Effect of GDP on Electric Energy Consumption

Effect of gross domestic product on Electric Energy ConsumptionA RegressionAnalysis of Energy Consumption with Cross-Country selective informationAbstractThis paper reviews four existent studies and performs a cross- bucolic multivariate regression analysis in distinguish to determine the relationship among galvanising strength white plague, universe, land argona size, and stinting growth as measured by gross domestic product using data from authoritative sources. Results from the statistical scrutinys confirm a incontrovertible correlation amidst the three regressors and the open variable.IntroductionEnergy is as much a part of us and our daily lives as is our very DNA. We lead and use postal code every single day even more than we may trustworthyize and it is available in an array of different forms. This analysis volition focus on goose egg in its electrical form, where it is derived from the flow of electric charge caused by electrical attraction or repulsion min gled with aerated particles (Helmenstine, 2017).Since zip is such an essential part of life as we know it, it is not surprising that the topic has made headlines time and time again. The New York clock claims that, in a new-fangled study, the United States was ranked eighth among twenty-three of the worlds top nil-consuming countries in efficiency, and that, according to Federal data, America loses as much as two-thirds of the power it generates through simple waste (Cavanagh, 2017). Understanding the impact of these statistics and deciding how to improve electric energy efficiency begins with interpreting the motive for and manipulation of electric energy. This regression eitherow for seek to quantify the effectuate of a selection of variables on electric energy consumption, specifically examining Gross house servant Product (gross domestic product), national populations, and land surface area size across diversified countries around the world, and to serve as a reference and aid for policy makers in estimating marginal energy capacity needs in accordance with fluctuations among these variables. I hypothesize that the coefficients on a countrys gross domestic product, population, and land mass are compulsive when regressed against national, annual electric energy consumption.Review of Previous LiteratureThere are a considerable number of studies that look at the do of a nations production level as an economic component of its energy consumption. One pioneering study by Kraft and Kraft (1978) compiled annualized ingestion data for the time period between 1947 and 1974. Using a bivariate Sims occasion test, results presented a causal, unidirectional relationship from gross national product (gross national product) to energy consumption for the United States. In order to adapt and distinguish my analysis from this 1978 study, I will focus on updated data from the time period between 2010 and 2015. Similarly, in order to improve general comprehensi bility, I will regress gross domestic product (GDP), rather than GNP, on electric energy consumption. GNP is a logical and effective variable to use since it quantifies a countrys production values regardless of the geographic location of the production, but GDP is the more commonly utilized method for calculating a countrys economic standing and success in the world, so GDP is the situation measure we will use.Mohanty and Chaturvedi (2015) interpreted an extensive assortment ofsecondhand findings to determine whether electric energy consumption driveseconomic growth or vice versa. Mohanty and Chaturvedi reviewed xlviiindependent studies to compare the presence and direction of a causalrelationship between economic growth and energy consumption. Twenty-six of thearticles examined suggested the existence of a causal relationship fromeconomic growth to energy consumption thirty-two found energy consumption to take a causal relationship to economic growth. football team analyses foun dsimultaneous causality between economic growth and energy consumption, andthree found no relationship either way. After reviewing the a posteriori research,Mohanty and Chaturvedi wherefore collected annualized data from India for the timeperiod from 1970-1971 to 2011-2012 and applied the two-step Engle-Grangertechnique along with the Granger causality/Block exogeneity Wald test. Resultssuggested that electric energy consumption does in fact fuel economic growth inboth the short run and the long run. However, this analysis revolves aroundIndian data, and the authors conclude that the lack of consensus on therelationship between energy consumption and economic growth is primarily aresult of country-specific economic structures, methodology adopted, and varyingperiod of study. In order to build upon this study, I will use a similar timeframe, from 2010-2015, and I will include data from one hundred seventycountries to estimate energy consumption amongst a diverse selection ofindust rial systems.Ameyaw et al (2007) argues that electricity performs an essential function in the economic development of most countries. The detail analysis specifically explores the causality nexus, the estimation of elasticity of energy consumption on economic growth and vice versa, in response to its importance in formulating and implementing energy consumption policy and environmental policy. Ameyaw et al targeted the study around Ghana after discovering that the country has not been evident or represented in much of the existent research. Amassing time series data for Ghana between 1970 and 2014, the study implements the Cobb-Douglas growth model and conducts the Vector Error Correction model in order to strategically verify the error correction adjustment. Finally, similar to the test performed by Mohanty and Chaturvedi, Ameyaw et al exercised the Granger Causality test to determine the direction of causality between electric energy consumption and economic growth. The observed findings revealed the existence of a unidirectional, causal relationship running from GDP to energy consumption. As a means of expanding upon this analysis, I will, as mentioned previously, use cross-country data and more recent data from 2015.Pao et al (2014) performed the final analysis which we will examine in this study. Data for this investigation were collected from Brazil during the time period between 1980 and 2008. Similar to Mohanty and Chaturvedi and to Ameyaw et al, Pao et al applied the Granger Causality test to the dataset. The results revealed a unidirectional, short causality from energy consumption to economic growth along with a bidirectional, robust causality between the two variables. A co-integration test was also implemented, and the topic was the indication of a long-run equilibrium relationship between variables with electric energy consumption seeming to be real GDP elastic, which suggests that energy consumption has a strong, despotic influence on varia tions in GDP. In the acknowledgement of previous literature, Ameyaw et al found evidence to support bidirectional, unidirectional, and no causality. This inconsistency was attributed not only to differences in location and economic structure, but also to the methodologies used in each analysis. The policy and social impacts of each outcome were explained, beginning with unidirectional causality from economic growth to energy consumption, as this paper seeks to prove. Such an outcome may, according to Ameyaw et al, imply that the implementation of energy conservation policies may have little or no adverse effect on economic growth. On the other hand, if a unidirectional causality is found to run from energy consumption to economic growth, then it is possible that reducing energy consumption could lead to a recession in economic growth, and that increasing energy consumption might positively contribute to a countrys economic growth. In contrast, the presence of bidirectional causality between energy consumption and GDP is seeming to mean that economic growth may beseech more energy while greater energy consumption might encourage economic growth. Accordingly, energy conservation attempts may inadvertently stunt economic growth. Finally, a lack of causality in either direction would indicate a draw close in GDP may not affect electric energy consumption, and that energy conservation policies may have no influence on economic growth. It is important to note that all of the data in this study were converted into natural logarithms prior to the empirical analysis so that this series can be interpreted in growth terms rather than raw values. Similar to this study, I will include policy recommendations in the conclusion according to the empirical results from my regression.Specification of the ModelFollowing the empirical literature in energy economics, it is logical to form a multivariate regression model between electric energy consumption and economic growth as followsECt = 0+ 1Popt + 2LAt + 3GDPt + ut,where ECrepresents energy consumption, Pop is population size, LA represents the landarea as determined by the physical size of a country, and GDP is real GDP. Theerror term, ut, is assumedto be independent and identically distributed (iid) with a mean of nada and a constantvariance. GDP, for this experiment, has been calculated as followsGDP = C + I + G + NE,where C isnational consumption, I is representative of investment, G is governmentexpenditure, and NE is net exports which is measured as total imports subtractedfrom total exports. In accordance with observed research, the computing devicecoefficient on GDPt is expected to be positive I providedhypothesize that the coefficients on Popt and LAt willalso be positive, such thatH0 1 0, 2 0, and 3 0H1 1 0, 2 0, and 3 0Data DescriptionData for this study has been collected for the time period between 2010 and 2015 across one hundred seventy countries around the world. The regression w ill be performed using the 2015 data for the following three independent variables population, land area, and GDP. Population is a sensible variable since it is logical to hypothesize that an area with higher(prenominal) population will have a more complex economic and social infrastructure and consequently greater demand for electric energy. Land area is reasonably expected to have the same effect on electric energy consumption as population does, since a larger country likely has a greater population and so on. The final variable to be regressed is GDP since it is a rational measure of economic growth and success. More developed countries, a.k.a. those with higher GDP, commonly have more advanced infrastructures and more taxing industrial and agricultural systems subsequently, greater demand for electric energy is inferential.Population and GDP data were compiled from the World Bank, aregularly updated, open-access center for international data and statistics. Toenhance comprehen sibility, GDP values have been familiarised for inflation toreflect afoot(predicate) U.S. dollars (USD). Electric energy consumption data were drawnfrom the U.S. Energy Information Administration (EIA), a government fundedorganization dedicated to collecting and analyzing impartial, independent energydata. Information from the EIAs public access website is trusted and used bylegislators, policy makers, and statisticians around the world. Figure 1. Cross-country scatter plots of the energy consumption and real GDP, 2015 Figure 1 is a scatter plot showing the relationship between electricenergy consumption (in billion Kilowatthours) and GDP (in real USD). Containingall one hundred seventy observations, a cluster in the bottom left corner isundeniable, given the exception of a few outliers. Figure 2 adjusts to show aclearer view of the majority of the data, excluding the top ten countries withthe highest GDP. Figure 2. Zoomed in view of Figure 1 to exclude outliers Figure 3. Summary s tatistics for the course of instruction 2015 Figure 3 shows the descriptive statistics of each variable with the full one hundred seventy observations included. ResultsThe following table, Figure 4, presents a summarization of the results from four crystallise regression tests performed on the datasetAs expected, the outcomes offer beta coefficients which estimate a positive correlation between each independent variable and the dependent variable. However, it is kindle to note that the intercept value is only statistically significant in the fourth regression, when all variables have been included. Simultaneously, the fourth regression possesses the highest R2 and adjusted R2, which proposes a reliable, positive relationship between the independent variables and electric energy consumption. Regardless of the insignificant intercept terms, each of the first three regressions is worth noting.In the first analysis, population alone is regressed against energy consumption. The coeffi cient on the population is positive and statistically significant at the 1% level. This indicates that countries with larger populations will, at least theoretically, have greater demand for electric energy. The magnitude of the coefficient estimator on population is minimal, such that a unitary increase in population will cause a subsequent increase in demand for electric energy by just 0.00000257 nevertheless, it is a positive influence, and that satisfies our originaly hypothesis. R2 and the adjusted R2 for this test are 0.56 and 0.55, respectively, indicating overall significance in explaining variance among the dependent variable.Land area is treated as the sole regressor in the second regression. Similar to the first regression, the coefficient on land area is positive and statistically significant at the 1% level. One key difference, however, is the value of the intercept term. The first regression shows a positive intercept, while the second has a negative one. The coefficie nt estimator value and magnitude are roughly the same though, with a value of 0.000232 and unsubstantial magnitude. R2 and the adjusted R2 are 0.48 and 0.47, respectively, signifying acceptable importance in explaining variance among the dependent variable.The final simple linear regression performed is the third test which considers GDP as the lone regressor. Again, like the previous two regressions, this test shows a positive coefficient on GDP that is statistically significant at the 1% level. The intercept value is positive, similar to the first regression and different from the second. The coefficient estimator is noticeably smaller in this regression, however, with a value of 0.000000000153. Such a low value suggests questionable magnitude and importance, especially when combined with the inferior R2 and adjusted R2 value of 0.43. The fourth and final regression completed is the test which regresses all three of our independent variables against energy consumption. This test i s the only one which has a statistically significant intercept, but it is similar to the other regressions in that the coefficient on each independent variable is positive and significant at the 1% level. The values on the intercept, population term, land area term, and GDP term are as follows -52.03, 0.00000136, 0.000129, and 0.0000000000498, respectively. The R2 and adjusted R2 share a value of 0.70, explaining an impressive percentage of variation among the dependent variable.ConclusionThe analysis in this paper shows that GDP, population, and land area size all have a positive impact on energy consumption. These effects are statistically significant, even at the 1% level. My results match those of much of the existent literature, including Kraft and Kraft (1978), who use data from 1947 to 1974. This analysis confirms their findings using recent data, suggesting that experimental methodologies adopted by individual researchers may play a bigger role in variations among results th an time periods do. The fact that there is such a lack of consensus among empirical results implies that policy makers should closely examine the techniques used to achieve the results they are given and thoroughly consider the differences in the economic structure of their country compared to countries included in studies. This is exactly what Ameyaw et al (2007) had in mind when they specified their test around Ghanas data, improving applicability of the results to environmental and energy conservation policy makers in the country of Ghana. The conclusions above, however, are indeed subject to a number of limitations. First, it is unclear to what extent these results can be applied to either individual country. Looking at global policy decisions, it is arguable, based on my results, that energy conservation attempts would likely have no negative impact on economic growth and development. However, previous literature has proposed that the relationship between economic growth and e nergy consumption is likely to differ among diverse countries with unique economic structures and geographic conditions. Second, there may be a host of other variables that affect electric energy consumption, such as funding available for, proficient advancement in, and national ability and willingness to adopt renewable energy sources as these sources may be more or less efficient and consequently wangle our interpretation of the energy consumption data. Including such quantities in my regression would increase the precision of the estimations and simultaneously help to eliminate potential omitted variable bias.The ways in which economic growth impacts electric energy consumption are not necessarily clear. A rise in economic growth may be associated with an initial increase in CO2 emissions, which could worsen economic activity or encourage individuals to seek alternative energy sources. As a result, GDP would fall while renewable energy consumption would grow exponentially. Such investigations, however, are left for future research.BibliographyAmeyaw, B., Oppong, A., Abruquah,L. and Ashalley, E. (2017). Causality Nexus of Electricity Consumption andstinting ingathering An Empirical Evidence from Ghana. Open Journal of Businessand Management, 05(01), pp.1-10.Cavanagh, T. (2017). Opinion Why Is America Wasting So Much Energy?. online Nytimes.com. Available atAccessed 2 Dec. 2017.Data.worldbank.org. (2017). GDP,PPP (current international $) Data. online Available at https//data.worldbank.org/indicator/NY.GDP.MKTP.PP.CDAccessed 2 Dec. 2017.Eia.gov. (2015). 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