Impact of sectoral decompositions of electricity consumption on economic growth in India: evidence from SVAR framework

The study examines the effects of electricity consumption from different sectors such as agricultural, commercial, domestic, industrial (HV), industrial (LV-MV) and miscellaneous sectors on economic growth over the period of 1981–2019 in the case of India. We used SVAR framework and concluded that the consumption of electricity from agriculture sector has a negative impact on economic growth, whereas the industrial (HV and MV-LV) and commercial electricity consumption has positive impact on economic growth. Similarly, electricity consumption by the domestic sector has less positive effect on economic growth. Further, we computed the total factor productivity growth (TFP) by using the DEA method and showed the effects of sector-wise electricity consumption on TFP as the robustness of our analysis. We obtain similar kind of results. From the policy perceptive, the study suggests that the government must speed up the construction of a power grid to improve the availability of electricity for achieving higher rate of economic growth.


Introduction
In the last 20 years, multiple research are carried out to examine the possible linkage of energy usage and economic development. It is being observed that the world's energy demand is continuously rising (Suganthi et al. 2012). The exponential growth of the human population, migration and urbanisation is the primary driver of global energy consumption growth ; Ouedraogo et al. (2013) and Chen et al. (2007Chen et al. ( , 2017). Electricity power is a prominent source of secondary renewable energy that is obtained through the conversion of primary energy. It is a form of crucial energy resource that is directly related to the country's economy and its citizen's development and wellbeing. Electricity is considered as a critical contributor in the advancement of economic and social development. Raising electrical power usage, particularly in the development of industrial sector, is a vital indicator of a country's increasing per capita income and economic well-being. The rapid development of India's economy has resulted in a substantial increase in electricity demand. Electricity production and consumption have a direct impact on economic growth and development quality. In this context Salahuddin and Alam (2016), Bhattacharya et al. (2016), Rafindadi and Ozturk (2016), Adedokun (2015), Sarwar et al. (2017), Cowan et al. (2014), Hossain and Saeki (2012) and Omri (2014) studied the possible association between the use of electricity and economic development activity with respect to four dimensions (like conservative, growth, neutrality and feedback). Since the economic reform of 1990, industrialisation, urbanisation and agricultural modernisation have all contributed to India's rapid economic development. The electricity consumption in India has witnessed a high surge from the year 2000. The number of residences having access to electric power has spiked dramatically from almost 55% in 2001 to excess of 80% in 2017. In 2014, an electrified Indian family used roughly 90 kilowatt-hours (kWh) per month, which was adequate to support four ceiling fans, four tube lights, a small refrigerator, a television and smaller kitchen equipment during typical Indian consumption efficiency levels, and hours. It accounts for three-quarters of Chinese monthly household consumption, a tenth of US consumption and a third of global consumption. In terms of sum total of power consumption in 2018-2019, the industry sector accounted for the highest proportion (42.0%), followed by the household (24.0%), agriculture (18.0%) and commercial sectors (10%). From 2009-2010 to 2018-2019, energy consumption in the industrial and home sectors grew substantially quicker than in other sectors, with CAGRs of 7.4% and 6.7%, respectively (Energy Statistics, 2019).
Further, electricity is an important driving force to promote the economic and social development. The increasing electricity consumption, especially industrial electricity consumption, is an important symbol of a country's economic development level. With the rapid development of India's economy, electricity demand is also growing rapidly. So, the production and consumption of electricity have a direct impact on the quality and speed of economic growth. Therefore, the purpose of this paper is to investigate an in-depth study of the relationship between electricity consumption in different sectors and economic growth in the case of India. Further, this paper will be beneficial for the policymakers to propose suggestions for achieving India's energy conservation, energy efficiency improvement and sustainable economic growth.
Our investigation differs from the current literature in three ways. First, although few studies (Nasreen and Anwar, 2014;Adedokun, 2015;Asafu-Adjaye (2000); Apergis and Payne, 2010; has focused on the relationship between electricity consumption and economic growth, however, the segregated electricity consumption such as the agricultural, commercial, domestic, industrial (both HV and LV-MV) and miscellaneous sectors lacks in previous literatures. Also, the effect of segregated electricity consumption gives better output as compared to the aggregate electricity consumption over the economic growth. Second, we have used economic freedom and urbanisation as the control variables into our growth equation, which are lacking in the previous studies. By ignoring such important control variables cause under specific bias in the model. Economically, these factors have also crucially determined the relationship between energy consumption and economic growth in developing countries like India. Finally, we have measured the total factor productivity (TFP) and observed the effects of electricity consumption over the TFP. No doubt this give us better results but also able to compare the results for robustness of our analysis.
The remaining of the research is organised as follows. The 'Literature review' section is discussing about the review of literature on economic growth and electricity usage. The 'Data sources and methodology' section represents the research methodology and the source of data. The 'Results and discussions' section reveals the empirical results of the study. The final section discusses the conclusions of the study.

Literature review
With the emergence of the industrial revolution, countries' energy use increased significantly and has continued to rise. Energy is a key input for achieving economic, social and industrial development and for increasing the welfare level. Since the oil crisis of the 1970s, there has been an increasing trend for energy usages in the case of both developed and developing countries (Cleveland and Stern, 2004).
In this context, we have established the relationship between electricity consumption and economic growth with the help of the production function. By following the work of Cleveland and Stern (2004) and Stern (2004), the relationship among them can be represented in the production functions. However, by looking into the interdependent nature of the variables, we have represented it in the system of Eq. (1).
where Y t is the economic growth, EC t is the electricity consumption from agriculture, commercial, domestic, high voltage industries, low and medium voltage industries and miscellaneous sector, UR t is the urbanisations and EF t is the economic freedom.
In this context Kraft and Kraft (1978) and Acaravci et al. (2015) shows the cause and effect association between economic activity and electricity use and reveals that energy consumption promotes economic growth in a one-way fashion. Some other studies (Nasreen and Anwar (2014); Adedokun (2015); Asafu-Adjaye (2000); Apergis and Payne (2010); ) support the feedback and conservative hypothesis between usage of electricity and economic growth. Despite much evidence supporting a unidirectional or bidirectional linkage among renewable energy deployment, and economic growth or any other contradictory research argument that no such relationship exists. In this case, Cowan et al. (2014) Using long-term panel data, investigated the link between economic activity, electric power use and carbon emissions for the BRICS countries from 1990 to 2010. In the cases of India, Brazil and China, the neutrality hypothesis was validated, as was the conservation hypothesis for South Africa and feedback hypothesis for Russia was also validated. However, for the countries like Bangladesh, India and Pakistan, from the year 1976 to 2009, Hossain and Saeki (2011) were unable to determine a causal association between electric power use, exports, remittances and economic activity. In another study, Joyeux and Ripple (2007) found no co-integration between household energy usage and GDP using panel data for some of the East Indian Ocean countries. For the period 1971-2007, Hossain and Saeki (2011) used a VECM model with two important variables on data from Nepal, India, Bangladesh, Sri Lanka, Pakistan and Iran. They identified a co-integration vector in support of the hypothesis for economic growth with the use of electricity in Bangladesh, as well as in India, Pakistan and Nepal; there is support for electricity-driven economic growth, as well as the conservative hypothesis application. Other studies in India demonstrate the impact of energy use on other economic activities. Kumari and Sharma (2016) found similar results between 1974 and 2014 using the same methods. In another study, Ghosh (2009) used a multivariate model to observe the association among electric power utilisation and development in economic growth, adding employment as a variable and substituting supply of electricity for variable electric power use. There was evidence of a co-integration link and one-way Granger causation from electricity supply to employment and GDP. Using a bivariate VAR model, Murray and Nan (1994) and Chen et al. (2007) concluded that India's economic development activity and electricity use were not co-integrated. Similarly, Abbas and Choudhury (2013) advocated for India's neutrality hypothesis. They concluded that, in order to ensure environmental sustainability, India's electricity consumption might be cut without hurting the country's economic growth. Srivastava (2016), on the contrary, used an correction element model of Granger causality on different state-level panel data to discover a two-way association among economic activity and electricity use in India. Similarly, Ahmad et al. (2014) explained the presence of co-integration among economic activity and electricity use, which forces each other, using the ARDL model. Further, we have addressed the summary of relevant literature in Table 6.

Data sources
The research uses the yearly data from 1981 to 2019 to examine the association between sector-wise electricity consumption and economic growth, i.e. agricultural, commercial, domestic, Industrial (HV), Industrial (LV-MV) and miscellaneous sectors of India. The electricity consumption variables are consumed in the agricultural, commercial, domestic, Industrial (HV), Industrial (LV-MV) and miscellaneous sectors. The sector-wise data are gathered from the Foundation database of Economic & Political Weekly. The degree of economic freedom and urbanisation are used as control variables in the model. The data on the degree of economic freedom and urbanisation are gathered from the World Bank Database's,World Development Indicator. Finally, total factor productivity data is consider as an output variable to conduct a robustness check of the results. We have taken the data at constant prices (2011US$) on real GDP as a proxy for economic growth. In order to measured the total factor productivity we have taken the data of real capital stock at constant prices' (2011US$) , the total primary energy consumption and labour force . The real capital stock and real GDP are collected from the database of Penn World (PWT9.0), while data about the labour force is collected from the Indicators of World Bank's, World Development reports and the UNCTAD database. Total primary consumption of energy data is gathered from the repository of US (EIA) Energy Information Administration.

Methodology
We have used the structural VAR to observe the short-run associations between sectoral decomposition of electricity consumption on economic growth in India. The SVAR framework is explained as follows.

SVAR framework
We have used the structural VAR approach model to investigate the relationship between electricity consumption and economic growth in India. We employ the structural VAR approach model proposed by Sims (1980) to show the dynamic relation between oil price, oil demand and economic growth. In most of the studies, the VAR approach is used to analyse the dynamic impacts of different disturbance terms on the variables in the model, as all variables are considered endogenous with the functions in lags (Tiwari, 2011). In such case, SVAR is treated as an attempt to solve the identification problem and utilised to predict the effects of specific policy change in the economy (Tiwari, 2011).
The VAR (p) model is written as follows: where Y t is a (4 × 1) vector comprising of the four endogenous variables, i.e. economic freedom, urbanisation, electricity consumption and economic growth. X t is the lag of the endogenous variables. ′ t is (4 × 1) vectors of residuals. The unrestricted VAR cannot be able to detect the shock of one variable to other variables; therefore, we have used the structural VAR (SVAR) model into our analysis. We represent the SVAR model as follows: where e t and t are vectors of residuals derived from reduced VAR and structural shocks, respectively, A and B are Kth matrices that define the linear relationship between VAR residuals and structural shocks. By following the theoretical relationship among the variable, we have given the restriction and the model is as follows.
where the coefficient a UR Y is the outcome of economic growth (Y) because of unrealised disturbance in the form of rapid urbanisation (UR), a EC UR is the response urbanisation (UR) due to unexpected shock in electricity consumption (EC) and a UR EF is the response of urbanisation due to unexpected shock in economic freedom. In the same way, EF is the result of economic growth (Y) due to unexpected shock in economic freedom and b EF Y is the economic freedom's reaction to an unexpected shock in economic growth. The coefficients e Y t , e EC t , e UR t , e EF t and Y t , EC t , UR t and EF t are the residuals from the corresponding equations in the reduced form VAR and structural disturbance term, respectively.

Measurement of total factor productivity (TFP)
Based on Färe et al. (1994), the Malmquist index can be written in Eq. (5): where (t) is the initial (reference) time period and (t + 1) is the final period. d t 0 x t , y t represents from the period t observation to the period (t + 1) technology. m 0 higher than 1 indicates a TFP growth between both periods while a value of m 0 lower than 1 indicates a TFP decline. The Malmquist in the equation below is representing the productivity of the production point (x t+1 , y t+1 ) relative to the production point (x t , y t ). This index is in fact a geometric mean of two output-based Malmquist TFP indices; one index uses the period (t) technology and the other period (t + 1) technology. To calculate this index, we then need to calculate the four component distance functions, which will involve 4 linear programs (similar to the conducted in calculating the Farrell technical efficiency measures) (Coelli 2008).

Results and discussions
We have checked the stationary of the variables by using Augmented Dickey and Fuller, 1979 and Phillips and Perron (PP) unit root test in order to avoid any kind of spurious relationship among them. once we confirm the form of stationary of the variables then we perform the vector auto regressive (VAR) model. It is because the assumptions of SVAR approach allows estimation of a co-integrating vector with I(1) time series. Using annual data from 1981 to 2019, a structural VAR model is applied to examine the empirical link among growth and electricity usage. The unit root (Dickey and Fuller (1979)) test is used to confirm that the variables are stationary at first difference form. The results are presented in Table 1.
At the level and first difference with intercept and trend, unit root tests were estimated. As recommended by Schwarz, the lag selection was done using the Schwarz information criterion (Pesaran and Shin, 1997). Table 1 shows the results of the ADF and PP unit root tests, which show that all of the variables at the level are non-stationary at first but become stationary after the first difference at the 1% level of significance. The ADF results are confirmed by the PP test, as shown in Table 1. A summary of the PP tests and ADF is given in Table 1, confirming that none of the variables is either I(2) or I(0), so the structural VAR framework can be estimated. Once after confirming the stationary property, we have presented the descriptive statistics and correlation matrix in Table 2 and Table 3 respectively. We observe that the average electricity consumption in agriculture sector is more as compared to commercial and domestic consumption in the case of India. Similarly in the case of high voltage industries, the electricity consumption is more than the low-and medium-voltage industries. Subsequently, the correlation matrix presented in Table 3 rules out the problem of multicollinearity as the values of the correlation coefficient is very small and most of the coefficients are statistically insignificant.
In the next step, the structural VAR model is estimated to investigate the short-run dynamics between the predictors. The outcomes of the model are shown in Tables 4 and 5. Table 4 shows that the coefficient of the outcome of economic growth because of the structural shock of electricity usage in the farming sector is significant statistically, which explains that 1% of structural shock of electricity consumption in the agricultural sector accounts to a decrease in economic growth by 0.34%. In contrast, the outcome of economic growth and development due to the structural shock of electricity consumption in the other sectors (such as commercial, domestic, industrial (HV) and industrial (LV-MV)) is statistically significant and affects positively to the economic growth. This represents that 1% of structural shock of electricity consumption in commercial, domestic, industrial (HV) and industrial (LV-MV) leads to a 0.47%, 0.021%, 0.57% and 0.62% rise in economic growth and development by the electricity usage in corresponding sectors respectively. The other structural coefficients like the response of economic freedom due to economic growth, the response of urbanisation due to growth and the response of growth due to economic freedom exhibit a positive short relationship between them.
The economic consequences of the structural shock in electricity usage are listed below. First, the statistically significant relationships clearly highlights a possibility that there is a short-run association among electricity usage (except the miscellaneous sector) and growth. That means electricity usage in all the sectors except the farming sector has a positive influence on the economic growth, whereas electricity usage in the agricultural sector has a negative influence on growth.
This is due to a number of issues in India's agriculture industry, including a lack of water, inadequate infrastructure, land degradation, inexperienced agricultural labour, the adoption of obsolete farming practises and rising oil/ petroleum prices. The other reason could be an inefficient application of electricity in the farm sector and a lack of awareness among the farming population of India to use energy-efficient high productive farming machinery.
On the contrary, Table 4 shows that the electricity consumption by the industrial sector (both HV and MV-LV) has a substantial beneficial impact on the economy. This represents that 1% of structural shock of electricity consumption in industrial (HV) and industrial (LV-MV) leads to a 0.57% and 0.62% increase in economic growth. This finding is in alignment with the widely held belief that increased energy usage leads to increased economic growth. Abbas and    (2000) also find that the electricity use has a considerable impact on economic growth. It can be explained in the following ways. First, the amount assigned to the industrial sector continues to expand on average, despite India's positive average growth in energy supply. Between 2000 and 2019, the industrial sector's share of electricity supply increased from 32.67 to 46.682%; however, the residential and commercial sectors' shares of electricity supply increased at a slower rate, from 21.27 to 24.76% and 6.44 to 8.24%, respectively (Central Electricity Authority, 2018). Tariffs in the industrial sector have been adjusted downward, according to the Central Electricity Authority, and these rates have been sustainable in many circumstances. The high cost of electricity in the industrial sector as a result of tariff increases could possibly explain the drop in electricity use in the industry.
Similarly, the sharp rise in demand of electricity, particularly from 1996, just the subsequent year after India joined the WTO, could be attributed to increased demand for heating, lighting, cooking and electric appliances, as well as export-oriented industrial expansion (Table 6). Another probable reason for increased growth due to industrial sector electricity consumption is the phenomenon of load shedding becoming less common after 2010, when India's electricity generation capacity was optimised to meet the economy's expanding demands. Furthermore, the industry is distinguished by the utilisation of cutting-edge, energy-efficient machinery. Therefore, it is really not surprising that with the average electricity usage in India increased, the industrial sector's contribution to GDP also increased during the period of study.
Likewise, Table 4 shows that electricity consumption by the domestic sector has a much less positive impact on economic growth. This represents that 1% of structural shock of electricity consumption in the industrial sector responds to an increase of 0.021% in economic growth. The results reveal that rising home energy use contributed to India's economic growth over the study period; this may have something to do with the fact that India's population is growing at an alarmingly fast rate. It is worth noting that the most frequent appliances were owned by a considerable section of the population, with cooking accounting for the highest volume of per year use of total energy. In addition, domestic electrical equipment absorbs 75% of all electricity utilised in households (Central Electricity Board, May 2016). As a result, income growth may appear to be a realistic component in the increase in domestic electricity demand, because higher income levels may enable the purchase and use of more appliances. McNeil and Letschert (2010) According to the report, refrigerators, air conditioners, chimneys and washing machines account for a large portion of the increase in electricity usage in emerging countries. Therefore, when the market is saturated due to the fact that the entire population possesses these enormous appliances, the demand for electricity savings from increasing the efficiency of these large appliances is growing. In this respect, higher income makes it easier to buy energy appliances because it is based on financial ability.
The growth due to increased domestic electricity consumption may also be attributed to some other underlying causes. This could be explained by rising urbanisation and changing lifestyles. Gupta (2018) adds to this by stating that urban lifestyles in emerging countries are getting more energy-intensive. Similarly, Karanfil and Li (2015) found that, with the exception of high-income nations, urbanisation is a significant influence in power use at all income levels, with urbanisation in upper-middle-income countries like India, being the most major driver of electricity use (World Bank, 2018). According to the World   Validate the conservation theory Rafindadi and Ozturk (2016) Trade, commerce, financial expansion, capital formation electricity use and economic activity Japan Support the feedback hypothesis these subsidies in the household sector will make energy use more price sensitive, potentially changing the current relationship between residential electricity consumption and growth. As evident from Table 4, electricity consumption by the commercial sector has a considerable positive effect on growth. This means that a 1% increase in electricity usage in the commercial sector boosts GDP growth by 0.47%. The findings suggest that during the study period, economic growth increased and it can be contributed by increased commercial electricity usage. This may have something to do with the fact that the commercial sector is developing at a high rate in India. This increase can be attributed to the development in the tourism, banking, retail, education and many other service sectors. The tourism industry has shown outstanding growth over the study period, due to which it has contributed significantly to GDP growth. A similar trend has also been observed for the hospitality sector.
Similarly, the unstructural shock of urbanisation on growth shows statistical significance except for industrial (HV) and miscellaneous sectors. In all the other sectors, the coefficient is statistically significant, which indicates that the response of growth due to electricity usage is increasing in all the sectors except in the case of home or domestic sector which is positive. This represents that 1% of structural shock of urbanisation leads to a 0.66%, 0.47% and 0.1.33% increase in GDP growth in agricultural, commercial industries (LV-MV), respectively.
In the next step, we estimate the impulse response function. The results are presented in Fig. 1. The results show the impact of a shock in electricity consumption in the agricultural sector on GDP growth and are presented in Fig. 1a. Over the first to tenth years, the agricultural electricity demand shock has statistically significant and negative effects on economic growth. The impact of a shock in electric power consumption on economic development is depicted in Fig. 1b.
From the first to the tenth years, the shock in commercial electricity use has a statistically favourable impact on GDP growth rate, with the highest effect of shock during the third and fourth years, after which the effect of shock begins to reduce gradually. Figure 1c highlights the effect of shock in domestic electricity consumption on GDP growth. This shows that the shock in domestic electricity consumption has a statistically positive impact on the GDP growth rate from the first to tenth years. Finally, Fig. 1 d and e show that the significant positive effects on economic growth are observed due to the shock of industrial electricity consumption (both HV and LV-MV).

Robustness checking
In order to obtain evidence of the robustness of our results, we calculate the total productivity growth (TFP) by using the DEA method. Moreover, the TFP growth index was constructed using the Malmquist index and was used as the output variable to examine the robustness of the results obtained by using the GDP growth as the output variable. The TFP growth index constructed uses not only labour and capital but also total primary energy consumption as a third dimension to include the effects of energy consumption. This aspect was not used before in studying the behaviour of electrifying consumption with growth variables. In several sectors, the relationship between TFP growth and electricity consumption is established using economic freedom and urbanisation as a robustness check for the established linkage between electricity consumption and economic growth in various sectors. Our results are consistent and are presented in Table 5. Table 5 shows that the coefficient of the response of TFP growth due to the structural shock of electricity consumption in the agricultural sector is statistically significant, which indicates that 1% of structural shock of electricity usage in the farm sector leads to a fall in TFP growth by 0.031%. In contrast, the response of TFP growth due to the structural shock of electricity consumption in the other sectors is statistically significant; the electricity consumption in these sectors affects the TFP growth positively. This represents that 1% of structural shock of electricity usage in commercial industrial (HV), industrial (LV-MV) and miscellaneous sectors leads to a 0.027%, 0.46%, 0.115%, 0.1771% and 0.15% increase in TFP growth by the electricity consumption in corresponding sectors respectively.
Other structural coefficients, such as economic freedom's response to TFP growth, urbanisation's response to TFP growth and TFP growth's response to economic liberty, have a short positive relationship. Similarly, the unstructured shock of urbanisation on growth shows statistical significance except for industrial (HV) and miscellaneous sectors. In all the other sectors, the coefficient is statistically significant, which indicates that the response of growth due to electricity usage in all the arenas except the domestic sector is positive. This means that a 1% urbanisation structural shock leads to 0.17%, 0.077% and 0.52% GDP growth in agricultural, commercial and manufacturing industries (LV-MV), respectively.

Conclusion and policy implications
During the period 1981-2019, this study analysed empirically the linkage between electricity usage in several sectors of India, including agriculture, home, commercial, industrial (HV), industrial (LV-MV) and miscellaneous sectors, and GDP growth. For each sector, various variables linked to electricity usage are proposed. Along with these variables, economic freedom and urbanisation were used as instrumental variables to construct the variable.
Economic growth, electricity consumption, urbanisation and economic freedom have all been included as independent variables in a multivariate time series model. Two unit root tests were performed to determine the sequence of integration for every series included in the model to ensure consistency of results. The results of the PP test support ADF's conclusions that all variables, including electricity consumption, are non-stationary at the level but become stationary at the first difference. The findings of this study were based on empirical evidence. Following that, structural VAR was used to see if there was a short-run link between the variables. The results show the coefficient of the response of a variable due to the structural shock of another variable. We observed that the coefficient of the outcome of economic growth due to the structural shock of electricity consumption in the agricultural sector is statistically significant, which indicates that 1% of structural shock of electricity usage in the farm sector accounts to a fall in growth of economy by 0.34%. In contrast, though the coefficient of the outcome of economic growth due to the structural shock of electricity consumption in the other sectors is statistically significant, the electricity consumption in these sectors affects the economic growth positively. Other structural coefficients, such as economic freedom's response to growth, urbanisation's response to growth and growth's response to economic freedom, have a short positive relationship. It revealed that economic growth, electricity consumption, urbanisation and economic freedom are significantly related among themselves (Salahuddin and Alam, 2016;Masuduzzaman, 2012 andGhosh, 2009). In India, population increase has a considerable impact on overall electricity consumption. Similarly, economic freedom has a significant impact on electricity consumption, which could be explained by the fact that countries with more economic freedom have stronger economies, with higher GDP per capita, which will eventually lead to increased electricity consumption to meet the increased demand to boost economic growth. From the policy perceptive, the study suggests in the following lines. First, in a big country like India, with diversity in all aspects of demography, there are significant variations in development from one corner to another from east to west or north to south of the country and from urban areas to rural areas. Similarly, according to income levels, areas can be divided into four groups. We must accelerate the installation of a power infrastructure in low-and middle-income areas to boost development. Simultaneously, we must work to make the rural power grid and electricity prices equal to those in cities. Second, urbanisation will have a significant impact on increased electricity demand (Solarin and Shahbaz, 2013).
To accommodate an increase in the urbanised population, the Indian government must consider increasing electricity power-producing capacity. The use of electrically operated appliances by city dwellers has a substantial impact on total electricity consumption. It would be critical to implement tax cuts and customs reductions for eco-friendly energy appliances and a restriction on importing inefficient electrical equipment that unnecessarily increases the use of electricity but is cheaper. Third, policymakers must recognise that promoting agriculture programs and drawing greater industrial investments to India will significantly increase electricity demand. Finally, electricity power construction must be incorporated into core public service construction to improve the availability of electricity for low-income communities and to improve development. We find that the consumption of electricity from agriculture sector has a negative impact on economic growth, whereas the industrial (HV and MV-LV) and commercial electricity consumption has positive impact on economic growth. This result is important from the policy formulation point of view. The government must think about the use of electricity consumption more on industrial and commercial consumption for achieving high economic growth.
Author contribution Debi Prasad Bal has developed the idea, has done the econometrics analysis and has written the methodology and analysis of results section. Sujit Patra has written the introduction section, overall correction of the manuscript and references. Seba Mohanty has collected all the data, interpreted the results and has written the review of literature and conclusions.

Data availability
The data and other material will be shared as per the request.

Declarations
Ethics approval We declare that the study is original in nature and followed all ethical aspects.

Consent to participate
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Competing interests
The authors declare no competing interests.