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

doi:10.5814/j.issn.1674-764x.2022.02.006

Abstract

Abstract:

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.

Key words: willingness-to-pay, green electricity, urban residents, Anyang City

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.

Figures/Tables 5

References 26

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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

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

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

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

Variable	Model 1		Model 2		Model 3
Variable	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