Effect of GDP on Electric Energy Consumption

Effect of GDP on Electric Energy Consumption A Regression Analysis of Energy Consumption with Cross-Country Data Abstract This paper reviews four existent studies and performs a cross-country multivariate regression analysis in order to determine the relationship among electric energy consumption, population, land area size, and economic growth as measured by GDP using data from authoritative sources. Results from the statistical tests confirm a positive correlation between the three regressors and the dependent variable. Introduction Energy is as much a part of us and our daily lives as is our very DNA. We need and use energy every single day even more than we may realize and it is available in an array of different forms. This analysis will focus on energy in its electrical form, where it is derived from the flow of electric charge caused by electrical attraction or repulsion between charged particles (Helmenstine, 2017). Since energy 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 Times claims that, in a recent study, the United States was ranked eighth among twenty-three of the world’s top energy-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 demand for and consumption of electric energy. This regression will seek to quantify the effects of a selection of variables on electric energy consumption, specifically examining Gross Domestic Product (GDP), national populations, and land 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 flu ctuations among these variables. I hypothesize that the coefficients on a country’s GDP, population, and land mass are positive when regressed against national, annual electric energy consumption. Review of Previous Literature There are a considerable number of studies that look at the effects of a nation’s production level as an economic component of its energy consumption. One pioneering study by Kraft and Kraft (1978) compiled annualized expenditure data for the time period between 1947 and 1974.   Using a bivariate Sims causality test, results presented a causal, unidirectional relationship from gross national product (GNP) 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 comprehensibility, 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 country’s production values regardless of the geographic location of the production, but GDP is the more commonly utilized method for calculating a country’s e conomic standing and success in the world, so GDP is the particular measure we will use. Mohanty and Chaturvedi (2015) interpreted an extensive assortment of secondhand findings to determine whether electric energy consumption drives economic growth or vice versa. Mohanty and Chaturvedi reviewed forty-seven independent studies to compare the presence and direction of a causal relationship between economic growth and energy consumption. Twenty-six of the articles examined suggested the existence of a causal relationship from economic growth to energy consumption; thirty-two found energy consumption to have a causal relationship to economic growth. Eleven analyses found simultaneous causality between economic growth and energy consumption, and three found no relationship either way. After reviewing the empirical research, Mohanty and Chaturvedi then collected annualized data from India for the time period from 1970-1971 to 2011-2012 and applied the two-step Engle-Granger technique along with the Granger causality/Block exogeneity Wald test. Results suggested that electric energy consumption does in fact fuel economic growth in both the short run and the long run. However, this analysis revolves around Indian data, and the authors conclude that the lack of consensus on the relationship between energy consumption and economic growth is primarily a result of country-specific economic structures, methodology adopted, and varying period of study. In order to build upon this study, I will use a similar time frame, from 2010-2015, and I will include data from one hundred seventy countries to evaluate energy consumption amongst a diverse selection of industrial systems. Ameyaw et al (2007) argues that electricity performs an essential function in the economic development of most countries. The detailed 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 rev ealed 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-run 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 outcome 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, positive influence on variations 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 country’s economic growth. In contrast, the presence of bidirectional causality between energy consumption and GDP is likely to mean that economic growth may demand more energy while greater energy consumption might encourage economic growth. Accordingly, energy conservation attempts may inadvertently stunt econo mic growth. Finally, a lack of causality in either direction would indicate a rise 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 Model Following the empirical literature in energy economics, it is logical to form a multivariate regression model between electric energy consumption and economic growth as follows: ECt = ÃŽ ²0 + ÃŽ ²1Popt + ÃŽ ²2LAt + ÃŽ ²3GDPt + ut, where EC represents energy consumption, Pop is population size, LA represents the land area as determined by the physical size of a country, and GDP is real GDP. The error term, ut, is assumed to be independent and identically distributed (iid) with a mean of zero and a constant variance. GDP, for this experiment, has been calculated as follows: GDP = C + I + G + NE, where C is national consumption, I is representative of investment, G is government expenditure, and NE is net exports which is measured as total imports subtracted from total exports. In accordance with observed research, the estimator coefficient on GDPt is expected to be positive; I further hypothesize that the coefficients on Popt and LAt will also be positive, such that: H0: ÃŽ ²1 ≠¤ 0, ÃŽ ²2 ≠¤ 0, and ÃŽ ²3 ≠¤ 0 H1: ÃŽ ²1 > 0, ÃŽ ²2 > 0, and ÃŽ ²3 > 0 Data Description Data for this study has been collected for the time period between 2010 and 2015 across one hundred seventy countries around the world. The regression will 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 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, a regularly updated, open-access center for international data and statistics. To enhance comprehensibility, GDP values have been adjusted for inflation to reflect current U.S. dollars (USD). Electric energy consumption data were drawn from the U.S. Energy Information Administration (EIA), a government funded organization dedicated to collecting and analyzing impartial, independent energy data. Information from the EIA’s public access website is trusted and used by legislators, 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 electric energy consumption (in billion Kilowatthours) and GDP (in real USD). Containing all one hundred seventy observations, a cluster in the bottom left corner is undeniable, given the exception of a few outliers. Figure 2 adjusts to show a clearer view of the majority of the data, excluding the top ten countries with the highest GDP. Figure 2. Zoomed in view of Figure 1 to exclude outliers Figure 3. Summary statistics for the year 2015   Figure 3 shows the descriptive statistics of each variable with the full one hundred seventy observations included. Results The following table, Figure 4, presents a summarization of the results from four separate regression tests performed on the dataset: As expected, the outcomes offer beta coefficients which estimate a positive correlation between each independent variable and the dependent variable. However, it is interesting 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 coefficient 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 coefficient 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 is 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. Conclusion The 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 than 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 Ghana’s 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 any 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 energy 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, technological advancement in, and national ability and willingness to adopt renewable energy sources as these sources may be more or less efficient and consequently alter our interpretation of the energy consumption data. Including such quantities in my regression would increase the precision of the estimations a nd 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. Bibliography Ameyaw, B., Oppong, A., Abruquah, L. and Ashalley, E. (2017). Causality Nexus of Electricity Consumption and Economic Growth: An Empirical Evidence from Ghana. Open Journal of Business and Management, 05(01), pp.1-10. Cavanagh, T. (2017). Opinion | Why Is America Wasting So Much Energy?. [online] Nytimes.com. Available at: [Accessed 2 Dec. 2017]. Data.worldbank.org. (2017). GDP, PPP (current international $) | Data. [online] Available at: https://data.worldbank.org/indicator/NY.GDP.MKTP.PP.CD [Accessed 2 Dec. 2017]. Eia.gov. (2015). International Energy Statistics. 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