The willingness to pay for Renewable Energy Sources (RES): the case of Italy

The willingness to pay for Renewable Energy Sources (RES): the case of Italy

S. Bigerna (Faculty of Economics, UTIU)P. Polinori (Department of Economics, Finance

and Statistics, University of Perugia)

Washington D.C. October, 9-12 2011

University of PerugiaDepartment of Economics Finance and Statistics

30th USAEE/IAEE Conference: Changing Roles of Industry, Government and Research

Outline

I. IntroductionII. Energy scenarioIII. Method and DataIV. Emprical findingsV. Conclusions



Introduction

… today’s economy is mainly based on fossil fuels that are finite and polluting

… consequences regarding the use of fossil energy have become evident

In this context RES are essential to reduce harmful emissions and to conserve no renewable resources



Policy ScenarioIn EU the RES Directive (2009/72/CE) known as “20-

20-20”, includes well-known environmental and energy targets for 2020.

20% of emission reduction20% of total energy satisfied by renewable resources, 20% of energy savings ………..…….. in reference to EU Directive 2009/72/CE Italian

goal is to attain the share of 17% in RES



One important feature of the RES is their high supply-generation cost …. Consequently:

….. high cost prevents the widespread uptake of renewable energy systems …..

But If a positive attitude exists to RES:…... it could affect consumers WTP augmenting the

premiums they are potentially apt to pay for such new technology

….. it could potentially reduce the needed amount of public funding.



Coherently with this Energy Scenario the primary purpose of this study is to estimate

consumers’ WTP for the development of the RES use in Italy

(We made a National Survey)



Green energy and WTP (State of Art)

Several surveys have been performed in the world.These studies are not very comparable because they

differ in terms of:

i) survey periodsii) countries and institutional contextiii) survey typologyiv) elicitation formatsv) applied methodology and econometric techniques

University of PerugiaDepartment of Economic, Finance and Statistics


Batley et al. (2001) [U.K.]Nomura and Akay (2004) [Japan]Ivanova (2005) [State of Queensland]Bollino (2009) [Italy]Zografakis et al. (2010) [Crete]Yoo and Kwak (2009) [Korea]

…. however by analyzing their empirical results all studies estimated a low WTP if compared with the additional cost due to the respective National Renewable Energy Target.



MethodLet us consider households direct utility function:

U = U(Xp, Xg, R)

positively related to:the private goods Xp (Xp1, ...., Xpn)

the composite public good Xg the public good R (RES use services)



Xg is a composite commodity of all the others public goods with unit prices and value equal to the tax charged to the households.

Households maximise U subject to their budget constraint that is:M = PpXp + Xgwhere M is the nominal income and Pp is a price vector of

private goods



Each household spends all its disposable income by purchasing private goods:

Md = M – Xg

Maximization framework provides a set of conditional demand functions:

di*(Pp, R, Xg, Md)



By substituting di* into U we obtain a conditional indirect utility function

V(Pp, R, Xg, Md)

Inverting V for Md we obtain the conditional expenditure function

E* = Md = E*(Pp, R, Xg, U)



Minimizing both the expenditure relating to private and public goods subject to the utility level we obtain the restricted expenditure function:

E = E(Pp, R, Xg, U)

Conditional expenditure function and restricted expenditure function are related as follows

E = E(Pp, R, Xg, U) = E*(Pp, R, Xg, U) + Xg



By changing the energy scenario we assume that the restricted expenditure function varies according to R:

R0 = scenario without RES in the energy portfolioR1 = scenario with RES in the energy portfolio



By holding M as a constant the WTP for the use of RES is given by the compensated surplus (CS):

CS = E(Pp, R0, Xg0, U0) - E(Pp, R1, Xg0, U0)CS = [E*(Pp, R0, Xg0, U0) + Xg0] – [E*(Pp, R1, Xg0, U0) + Xg0]CS = E*(Pp, R0, Xg0, U0) – E*(Pp, R1, Xg0, U0)

where U0 is the utility level of the household without RES program.This estimate of compensating surplus is a measure of the

WTP for “RES use” service



Cont’ d Method

Elicitation formatWe try to deal with two questions:1) Consumers have a range of economic values, or a

valuation distribution in their mind instead of a single point economic value estimation

2) Overestimation of WTP typically occurs in contingent valuation studies



To dale with the first question we adopt a variant of the payment card approach…..

1) payment card method is consistent with important guidelines (e.g. U.K. Government guidelines)

2) many scholars assert that this method could be more intensively employed in CV studies (Champ et al. 2003; O'Garra and Mourato 2007; Atkinson et al. 2005).

Payment Card allows us to consider that consumers have a range of economic values in their mind



To mitigate hypothetical bias Cheap Talk is often used

… participants are explicitly warned about hypothetical bias and are asked to respond to the valuation question as if the payment were real

However Cheap Talk might have little or no effect on some people



In order to reduce the overestimation risk ….

We adopted a “certainty correction method” proposing five types of acceptance intensity:

• “definitely yes” and “definitely no” (DY, DN),• “probably yes” and “probably no” (PY, PN)• “not sure or don’t know” (DK)

Consequently we adopt a SPC approach





Bid (€) DN PN DK PY DY Interval data0 0% 25% 50% 75% 100% 0 [NO]

0.05 0% 25% 50% 75% 100% 0.001 - 0.0500.1 0% 25% 50% 75% 100% 0.051 - 0.1000.15 0% 25% 50% 75% 100% 0.110 - 0.1500.3 0% 25% 50% 75% 100% 0.151 - 0.3000.5 0% 25% 50% 75% 100% 0.301 - 0.5000.75 0% 25% 50% 75% 100% 0.501 - 0.750

1 0% 25% 50% 75% 100% 0.751 - 1.0001.5 0% 25% 50% 75% 100% 1.001 - 1.5002 0% 25% 50% 75% 100% 1.501 - 2.0005 0% 25% 50% 75% 100% 2.001 - 5.00010 0% 25% 50% 75% 100% 5.001 - 10.00015 0% 25% 50% 75% 100% 10.001 - 15.00020 0% 25% 50% 75% 100% 15.001 - 20.00030 0% 25% 50% 75% 100% 20.001 - 30.00050 0% 25% 50% 75% 100% 30.001 - 50.000100 0% 25% 50% 75% 100% 50.001 - 100.000

200+ 0% 25% 50% 75% 100% 100.001 - 200 [+]

Instruct the respondent to circle an aswer for each of 17 prices

Cont’ d Method

Payment card data may be analyzed in several ways.The choice of which model to use in regression

analysis is mainly affected by the data.Three aspects are relevant (Cameron and Huppert

1989; Whitehead et al. 1995; O’Garra and Mourato 2006): 1) the number of zero responses; 2) the size of the intervals; 3) the percentage of data that is point estimates.



• In our case the limited number of zeros and of point estimated WTP jointly to small size of the intervals suggests we use interval regression method

• So …… respondents maximum WTP may lie between the value recorded on the card and the higher value of the next card.



Cont’ d Method

WTP probability associated with the choice of the respondents is:

P(ti) = P(tli < WTPi ≤ tui )

WTP is non-negative and its distribution is skewed we use a lognormal conditional distribution:

log WTPi = xi' * β + εi [εi ~ N(0, σ)]



The probability of choosing ti can be written:

P(ti) = Φ [(log tui − xi'*β)/σ]− Φ[(log tli − xi'*β)/σ]

where Φ is the standard normal cumulative density function.



The corresponding log likelihood function can be written:

T log L =∑log[Φ[(log tui − xi'*β)/σ]−Φ[(log tli − xi'*β)/σ]] i= 1



We estimate the optimal values of β and σ, and the mean and median WTP (Cameron and Huppert, 1989; Hanemann and Kanninen, 1999):

Median WTP = exp(xi

’β)

Mean WTP = exp(xi'β) exp (σ2/2 )

and we have computed the confidence interval according to Krinsky and Robb’s simulation model.



DataIn a typical CV study a policy scenario is proposed to

interviewees and their WTP to attain it is subsequently elicited.

Respondents were asked to consider the benefits to themselves of developing the RES use in Italy.



Each respondent was confronted with a range of:

(i) general questions concerning RES and their

potential development;

(ii) questions on knowledge about Italian energy

system;

(iii) bids in order to support RES development in Italy



In order to derive WTP a national survey with 1.019

interviews was admnistered at the end of

November 2007

This is a very good period because before 2008-2009

…… financial crisis alters the long run consumers

perception



The stratified sample is representative of 46.8 million individuals, residents of Italy, and the survey was conducted by Istituto Piepoli.

The sample is highly representative of Italian population in terms of:

male-female ratiogeographical and urban location

demographic characteristicseducation and income distribution



The profile of the typical interviewee is:men aged 47

highly educatedmarried family with one child

income is around 35 000 €home owner

About the topic of survey the interviewee believes that the Italian energy scenario will lot worse in the next ten years, he knows the RES, his knowledge is really accurate and he consider RES a strategic opportunity for Italy.



Empirical findings



0.000

0.200

0.400

0.600

0.800

1.000

1.200

Figure 2: WTP Survivor function

DY as yes DY, PY as yes DY, PY, DK as yes

As expected, the results show that:

1) the proportion of respondents who are willing to pay decreases with the amount submitted

2) the proportion is larger when “yes category” includes also PY and DK responses

3) this is especially evident at the right most end of the tail, for amounts greater than € 5



Main findings:1. knowledge of RES and conviction that RES could

play an important role in Italian energy scenario positively affects the WTP

2. higher level of education and a better employment (which proxies higher income) are associated, coeteris paribus, with higher WTP

3. men are willing to pay less if compared with women4. older respondents are willing to pay less if

compared to younger ones ……



5. residents in North and Center Italy exhibit a higher WTP

6. people who live in municipalities greater than 100,000 inhabitants are willing to pay less

7. household size influences negatively the WTP8. “acting consistently” has a negative influence on

the WTP



Finally• Based on the estimated parameters it is possible to

compute the mean and median WTP of the sample, and then to compute the total WTP for Italy

Tot WTP = WTP(monthly) x 2 x 6(Bimonthly bill) x Nr. households

• The total WTP may be compared with the total annual subsidy needed in Italy to comply with the EU climate change package goals by 2020



Annual electric Households Total annual Annual subsidy Market sustainability

bill (Nr.) (Nr.) WTP (Euro) cost (Euro) (a)

of RES (%)

No parametric computation LBM 3.47 454,098,274 12.97%

KM 6.01 786,492,977 22.47%

Parametric estimation (median)Interv. Data Regr. (I) model 5.05 660,863,483 18.88%

Interv. Data Regr. (II) model 7.06 923,900,235 26.40%

Interv. Data Regr. (III) model 9.95 1,302,097,357 37.20%

Parametric estimation (mean)Interv. Data Regr. (I) model 12.16 1,591,306,921 45.47%

Interv. Data Regr. (II) model 15.95 2,087,281,693 59.64%

Interv. Data Regr. (III) model 24.14 3,159,058,312 90.26%

(a) The figure is an estimate of the additional costs necessary to achieve 17% of energy produced from renewable

6 21,810,676 3,500,000,000

Table 6: Policy implicationsMean/Median WTP

(Euro)



ConclusionsWe can see that a measure of the market

sustainability of RES , i.e. the cover capacity range, lies between 19% and 37%, according to different estimation models….. however:

1) we find a substantial willingness of consumers to partially cover the cost of RES

2) uncertainty plays a crucial role counting for 8% -19% of the annual goal (Choice of the SPC OK)



3. in table 6 we consider the full incremental cost that we arbitrarily ascribe entirely to the “small consumers” (eg low voltage clients). Maybe if we consider all the clients, market sustainability could noticeably increase

4. if we consider the current burden paied by italian households we notice that the actual additional cost due actually to renewables is less than all the elicited values in our models



This means that:

-- a further margin could exist in the Italian context;-- Italians (Are? Were? -2007-) ready to pay more for RES

according to the European target

Perhaps Italian citizens need appropriate information and education campaigns finalized to:

a) better explaining all the advantages linked to the renewable energy use

b) reducing erroneous evaluations on the costs of renewable energies



Thanks for attention

... suggestions and questions are [email protected]



The willingness to pay for Renewable Energy Sources (RES): the case of Italy

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