Measuring willingness to pay for electricity: The case of New Brunswick in Atlantic Canada

Abstract

Recently, electricity markets around the world have been going through transformation process that eventually leads to a higher competition among electricity providers. In this regard, the role of consumer preferences increases, especially if new electricity products are offered. Traditionally, consumer preferences have been expressed in terms of customers’ willingness to pay. Therefore, the goal of this study is to provide a practical framework for estimation of customers’ willingness to pay for electricity. Specifically, dynamics of regional residential willingness to pay for electricity in the province of New Brunswick in Atlantic Canada is analyzed. First, theoretical framework to evaluate consumer preferences is developed followed by empirical approach to define willingness to pay over period of 1991–2013 on the basis of revealed preferences method. Finally, dynamics of the residential willingness to pay for electricity is analyzed with the help of advanced time series analysis. Our study shows that residential willingness to pay for electricity in the province of New Brunswick had been increasing over study period. Moreover, it has accelerated significantly since 2005. The designed methodology and empirical work will help electricity providers identify new electricity products with the highest willingness to pay by consumers. Overall, implementation of the results of this study can improve economic efficiency of provincial electricity market.

Keywords

Consumers’ willingness to pay indirect utility revealed preferences autoregressive model electricity market

Introduction

Electricity markets all over the world are going through a transformation process. From an economic standpoint, this process is mostly associated with the loss of natural monopoly power by traditional electric utilities due to technological changes and the use of alternative sources of electricity generation, especially renewable resources. The latter produced a new term such as “green electricity” and a very intensive discussion around willingness to pay (WTP) by consumers for this product.

In general, customer’s WTP for electricity as a product with certain characteristics is not well defined, but this concept is essential for predicting the success of various energy policies and initiatives.¹ Economic theory provides us with two basic methods to define customer’s WTP: (i) revealed preferences method and (ii) stated preferences method. While the former is based on market data, the latter is based on a survey approach. In this study, we use the revealed preferences method to define WTP for electricity in the province of New Brunswick. As stated by Liu et al.,² among all the revealed preference methods, the hedonic price approach is by far the most widely used method. This method is based on an individual’s demand for the characteristic of a product by estimating the relationship between the prices of the product and its characteristics. Eventually, it estimates an individual’s WTP for the product’s characteristics. This very idea is a backbone of our methodology along with another revealed preferences method namely market price approach. According to our literature review, majority of empirical studies to define WTP for electricity are based on stated preferences method with Contingent Valuation Methodology as the most popular technique. Application of the revealed preferences method is rare, and that is why our study attempts to fill this gap.

Regardless of the method used, international literature presents a consistent picture of an increasing WTP for energy from renewable or green sources over time and relatively low levels of take up of green energy tariffs offered by electricity firms in most nations. For example, Grilli³ has analyzed 34 papers dedicated to WTP for green electricity to make this conclusion. Despite a high level of customer intent, the actual take up of green energy provision has been relatively low. A range of explanations for this large divergence between stated and actual behavior have been forwarded including free rider problems⁴; upward bias in contingent valuation^5,6; “bandwagon effects”; the need for a critical mass of green customers and reciprocity; lack of knowledge as to green power availability; hesitancy in switching electricity supplier; distrust of energy product suppliers and cost concerns.⁶

This stream of literature presents a very useful framework to measure consumers’ WTP for new electricity products in general including green electricity. As a matter of fact, all the above-mentioned studies used stated preferences method to evaluate WTP for electricity. On the other hand, these studies raised a very important problem – the difference between stated WTP by electricity consumers and their real behavior. Despite hypothetical desire to have new electricity products and broader choice, mostly consumers of electricity stick to a status quo. In our opinion, in order to change this pattern of behavior, providers of electricity have to take on more aggressive approach in terms of the design and provision of new products. In our opinion, conceptual framework based on the so-called customer perceived value (CPV) of electricity coupled with revealed preferences method can address this problem in a systematic way.

In electricity markets under natural monopoly, consumer preferences have played a secondary role. However, within transforming electricity markets, these preferences are becoming one of the major driving forces in provision of the “right” electricity products. In our study, by the right electricity products we mean the products that bring the highest CPV and eventually maximize total welfare. In this study, we develop our methodology for this new conceptual framework under transforming electricity market, and show a practical way to define and measure the CPV of electricity via WTP. The latter is applied to the electricity market in the province of New Brunswick in Atlantic Canada.

In New Brunswick, the electricity market has been organized as a regulated, vertically integrated monopoly. Under such a monopoly structure, a government agency controls and operates the whole electricity network that includes generation, transmission, and distribution of electricity within a certain region forming the so-called public utility.

In other provinces, electricity markets are changing such that there is greater competition in generation and more options and alternatives available to customers. Hence, focusing on the electricity market in New Brunswick, we assume that electricity market in the province will eventually follow national as well as international trends with increasing role of customers’ WTP for electricity. Aligning electricity prices with consumers preferences based on their WTP has the potential to improve allocative efficiency in the New Brunswick’s electricity market.

Theoretical background

Suppose there are J electricity products index by j = 1,…, J and all other goods and services Z bought by a typical consumer at a price P_Z. It means that each consumer divides her income Y between the J electricity products and all other goods and services Z. In such a case, the ith consumer problem can be written as

\begin{array}{l} \max U_{i} {(X_{j}, Z)}_{i} \\ S . t . \\ P_{j} + P_{Z} Z = Y_{i} \end{array}

(1)

where

U_i(.) is the ith consumer utility function;

X_j are the characteristics (attributes) of the jth electricity product;

P_j is total consumer spending on electricity products which is $P_{J} = \sum_{1}^{J} p q j_{j}$

p_j is the price of the jth electricity product;

q_j is the quantity of the jth electricity product;

Z is the quantity of all other goods and services (also known as a composite or outside good);

P_Z is the consumer price index (CPI) which is the price of the composite good;

Y_i is the ith consumer money income.

Solving the budget constraint for Z

Z = \frac{Y_{i} - P_{j}}{P_{Z}}

(2)

and substituting it into original utility function we obtain conditional indirect utility function which reflects the highest level of utility the ith consumer obtains given characteristics of the jth electricity product, total spending on electricity, CPI, and personal income

U_{i} (.) = U_{i} (X_{j}, \frac{Y_{i} - P_{j}}{P_{Z}}) = U_{i j}^{*} (X_{j}, P_{J}, P_{Z}, Y_{i})

(3)

McFadden⁷ notes that in general there are observable factors in the ith consumer utility function but unobservable to a researcher, and therefore indirect utility for the jth product should be written as

U_{i j}^{*} (X_{j}, p_{j}, P_{Z}, Y_{i}) = V_{i j} (X_{j}, p_{j}, P_{Z}, Y_{i}) + ε_{i j}

(4)

where V(.) is the observable indirect utility and ε _ij is observed by the ith consumer but not by a researcher.

The last expression (4) is known as the random utility model introduced by McFadden.⁷ Later, McFadden⁸ showed that indirect utility function V_ij(.) can be expressed in terms of economic factors and hedonic characteristics (attributes) of a good or service. It means that the above-presented expression can be written as follows

\begin{matrix} U_{i j}^{*} (X_{j}, p_{j}, P_{Z}, Y_{i}) = V_{i j} (X_{j}, p_{j}, P_{Z}, Y_{i}) + ε_{i j} = F_{i j} (p_{j}, P_{Z}, Y_{i}) + H_{j} (X_{j}) + ε_{i j} \end{matrix}

(5)

where F_ij(.) is a function that depends on economic factors such as the price of the jth product p_j, individual income Y_i, and the CPI P_Z, while H_j(.) is the hedonic function that depends on the jth product’s characteristics. In reality, different groups of consumers may pay different prices for the same electricity product: For example, residential customers and industrial customers. Therefore, in terms of electricity as our target product, we can re-define the above-presented variables as follows: p_ij is the price of the jth electricity product for the ith consumer group, Y_i is average income of the ith consumer group, and P_Z is the CPI. Vector X_j should include technical characteristics of electricity such as reliability, safety, unit cost of electricity production and others. For example, some fundamental technical characteristic of electricity are presented in hedonic function developed by Zhou et al.⁹

In our previous research,¹⁰ we stated that CPV of electricity is the consumer’s WTP for a specific electricity product as seen by a provider of that product. Since the provider of the product cannot observe variables in the consumer’s utility function, the above framework of a random utility should be used. However, our model is expressed in terms of indirect utility while we need to express it in terms of the WTP.

According to Haab and McConnel,¹¹

although willingness to pay does not need a utility function for its derivation, it is useful to show that it can be derived from the indirect utility function… As noted, the linear random willingness to pay model and the linear random utility model produce identical parameter estimates and identical welfare estimates.

The very same conclusion is presented by Twerefou¹² with respect to electricity market.

From a theoretical viewpoint, WTP can be expressed as the change in indirect utility due to a change in a product’s price holding all other factors constant. Eventually, it means that WTP, CPV, and indirect utility depend on the same set of variables. Therefore, given the above discussion, we can set up the following model for the jth electricity product with respect to the ith consumer group

C P V_{i j} = W T P_{i j} = F_{i j} (p_{i j}, P_{Z}, Y_{i}) + H_{j} (X_{j}) + ε_{i j}

(6)

Furthermore, since we are interested in electricity products in a specific region, macroeconomic characteristics of that region will definitely affect the consumer’s choice, and therefore they also should be reflected in the above model as follows

\begin{matrix} C P V_{i j} = W T P_{i j} = F_{i j} (p_{i j}, P_{Z}, Y_{i}) + F (m a c r o) + H_{j} (X_{j}) + ε_{i j} \end{matrix}

(7)

where F(macro) is a function of regional macroeconomic factors such as economic growth, unemployment and others.

In addition, as empirical work has shown, social characteristics such as gender, education, religion, party affiliation and others play an important role in consumer’s choice. Also, environmental characteristics such as level of emissions and climate variables such as air temperature are among significant factors behind electricity demand. Thus, those characteristics should be also included into our hedonic function H_j(.). Some of those characteristics will be common for all consumer groups while others will be group specific. Therefore, we suggest the following notation:

T_j is the vector of electricity’s technical characteristics associated with the jth product;

S_i is the vector of social characteristics associated with the ith consumer group;

E_ij is the vector of environmental characteristics including climate variables associated with the jth electricity product and the ith consumer group.

Taking into account the above notation, our final model becomes

\begin{matrix} C P V_{i j} = W T P_{i j} = F_{i j} (p_{i j}, P_{Z}, Y_{i}) + F (m a c r o) + H (T_{j}) + H (S_{i}) + H (E_{i j}) + ε_{i j} \end{matrix}

(8)

In terms of empirical work, it is necessary to evaluate WTP for electricity products as our dependent variable and to identify and measure all factors included on the right-hand side of equation (8). Eventually, equation (8) can be used to rank significance of all factors – economic, technical, social, and environmental – for customer’s CPV in order to design and provide right electricity products for various consumer groups. However, this study is dedicated to evaluation of the WTP for electricity which we later use as a proxy for CPV in estimating equation (8).

WTP for electricity

Briedert et al.¹³ emphasized that pricing decisions are closely related to the understanding of the nature of WTP. They claim that usually firms follow strategies based on an “intuitive” pricing approach without knowing consumers’ behavior and their responses to the changes in prices. Recent empirical studies have shown that even small variations in a product’s price and consumer behavior might have significant impact on a firm’s revenues and profits.¹⁴ Therefore, accurate estimation of the consumers’ WTP is important not just from an economic standpoint but also from a standpoint of business strategies.

As already mentioned in the Introduction, in economic literature, two groups of methods are discussed with respect to WTP estimation: (i) stated preferences method and (ii) revealed preferences method.^2,13,15 The first one is mostly associated with a survey approach when consumers are asked their WTP directly.

In turn, the revealed WTP method is based on the premise that if a good, service, or resource being valued has a market, then it will have a market price and consumers will reveal their preferences for that particular product by paying for it at the market price. Thus, the market price can be used to assess the value of the good, service, or resource. In environmental economics, five main methods have been developed within this group: Travel Cost Method, Averting Behaviour Method, Market Price Method (MPM), Hedonic Price Method (HPM), and the Production Function Method.

Recently, many studies have been carried out to estimate consumers’ WTP for electricity. For example, Roe et al.¹ estimated consumers’ WTP for environmental attributes of electricity in the US. They used both the stated preferences method – conjoint analysis – and the revealed preferences method – the HPM. The conjoint survey showed that “a wide array of population segments is willing to pay small amounts for tangible reductions in air emissions”, whereas the HPM indicated that the US customers are willing to pay higher premiums for green electricity, especially from renewable sources.

Twerefou¹² applied one of the most widely used stated preferences methods – the contingent valuation method (CVM) – to identify consumers’ WTP for improvements in electricity supply in Ghana. He compared the existing situation with his results and concluded that consumers’ WTP is in 1.5 times higher than the existing market price of electricity.

Diaz-Rainey and Ashton¹⁶ applied CVM to estimate WTP for green electricity in UK. Based on their survey, they derived relationship between WTP for green electricity and various groups of variables – attitudinal, demographic, and socio-economic. They concluded that attitudinal variables dominate. In their opinion, the dominance of attitudinal variables has strong implications for marketing: customer profiling should look beyond demographic variables and take into account attitudes.

We have applied HPM in our study dedicated to the electricity price in the province of New Brunswick in Canada.¹⁷ We found that along with the technical characteristics’ demand side, the characteristics of electricity as well as social and environmental attributes of electricity are very important determinants. However, in our opinion, the price of electricity is a poor indictor of the consumer preferences compared to the CPV on the basis of WTP. That is why in this study, we developed a practical framework to compute WTP for electricity to use it as a dependent variable in our analysis of the relationship between CPV and its determinants as expressed by equation (8).

Estimating WTP for electricity in the province of New Brunswick

In this study, in order to define WTP, we applied the MPM. Usually the MPM method calculates the area under inverse market demand from zero to actual consumption according to interpretation of the inverse market demand as marginal WTP. Also, total economic surplus – the sum of consumer and producer surpluses of a product – can be used as a proxy for the value of the good and consequently for the WTP. Since market for electricity is well developed and information on the price of electricity and its consumption is available, the use of the MPM becomes obvious.

Choynowski¹⁸ described a practical way to apply MPM to estimate WTP for electricity. He used a derived demand for electricity and its price elasticity to identify the area under the demand curve which is total willingness to pay (TWTP) by definition.

We applied this methodology to estimate TWTP for electricity by urban residential customers in the province of New Brunswick in Atlantic Canada. The following data were used in our estimation: annual electricity consumption, average electricity price, and price elasticity of the demand for electricity. Similar to Choynowski,¹⁸ we assumed a linear inverse demand for electricity by urban residential customers as

P = b - s Q

(9)

where Q is the quantity of electricity consumed by urban residential customers in kilo-watt

\times

hours (kWh); P is the electricity price in cents per kWh; s is the slope of the inverse demand for electricity, and b is the vertical intercept of the inverse demand. Slope s in equation (9) can be obtained from the value of the point price elasticity of demand since they are related as follows

e = \frac{1}{s} \times \frac{P}{Q}

(10)

where e is the point price elasticity of inverse demand and s is the slope of the linear inverse demand function presented by expression (9).

Price and consumption of electricity by urban residential customers for 1991–2013 period were obtained directly from the provider of electricity in the province, namely NB Power. Price elasticity of demand was taken from Ryan and Razek.¹⁹. Figure 1 shows the approach graphically.

Figure 1.

Graphical approach to assess TWTP.

In general, numerical value of TWTP, which is the shaded area in Figure 1, can be found as

TWTP = \int_{0}^{Q^{*}} f (Q) d Q

where f(Q) is the inverse demand function. Since we assumed linear inverse demand then

TWTP = 0.5 \times (b - P^{*}) \times Q^{*} + P^{*} \times Q^{*}

(11)

where b is defined as

b = P^{*} + \frac{P^{*}}{e}

Based on the approach presented above, we computed annual TWTP, TWTP per kWh and annual TWTP per capita. Graphical representation of these three time series over 1991–2013 period for urban residential customers in New Brunswick is presented in Figure 2.

Figure 2.

Dynamics of TWTP over 1991–2013 period.

Dynamics of the WTP for electricity in the province of New Brunswick

According to Figure 2, all three time series exhibit obvious upward trending behavior over time which is further formally tested on the basis of time series analysis. Since all three time series show similar dynamics, we chose for our dynamic analysis the TWTP series expressed in terms of cents per kWh. We examined the chosen TWTP time series with the help of linear autoregressive model.²⁰ In general, autoregressive model is a model of a dynamic stochastic process based on realization of the past values of an economic variable – TWTP for electricity in this case.

Prior to estimation of a specific model, some statistical procedures were applied. First, we transformed our TWTP series into a series in natural logarithms. Logarithmic transformation is a common way in econometrics to address non-linearity in time series. Second, we examined the time series for the presence of a unit root using the Dickey–Fuller test. The test showed the unit root which means that the natural logarithm of TWTP series is non-stationary. However, taking into account obvious trending behavior, we can conclude that our series is trend stationary which was further tested formally. According to Wooldridge²¹ and Maddala and Kim,²² the problem of trend-stationary process can be solved by including a deterministic linear time trend. Finally, we constructed autocorrelation function and partial autocorrelation function in order to identify potential models to be estimated.

Eventually, our preliminary statistical work led to the identification of the following competing models

Model 1 : y_{t} = a_{o} + a_{1} y_{t - 1} + e_{t}

(12)

Model 2 : y_{t} = a_{o} + a_{1} y_{t - 1} + a_{3} t i m e + e_{t}

(13)

Model 3 : y_{t} = a_{o} + a_{1} y_{t - 1} + a_{2} y_{t - 2} + e_{t}

(14)

Model 4 : y_{t} = a_{o} + a_{1} y_{t - 1} + a_{2} y_{t - 2} + a_{3} t i m e + e_{t}

(15)

Model 5 : y_{t} = a_{o} + a_{1} y_{t - 1} + a_{2} y_{t - 2} + a_{3} t i m e + a_{4} d * t i m e + e_{t}

(16)

where

y_t is the natural logarithm of WTP for electricity by urban residential customers in cents per kWh;

time is the time trend (year);

d is dummy: it is 0 for 1991–2004 period and 1 for 2005–2013 period.

All the five models are autoregressive models: Model 1 is autoregressive of order one or AR(1); Model 2 is AR(1) with a linear time trend; Model 3 is AR(2); Model 4 is AR(2) with a linear time trend, and Model 5 is AR(2) with a linear time trend with break point in year 2005. All the five models were estimated in E-VIEWS and STATA, and the results of these estimations turned to be the same.

Table 1 shows t-statistics for each coefficient under each model as well as R²-adjusted.

Table 1.

Results of model estimation.

Coefficients	Model 1	Model 2	Model 3	Model 4	Model 5
a ₀	–0.03	1.36	1.26	2.69	3.99
a ₁	36.41	9.10	10.15	10.01	9.03
a ₂	–	–	–4.25	–4.96	–4.66
a ₃	–	1.45	–	2.34	2.22
a ₄	–	–	–	–	2.57
R²-adjusted	0.9844	0.9852	0.9912	0.9930	0.9947

As can be seen from the table, only Models 4 and 5 have all coefficients statistically significant at 95% level. These models contain upward time trend which we previously observed in the raw data. Estimation output for these models are presented below

Model 4 : y_{t} = 0.364028 + 1.615451 y_{t - 1} - 0.791909 y_{t - 2} + 0.006453 t i m e

Model 5 : y_{t} = 0.538599 + 1.426283 y_{t - 1} - 0.676427 y_{t - 2} + 0.005361 t i m e + 0.003116 d * t i m e

First of all, residuals in both models are white noise which means that the models explain the existing dynamics rather well.

Conclusion

The purpose of this study was to identify residential consumers’ WTP for electricity and analyze its dynamics over time in the province of New Brunswick in Atlantic Canada. Theoretically, CPV for electricity is WTP by customers as seen by the provider of electricity. Technically, WTP for electricity can be computed from market data, particularly electricity price, consumption of electricity and price elasticity of inverse demand for electricity. Using this revealed preferences empirical approach, WTP for electricity was estimated for the period from 1991 to 2013. We found that WTP is a better representation of consumers’ preferences than price. For example, in 2013 the electricity price per kWh was 9.9 cents while the estimated WTP per kWh was 18.15 cents.

In terms of dynamics, WTP for electricity by urban residential customers in the province of New Brunswick follows autoregressive process with positive linear time trend. The existence of this process statistically proves an increasing over time WTP for electricity in the province of New Brunswick similar to conclusions reported in other studies cited in this paper. Based on the time series analysis of the WTP series, we can conclude:

WTP by urban residential electricity customers in the province of New Brunswick has been increasing over the whole period of 1991–2013.

The increase in WTP has accelerated since 2005.

On average, currently urban residential electricity customers’ WTP is increasing at the rate of $0.010085 per kWh per year.

Current study also provides a methodology to define fundamental structure of the WTP for electricity. All factors/determinants influencing WTP are divided into micro- and macroeconomic factors as well as technical, social, and environmental. The proposed methodology also links WTP for electricity to its CPV and indirect utility. This framework will be used in our further research to identify a specific set of fundamental determinants of WTP. The presented structure of our model will help us rank significance of these determinants for different consumer groups to provide new products in order to maximize overall welfare.

The work presented in this study is limited by available secondary data and assumptions we made. In our analysis, WTP was calculated over the period of 1991–2013 due to data availability in terms of electricity prices and actual consumption by residential customers provided by the electricity provider in the province of New Brunswick – NB Power. Another limitation of our analysis is the linearity assumption of the inverse demand for electricity by residential customers. On the other hand, this approach is widely used in the literature to define WTP for electricity.¹⁸ The last major limitation of our research is the assumption of constant point price elasticity over our study period. In future work, we intend to address all these limitations in order to improve our empirical framework.

Footnotes

Acknowledgements

The authors would also like to acknowledge J Herbert Smith Center (TME) located at the University of New Brunswick for providing research facilities and other support.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors acknowledge the financial support from Mitacs and Siemens.

Yuri Yevdokimov is a professor at the University of New Brunswick, Fredericton, Canada. He holds a joint appointment in the departments of economics and civil engineering. Dr. Yevdokimov has PhD in Economics from the University of Manitoba, Canada and MSc equivalent in engineering from Sumy State University in Ukraine. Dr. Yevdokimov’s research interests are in the field of economic evaluation of climate change impacts, sustainable transportation, sustainable energy and emerging market economies.

Viktor Getalo is a research assistant at the J Hubert Smith Centre for Technology, Management and Entrepreneurship (TME) at the University of New Brunswick, Fredericton, Canada. He holds a Ph.D equivalent in mechanical engineering from Ukraine and MA in economics from the University of New Brunswick.

Dhirendra Shukla is the J Herbert Smith Chair for TME at the University of New Brunswick, Fredericton, Canada. He obtained his MBA from the Telfer School of Management at the University of Ottawa, Canada and completed his PhD in Entrepreneurial Finance from the King's College London, University of London, UK. His current research interests are in venture capital, syndication, entrepreneurship, corporate finance, performance and governance, and boards of directors.

Tugcan Sahin is a PhD candidate in Interdisciplinary Program at the University of New Brunswick, Fredericton, Canada. Currently he is a management consultant for Strategic Planning at Siemens, Munich, Germany.

References

Roe

Teisl

Levy

et al . US consumers’ willingness to pay for green electricity. Energy Policy 2001; 29: 917–925.

Liu

J-L

Changa

Pe-I

Den

S-J.

Consumer willingness to pay for energy conservation: a comparison between revealed preferences and stated preferences Mathod. Procedia Environ Sci 2013; 17: 620–629.

Grilli

Renewable energy and willingness to pay: evidences from meta-analysis. Econ Policy Energy Environ 2017; 1–2: 253–271.

Wiser

Pickle

Green marketing, renewables, and free riders: increasing customer demand for a public good. Berkeley, CA: Lawrence Berkeley National Laboratory, 1997.

Gosling

Craik

Martin

et al . The personal living space cue inventory: an analysis and evaluation. Environ Behav 2005; 37: 683–705.

Wiser

Using contingent valuation to explore willingness to pay for renewable energy: a comparison of collective and voluntary payment vehicles. Working Paper LBNL-53239, Lawrence Berkeley National Laboratory, Berkeley, CA, 2003.

McFadden

The choice theory approach to market research. Marketing Sci 1986; 5: 275–297.

McFadden

Economic choices. Am Econ Rev 2001; 3: 351–378.

Zhou

Approach on pricing electricity for large consumers based on hedonic price model. DRPT, April 2008, pp. 348–354.

10.

Sahin

Yevdokimov

Modelling customer perceived value in electricity markets: the case of Atlantic Canada. In: International conference on energy finance, Hangzhou, China, 30 June 2016.

11.

Haab

McConnel

Valuing environmental and natural resources. Cheltenham, UK/Northampton, MA: Edward Elgar, 2002.

12.

Twerefou

Willingness to pay for improved electricity supply in Ghana. ME 2014; 5: 489–498.

13.

Briedert

Hahsler

Reutterer

A review of methods for measuring willingness-to-pay. Innovative Marketing 2006; 2: 8–32.

14.

Le Gall-Ely. Definition, measurement and determinants of the consumer’s willingness to pay: a critical synthesis and avenues for further research. Rech Appl Marketing 2009; 24: 91–112.

15.

Marn

Roegner

Zawada

Pricing new products. McKinsey and Company, Quaterly Report, #3, 2003.

16.

Diaz-Rainey

Ashton

Profiling potential green electricity tariff adopters: green consumerism as an environmental policy tool?

Bus Strategy Environ 2011; 20: 456–470.

17.

Yevdokimov

Sahin

Getalo

et al. Leveraging customer perceived value within transformation of electric utilities: the case of New Brunswick. In: 40th IAEE international conference, Singapore, 18–21 June 2017.

18.

Choynowski

Measuring willingness to pay for electricity. Asian Development Bank, Economics Research Department Technical Note Series #3, Asian Development Bank, 2002.

19.

Ryan

Razek

The likely effect of carbon pricing on energy consumption in Canada. Sustainable Prosperity, University of Ottawa, Background Paper, 2012.

20.

Enders

Applied econometric time series. 4th ed. Alabama: University of Alabama, 2014.

21.

Wooldridge

Introductory econometrics: a modern approach. 5th ed. Nashville: South Western College Publications, 2012.

22.

Maddala

Kim

Unit roots, cointegration and structural change. Cambridge: Cambridge University Press, 1998.