This study has investigated the relationship between energy consumption and economic growth in Brazil during the period of 1980-2008. The co-integration test indicates a long-run equilibrium relationship between variables, andenergy consumption appears to be real GDP elastic. This elasticity suggests that energy consumption has a great positive influence on changes in income. The causality results from the error correction model reveal a unidirectional short-run causality from energy consumption to economic growth and a bidirectional strong causality between them. These findings suggest that Brazil should adopt a dual strategy of increasing investment in energy infrastructure, and stepping up energy conservation policies to reduce any unnecessary waste of energy, in order to avoid having a negative effect on economic growth by reducing energy consumption. In contrast, energy conservation is expected to increase the efficient use of energy and, therefore, enhance economic growth.
Energy is the foundation of economic development and constitutes one of the vital infrastructure investments in social development. Both economy and energy consumption in Brazil have been growing rapidly. In the recent five years (2003-2008), Brazil has experienced greater growth rates in both energy use (4.18%) and income (4.81%) than the global growth rates for corresponding variables. The world’s recent five-year growth rates in energy use and real GDP are 2.97% and 3.43%, respectively. The Olympic Committee has chosen Brazil as the host country for the 2016 Olympic Games, highlighting the fact that Brazil is one of the future bright stars of the world. Official energy projections for Brazil indicate a continuing increase in demand for energy, in the next two decades.
There are numerous studies that deal with the causality relationship between energy consumption and economic growth. The findings from the studies vary not only across countries but also across methodologies for the same country. In a summary of the literature on the causal relationship between energy consumption and economic growth, there is evidence to support bidirectional or unidirectional causality, or no causality, between energy consumption and economic growth.
Evidence in either direction will have a significant bearing on policy. If, for example, there is unidirectional causality running from economic growth to energy consumption, it could imply that energy conservation policies may be implemented with little or no adverse effect on economic growth. Unidirectional causality running from economic growth to energy consumption was revealed by Ghosh [
In contrast, if a unidirectional causality runs from energy consumption to economic growth, reducing energy consumption could lead to a fall in economic growth while increasing it may contribute towards a country’s economic growth. Unidirectional causality running from energy consumption to economic growth was revealed by Shiu and Lam [
On the other hand, if bidirectional causality is found, economic growth may demand more energy whereas more energy consumption may induce economic growth. Energy consumption and economic growth may complement each other and energy conservation measures may negatively affect economic growth. For example, Jumbe [
Finally, no causality in either direction would indicate that energy conservation policies may not affect economic growth, and rise in real income may not affect electricity consumption. Chen et al. [
The purpose of this study is to investigate the causality relationship between energy consumption and economic growth, and to obtain policy implications from the results in Brazil. This purpose is accomplished by the following steps: First, stationarity and co-integration are tested; second, error-correction models are estimated to test for the Granger causality; finally, the F-tests are performed to determine the joint significance levels of causality between the two variables.
The remainder of this paper is organized as follows: Section 2 outlines the model and methodology. Section 3 discusses the data and empirical findings. The final section summarizes and concludes the paper.
For modeling purposes, all of the data were converted into natural logarithms prior to conducting the empirical analysis. Thus, the series can be interpreted in growth terms after taking the first difference into account.
Following the empirical literature in energy economics, it is plausible to form a long-run relationship between energy consumption and economic growth in linear logarithm form, as follows:
where LEC and LGDP represent natural logarithms of energy consumption and real GDP, respectively. The error term, ut, is assumed to be independent and identically distributed with a zero mean and a constant variance. The long-run income elasticity is given by:
The signs of β1 is expected to be positive because a higher level of economic growth should stimulate energy use.
The empirical analysis tests for the existence of a long-term relationship between the variables in Equation (1) while using the vector error-correction model to capture the Granger causality between variables. A three-step procedure is performed. First, we check the integration order of each variable, since various co-integration tests are only valid if the variables have the same order of integration. The three unit root tests Augmented Dickey-Fuller (ADF) [
Second, when all of the series of the same order are integrated, the Johansen maximum likelihood method [
Finally, if all of the variables are I(1) and co-integrated, the error correction model (ECM) is used for correcting any disequilibrium in the co-integration relationship, captured by the error-correction term (ECT), as well as testing for long-run and short-run causality among the co-integrated variables. The ECM for Equation (1) is specified as follows:
where
is derived from the long-term co-integration relationship described in Equation (1). The sign Δ is the first-dif- ference operator; the optimum lag lengths ni and ki are determined on the basis of Akaike’s information criteria (AIC); and µit are the serially uncorrelated error terms. The parameter δ1 is interpreted as being the speed of the adjustment coefficient which measures the speed at which the values of LEC come back to long-term equilibrium levels, once LEC violates the long-run equilibrium relationship. The negative sign of the estimated speed of adjustment coefficient is in accord with the convergence toward long run equilibrium [
The ECM represented by Equation (3) includes both the dependent variables with their own lags and the previous disequilibrium in terms of ECTt-1. This specification can test the short-run and long-run causality among co-integrated variables. In terms of short-run causality in Equation (3), the causality runs from the real output to energy consumption if the joint null hypothesis, γ12i = 0, "i is rejected via a Wald test, whereas the causality runs from energy consumption to the real output if the joint null hypothesis γ21i = 0, "i is rejected. With respect to long-run causality if the null hypothesis δ1 = 0 is rejected, energy consumption respond to deviations from the long-run disequilibrium. If the null hypothesis δ2 = 0 is rejected, then the real output responds to deviations from the long-run equilibrium. Finally, the strong Granger-causality runs from the real output to energy consumption if the null hypothesis γ12i = δ1 = 0, "i is rejected, whereas the strong Granger-causality runs from energy consumption to real output if the null hypothesis γ21i = δ2 = 0, "i is rejected.
This study collects annual data on energy consumption and real GDP for the period between 1980 and 2008 from the Energy Information Administration (EIA) and the World Development Indicators (WDI). Real GDP is measured in US dollars at 2000 prices. Energy consumption is measured in BTU (British thermal unit).
For the time period between 1980 and 2008, the energy consumption-income relationship (
Time series plots of the energy consumption and real GDP, 1980-2008
. Summary statistics for Brazil, 1980-2008.
Energy consumption (Billion Btu) | Real GDP (constant 2000 US$ Billions) | ||||
---|---|---|---|---|---|
Mean | S.D. | CV (%) | Mean | S.D. | CV (%) |
6823.80 | 2063.677 | 30.24 | 577.411 | 124.920 | 21.64 |
. Average growth rates in percentages to 2008 for each variable.
Brazil | World | |||
---|---|---|---|---|
Energy consumption | Real GDP | Energy consumption | Real GDP | |
15 year growth | 5.15 | 3.25 | 2.41 | 3.11 |
10 year growth | 2.75 | 3.36 | 2.57 | 3.09 |
5 year growth | 4.18 | 4.81 | 2.97 | 3.43 |
The ln(energy consumption)-ln(GDP) plots for Brazil, 1980-2008
. Coefficients of Equations (1).
Dep. var. | Indep. var. | ||||
---|---|---|---|---|---|
LGDP | Intercept | Adj-R2 | JB | p-val. | |
LEC | 1.454* (24.977) | −0.431 (−1.168) | 0.9570 | 1.909 | 0.385 |
Note: Figures in parenthesis indicate t-statistics. * indicates the rejection of a null hypothesis at 1% level of significance.
. Results of unit roots tests.
ADF | PP | KPSS | ||||
---|---|---|---|---|---|---|
Level | 1st diff. | Level | 1st diff. | Level | 1st diff. | |
LEC | −0.2761 | −4.2798* | −0.3208 | −4.2760* | 0.6629** | 0.0882 |
LGDP | 2.2491 | −4.2497* | 0.5498 | −4.2427* | 0.6664** | 0.1337 |
Note: All unit roots (except the KPSS) have a null hypothesis in that the series has a unit root against the alternative of being stationary. The null of KPSS states that the variable is stationary. Individual intercepts are included in test regressions. * and ** mean that the null of the unit root test is rejected at a 1% and 5% level. The lag lengths are selected using AIC.
ence, indicating that they are integrated at order one i.e., I(1).
The next step is to test whether LEC and LGDP are co-integrated and
Co-integration implies the existence of causality, at least in one direction. However, it does not indicate the direction of the causal relationship. Hence, to shed light on the direction of causality, ECM based causality tests are performed. The short-run χ2-statistics, long-run t-statistics and joint F-statistics for Equation (3) are reported in
This study has investigated the causality relationship between energy consumption and economic growth in Brazil during the period of 1980-2008. Granger causality test was used to examine the causal relationship between variables. Prior to testing for causality, the ADF, PP and KPSS unit root tests and Johansen co-integration rank test were used to examine the unit roots and the co-integration. The Johansen co-integration test indicates a long-run equilibrium relationship between energy consumption and economic growth, and energy consumption appears to be real GDP elastic. A 1% increase in the growth of income will lead to an increase of growth in energy consumption by 1.454% in the long run. This elasticity suggests that energy consumption has a great positive influence on changes in income.
A bidirectional strong Granger causality between economic growth and energy consumption implies that the two variables are jointly determined and affected at the same time. That is, an increase in energy consumption raises economic growth and vice versa. This can be explained by at least three factors: scale, technique effects, and energy efficiency. Firstly, the scale effect occurs as energy consumption increase with the size of the economy. Secondly, the energy-income relationship depends on the techniques of production. An improvement in the techniques of production, i.e., the technique effect, may reduce the amount of energy use and increase profitability per unit of production. Finally, in pursuit of continuing economic growth, Brazil’s government will need to put more effort into improving the energy efficiency of energy appliances and equipment, reducing the loss in power transmission and distribution, and introducing various kinds of tariff reforms to control energy consumption patterns. Figures for 2007 show that Brazil consumed 10046 Btu of energy for every dollar of GDP output at market exchange rates, which is only marginally higher than the world energy intensity of 9800 Btu. So, Brazil was the most efficient energy user. The disconcerting note in Brazil’s record of energy use is that, while
Causality results
. Results of Johansen’s co-integration test.
Eigenvalue | Trace Stat. | 5% critical value | Max Eigen. Stat. | 5% critical value | Number of co-integrations |
---|---|---|---|---|---|
0.673 | 36.765* | 25.872 | 29.023* | 19.387 | None |
0.258 | 7.742 | 12.518 | 7.742 | 12.518 | At most 1 |
Note: The optimal lag lengths are selected using AIC. * indicates the rejection of a null hypothesis at 5% level of significance.
. Results of causality tests.
Source of causation (independent variables) | |||||
---|---|---|---|---|---|
Short-run | Long-run | Joint (short-run/long-run) | |||
χ2-statistics | t-statistics | F-statistics | |||
ΔLEC | ΔLGDP | ECT | ΔLEC/ECT | ΔGDP/ECT | |
ΔLEC | 1.116 | −0.199* | 5.640** | ||
ΔLGDP | 9.527* | −0.358* | 11.962* |
Note: The optimal lag lengths are selected using AIC. * and ** indicate a 1% and 5% level of significance, respectively.
energy intensity has decreased by an annual average rate of 0.27% in the South and Central American region as a whole, Brazil has shown an annual average increase of 0.26% in energy intensity since the nineties. But it is still credible that the energy intensity in Brazil is almost a third lower than that of Venezuela, the largest source of oil in South America. However, Brazil is the 10th largest energy consumer in the world and the third largest in the Western Hemisphere, behind the United States and Canada. Thus, an improvement in energy efficiency is essential. Thus, Brazil should adopt a dual strategy of increasing investment in energy infrastructure, and stepping up energy conservation policies to reduce any unnecessary waste of energy, in order to reduce emissions and avoid having a negative effect on economic growth by reducing energy consumption. In contrast, energy conservation is expected to increase the efficient use of energy and, therefore, enhance economic growth.
*Corresponding author.