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

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.

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.

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.

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.

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

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

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….

(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

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)

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

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].

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 2011-2015 $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 $ 1.8-2.2 [32] Japan CV-DBDC 2000 $17 [20] renewable energy CV-OE 10%：$5.77 20%：$15.04 [14] 2014 $3.10 [19] $1.26

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 -29.527 Pseudo R（Adjusted R2） 0.0931 0.3023 Total 407 274

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

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

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 -21.586 -55.879 -51.154 -24.77 -23.787 -51.094 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

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

Results and Discussion The influence of personal characteristics (gender, age and income) Average for male significantly higher than female (CNY 38.206 over 27.138/mo) Almost same for residents under or above age 34 Exception for young respondents: income increases and more positive

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

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

Q & A session

Thank you!