Effectiveness of state incentives for promoting wind energy: A panel data examination

Abstract

Over the last decade, India has started to concentrate earnestly on renewable energy. The Indian government, as well as different state governments, are adopting policy instruments such as feed in tariff, captive consumption, renewable purchase obligation and generation based incentive etc. aimed at renewable energy development. This paper evaluates the effectiveness of state level incentives for the development of wind energy in India. Fixed effect panel data modelling technique of econometric analysis is used to analyse the data of 26 Indian states in 11 years. The results show that feed in tariff and captive consumption are the significant predictors of wind energy development. However, renewable purchase obligation does not affect wind energy significantly.

Graphical abstract

Keywords

India / wind energy development / state incentives / econometric analysis / panel data

1 Introduction

Energy development is an important objective of every country [1]. As a result of economic liberalisation in 1991, energy demand has increased substantially in India in the last decades. Moreover, this demand is likely to increase in the future due to the combination of economic growth and rise in population [2]. But like many other developing countries, India has also fulfilled most of its energy demand from conventional sources such as coal, oil and gas etc. [3]. But non renewable energy sources suffer from various limitations such as environment distortion, emission of greenhouse gases and burden on foreign exchange reserves [4]. Due to these limitations and consumers’ growing concern for the environment, the Indian government has started to concentrate on the developing renewable source of energy. The Ministry of New and Renewable Energy (MNRE) which manages renewable energy in India, includes small hydro project (<25 MW), biomass power, bagasse cogeneration, urban and industrial waste power, wind and solar power in grid-connected commercial renewable energy. By March 31, 2013, renewable energy capacity had reached 27542 MW, accounting for 12.3% of the 223344 MW total installed capacity in India (including coal, gas, nuclear etc.) (Ministry of Power). Figure 1 shows the proportion of various energy sources in India’s energy mix.

Worldwide, wind energy is more beneficial than other renewable energy sources due to its cost competitiveness and technological maturity [5]. Hence, wind energy has the highest share in all renewable energy technologies after excluding hydropower (Renewables Global Status Report 2013). Wind power is the leading renewable power in India, accounting for 69% of the total renewable energy capacity. According to the Centre for Wind Energy Technology, between 2005 and 2009, wind energy generation has increased from 24874.07 million units to 59208.00 million units. Till the end of 2012, wind energy had been installed in 10 Indian states. But there is a big disparity in installed capacity as Tamil Nadu has as high as 6987 MW wind energy whereas West Bengal and Odisha have very limited capacity. This inequality in installation might have been caused by different policies of state governments, wind conditions, power sector regulatory environment, and etc.

As mentioned in Indian constitution, electricity has to be managed by the central government in conjunction with state governments, hence both governments have issued various policies, financial incentives and made several regulatory changes to develop wind energy [6]. The objective of the paper is to examine the effect of a particular incentive in the development of wind energy empirically, using the fixed effect panel data modelling technique.

2 Literature review

Wind energy literature has largely concentrated on the specific set of topics such as the describing current status, development in wind energy industry in different countries and future opportunities and challenges [7−12]. Other types of studies includes feasibility and effect of large scale integration of wind energy into the power grid [13], simulation models to find out optimum policies and incentives to develop renewable energy [14] as well as drivers for wind energy development, including private participation, public acceptance, network stability and project planning [15]. The feasible model for renewable energy development and the strategies for increasing renewable energy market penetration using the geographical information system (GIS) based analytical method have been financially studied [16,17]. The impact of different diffusion methods on renewable energy using the logistic regression and innovation diffusion theory has also been studied [18,19]. The innovation diffusion theory has been used to analyse the role of government organizations in the creating infrastructure for wind energy [20]. The structural equation modelling technique has been used to measure the influence of renewable energy on the gross domestic product (GDP) of countries while the qualitative methodology like interview method and literature reviews has been used to investigate the issue of renewable energy intermittency [21].

The role of incentives issued by various states for renewable energy development has been examined econometrically [18,22,23]. The cross-sectional data and panel data have been used to examine the impact of government incentives and policies in renewable energy development [18,23]. It is argued that due to the failure of the conventional energy market, incentives are necessary to increase consumers’ interest in renewable energy. The capital cost in wind energy is relatively higher than conventional energy technologies, but since it does not require any fuel inputs, it delivers saving over the conventional technology. The cost competitiveness of wind power generally depends on the overall cost of the wind turbine, availability of public subsidies and expected savings from conventional energy [24]. The current status and developments in wind energy sector have been studied [4,25,26]. However, despite of large amount of research related to renewable energy and wind energy, the knowledge about factors influencing wind energy development remains limited. Moreover, systematic scholarly work that empirically investigates the influence of policies and incentives on wind energy development is lacking. Hence, to address this gap in the literature, this paper analyzed the role of incentives in the development of wind energy in the Indian energy market.

3 Background

3.1 Development of wind energy in India

Due to the oil shock of late 1970s, energy planners all over the world started looking for alternative sources of energy. At that time, the sudden increase in oil price affected the India’s balance of payment situation badly. This prompted the Indian government to focus on renewable energy with the aim of becoming self-reliant in energy. In 1981, the Indian government constituted Commission for Additional Sources of Energy (CASE) to formulate and implement policies for the development of renewable energy. In 1982, a new department, the Department of Non-Conventional Energy (DNES) was created in the then Ministry of Energy and was given the responsibility of taking care of CASE. In 1992, the Indian government changed the DNES into a separate ministry, the Ministry of Non-Conventional Energy Sources (MNES) and became the first country in the world to create a dedicated ministry for renewable energy. In October 2006, this ministry was renamed the Ministry of New and Renewable Energy (MNRE).

Due to the efforts of the Indian government, MNRE and various state governments, Indian wind energy capacity grew from merely 1666.8 MW to 17351.50 MW between 2001 and 2011 (Centre for Wind Energy Technology). During this period, recorded cumulative annual growth rate of wind energy (CAGR) was 26%, and the share of wind energy in the total energy increased from merely 2% in 2003 to 9% in 2013. With a 68% of total renewable energy capacity, wind energy is the most installed renewable energy technology in India. Tamil Nadu, Gujarat, and Maharashtra are the states which have contributed most to this growth. Tamil Nadu has an installed capacity of approximately 7000 MW while both Gujarat and Maharashtra have installed a capacity of around 3000 MW.

3.2 Drivers of wind energy development

Previous studies have mentioned that the development of wind energy in India is mainly influenced by technological development, supportive government policies, electricity deficit and social and economic factors such as population growth rate, GDP etc. This section provides details about the above mentioned factors which have contributed to the growth of wind energy in India.

3.2.1 Technological development

Global Wind Energy Council (GWEC) noted that India is fast developing as a major centre for wind turbine manufacturing due to its right mix of policies. Global heavyweights such as Suzlon, Vestas, Enercon, GE, Gamesa and Siemens have established manufacturing facilities in the country. By 2012, India had a wind turbine manufacturing capacity of 9500 MW with 16 wind turbine manufacturers. These manufacturers are manufacturing the state-of-the-art Class II and Class III turbines suitable for medium to low wind regime of India. The average size of wind turbine also increased slowly from just 767 kW in 2004 to 1117 kW in 2009 [27]. Due to these larger turbines, the plant utilisation factor also subsequently increased from 10% to 12% in 1998 to 20% to 22% in 2010 [27]. These technological developments are decreasing the cost of generating energy from wind.

3.2.2 Regulatory changes

The main aim of renewable energy policy is to significantly increase the share of renewable power in India’s energy mix [28]. The Indian government has made crucial changes in energy laws and policies to achieve this objective.

India has implemented several provisions regarding the renewable energy sources with the enactment of Electricity Act in 2003. Section 3 (1) and 3 (2) of the Electricity Act mandates the central government to make national electricity and tariff policy after consulting state governments. The goal of these policies is to develop energy from every possible source including coal, nuclear, hydro, natural gas and renewable energy. Sections 61 and 86 of the Electricity Act instructs all State Electricity Regulatory Commissions (SERCs) to promote generation of electricity from renewable energy sources by determination of tariff and laying down minimum proportion of total electricity to be procured from renewable energy resources.

3.2.3 Wind energy incentives

As mentioned earlier, electricity in India is managed jointly by the central and state governments. Hence, the central government decides its set of policies and incentives for the development of wind energy while each individual state issues its own policies in line with the central government schemes [29].

Central government incentives for wind energy development

The Electricity Act of 2003 made major changes in the Indian energy sector such as open access to transmission, deregulation of power generation and allowing SERCs to fix the renewable energy obligations. Currently, the Indian government provides a number of incentives to renewable energy such as accelerated depreciation (AD), generation based incentive (GBI), income tax exemptions, renewable energy credits (REC) and clean development mechanism (CDM). The GBI scheme issued by the Indian government recently provides incentive exclusively to independent power producer (IPP) for feeding wind energy into the grid. Under this scheme, GBI of Indian rupee (INR) 0.50/kWh is given to renewable energy generator for the electricity fed into the grid.

The Indian government also provides AD benefit to investors for putting up wind energy projects. By this benefit, the wind energy investor can claim 80% of the cost of wind energy generator as depreciation in the first year. AD is very helpful in reducing the tax liability of the wind energy investor. In India Wind Energy Outlook, 2011, the GWEC observed that AD played a crucial role in the growth of Indian wind energy sector [27]. Moreover, for the first 10 years, the Indian government will not charge any tax on the income generated by the sale of wind energy. A National Clean Energy Fund is also available to provide funds for research and development in renewable energy. This fund provides capital by imposing a cess on coal, peat and lignite. These policies are issued with the sole objective of increasing the share of wind energy.

State government incentive for wind energy development

Various states have determined the feed-in tariff (FIT) for selling wind energy to electricity companies. FIT is a price guaranteed by SERCs for wind energy. This tariff varies across the states depending upon project cost, state resources and tariff regulation in the respective state. Till 2011, thirteen states had FIT incentive for wind energy. Various state governments also allow industrial energy consumers to install wind energy project for their captive consumption (CC). Some large energy intensive industries such as cement and textile are generating wind energy to meet their captive requirement [30]. Ghosh et al. [31] has reported that approximately 80% wind energy is used for captive consumption.

State governments also provide various other financial incentives to promote wind energy. These financial incentives include subsidies provided at installation or during operation of the project. With these incentives, state governments wish to reduce those financial hurdles which make renewable technologies less attractive in comparison to the conventional sources. Maharashtra provides the subsidy for wind energy projects. This state government of Maharashtra returns 50% of the evacuation cost at the end of the first year. According to GWEC, the average evacuation cost of wind power project is 4 to 5 million per megawatt.

As mentioned earlier, the Electricity Act of 2003 provides provision for fixing renewable energy to be procured by different entities. These entities include distribution companies, open access users and captive consumers who are buying or generating energy from non-renewable sources. These renewable purchase obligation (RPO) targets vary according to the conditions of respective state. It can be as high as 10.15% in Tamil Nadu or as low as 0.25% in Karnataka. Some states, like Sikkim and Arunachal Pradesh, have not even decided their RPO targets [32]. This policy also has penalty provision if anybody fails to meet their RPO target. This policy also motivates companies to develop renewable energy projects. Some states have issued RPO exclusively for wind energy. To overcome the disparity in the renewable energy conditions among states, the Indian government has launched the renewable energy credits (REC) mechanism [32]. The REC framework provides an open market from where obligated entities can purchase RECs to meet their RPO obligations. The Indian government has set the value of one REC equivalent to one MWh of renewable energy fed into the grid [32]. Since March 2011, trading of RECs is happening on the platforms of power exchange of India (PXI) and Indian energy exchange (IEX) [33].

To encourage manufacturing of wind turbine and its components, some state governments do not put excise duty on various parts of the wind turbine. Tamil Nadu charges less price for the electricity provided to the wind turbine manufacturing units. The Indian government has also established special economic zones to promote export of renewable energy technologies. Some states, like Gujarat and Rajasthan, facilitate project developers by providing land to set up wind energy project.

3.2.4 CDM incentive

The role of Clean Development Mechanism (CDM) is vital for the promotion of renewable energy technologies since CDM constitutes the largest source of carbon mitigation finance to the developing world, especially India. CDM is one of the three flexible mechanisms, i.e. emission trading, joint implementation and CDM, introduced by the United Nation Framework Convention on Climate Change (UNFCCC) in Kyoto Protocol. CDM is compensating the mechanism which enables the trading of emission reductions from sustainable projects installed in developing countries. India’s growing CDM market has attracted a large amount of capital inflow of global finance for energy. Large amounts of carbon funds operated by Asian Development Bank (ADB) and International Finance Corporation (IFC) for their client companies have been invested in the CDM projects of India. These funds finance the project in exchange of the expected CERs to be generated by the project. India is among the biggest recipients of CDM finance, having the second largest number of registered projects after China. Till September 2014, out of the total 7773 registered CDM projects, 3621 projects were from China and 1507 projects from India. Moreover, out of these 1507 registered projects, two thirds were from the renewable energy sector of which the majority were from wind.

Several states have created single window clearance and CDM promotional center with the aim of creating an enabling policy environment. The distribution of renewable energy based CDM projects has been skewed across India. More than 90% of renewable energy based CDM projects have come up in the states of Andhra Pradesh, Chhattisgarh, Gujarat, Himachal Pradesh, Karnataka, Maharashtra, Punjab, Rajasthan, Tamil Nadu and Uttar Pradesh. Wind energy is having the largest share of renewable energy based CDM projects with 820 registered project, totalling to 14706 MW followed by biomass, hydro and solar. Till 2020, India envisages 65 GW of electricity generation from wind power, saving 173 million tons of CO₂ emissions each year [34].

Since CDM is the incentive facilitated by the United Nations and does not have any variation across states, it has not been considered in this paper.

4 Empirical analysis: methodology and data

To examine the effectiveness of state incentives in the development of wind energy, the annual wind energy installation, states’ wind power potential and other socioeconomic factors were empirically analysed. Of the total 28 states and seven union territories (UTs), this paper has considered only those states which are having the wind energy potential according to the CWET. CWET is a government organisation having the responsibility of conducting the wind resource assessment in the country. After removing the states with the negligible potential of wind energy, 28 units are left. Due to the non-availability of data related to the gross domestic product (GDP) of two UTs, Daman & Diu and Lakshadweep were removed from the study. This paper only considered the data obtained from 2001 to 2011, since during this time period wind energy has received a lot of attention and the states have made available number of incentives for wind energy. After filtering with the above mentioned data limitations, only 26 states were valid, totalling to 286 observations. The summary statistics of the data is listed in Table 1.

4.1 Dependent variable

This paper examines the annual grid connected wind capacity installed in a state (in megawatt, MW) from 2001 to 2011 (INSTAL). This data was procured from CWET since it keeps track of the annual installation of wind energy in every state [26]. As is presented in Table 2, during this period, the wind energy in India increased considerably from 1666.7 MW to 17351.5 MW. This wind energy capacity was mainly installed in Tamil Nadu, Gujarat, Maharashtra, Andhra Pradesh, Rajasthan, Madhya Pradesh and Karnataka. In India, the installation of wind energy is quite concentrated across three states namely Tamil Nadu, Gujarat and Maharashtra. These three states alone accounts for 75% of the total installation. However, in recent times, other states, like Madhya Pradesh, Rajasthan and Andhra Pradesh, have also installed a good amount of wind energy. This disparity in installation across states suggests the effect of state incentives in enabling wind energy deployment.

4.2 State government incentive

When the sample size is small, the selection of policies should be parsimonious [36]. However, this is not relevant to this paper since the analysis of 26 of the total 35 units has been conducted. This paper proposes that the states with better incentives will have a higher installation of wind energy as incentives motivate investment in wind energy. The data related to state incentives was collected from various sources such as wind tariff order of MNRE and SERCs. However, MNRE keeps a record of on-going policies. It does not have the information related to the older policies which have expired. Hence, a large number of reports and SERCs’ tariff orders have been referred to in collecting information on policies which have expired. Although every effort has been made to collect data of incentives in each state, it is possible that some incentives may have been left. All the information related to major incentives issued by MNRE and SERC was collected diligently.

Four major incentives, namely, FIT, CC, RPO and special RPO for wind (WRPO) available to wind energy were studied in this paper. WRPO and RPO are command and control incentives under which obligated entities have to purchase a minimum percentage of total energy from renewable sources [36]. FIT is the price at which wind energy will be purchased by the government while with CC big energy consumers can set off their energy consumption. This paper hypothesizes that positive incentives i.e. FIT and CG, will lead to more wind energy development than mandatory policies, like RPO and WRPO.

In India, FIT incentive is offered by 13 states, but as shown in Table 3, this incentive varies in terms of price and duration of power purchase agreement (PPA). For example, Tamil Nadu gives INR 3.39 per kWh as FIT, while Uttarakhand gives INR 5.15/kWh. The state governments provide FIT for wind energy to ensure fair returns from wind energy projects. But the revenue from wind energy also depends on the plant load factor (PLF) of the wind project in the state. PLF is the capacity utilisation factor of the wind turbine and is calculated by dividing the actual electricity generation to the total capacity of a wind turbine. Hence, the state with a lower PLF provides a higher FIT to ensure a competitive return on the project while the state with a higher PLF gives a lower FIT. For example, Uttarakhand assumes a lower PLF (20%) in comparison to Tamil Nadu (27.15%), hence, Uttarakhand gives a higher FIT.

At the beginning of 2001, only seven states had some forms of incentives like FIT and CC for wind energy. However, in 2011, each of the twenty-two states had at least one incentive. Six states i.e. Gujarat, Andhra Pradesh, Karnataka, Maharashtra, Rajasthan and Madhya Pradesh had four incentives and the rest of the states had one or two incentives. However, four states i.e. Arunachal Pradesh, Andaman & Nicobar, Sikkim and Puducherry had no incentive for wind energy development. RPO was the most prevalent incentive in 2011. It was implemented by 22 states. Six states had special renewable purchase obligation for wind energy (WRPO). FIT and CC were available in 12 and 10 states respectively.

4.3 Control variables

This analysis includes state per capita GDP as a control variable. Consistent with previous environmental policy studies [37, 38], this paper hypothesizes that if other things are equal, the states with higher wealth will have more renewable energy deployment because they have more resources to invest in renewable energy. The states with higher per capita GDP are more likely to invest higher resources for wind energy even if fewer incentives are available. This variable is denoted as SGDP in the analysis. It denotes per capita GDP of a state in a given year, with 2005 as the base year. The data related to SGDP was procured from the Planning Commission of India. The state population (POPU) was included in the analysis as the second socioeconomic control variable. State population data was taken from the Census Bureau of India. In India, population data is recorded every ten years, hence this paper multiplied the population of 2001 with exponential growth to obtain the annual population data. The increase in population put more burden on limited fossil fuel resources. This paper assumes that the states with higher population are more likely to increase its energy capacity to satisfy the growing electricity demand and renewable energy can be a viable option for this. It can also be possible that the rise in population is associated with the increase in baseload fossil fuel generation.

Wind energy is different from other energy sources as it is geographically highly variable and cannot be transfer across regions [18]. The development of wind power in a state largely depends on the quality of wind resource. The wind energy potential (POTEN) variable is time-invariant, hence it is included in this paper separately from the state fixed effects. The data from CWETs estimation of wind energy generation potential in each state have been taken for this variable. These data are very reliable as CWET does wind resource assessment study on wind data collected from a widespread network of 790 wind monitoring stations in 31 states and union territories. The only note of caution for using this data is that this is theoretical resource potential. This paper does not transform theoretical resource potential into electricity potential because this conversion requires critical analysis of technical and economic feasibility, which is beyond the scope of this paper.

The falling price of wind turbines can improve the return on investment in wind energy technology. The total cost of a wind energy project differs significantly across states due to the difference in labour, logistics and installation costs. But historical data of wind turbine installation costs is available only at the national level. Hence, wind turbine price factor was not included in the model. The impact of this factor on wind energy development can be captured by the state and year fixed effects.

The wind energy potential, per capita state GDP, population growth rate and annual installation data showed the non-normal distribution and were normalized by a natural logarithmic transformation. The results should be interpreted in the form of elasticity, which means percentage change in wind installation due to 1% change in population, GDP or potential. The data of annual wind energy installation was changed to normal by taking the log of (1+INSTAL_it) to keep observations for the states who had zero wind energy installation for some years. As the natural log of zero is undefined, this transformation is suitable when the dependent variable is equal or greater than zero. Because the use of 1 as constant is essential in this situation, the final results can be sensitive to the choice of constant.

4.4 Model specification

The two model specifications estimated in this paper are

(Model 1)\mathrm{I}\mathrm{N}\mathrm{S}\mathrm{T}\mathrm{A}\mathrm{L}={\beta }_{\mathrm{0}}+{\beta }_{\mathrm{1}}\mathrm{F}\mathrm{I}\mathrm{T}+{\beta }_{\mathrm{2}}\mathrm{C}\mathrm{C}+{\beta }_{\mathrm{3}}\mathrm{R}\mathrm{P}\mathrm{O}+{\beta }_{\mathrm{4}}\mathrm{W}\mathrm{R}\mathrm{P}\mathrm{O}+\gamma C_{it}+T_i+{\epsilon }_{it},

(Model 2)\mathrm{I}\mathrm{N}\mathrm{S}\mathrm{T}\mathrm{A}\mathrm{L}={\beta }_{\mathrm{0}}+{\beta }_{\mathrm{1}}\mathrm{Y}\mathrm{F}\mathrm{I}\mathrm{T}+{\beta }_{\mathrm{2}}\mathrm{Y}\mathrm{C}\mathrm{C}+{\beta }_{\mathrm{3}}\mathrm{Y}\mathrm{R}\mathrm{P}\mathrm{O}+{\beta }_{\mathrm{4}}\mathrm{Y}\mathrm{W}\mathrm{R}\mathrm{P}\mathrm{O}+\gamma C_{it}+T_i+{\epsilon }_{it},

where i represents the state and t denotes the year. C_it denotes the state fixed effect, and T_i is the time fixed effect. The error term, ε_it, is independent and identically distributed across the state and year.

In the first specification, dummy variables were used to indicate the existence of four types of incentives, i.e. FIT, CC, RPO and WRPO, which were available for wind energy technology. For each type of incentive, the dummy variable was coded 1 for a year if the incentive was available and as 0 in case it was not available. By including separate variables for every incentive, individual, as well as joint effect can be examined empirically [39].

In the second specification, the effect of these four incentives, since their introduction, was tested. This specification studies the increase in the impact of state level incentives over time, as the awareness and understanding of these incentives in public increases. Howell-Moroney [40] also found that as the understanding of an incentive increases or its scope gets enlarged to include more beneficiaries, its influence gets increased. In this specification, the duration variable was coded as 1 for the first year of implementation, 2 for the second year, 3 for the third year, 4 for the fourth year and so on. This model includes four incentive variables which are denoted as YFIT (yearly feed in tariff), YCC (yearly captive consumption), YRPO (yearly renewable purchase obligation) and YWRPO (yearly special rpo for wind energy).

4.5 Model estimation

Due to the availability of ample heterogeneity across states, Hausman test was performed on both specifications to test if fixed effects were necessary. The Null hypothesis for Hausman test was that the state does not have heterogeneity. During Hausman test, this hypothesis got significantly rejected, which concluded the presence of major differences between the states which could not be captured by explanatory variables. Hence, it is vital to control these unobserved differences. This analysis included state dummy variable as an explanatory variable to capture these state difference. By including the state dummy variable, the constant term started varying for each state and avoided heterogeneity bias which would have occurred in case a common constant term was included for all states.

An extensive increase in installation of wind energy over the years indicates the presence of unobserved time effects which are common to all states. Some examples of these effects can be an increase in the concern for the environment, reduction in the cost of a wind turbine, etc. To remove heterogeneity bias, time fixed effects were incorporated in the paper through year dummy variables.

Many times, time-series cross-sectional studies have problem panel heteroskedasticity, contemprous correlation and serial correlation [41]. To test the presence of autocorrelation, Wooldridge test was conducted. This test showed no autocorrelation was present in the model. Besides this modified Wald test was performed to test the existence of group-wise heteroskedasticity in the model. This test showed the presence of heteroskedasticity. Frees test was done to find out the existence of contemporaneous autocorrelation among states. The results of this test showed that a contemporaneous autocorrelation was present. Hence, the Prais-Winston estimation with the panel corrected standard errors (PCSE) method was used in both specifications. PCSE use ordinary least square coefficient estimates and residuals to calculate standard errors that are correct for panel heteroskedasticity [42]. This method is used widely in public policy and political science econometric analysis as panel heteroskedasticity is prevalent in these studies. This method removed the autocorrelation problem.

5 Empirical results

The results of this regression analysis are shown in Table 4. Models 1 and 2 present the effect of the four state incentives on the wind turbine installation data from 2001 to 2011 under two specifications. Model 1 examines the average effect of a state incentive while Model 2 tests the incremental effect of state incentive over the period. The results indicate that the states offering feed in tariff and captive consumption have a higher installation of wind energy than the states not offering these incentives. Alternatively, the states that have RPO do not have more installation of wind energy than the states not giving this incentive. The investors putting up wind energy project due to the feed in tariff and captive consumption can also use accelerated depreciation benefit of the central government. Hence, the impact of captive consumption and feed in tariff should be studied in the context of the combined presence of central and state government benefits.

The effect of feed in tariff and captive consumption indicates the criticality of these incentives for market deployment. If other variables are held constant, the states having a captive generation have 280% more deployment than the states not having this incentive. In the following years, the states offering captive consumption have 9% more wind energy every year, as compared to the states not offering this benefit.

The possible reason for the differential effect of captive consumption and feed in tariff in relation to RPO can be the design of incentives and ease with which these benefits can be obtained. Large energy demanding companies are allowed to take the benefit of accelerated depreciation with captive consumption. The combination of these benefits increases the rate of return significantly as by installing wind power project for their captive use expensive consumption of manufacturing plant can be completely set off with the energy generated from a wind turbine. Moreover, documentation for this process is very easy and can be simply executed with the state government. In the case of CC and FIT, wind energy generators can easily sign an agreement with the state government even before installing the wind energy project. This agreement protects wind energy generators from future uncertainties.

In principle, RPO should also have a positive impact like FIT and CC as this benefit can also be used with the generation based incentive or accelerated depreciation benefit of the central government. But results indicate that RPO do not have any effect on installation of wind energy. This may be because of the RPO structure, as it is an obligation only of captive consumer who are buying or generating energy from non-renewable sources, open access user and electricity distribution companies. Moreover, the penalty provision was not strict when an obligated entity does not fulfil its RPO requirement. The effect of WRPO is positive on the development of wind energy in Model 1. But in Model 2 this variable becomes negative, indicating that, in the long run, this incentive does not have any impact on wind energy development. The change of positive effect of WRPO in Model 1 to negative in Model 2 indicates that, in the long run, this incentive becomes ineffective if this is not enforced by stringent penalty provision.

In short, the result of this analysis indicates that the wind energy market is more responsive to the simplicity of the incentive offered and the easiness in obtaining these benefits. The feed in tariff and captive consumption are simple and easily available during the life of the project, hence have a significant effect on the wind energy development. However, RPO and WRPO do not have any effect on the development of wind energy since they are complex to implement.

Moreover, the findings indicate that wind energy installation is increasing every year in the states who are offering FIT and CC. The effect of these variables in Model 2 is consistent with those in Model 1 but have a smaller impact. The coefficients in Model 1 highlights the average impact of an incentive for the entire study period while the coefficients in Model 2 show an incremental impact of offering the incentive for another year. The results of Model 2 show that these incentives become more effective over the year, as with the passage of time, both policy makers and investors gain experience. With the increase in experience, both parties understand each other better and the rules and regulations a policy involves. On the whole, the results indicate that the states with captive consumption and feed in tariff have more deployment in wind energy than the states which do not have these incentives. Also, wind energy installation over the period increase much faster in the states having these incentives than in the states not having these incentives.

These results also show that the states with higher per capita GDP have installed more wind energy during the study period. This result validates the understanding that, the rich states have more funds to invest in new and risky technologies. Previous studies have also found the positive relation between renewable energy consumption and income [43]. The wind energy industry suffers from the disadvantage of having a high start-up cost. This makes the payback period in wind energy industry long. But the advantage of this sector lies in its less recurring cost and consistency in revenue during the life of the project. Due to the requirement of high initial capital to start this project, investors with higher income have the capacity to implement this technology.

The analysis indicates, too, that the state’s wind energy potential has a positive effect on wind energy development in the state. In 1980s, India’s wind resource assessment programme was started. During this programme, some good locations for wind power projects in the coastal areas of Gujarat and Tamil Nadu were found. Subsequently, the Indian government started putting up demonstration projects. Due to the overwhelming success of these projects, the commercial activity of wind energy also started in these states. This can be the reason for a high potential for wind energy having a positive relation with the development of wind energy.

Another control variable, population, is also found to have a positive impact on the development of wind energy in Model 2. This incremental effect of the population growth rate variable on wind energy development indicates that an increase in population every year encourages the state government to increase wind energy installation. Previous studies have noted that the increase in population, also increase the demand for energy. Hence, to satisfy this demand, the state has to look for better energy management. This result is a positive sign for wind energy industry as the demand for electricity will continue to increase in the near future due to its rising population. Dummy variables for the state and time have captured variation across the state and over time. These results are robust for different specifications and omission of time dummies.

6 Discussion and conclusions

This paper analyzed the effect of state incentives on the market development of wind energy in line with the increasing interest of the investor, the government and the general public in wind energy. The impact of four types of incentives issued by Indian states based on the 11 years of state level data was examined.

The findings of this paper reveal the mixed effect of state incentives on wind energy till date. The results of this paper suggest that the states having captive consumption and feed in tariff benefit have more wind energy installation than the states which do not have this incentive. The point of concern is that RPO is not found to have any effect on wind energy development. Moreover its incremental effect is also negative. The states which have RPO do not have significantly more deployment of wind energy than the states not having RPO. This suggests that RPO does not seem to have any effect on wind energy development. This finding is consistent with the findings of Wiser et al. [44] that despite some success stories, most of the states do not meet their RPO targets. The role of GDP on wind energy development is also found to be significant as the state with a higher per capita GDP have more installation of wind energy than the state with a lower GDP. The higher cost of generating energy from wind than the conventional energy sources could be a probable reason for this finding. But as the cost of generating wind energy is decreasing and at good sites wind energy is already competitive with conventional energy technologies, GDP factor will not be relevant in the long run [45]. The effect of population on wind energy is also found to be positive. The population increase in a state puts an extra pressure on the limited energy resources; hence it becomes inevitable for the state government to develop more sustainable technologies to meet the energy requirement. Wind energy is the most commercialized renewable energy technology in India as well as the world. This can be the reason for the positive relationship between population and wind energy development.

This paper is useful for renewable energy stakeholder in examining the impact of state government incentives. This paper also identifies the factors which negatively affects the renewable energy. Implementation of RPO without strict enforcement of penalty provision is not contributing to the renewable energy development. Nowadays, as many states are implementing RPO incentive or consistently revising this policy, these results can be an important indication for policymakers. An important finding for policymakers is that they should strongly enforce penalty provision while designing the RPO policy.

Several caveats of this paper must be noted. This paper does not consider the strength, scale and scope of each incentive. These factors should be considered in future researches. For example, Tamil Nadu’s feed in tariff of INR 3.50/kWh and Maharashtra’s feed in tariff of INR 5.13/kWh are treated equally in this paper, as it considers only the existence of this incentive. This paper does not study this deviation in feed in tariff between states and over time. Future studies should also consider the effect of third party sale for development of wind energy. Off late, many projects have been commissioned which are not selling power to state utilities, but sells it to large energy intensive organisations at a mutually agreed price. These industries give a much higher price for wind energy than the state government, which is motivating many independent power producers to put up wind power project. The third party sale benefit is also contributing to the wind energy development. The role of this benefit in the development of wind energy can be examined in future studies.

A few years back, the government has started the market for RECs to facilitate obligated entities in fulfilling the RPO requirement by buying REC from power exchanges. The impact of this benefit on the deployment of wind energy can be studied in the future.

This paper only considered the data collected from 2001 to 2011, but in April 2012, the central government had stopped the accelerated depreciation benefit [46]. This benefit has played a vital role in the development of wind energy. The removal of this incentive has affected the wind energy sector substantially since the annual installation was drastically reduced to 1698.8 MW in 2012. In 2011 wind energy installation was 3196.9 MW. This paper studied the data till 2011, but the situation has changed after this year. Since new benefits like third party sale and REC are not considered in this paper, there is a need for continuous evaluation and analysis of states’ wind energy policies.

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