The impact of solar water pumps on energy-water-food nexus: Evidence from Rajasthan, India

Energy Policy 129 (2019) 598–609

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The impact of solar water pumps on energy-water-food nexus: Evidence from Rajasthan, India

T

Eshita Gupta Infrastructure and Government Services, KPMG India & Centre for Research on Economics of Climate, Food, Energy and Environment, India

ARTICLE INFO

ABSTRACT

JEL codes: Q10 Q20 Q30 Q40 Q50

This paper seeks to identify the causal impact of solar water pumps on the consumption of water, electricity and diesel as well as the gross cropped area under fruits and vegetables and profits of farmers. We use the differencein-differences approach with a sample of 414 rural farmers from six districts of Rajasthan for this purpose. We find that the adoption of the solar water pump has impacted energy-water-food nexus by increasing average groundwater consumption of adopters over baseline in the districts of Jaipur and Sikar by 16–39%; reducing average electricity consumption of adopters over baseline in Jaipur and Sikar by 1–17% and in Sri Ganganagar by 17–134%; reducing average diesel consumption in Bikaner, Jaisalmer, Sri Ganganagar and Chittorgarh by 50–106%; increasing food security of adopters as indicated by an increase in average cropping intensity in Jaipur and Sikar by 2–10% and in Bikaner by 2–9%, and expansion in gross cropped area under fruits and vegetables in Jaipur by 12–53% and Bikaner by 10–116%; increasing income security with higher annual profits for solar pump adopters in Jaipur and Sikar by 3.1–41.5% and in the diesel-using districts by 14–76%.

Keywords: Solar pumps Energy-water-food nexus Rajasthan Impact evaluation

1. Introduction The Solar Pumping Programme was introduced by the Ministry of New and Renewable Energy (MNRE) of the Government of India in the year 1992. The programme increased rapidly with the advent of the offgrid PV scheme of the Jawaharlal Nehru National Solar Mission (JNNSM), the underlying aim of which is to strengthen water, energy and food security in rural India. Under the scheme, roughly.147 million solar pumps had been installed in India by December 2017 though it is miniscule in comparison with the over 30 million electric and diesel pumps currently installed in the country (MNRE, 2017). Interestingly, more than 70% of these solar pumps had been installed in just the past three years. Hence, India is implementing an ambitious plan to expand the installation of solar water pumps for irrigation from the current.15 million to 1 million by 2022 under this programme (MNRE, 2017). At present, MNRE provides a capital subsidy of up to 30% to farmers for installing the pumps while the state government offers 50–70%“of the cost. The remaining share of the cost is to be paid by farmers. The solar pump scheme for irrigation began in Rajasthan in 2010 with an 86% subsidy. The remaining 14%, which is equivalent to the cost of only the pump set, was to be paid by the farmer which amounted to about USD 841–540. Over the years, the subsidy figure has been reduced from 86% to 70% or even 60%. At present, there are three, very transparent eligibility criteria for the subsidy: 1) the farmer should own at least 0.5 Ha of land; 2) the land should have a diggi [or farm pond] or minisprinklers; and 3) the drip irrigation system should be installed in a portion of the farm. Currently, farmers who already have electric

connections for irrigation are provided with a smaller subsidy, amounting to about 30% of the total cost of the solar pump set. Irrigation, which typically relies on access to energy inputs, plays a crucial role in breaking the vicious cycle of poverty among farmers by providing food and income security (Ferroukhi et al., 2015). An important question then is: How does solar pump adoption impact water, energy, food and income security of a farmer? The present paper attempts to answer this question through the lens of the energy-water-food nexus. Our study quantifies some of the resource nexus trade-offs and synergies for rural farmers in Rajasthan. In rural areas, the quality of the grid supply is low as there is an acute shortage in the electricity supply. By providing access to clean and reliable energy via a subsidized, low-cost solar water pump, the solar pumping programme can potentially help farmers to overcome electricity and diesel supply constraints; increase farmer's control over water supply; increase production of existing crops; extend the cultivated area with old or new crops; increase profits; reduce the carbon footprint of its energy consumption. While the solar water pumps promise enormous benefits to farmers, the free solar energy-groundwater nexus can lead to depletion of groundwater in the same way that pumps energized by cheap electricity had done in the past (Strand, 2010; Bassi, 2018; Briscoe and Malik, 2006). The key advantage of solar pumps relative to electricity or diesel pumps is that it is energy smart as it can reduce carbon emissions by substituting fossilfuel-based pumps. Unlike the large volume of literature on the impacts of electric and diesel-based irrigation technologies in developing countries, there is little on the impacts of solar water pumps on resource use, environment, and food and income security. A qualitative primary

https://doi.org/10.1016/j.enpol.2019.02.008 Received 12 August 2018; Received in revised form 31 December 2018; Accepted 2 February 2019 0301-4215/ © 2019 Elsevier Ltd. All rights reserved.

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field survey of 107 solar pump adopters in Rajasthan by Kishore et al. (2014) found evidence of a large reduction in diesel consumption but little reduction in electricity consumption; improvement in water use efficiency but no change in quantity of water used. But the study reported an increase in area under irrigation and in crop productivity by roughly 5–10%. Similarly, GIZ (2013) found that access to solar-powered irrigation systems in Bihar enabled farmers to switch from deficit irrigation to the full irrigation resulting in higher crop yields and profits. Honrao (2015) studied rural villages in Maharashtra and found that replacement of diesel pumps by solar powered irrigation pumps resulted in significant savings in the input costs and increased productivity and profits. SEWA (2015) found that solar pumps reduce production costs significantly for Gujarat saltpan framers and resulted in 161% increase in profits for a solar pump farmer when compared to diesel pump using farmers. The key limitation of above studies was the small sample size and/or the absence of a control group for comparison. A study by Burney et al. (2010), which is of relevance to the present study, assessed the impact of solar-powered drip irrigation on food security as measured by farmer household income and nutritional intake in the rural Sudano-Sahel region of West Africa. Using a matchedpair comparison of villages in northern Benin (two treatment villages, two comparison villages), they found that solar-powered drip irrigation significantly augments both household income and nutritional intake, particularly during the dry season, while being cost effective compared to alternative technologies. Similar studies have been done in other African countries such as Egypt, Zambia, Kenya, Benin and Zimbabwe (IRENA (2016); Egypt Network for Integrated Development (ENID), (2013); Portia (2014); Solar Electric Light Fund (SELF), (2015); Magrath (2015)). In line with previous studies, the key finding of the current study is that the solar water subsidy programme in India has had a positive impact on poverty reduction as it has increased the food security and income of farmers, while reducing the consumption of fossil fuels such as diesel and electricity, which are associated with a high degree of carbon emission. However, there is evidence of increased groundwater extraction by an average farmer in groundwater-using districts such as Jaipur and Sikar. On the other hand, in the canal irrigated districts of Jaisalmer, Sri Ganganagar and Bikaner, this is a winwin policy with a significant fall in expensive and dirty diesel consumption with no impact on groundwater extraction. Most farmers in these regions are dependent on canal irrigation and use solar pumps for distributing water from a diggi (water storage tank) instead of groundwater extraction. The study adds to the existing literature on the subject in a number of ways. Firstly, it is the first econometric study to apply a rigorous difference-in-differences approach to micro data collected through a primary survey in order to quantify the impacts of solar water pump adoption on resource use, and food and income security of farmers. Secondly, it contributes to the empirical literature on the energy-water-food nexus by providing evidence on the strength of key interdependencies among these three sectors. Thirdly, it adds to energy policy research by highlighting the need for internalization of social and environmental externalities and formulating cross-sectoral policies in an integrated manner.

dependent on groundwater irrigation and faced with a critical overextraction threat. In canal irrigated areas, many farmers store canal water in diggi or water tanks. In regions such as Jaipur and Sikar where a reliable electricity supply is available, farmers use electric pumps to extract groundwater or distribute water from a diggi to the fields in addition to extracting water through the recently adopted solar pumps. In regions, with a less reliable supply such as Jaisalmer, Bikaner and Sri Ganganagar, farmers use a combination of electric and diesel pumps in addition to solar pumps. There are two main cropping seasons in Rajasthan: kharif (June-September) and rabi (October-April). While the main crops during the kharif season are bajra, pulses, jowar, maize and ground nuts, key crops during the rabi season include wheat, barley, gram, pulses and oilseeds. In addition to these traditional crops, farmers cultivate fruits and vegetables (such as oranges, pomegranate, cauliflower, cucumber, green apple, dates, potatoes, onions and tomatoes), the production of which is relatively more sensitive to water and temperature requirements. 3. Theoretical model and key hypothesis We develop a farm-level model for profit maximization where a farmer has access to a different mix of irrigation pumps. We do not model pump choice as we assume that pump types are exogenous. Our model is inspired by Badiani et al. (2012). Consider a farmer who has access to all three types of pumps–solar, electric or diesel though, in the interest of generality, two crops–a traditional crop (such as maize and wheat) and a new crop (such as fruits and vegetables)–are assumed. The production function: Each crop is produced with a CobbDouglas production function. There are two inputs-Land (L) and Water (W ) . The amount of water demand for a given farmer is a sum of both, ground water demand (WitG ) and canal water demand (WitC ). In our data farmers are either ground water users or canal water users and not both. The total water demand for both types of farmers can be met by solar, electric or diesel pump. Let production function of a old crop and a new crop be given by Fito and Fit1 respectively.

Fito = Loit (

G it (Wit

Fit1 = (L + LRit

+ WitC )) Loit ) ((1

(1) G it )(Wit

+ WitC ))

(2)

where, Loit is land cultivated under old crops, L is land owned which is assumed to be fixed over the period of analysis, LRit is net land rented in, it is the percentage of water allocated to under old crops. Water Demand: Let W G = Wits + Witeu + Witef + Witd be total ground water demand, where Wits , Witeu , Witef and Witd denote ground water demand met by solar, electric pumps with unit pricing, electric pumps with flat rate and diesel pumps, respectively. As the supply of canal water is fixed by the government, solar adoption is likely to impact the total canal water distributed from a diggi (water storage tank) to agricultural fields by different types of pumps but not the total annual canal water consumption.1 Let W C = Witsc + Witeuc + Witefc + Witdc be total canal water distributed which is also equal to canal water supplied (WitCS ), where Witsc , Witeuc , Witefc , Witdc denote canal water distributed by solar, electric pumps with unit pricing, electric pumps with flat rate, and diesel pumps, respectively. Demand for energy is derived demand and is assumed to be proportional to water demand met. Electricity Demand: Electricity use is directly proportional to the ground water extracted by electricity or the canal water distributed by electric pumps. It can be presented as:

2. Background The Indian state of Rajasthan has been a pioneer in promoting solar water pumps accounting for about 37,000 pumps, which is 25% of the total pumps installed in the country up to December 2017. Rajasthan receives about 6–7 kWh/m2/day of solar insolation, with 325 sunny days in a year, on average, making it the most promising region of the country for harnessing solar energy. In terms of area, Rajasthan accounts for 10.5% of India, with 15.7 million hectares of land, of which only 35–38% is irrigated. Although the average land holding size in the state, at 3 ha, is large, the land is less fertile while the soil structure is either arid or semi-arid. While the northern and northwestern regions are irrigated by the Indira Gandhi canal, other regions are largely

Eitgw = g (x it , µc )(Witeu + Witef )

(3)

1 It is reasonable to assume that the total canal water supplied to a farmer is completely distributed in the field.

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E. Gupta

EitC = h (µde )(Witeuc + Witefc )

(4)

Eit = Eitgw + EitC

(5)

•D •D •S •S

it

it

where g (x it , µc ) determines the amount of electricity required to get one unit of ground water. This is likely to be function of current ground water table/stock (xit ) and other cluster level characteristics as denoted by (µc ) . h (µde ) determines the amount of electricity required to distribute one unit of canal water. Due to limited hours of electricity supply and a fixed capacity of a electric pump there exist a maximum possible electricity consumption for a farmer, which we denote by Ei . This implies Eit Ei . Solar Demand: Solar pump use is directly proportional to the ground water extracted or the canal water distributed by solar pumps. It can be presented as:

Sitgw = g (x it , µc )(Wits )

(6)

SitC

(7)

=h

(µds )(Wits )

Sit = Sitgw + SitC

it

Lagrangian for profit maximization for a farmer with all three types of pumps is given by:

+ P1t (L + LRit eu (x

=

2

eu (x , µ ) W eu it it c

=

m (µdd )(Witdc )

Fitef

G it )(Wit

h (µde )(Witeuc )

it

it Witeu

µc ) Witd

Witef

Witeu

Si h (µds )

Witsc

3

Di l (x it , µc )

Witd

Witdc

Witsc

Witefc

Witeuc

Witdc )

rit LRit

it

it

it Witeuc

it

Witefc it

Witdc it Witsc

d (x , µ )(W d ) it it c

it

(12)

Loit it

LRit

= Wits + Witeu + Witef + Witd = WitCS = Witsc + Witeuc + Witefc + Witdc s sc it = 0 & Wit = 0 if non-adopter of solar pump eu euc = 0 W & it it = 0 if not using unit electric meter ef efc = 0 W & it it = 0 if not using flat meter dc d it = 0 & Wit = 0 if not using diesel pump G it = 0 if not using ground water C it = 0 if not using canal water Ei or g (x it , µc )(Witeu + Witef ) Ei if using ground water it Ei or if using canal water it G

it

(14)

Fito Witd

Fit1

+ P1t

Witef

+ P1t

Witef Fit1 Witd

=

eu (x

µc ) +

it ,

µc ) +

= Pot

Fito Witefc

= Pot

Fito Witdc

= Pot

Fito Fit1 = sc + P1t Wit Witsc

= Pot = Pot = Pot

Fito Loit

+ P1t

+ P1t

Fit1 Witdc

= P1t

it

= P1t

Loit

Fit1 L it

+

l 2

+

3

(17) 4

l 1

+

4

= m (µdd ) +

Fito F1 + P1t it = r LRit LRit Fito

l 1

=

Fit1

(15) (16)

Fito Fit1 = h (µde ) + euc + P1t Wit Witeuc

Fit1 Witefc

1

1

d (x

=

it ,

= Pot

C

it

Fito

= Pot

= Pot

Witd

2

Fito Fit1 = eu + P1t Wit Witeu

= Pot

Witef

subject to:

•W •W •W •W •W •W •W •W • EE •

l 2

Fito F1 + P1t its = Wits Wit

= Pot

Wits

+ WitC ))

rit LRit

Wits

it ,

(13)

(11)

Loit ) ((1

Witefc

d ((x

First order conditions are:

G C it (Wit + Wit ))

+ P1t (L + LRit

Witeuc

Di m (µdd )

CS 4 (Wit

h (µde )(Witeuc )

Ei g (x it , µc )

1

Si g (x it , µc ) l 3

where l (x it , µc ) determines the amount of diesel required to get one unit of water. m (µdd ) determines the amount of diesel required to distribute one unit of canal water. Similar to electricity consumption maximum limit, there is a maximum possible diesel consumption given capacity of diesel pump. We denote this by Di . This implies Dit Di . Pumping Cost: Per unit cost of pumping is zero with flat meter or solar. For flat metered farmers, we denote annual fixed electricity cost by Fitef . For unit metered farmers, eu (x it , µc ) represents per unit cost of pumping by unit metered electricity pump. d ((x it , µc ) represents per unit cost of pumping by diesel pump. Profit: Let it denote profit of farmer i in period t, then it = Pot Loit (

Fitef

µc ) Witeu

Ei h (µde )

l 1

(10)

Dit = Ditgw + DitC

it ,

m (µdd )(Witdc )

(9)

m (µdd )(Witdc )

Loit )

ef efc s eu d sc euc dc it )(Wit + Wit + Wit + Wit + Wit + Wit + Wit + Wit ))

((1

where g (x it , µc ) determines the amount of solar energy required to get one unit of ground water. This is likely to be function of current ground water table/stock (xit ) and other cluster level characteristics as denoted by (µc ) . h (µds ) determines the amount of solar energy required to distribute one unit of canal water. Due to limited hours of solar supply and a fixed capacity of a solar pump there exist a maximum possible solar consumption for a farmer, which we denote by Si . This implies Sit Si . Diesel Demand: Demand for diesel is assumed to be proportional to water demand met by diesel. It can be presented as:

DitC

+ Witeu + Witef + Witd + Witsc + Witeuc + Witefc + Witdc ))

s it (Wit

L = Pot Loit (

(8)

Ditgw = l (x it , µc )(Witd )

Di or l (x it , µc )(Witd) Di if using ground water Di or m (µdd )(Witdc ) Di if using canal water Si or g (x it , µc )(Wits ) Si if using ground water Si or h (µds )(Witsc ) Si if using canal water

it

4

(18) (19)

4

+

l 3

(20) (21) (22) (23) (24)

Eqs. (14) – (17) implies that a farmer extracts ground water using solar/electricity/diesel pump to equalize the sum of value of marginal product of water from traditional and new crops to the marginal cost of water extraction by a given pump. The marginal cost of extraction by a given pump is the sum of per unit cost of pumping and shadow price of the additional unit of water by that pump. There are no per unit pumping cost in case of solar and flat metered pump. 600

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Similarly, Eqs. (18) – (21) explain optimal distribution of canal water by a given farmer. Eq. (22) implies farmer allocates cultivated land area amongst traditional and new crops to equate value of its marginal product in these two crops. Eq. (23) shows that farmer selects optimal rented cultivated land area to equate sum of value of its marginal product in traditional and new crops to the value of per unit land rent. Eq. (24) shows that farmer allocates water to traditional and new crops such that value of marginal products is same for both. It is reasonable to assume that the willingness to pay for an additional unit of water will be same for a farmer whether that additional unit of water is extracted by a solar pump, an electric pump or a diesel pump. This implies that 1 = 2 = 3 for a ground water using farmer. Similarly, 1l = 2l = 3l for a canal water using farmer. From the above model, we derive following hypothesis to be tested for the energywater-food nexus: Hypothesis 1: Water consumption is positively related with the adoption of solar water pumps. For rainfed adopters, water consumption is expected to increase as solar water pumps provide easy access to groundwater for irrigation. For electric or diesel pump using farmers, water consumption is expected to increase with solar pump adoption if they have unmet water demand, i.e., 2 0 and/or 3 0 . Here, an additional solar pump will complement the existing pumps to meet the newly created irrigation demand of the farmers. As the marginal cost is zero for solar and flat meters, farmers are likely to first use the cheapest source of energy and then move on to more expensive options. With solar adoption, water consumption may not change if the farmer uses a solar pump as a substitute rather than as a complement. This is likely to be the case when a solar pump is able to meet all the groundwater requirements of a farmer, i.e., Wit Wits . Hypothesis 2: Electricity consumption is negatively related with the adoption of solar water pumps. As discussed above solar pump can substitute electric pump completely or partially depending on the total water requirement of a farmer and cost of pumping. When Wit Wits + Witeu + Witef or WitC Witsc + Witeuc + Witefc , farmers have incentive to substitute Witeu or Witeuc by Wits or Witsc , respectively, to the extent possible to save high pumping costs or distribution cost due to per unit electricity pricing. The hypothesis of declining electricity consumption may be rejected if farmers have large unmet irrigation demand that they expect to meet by a new solar pump. Also in case of flat meters where farmers have to pay fixed charges, we expect small or no effect on electricity consumption. Hypothesis 3: Diesel consumption is negatively related with the adoption of solar water pumps. As in the case of electricity consumption, solar pump is likely to Wit Wits + Witd substitute diesel consumption when or WitC Witsc + Witdc , farmers have incentive to substitute Witd or Witdc by Wits or Witsc , respectively, to the extent possible to save high pumping costs or distribution cost due to high diesel prices. If hypothesis 3 is rejected, then farmers may have large unmet irrigation demand that they expect to meet by a new solar pump with 3l 0 . Hypothesis 4: Cropping Intensity is positively related with the adoption of solar water pumps. The cropping intensity is defined as a ratio of gross cropped area and net cultivated area. Access to a solar pump enables farmers to cultivate more intensively because it increases access to energy and water leading to an increase in cropping intensity or the number of times that the net cultivated area is cropped. Hypothesis 5: Gross cropped area under fruits and vegetables are positively related with the adoption of solar water pumps. As the water access of the energy-constrained farmer increases, they are able to change their cropping pattern towards higher remunerative crops, such as fruits and vegetables, with specific water requirements. As the electricity supply is erratic in the rural villages of Rajasthan, access to a solar pump will enable farmers to meet the specific water requirements of these crops by providing a reliable water supply during day time. Thus, the gross cropped area under fruits and vegetables is

expected to increase with the adoption of a solar pump. Hypothesis 6: Profits are positively related with the adoption of solar water pumps. Increase in cropping intensity, increase in GCA under fruits and vegetables, and decrease in energy cost are expected to increase the profits of farmers. 4. Study design In this study, we analyzed the impact of the programme for the 2011, 2012, 2013, 2014 and 2015 period, using a quasi-experimental difference-in-differences study design which took into consideration the various resource and welfare outcomes for solar pump adopters, before and after they were treated, as compared to matched control farmers over the same time period. The study selected its sample of 434 farmers (289 adopters and 145 non-adopters) using a multistage, stratified, random sampling procedure. The first step was to draw a sample of 13 tehsils/blocks from the six districts having a large number of solar pump adopters, i.e., Jaipur, Sikar, Jaisalmer, Chittorgarh, Bikaner and Sri Ganganagar. As the impact of the solar pump is expected to be very different in different agro-climatic conditions, blocks were selected to capture this variation in agro-climatic conditions and related features such as differences in the irrigation system (tube well/canal irrigated areas) and differences in farming contract (sharecroppers/owners). The second step was to form homogeneous village clusters within blocks based on cropping pattern and water-table depth. Fig. 1 display all the sampled villages by red dots. The third step was to draw a sample of adopters and non-adopters of solar pumps from each homogeneous village cluster. Given the fewer number of adopters of solar pumps relative to non-adopters, we collected our data using a case-control design survey. We obtained a list of all the adopters (referred to as “the cases”) through accessing data that is readily available with the Department of Horticulture in the respective village clusters. A sample of adopters from each cluster was created by randomly selecting some from the full list. For comparison, these cases have been supplemented with a sample from populations in nearby regions, with similar cropping patterns and socio-economic characteristics, who meet the eligibility criteria for obtaining solar pumps but have not adopted them (referred to as “the controls”). To reduce self-selection bias, we selected as much of the sample of non-adopters as possible from the waiting list. At the beginning of every year, the government announces the level of subsidy so that interested farmers can apply for the solar water pump. The Department of Horticulture then selects, through conducting an open lottery, the required number of applicants at the district level to meet their yearly targets. The Department then prepares a waiting list of all the remaining candidates. On average, 40% of our sample of controls (nonadopters) has been drawn from the waiting list of farmers for the year 2014–15 in each village cluster. Where such non-adopters were unavailable as was the case in many clusters, we selected non-adopters who were interested in adopting a solar pump but had not yet applied or who had backed out of the scheme altogether for whatever reason (See Table 4 in the Appendix). We surveyed adopters and non-adopters from each village cluster randomly while attempting to maintain a sample size ratio of 2:1, the reason for this being that the control group, i.e., the non-adopters, had to be as similar as possible to the treatment group, i.e., the adopters, with the obvious exception of solar water pump ownership. Both cases and controls were interviewed regarding their outcomes on water consumption, energy consumption, producer surplus, cropping patterns, and socio-economic characteristics. In our sample, we have solar pump adopters from four different years: 12 for 2011; 13 for 2012; 14 for 2013; and 15 for 2014 (See Table 1). All districts show the maximum number of adopters for the year 2013–14. The before-solarpump period for each treated farmer has been defined as the years prior to the year that the farmer adopted the solar pump. The same before601

Energy Policy 129 (2019) 598–609

E. Gupta

Fig. 1. Sample villages from selected districts.

decisions by farmers. The first problem is the endogeneity of access to solar pumps due to purposive targeting rules. For example, in Rajasthan, farmer should meet the three eligibility criteria for the subsidy as discussed above. The second source of bias is the self-selection into the programme due to nonbinding programme participation. Farmers have to pay about 14–30% of the cost of the pump. Therefore, not all farmers would be willing to adopt solar pumps on their farms. Participation in the solar water pump programme is likely to be correlated thus with socio-economic characteristics such as education and income, which may determine the probability of solar pump adoption by a farmer. The proposed approach compares outcomes before and after a programme intervention for the treatment group with a comparison group. We estimate the average treatment effect on the treated farmers (ATT), given a vector of farmer characteristics. In order to understand the key determinants of our outcome variables, in Section 3 we have formulated a simple theoretical model to assess a farmer's profit maximization problem. The first order conditions from the theoretical model show that key outcome variables will be a function of a number of exogenous factors: energy access as measured by electric pump ownership and capacity; diesel pump ownership and capacity; solar pump ownership and capacity; groundwater level; size of agricultural land holding; irrigation system (whether using drip or practicing flood); diggi/water storage tank ownership and volume; farmer fixed effect to account for factors that remain fixed over the period of analysis such as education, age, experience, management efficiency, level of awareness, agricultural assets, and cluster-level factors such as

Table 1 District wise and year wise Adopters in sample. Year

Jaipur

Sikar

Jaisalmer

Bikaner

Sriganganagar

Chittorgarh

2011-12 2012-13 2013-14 2014-15 2015-16

13 35 12

2 28 13

1 4 17 9 1

4 18 33 11

3 17 26 7 1

4 10 6 1

period has been assigned to the control group as well. We collected both before and after data on outcome variables using a recall survey for the 2011–12 to 2015–16 period. In order to minimize memory-based errors in questions about past behavior, we provided clues that could be associated with the behavior of interest (e.g. what crops did you grow before adopting the solar pump), referred to meaningful events to anchor the time-frame, and used warming-up questions to trigger memories related to the event of interest (Boyce and Mauch, 1992). 5. Econometric model We have used the difference-in-differences method to estimate the causal impact of solar water pump adoption on selected outcome variables. This approach helps in accounting for two key identification problems: non-randomized allocation of solar pumps and adoption 602

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quality of soil and water; and cluster-specific trends to account for cluster- level variables that change over time such as input and output prices. In order to see how a given outcome variable changes with solar water adoption, we estimate reduced form equation by expressing each outcome variable as a function of all exogenous variables, solar adoption dummy, before and after dummy and the interaction of solar adoption dummy and before and after dummy as shown below:

Oit =

1 Di

+

Adopter

6 µct +

After + 2 Dt z + 7 i it

+

Adopter After D 3 Dit

+

4 Yearit

+

In the three canal using districts, the quality of the groundwater is not suitable for irrigation which is why few farmers use it. In Bikaner and Jaisalmer, the groundwater depth is very high in many places making it difficult to extract and use for irrigation. In Jaipur and Sikar, farmers use only electric pumps for irrigation with average electricity capacity ranging from 10 to 13 HP. In canal irrigated areas, farmers hardly use electricity due to poor electricity access in these areas and are instead largely dependent on costly diesel use. We found average cropping intensity and gross cropped area under fruits and vegetables to be highest in Jaipur, Chittorgarh and Sri Ganganagar. These areas also have relatively better water access than the other three districts. We found Sri Ganganagar to have the highest average annual profits followed by Bikaner and Jaisalmer.

5 Xit

(25) Where i is individual farmer and t represents year, Oit denotes the key dependent/outcome variable of interest- water consumption (Wit ); diesel consumption (Dit ); electricity consumption (Eit ); cropping intensity (CIit ) ; Gross cropped area under fruits and vegetables (GCAFVit ) ; Annual profits (APit ) ; z i are farmer fixed effect, µct are cluster specific trend, Di Adopter is a dummy that takes value one for adopters and 0 for non-adopters, DtAfter is a dummy that takes value one for the period after adoption and 0 for the period before adoption. Xit are other exogenous controls-size of land holding, water table, electric meter type and animals possessed. The key coefficient of interest is 3 that measures difference-in-difference impact of the solar water pump on the outcome variable as estimated from its corresponding reduced form equation. This is a basic model. This is extended to study if this impact of solar pump adoption on the key outcome variables varies with land size and electric/diesel pump capacity in the following manner:

Oit =

+ 2 DtAfter + 3 DitAdopter D After Adopter After D ElecCap + 6 Yearit + 5 Dit

1 Di

+

Adopter

+

4 Dt

7 Xit

After

+

7. Regression results For each pair of district and outcome variable, we have estimated two different models. M1 is a basic specification with solar pump adoption dummy, solar pump before and after time dummy, and the interaction between these two dummies. The interaction dummy gives the difference and differences estimate of the impact of solar pump adoption on the outcome variable for an average farmer. For understanding heterogeneity in impacts, M2 extends M1 by interacting the adoption dummy, time dummy and combination of adoption and time dummy by two variables: available electric and diesel pump capacity and agricultural land holdings. As electric and diesel capacity may be endogenous, we have estimated M2 only if we found them to be nonendogenous for a given outcome variable. The average effect from M2 is the same as in M1. Both models account for the farmer fixed effect, which controls for all the farmer-level factors that are fixed over the period of analysis such as the number of farming assets, level of education and age of farmer, main occupation of the farmer, and experience and efficiency in farm management. In addition, both models also account for factors that change over time such as groundwater level, agricultural land holdings, number of animals possessed, and electricity connection type by explicitly including them in the regression and by allowing cluster by year fixed effects to control cluster specific factors that may change over the period of study such as input and output prices of crops and wages of skilled and unskilled labor. As there are adopters from different years, we account for adoption heterogeneity by adoption year dummies. Some other control variables were also considered such as irrigation system (i.e., using drip or practicing flood), diggi/water storage tank ownership and volume, etc. but these were dropped from the final regressions due to possible endogeneity problems.

ElecCap

8 µct

+

9 zi

+

it

(26) The key coefficients of interest are 3 and 5, which jointly give difference-in-difference impact of the solar water pump on the outcome variable as estimated from its corresponding reduced form equation. In this model, 3 gives the impact of solar pump adoption on the outcome variable when electricity capacity of the farmer is zero. The estimated impact of solar pump adoption increases linearly at the rate of 5 with per unit increase in the electric capacity of the pump owned by a farmer. Similar model is estimated to study if impact of solar pump adoption on outcome variables varies with landholding size. 6. Summary statistics Table 2 displays the sample mean of key variables for adopters and non-adopters in all the 6 districts during the baseline. All these variables are explained in detail in the appendix. As we have the highest adoption of solar pumps for 2013–14, the period before 2013–14 is considered the baseline. Thus, we have dropped adopters prior to 2013–14 in calculating the baseline averages. The last column gives differences in mean of key variables. For all districts and variables, we observe good matching between adopters and non-adopters except for Chittorgarh, where we have a small sample. Therefore, we need to exercise caution in interpreting results from Chittorgarh. In the case of Jaipur, we had to drop 16 farmers in order to obtain good matching between the treatment and control group. The new sample has therefore come down to 93 farmers from the original 109. In the case of agricultural land holdings, we find Chittorgarh, Sikar and Jaipur to have relatively smaller landholdings with the average size ranging between 5 and 11 acres as compared to the northern and western districts of Bikaner, Sri Ganganagar and Jaisalmer with the average size ranging between 19 and 40 acres. While these three districts with small landholdings are completely dependent on groundwater irrigation, the remaining three districts with large landholdings are very much dependent on canal irrigation. There are a few areas within the northern and western districts that use groundwater for irrigation, for e.g., Pokhran in Jaisalmer, which is completely dependent on groundwater irrigation.

7.1. Average impact (Model M1 results) Table 3 presents difference-in differences estimates from model M1. In the main text, we only report the difference-in-differences coefficient corresponding to the interaction of solar pump adoption dummy and time dummy. The Table gives the causal impact of solar water pump adoption for an average farmer in each selected district and for each outcome variable. In addition to the mean impact, we also report a 95% confidence interval in square brackets. In the appendix, we have reported the full regression results for all the estimated models. Figs. 2 and 3 plot the percentage changes in outcome variables for adopters relative to average baseline values for outcome variables. The results for each outcome variable is discussed in detail below: 7.1.1. Impact on water consumption The annual total water consumption of farmers increases for solar pump adopters in two major groundwater using districts: Jaipur and Sikar. The estimates show that the average causal effect of solar pump adoption on annual water consumption is 35 lac liters or 17% in Jaipur with a 95% confidence interval of [6,64] lac liters or [3%, 29%]. The 603

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Table 2 Key variables in the baseline (before 2013–14). District

Variable

Mean of A

Mean of NA

Difference

N

A

NA

Jaipur

Land Owned (acres) Electric Capacity (HP) Water Table (Feet) Number of Farming Assets Highest Level of Education (years)

7.8 13.2 258 5.9 7 209.7

7.3 14.9 250 5.3 7.4 218.6

.50 − 1.7 8 .6 − 0.4 − 8.8

93

60

33

63

43

20

49

30

19

103

66

37

78

54

24

29

21

8

Sikar

Jaisalmer

Bikaner

Sriganganagar

Chittorgarh

Water Consumption (105 Litres) Electricity Consumption (HKWH) Cropping intensity Gross Cropped Area FV (acres) Annual profits (Thousand Rupees) Land Owned (acres) Electric Capacity (HP) Water Table (Feet) Number of Farming Assets Highest Level of Education (years) Water Consumption (Lakh Litres) Electricity Consumption (HKWH) Cropping intensity Gross Cropped Area FV (acres) Annual profits (Thousand Rupees) Land Owned (acres) Diesel Capacity (HP) Electric Capacity (HP) Number of Farming Assets Highest Level of Education (years) Diesel Consumption (Litres) Electricity Consumption (HKWH) Cropping intensity Gross Cropped Area FV (acres) Annual profits (Thousand Rupees) Land Owned (acres) Diesel Capacity (HP) Electric Capacity (HP) Number of Farming Assets Highest Level of Education (years) Diesel Consumption (litres) Electricity Consumption (HKWH) Cropping intensity Gross Cropped Area FV (acres) Annual profits (Thousand Rupees) Land Owned (acres) Electric Capacity (HP) Diesel Capacity (HP) Number of Farming Assets Highest Level of Education (years) Diesel Consumption (Litres) Electricity Consumption (HKWH) Cropping intensity Gross Cropped Area FV (acres) Annual profits (Thousand Rupees) Land Owned (acres) Electric Capacity (HP) Diesel Capacity (HP) Water Table (Feet) Number of Farming Assets Highest Level of Education (years) Diesel Consumption (Litres) Water Consumption (Lakh Litres) Electricity Consumption (HKWH) Cropping intensity Gross Cropped Area FV (acres) Annual profits (Thousand Rupees)

94.4 1.96 3 34.2 9.2 10.2 272.9 2.75 7.5 154 63.5 1.53 1.1 42.7 25.6 9.9 3.4 6.5 5.8 925.7 27.2 1.5 1.2 95 19.9 11.7 2.9 6.7 6.6 443 57.4 1.7 2.5 189 30 1.6 24.8 8.8 8 942 11.9 1.86 3.7 509 9.8 6 8.5 219 6.4 7 663 547 32 1.85 3.6 103

98.6 1.93 4 28.9 11 11.5 260 3 7.1 136 57.5 1.57 2 63 30.7 7.3 0.7 5.6 6.4 256.8 4.4 1.4 1.1 153 19 15 4.9 6.3 6.8 548 65.9 1.7 2.2 251 25 3.2 28.5 8.5 7.8 994 4.3 1.9 1.74 592 5.4 4.6 5 98 2.6 4 512 198 20 1.9 1.8 59.7

− 4.2 0.03 1* 5.6 − 1.8 − 1.3 12.8 − .3 0.4 17 5.9 − 0.035 − .85 − 20* − 5.1 2.6 2.7 .88 − 0.61 668*** 22.7 0.1 0.1 − 58* 0.9 − 3.48 − 1.95 0.35 − 0.19 − 105 − 8.5 0 0.32 − 62 5 − 1.58 − 3.6 0.3 0.2 − 52 7.6 − 0.04 1.9* − 83 4.3*** 1.4 3.4 122** 3.8*** 3*** 151 349** 12 − 0.05 1.8** 43*

* p < 0.1. ** p < 0.05. *** p < 0.01.

average impact in Sikar at 77 lac liters or 50% is more than double the impact in Jaipur with a 95% confidence interval of [45,109] lac liters or [29%,71%]. However, it is worth noting that 37% of adopters in our sample from Sikar are rainfed farmers with zero baseline groundwater consumption, resulting in a much lower average baseline water

consumption for solar pump adopters in Sikar and a much higher percentage increase from a low base as compared with Jaipur. Jaipur and Sikar are neighboring districts and share many common characteristics. For example, they both use electric pumps for extracting groundwater for irrigation and have the same level of groundwater table. We 604

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Table 3 Marginal impact of solar pump adoption on key outcome variables. VARI ABLES Water Consumption (lac lit) Electricity Consumption(HKWH) Diesel Consumption(lit) Cropping Intensity Gross Cropped Area Fruits veg (acres) Annual profits (000'Rs)

(1) Jaipur

(2) Sikar

(3) Jaipur & Sikar

35** [6,64] − 11** [− 21,− 0.9]

77*** [45,109] − 2.2 [.5,− 9]

51*** [30,72] − 7.7** [− 14,− 1]

0.03 [− 0.05,0.11] 1.0*** [0.35,1.6] 7.7* [− 1.4,16.8]

0.3*** [.12,.44] 0.5* [− 0.03,1.1] 13 [− 3.5,30]

0.12*** [0.04,0.22] .83*** [0.4,1.3] 9.4** [1.4,17]

(4) Bika ner

6.8 [− 16,29] − 394** [− 749,− 40] 0.13*** [0.04,0.20] 1.6** [0.26,2.9] 157*** [51,261]

(5) Srigan ganagar

− 8.7** [− 16,− 2] − 709*** [− 1101,− 315] 0.04 [− 0.07,0.16] − 0.05 [− 1.2,1.13] 159** [11,307]

(6) Jaisal mer

(7) Chittor garh

(8) All Diesel Districts

6.6 [− 3,15] − 556** [− 993,− 80] 0.1 [0.04,0.33] 0.8 [− 1.06,2.6] − 47 [− 351,256]

215* [− 45,455] 0.08 [− 7,7] − 354*** [− 578,− 130] 0.2* [− 0.04,0.38] 0.5* [− 0.07,1] 24 [− 30,78]

1.3 [− 8,10] − 543*** [− 765,− 346] 0.10*** [0.03,0.16] 0.7* [− 0.04,1.4] 112*** [35,189]

95% confidence interval in parentheses. * p < 0.1. ** p < 0.05. *** p < 0.01.

Fig. 2. Marginal impact on resource variables as % of mean of adopters in baseline.

hypothesis. In Sikar, which is another major electricity-using district but with relatively lower average electricity capacity than Jaipur, we found a negative but insignificant impact of solar pump adoption on electricity consumption. One reason for the insignificant result could be the high number of rainfed solar pump adopters (with no electric pump) in the sample of solar pump adopters in Sikar. However, in the pooled model of Jaipur and Sikar, we do find a negative and significant average impact on electricity consumption of 7.7 HKWH or 9.54% despite a wide 95% confidence interval of [-14,-1] HKWH / [-17%,1.23%] (Table 3, Fig. 2, Table 7 in the Appendix). Among the remaining four diesel-using districts, we find a significant and negative impact in only one district, Sri Ganganagar, where about 24% of solar pump adopters use the electric pump. There was a decrease in electricity consumption by 8.7 HKWH or 73% with a 95% confidence interval of [-16,-2] HKWH / [-134%,-17%] (Table 8 in the Appendix).

therefore estimate and report results from a pooled model. Due to the large sample size and greater variation, the pooled model estimates exhibit much lower standard errors and provide more precise estimates as compared to the district-specific model estimates. The results show that the annual water consumption of a solar pump adopter increases by 51 lac liters or 27.5% on average in this area with a 95% confidence interval [30,72] lac liters or [16%,39%] [see Table 3, Fig. 2, See Table 5 in the Appendix]. These findings corroborate our first hypothesis from the theoretical model. In the case of Chittorgarh, which has relatively good groundwater levels and where farmers use both electric and diesel pumps for irrigation, the estimates are significant at the 10% level of confidence (Table 6 in the Appendix). 7.1.2. Impact on electricity consumption We found evidence of decline in electricity consumption by 11 HKWH or 11.7% on average with a 95% confidence interval of [-21,0.9] HKWH/[-21%,-1%] after solar water pump adoption in one major groundwater-using district: Jaipur. This result is in line with our second

7.1.3. Impact on diesel consumption We found strong evidence of reduction in annual diesel 605

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consumption after solar pump adoption in all the four diesel-using districts, Bikaner, Sri Ganganagar, Jaisalmer and Chittorgarh. As estimated from the pooled model, the annual diesel consumption of solar pump adopters in these four districts fell by 556 liters or 77% on average after solar pump adoption with a 95% confidence interval of [-765,-346] liters / [-106%, −50%] (Table 9 in the Appendix, Fig. 2). As seen in district-specific models, Sri Ganganagar has experienced the largest average decline of 709 liters or 75% with a 95% confidence interval of [-1101,-315], followed by 537 liters or 59% in Jaisalmer with a 95% confidence interval of [-999,-80], 394 liters or 89% in Bikaner with a 95% confidence interval of [-794,-40] and 354 liters or 60% in Chittorgarh with a 95% confidence interval of [-578,-130] (Table 3, Table 9 in the Appendix).

three districts, Jaipur, Sikar and Bikaner. In Jaipur, GCA under fruits and vegetables increased on average by 1 acre or 33% with a 95% confidence interval of [0.35,1.6]/[12%,53%] for solar pump adopters. In Sikar, we found a positive impact of.5 acres or 45% but it is significant at the 10% level of confidence. In the pooled model, we find a positive and significant average impact of about 0.8 acres or 36% for adopters with a 95% confidence interval of [0.4,1.3]/[18%,59%] (see Table 3, Fig. 3, Table 13 in the Appendix). In Bikaner, GCA under fruits and vegetables expanded by 1.6 acres on average for solar pump adopters, representing about a 64% increase over the baseline GCA under fruits and vegetables for solar pump adopters. The 95% confidence interval ranges between [0.26, 2.9]/[10%,116%] (see Table 14 in the Appendix).

7.1.4. Impact on cropping intensity We found that two out of the six districts experienced improvements in cropping intensity after adoption of solar water pumps. Sikar, with a high number of rainfed solar pump adopters, showed the maximum increase in average cropping intensity with.3 or 20% with a 95% confidence interval of [0.12,0.44]/[8%,29%]. A number of rainfed farmers in Sikar, who were previously only able to cultivate a kharif crop during the monsoon period, were now also able to cultivate a crop during the rabi season due to access to the solar water pump resulting in a doubling of their cropping intensity. In Jaipur, we did not find any significant impact on cropping intensity. In the pooled model for Jaipur and Sikar, we found a positive significant impact of.12 or 5.6% with a 95% confidence interval of [0.04, 0.22]/[2%,10%] (See Table 3, Fig. 3, Tables 10, 11 in the Appendix). Similarly, among diesel-using districts, only Bikaner shows a positive significant impact on cropping intensity of 0.13 or 6% with a 95% confidence interval of [0.04, 0.20]/ [2%,9%] (see Table 12 in the Appendix). In Bikaner, many farmers reported improvement in cropping intensity after the adoption of solar pumps as it enabled them to grow a zaid crop since they could save and store water in the diggi from the previous season and use the solar pump for distributing water from the diggi. It had not been profitable for them to grow a zaid crop earlier due to the cost of diesel and the unreliable electricity supply during that season.

7.1.6. Impact on annual profits We found evidence of improvement in annual profits in three districts, Jaipur, Sri Ganganagar and Bikaner. In Jaipur, the average impact on annual profits is positive at INR 8000 or 23% with a 95% confidence interval of [1.4,16.8] thousand rupees but significant at 10%. In Sikar, we found a positive but insignificant impact on the annual profits of adopters. In the pooled model for Jaipur and Sikar, we obtained a significant and positive impact of about INR 9000 or 22% with a 95% confidence interval of [1.4,17]/[3.4%,41.5%] thousand rupees (Table 3, Fig. 3, Tables 15 and 16 in the Appendix). There is strong evidence of improvement in annual profits of solar pump adopters in two diesel-using districts, Bikaner and Sri Ganganagar. Sri Ganganagar experienced the highest average increase at INR 159,000 (or 30% over the baseline average for adopters) followed by Bikaner that showed an average annual increase of INR 157,000 (or 83% over the baseline average for adopters). In the case of Jaisalmer and Chittorgarh, we did not find evidence of increase in profits. In the pooled model for four diesel-using districts, we found a positive and significant impact of INR 112,000 or 45% with a 95% confidence interval of [14%,76%] (Table 17 in the Appendix). 7.2. Heterogeneous impacts (Model M2 results) The estimates reported in Table 3 give the average impact for each district, which could therefore mask some interesting heterogeneous impacts. Here we analyze whether the impact on key outcome variables

7.1.5. Impact on gross cropped area under fruits and vegetables We found evidence of increasing GCA under fruits and vegetables in

Fig. 3. Marginal impact on food and income security indicators as % of Mean of Adopters in Baseline. 606

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Fig. 4. Marginal effect of solar pump adoption on water consumption in Jaipur and Sikar.

resources. Increased water access for solar pump adopters has led to increase in cropping intensity, gross cropped area under fruits and vegetables, and annual profits of solar pump adopters. We found this impact to be higher for small farmers and thus the policy seems to have positive distributional impacts and social externalities. Another key empirical finding is a significant reduction in electricity consumption and diesel consumption, which are associated with high degree of carbon emissions. The substitution effect for diesel is much higher compared to electricity due to its high price. In line with the literature on the impact of agricultural subsidies for promoting irrigation technologies, our study finds significant positive impact on poverty reduction as indicated by higher profits for farmers. Here, diesel using districts witnessed significant cost savings due to substitution of costly diesel by solar pumps. Many small farmers in areas with little or no electricity access were dependent on costly diesel. Access to a solar pump has significantly improved their profitability. Relatively, profits increased much more for diesel using districts than electricity using districts. The above analysis has many important policy implications. It suggests that policy makers should engage in a detailed mapping of all the regions with respect to the region's suitability for solar water pumps.2 In regions where farmers already use pumps to distribute canal

varies by: a) electric and diesel capacity and b) land holding size. Figs. 4, 5 and 6 shed some light on this. These figures plot the marginal impact on key outcomes from model M2. In Jaipur and Sikar, which use only groundwater for irrigation using electric pumps, the marginal impact on water consumption is relatively higher for small or energy constrained farmers than for large farmers. This also implies that farmers with lower electricity capacity are more likely to use the solar pump as a complement than a substitute. At the same time, water extraction increases with land holding size in these districts. The marginal impact on cropping intensity is positive and significant till 16 HP electricity capacity. The positive relationship is expected to be relatively weaker for areas with higher energy and water access prior to access to a solar pump. The marginal impact on annual profits is positive and significant till 14 HP. 8. Conclusions and policy implications This study finds that there is a substantial difference in the solar pump impact on energy-water food nexus for a farmer depending on geographical area, purpose for which solar pump is used, amount of land holding and amount of electric and diesel capacity possessed. In particular, we find that for regions that use ground water for irrigation, solar pump adoption has led to increase in their water consumption for an average farmer. We find this effect to be more pronounced for farmers that had no pumps or that possessed small electric pumps before solar pumps. In contrast, for districts that use canal water and use solar pump for lift irrigation or to distribute water from tanks to the fields, there are no negative externalities on common ground water

2

Food and Agricultural organization (FAO) has developed a toolbox on solarpowered irrigation systems which provides a list of all the types of questions that need to be studied before installing solar pumps and which may be used as a starting point for studying the suitability of solar pump adoption in a given region (FAO, 2017). 607

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Fig. 5. Marginal effect of solar pump adoption on cropping intensity in Jaipur and Sikar.

water, solar pumps provide a win-win solution as farmer profits expand and fossil fuel consumption falls with no impact on groundwater extraction. As solar pumps are highly profitable in diesel-using districts, the government can specifically target poor farmers for the subsidy in these regions as rich farmers are likely to adopt solar pumps even without subsidies due to the high cost reductions. In areas where solar pump is used for ground water, government should minimize the negative impact on groundwater stock in the long run. It is imperative to do optimal sizing of these pumps such that they don't deplete the aquifer in a given region. In the current study, nearly all adopters have a 3 HP pump which is relatively small compared to the existing average electric capacity of 13 HP in Jaipur and 10 HP in Sikar. Still, we find strong evidence of increasing pressure on ground water. The extension of subsidy to larger solar pumps of 7-10 HP without any quantity and price regulations can result in unsustainable use of the ground water. We need innovative policies for managing ground water level in a sustainable way. As done in few other countries, ground water use by all agricultural pumps must be monitored to formulate policies that give farmers correct incentives. For example, subsidized pumps policy can put restrictions on the volume of water that can be extracted from such pumps. There can be level sensors built in the pumps that automatically stops a pump if water level drops below a given level. In India, government has started measuring ground water use by solar pumps with remote sensing chips in the all newly installed solar water pumps. Another measure that policymakers may consider adopting to ensure sustainable use of the water is assigning a value to the

groundwater. This can be done by connecting solar adopters to the grid and thus incentivizing efficient water use on farms as done in Karnataka (Shah et al., 2016, 2014). Similarly, solar pumps can be promoted as a service model, if feasible in a given region, with farmers paying for the per unit groundwater extracted (Agrawal and Jain, 2018). Furthermore, setting correct electricity prices can also play a major role in the successful adoption and use of solar pumps by farmers. With high electricity prices, we can expect greater substitutions in favor of solar as has been the case with diesel consumption. The analysis presented here should be applied to other regions for better understanding of the impacts of solar pump adoption in different agro-climatic zones. This will be required for aggregating impacts at the country level. Important extensions include incorporating within-year heterogeneity such as variation in estimated impacts by seasons, much detailed distributional analysis (possible with higher sample size) for targeting right population groups and regions for the subsidy program, using actual metered data in place of recall data wherever possible, and most important, estimation of net benefit on the environment along with complete cost-benefit analysis of the program incorporating all social and environmental externalities. Acknowledgements I would like to thank the South Asian Network for Development and Environmental Economics (SANDEE) at the International Center for Integrated Mountain Development (ICIMOD) for providing the research grant for this study. I am extremely thankful to Celine Nauges for her guidance at different stages of this paper. I also acknowledge comments 608

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Fig. 6. Marginal effect of solar pump adoption on annual profits in Jaipur and Sikar.

and insights that I received from several SANDEE advisors and peers at several Research and Training Workshops. I would also like to acknowledge SP Singh, from the Department of Horticulture, for providing all the required data and facilitating the study in the field. I thank Deepali Vaish and Bedashree for providing research assistance for this study. The opinions expressed in this paper are those of the author and should be not attributed to SANDEE, ICIMOD or their sponsors.

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Appendix A. Supplementary data Supplementary data associated with this article can be found in the online version at doi:10.1016/j.enpol.2019.02.008. References Agrawal, Shalu, Jain, Abhishek, 2018. Sustainable deployment of solar irrigation pumps: key determinants and strategies. Wiley Interdiscip. Rev. Energy Environ. e325. Badiani, Reena, Jessoe, Katrina K., Plant, Suzanne, 2012. Development and the environment: the implications of agricultural electricity subsidies in india. J. Environ. Dev. 21 (2), 244–262. Bassi, Nitin, 2018. Solarizing groundwater irrigation in india: a growing debate. Int. J. Water Resour. Dev. 34 (1), 132–145. Boyce, Carolyn M., Mauch, Marilyn C., 1992. Evidence of anchoring in a survey recall task. J. Off. Stat. 8 (1), 97. Briscoe, John, Malik, R.P.S., 2006. India's Water Economy: Bracing for A Turbulent Future. Oxford University Press, New Delhi. Burney, Jennifer, Woltering, Lennart, Burke, Marshall, Naylor, Rosamond, Pasternak, Dov, 2010. Solar-powered drip irrigation enhances food security in the sudano-sahel.

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