Renewable and Non-Renewable Energy ...

Renewable and Non-Renewable Energy Consumption and Economic Growth in India Abstract Ramphul Ohlan The emergence of renewable energy as an alternative to non-renewable energy sources has garnered a great deal of attention in crafting of sustainable energy policies. Motivated by this, the present study investigates the impact of renewable and non-renewable energy use on economic growth in India within the energy consumption–growth framework over the period 1971-2012. A newly proposed Bayer and Hanck combined test and autoregressive distributed lags bound testing approach to cointegration and vector error correction model for Granger casualty are used in a multivariate framework wherein trade openness and financial development are included as additional variables. Empirical evidence confirms the existence of a long run equilibrium relationship among the competing variables. The results indicate that non-renewable energy consumption has a long run significant positive effect on India's economic growth. In sharp contrast, the long run elasticity of economic growth with respect to renewable energy consumption is found to be statistically insignificant. In addition, it is shown that a bidirectional causality exists between nonrenewable energy use and economic growth in both the long run and short run. Based on the findings, it is suggested that a non-renewable energy conversation policy may retard economic growth in India if initiated without due regard to renewable energy sources. Keywords: Non-renewable energy, Renewable energy, Economic growth, India, Causality 1. Introduction Non-renewable energy has fueled the world economic growth for many decades. The depletion of non-renewable energy sources and the problem of global warming, however, have recently attracted wide attention toward developing alternative energy sources (Simsek and Simsek, 2013). With the advances in technology and quest for environmental sustainability, renewable energy sources are becoming increasingly significant alternatives. At the same time, renewable energy sources in most of developing and emerging economies are largely undeveloped while simultaneously these countries are participating in a global transition to clean and low-carbon energy systems. So, the emerging issue for empirical investigation in energy led economic growth literature is that whether a transition from nonrenewable energy to renewable energy can sustain economic growth in developing countries (Maji, 2015; Bhattacharya, et al. 2016). Indeed, examination of the relative effects of renewable and non-renewable energy sources on economic growth provides valuable insights to design and implement sustainable energy and environmental policies (Apergis and Payne, 2012; Omri, 2014). The objective of the current study is to examine the impact of both renewable and non-renewable energy use on economic growth in India. The choice of India for empirical 1

analysis is actuated by the subsequent main reasons. Firstly, India is one of the fastest growing energy dependent countries (Cheng, 1999; Paul and Bhattacharya, 2004; Mallick, 2009; Wolde-Rufael, 2010; Mandal and Madheswaran, 2010; Ramakrishna and Rena, 2013; Ohlan, 2015). Indeed, it is the world's third largest consumer of energy after China and United States with a consumption level of 872 million tons of oil equivalent in 2014. Secondly, India's energy mix is increasingly dominated by fossil fuel. For instance, the share of fossil fuel in total energy consumption has remarkably gone up from 37.05% in 1971 to 73.64% in 2012, a 36.59 percentage points increase. Thirdly, this transformation in the composition of energy consumption has been closely tied to a spectacular economic growth that has brought rising levels of prosperity and social benefits to the country. Additionally, in regard of India's current energy scenario, it may be noted that one third of its total energy demand is met by imported fossil fuels. Although, India's energy consumption per capita continues to be far below the developed countries. However, the same is expected to rise sharply in the near future exerted by a quest for improved quality of life and scope for rapid development of manufacturing sector under recent initiatives, viz. Make in India, National Industrial Corridors, Digital India and Startup India. The growth in energy supply in the country is not likely to keep pace with increasing demand on the other hand. Consequently, the country's reliance on energy imports is expected to increase further in the years to come. Any shortfall in fossil fuel import due to an unforeseen geopolitical situation may cause acute energy scarcities which can consequently impede India's economic growth (Sen et al., 2015). Moreover, the energy and climate change agenda has taken center stage in the domestic and international policy arena. India is ambitiously well placed to build on this momentum. In order to meet the ambitious target of green growth pledged in Intended Nationally Determined Contributions of climate conference held in Paris in 2015, India has set to significantly reduce the share of fossil fuel-based sources in total energy consumption over the next 15 years. Accordingly, the development of alternative energy sources and energy conservation have emerged as chief policy objectives in the country. Given underdeveloped renewable energy sources, low level of supportive infrastructure, lack of access to advance technology and financial barriers, efforts to reduce use of non-renewable energy can underpin India's future economic growth. In order to determine which type of energy use is more important to sustain the pace of India's economic growth, it is imperative to examine the long-run and causal relationships between renewable and non-renewable energy consumption and economic growth. 2

So far, no country-specific published study has appeared to simultaneously estimate the short and long run effects of renewable and non-renewable energy use on India's economic growth. The task of the present study is to extend the research on investigation of the relative effects of renewable and non-renewable energy use on economic growth to the case of India. In this way, it advances relevant policy implication for India's energy mix. The discussion in the paper is structured as follows: after introducing the study, Section 2 deals with the literature review of the multi-country and country specific studies on renewable energy consumption and economic growth. Section 3 summarizes the data and describes the methods of analysis used in the study. Section 4 presents and analyzes the empirical findings. Finally, Section 5 concludes the paper and provides policy implications. 3. Methodology The present study ascertains the influence of both renewable and non-renewable energy use on economic growth in India. To achieve this, annual data on energy use per capita were taken from BP Statistical Review of World Energy June 2015. Gross Domestic Product (GDP) per capita (constant 2005 US$) was used as a proxy for economic growth while trade openness (exports + imports as a share of GDP) and financial development (ratio of money supply to GDP) were used as additional variables. The study covers the period from 1971 to 2012 which is dictated by the availability of data. The trend and descriptive statistics of the data series used in the study are presented in Fig. 1 and Table 1 for ready reference. As seen, the log of economic growth, non-renewable energy consumption per capita, trade openness and financial development have shown rising trends and are strongly correlated. It is intuitively clear that energy consumption, economic growth, trade openness and financial development have characterized the Indian economy over the last three decades. While, the series of per capita renewable energy consumption has witnessed a downward trend till 2002 and thereafter it is gradually rising. It indicates that during the 2000s renewable energy consumption has also outpaced the population growth. In addition, there is not any unusual feature in our data.

3

Fig. 1 Trends in variables. Table 1 Descriptive Statistics Statistic

Growth (lnY)

Mean Median Maximum Minimum Standard deviation Skewness Kurtosis Jarque bera (Probability) Observations

Renewable energy (lnR)

1.2587 1.2119 1.6537 1.0200 0.1933

5.0897 5.0893 5.1694 5.0153 0.0537

Nonrenewable energy (lnN) 5.3170 5.3625 6.1297 4.6279 0.4648

0.5688 2.1321 3.5815 (0.1668) 42

0.1165 1.5361 3.8451 (0.1462) 42

0.0232 1.7794 2.6112 (0.2710) 42

Trade Financial openness development (lnO) (lnD) 3.1158 4.7423 3.0120 4.3918 4.1284 7.9932 2.1409 2.3165 0.5483 1.7371 0.3194 2.1028 2.1228 (0.3460) 42

0.4562 2.0656 2.9848 (0.2248) 42

3.1 The Model To estimate the influence of renewable energy consumption and non-renewable energy consumption on economic growth, the functional form of the model is developed as: Yt = f( Rt, Nt, Ot, Ft,)

(1)

where Y= per capita GDP, R = renewable energy consumption per-capita, N = non-renewable energy consumption per-capita, O = trade openness, F = financial development. In accordance with the growth literature (e.g., Murthy et al., 2014; Sebri and Ben-Salha, 2014; Shahbaz et al., 2016), trade openness and financial development are considered as additional factors affecting India's economic growth. 4

Using log-linear transformation of the variables Eq. (1) can be written in a time-series econometric specification as follows: lnYt = β0+ β1ln Rt + β2ln Nt + β3ln Ot + β4ln Ft + φt

(2)

where ln is natural log, β1, β2, β3 and β4 are elasticities of economic growth with respect to renewable energy consumption per capita, non-renewable energy consumption per capita, trade openness and financial development, respectively, β0 is intercept parameter and φ is the error term. 3.2 Cointegration Analysis The long-run relationship among the variables is investigated by applying a joint cointegration test proposed by Bayer and Hanck (2013). This test provides uniform and reliable cointegration results by integrating the findings of four cointegration approaches, namely Engle and Granger (1987), Johansen (1995), Boswijk (1994) and Banerjee et al. (1998) which are expressed by EG, JOH, BO and BDM respectively. This formula is as follows: EG − JOH = −2[ln(PEG) + ln(PJOH)]

(3)

EG − JOH − BO − BDM = −2[ln(PEG) + ln(PJOH) + ln(PBO) + ln(PBDM)]

(4)

where PEG, PJOH, PBO and PBDM represent the probability values of EG, JOH, BO and BDM tests respectively. To conclude whether long-run association is present or not among the series, the Fisher statistic is applied. One can reject the null of no cointegration hypothesis if the calculated Fisher statistics is below the critical value provided by Bayer and Hanck (2013) and vice-versa. Following Shahbaz et al. (2016), Rafindadi and Ozturk (2015), Solarin and Shahbaz (2015) the results of this test are confirmed by applying autoregressive distributed lag (ARDL) approach developed by Pesaran et al. (2001). An unrestricted error correction model (UECM) is derived from the ARDL bound testing approach which integrates the short-run dynamics with the long-run equilibrium without losing any long-run information. The UECM takes the following form: p

q

r

s

t

i 1

j 0

k 0

l 0

m 0

InYt  1    i InYt i    i InRt  j    j InNt k    k InOt l    l InFt m Y InYt 1   R InRt 1   N InNt 1   O InOt l   F InFt 1  t

(5 )

5

where ∆ is the first difference operator and μ is the error term. Pesaran et al. (2001) suggested F-test for joint significance of the coefficients of the lagged level of the variables. For example, the null hypothesis of no cointegration among the variables in Eq.5 is H0: αY = αR = αN = αO = αF = 0 against the alternative hypothesis of cointegration H0: αY ≠ αR ≠ αN ≠ αO ≠ αF ≠ 0. A limitation of ARDL model is that it cannot be applied to determine the existence of cointegrating relations if explanatory variables are integrated of order two, I(2). The Augmented Dickey-Fuller (ADF) (Dickey and Fuller, 1979), Phillips-Perron (PP) (1988) and Vogelsang and Perron (1998) unit-root tests are applied to ensure that the variables are not (I(2). Likewise, the cumulative sum (CUSUM) and the cumulative sum of squares (CUSUMSQ) tests suggested by Pesaran and Shin (1999) are applied to check the long-run stability of the parameters to be estimated. 3.3 Causality Analysis To complement the above, we have also carried out Vector Error-Correction Model (VECM) Granger-causality test. In the presence of cointegration among the series, the VECM can be presented as given below:  11,i 12,i 13,i 14,i 15,i   InYt 1   InYt   1            21,i  22,i  23,i  24,i  25,i   InRt 1   InRt    2  P (1  L)  InN t     3    (1  L)   31,i  32,i  33,i  34,i  35,i    InN t 1         i 1    41,i  42,i  43,i  44,i  45,i   InOt 1   InOt    4      InF      t   5    51,i  52,i  53,i  54,i  55,i   InFt 1   1t  1        2t   2    3  ( ECTt 1 )   3t        4t   4       5  5t 

 6

where (1−L) is showing the difference operator, ECTt−1 is the one period lagged error correction term, derived from cointegrating vector while μ1t, μ2t, μ3t, μ4t and μ5t are residual terms. The statistical significance of ECTt−1, testing applying t-test statistic, confirms the existence of long-run Granger-causality while that of Wald's test  2 statistic for the combined significance of lagged values of variable exhibits short-run dynamics. 4. Results and Discussion 4.1 Unit Root Tests The traditional ADF and PP unit root tests are used to determine the integration properties of the series. The results presented in Table 2 reveal that no series is stationary in 6

their levels since the unit root tests do not reject the null hypothesis for the series in levels. On the other hand, however, the null hypothesis of the presence of unit root is rejected once the series are in the first difference. In other words, the per capita GDP, per-capita renewable energy consumption, per-capita non-renewable energy consumption, trade openness and financial development series are non-stationary in level and become stationary process in their first differences. In short, the variables are integrated of order one, I(1). Table 2 ADF and PP Unit Root Tests Results Variable

ADF Constant and trend 3.5951 (0) -1.1231 (0) -2.7541(5) -2.1952 (0) 1.0194 (0) -2.3093 (0) -0.4306(0) -2.1683 (0) 0.1540 (1) -2.3014 (1) -5.8847* (0) -7.9387* (0) -2.9554* (0) -3.3423* (0) -6.1162* (0) -6.1725* (0) -8.6057* (0) -8.4881* (0) -3.9435* (0) -3.9727*(0) Constant

lnYt lnRt lnNt lnOt lnFt ∆lnYt ∆lnRt ∆lnNt ∆lnOt ∆lnFt

Constant 4.6664 (3) -1.3063 (4) 1.0209(1) -0.4306 (0) 0.4887 (2) -5.9475* (4) -2.8986* (4) -6.1180* (2) -8.3433* (3) -3.7547* (4)

PP Constant and trend -1.1231 (0) 1.0680 (1) -2.4812 (3) -2.1835 (3) -1.6903 (1) -8.6281*(4) -3.2800* (5) -6.1720* (1) -8.2396* (3) -3.6513*(5)

Inference

Nonstationary Nonstationary Nonstationary Nonstationary Nonstationary Stationary Stationary Stationary Stationary Stationary

Note: ( ) indicate lags and bandwidths for ADF and PP unit root tests respectively. * denotes the rejection of null hypothesis of the presence of a unit-root at 1% level of significance. The results of ADF and PP unit root tests become questionable in the presence of structural break. Consequently, the integration properties of the analyzed variables are confirmed using Vogelsang and Perron (1998) break point unit root test. The results reported in Table 3 clearly show that all the variables are 1(1). Since the series are integrated of order (1), we can confidently apply Bayer and Hanck combined cointegration test and ARDL bound testing approach to detect the possible presence of a long run equilibrium relationship among the variables. Table 3 Results of Break Point Unit Root Test Variable

Constant

lnYt lnRt lnNt lnOt lnFt ∆lnYt ∆lnRt

1.0480 (0) -3.1774 (0) -0.2619 (0) -1.9278 (1) -3.5447(4) -7.5004* (0) -4.4718*(0)

Break Year 1993 1991 1983 1990 1999 2002 2002

Constant and trend -2.8152 (0) -0.7185 (0) -3.0152(0) -4.2549(0) -3.3881 (1) -8.1587*(0) -4.0464 (0)

Break Year 2004 2004 1999 1984 2001 2002 2001

Inference Nonstationary Nonstationary Nonstationary Nonstationary Nonstationary Stationary Stationary 7

∆lnNt ∆lnOt ∆lnFt

-6.6226*(0) -9.0374*(0) -5.5048(3)

2009 1986 1999

-6.5769* (0) -9.0785* (0) -6.5362*(3)

2009 1986 1999

Stationary Stationary Stationary

Note: ( ) shows lags for Vogelsang and Perron (1998) unit root test. * denotes the rejection of null hypothesis of the presence of a unit-root at 1% level of significance. 4.2 Testing for Cointegration Reported in Table 4, Fisher statistics for Bayer and Hanck (2013) EG-JOH-BO-BDM test provide the evidence of existence of two cointegrating vectors at 1% level of significance when economic growth and renewable energy consumption are treated as dependent variables. The implication of these results is that there is a long run equilibrium relationship among economic growth, energy consumption from renewable and non-renewable sources, trade openness and financial development in India. Table 4 Bayer and Hanck Cointegration Test Results Estimated model F(lnYt|lnRt, lnNt, lnOt, lnFt) F(lnRt|lnYt, lnNt, lnOt, lnFt)

EG-JOH

Cointegration

55.496234*

EG-JOHBO-BDM 111.03979*

55.299796*

114.73517*

Yes

Yes

Note: * shows rejection of the null of no cointegration hypothesis at 5 per cent level of significance. Critical values at 5% level are 15.845 (EG–JOH) and 30.774 (EG– JOH–BO– BDM), respectively. We used ARDL bounds test to confirm the presence the long run equilibrium relationships among the analyzed variables. The leg length in ARDL model was selected based on Schwarz Bayesian Criterion (SBC). As can be seen from the results presented in Table 5 that the estimated value of the F-statistic is above the upper limit of the bound when InYt and lnRt are used as a dependent variable. It means that the variables are co-integrated. These results supported the results those obtained using Bayer and Hanck method reported in Table 4. Therefore, we are able to conclude that there are two cointergating vectors among lnYt, lnRt, lnNt, lnOt, and lnFt in India over the period from 1971 to 2012. Table 5 Results of ARDL Model of Cointegration Estimated ARDL model

Optima l lag length

F-statistics

Lower Bound critical value at 5% level

Upper Bound critical value at 5% level

Cointegration

F(lnYt|lnRt, lnNt, lnOt, lnFt) F(lnRt|lnYt, lnNt, lnOt, lnFt)

(1,1,0,0 ,0) (1,0,0,0 ,0)

6.3047*

3.2055

4.4778

Yes

9.3581*

3.2055

4.4778

Yes

8

Note: * indicates rejection of the null hypothesis of no cointegration at 5% level of significance. 4.3 Long-run and Short-run Elasticities Estimates The long run elasticities of economic growth with respect to energy consumption from renewable and non-renewable sources, trade openness and financial development estimated using the underlying ARDL model are shown in Table 6. The results of the study indicate that non-renewable energy consumption has a statistically significant positive effect on economic growth in India. Specifically, the magnitude of 0.255 implies that a 1% increase in non-renewable energy consumption per capita leads to a 2.55% increase in GDP per capita in the long-run. It means that non-renewable energy consumption is catalyst to sustain India's fastest economic growth. The result corroborates the findings of Wolde-Rufael (2010). In sharp contrast, the estimate for the coefficient of the effect of renewable energy consumption on economic growth is statistically insignificant at 5% level of significance. In addition, the openness is also found to have a positive effect on India's economic growth in long-run with a fair degree of 0.10% per every 1% increase. Although, financial development is found to have a positive influence on economic growth in India, but its coefficient is not statistically significant at the 5% level. In sum, non-renewable energy consumption and trade openness promote India's economic growth; however, renewable energy consumption is ineffective on economic growth in the long run. These results are consistent with the finding of Dogan (2015) for the effect of electricity consumption from renewable and non-renewable sources on Turkey's economic growth. The policy implication is clear. A heavy reliance on the use of renewable sources of energy is not desirable to maintain India's long-run economic growth.

Table 6 ARDL Model Long Run Coefficients Estimates Variable lnRt lnNt lnOt lnFt Constant

Coefficient 0.1084 0. 2554* 0. 1048** 0. 0170 -1.0209

Standard Error 0.2327 0.0628 0.0432 0.0165 1.3608

T-Ratio [Prob] 0.4658 [0.644] 4.0620 [0.000] 2.4286 [0.021] 1.0334 [0.309] -0.7502 [0.458]

Note: *, ** denote significance at 1% and 5% level respectively. Prob = Probability. Considering the short-run analysis, the results of error correction representation for the selected ARDL model given in Table 7 show that energy consumption from both renewable and non-renewable sources matters for economic growth. An interesting part of 9

the short run result is that a the 1% increase in energy consumption from renewable sources stimulates economic growth by 0.98%, at 1% level of significance. This depicts a potential role of renewable energy in economic growth. Therefore, the Government of India can pay more focused attention to exploration of renewable energy sources to fully harvest its advantage in the years to come. Similarly, a 10% increase in energy consumption from nonrenewable sources promote economic growth by 0.76%. Besides, trade openness also significantly contributes to economic growth in India. Table 7 Results of Error Correction Representation for the ARDL Model Variable ∆lnRt ∆lnNt ∆lnOt ∆lnFt Ecmt-1 Diagnostic tests

Coefficient Standard Error T-Ratio [Prob] 0.98097* 0.30123 3.2565 [0.003] 0.076894** 0.028542 2.6941 [0.011] 0.031572** 0.013838 2.2816 [0.029] 0.0051270 0.0056252 0.91144 [0.368] -0. 30109* 0.093752 - 3.2115 [0.003] R2 = 0.509; F-Stat. F(5,35) = 7.0488*[0.000]; DW-statistic = 2.3532

Note: *, ** denote significance at 1% and 5% level respectively. Prob = Probability. As a summary of the short run analysis, energy consumption from both renewable and non-renewable sources and trade openness positively affect India's economic growth. As expected, the coefficient of lagged error correction term (ECMt-1) is found to be negative with the value of -0.75 (˂1) and statistically significant at the 5% level of significance. It provides the stability of the error correction model and confirms the presence of cointegration among the variables. In addition, the coefficient of ECMt-1 shows that deviations from the long-run equilibrium are corrected by 75% in every year. Moreover, the estimated ARDL model passed the usual diagnostic tests. The goodness of fit of the model was above 0.5 which is preferred in econometric analysis. In testing the stability of the regression coefficients, the CUSUM and CUSUMSQ plots for selected ARDL model are shown in Figures 2 and 3, respectively. As seen, the CUSUM and the CUSUMSQ test statistics are with the critical bounds, indicating that the estimated parameters are stable over the time. Therefore, the results of the select model can be used for policy decision making purposes.

10

20

10

0

-10

-20 1972

1982

1992

2002

2012

Dotted straight lines represent critical bounds at 5% significance level

Figure 2: The Results CUSUM Test 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 -0.2 -0.4 1972

1982

1992

2002

2012

Dotted straight lines represent critical bounds at 5% significance level

Figure 3: The Results CUSUMSQ Test

4.4 Granger-causality Analysis Consistent with the evidence of the cointegration relationship between the variables under analysis, we now turn to causality dynamics. The results obtained from VECM Granger causality analysis are given in Table 8. The correctly signed and statistically significant coefficients of the error correction terms for ∆lnYt and ∆lnNt equations show the 11

existence of feedback effect between non-renewable energy and economic growth in the long run. This implies that adoption of non-renewable energy conversation policy may adversely affect economic growth in India which in turn will further decrease the demand for nonrenewable energy. Based on the statistical significance of the coefficients of ∆lnNt and ∆lnYt in these equations the same inference is drawn for short run as well. This finding is in line with the conclusion reached in Apergis and Payne (2012) for emerging market economies. Table 8 Results of VECM Granger Causality Test Dependent Type of causality variable Short run causality (Wald Test  2 statistic)

  ln Y

t 1

∆lnYt ∆lnRt ∆lnNt ∆lnOt ∆lnFt

…

  ln R

t 1

  ln N

t 1

13.5729*** 9.6211** [0.0088] [0.0473] 1.2328 … 4.2248 [0.8677] [0.3764] 10.3076** 7.2214 … [0.0356] [0.1246] 1.5655 3.2719 3.8202 [0.8150] [0.5122] [0.4309] 2.1909 15.1360** 7.2619 [0.7007] [0.0044] [0.1227]

Long run causality

 ln O

t 1

5.6967 [0.2230] 2.1294 [0.7120] 23.1698** [0.0001] …

  ln F

t 1

2.3278 [0.6757] 9.1017* [0.0586] 10.4809** [0.0331] 0.1619 [0.9969] 10.2571** … [0.0363]

ECMt−1 [P] -0.3874** [0.0467] 0.2128 [0.1216] -0.9242*** [0.0641] -2.4815 [0.2293] 1.3198[0.5513]

Note: *, **, and *** indicate significance at 1%, 5% and 10% levels respectively. [ ] = P = Probability. In addition, financial development, renewable energy and trade openness are found to Granger cause to economic growth in the long run. With regard to second equation, in which renewable energy is dependent variable, error correction term is statistically insignificant which means that economic growth does not drive use of renewable energy in India in the long run. Furthermore, renewable energy Granger causes economic growth in the short-run, but reverse is not found. Hence, India is an energy-dependent economy and any energy or environmental policy drawn up in an attempt to conserve energy will jeopardize the process of economic development. 5. Concluding Remarks and Policy Recommendations This study has extended the research on the long-run and short-run impacts as well as casual relationship among energy consumption from renewable and non-renewable sources and economic growth using a multivariate framework wherein trade openness and financial development are included as additional variables over the period 1971–20012 in the case of 12

India. The results of Bayer and Hanck combined test and autoregressive distributed lags (ARDL) bound testing approach to cointegration reveal the existence of a long-run equilibrium relationship among economic growth, renewable energy consumption, nonrenewable energy consumption, financial development and trade openness. This long run relationship indicates that a 10% increase in non-renewable energy consumption increases real GDP by 6.28%; however, elasticity of economic growth with respect to renewable energy consumption is found to be statistically insignificant. These findings highlight the importance of non-renewable energy sources within the Indian energy portfolio. The short-run estimates show that both renewable and non-renewable energy sources matter for economic growth. A 1% increase in renewable and non-renewable energy consumption stimulate economic growth by 0.98% and 0.07%, respectively, in short run. The results of the error correction model show the presence of bidirectional Granger causality between non-renewable energy consumption and economic growth in India in both the shortrun and long-run. The existence of feedback effect between non-renewable energy consumption and economic growth suggests that the adoption of energy conversation policies will not only limit the India's economic growth but also reduce demand for non-renewable energy in return. This finding suggests that the Government of India should continue to aim for sustained non-renewable energy exploring policies. In the case of renewable energy consumption, we find a unidirectional causality running from renewable energy consumption to economic growth in the short-run. Based on these findings, an increase in non-renewable energy consumption, as a direct input into the production of goods and services, has a positive impact on India's economic growth. Similarly, an increase in economic growth also affects the consumption of non-renewable energy as the demand for goods and services (inclusive of energy) expands with the level of income. In addition, government policy initiatives such as renewable energy portfolio standards, renewable energy production tax credits, rebates for the installation of renewable energy systems, and the establishment of markets for renewable energy certificates would assist in the expansion of renewable energy. Furthermore, the expansion of renewable energy would also assist in reducing the dependence on foreign energy sources, the susceptibility to volatile international oil and natural gas prices, and the long-run environmental consequences of carbon emissions. This is applicable not only to India, but also for several other developing countries with abundant renewable energy sources.

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