Determinants Of Energy Demand Research Articles

Climate policy uncertainty and its impact on energy demand: An empirical evidence using the Fourier augmented ARDL model

Global climate change and its subsequent impact on energy demand present pressing issues for policymakers. Existing literature presents various determinants of energy demand, but the intricate relationship between energy demand and climate policy uncertainty (CPU) remains underexplored. Utilizing the Fourier-augmented ARDL (FA-ARDL) model and drawing from monthly data spanning March 1995 to August 2022, we investigate the impact of CPU on energy demand in China. Our study finds a significant long-run co-integration between climate policy uncertainty (CPU) and renewable energy demand. The FA-ARDL analysis shows that CPU negatively impacts renewable energy demand in both the short and long term, as it leads to higher renewable energy prices. These increased prices deter stakeholders from investing in or adopting renewable technologies, making renewables less competitive compared to traditional energy sources. Our findings are helpful for policymakers to communicate the climate objectives since mitigating climate-related uncertainties would substantially drive renewable energy consumption.

Building Function Recognition Using the Semi-Supervised Classification

The functional classification of buildings is important for creating and managing urban zones and assisting government departments. Building function recognition is incredibly valuable for wide applications ranging from the determination of energy demand. By aiming at the topic of urban function classification, a semi-supervised graph structure network combined unified message passing model was introduced. The data of this model include spatial location distribution of buildings, building characteristics and the information mined from points of interesting (POIs). In order to extract the context information, each building was regarded as a graph node. Building characteristics and corresponding POIs information were embedded to mine the building function by the graph convolutional neural network. When training the model, several node labels in the graph were masked, and then these labels were predicted by the trained model so that this work could take full advantage of the node label and the feature information of all nodes in both the training and prediction stages. Quasi-experiments proved that the proposed method for building function classification using multi-source data enables the model to capture more meaningful information with limited labels, and it achieves better function classification results.

Systematic map of determinants of buildings’ energy demand and CO2 emissions shows need for decoupling

Buildings are responsible for a major share of global final energy consumption and carbon dioxide (CO2) emissions. An analysis of the worldwide observed drivers of demand can highlight the policy actions most suited to drive the decarbonization of the building sector. To contribute to such an analysis, we carry out a mapping of the literature on determinants of energy demand and CO2 emissions from buildings. The work includes a list and classification of relevant studies in an on-line geographical map, a description of trends and gaps, and a narrative review. We identify 4080 articles in the Scopus and the Web of Science databases, of which 712 are relevant after screening at the title and abstract level, and 376 are included for data extraction. The literature base mostly addresses electricity and water use, in North America and Europe (57% of the literature) and Asia (27%). Econometric modeling approaches using panel data to calculate demand elasticities, dominate. These findings highlight gaps in terms of the studied variables (only 5% focus on CO2 emissions while a mere 1% have a lifecycle perspective), geographical scope (only 5% of the articles focus on Africa, 7% on Latin America and the Caribbean, and 5% on Oceania), and methodological approach (only 5% use qualitative methods). We confirm that worldwide, income, energy price and outdoor temperature are unequivocal drivers of buildings energy demand and CO2 emissions, followed by other indicators of scale such as population or heated floor area. Our analysis makes it clear that decoupling from rising wealth levels has not been observed. This will continue to challenge reductions in energy use and CO2 emissions from buildings in line with climate targets. Macroeconomic policies focusing on the impacts of income, energy price, population and growing floor area are needed in combination with technical policy to reduce the impact of outdoor climate.

Determinants of energy demand and willingness to pay for improved energy source among household in Mubi metropolis, Adamawa – Nigeria

This paper examines the determinants of households energy demand and energy use in order to provide useful information on energy consumption pattern of Mubi metropolis in Adamawa State – Nigeria. The paper explore various energy sources such as traditional biomass, wood fuel, kerosene, LPG, solar, wind, and electricity and the willingness of the households to shift from one energy source to the other. Secondary sources of data were used extensively for this work and the data were collected from Yola Electricity Distribution Company Mubi branch (YEDC, Mubi) and NNPC depot Yola. The study utilizes econometrics methods of regression analysis and findings from the results shows that energy is the key factor to the welfare of the households and individual energy consumption majorly depends on households’ income and energy prices. This paper reveals that previous studies use limited economic models for analysing household energy consumption pattern which limits its scope of analysis. The paper suggest that there is a need to make a general awareness in Mubi on the importance of using clean energy sources like; LPG and Electric Cookers which improve health and non-hazardous because of the fact that negative relationship exist between household’s energy consumption and energy price.

Human capital and export diversification as new determinants of energy demand in the United States

This paper investigates how education and export diversification contribute to energy demand by also incorporating the role of natural resource, oil prices and income in driving energy demand function for the United States (U.S.) economy. In doing so, we apply the unit root test of Kim and Perron (2009) and the bootstrapping autoregressive-distributed lag (ARDL) cointegration approach developed by McNown etal. (2018), which accommodates the presence of a single unknown structural break in the series. The empirical results confirm that the variables are cointegrated in a long-run relationship. Education is negatively linked to energy demand, and export diversification also decreases this demand in the long-run. On the other hand, economic growth increases energy consumption, while oil prices reduce energy demand in the long-run. Natural resource production affects energy consumption positively. Further, the Vector Error Correction Mechanism (VECM)'s Granger causality analysis reveals a feedback effect between education and energy demand. Export diversification causes energy demand, and in return, energy demand causes export diversification. A bidirectional causality also exists between economic growth and energy demand. The paper also discusses the potential implications of the results.

Impact of Ageing and Generational Effects on Household Energy Consumption Behavior: Evidence from Pakistan

Demographic shift is a worldwide phenomenon, which is mainly common among industrialized nations. However, in the age of fast technology transfer and globalization policy makers cannot undervalue population aging in developing countries, like Pakistan. The relationship between population aging, combined with joint family system, and energy demand has gained importance in Pakistan during the recent times. On the basis of a detailed analysis of micro data spanning over period of 16 years, this study explores the role of generational behavior towards energy consumption, while considering the effects of cohort and age, along with other determinants of energy demand. The decomposition of energy consumption exhibits significant differences in cohort and age effects. The study concludes that, in addition to aging effects, policy makers cannot ignore the recent generation’s trends of spending increasingly more on energy than previous generations.

Determinants of energy demand in African frontier market economies: An empirical investigation

Since the turn of the twenty-first century, the economic performance of Africa has been remarkable. We identify the key factors driving energy consumption from seven frontier market economies in Africa. More specifically, the role of economic, financial, and trade integrations are explored as the source of overall energy demand. First, we establish cross-sectional dependence and heterogeneity across countries. Second, the long-run elasticities of energy consumption reflect the key role of stock market indicators, along with industrialisation and trade openness while foreign direct investment (FDI) inflows have a reducing effect. In this respect, the long-run energy elasticities on individual countries show a considerable variation. Finally, the heterogeneous panel non-causality test confirms that the energy consumption has a feedback relationship with stock market indicators and industrialisation. Our findings reflect that the environmental planning should comprise development in financial and trade sectors in boosting economic growth and increasing energy demand for these countries.

Determinants of energy demand in the French service sector: A decomposition analysis

This paper analyzes the changes in the energy consumption of the service sector in France over the period 1995–2006, using the logarithmic mean Divisia index I (LMDI I) decomposition method. The analysis is carried out at various disaggregation levels to highlight the specifics of each sub-sector and end-use according to their respective determinants. The results show that in this period the economic growth of the service sector was the main factor that led to the increase in total energy consumption. Structure, productivity, substitution and intensity effects restricted this growth, but with limited effect. By analyzing each end-use, this paper enables a more precise understanding of the impact of these factors. The activity effect was the main determinant of the increase in energy consumption for all end-uses except for air conditioning, for which the equipment rate effect was the main factor. Structural changes in the service sector primarily impacted energy consumption for space heating and cooking. Improvements in productivity limited the growth of energy consumption for all end-uses except for cooking. Finally, energy efficiency improvements mainly affected space-heating energy use.

Ant colony optimization approach to estimate energy demand of Turkey

This paper attempts to shed light on the determinants of energy demand in Turkey. Energy demand model is first proposed using the ant colony optimization (ACO) approach. It is multi-agent systems in which the behavior of each ant is inspired by the foraging behavior of real ants to solve optimization problem. ACO energy demand estimation (ACOEDE) model is developed using population, gross domestic product (GDP), import and export. All equations proposed here are linear and quadratic. Quadratic_ACOEDE provided better-fit solution due to fluctuations of the economic indicators. The ACOEDE model plans the energy demand of Turkey until 2025 according to three scenarios. The relative estimation errors of the ACOEDE model are the lowest when they are compared with the Ministry of Energy and Natural Resources (MENR) projection.

Analysis of demand for electricity in the United States

We investigate the lack of general agreement concerning the determinants of energy demand and consider residential, commercial and industrial demand for electric energy. Our Halvorsen-type models permit consistent estimation of total elasticities of demand for the three sectors. The estimated own-price elasticity is above unity for the commercial demand but less than unity for the residential and industrial demands.

Energy Shocks and the Demand for Energy

This paper discusses developments in energy consumption and oil markets since the early 1970s, highlighting the fall in energy and oil consumption per unit of output and the accompanying reduction in industrialised countries' exposure and vulnerability to energy shocks. In an attempt to analyse the determinants of aggregate output, a very simple model is subsequently presented to discuss the major transmission channels of energy price shocks and the related risk of disturbances to long-term equilibrium growth. The focus is then turned on investment and the capital stock and their role in the determination of energy demand. Energy demand equations are empirically derived for seven major countries, distinguishing in each case between demand for energy in industry, transportation and the commercial and residential sectors.

Energy Use in Institutional Buildings: Estimates From State Energy-Audit Surveys

This article examines the determinants of energy demand for nearly 9,000 institutional buildings in the United States. The data were collected, as part of the federal Institutional Conservation Program, by state energy offices using mail surveys. The article presents energy demand estimates adjusted for differences in state surveys as well as for nonresponse bias, as functions of energy prices, building characteristics, and fuel-type variables for approximations of the installed heating ventilation and air-conditioning equipment. Energy price elasticities are found to vary from −.28 for schools to −1.05 for hospitals.

Long term forecasting of energy demand in the developing countries

Forecasting long-term energy demand should be an important component of energy planning in developing countries. Traditional aggregated econometric metods are not well adapted and technico-economic approaches which look in more details at the determinants of energy demand seem better suited. This paper outlines a possible approach to the modeling of energy demand in developing countries.

A Systems View of Energy and Land Use

The role of land use as an underlying determinant of energy demand is often neglected. A systems framework to study land use and energy utilization has been developed. With input from planning organizations, consulting firms, and federal policy makers, the systems models are being used to assess the impact of strategies to achieve energy-conserving land use patterns.

The determinants of energy demand in the EEC area

In this study the energy demand of a fairly homogeneous group of advanced countries is investigated, both individually by country and in total. The aim is to build an energy demand model on economic a priori criteria. From the alternative models tried the one based on a pooled cross-section time-series technique produces the most promising results as it is able to pick up a significant price elasticity. The forecasting performance of the pooled model suggests that there has not been a shift of the demand curve during the crisis period 1971–1974 but simply a movement along it. Finally, based on the findings of the study, forecasts of the EEC energy requirements in 1980 are made.

Residential Demand for Electric Energy

A serious impediment to the design of appro,A-IL priate public policies with regard to the ''energy crisis is the lack of general agreement concerning the determinants of energy demand. This paper considers the determinants of residential demand for electric energy. The results indicate that the long-run own-price elasticity of demand is equal to at least unity, contrary to the common assumption that demand is not responsive to price.