the willingness to pay (wtp) of lse students and staff for energy saving devices in lse buildings

GY240 Research Techniques (Spatial, Social, Environmental)

Candidate: Sebastian Herran

The Willingness To Pay (WTP) of LSE students and staff for energy saving devices in LSE buildings.

Abstract

In order to develop the proper policies which could supplement the Environmental Policy at the LSE, it is necessary to understand the students’ and staff’s preference and determinants for energy saving measures. The study attempts to analyse LSE student’s willingness to pay (WTP) for two energy saving devices, namely the onEPuck and the EcoButton, around LSE campus buildings by applying a Contingent Valuation (CV) method. A binary logistic regression method is applied to determine the main explanatory factors that influence the willingness to pay. The project found that the factors determining WTP for each device differed and present potential explanations; namely direct global preoccupation for the environment and efficiency gains indirectly linked to care for the environment.

Introduction and Literature review

Introduction

The London School of Economics (LSE) is strongly committed to environmental behaviour, imposing measures through their Environmental Sustainability Policy. This policy seeks to improve LSE’s environmental and social impacts “by delivering environmental enhancements where possible” (LSE Sustainability Website). The ‘Energy and Carbon’ objective is to ‘reduce consumption and increase energy efficiency in buildings and equipment to reduce LSE’s carbon footprint” (LSE Enviromental Policy 2014:1).

This project considers the installation of energy saving devices that reduce energy consumption from personal computers and mobile phones. The two devices analysed are Ecobutton, a device that places your personal computer in the lowest power setting, and onE Puck, a portable mobile phone charger that uses heat disparity from a beverage to charge your phone. The method of payment is per annum through tuition fees or staff remuneration deductions.

Literature Review

Energy efficiency and conservation are critical elements in energy policy dialogue, and have taken a renewed importance as global climatic change concerns have intensified (Gillingham et al 2009). Literature highlights the importance of closing the ‘energy efficiency gap’ (Jaffe et al 2004) which has led to underinvestment and a slowing of adopting energy-efficient technologies. One explanation of this gap in the residential sector is the ‘Principle-Agent problem’ (Anderson 2013). This project will encounter this issue, as students do not directly pay for energy use. Higher education is a growing sector, with student numbers increasing by a factor of five over the past thirty years (Carbon Trust 2015). This means energy consumption at universities provides an opportunity for future energy use reductions.

Evaluating the population’s WTP for energy-saving measures such as energy efficient standards and labelling (Masjuki et al 2000, Webber 2000) conclude that consumers do significantly value

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energy saving attributes. With regard to energy saving behaviour, attitudes range from “different forms of uncertainty to very high levels of concern” (UK Energy Research Centre 2013). Its clear that “behavior change is possible, with basic environmentally friendly actions, such as switching off unused lights and recycling” (Guardian Sustainability 2015). “People who recycle typically take a conserving approach to most things and recognize that individuals can make a difference by sending less refuse to the local landfill” (Painter 2015:1). Literature highlights two determinants of using energy-saving, namely, direct global preoccupation for reducing carbon emissions and indirect efficiency gains leading to climate change mitigation. This project will therefore question respondents on their interest in reducing emissions, ie directly saving energy, and their involvement in activities that indirectly contribute, such as recycling.

Research design

The valuation of non-market amenities and services, through contingent valuation, “has become an increasingly important tool for policy-makers” (Howe et al 1994:385). The acceptance of the contingent valuation method (CVM) or willingness to pay (WTP) methodology offers a method of quantifying the value of public good characteristics such as energy saving measures. The contingent valuation method asks respondents would they be willing to pay in a hypothetical market situation for some public good (Arrow et al 1993). The justification of using the Binary Logistic Model is its the common method to value dichotomous variables in a contingent valuation (Peterson 2010) - in line with this project.

An online survey was used to collect data from LSE members. Online surveys were conducted due to ease of use, cost and distributional range. Our target was to obtain over 100 responses, as the more responses; the more accurate the sample is of the population (Studenmund 2005). Toluna Quick Surveys provided a summary of raw data in a neat and organised presentation, supplemented by statistical graphs such as pie and bar charts. The online data collection method was preferred as the survey was conducted during the last couple weeks of holiday, when not many students and staff would have been on campus to conduct face-to-face interviews, and finished at the start of term, when many students were time-constrained due to revision. In addition, face-to-face interviews are also more “susceptible to social desirability bias due to the presence of the interviewer” (Duffy et al 2007:638). The survey aimed to collect the raw data needed to conduct the investigation while gaining a better understanding of the respondents’ preferences and behaviours towards energy-saving.

Survey design

The survey began with a filter question to ensure the sample contained only LSE staff and students. Following this were a set of behavioural questions to assess the respondent’s level of active interest towards environmental sustainability. Once this was recorded, the survey presented the contingent scenario with dichotomous choice value elicitation questions followed up with preferences on devices and methods of payment. The final section of the survey aimed to record socio-economic and demographic characteristics of the respondents, however respondents had the option not to answer due to the varying sensitivity of the answers. Survey design is crucial and must be rigorously piloted before conduction (Colosi 2006). Hence before conducting the survey it was piloted and feedback, such as descriptions, lengths were amended.

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Data analysis and findings

Description of the main trends and patterns in the data

Figure.1 – A pie chart showing preference of respondents between the two devices.

Appendix 1 illustrates students are WTP for energy saving devices, however, Figure 1 demonstrates 52.04% of respondents preferred onE Puck to EcoButton. Comments received highlighted two possible explanations. Firstly, a large majority were intrigued by onEPuck as they thought it ‘interesting and different’. Secondly, respondents wondered whether EcoButton had any negative effects on the computer’s performance, thus were less attracted to the device. However, the difference is small and therefore both devices serve of interest.

Figure 2 – A bar chart showing percentage of respondents WTP £20 and £30 for at least one device.

WTP £20 for at least one of the devices

WTP £30 for at least one of the devices

0% 10% 20% 30% 40% 50% 60% 70%

66%

41%

34%

59%

NoYes

Figure 2 demonstrates at £30, 59% of respondents weren’t WTP for one device. In contrast, the WTP £20 shows 66% of respondents would so is reasonable to choose our initial value elicitation bid as the dependent variable in the Binary Logit models.

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Presentation of the test – Binary Logistic Model

To quantify effects of the explanatory variables binary logit models will be used because it can analyse discrete data. The final regression models include the following variables:

Respondent’s behavioural independent variables:

1) Whether take an active interest in reducing their carbon emissions2) Whether recycle paper3) Use power saving mode on their computer

Demographic independent variables:

1) Gender2) Age3) Income

Note: All independent variables used in final models were tested for multicollinearity, using a Pearson Correlation Matrix, none of them showed very significant multicollinearity, even with respect to our small sample size, as seen in Appendix 2. In addition, description and coding of independent variables is presented in Appendix 3.

The behavioural variables are selected to cover the perceived direct environmental preoccupation (reduce emissions, power saving) as well as indirect private actions related to environmental sustainability (recycle).

The regression models will look like the following:

WTP = β0 + β1X1i + β2X2i + … + βkiXki + ϵi

WTP = dependent variableβ0 = constant termβ =regression coefficientsX =independent variables

= the error termϵ

The following hypotheses will also be tested and P-values used to test significance

H0: The independent variables do not affect the WTP for the energy saving device at LSEH1: The independent variables do affect the WTP for the energy saving device at LSE

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Results of the test

Table 1: The results of the binary logit regression for onEPuck device

Variables in the Equation

B S.E. Wald df Sig. Exp(B)95% C.I.for EXP(B)

Lower Upper

Reduce_Emissions -0.241 0.525 0.211 1 0.646 0.786 0.281 2.198

Recycle 2.163 1.217 3.157 1 0.076 8.7 0.8 94.578

Power_Saving 0.507 0.504 1.012 1 0.314 1.66 0.619 4.454

Male 0.175 0.552 0.101 1 0.751 1.192 0.404 3.516

Age -0.031 0.217 0.02 1 0.888 0.97 0.634 1.483

Income 0.08 0.093 0.742 1 0.389 1.084 0.903 1.301

Constant -2.303 1.463 2.479 1 0.115 0.1

Out of 6 explanatory variables tested against the WTP £20, only 1 was statistically significant. Remaining 5 had P-values fairly high, higher than any conventional significance level to infer a relationship on WTP.

The data shows if a respondent did recycle paper, then they were more WTP for the device as this increases the logit or the estimated log-odds of WTP by 2.136 (B-coefficient). We can infer the probability that respondents are WTP, who have the average mode characteristics, recycle paper but don’t actively reduce emissions or use power saving mode, is approximately 0.643. This calculation is illustrated in Appendix 4, where average mode characteristics are justified. We can conclude at the 10% significance level we can reject the null hypothesis and infer that, controlling for all other variables in the model, there is a reasonably significant relationship.

The other variables highlight no significance between them and WTP £20. Perhaps one reason why the data is insignificant is because of limited sample size. Another reason could be that our WTP question included only one threshold for the initial bid: £20. This project’s limitation highlights that normally WTP surveys randomly ask for different values to different people which increase the precision of the results. This is because they are less dependent on the amount suggested by the researcher.

Table 2: The results of the binary logit regression for the EcoButton

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B S.E. Wald df Sig. Exp(B)95% C.I.for EXP(B)

Lower Upper

Reduce_Emissions 1.34 0.584 5.262 1 0.022 3.821 1.215 12.01

Recycle 0.75 1.102 0.464 1 0.496 2.118 0.244 18.36

Power_Saving -1.139 0.568 4.022 1 0.045 0.32 0.105 0.974

Male -0.168 0.588 0.082 1 0.774 0.845 0.267 2.673

Age 0.582 0.254 5.245 1 0.022 1.789 1.087 2.943

Income 0.027 0.098 0.076 1 0.783 1.028 0.847 1.246

Constant -1.931 1.425 1.836 1 0.175 0.145

Table 2 highlights out of 6 explanatory variables only 3 were statistically significant. Taking interest in reducing carbon emissions had a B-coefficient of 1.34, and Age also had a positive relationship on WTP with a B-coefficient of 0.582. Power saving had a negative relationship recording a B-coefficient of -1.139. Combining the results, the probability that respondents are WTP for Ecobutton, who have the average mode characteristics, actively reduce emissions, use the power saving mode but do not recycle, is approximately 0.255, as illustrated in Appendix 5.

We can conclude at 5% level these three variables were statistically significant and can infer that, controlling for other variables, they reject the null hypothesis.

Interpretation of the substantive meaning of results and policy implications

A basic preliminary conclusion can be deducted from the results suggests that respondents are more likely to be WTP for the devices if they already engage in energy-saving measures as more inclined to adopt further measures.

A deeper interpretation involves some basic (and potentially inaccurate) assumptions. Firstly, we can categorize ‘actively reducing emissions’ and ‘power saving mode’ as actions that are directly related to reducing energy use. Conversely, although recycling paper aids in reducing carbon emissions, it has been highlighted to relate to efficiency and land-fill concerns. There is a clear link from the results that respondents that have a global preoccupation about energy saving are more probable to be WTP for the EcoButton device. For onEPuck, the results suggest they do not associate the device directly with climate change mitigation, but potentially efficiency. If this wasn’t the case then the results from the model would be similar. The EcoButton is simply a standard device, making appliances more efficient without changing habits ie use the device in the same way thus forcing people into energy saving attitudes. In contrast, onEPuck is more of a controlled change, altering the way of phone charging. Recycling also changes habits and therefore can potentially justify our reasoning. With regard to policy implications, results highlight that policy strategy should be dependent on whether it is attempting to enhance the population’s environmental awareness through changing daily habits. As discussed above, this project situates these devices either side of that argument.

Conclusions

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In conclusion, the project has assessed the WTP for two energy devices and found that these decisions are potentially motivated by direct and indirect environmental preoccupation. There are several limitations to bear in mind; firstly the limited sample size and secondly, that WTP values could be an overestimation due to individuals stating their behavioural intentions rather than on revealed economic decisions (Banfi et al 2008). In addition, the perception of a further increase in university fees, given the 2012/2013 national fee rise, may have deterred students from being willing to pay. An alternative payment mechanism was also questioned, as seen in Appendix 5, presenting further area for research. Whether people were willing to change their daily habits for climate change mitigation also presents an interesting enhancement of this research. Finally, results from a separate regression (Appendix 6) demonstrate that owning one of the devices has an impact on WTP for the other; hence exploring the impact that these devices have on raising environmental awareness would be useful for policymakers.

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References

Duffy,B Smith,J Terhnian,G Bremer,J – Comparing data from online face-to-face surveys International Journal of Market Research Vol 47 Issue 6

Designing an effective questionnaire – Laura Colosi (2006) Cornell University

Using PROC LOGISTIC to Estimate Willingness to Pay for Fresh Produce - Mikael Peterson, California Polytechnic State University, San Luis Obispo, CA (2010)

“Green Clothes” A survey of people’s willingness to pay for environmentally friendly clothes Elsa Levinson (2009).

What motivates consumers to make ethically conscious decisions? Guardian Sustaibnability Blog (2015)

Why Do People Choose to Recycle? Sally Painter (2015)

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Appendix – final questionnaire and any supporting tables, charts or graphs

Appendix 1

Figures 3 and 4 illustrate that the majority of respondents have both purchased a portable phone charger and used the standby mode on their computers, which gives an indication that the respondents are WTP for energy saving devices.

Figure 3 Figure 4

Appendix 2 -

Table 3 – Pearon’s Correlation Matrix of independent variables

Reduces Emission

s RecyclePower Saving

Age in 5 Classes

Income in 9 Classes

Dummy 1=Male

Pearson Correlation

1 .269** .146 .091 -.179 -.087

Sig. (2-tailed) .007 .151 .377 .106 .399N 98 98 98 96 83 97Pearson Correlation .269** 1 .289** .084 -.180 -.078

Sig. (2-tailed) .007 .004 .414 .103 .446N 98 98 98 96 83 97Pearson Correlation

.146 .289** 1 -.001 -.254* -.158


.091 .084 -.001 1 -.066 .032


-.179 -.180 -.254* -.066 1 .171


-.087 -.078 -.158 .032 .171 1

Sig. (2-tailed) .399 .446 .122 .757 .124N 97 97 97 95 82 97

*. Correlation is significant at the 0.05 level (2-tailed).

Recycle

Power Saving

Age in 5 Classes

Income in 9 Classes

Dummy 1=Male

**. Correlation is significant at the 0.01 level (2-tailed).

Correlations

Reduces Emissions

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Table 3 shows there is no indication of significant multicollinearity between the variables as this is usually highlighted by values close to -1 or 1 (Studenmund 2005).

Appendix 3 – Description and coding of the variables

Variable DescriptionReduce Emissions Does the respondent take an active interest in reducing their carbon

emissions? (Yes=1; No=0)Recycle Does the respondent recycle paper? (Yes=1; 0=No)Power saving Does the respondent use the power saving mode on their computer?

(Yes=1; No=0)Income Household yearly income categorized into 9 classes (Under £18,000 =1;

£18,000 - £24,999 =2; £25,000 - £39,999 =3; £40,000 - £59,999 =4; £60,000 - £79,999 =5; £80,000 - £99,999 =6; £100,000 - £149,999 =7; £150,000 - 199,999 =8; Over £200,000 =9)

Age What is your age split into 5 classes (18-21 = 1; 22-25 = 2; 26-30 = 3; 31-40 = 4, 40+ =5)

Gender What is your gender? (Female = 0; Male =1)

Appendix 4 –

The equations below present the calculations for the probabilities that respondents were willing to pay for each device, given the statistical results from the Binary Logistic Model. Controlling for the insignificant variables, I have used the average mode for each demographic and socio-economic characteristic. Although this method of calculation provides an estimate, it is however a limitation of the project which could be significantly improved by calculating for each gender, age and income class of the respondents.

Figure 5 – A Pie Chart Showing the number of male and female respondents.

Figure 5 demonstrates that the majority of respondents were male and I will therefore use ‘male’ as the average mode gender when calculating the probability.

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Figure 6 – A Bar Chart to show the mode range of ages of the respondents

Figure 6 shows that the majority of the respondents were between the ages 18-21 years old and therefore I will use this class group in my calculation.

Figure 7 –A bar chart demonstrating the mode income class

Figure 7 demonstrates that the majority of the respondents claimed their household income exceeded £200,000 and I will therefore include this income bracket in my calculation.

Calculation of probabilities:

Using the equation to calculate the probability:

P= exp( +Xiα i)β

1- exp( +Xiα i)β

For the onEPuck device:

P(Reducing_Emissions=0,Recycling=1,Power Saving=0,Gender=1,Age=1,Income=9) =

exp( -2.303 + (0 x -0.241) + (1 x 2.163) + (0 x 0.507) + (1 x 0.175) + (1 x -0.031) + (9 x 0.08)

1 - exp( -2.303 + (0 x -0.241) + (1 x 2.163) + (0 x 0.507) + (1 x 0.175) + (1 x -0.031) + (9 x 0.08)

= 0.6425888894215898266

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For the EcoButton:

P(Reducing_Emissions=1,Recycling=0,Power Saving=1,Gender=1,Age=1,Income=9) =

exp( -1.931 + (1 x 1.34) + (0 x 0.75) + (1 x -1.139) + (1 x -0.168) + (1 x 0.582) + (9 x 0.027)

1 - exp( -1.931 + (1 x 1.34) + (0 x 0.75) + (1 x -1.139) + (1 x -0.168) + (1 x 0.582) + (9 x 0.027)

= 0.25483298588

Appendix 5 -

Figure.8 – A pie chart showing the preferred method of payment for the onEPuck from staff resonses.

Figure 5 shows that a strong majority (81.82%) of staff members preferred not to pay via salary deductions. Feedback highlighted that not all staff members consumed beverages at LSE and therefore would only like those who use it to pay the additional cost. Another key factor stated was that by paying at LSE facilities, they were able to note their contribution each time they bought a beverage.

Figure.6 – The preferred method of payment for the onEPuck from student respondents.

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Figure 6 shows that 55.41% of the student respondents preferred to pay through additional payments at LSE facilities as opposed to an increase in the tuition fee. Comments collected in the survey highlighted mixed thoughts. On one hand, for students who drink more than 100 cups of coffee a year, it is actually cost effective to pay via tuition fees. In addition to this students signalled that they aim to keep their daily expenditure to a minimum and may therefore be put off by the additional cost every time they wanted a beverage. However, on the other hand, the majority of the respondents claimed that due to the varying frequencies use of LSE’s catering facilities, it wasn’t equitable to pay via tuition as some students used them more than others.

Appendix 6

Table 4 – A binary logistic model for the WTP for the onEPuck including the WTP for the Ecobutton as an independent variable.


B S.E. Wald df Sig. Exp(B)

Step 1a

Reduce_Emissions -1.475 .715 4.259 1 .039 .229

Recycle 2.319 1.541 2.265 1 .132 10.166

Power_Saving 1.748 .713 6.006 1 .014 5.742

Male .298 .699 .181 1 .670 1.347

Age -.565 .296 3.659 1 .056 .568

Income .023 .126 .034 1 .854 1.023

ecobutton_WTP£20 3.773 .900 17.590 1 .000 43.524

Constant -2.931 1.847 2.518 1 .113 .053

a. Variable(s) entered on step 1: Reduce_Emissions, Recycle, Power_Saving, Male, Age, Income,

ecobutton_WTP£20.

Table 5 – A binary logistic model for the EcoButton with the WTP for the onEPuck as an independent variable.


B S.E. Wald df Sig. Exp(B)Step 1a Reduce_Emissions

1.973 .723 7.456 1 .006 7.194

Recycle -.435 1.373 .100 1 .752 .647

Power_Saving-1.967 .791 6.185 1 .013 .140

Male -.092 .727 .016 1 .900 .912

Age .954 .368 6.714 1 .010 2.596

Income -.042 .122 .118 1 .731 .959

onepuck_WTP£203.576 .852 17.625 1 .000 35.730

Constant -3.136 1.859 2.845 1 .092 .043

a. Variable(s) entered on step 1: Reduce_Emissions, Recycle, Power_Saving, Male, Age, Income,

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onepuck_WTP£20.

Table 4 and 5 shows that if respondents were WTP for one device then they were WTP

for the other, which has implications for policy regarding improving environmental

awareness. This can be interpreted as policymakers may want to promote devices that

raise environmental awareness in order to increase usage of other energy saving

technologies.

Appendix 7

Description of LSE’s environmental policy

One of LSE's Environmental Sustainability Policy objectives is to "...Reduce consumption and increase energy efficiency in buildings and equipment in order to reduce the school's carbon footprint..." and they strive to achieve this through the Carbon Management Plan - a directive to achieve a reduction of 54% in carbon emissions from the baseline year of 2005/6 (14,484 tonnes CO2e) by 2020.

A project proposed to decrease LSE's carbon footprint is through the installation of energy saving devices that reduce the energy consumption from personal computers and mobile phones.

The two energy saving devices under consideration are the Ecobutton, a device that places your personal computer in the lowest power setting when pushed, and the onE Puck, a portable mobile phone charger that uses the heat disparity from a beverage to charge your phone.

Description of the devices

onE Puck

The onE Puck is a portable charger that uses the heat disparity from your hot or cold beverage to charge your phone. In ideal conditions, it charges your phone just as fast as normal and saves 5 watts of energy for every hour spent charging your phone with the device instead of a mains power supply.

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Further information can be found here: https://www.kickstarter.com/projects/epiphanylabs/epiphany-one-puck

In the proposed scheme, several devices would be placed around cafes around campus including the Garrick, the Three Tuns and in the Saw Swee Hock centre.

Ecobutton

The Ecobutton helps decrease the energy consumed by computers and laptops. By connecting via a USB port, it can place the computer into a power-saving "ecomode" at the press of a button. It saves an estimated annual average of $100 in electricity costs and 0.45 carbon units per device.

More information can be found here: https://www.ecobutton.com

Under the proposed scheme, an Ecobutton would be installed on all LSE computers.

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The questionnaire used for this project:

Filter

1. Are you a student and/or staff member at LSE?

Household behaviour/attitude

2. Do you take an active interest in reducing your carbon emissions?

3. Are you a member of an environmental society/organisation?

4. Do you recycle paper?

5. Do you use the power saving mode on your personal computer?

6. Do you turn your computer screen off or place your computer on standby if you leave it unattended for more than 5 minutes?

7. Do you charge your mobile phone at least three times per week?

8. Have you ever purchased or used a portable mobile phone charger before?

9. Are you primarily a …?

10. What academic department are you in? (If staff member please select “staff”)

11. Have you ever heard of and/or used the onE Puck or EcoButton devices before?

If no, then descriptions of LSE’s Enviromental Policy and descriptions of the devices were presented as seen in Appendix 7.

Contingent scenario with dichotomous choice value elicitation questions

onE Puck (Student)

12. Would you be willing to pay an additional 20p on top of your beverage at LSE catering facilities for the installation of the onE Puck?

13 (i). If answered ‘no’. Would you be willing to pay an additional 10p for the installation?

13 (ii) If answered ‘yes’. Would you be willing to pay an additional 30p for the installation?

14. Would you be willing to pay an additional £20 per annum on top of your tuition fees?

15 (i) If answered ‘no’. Would you be willing to pay an additional £10 per annum?

15 (ii) If answered ‘yes’. Would you be willing to pay an additional £30 per annum for the installation?

16. Which would be your preferred method?

17. Why is this your preferred method?

Ecobutton (Student)

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18. Would you be willing to pay an additional £20 per annum on top of your tuition fees to have the ecobutton installed on all LSE computers?

19 (i). If answer ‘no’. Would you be willing to pay £10 per annum for the installation?

19. (ii) If answer ‘yes’. Would you be willing to pay £30 per annum for the installation?

20. Would you be willing o pay an additional 20p on top of your beverage at LSE catering facilities for the installation of the onE Puck devices?

21 (i). If answer ‘no’. Would you be willing to pay an additional 10p?

21 (ii). If answer ‘yes’. Would you be willing to pay an additional 30p?

onE Puck (Staff )

22. Would you be willing to have £20 deducted per annum for the installation?

23(i). If answer ‘no’ would you be willing to have £10 deducted for the installation?

23 (ii). If answer ‘yes’. Would you be willing to have £30 deducted for the installation?

24. Which would be your preferred method?

25. Why is this your preferred method?

EcoButton (staff)

26. Would you be willing to have £20 deducted per annum in order to have the EcoButton installed to all computers at LSE?

27 (i). If answer ‘no’. Would you be willing to have £10 deducted for the installation of devices?

27 (ii). If answer ‘yes’. Would you be willing to have £30 deducted for the installation of devices?

28. Would you be willing to pay for the installation of both devices?

29. If you could only have one device installaed, which would you prefer to have?

Socio-economic and demographic questions

30. What is your gender?

31. What is your age?

32. What is your household income?

General comments

33.Do you have any general comments?

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the willingness to pay (wtp) of lse students and staff for energy saving devices in lse buildings

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