Willingness to Pay for Green Electricity in Tianjin, China: Based on Contingent Valuation Method

Transcription

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

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. 2

3 Introduction Green electricity (GE) is the major form of renewable energy. China is projected to consume 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. 3

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

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. 5

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 6

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

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 1. Are you willing to bear additional cost between the gap of traditional power and cleaner one? 2. If yes, how much? (family elicitation technique of OE) 3. If no, 6 options to choose Personal info: gender, age, education. 8

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 9

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) 10

11 Table 1: Summary of variables used in the models Variables Definition Mean Std. Dev. Min Max Environment Awareness of the environmental issues Belief Belief on the governance. authority for environmental Knowledge Dummy variable, 1 if the respondent knows about renewable energy Behavior Previous behavior to energy conservation and emissions reduction Gender Dummy variable, 1=male, 0=female Age Age of the respondents Dummy variable, 1 if someone have a bachelor Education degree Member Total number of household members Income Total monthly household income Disease Dummy variable, 1 if someone in the respondent s family has a respiratory disease Whether Dummy variable, 1 if the respondent is willing to pay for green electricity WTP Willingness to pay for green electricity (CNY)

12 Table 2: Motives for non-positive WTP responses Table 3: Reasons for the non-positive WTP responses [7] Motives 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%) The government and polluters should be responsible for the extra expense. 85 (57.43%) Protest responses 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%) 12

13 Table 3: Summary of studies on the WTP for green electricity Reference Survey Object of study Method Year WTP Annual/monthly area [1] China, green electricity CV-PC 2010 $ Per month Jiangsu [2] China, Renewable electricity CVM-SBDC $ Per month Beijing 2015 [16] Korea green electricity CVM-DBDC 2010 $1.35 Per month [40] US green electricity CE 1997 $73.55 Per year [4] Slovenia Green electricity CE 2008 EUR 4.2 Per month [33] Crete Renewable energy CVM-DBDC quarterly [5] Korea Green electricity CV-SBDC 2006 $ Per month [32] Japan Green electricity CV-DBDC 2000 $17 Per month [20] US renewable energy CV-OE %:$5.77 Per month 20%:$15.04 [14] Korea renewable energy CV-DBDC 2014 $3.10 Per month [19] Korea Renewable electricity CV-DBDC 2010 $1.26 Per month 13

14 Table 4: Factors for WTP Variables Logit model Multiple linear regression Environment * Belief 0.304** 9.058*** Knowledge 0.421* Behavior 0.466*** Gender *** Age 0.284** *** Education 0.508* Member 0.212* Income *** Disease 0.442* 7.368** Constant Pseudo R(Adjusted R 2 ) Total

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 15

16 Table 5 & 6: Results from Logit regression and MLR Logit Logit Logit Logit Male Female Older Younger Disease Healthy Familiar unfamiliar Environment ** * Belief 0.335** * ** ** Knowledge * Behavior 0.347* 0.584*** *** 0.498* 0.445*** 0.444*** 0.579** Gender * ** * Age 0.368** * ** Education ** Member * ** ** Income * Disease 0.717** ** * _cons Obs Adj R 2 Pseudo R

17 MLR MLR MLR MLR Tianjin University Male Female Older Younger Disease Healthy Familiar unfamilia r Environment 6.671* ** 8.995* Belief *** 6.676*** 8.911*** 9.296*** 7.384*** *** 8.939*** 9.064*** Knowledge Behavior Gender *** *** *** 9.079*** *** 8.599* Age * * ** *** Education Member Income *** 7.197*** 9.386*** *** *** 7.836*** *** 6.909*** Disease *** ** ** _cons Obs Adj R Pseudo R 2 Mean WTP Max WTP Min WTP Std. Err

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 18

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 19

20 Conclusions and Policy Implications Conclusion 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 20

21 Conclusions and Policy Implications 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 21

22 22 Tianjin University Q & A session

23 Thank you! 23