evidence from post-communist economies

Considering diverse national contexts, there are differences in the capacity of countries in terms of their ability to attain sustainability in its three main dimensions. The present study puts the economic, social, and environmental indicators face-to-face from 19 post-communist economies across the 2006–2020 period. It emphasizes the main vulnerabilities at the level of the analyzed countries, concentrates on these weak points, and offers concrete explanations regarding the main social and economic factors, exerting a negative influence on them. Consequently, placing climate and energy, with their major components, i.e., energy use, energy savings, greenhouse gases, and renewable energy, at the center of the analysis, as the major weak points of environmental wellbeing within the analyzed group of countries, the nature of the influence of human and economic wellbeing upon each of them is evidenced using panel data-specific methods (pooled, fixed, and random effects). The general results obtained showed the following: 1) the components of environmental wellbeing registered a different evolution among post-communist economies; 2) climate and energy components were the main vulnerabilities in terms of environmental sustainability; 3) these environmental components were closely linked to both components of economic and social dimensions; and 4) the determinants of energy use, energy savings, greenhouse gases, and renewable energy were different in the selected group of countries. This study draws attention to the fact that the patterns of development applied in the group of post-communist economies seem to strengthen sustainable goals, especially with regard to economic and human wellbeing. Moreover, while directing its focus on the main urgent environmental vulnerabilities and encouraging their strengthening by not putting the economic dimension in the center of interest, it supports the theoretical perspective of sustainable wellbeing, based on sustainability and ecological economics.

Assessment of sustainability and uncertainties of oil markets: mediating determinants of energy use and CO2 emissions.

The current paper investigates the sustainability growth problem in the USA and evaluating the co-integration relationship among all variables, including oil, carbon emission, and consumption of energy. We also determine the impacts of energy consumption on the USA economic growth, government spending, and trade openness. We used the co-integration and popular lag model (ARDL) to find the long-term and short-term relationships between all study variables. The empirical results show that (1) the crude oil prices increase and adverse impact on energy demand and government expenditure during the study periods, and CO2 emission negatively affects USA economic growth. In addition, (2) the innovative accounting method (ICA) results, we used further research to research the causality between study variables. The empirical results propose that oil prices affect the country's economy responsible for more energy consumption, and the causal effect between consumption of energy and economic growth is not relevant. Wastage of energy allows the system to produce more CO2 emissions. Model results find that the one-sided causal effects of economic growth and CO2. We see during the analyses that (CO2) emissions will negatively impact the country's economy. Therefore, country policymakers are expected to change fossil fuel energy to non-fossil fuel energy as an essential component of the USA's economic growth policies.

Heterogeneity in price responsiveness for residential space heating in Germany

More than 80% of the energy expenditures of private households in Germany are spent on space heating and hot water preparation. This creates opportunities for policymakers trying to influence energy consumer behavior. However, for these measures to be effective and efficient, the factors that determine energy usage need to be known. In this paper, we identify the determinants of heating and hot water expenditures for German households, using a panel dataset derived from yearly residential household surveys covering the years 1996–2014. Furthermore, we test for heterogeneity between households using different methods. For the full sample, we find an own-price demand elasticity of heating expenditures ranging from 0.573 to 0.690. A large number of technical and socio-demographic factors are significant determinants of energy use. Additionally, we discover significant heterogeneity in price elasticity between different socioeconomic groups. Our findings have implications for evaluating the effectiveness of policy measures that target influencing energy use across different groups of consumers.

Empirical and modelled energy performance in Kuwaiti villas: Understanding the social and physical factors that influence energy use

This paper investigates the physical and social determinants of energy use in four case study villas in Kuwait. The investigation consisted of a 12 month mixed-method longitudinal monitoring and surveying study followed by building energy modelling. Monitoring and surveying provided empirical data about household energy use, internal temperatures, building and system characteristics and occupant energy-use behaviours. This data was used to ground models to better predict energy use. Models grounded with empirical data show that space cooling accounts for 50% to 75% of total energy use in villas. Results suggest that drivers of cooling energy are varied, with 30% of cooling energy in some villas attributable to heat gains from appliances. Furthermore, while the study found no distinct reduction in measured and modelled annual energy use intensities of villas with improved levels of efficiency (insulation and double glazing), such measures did contribute to more uniform internal temperatures. The study also identified key occupant energy-consuming behaviours including a tendency not to adjust air conditioning (AC) thermostat settings during periods of summer travel and lack of regular AC maintenance. The approach and findings of the study can inform future studies in Kuwait seeking to understand energy use in a nationally representative sample.

Appraisal of the Determinants of Energy Use in LAPAI Local Government

The paper investigates the determinants of energy use in Lapai Local government area of Niger state, Nigeria using a cross-sectional data randomly collected from 117 respondents in the Lapai. Employing the descriptive statistics and Multinomial Logistics regression model to capture the characteristics of the respondents and the determining factors of energy use in Lapai respective. The empirical result obtained revealed that people use more of the traditional energy sources (firewood and charcoal) because of its availability and affordability. Income level, family size, educational status, and occupational status are the major determinants of the use of the modern energy sources relative to traditional energy sources. The study thus recommends The Government through ministry of power and environment should come up with suitable policies that will shift the attention of using biomass energy source to modern energy source, Government and environmental agencies should introduce safe and sufficient energy source in other to reduce burden on the use of biomass in the country so as to reduce the level of pollution and erosion. The actions of the local government authorities towards the improvement in the availability and access to modern energy source so a s to reduce the use of traditional energy sources as well as improvement in the income level and access to education of individuals in the local government.

Determinants of energy use in the informal food sector

ABSTRACTAlthough enterprises in the informal food sector require energy to transform, cook and process food, energy-use patterns in this sector are not well understood by policymakers and the local-level authorities who regulate their trading activities. This paper reviews relevant literature and presents empirical data collected in Rwanda, Senegal and South Africa on the use of traditional and modern energy sources by informal food sector operators. Our sample includes male- and female-operated enterprises in the urban centres of three African countries where the informal food sector is important, not only for providing the convenience of affordable and readily prepared meals, but also as a source of income for women and men in developing countries. Multiple fuel-use and energy-stacking strategies are common among informal food enterprises and policy needs to acknowledge this if it is to intervene in ways that will benefit both enterprises and regulators.

Appraisal of the Determinants of Energy Use in LAPAI Local Government

The paper investigates the determinants of energy use in Lapai Local government area of Niger state, Nigeria using a cross-sectional data randomly collected from 117 respondents in the Lapai. Employing the descriptive statistics and Multinomial Logistics regression model to capture the characteristics of the respondents and the determining factors of energy use in Lapai respective. The empirical result obtained revealed that people use more of the traditional energy sources (firewood and charcoal) because of its availability and affordability. Income level, family size, educational status, and occupational status are the major determinants of the use of the modern energy sources relative to traditional energy sources. The study thus recommends The Government through ministry of power and environment should come up with suitable policies that will shift the attention of using biomass energy source to modern energy source, Government and environmental agencies should introduce safe and sufficient energy source in other to reduce burden on the use of biomass in the country so as to reduce the level of pollution and erosion. The actions of the local government authorities towards the improvement in the availability and access to modern energy source so a s to reduce the use of traditional energy sources as well as improvement in the income level and access to education of individuals in the local government.

A data-driven predictive model of city-scale energy use in buildings

Many cities across the United States have turned to building energy disclosure (or benchmarking) laws to encourage transparency in energy efficiency markets and to support sustainability and carbon reduction plans. In addition to direct peer-to-peer comparisons, the benchmarking data published under these laws have been used as a tool by researchers and policy-makers to study the distribution and determinants of energy use in large buildings. However, these policies only cover a small subset of the building stock in a given city, and thus capture only a fraction of energy use at the urban scale. To overcome this limitation, we develop a predictive model of energy use at the building, district, and city scales using training data from energy disclosure policies and predictors from widely-available property and zoning information. We use statistical models to predict the energy use of 1.1million buildings in New York City using the physical, spatial, and energy use attributes of a subset derived from 23,000 buildings required to report energy use data each year. Linear regression (OLS), random forest, and support vector regression (SVM) algorithms are fit to the city's energy benchmarking data and then used to predict electricity and natural gas use for every property in the city. Model accuracy is assessed and validated at the building level and zip code level using actual consumption data from calendar year 2014. We find the OLS model performs best when generalizing to the City as a whole, and SVM results in the lowest mean absolute error for predicting energy use within the LL84 sample. Our median predicted electric energy use intensity for office buildings is 71.2kbtu/sf and for residential buildings is 31.2kbtu/sf with mean absolute log accuracy ratio of 0.17. Building age is found to be a significant predictor of energy use, with newer buildings (particularly those built since 1991) found to have higher consumption levels than those constructed before 1930. We also find higher electric consumption in office and retail buildings, although the sign is reversed for natural gas. In general, larger buildings use less energy per square foot, while taller buildings with more stories, controlling for floor area, use more energy per square foot. Attached buildings – those with adjacent buildings and a shared party wall – are found to have lower natural gas use intensity. The results demonstrate that electricity consumption can be reliably predicted using actual data from a relatively small subset of buildings, while natural gas use presents a more complicated problem given the bimodal distribution of consumption and infrastructure availability.

Performance Evaluation of Well-Insulated Versions of Contemporary Wall Systems—A Case Study of London for a Warmer Climate

Climate change and its consequences are of a great concern and the likely increasing temperature would add more dilemmas for the choice of passive design options. The performance of building envelopes is one of the key determinants of energy use and thermal comfort. This research presents an evaluation of commonly used construction systems (lightweight and heavyweight) with different levels of thermal mass. The performance of different construction combinations is quantified on the basis of their impact on thermal comfort and energy consumption for current and future time slices in London, UK where climate change impact is expected to be extreme. A flat model is examined as a case study to model the performance of the construction systems with low, medium and high level of thermal mass. The dynamic thermal simulation software used was DesignBuilder, which employs EnergyPlus as its calculation engine. In essence, this study establishes a new approach for assessing the performance of well-insulated contemporary construction systems on the basis of overall annual thermal comfort hours and energy consumption. Results indicate limited advantage of heavyweight construction systems in a changing climate.

Benefits and challenges of urbanization and low carbon energy needs in India

Urbanization has occurred rapidly in India principally due to social, economic and political drivers and has offered improved quality of life, access to amenities and economic opportunities for many. However, this has been accompanied by challenges that include insufficient energy, lack of urban infrastructure, and poor delivery of basic ser-vices, resulting in undesirable environmental impacts, congestion, and urban sprawl. India’s urbanization has placed tremendous demand on the country’s resources. Providing energy to all while maintaining a low carbon footprint is a global priority. Although economic development is anchored by both urbanization and industrialization, urbanization itself is a major determinant of energy use, including energy use related to transportation. Deficiencies in urban planning and management have to be overcome if India’s urban environment is to meet the rising expectations of an expanding urban population and provide an environment consistent with rapid, inclusive and sustainable growth. India's energy demand in 2030 is likely to be double that of current demand. Achieving a greener future in a sustainable way with low energy costs can be addressed by measures such as preferential policies towards renewables, investment in technology and empowerment of local government to meet the low carbon energy needs in India.

Estimating energy savings from behaviours using building performance simulations

ABSTRACTOccupants’ behaviours are a major determinant of energy use in buildings. The related savings potential has been insufficiently exploited. Although research has addressed behavioural savings through real-world interventions and quantitative modelling approaches, it has not yet explored the full variety of household activities. This work proposes an integrative modelling approach of energy behaviours in the residential setting as a tool to estimate the behavioural impact of households on energy consumption. It uses building energy performance simulation (BEPS) tools to exploit the behavioural savings potential associated with usage and investment energy behaviours when using different energy services in daily household activities. Simulations have estimated significant behavioural savings potential associated with energy behaviours, which may be materialized if some forms of behaviour are induced. Investment behaviours have higher savings potential than usage behaviours, and the behavioural savings potential per energy service is proportional to the energy consumption breakdown. BEPS tools enable a quantitative estimate of the behavioural impact on energy consumption, but further improvements to these tools are needed to incorporate the complexity of behavioural dimensions. Estimating the behavioural savings potential is important for a more effective design of behaviour change interventions, which in turn will support more effective energy efficiency policies.

Production Risk, Energy Use Efficiency and Productivity of Korean Industries

Korea imports all of its primary energy, which leads to high dependency and vulnerability related to its energy supply. Efficiency in the use of energy is a way to reduce dependency and emissions. This study provides empirical results of the stochastic production process in energy use. Special attention is given to the factors that increase the risk or variation of using more of the energy input in production. A dynamic panel model is specified and applied to 25 Korean industrial sectors over the period 1970-2007. The determinants of energy use are identified and their effects in the form of elasticities of energy use are estimated. Stochastic production technology is applied to estimate an energy demand model based on an inverted factor demand. The findings reveal that: first, there are large variations in the degree of overuse or inefficiency in energy use among the individual industries as well as over time; second, information and communication technology (ICT) capital and labor are substituting for energy; and third, ICT capital input decreases the variability of energy demand while non-ICT capital, material and labor increase the variability of energy demand. The results suggest that technical progress contributes more to the increase in the mean energy demand than to the reduction in the level of risk. It is recommended that industries increase their level of ICT capital as well as digitalize and invest more in R&D activities and value added services to reduce the uncertainty related to their demand for energy.

Social, Psychological and Technological Determinants of Energy Use

The traditional supply-side model of energy generation and use is changing rapidly. Two major, inter-dependent problems that electric power grids currently face are sustainability in the long run and dealing with the peaks and lows of demand in the short term.

Determinants of energy use in UK higher education buildings using statistical and artificial neural network methods

Studies were carried out using Display Energy Certificate (DEC) data for university buildings to investigate building parameters as determinants of energy use. A preliminary statistical analysis was conducted of annual electricity and heating fuel consumption across a UK-wide dataset focusing on building activity and internal environment. Using data for London university buildings only, a pilot study was also undertaken to assess the use of an artificial neural network (ANN) method for analysing a wider range of energy use determinants. For University Occupier Buildings (UOB) it was found that generally electricity use is high and heating fuel use is low relative to the CIBSE TM46 benchmarks for the University campus category. For the London university dataset there was appreciable variation in energy use between different university-specific building activities. Activity was also shown to have a high ANN causal strength together with material, environment and glazing type. Prediction performance of the ANN improved with the addition of building parameters: for electricity use the ANN mean absolute percentage error reduced to 34%, a 30% reduction relative to a theoretical benchmark-based approach; for heating fuel use it reduced to 25%, a 49% reduction against the benchmark-based approach. Prediction performance appeared to be restricted however, perhaps owing to the limited number of training patterns. From the pilot study the ANN methodology appears to be viable for use in analysing building energy use determinants. A broader follow-up study is planned accordingly and various measures to develop the ANN methodology are presented.

A review of intervention studies aimed at household energy conservation

This article reviews and evaluates the effectiveness of interventions aiming to encourage households to reduce energy consumption. Thirty-eight studies performed within the field of (applied) social and environmental psychology are reviewed, and categorized as involving either antecedent strategies (i.e. commitment, goal setting, information, modeling) or consequence strategies (i.e. feedback, rewards). Particular attention is given to the following evaluation criteria: (1) to what extent did the intervention result in behavioral changes and/or reductions in energy use, (2) were underlying behavioral determinants examined (e.g. knowledge, attitudes), (3) to what extent could effects be attributed to the interventions and, (4) were effects maintained over longer periods of time? Interestingly, most studies focus on voluntary behavior change, by changing individual knowledge and/or perceptions rather than changing contextual factors (i.e. pay-off structure) which may determine households’ behavioral decisions. Interventions have been employed with varying degrees of success. Information tends to result in higher knowledge levels, but not necessarily in behavioral changes or energy savings. Rewards have effectively encouraged energy conservation, but with rather short-lived effects. Feedback has also proven its merits, in particular when given frequently. Some important issues cloud these conclusions, such as methodological problems. Also, little attention is given to actual environmental impact of energy savings. Often, an intervention's effectiveness is studied without examining underlying psychological determinants of energy use and energy savings. Also, it is not always clear whether effects were maintained over a longer period of time. Recommendations are given to further improve intervention planning and to enhance the effectiveness of interventions.

The analysis of the factors affecting energy use in the commercial buildings of the Wellington CBD

This paper describes the methods used and the results obtained during a study of energy consumption in the commercial buildings of the Central Business District of Wellington, the capital city of New Zealand. Data was collected on the energy consumption, floor area and occupancy of 1,107 buildings, together with their design and operational characteristics. Analysis of the data demonstrated that both floor area and number of occupants provide useful bases for comparison of the intensity of energy use in buildings. Categorising premises within buildings into activity groups produced significantly different mean energy intensities for each activity. The factor most often associated with energy use for all activity groups, and apparently, therefore, the major determinant of energy use in these buildings, was the complexity and degree of centralisation of the HVAC services. The information derived from this data constitutes the most comprehensive picture yet produced for a major collection of New Zealand's commercial buildings. It provides a basis for comparisons with other centre's data and for the eventual production of a national data base.

Workpiece gripping mechanism for industrial robots : Bobby L Higgins assigned to Thermwood Corporation

Many cities across the United States have turned to building energy disclosure (or benchmarking) laws to encourage transparency in energy efficiency markets and to support sustainability and carbon reduction plans. In addition to direct peer-to-peer comparisons, the benchmarking data published under these laws have been used as a tool by researchers and policy-makers to study the distribution and determinants of energy use in large buildings. However, these policies only cover a small subset of the building stock in a given city, and thus capture only a fraction of energy use at the urban scale. To overcome this limitation, we develop a predictive model of energy use at the building, district, and city scales using training data from energy disclosure policies and predictors from widely-available property and zoning information. We use statistical models to predict the energy use of 1.1 million buildings in New York City using the physical, spatial, and energy use attributes of a subset derived from 23,000 buildings required to report energy use data each year. Linear regression (OLS), random forest, and support vector regression (SVM) algorithms are fit to the city's energy benchmarking data and then used to predict electricity and natural gas use for every property in the city. Model accuracy is assessed and validated at the building level and zip code level using actual consumption data from calendar year 2014. We find the OLS model performs best when generalizing to the City as a whole, and SVM results in the lowest mean absolute error for predicting energy use within the LL84 sample. Our median predicted electric energy use intensity for office buildings is 71.2 kbtu/sf and for residential buildings is 31.2 kbtu/sf with mean absolute log accuracy ratio of 0.17. Building age is found to be a significant predictor of energy use, with newer buildings (particularly those built since 1991) found to have higher consumption levels than those constructed before 1930. We also find higher electric consumption in office and retail buildings, although the sign is reversed for natural gas. In general, larger buildings use less energy per square foot, while taller buildings with more stories, controlling for floor area, use more energy per square foot. Attached buildings – those with adjacent buildings and a shared party wall – are found to have lower natural gas use intensity. The results demonstrate that electricity consumption can be reliably predicted using actual data from a relatively small subset of buildings, while natural gas use presents a more complicated problem given the bimodal distribution of consumption and infrastructure availability.

Reducing Energy Consumption in Housing: An Assessment of Alternatives

Physical characteristics of housing far outweigh the importance of household behavior as determinants of energy use. Analysis of residential energy consumption in two metropolitan areas in the north central United States suggests that household behavior affects energy use only within relatively narrow limits determined by the design, construction, and condition of the dwelling. The greatest reduction in per-unit energy consumption can result from changing the design of newly constructed housing. Savings of 20 to 50 percent appear to be readily attainable. Alterations of existing housing would have a more modest effect, from 5 to 10 percent, whereas feasible changes in occupant behavior would reduce energy use only 2 to 5 percent.