Disentangling stigma from actuarial risk: the cautionary story of nuclear power USAEE 35 th Annual Meeting Nov 13, 2017, Houston, TX

Size: px

Start display at page:

Download "Disentangling stigma from actuarial risk: the cautionary story of nuclear power USAEE 35 th Annual Meeting Nov 13, 2017, Houston, TX"

Cori Jemima Spencer
6 years ago
Views:

1 Disentangling stigma from actuarial risk: the cautionary story of nuclear power USAEE 35 th Annual Meeting Nov 13, 2017, Houston, TX Ahmed Abdulla Parth Vaishnav Brian Sergi David Victor

2 2 Outline Motivation and background What has been done in the past What we did, and why it is different Does the nuclear label have an effect that is additional to the catastrophic risk? What is the practical consequence of this effect? So what?

3 People perceive nuclear power as being extraordinarily risky 3 Slovic, P., Fischhoff, B. & Lichtenstein, S. Facts and Fears: Understanding Perceived Risk Societal Risk Assessment. in Societal Risk Assessment (eds. Schwing, R. C. & Albers, W. A., Jr.) (Springer, 1980).

4 People perceive nuclear power as being extraordinarily risky 4 Fischhoff, B., Slovic, P., Lichtenstein, S., Read, S. & Combs, B. How safe is safe enough? A psychometric study of attitudes towards technological risks and benefits. Policy Sci 9, (1978).

5 Stigma associated with nuclear power, has been studied as a labeling effect 5 van Rijnsoever, F. J., van Mossel, A. & Broecks, K. P. F. Public acceptance of energy technologies: The effects of labeling, time, and heterogeneity in a discrete choice experiment. Renewable and Sustainable Energy Reviews 45, (2015).

6 Nuclear power evokes negative emotions regardless of average (levelized?) actuarial risk 6 Life-cycle analyses are used to assess the total risk associated with power generation (i.e., risks associated with building plants, producing power, dis- mantling the plants). In Switzerland, about 63 TWh of electricity are produced a year. If the total amount of electricity were produced by X, there would be about two fatalities each year. X = photovoltaic power plants X = nuclear power plants If you think about solar power [nuclear power], what feelings are evoked? What feelings do you experience due to this information about two fatalities per year? How acceptable do you consider the risk of two fatalities per year? Siegrist, M. & Sütterlin, B. Human and Nature- Caused Hazards: The Affect Heuristic Causes Biased Decisions. Risk Analysis 34, (2014).

7 One of the concerns with nuclear is that it poses a catastrophic risk 7 The technology is perceived to kill or hurt lots of people in a single accident The average (levelized) risk is less relevant Fischhoff, B., Slovic, P., Lichtenstein, S., Read, S. & Combs, B. How safe is safe enough? A psychometric study of attitudes towards technological risks and benefits. Policy Sci 9, (1978).

8 We separate fear of the catastrophic aspect of risk from everything else. Why? 8 The catastrophic nature of the risk is Actuarial Amenable to engineering solutions Catastrophic = low probability + high consequence Solve by Lowering probability even more: safer designs Making consequences smaller: smaller reactors(?) Delineating the catastrophic risk perception from everything else tells us something about how much of the fear is NOT amenable to solutions above

9 Is there a stigma separate from the catastrophic risk? 9 Your local electric utility plans to construct a new hydroelectric dam that generates enough electricity to power 700,000 homes. Like all facilities that produce electricity, there is a risk associated with this project. While accidents at are very rare, the worst possible accident can result in approximately 2,000 deaths. To what extent do you support the development of this power plant? Hydroelectric dams Hydroelectric power plants Nuclear power plants

10 Hydroelecric power plants preferred to nuclear power plants (p = 0.002, n = 850) 10

11 There is an age effect for nuclear but not for others 11

12 What effect does this stigma have on people s portfolio choices? 12

13 Blinded participants build portfolio with 26% nuclear vs 19% when label is revealed 13 p < 0.01 This is 350TWh of additional nuclear power 40 large plants in addition to the current 99

14 approach. We bootstrapped a regression model by randomly selecting, with replacement, a synthetic sample of the same size as our full sample (i.e., n = 1226). We performed an OLSR on this sample. This process was repeated 10,000 times, and the median values of the coefficients are reported as the effects. A p-value is estimated by calculating the proportion of runs in Blinded which participants the sign of the effect is different from build the median value. portfolio The purpose of this regression with was to evaluate the effect of blinding participants while controlling for other factors that might have influenced their decision (e.g., the position in which nuclear power appeared, or whether 25% nuclear vs 18% when label is revealed the survey instrument was anchored to the current US portfolio). The equation we used was as follows: 14 pnuc i = blind i + anchor i + oct i + dec i + apr i + pos2 i + pos3 i + pos4 i + pos5 i + pos6 i + nep i + male i + partscale i + e i where, pnuc i is the proportion of nuclear power in respondent i's portfolio; blind i is a dummy variable that takes the value of 1 if the names of the technologies were not revealed to the respondent; anchor i is a dummy variable that takes the value of 1 if the version of the survey instrument presented to respondent i was anchored to the current US portfolio; oct i, dec i, are dummy variables that take the value of 1 if respondent i was surveyed during the October 2016, December 2016 runs of our survey; apr i is a dummy variable that takes the value of 1 if the respondent i was surveyed during the replication run of our survey, which occurred in April 2017; posx i is a dummy variable, which takes the value of 1 if nuclear power was in the X th position in the version of the survey instrument that was presented to respondent i; nep i is the score of respondent i on the New Ecological Paradigm (NEP), in such a way that a lower score indicated greater sympathy to pro-ecological attitudes; male i is a dummy variable, which takes the value of 1 if the respondent i is male; partscale i is a measure of how respondents self-identify on a six-point Likert scale that goes from 1 (very liberal) to 7 (very conservative).

15 MTurk sample 15

16 16 Wider implications of the study Nuclear industry should focus on the stigma as much as on the technology For energy system modelers Need to account for more than techno-economics of energy sources Perceptions may impose hard constraints For other nascent technologies Monitor public perceptions: they may become unmoored from actuarial risk Find out people s mental models: address what they are afraid of; not merely what you can

17 17 Acknowledgements This work was supported by the UC San Diego Frontiers of Innovation Scholars Program; the UC San Diego School of Global Policy and Strategy, which is supported by the Electric Power Research Institute; and the Center for Climate and Energy Decision Making at Carnegie Mellon University, which is supported by the U.S. National Science Foundation (SES ).

18 18 During the production of solar panels in China, three workers are killed by a falling object. The workers killed in the accident were 35 years old. The average wage of the workers was equal to CHF 4,900 ($ 5,200) a year. During the construction of a nuclear power plant in China, three workers are killed by a falling object. The workers killed in the accident were 35 years old. The average wage of the workers was equal to CHF 4,900 ($ 5,200) a year. How much should each family receive as compensation for the cause of death? How tragic is this incident in your view? (0 to 100) Mean compensation: CHF 50,000 Tragedy scale: µ = 85.32, σ = Mean compensation: CHF 100,000 Tragedy scale: µ = 78.64, σ = 20.54

Approaches for Informing Public Preferences for CCS and Other Low Carbon Technologies

Approaches for Informing Public Preferences for CCS and Other Low Carbon Technologies IEA GHG Social Research Network November 18, 2010 Lauren Fleishman, PhD Candidate Wändi Bruine de Bruin and M. Granger