This paper has presented the first empiricalanalysis of energy consumption for Cyprus. Using annual data from 1960 to 2004, we have examined residential and commercial electricity use, thereby focussing on the two most expanding sectors consuming a continuously higher proportion of electricity, which is the fastest-growing energy form in the island. We analysed the dynamic interaction between electricity use, income, prices and the weather applying widely used time series analysis techniques such as unit root and cointegration tests, Vector Error Correction models, Granger causality tests and impulse response functions. Results show that the long-term impact of income and prices on electricity use is significant, with elasticities similar to those reported for other countries (above unity for income, less than 0.5 for prices in absolute terms). Conversely, weather fluctuations seem to be the most significant cause of short-term variation in electricityconsumption (albeit with small elasticity values), while the effect of income and prices is not significant in the short run. Granger causality tests indicate that electricity prices can be treated as purely exogenous, income and prices clearly Granger- cause electricity use, and there is bidirectional causality between residential electricityconsumption and private income. Overall, the commercial sector is less elastic to changes in income, prices and the weather, and after a one-time shock it tends to revert to equilibrium much faster than the residential sector.
The variable PCRDIjt is defined as the per capita real disposable income in a state j in year t. PCRDIjt is included in the model to “ control ” for the potential impact on commercial and industrial electricityconsumption of greater the household demand for services and goods in state j in year t resulting from a higher per capita real disposable income. In other words, as PCRDIjt rises, so does the consumer demand for consumer goods and services in state j in year t. To the extent that these household services and goods are provided within state j in year t, the greater will be the commercial and industrial consumption of electricity within state j in year t, ceteris paribus. This commercial and industrial demand for electricity is in effect a derived
The unidirectional causality running from GDP to electricityconsumption of this empiricalanalysis would have important policy implications on Vietnam’s economic policies. Although electricityconsumption does not have effects on economic growth in both short-run and long-run, electricity management should be concerned. Because of inefficient electricityconsumption, electricity losses, and artificial low electricity prices, it is hard for electricityconsumption translated into economic growth. Vietnamese government should gradually privatize electricity sector, and eventually creates a competitive electricity market. These activities would create a more competitive electricity market, in which electricity price is determined by market forces, and electricity is consumed more efficiently. It would lead to significantly reduction of ineffective electricity use and eventually electricity shortage. Vietnamese government should restructure power supply to meet an increasing demand for electricity. According to the economic development strategy 2011-2020, annual economic growth is 7-8%, so it creates a higher demand for electricity, which is estimated to increase by 17% yearly. This figure would be higher if domestic electricity supply does not increase sufficiently.
multiple regression models and it was discovered the performance of the IGMMRM is higher than other two models based on historical data. The local kernel regression (ELKR) is introduced by  and how it can be parallelized for large-scale decentralized smart grid scenarios, it can be applied to a new and expensive training process if the data archive is changed. The assignment to local models saves computations, as only the kernel regression model with the closest codebook vector is taken into account for prediction. ELKR has shown signiﬁcantly higher accuracies than common kernel regression or back-propagation, and competitive results compared to linear multiple regression (LMR). The performance of forecasting techniques to formulate prediction models for electricityconsumption is an essential key factor for the development of any country . This is achieved if demand for electricity is predicted accurately. Energy analysts need exquisite guidelines to choose the most appropriate predictive technique in order to provide accurate forecasts of electricityconsumption trends. This paper presents the use of MNM technique to predict electricityconsumption considering historical data from Universiti Malaysia Sarawak; by considering data from 2009 to 2012. The real data consists of electricityconsumption measurements taken during different months for the respective years. The remainder of this paper is organized as follows: Section 2 outlines the models used; Section 3 discusses the empiricalanalysis and results and the last section summarizes and concludes the paper.
Finally, some more variables are significant at the ten percent level (es3, reduc0 and reduc2), but the sign of their coefficient is hardly interpretable or even contradictory. If the respondent answered that she “does not know” the importance of energy saving (es3), she knows more probably her electricity product. The contrary would have been logical. We also expected everyone acting so as to reduce his electricityconsumption, even in different ways - turning off lights (reduc0) or using low intensity bulbs (reduc2) - to have a greater knowledge of the product she has at home. We however do find opposite signs for these variables. The fact that the coefficients are only slightly significant proves that the effects are not well established.
Jordaan, J. A. (2004). Estimating FDI-induced externalities when FDI is endogenous: A comparison between OLS and IV estimates of FDI-induced externalities in Mexico. London School of Economics and Political Science, Research Paper in Environment and Spatial Analysis, 92, 28.
Secondly, there are many studies on household electric power consumption, which mainly focus on the factors affecting the energy consumption of farmers and the negative impact on the environment. China's urban life energy consumption has spatial agglomeration and imbalance, its aggregation shows the positive spatial correlation of the rising trend in the fluctuation, and has the extended accumulation in the time dimension. The impact of the education level of the diverted population, the price of energy, the net income of household agriculture, the size of the household and the availability of biomass energy are introduced into the impact analysis, which further analyzes its causes. Jiang and Neill (2014) and others found that the higher the level of household education, the more inclined to the use of liquefied petroleum gas, solar energy and other new energy-based. Lou Bojie's research results show that household energy consumption increases with the level of the family economy, and Liu shuang and so on research has also proved this point. In many remote rural areas, the long-term development and utilization of traditional biomass energy has led to serious arbors in rural areas. The ecological problems such as water and soil erosion are continuing and the environmental problems in rural areas are still very serious. Xu Yongbing and Zhang Yun (2015) found that the annual consumption of coal in rural areas of Hebei Province has reached 40 million tons, and the scattered coal in the rural areas accounts for 11.9% of the total coal consumption. The contribution rate of these three pollutants has reached the dust 23.9%, 16.9% of sulfur dioxide and 4.9% of nitrogen oxides, of which only rural heating in winter to consume a total of 75% of the total amount of coal in rural areas, the equivalent of the province's emissions of all power plants pollutants. Therefore, in the extremely serious environmental pollution today, optimize the rural energy consumption structure is very important. Although there are a lot of researches on the influence factors of energy consumption in previous scholars, few scholars regard labor transfer as the influencing factor.
This chapter offers a first explanation of the channels that could give rise to differ- ences in the effects of the timing of daylight in the North and the South. As I show, people living in the North and the South get the same overall amount of daylight over the year in principle. However, the North is much colder and has a larger seasonal variation in sunrise times. A simple analysis suggests that in the hot South later sun- rise could lead to lower residential electricityconsumption if this shifts the hours of human activity into the colder morning hours. Such a change could result in a reduced demand for cooling, which is one of the major sources of residential electricity con- sumption in hot areas. However, in the North, temperature-related arguments cannot explain why early sunrise would reduce electricityconsumption since most heating uses fossil fuels. I argue that the extent of people’s waking hours at home (versus at work) can generate a situation where early daylight is associated with lower residen- tial electricityconsumption through changes in the demand for lighting in the dark mornings.
Lower transportation and communication costs results in greater market size and access, more access to information are the combined results of energy and other infrastructure (Toman & Jemelkova, 2002). In general, one can say that energy is an important factor for development, even how hard energy on its own is not easy to produce and distribute. Extending access to affordable electricity energy services is a core point to developing countries, and in that perspective for example Rwandan Energy Utility Corporation Limited has been created, operated with both public and internal generated funds (MININFRA, 2012). Applying such concepts one needs to take into account other factors. For example, a high share of energy expenditure could be due to a high level of consumption (as a result of large household size or high levels of discretionary use or low efficiency of use), more energy might be spent on cooking and lighting, or it could be due to high unit prices of energy, or it could be due to exceptionally low levels of income (Foster & Tre, 2000). However, as electricity energy is linked to economic development of the whole economy, the access to electricity by the citizen of Rwandan economy as a core factor to its development, still at a low level of 19.4% in 2014 as indicated by World Bank (2018) development indicators. This paper highlights the macroeconomic variables which determine the Access to electricity in Rwanda, examines the causal relationship between them thereby drawing up policy recommendations for policy makers throughout the period of 1997 to 2012.
Empirical findings have shown that there are mixed results in terms of the four hypotheses and electricityconsumption - economic growth nexus is still an issue that remains to be resolved. There exist the contractionary results and no consensus about the existence of relationship and direction of causality in the literature and Turkey is no exception. Several of these studies employ bivariate models to estimate the relationship between energy consumption and economic growth. An unidirectional causality relationship running from GNP to energy consumption but not vice versa as found by Kraft and Kraft (1978). Eden (1984) apply Sims’ technique and use the updated data US (1947–1979) to reexamine the relationship between energy consumption and GNP. They find that find no viable relationship between energy consumption and GNP. Kouakou (2011) investigates the causal relationship between the electric power industry and the economic growth for the case of Ivory Coast by adopting data from 1971 to 2008. The eventual of his study found that there is a bidirectional relationship running from electricityconsumption to economic growth and from economic growth to electricity use in the short run. The model supports also evidence of short run unidirectional causality running from electricityconsumption per capita to industrial value added. Shahbaz et al. (2011) studies the relationship between energy (renewable and nonrenewable) consumption and economic growth by employing Cobb–Douglas production function in the case of Pakistan between the periods 1972–2011. The results from the Granger causality analysis confirm the existence of feedback hypothesis between renewable energy consumption and economic growth, nonrenewable energy consumption and economic growth, economic growth and capital.
Energy (electricity) is one among the major indicators of any country. One of the most vibrant constructing slabs of hominid’s life is energy . Energy is a core factor for fiscal development of any country(D. M. s. islam & Ali). In contemporary years, it was examined that most of the oil trade in markets are fronting oil insufficiency due to bulky intake and on the other hand oil fabricating economies are not capable to encounter loads of their customers due to bulky amount of demand of the world(Mallick) .
This study uses the Blinder-Oaxaca decomposition to investigate what are the main reasons that contribute to the changes of household electricity con- sumption. The household data in Taiwan over the period 1985-2015 are used. The empirical results indicate that the changes of household electricity con- sumption are driven by different factors across these three decades. The in- crease in household electricityconsumption is mainly attributed to the changes in the coefficients effect of the determinants. In particular, the coeffi- cients effect of household size plays the most important role. The declining of household size leads to electricityconsumption per capita increases due to the loss of economies of scale. As for the contribution of the endowments effect, the number of air condition and household income are the most important factors. Moreover, the coefficients effect of household size is crucial both for high-income and low-income households. Therefore, the policy implication means that the electricity pricing policy should take household size into con- sideration so as to offer electricity-saving incentives for households with smaller family size. Besides, some strategies, such as improving energy effi- ciency of appliances and providing the subsidy for the investment in ener- gy-efficient appliances, should have a higher priority.
from the existing literature for some aspects. First, as being distinguished from the previous works, it employs not only the cointegration and Granger causality methods but also the ARDL method in order to clarify the direction of relationship with elasticities of electricity intensities. Second, it tries to discover the relationship between industrial produc- tion and electricityconsumption in the industrial sector for both developed and emerging economies in terms of causalities and price elasticities. Furthermore, although studies in the literature based on GDP and aggregate electricity consump- tion or their per capita levels (hence found different results in terms of cointegration and causality), it analyses electricityconsumption and economic growth both aggregate and per capita levels at the same time in order to clarify this difference. Thus, it utilizes ARDL method together with cointegration relationship, causality relationship and elasticities; it extends the empirical literature of energy intensity both to the electricity subcomponent and industry production as being first study in the literature.
import is an important factor for inflation. In their study on electricityconsumption and growth, Karagol, Erbaykal and Ertugrul (2007) determined co-integration between variables and concluded that this relation is positive over the short run but negative in the long run. In the study on effect of input/output price model and energy prizes on industrial expenses, Aydin (2012) stated that any increase in petroleum prices affects production costs of mostly land, air and sea transportation respectively in a decreasing order; Ulusoy (2006) showed that any type of energy consumption has no direct effect but increases of the share of investments in the national income; Kar ve Kinik (2008) stated that there is a two-way causality between electricityconsumption and economic growth and also one-way causality from total electricityconsumption to economic growth; Terzi (1998) showed that energy demands in trade and industry sectors are inelastic from income. However, from the result that price elasticity is meaningful in only the trade sector, it can be said that price policies, aiming to reduce the demand of electricity in trade and industry sector, would not be effective and that policies to increase electricity supply of economic growth are required. Using electricity and growth data, Agir and Kar (2010) deduced that electricityconsumption of provinces of Turkey contributes positively both for income and added-value levels and it is a potential constraint for growth; Bakirtas, Karbuz and Bildirici (2000) concluded that from being, growth and electricity variables are co-integrated in the long term and income elasticity of electricityconsumption is quite high, electricityconsumption will continue to increase rapidly in the future. Azgun (2011) tested relation of total electricityconsumption and its sub-components with GDP and found a long term relation. As for Cetin and Seker (2012), they presented results regarding adverse effects of energy shortage on the economy. Consequently, there are many empirical studies indicating that electricityconsumption is in a linear relation with economic growth and social development.
For our campus dataset, we conduct out-of-sample experiments for two testing periods of 12 months each: Jul 2011 - Jun 2012, and Jul 2012 - Jun 2013. For brevity, we report our observations for the first period only. Analysis of our experiments for the second period yielded similar results, hence we believe our conclusions to be robust. We build one prediction model per building. The averaging and RT models are trained over a period of two years: Jul 2009 - Jun 2011 for the first experiment, and Jul 2010 - Jun 2012 for the second. Since RT is a data-rich model, it can be costly to train as more features are included. For this reason, we picked the feature combination that offers the best prediction accuracy, based on our previous work : day of the week, semester, temperature, and holiday/working day flag. TS is trained using a sliding window of 8 weeks preceding the prediction period, and predictions are made for three horizons: 1, 4, and 24 hours. In the first case, the model is retrained every hour, while in the last two cases, the model is retrained every 4 hours. The objective of using three prediction horizons is to examine how performance changes as a function of how far ahead in time the predictions are made. After a sweep test, we determined the optimal TS parameters ((p, d, q) = (8, 1, 8)) for both testing periods.
Despite the recent developments and renewed Government commitment to expand electricity generation, there is limited empirical work on the relationship between elec- tricity consumption and output growth in Uganda. Earlier micro-founded work by Reinikka and Svensson (2002) suggested that electricity constrained firms may make suboptimal investments—for example in own electricity generation—often at the expense of productive investment. Further, enterprise level surveys indicate that elec- tricity constraints have the biggest drag on firm-level performance and continue to be among the severest constraints that firms have to deal with (World Bank 2013; Mawe- jje 2013). This evidence is suggestive of the important relationship between electricityconsumption and firm growth. However, results from macro-founded research in other developing countries comparable to Uganda have not been conclusive, especially on the direction of causality (Akinlo 2008; Odhiambo 2010).
Energy is an input in an integral part of economic development. An increase demand for energy is a natural consequence of expanding economic activity. The scale of its use is closely associated with its capabilities and the quality of life that its members experience. Worldwide, great disparities are evident among nations in their level of energy use, prosperity, health, political power and demand upon the world’s resources. In present the India’s final energy demand grows faster than the development of its natural resources. To tackle the long run constraints of the present demand and supply trends, drastic changes in the management of the sector is required. India accounts for about 2.4percent of the world’s total annual energy production and for about 3.3percent of the world’s total annual energy consumption. The world wide general evident is that there is positive correlation between percapita income and percapita energy consumption. Now days, the percapita energy consumption is regarded as one of the important indices of economic development.
The low, medium and high variants of projected population from the United Nations World Population Prospects (2000 Revision) are used to project the number of smokers as well as total cigarette consumption to 2010 and 2025. The medium variant is used as the baseline. We report these two years mainly because of 1) the relative proximity of 2010, which nevertheless allows some of the cumulated reduction effect to kick in; and 2) the period up until 2025 is likely to be of greatest interest to the current generation of tobacco workers and growers, because by 2025 a large proportion of them will no longer be involved in the tobacco sector because of death or retirement. We then multiply these population projections by our own projections of per capita consumption and prevalence rates.
This study also explores causal relationship between the variables in terms of the three error-correction based Granger causality models: i) Weak (short-run) Granger causality, ii) Long-run Granger causality, and iii) Strong Granger causality. According to results from three kinds of Granger causality, there are evidences of a unidirectional short-run, long-run and strong causalities running from the electricityconsumption per capita; evidences of a unidirectional short-run and strong causalities running from employment ratio to real GDP per capita. But, there is no causal evidence from the real GDP per capita to electricityconsumption per capita (table no. 6 and figure no. 4). These results confirms “Growth hypothesis” for Turkey which suggests that electricityconsumption plays an important role in economic growth. Thus, any reducing (increasing) in electricityconsumption could lead to a fall (rise) in growth of Turkish economy.
Given the thrust on the deregulation of electricity markets in India since 2003, the short term electricity market with power exchanges in particular have evolved rapidly to support the growth of the power markets in an efficient manner. Since their year of inception 2008, power exchanges are now more efficient and are able to mitigate risks arising from price volatility for the participants to a large extent. The two power exchanges Indian Energy Exchange (IEX) and Power Exchange of India Limited (PXIL) have aided in better utilisation of electricity generated in the country and have taken care of unmet demand for power. Volumes on the power exchange have grown almost 14 times. But the short term market in India is yet to achieve its full potential. In 2013-14, the two power exchanges witnessed constraints on the volume of electricity due to congestion in transmission. During the year 2013-14, the actual transacted volume on power exchanges was 30029.62 MU whereas unconstrained volume was 35621.04MU, leading to a gap of 5591.42 MU amounting to 16% as a percentage of the unconstrained volume.