1. Li-Qiu Liu College of Management and Economics, Tianjin University
Willingness to Pay for Green Electricity in Tianjin, China: Based on Contingent Valuation Method
Li-Qiu Liu
College of Management and Economics, Tianjin University
June 20, 2017,
 the 40th  IAEE, Singapore

2. Introduction
East and Central China: suffers from frequent occurrences of haze
Renewable energy: reduce pollutant emissions and improve the quality of air.
One of the countermeasures for effectively relieving haze.

3. Introduction
Green electricity (GE) is the major form of renewable energy.
China is projected to consume 6.8–7.2 TWh of electricity in 2020, and its corresponding proportion in terminal energy consumption will increase to over 27%.
The proportions of carbon emissions resulting from power generation will also increase.

4. Introduction
China aims to realize the goal of increasing the proportion of non- fossil energy in primary energy consumption to 15% by 2020, and to 20% by 2030.
It is urgent to boost the development of green electricity.

5. Introduction
The Beijing–Tianjin–Hebei region: China’s pilot power load and green electricity importing areas.
Tianjin: prototype and epitome of quite a few cities in North China.
Referential significance and may guide future power policy adjustment.

6. Literature Review
Willingness-to-pay is a widely adopted concept in the analysis of the value of public goods.
Examples: CO2 emission reduction, new energy vehicles, bioethanol, reliable electricity services and green electricity.
WTP differs significantly among regions and time
Direct survey is commonly used for WTP research

7. Contingent valuation method
Direct survey
Customer survey
Choice experiment
Contingent valuation method
Expert judgement
Literature Review
Multiple limitations in China
More flexible and applicable

8. Methods
Survey design: identify influence of factors (Pre-test survey: 40 samples)
Viewpoints on environment, corresponding protection measures, and trustfulness on the policies
Extra money willing to pay for green electricity: 3 questions
Are you willing to bear additional cost between the gap of traditional power and cleaner one?
If yes, how much? (family– elicitation technique of OE)
If no, 6 options to choose
Personal info: gender, age, education….

9. (A.) The additional cost should be paid by the government and polluters; (B.) I lack trust in the government and related departments; (C.) I am very willing to pay, but the family income is not enough to bear the additional cost; (D.) The air quality is good enough and does not need to be improved ; (E.) The cost has been included in the taxes and fees; (F.) I have other reasons not listed.
Protest response
Genuine zero response

10. Results and Discussion
Total 468 questionnaires: 407 valid (effective response 86.97%)
70.8% expressed awareness of green electricity
67.3% willing to pay for green electricity
65.6% university bachelor degrees or higher education
35.9% have relatives suffered from respiratory disease
Higher level of education than national average
Strong awareness of environmental issue
Higher incidence of respiratory disease (poor air quality)

11. Table 1: Summary of variables used in the models
Definition
Mean
Std. Dev.
Min
Max
Environment
Awareness of the environmental issues.
3.930
0.670 1 5
Belief
Belief on the authority for environmental governance.
2.826
0.981
Knowledge
Dummy variable, 1 if the respondent knows about renewable energy.
0.708
0.455
Behavior
Previous behavior to energy conservation and emissions reduction.
3.397
0.884
Gender
Dummy variable, 1=male, 0=female
0.526
0.500
Age
Age of the respondents
2.319
1.119 6
Education
Dummy variable, 1 if someone have a bachelor degree
0.656
0.476
Member
Total number of household members
3.533
1.091 8
Income
Total monthly household income
2.359
1.159
Disease
Dummy variable, 1 if someone in the respondent’s family has a respiratory disease.
0.359
0.480
Whether
Dummy variable, 1 if the respondent is willing to pay for green electricity
0.673
0.470
WTP
Willingness to pay for green electricity (CNY)
32.631
27.344
200

12. Table 2: Motives for non-positive WTP responses
Description
Number (%)
Genuine zero responses
Air quality is good enough, therefore, there is no need to pay an extra amount of money.
0 (0)
Household income is too low to afford it although I’m glad to pay more.
15 (10.14%)
Protest responses
The government and polluters should be responsible for the extra expense.
85 (57.43%)
I lack trust to the government and related departments.
27 (18.24%)
I have paid enough costs and taxes, and therefore I do not want to pay more.
21 (14.19%)
Total
148 (36.36%)
Table 3: Reasons for the non-positive WTP responses[7].

13. Table 3: Summary of studies on the WTP for green electricity
Reference
Survey area
Object of study
Method
Year
WTP
Annual/monthly
[1]
China, Jiangsu
green electricity
CV-PC
2010
$1.15–1.51
Per month
[2]
China,
Beijing
Renewable electricity
CVM-SBDC
$2.7–3.3
[16]
Korea
CVM-DBDC
$1.35
[40] US CE
1997
$73.55
Per year
[4]
Slovenia
Green electricity
2008
EUR 4.2
[33]
Crete
Renewable energy
2007
€16.33
quarterly
[5]
CV-SBDC
2006 $
[32]
Japan
CV-DBDC
2000
$17
[20]
renewable energy
CV-OE
10%：$ %：$15.04
[14]
2014
$3.10
[19]
$1.26

14. Table 4: Factors for WTP Variables Logit model
Multiple linear regression
Environment
0.097
3.985*
Belief
0.304**
9.058***
Knowledge
0.421*
0.176
Behavior
0.466***
0.447
Gender
-0.372
14.869***
Age
0.284**
-3.777***
Education
0.508*
-4.583
Member
0.212*
-1.015
Income
-0.143
10.145***
Disease
0.442*
7.368**
Constant
-3.669
Pseudo R（Adjusted R2）
0.0931
0.3023
Total
407
274

15. Results and Discussion
The influence of health conditions on WTP
Two tables below present the results from Logit regression and MLR methods separately
More sensitive with older respondents and the ones having relatives suffering respiratory disease
Not for young people or people unfamiliar with the topic
Behavior only if no such relatives

16. Table 5 & 6: Results from Logit regression and MLR
Male
Female
Older
Younger
Disease
Healthy
Familiar
unfamiliar
Environment
-0.326
0.653**
0.335
-0.004
0.659*
0.009
-0.013
0.318
Belief
0.335**
0.233
0.409*
0.216
0.452**
0.213
0.344**
0.209
Knowledge
0.16
0.611*
0.324
0.366
0.495
0.432
Behavior
0.347*
0.584***
0.278
0.537***
0.498*
0.445***
0.444***
0.579**
Gender
-0.485*
0.099
-0.568**
-0.554*
-0.104
Age
0.368**
0.114
0.434*
0.211
0.306**
0.305
Education
0.566
0.494
0.678
0.51
0.269
1.001**
Member
0.212
0.249
0.363*
0.095
0.485**
0.125
0.400**
-0.046
Income
-0.18
0.025
-0.003
-0.251*
0.085
-0.207
-0.078
-0.227
0.717**
0.065
0.976**
0.162
0.567*
0.108
_cons
-2.087
-5.977
-4.352
-1.965
-8.117
-2.277
-3.46
-3.829
Obs
214
193
148
259
146
261
288
119
Adj R2
Pseudo R2
0.1001
0.1321
0.1248
0.0845
0.1598
0.0716
0.1014
0.1069

17. MLR
Male
Female
Older
Younger
Disease
Healthy
Familiar
unfamiliar
Environment
6.671*
0.299
0.131
7.179**
8.995*
1.705
4.246
5.078
Belief
10.361***
6.676***
8.911***
9.296***
7.384***
10.104***
8.939***
9.064***
Knowledge
0.012
-1.302
1.366
-0.255
0.718
0.611
Behavior
0.105
0.943
-1.506
1.215
-0.053
1.887
-0.177
3.847
Gender
13.309***
14.429***
26.190***
9.079***
16.000***
8.599*
Age
-3.401*
-3.529*
-3.088
-3.637**
-4.792***
-1.16
Education
-6.446
0.104
-4.768
-2.939
-4.239
-1.228
-7.485
4.708
Member
-0.53
-1.577
-0.447
-0.521
-1.114
-0.603
-1.723
0.4
Income
12.788***
7.197***
9.386***
11.248***
13.416***
7.836***
10.946***
6.909***
14.307***
-0.446
8.982**
5.874
8.469**
1.718
_cons
-37.66
0.05
-24.77
Obs
136
138
108
166
109
165
203 71
Adj R2
0.3499
0.1836
0.27
0.2989
0.3474
0.278
0.2773
0.3621
Pseudo R2
Mean WTP
38.206
27.138
31.407
33.428
35.239
30.909
34.443
27.451
Max WTP
200
100
Min WTP
Std. Err
30.52
22.601
25.82
28.339
31.317
24.316
28.805
22.019

18. Results and Discussion
The influence of knowledge, behavior and belief
Respondents familiar with clean energy higher positive responses than others
Behavior significantly positive in Logit regression but nothing major in MLR
Trustfulness for the government: positively correlated with all groups in MLR

19. Results and Discussion
The influence of personal characteristics (gender, age and income)
Average for male significantly higher than female (CNY over /mo)
Almost same for residents under or above age 34
Exception for young respondents: income increases and more positive

20. Conclusions and Policy Implications
Knowledgeable about clean energy: high WTP for green electricity;
but many lack this knowledge
Mainly influenced by consciousness (behavior of households, belief towards the government, have relatives with respiratory disease…)
Education level: positively related if has the knowledge, but no influence for people without this knowledge
Age differs not as expected

21. Conclusions and Policy Implications
Improving the transparency of governance-- an effective way to WTP
Electricity pricing mechanism should be reformed in the future plan
Has limitations since not all consumers are familiar with green electricity

22. Q & A session

23. Thank you!