Carbon Emission and Sustainable Development

Who Is More Willing to Pay for Green Electricity? A Case Study of Anyang City, China

  • HOU Peng , 1, 2, * ,
  • LIU Xiaojie , 1, * ,
  • CHENG Shengkui 1
  • 1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
* HOU Peng, E-mail: ;
LIU Xiaojie, E-mail:

Received date: 2021-03-15

  Accepted date: 2021-05-16

  Online published: 2022-03-09

Supported by

The National Natural Science Foundation of China(71874178)


Green electricity (GE) is of great importance for effectively combating climate change and global Sustainable Development Goals (SDGs). The willingness-to-pay (WTP) for GE by end users has great influence on its widespread application, especially at the household level. Researchers have analyzed the mechanisms of residents’ WTP and predicted how much they could afford for GE. However, most of these studies have focused on developed (affluent) countries and regions, and residents’ WTP as well as the corresponding monetary amount of their WTP were not considered together in previous studies. In this study, we try to fill the gap of the inadequate research of residents’ WTP in underdeveloped areas through an analysis of the WTP of urban residents in a small Chinese city (Anyang). A total of 274 household-scale samples were collected by face-to-face interviews in December 2019. We found that approximately 60% of respondents gave a positive response to WTP, with an average value of 8.39 yuan per month. Educational attainment, per capita disposable income in the household, the length of residents’ time living in urban areas and the rate of neighbors’ approval of WTP are all positively correlated with the tendency of the residents’ positive response toward WTP. The three factors of per capita disposable income, the rate of neighbors’ approval of WTP and the degree of residents’ environmental concerns are positively correlated with the amount of residents’ WTP. More importantly, we demonstrated the existence of a weak “Herd Effect” in residents’ views of WTP. Finally, some suggestions for policymakers are given, such as raising household income through retraining and raising residents’ environmental awareness through education and community advertisements. This study also highlights that the widespread application of GE at the household level will be of great assistance to the GE industry overall. Therefore, the rising economic cost of GE should not be shared by the households in the long term.

Cite this article

HOU Peng , LIU Xiaojie , CHENG Shengkui . Who Is More Willing to Pay for Green Electricity? A Case Study of Anyang City, China[J]. Journal of Resources and Ecology, 2022 , 13(2) : 231 -237 . DOI: 10.5814/j.issn.1674-764x.2022.02.006

1 Introduction

Electricity generated from conventional resources (ECR) has been criticized for its negative external environmental affects (mainly the emissions of greenhouse gases) (Armaroli and Balzani, 2011; Oberschelp et al., 2019), though it has been providing energy for societies since the Industrial Revolution. Electricity supplies humans with energy in most basic modern items and affairs at the household scale, such as indoor cooking, lighting, cooling and heating, travelling and entertainment. Societies are increasingly moving toward an electricity-extensive world, even in the least developed countries and regions. According to International Electricity Agency (IEA), the household sector consumed 5776.50 TWh electricity in 2017, accounting for 27.03% of the total final electricity consumption worldwide (IEA, 2019). Therefore, energy switching (Krausmann et al., 2013) to cleaner and greener energy at the household level (Damette et al., 2018) is of considerable importance and urgency for society at multiple levels. These include achieving the global consensus Paris Climate Target (keeping the global average temperature increase to well below 2℃ above pre- industrial levels, and ideally limiting it to 1.5 ℃) (UN, 2015a), as well as various multi-field Sustainable Development Goals (SDGs) (UN, 2015b). In the context of the concurrent ever-increasing demand for electricity and deteriorating situation of the global environment (Liu et al., 2007; Carleton and Hsiang, 2016), the Green Electricity (GE) industry has been given a position of high hope by many segments of society (Gan et al., 2007).
The GE industry is characterized by substantial upfront economic costs (Schmidt, 2014; Egli and Schmidt, 2018) mainly for the technology investment and ancillary facilities. On the other hand, the GE industry enjoys fiscal subsidies from governments because of its heavy-capital occupation and expected positive externalities. Yet the large-scale economic costs are expected to be shared partly by end-users (including households) at least in the medium term. Therefore, GE is a (semi-) luxury energy product to some extent, especially for low-income households (Zhang and Wu, 2012). Andor et al. (2018) demonstrated that residents were less willing to pay a premium for GE when they were informed about the GE industry’s tax exemptions. It can easily be recognized as a double-benefit if the GE industry enjoys fiscal subsidies while at the same time providing GE for end-users in a higher price. Considering the “Rational-Person Hypothesis” in economics, we are absolutely convinced that most residents can accept GE on the condition that the price of GE is less than or equal to that of ECR. Thus, when we analyze or assess residents’ willingness to pay (WTP) for GE, we are actually evaluating the GE’s premium which is derived from its positive environmental externalities in the eyes of the residents (i.e., How much more are they willing to pay for GE than ECR?). Survey respondents’ environmental awareness (Murakami et al., 2015; Štreimikienė and Baležentis, 2015), educational attainment (Dogan and Muhammad, 2019), and per capita disposable income (Oliver et al., 2011; Arega and Tadesse, 2017) were found to be considerably positively correlated with their WTP in previous studies. The contingent valuation method (CVM) was widely used in assessing residents’ WTP for GE (Nomura and Akai, 2004; Lee and Heo, 2016; Xie and Zhao, 2018), which is to a great extent equivalent to the premium prediction of GE. Besides, Borchers et al. (2007) and Su et al. (2018) argued that respondents’ WTP for GE from different sources (i.e., hydroelectric, wind power, solar power, geothermal energy, etc.) were unequal, and solar power ranked the first.
As the most populated country and the largest developing country in the world, the energy-switching at the household level is quite important and urgent in China. It is also of great significance in the dimensions of politics, and the nexus between human well-being, sustainable development and environmental protection. However, previous studies of residents’ WTP mostly focused on developed (affluent) countries and regions, while 74.46% of all Chinese residents still lived in small- and middle-size cities in 2019 (CNBS, 2019). In this study, we try to fill the gap of the inadequate study of residents’ WTP for GE. We argue that the residents’ WTP is a two-staged issue, involving both whether and how much they are willing to pay for GE. Based on a first-hand dataset and information from interviewing urban households in Anyang, a typical small-size city in China, we ultimately identified and assessed the key variables influencing residents’ WTP. We believe this study can not only help the respondents to improve their recognition of GE, but also provide policymakers with valuable information and advice on the (potential) future propaganda and promotion of GE.
The remaining sections of this paper are organized as follows. We describe the background of the survey region and data description in section 2, while the methodology is introduced in section 3. The results of the analyses are given in section 4, followed by the policy implications (section 5) and the conclusions (section 6).

2 Survey region and design

2.1 Survey region

Anyang is located in the northern-most part of Henan province, at the juncture of three provinces with Hebei province to the north and Shanxi province to the west. Anyang was chosen as the case study area for its typical representation of Chinese small- and middle-size cities. In 2018, Anyang held a total population of 5.92 million (AMBS, 2019), 50.20% of which lived in the nine county-level urban districts. The per capita income of urban households of Anyang City closely approaches the national average (38319.44 to 39250.8 yuan). The urbanization rate of Anyang was almost 10% lower than the national average (CNBS, 2019), however, a considerable spatial distinction was confirmed among the different districts. Specifically, Wenfeng District held the highest urbanization rate (UR) of 88.31%, which was nearly 2.74 times that of the lowest one, Neihuang County, with an UR of 32.19%. Beiguan District fell slightly behind Wenfeng District, ranking second with an UR of 85.88%. The UR of Yindu District, Longan District, Linzhou City and Tangyin County fluctuated between 48% and 70%, while Anyang County and Hua County were just a little higher than Neihuang County, with the UR of 36.72% and 33.09%, respectively.

2.2 Survey design

In the beginning, a pilot investigation of 30 households was conducted in August 2019. We then made some adjustments and amendments to the questionnaire according to the feedback information. The final questionnaire consisted of two parts. The first part consisted of the demographic characteristics of both the respondents and their family members: gender, age, educational attainment and the status of marriage. The second part characterized the households’ consumption of energy and their WTP for green electricity, and a group of scenarios were presented to gauge the respondents’ awareness of relevant energy and environmental issues and their common daily behavior.
Eight postgraduates from Henan Agricultural University participated in our survey as investigators, and a one-day specialized training was organized to improve their understanding of the survey and to help them master some practical investigation skills. We also arranged rehearsals in order to enhance the investigators’ adaptability and flexibility in the upcoming interviews. The formal survey was applied from December 16 to 29, 2019, under the great assistance of community workers. The predesigned sample size was 300 households, 17 of which quit during the interviewing process for personal reasons, and nine questionnaires were finally excluded as those respondents failed or refused to answer the key questions. Therefore, the following analysis was based on the remaining 274 sample households (Fig. 1).
Fig. 1 The spatial distribution of household samples in Anyang City
Table 1 shows the demographic characteristics of the respondents. More than half of the respondents in our survey were female (185, 64.69%), and the average age of the respondents was 52.35 years old. The average family size was 4.13 persons, while households with ≥3 members accounted for 81.82% of the total number of households. The average educational attainment of respondents was 10.58 years, fluctuating from 0 to 16 years. The monthly labor income varied in a range of 200 yuan and 7500 yuan per capita, with an average value of 1798.42 yuan in 2018. We also collected data for the length of time the respondents lived in urban areas, and the average value was 28.37 years.
Table 1 Descriptive statistics of the measured variables (n=274)
Item Definition Mean S.D.
Gender male=1, female=0 0.35 0.48
Age (yr) Age of the respondents 52.35 13.09
Family size (persons) Total number of household members 4.13 1.54
Education (yr) Educational attainment level 10.58 3.48
Perincome (yuan) The monthly labor income per capita in the household 1798.42 1019.19
Length (yr) The length of time the respondents had lived in cities 28.37 21.56
Rate The ratio of those “Willing” (WTP=1) to the total number of sample households in the same community. (a closed interval from 0 to 1) 0.60 0.22
Pollution Respondent strongly disagrees with the opinion that the development of renewable energy can help to manage environmental pollution=1, partially disagree with the opinion =2, neutral on the opinion =3, partially agree with the opinion =4, absolutely agree with the opinion =5 4.47 0.65

Note: Labor income was defined as the sum of (retired) wage and the transfer income from government or other family members, while property income was excluded considering the feedback information obtained in the pre-investigation (i.e., most respondents refused to tell us their property income).

3 Methods

The answer of respondents’ WTP for GE produced a one-tailed distributed dataset (equal to or greater than zero), and it actually was a two-staged decision to some extent. In the first stage, the respondents made a decision about whether they were willing to pay a premium for GE or not. In the second stage, they would accept a hypothetical premium for GE in their mind. Those unwilling to pay a premium for GE in fact paid “a value of zero” for GE. We in-troduced a Probit model ahead of the OLS regression model in each model set to demonstrate whether the variables contributing to respondents’ decision to pay for GE could also increase the magnitude of their WTP. The software of STATA (14.0) was used in data clean-up and analysis.
In the Probit model, the samples were divided into two subgroups according to their WTP (0 or 1).
$Pr\left( {WT{P_i}} \right) = \left\{ {\begin{array}{*{20}{c}}{0,\;WTP_i^{\rm{*}} \le 0}\\{1,\;WTP_i^{\rm{*}} > 0}\end{array}} \right.$
$\;WTP_i^* = {\beta _0} + {\beta _i}{X_i} + {\varepsilon _i} {\varepsilon _i}~N(0,\;{\sigma ^2})$
In equation (1), $Pr(WT{P_i})$ is an unobserved variable that measures whether respondent is willing to pay or not, and it takes the value of 1 if $WTP_i^* > 0$, and 0 otherwise. $WTP_i^*$ is the latent variable measuring the probability of respondents’ WTP. In equation (2), ${X_i}$ refers to the column vector set of independent variables described in Table 1. ${\beta _i}$ is the concomitant parameter of ${X_i}$. ${\varepsilon _i}$ is the residual term, which obeys the Bivariate Normal Distribution and has a mean value of zero.
In the second step, the OLS regression model, equation (3), was applied to analyze the magnitude of the respondents’ WTP.
$\;E\left( {{Q_i}{\rm{|}}WT{P_i}} \right) = {\alpha _0} + {\alpha _i}{X_i} + {\delta _i}$
where ${Q_i}$ refers to the magnitude of the respondents’ WTP for GE; ${\delta _i}$ is the residual term; ${\alpha _i}$ denotes the concomitant parameter of ${X_i}$.

4 Results

Approximately 60.98% of the respondents in our study were willing to pay a premium for GE, with an average value of 8.05 yuan per month. A considerable spatial heterogeneity of both WTP and the magnitude of WTP was observed in the sample households. On the whole, respondents in the core districts (the center of Anyang) were more prone to make a positive decision, but with a lower magnitude of WTP, than those in the peripheral districts (Table 2). Specifically, about 80.65% of respondents in Yindu District were willing to pay for GE, and Hua County took second place on the list, with a positive rate of 75%. Neihuang County and Wenfeng District followed behind, with positive percentages of 69.23% and 66.67%, respectively. Linzhou City, Longan District, Tangyin County, Beiguan District and Anyang County ranked fifth to ninth, with ˂60% positive rates of WTP.
Table 2 The differences of respondents’ WTP among districts
Name of district Sample size WTP=1 (YES) (%) Magnitude of WTP (yuan)
Wenfeng District 24 66.67 6.00
Beiguan District 24 44.44 6.67
Longan District 16 50.00 3.34
Yindu District 31 80.65 6.66
Anyang County 17 41.18 9.98
Linzhou City 62 55.56 6.18
Tangyin County 29 48.39 11.08
Neihuang County 24 69.23 8.01
Hua County 47 75.00 11.93
Average -- 60.98 8.05
In our survey, we also asked the respondents “Q14: How would you feel if the utilization of GE led to a bigger household electricity bill” (Fig. 2), and found that the number of respondents answering “(totally) unacceptable” (125) nearly equaled the number answering “Acceptable (within small increment)” (127). However, 88.98% of the “acceptable” respondents would only accept a tiny increment. The other 22 respondents held a neutral attitude toward this question.
Fig. 2 The distribution of respondents’ answers on Question 14: “How would you feel if the utilization of GE led to a bigger household electricity bill”.
In the econometric models, we added the WTP-related independent variables into the models in sequence, and the results are presented in Table 3. The demographic characteristics of families were first considered in model 1. We found in model 1 that Education was positively correlated with respondents’ WTP decision and the quantity of their WTP with statistical significance levels of 1% and 5%, as was the variable of Perincome. An upgrade of respondents’ educational attainment from illiteracy to postgraduate (7 grades in total) could bring 2.01 yuan more per month to their WTP. The coefficient of Perincome indicated that respondents would pay 0.20 yuan more per month for WTP for every 100 yuan increment in income. Family size was positively correlated with the quantity of respondents’ WTP at a 10% significance level, indicating that larger families would tend to pay 0.97 yuan more per month for each additional person. Gender and Age didn’t pass the statistical test in model 1. It is easy to understand that respondents with higher educational attainment are more prone to have higher and better environmental awareness, which is beneficial for their positive decision of WTP. Furthermore, the affluent households are more likely to pay, and also willing to pay a higher premium for GE, mainly because the limited increment of their electricity bills would have only subtle (or even no) effects on their normal life.
Table 3 Estimation results of the Probit model and the OLS model
Variable Model 1 Model 2 Model 3
Probit OLS Probit OLS Probit OLS
Gender -0.298(0.182) 0.131(1.803) -0.33*(0.195) 0.287(1.804) -0.343*(0.197) -0.025(1.804)
Age -0.009(0.007) 0.063(0.073) -0.01(0.009) 0.075(0.08) -0.01(0.009) 0.073(0.079)
Education 0.429***(0.098) 2.005**(0.92) 0.329***(0.105) 1.677*(0.953) 0.323***(0.106) 1.498(0.954)
Family size 0.017(0.057) 0.971*(0.568) 0.024(0.062) 0.917(0.58) 0.023(0.062) 0.89(0.578)
Perincome 0.0004***(0.000) 0.002**(0.001) 0.0004***(0.000) 0.002**(0.001) 0.0003***(0.000) 0.002*(0.001)
Length - - 0.008*(0.005) -0.002(0.046) 0.009*(0.005) 0.005(0.046)
Rate - - 2.944***(0.493) 6.414*(3.865) 2.938***(0.493) 6.516*(3.848)
Pollution - - - - 0.088***(0.139) 2.384**(1.298)
Constant -1.207*(0.661) -9.6(6.539) -2.706***(0.774) -12.511*(6.763) -3.06***(0.958) -22.074**(8.51)
R2 - 0.049 - 0.059 - 0.071

Notes: The values in brackets are the T-statistic values of the estimated coefficients of the corresponding variables. Model 1 comprises household demographic variables alone, while several more external variables are introduced into Model 2 and Model 3. ***, **, * mean P<0.01, P<0.05, P<0.1 respectively.

In models 2 and 3, the variables of Length, Rate and Pollution were added in sequence. As we added these three more variables into the Probit model, the coefficients of “Gender” in model 2 and model 3 each showed a statistical significance level of 10%. Thus, it could be predicted that women were more prone to make a positive decision of WTP for GE than men, but there was not considerable evidence that the women were willing to pay a higher premium than men. The gender-induced difference of the WTP decision can be explained by the feature that women always tend to express their inner feelings and give positive responses to questions, while men usually make judgments on apparent economic gains or losses. In model 2, we found the length of time the respondents had lived in urban areas was conducive to their positive decision for WTP at a statistical significance level of 10%, but it could not stimulate the respondents to pay a higher premium for WTP. We also confirmed that the respondents’ decision of WTP, as well as its quantity, were greatly influenced by their neighbor’s decisions at the 1% and 10% significance levels, respectively. This observation can be explained to some extent by the ubiquitous “Herd Effect” in human behavior. However, as the hypothetical usage of GE is an environmentally-friendly choice in the face of environmental pollution, but one which requires some concessions and sacrifices on individual wealth, the respondents being influenced by their neighbors could be defined as a weak “Herd Effect”.
In model 3, we found that the greater extent to which respondents agreed with the opinion that “GE can help to alleviate environmental pollution”, the more likely they were to pay and also to pay a higher premium for GE, which passed at a significance level of 5%. This can be explained by the respondents’ personal expectations for a better natural environment.

5 Policy implications

The development and application of GE at the household level will undoubtedly contribute to society’s fight with environmental challenges (GHG emissions). However, it is a complicated issue which requires cooperation from consumers, suppliers and government agencies.
It is a fact that GE suppliers are facing huge financial pressures in the early stages of technology innovation as well as the securing the necessary investments, thus fiscal subsidies towards the suppliers are of great importance for the sustainable development of GE. On the other hand, these subsidies can also help to alleviate the economic pressure on end-users (households) indirectly. In the short term, a series of measures can be taken to improve the recognition and decision on GE at the household level. First, households’ labor income can be increased by retraining the unemployed family members and raising their educational attainment levels, so that they can find high-wage work and more stable employment. What’s more, improving urban residents’ environmental awareness is also an indispensable measure. School education is definitely the first choice for improving an individual’s environmental concerns, especially those issues closely related with their daily life. In addition, online (TV and Internet) advertising and offline propaganda (community activities and special brochures) in communities and companies are important supplements for helping to improve the public’s awareness of environmental protection and sustainable development. However, in the long term, the development of the GE industry should avoid relying entirely on fiscal subsidies and increase retail prices gradually. In the process, the innovation of GE-related technologies, the improvement in the level of company management, as well as the continuously increasing application of GE at the household level can all help to offset and cover the rising costs. We believe the wide application of GE at the household level will become a win-win campaign in the near future.

6 Conclusions

The results of this survey confirmed that households with higher income and stronger environmental concerns were more prone to make a positive decision on WTP, and these households are also more likely to pay a higher premium for GE, which is consistent with similar studies conducted previously in developed countries or affluent regions (Nomura and Akai 2004; Arega and Tadesse 2017). It is easy to understand that higher labor income equals stronger affordability in daily life, which provides an economic buffer for households burdened with a higher electricity bill resulting from the usage of GE. In addition, the quantity of the GE premium keeps pace with respondents’ environmental concerns. Men tended to be less likely to pay a premium for GE, and the respondents’ WTP is significantly influenced by the decisions of their community neighbors. Considering that we interviewed the respondents in different places (primarily in their houses or community offices), it was impossible for them to communicate with each other during the interview process. But neighbors freely talking and communicating in their everyday lives is a common phenomenon. Therefore, the consistent decision in different spaces can be defined as a weak “Herd Effect”.

We wish to thank the community workers for their help in contacting the sample households, and we also appreciate the eight postgraduates from Henan Agricultural University for joining our investigation. They are: Feng Manjing, Qu Yalong, Sheng Ge, Yang Xi, Zheng Menghua, Wang Yanyan, Shi Yaqi and Wang Yanqiang.

Andor M A, Frondel M, Sommer S. 2018. Equity and the willingness to pay for green electricity in Germany. Nature Energy, 3(10): 876-881.


AMBS (Anyang Municipal Bureau of Statistics). 2019. Anyang statistical yearbook. Beijing, China: China Statistics Press. (in Chinese)

Arega T, Tadesse T. 2017. Household willingness to pay for green electricity in urban and peri-urban Tigray, Northern Ethiopia: Determinants and welfare effects. Energy Policy, 100: 292-300.


Armaroli N, Balzani V. 2011. Towards an electricity-powered world. Energy & Environmental Science, 4(9): 3193-3222.

Borchers A M, Duke J M, Parsons G R. 2007. Does willingness to pay for green energy differ by source? Energy Policy, 35(6): 3327-3334.


Carleton T A, Hsiang S M. 2016. Social and economic impacts of climate. Science, 353(6304). DOI: 10.1126/science.aad9837.


CNBS (Chinese National Bureau of Statistics). 2019. China city statistical yearbook (2018). Beijing, China: China Statistics Press. (in Chinese)

Damette O, Delacote P, Lo G D. 2018. Households energy consumption and transition toward cleaner energy sources. Energy Policy, 113: 751-764.


Dogan E, Muhammad I. 2019. Willingness to pay for renewable electricity: A contingent valuation study in Turkey. The Electricity Journal, 32(10): 1-6.

Egli F, Steffen B, Schmidt T S. 2018. A dynamic analysis of financing conditions for renewable energy technologies. Nature Energy, 3(12): 1084-1092.


Gan L, Eskeland G S, Kolshus H H. 2007. Green electricity market development: Lessons from Europe and the US. Energy Policy, 35(1): 144-155.

International Energy Agency. 2019. Elctricity Information (2019 edition). Paris, France: IEA.

Krausmann F, Erb K H, Gingrich S, et al. 2013. Global human appropriation of net primary production doubled in the 20th century. Proceedings of the National Academy of Sciences of the USA, 110(25): 10324-10329.


Lee C Y, Heo H. 2016. Estimating willingness to pay for renewable energy in South Korea using the contingent valuation method. Energy Policy, 94: 150-156.


Liu J G, Dietz T, Carpenter S R, et al. 2007. Complexity of coupled human and natural systems. Science, 317(5844): 1513-1516.


Murakami K, Ida T, Tanaka M, et al. 2015. Consumers’ willingness to pay for renewable and nuclear energy: A comparative analysis between the US and Japan. Energy Economics, 50: 178-189.

Nomura N, Akai M. 2004. Willingness to pay for green electricity in Japan as estimated through contingent valuation method. Applied Energy, 78(4): 453-463.


Oberschelp C, Pfister S, Raptis C E, et al. 2019. Global emission hotspots of coal power generation. Nature Sustainability, 2(2): 113-121.


Oliver H, Volschenk J, Smit E. 2011. Residential consumers in the Cape Peninsula’s willingness to pay for premium priced green electricity. Energy Policy, 39(2): 544-550.


Schmidt T S. 2014. Low-carbon investment risks and de-risking. Nature Climate Change, 4(4): 237-239.


Štreimikienė D, Baležentis A. 2015. Assessment of willingness to pay for renewables in Lithuanian households. Clean Technologies and Environmental Policy, 17(2): 515-531.


Su W H, Liu M L, Zeng S Z, et al. 2018. Valuating renewable microgeneration technologies in Lithuanian households: A study on willingness to pay. Journal of Cleaner Production, 191: 318-329.


United Nations. 2015a. The paris agreement. Paris, France: UN.

United Nations. 2015b. Transforming our world: The 2030 agenda for sustainable development. New York, USA: UN.

Xie B C, Zhao W. 2018. Willingness to pay for green electricity in Tianjin, China: Based on the contingent valuation method. Energy Policy, 114: 98-107.


Zhang L, Wu Y. 2012. Market segmentation and willingness to pay for green electricity among urban residents in China: The case of Jiangsu Province. Energy Policy, 51: 514-523.