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Establishing an objective system for the assessment of public acceptance of nuclear power in China
Nuclear Engineering and Design 238 (2008) 2834–2838
Contents lists available at ScienceDirect
Nuclear Engineering and Design journal homepage: www.elsevier.com/locate/nucengdes
Establishing an objective system for the assessment of public acceptance of nuclear power in China Changxin Liu a,∗ , Zuoyi Zhang a , Steve Kidd b a b
INET, Tsinghua University, Beijing, China World Nuclear Association, London, UK
a r t i c l e
i n f o
Article history: Received 9 August 2007 Received in revised form 31 March 2008 Accepted 1 April 2008
a b s t r a c t This paper proposes a way of quantifying the public acceptance of nuclear power. Based on initial qualitative analysis, sampling surveys and statistical theory, an assessment system (including assessment performances, weighing coefficients and an assessment model) is established to quantify the status of public acceptance of nuclear power in China. Using this system, quantitative indicators are derived, their trends over 5 years are described, the main factors influencing the public acceptance of nuclear power in China are derived and, accordingly, necessary improvements are suggested. Utilizing this system over the longer term could provide useful reference data for decision makers. © 2008 Elsevier B.V. All rights reserved.
1. Introduction
2. Qualitative analysis to establish the assessment system
The “public acceptance of nuclear power” is simply the general public’s attitude towards nuclear development; its importance has drawn substantial attention in recent years. It derives from public perceptions of nuclear technology risk and also from complex social, cultural and historical factors. Many studies have been completed on this topic. Based on risk perception and decision theory, European and American experts have analyzed relevant factors affecting public acceptance of nuclear power, such as demographic differences, the character of nuclear technology, trust, values, policy aspects, media exposure, etc. (Grimston and Beck, 2002; Sjoberg, 2003). Korean experts found, through survey analysis, that public acceptance of nuclear power varies with sex, education and information channels (Lee and Lee, 1999). Chinese experts pointed out that the variables such as the perceived benefit, risk, dread and trust were important factors affecting the public’s attitude (Shi et al., 2002). All the studies help to understand the public acceptance of nuclear power, and to provide suggestions to improve this. Nevertheless, what has been done is mainly qualitative and cannot further describe the status of public acceptance of nuclear power in a quantitative way. Using survey data and statistical theory, this paper aims to establish an assessment system for the public acceptance of nuclear power to derive a numeric indicator summarizing it. This would be valuable to decision-makers.
With reference to the previous studies, it would seem that the public acceptance of nuclear power is the result of judging the relative benefits and risks by the public, which are very subjective in character. Objective factors like age, sex, education and technology characteristics may have impacts on the public acceptance of nuclear power, but cannot be used as performance criteria to assess the public’s attitude. We therefore use four subjective items to assess public acceptance, as follows:
∗ Corresponding author. Tel.: +86 10 68555597; fax: +86 10 68527188. E-mail address: [email protected] (C. Liu). 0029-5493/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.nucengdes.2008.04.001
(a) (b) (c) (d)
opinion on the benefits of nuclear power (electricity); judgment of the risks; knowledge of nuclear power; trust in the parties concerned.
The four items are relatively abstract and need to be sub-divided into nine separate issues to be measured, as follows: • Opinions on benefits: (1) benefit to the national power supply; (2) benefit to lower electricity prices; (3) benefit to environmental protection. • Judgment on risks: (4) judgment on operational risks of nuclear power plants; (5) judgment on the risks of nuclear waste (nuclear proliferation is not yet included here). • Knowledge of nuclear power: (6) how much nuclear knowledge the public has; (7) self-assessed familiarity with nuclear power. • Trust in the parties concerned: (8) trust in governmental agencies; (9) trust in nuclear experts.
C. Liu et al. / Nuclear Engineering and Design 238 (2008) 2834–2838
2835
Table 1 Contingency table of Anat and Bene Bene
Strongly support Support No opinion
Anat
Oppose Strongly oppose ni*
Strongly agree
Agree
No opinion
Disagree
Strongly disagree
n*j
279 200.4 169 203.4 31 70.8 8 10.0 2 4.5 489
106 143.4 183 145.6 52 50.7 6 7.1 3 3.2 350
14 49.6 49 50.3 51 17.5 4 2.5 3 1.1 121
1 5.7 7 5.8 5 2.0 1 0.3 0 0.1 14
2 2.9 0 2.9 3 1.0 1 0.1 1 0.1 7
402
The public’s thoughts on these issues belong to the discipline of sociology. According to sociological theory, any social phenomenon has both uncertainty and statistical certainty, and probability is the mathematical tool to describe the statistical certainty. So, we can quantify the public acceptance of nuclear power through statistical theory. To facilitate the analysis afterwards, the nine issues above are defined as the independent variables, noted by Bene , Beco , Benv , Rope , Rwas , Kkno , Kfam , Tgov , and Texp , the public acceptance of nuclear power is defined as the dependent variable, noted by Anat . Public acceptance is a rather abstract concept, not easily measured, so the public’s attitude to national nuclear power development is used.
3. Choosing assessment performances Assessment performances are the bases of an assessment system; they are the factors that can be used to judge public acceptance. Because not all the nine factors above are statistically associated with public acceptance, they need first to be tested through survey data before becoming assessment performances. Data used here is from a public survey in 2006. A way called “multistage random sampling” was used to poll, sample size 988 (including seven invalid), sampling error 3%, and confidence coefficient 95%. Interviewees distributed in 31 provinces in China, and about 30 people from each province. Correlation analysis (a significance test) is carried out between every independent variable and the dependent variable (Lv, 1998). If there exists statistical association, the independent variable can be introduced as an assessment performance. Examples of the Anat and Bene analysis and the Anat and Rwas analysis are given below. We formed a contingency table using the survey data of Anat and Bene as shown in Table 1. In each cell, the upper is the real frequency nij , the lower is the expected frequency Eij . The expected frequency is defined by Eij = ni* × n*j /n assuming the variables are not correlated, n is the total frequency, ni* ,n*j are marginal sums of frequency distribution. Introducing a statistics: 2 =
(nij − Eij )2 i
j
Eij
The bigger the 2 value, the stronger the relationship between two variables. According to statistical theory, the statistics above follows the distribution of 2 with a freedom degree of 16 (significance level
408 142 20 9 981
0.01) i.e.: 2 =
(nij − Eij )2 i
j
Eij
∼ 20.01 (16)
By calculating the data in Table 1, we get 2 = 210.45, then compare this value with the critical value 20.01 (16) = 32, then we have 2 20.01 (16). So, Bene and Anat are clearly correlated. Then to analyze if Rwas and Anat are correlated or not we formed another contingency table using survey data. After calculating 2 = 32.124, to compare with 20.01 (16), we have 2 ≈ 20.01 (16). So, the Rwas and Anat are not correlated. When doing correlation analysis, if there are many cells with low expected frequencies, the influence of those cells on 2 should be handled carefully. If the difference between expected frequency and real frequency is rather large for most other cells, the influences of those cells could be omitted. If not the case, to avoid mistakes, the conclusion should be drawn by direct comparison of expected frequency and real frequency. Assessing all the nine independent variables and calculating the 2 statistics, we get Table 2. Comparing each 2 value with the critical value 20.01 (16) = 32, we get the variables correlated with the public acceptance of nuclear power, namely Bene , Beco , Benv , Rope , Kkno , Kfam , Tgov , and Texp . These factors can therefore be the assessment performances. The value of Rwas is close to the critical value, no significant correlation is observed, and so Rwas cannot be an assessment performance. This may be explained that most Chinese are lacking the knowledge of nuclear waste, their worries are mainly on the severe nuclear accident. Beside the analysis carried out above through contingency analysis, two other methods, namely the rank sum test and the cumulative frequency test, were also applied, and the results are the same as the above.
Table 2 2 value of each independent variable to the dependent variable Independents
2
Bene Beco Benv Rope Rwas Kkno Kfam Tgov Texp
210.45 92.01 141.68 169.60 32.12 51.17 64.03 35.79 99.47
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C. Liu et al. / Nuclear Engineering and Design 238 (2008) 2834–2838
Fig. 1. Assessment performance layers of public acceptance.
4. Establishing the assessment system
Table 3 Correlation coefficients of the performances
To establish the assessment system of the public acceptance of nuclear power, it is necessary to include the establishment of the assessment performance layers, the calculation of the weighing coefficient of performances and the construction of assessment model.
Performances
Cramer’s V
Bene Beco Benv Rope Kkno Kfam Tgov Texp
0.232 0.153 0.190 0.208 0.114 0.128 0.096 0.16
4.1. Establishment of the assessment performance layers Three layers are defined here: the destination layer, first performance layer and second performance layer. The destination layer is the public acceptance of nuclear power, the first layer is the four factors received from qualitative analysis, the second layer consists of the assessment performances derived above, as shown in Fig. 1. 4.2. Calculation of the weighing coefficient of performances In the assessment system, each performance has a different degree of impact on the public acceptance of nuclear power, and the weighing coefficient is the measure to show this relative importance. Only by having reasonable weighing coefficients, will the assessment result also be reasonable. In various fields, the ways to determine the weighing coefficients for variables, performances or factors can be very different, and generally include both subjective and objective methods (Wang, 1994). Considering that we have the survey data and the opinions are rather different between the experts and the general public (Slovic and Fischoff, 1980), we define all the weighing coefficients of the first layer as 1, and calculate the weighing coefficients of the second layer via the survey data. Firstly, we calculate the correlation coefficients between the independent variables (assessment performances) and the dependent variable; the bigger the coefficient is, the stronger the relationship between the independent variable and the dependent variable. Then, we make appropriate adjustment to obtain the weighing coefficients. Cramer’s V coefficient is used here:
V=
2 , n × min[(r − 1), (c − 1)]
r and c are the degrees of freedom of the independent and the dependent, respectively.
After calculation, we get Table 3. To do unitary transformation to the correlation coefficient above, we get the unitary coefficient, as shown in Table 4. Considering that the weighing coefficient is not necessary to be strictly precise, we do approximate estimation to get the weighing coefficient, shown also in Table 4. 4.3. Construction of the assessment model To get a numeric value to describe the public acceptance of nuclear power quantitatively, we introduce a variable Z, whose maximum value is 100. This variable is called the Index of the Public Acceptance of Nuclear Power, and it is defined by equation below, and this equation is also the assessment model. Z=
8 5
wj
j=1
vi × aij
i=1
aij is the percent of people with specific opinion among the total,
5
a = 100; vi is the value corresponding to specific to any j, i=1 ij attitudes, separately taking 1, 0.8, 0.6, 0.4, 0.2; wj is the weighing coefficient of performances in the second layer (see Table 4), 8 w = 1. j=1 j 5. Application of the assessment system During the past, when talking of the public acceptance of nuclear power, the public’s answers to “support nuclear power development or not?” or “support utilizing nuclear power to produce electricity or not”, or “support the building of a new nuclear power
Table 4 Unitary coefficients and weighing coefficients of the assessment performances Performances
Bene
Beco
Benv
Rope
Kkno
Kfam
Tgov
Texp
Unitary coefficients Weighing coefficients
0.1811 0.2
0.1194 0.1
0.1483 0.15
0.1624 0.15
0.0890 0.1
0.0999 0.1
0.0749 0.1
0.1249 0.1
C. Liu et al. / Nuclear Engineering and Design 238 (2008) 2834–2838 Table 6 Quantitative descriptions of factors affecting public acceptance
Table 5 aij of year 2006 vi
Bene
Beco
Benv
Rope
Kkno
Kfam
Tgov
Texp
Factors
1 0.8 0.6 0.4 0.2
49.9 35.7 12.3 1.4 0.7
20.8 27.3 38.2 10.2 3.5
24.2 33.3 29.9 10.1 2.4
8.2 50.5 33.7 6.4 1.2
2.3 9.6 24.3 29.1 34.6
0.5 7.0 64.1 23.5 4.9
27.2 35.9 15.6 15.7 5.5
28.3 45.8 17.9 5.9 1.9
Bene Beco Benv Rope Kkno Kfam Tgov Texp
plant or not?” are often quoted, but the answer to one question brings confusion, and even different conclusions (Rosa and Dunlap, 1994; Blee, 2001). This assessment system of public acceptance of nuclear power uses a comprehensive evaluation method based on a series of questions, and is more objective and therefore accurate. Furthermore, the assessment system can highlight the main factors and their relative impacts, providing good guidance on ways to improve the public acceptance of nuclear power. 5.1. Indices of the public acceptance of nuclear power To obtain the index of public acceptance for the year 2006 as an example, we firstly got the aij values from the survey of year 2006, shown in Table 5. Putting the aij into the assessment model, we get: Z2006 =
8 5
wj
j=1
2837
vi × aij
i=1
= 0.2 × (1 × 49.9 + 0.8 × 35.7 + 0.6 × 12.3 + 0.4 × 1.4 + 0.2 × 0.7)
vi × aij
86.54 70.34 73.3 71.62 43.12 54.94 77.06 78.42
influential in the public acceptance of nuclear power. These factors should be emphasized in communications with the general public. 5.3. Status of factors affecting public acceptance
5
In the assessment model, the value of i=1 vi × aij highlights the quantitative description of each of factors affecting public acceptance, as shown in Table 6. We can see that at present in China, the public’s opinions on all the benefits of nuclear power, the trust in government and experts and their judgment on risks of the existing operating nuclear power plant are quite favorable, but the familiarity and knowledge of nuclear power is relatively poor. We should therefore give much more attention to extending the knowledge about nuclear power to the general public, to improve their familiarity. 6. Conclusions
+0.1 × (1 × 20.8 + 0.8 × 27.3 + 0.6 × 38.2 + 0.4 × 10.2 + 0.2 × 3.5) +0.15 × (1 × 24.2 + 0.8 × 33.3 + 0.6 × 29.9 + 0.4 × 10.1 + 0.2 × 2.4) +0.15 × (1 × 8.2 + 0.8 × 50.5 + 0.6 × 33.7 + 0.4 × 6.4 + 0.2 × 1.2) +0.1 × (1 × 2.3 + 0.8 × 9.6 + 0.6 × 24.3 + 0.4 × 29.1 + 0.2 × 34.6) +0.1 × (1 × 0.5 + 0.8 × 7 + 0.6 × 64.1 + 0.4 × 23.5 + 0.2 × 4.9) +0.1 × (1 × 27.2 + 0.8 × 35.9 + 0.6 × 15.6 + 0.4 × 15.7 + 0.2 × 5.5) +0.1 × (1 × 28.3 + 0.8 × 45.8 + 0.6 × 17.9 + 0.4 × 5.9 + 0.2 × 1.9) = 71.43
This value is the index of the public acceptance of nuclear power for year 2006. In the same way, we can get the indices for the years 2002–2005; they are Z2002 = 72.96, Z2003 = 68.92, Z2004 = 70.74, Z2005 = 69.34. The trend is shown in Fig. 2. We can see that public acceptance of nuclear power in China is quite good and being maintained at a relatively high level. 5.2. Important factors for communicating with the public During the process of assessment, from unitary coefficients in Table 4, we discover that the opinions on the benefit to power supply, the safety judgment on nuclear power plants, the trust in experts and benefit to environment protection each have bigger coefficients than the others, meaning that these factors are more
Based on the survey and statistical theory, the assessment system was established to quantitatively describe the public acceptance of nuclear power that was previously assumed, having only a qualitative character. This paper proposes a tool to describe the degree of the public opinion more comprehensively while simply. For “comprehensively”, a series of questions are used, instead of only one or two questions like “do you support the nuclear power?” For “simply”, an assessment system is constructed to draw an index. Five indices of the public acceptance of nuclear power were calculated, to describe the present situation and trends of public acceptance of nuclear power in China. This provides useful references for governmental agencies, the nuclear industry and investors for when they must make important decisions. During the process of quantitative assessment, the main factors affecting the public acceptance of nuclear power in China were derived, pertinently providing suggestions to improve the public’s attitude. The objective of this paper is to highlight the position of the general public in China, on a national basis. The methodology could also be applied to another country, or to local people in specific areas, to allow comparisons between different countries or regions. Alternative assessment systems could also be developed. Moreover, the methodology used in this paper could also be used to study the public acceptance of gene-modified food, etc. References
Fig. 2. Indices of public acceptance over 5 years.
Blee, D., 2001. Lessons learned? Selected public acceptance case studies since Three Mile Island. Nuclear Energy 1 (40). Grimston, M., Beck, P., 2002. Double or Quit? The Global Future of Civil Nuclear Energy. Royal Institute of International Affairs, London. Lee, K.J., Lee, Y.E., 1999. Public Acceptance of Nuclear Energy in Korea. In: Proceedings of the Fourth Nuclear Energy Symposium Energy Future in Asia/Pacific Region, Taiwan, China. Lv, S., 1998. Social Statistics. Beijing University Press, Beijing. Rosa, E., Dunlap, R., 1994. Poll trends: nuclear power—three decades of public opinion. Public Opinion Quarterly 58, 295–324.
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L. Sjoberg, 2003. Risk communication between experts and the public: perceptions and intentions. SSE/EFI Working Paper Series in Business Administration. Shi, Z., Zhang, Z., Xue, L., 2002. Study on risk acceptance of nuclear power. Nuclear Science and Engineering 22 (3).
Slovic, P., Fischoff, B., 1980. Facts and Fears—Understanding Perceived Risk. Societal Risk Assessment: How Safe is Safe Enough. Plenum Press, New York, p. 181–214. Wang, Q., 1994. Guidance of Statistic Performance. Northeast Financial University Press, Liaoning.
Contents lists available at ScienceDirect
Nuclear Engineering and Design journal homepage: www.elsevier.com/locate/nucengdes
Establishing an objective system for the assessment of public acceptance of nuclear power in China Changxin Liu a,∗ , Zuoyi Zhang a , Steve Kidd b a b
INET, Tsinghua University, Beijing, China World Nuclear Association, London, UK
a r t i c l e
i n f o
Article history: Received 9 August 2007 Received in revised form 31 March 2008 Accepted 1 April 2008
a b s t r a c t This paper proposes a way of quantifying the public acceptance of nuclear power. Based on initial qualitative analysis, sampling surveys and statistical theory, an assessment system (including assessment performances, weighing coefficients and an assessment model) is established to quantify the status of public acceptance of nuclear power in China. Using this system, quantitative indicators are derived, their trends over 5 years are described, the main factors influencing the public acceptance of nuclear power in China are derived and, accordingly, necessary improvements are suggested. Utilizing this system over the longer term could provide useful reference data for decision makers. © 2008 Elsevier B.V. All rights reserved.
1. Introduction
2. Qualitative analysis to establish the assessment system
The “public acceptance of nuclear power” is simply the general public’s attitude towards nuclear development; its importance has drawn substantial attention in recent years. It derives from public perceptions of nuclear technology risk and also from complex social, cultural and historical factors. Many studies have been completed on this topic. Based on risk perception and decision theory, European and American experts have analyzed relevant factors affecting public acceptance of nuclear power, such as demographic differences, the character of nuclear technology, trust, values, policy aspects, media exposure, etc. (Grimston and Beck, 2002; Sjoberg, 2003). Korean experts found, through survey analysis, that public acceptance of nuclear power varies with sex, education and information channels (Lee and Lee, 1999). Chinese experts pointed out that the variables such as the perceived benefit, risk, dread and trust were important factors affecting the public’s attitude (Shi et al., 2002). All the studies help to understand the public acceptance of nuclear power, and to provide suggestions to improve this. Nevertheless, what has been done is mainly qualitative and cannot further describe the status of public acceptance of nuclear power in a quantitative way. Using survey data and statistical theory, this paper aims to establish an assessment system for the public acceptance of nuclear power to derive a numeric indicator summarizing it. This would be valuable to decision-makers.
With reference to the previous studies, it would seem that the public acceptance of nuclear power is the result of judging the relative benefits and risks by the public, which are very subjective in character. Objective factors like age, sex, education and technology characteristics may have impacts on the public acceptance of nuclear power, but cannot be used as performance criteria to assess the public’s attitude. We therefore use four subjective items to assess public acceptance, as follows:
∗ Corresponding author. Tel.: +86 10 68555597; fax: +86 10 68527188. E-mail address: [email protected] (C. Liu). 0029-5493/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.nucengdes.2008.04.001
(a) (b) (c) (d)
opinion on the benefits of nuclear power (electricity); judgment of the risks; knowledge of nuclear power; trust in the parties concerned.
The four items are relatively abstract and need to be sub-divided into nine separate issues to be measured, as follows: • Opinions on benefits: (1) benefit to the national power supply; (2) benefit to lower electricity prices; (3) benefit to environmental protection. • Judgment on risks: (4) judgment on operational risks of nuclear power plants; (5) judgment on the risks of nuclear waste (nuclear proliferation is not yet included here). • Knowledge of nuclear power: (6) how much nuclear knowledge the public has; (7) self-assessed familiarity with nuclear power. • Trust in the parties concerned: (8) trust in governmental agencies; (9) trust in nuclear experts.
C. Liu et al. / Nuclear Engineering and Design 238 (2008) 2834–2838
2835
Table 1 Contingency table of Anat and Bene Bene
Strongly support Support No opinion
Anat
Oppose Strongly oppose ni*
Strongly agree
Agree
No opinion
Disagree
Strongly disagree
n*j
279 200.4 169 203.4 31 70.8 8 10.0 2 4.5 489
106 143.4 183 145.6 52 50.7 6 7.1 3 3.2 350
14 49.6 49 50.3 51 17.5 4 2.5 3 1.1 121
1 5.7 7 5.8 5 2.0 1 0.3 0 0.1 14
2 2.9 0 2.9 3 1.0 1 0.1 1 0.1 7
402
The public’s thoughts on these issues belong to the discipline of sociology. According to sociological theory, any social phenomenon has both uncertainty and statistical certainty, and probability is the mathematical tool to describe the statistical certainty. So, we can quantify the public acceptance of nuclear power through statistical theory. To facilitate the analysis afterwards, the nine issues above are defined as the independent variables, noted by Bene , Beco , Benv , Rope , Rwas , Kkno , Kfam , Tgov , and Texp , the public acceptance of nuclear power is defined as the dependent variable, noted by Anat . Public acceptance is a rather abstract concept, not easily measured, so the public’s attitude to national nuclear power development is used.
3. Choosing assessment performances Assessment performances are the bases of an assessment system; they are the factors that can be used to judge public acceptance. Because not all the nine factors above are statistically associated with public acceptance, they need first to be tested through survey data before becoming assessment performances. Data used here is from a public survey in 2006. A way called “multistage random sampling” was used to poll, sample size 988 (including seven invalid), sampling error 3%, and confidence coefficient 95%. Interviewees distributed in 31 provinces in China, and about 30 people from each province. Correlation analysis (a significance test) is carried out between every independent variable and the dependent variable (Lv, 1998). If there exists statistical association, the independent variable can be introduced as an assessment performance. Examples of the Anat and Bene analysis and the Anat and Rwas analysis are given below. We formed a contingency table using the survey data of Anat and Bene as shown in Table 1. In each cell, the upper is the real frequency nij , the lower is the expected frequency Eij . The expected frequency is defined by Eij = ni* × n*j /n assuming the variables are not correlated, n is the total frequency, ni* ,n*j are marginal sums of frequency distribution. Introducing a statistics: 2 =
(nij − Eij )2 i
j
Eij
The bigger the 2 value, the stronger the relationship between two variables. According to statistical theory, the statistics above follows the distribution of 2 with a freedom degree of 16 (significance level
408 142 20 9 981
0.01) i.e.: 2 =
(nij − Eij )2 i
j
Eij
∼ 20.01 (16)
By calculating the data in Table 1, we get 2 = 210.45, then compare this value with the critical value 20.01 (16) = 32, then we have 2 20.01 (16). So, Bene and Anat are clearly correlated. Then to analyze if Rwas and Anat are correlated or not we formed another contingency table using survey data. After calculating 2 = 32.124, to compare with 20.01 (16), we have 2 ≈ 20.01 (16). So, the Rwas and Anat are not correlated. When doing correlation analysis, if there are many cells with low expected frequencies, the influence of those cells on 2 should be handled carefully. If the difference between expected frequency and real frequency is rather large for most other cells, the influences of those cells could be omitted. If not the case, to avoid mistakes, the conclusion should be drawn by direct comparison of expected frequency and real frequency. Assessing all the nine independent variables and calculating the 2 statistics, we get Table 2. Comparing each 2 value with the critical value 20.01 (16) = 32, we get the variables correlated with the public acceptance of nuclear power, namely Bene , Beco , Benv , Rope , Kkno , Kfam , Tgov , and Texp . These factors can therefore be the assessment performances. The value of Rwas is close to the critical value, no significant correlation is observed, and so Rwas cannot be an assessment performance. This may be explained that most Chinese are lacking the knowledge of nuclear waste, their worries are mainly on the severe nuclear accident. Beside the analysis carried out above through contingency analysis, two other methods, namely the rank sum test and the cumulative frequency test, were also applied, and the results are the same as the above.
Table 2 2 value of each independent variable to the dependent variable Independents
2
Bene Beco Benv Rope Rwas Kkno Kfam Tgov Texp
210.45 92.01 141.68 169.60 32.12 51.17 64.03 35.79 99.47
2836
C. Liu et al. / Nuclear Engineering and Design 238 (2008) 2834–2838
Fig. 1. Assessment performance layers of public acceptance.
4. Establishing the assessment system
Table 3 Correlation coefficients of the performances
To establish the assessment system of the public acceptance of nuclear power, it is necessary to include the establishment of the assessment performance layers, the calculation of the weighing coefficient of performances and the construction of assessment model.
Performances
Cramer’s V
Bene Beco Benv Rope Kkno Kfam Tgov Texp
0.232 0.153 0.190 0.208 0.114 0.128 0.096 0.16
4.1. Establishment of the assessment performance layers Three layers are defined here: the destination layer, first performance layer and second performance layer. The destination layer is the public acceptance of nuclear power, the first layer is the four factors received from qualitative analysis, the second layer consists of the assessment performances derived above, as shown in Fig. 1. 4.2. Calculation of the weighing coefficient of performances In the assessment system, each performance has a different degree of impact on the public acceptance of nuclear power, and the weighing coefficient is the measure to show this relative importance. Only by having reasonable weighing coefficients, will the assessment result also be reasonable. In various fields, the ways to determine the weighing coefficients for variables, performances or factors can be very different, and generally include both subjective and objective methods (Wang, 1994). Considering that we have the survey data and the opinions are rather different between the experts and the general public (Slovic and Fischoff, 1980), we define all the weighing coefficients of the first layer as 1, and calculate the weighing coefficients of the second layer via the survey data. Firstly, we calculate the correlation coefficients between the independent variables (assessment performances) and the dependent variable; the bigger the coefficient is, the stronger the relationship between the independent variable and the dependent variable. Then, we make appropriate adjustment to obtain the weighing coefficients. Cramer’s V coefficient is used here:
V=
2 , n × min[(r − 1), (c − 1)]
r and c are the degrees of freedom of the independent and the dependent, respectively.
After calculation, we get Table 3. To do unitary transformation to the correlation coefficient above, we get the unitary coefficient, as shown in Table 4. Considering that the weighing coefficient is not necessary to be strictly precise, we do approximate estimation to get the weighing coefficient, shown also in Table 4. 4.3. Construction of the assessment model To get a numeric value to describe the public acceptance of nuclear power quantitatively, we introduce a variable Z, whose maximum value is 100. This variable is called the Index of the Public Acceptance of Nuclear Power, and it is defined by equation below, and this equation is also the assessment model. Z=
8 5
wj
j=1
vi × aij
i=1
aij is the percent of people with specific opinion among the total,
5
a = 100; vi is the value corresponding to specific to any j, i=1 ij attitudes, separately taking 1, 0.8, 0.6, 0.4, 0.2; wj is the weighing coefficient of performances in the second layer (see Table 4), 8 w = 1. j=1 j 5. Application of the assessment system During the past, when talking of the public acceptance of nuclear power, the public’s answers to “support nuclear power development or not?” or “support utilizing nuclear power to produce electricity or not”, or “support the building of a new nuclear power
Table 4 Unitary coefficients and weighing coefficients of the assessment performances Performances
Bene
Beco
Benv
Rope
Kkno
Kfam
Tgov
Texp
Unitary coefficients Weighing coefficients
0.1811 0.2
0.1194 0.1
0.1483 0.15
0.1624 0.15
0.0890 0.1
0.0999 0.1
0.0749 0.1
0.1249 0.1
C. Liu et al. / Nuclear Engineering and Design 238 (2008) 2834–2838 Table 6 Quantitative descriptions of factors affecting public acceptance
Table 5 aij of year 2006 vi
Bene
Beco
Benv
Rope
Kkno
Kfam
Tgov
Texp
Factors
1 0.8 0.6 0.4 0.2
49.9 35.7 12.3 1.4 0.7
20.8 27.3 38.2 10.2 3.5
24.2 33.3 29.9 10.1 2.4
8.2 50.5 33.7 6.4 1.2
2.3 9.6 24.3 29.1 34.6
0.5 7.0 64.1 23.5 4.9
27.2 35.9 15.6 15.7 5.5
28.3 45.8 17.9 5.9 1.9
Bene Beco Benv Rope Kkno Kfam Tgov Texp
plant or not?” are often quoted, but the answer to one question brings confusion, and even different conclusions (Rosa and Dunlap, 1994; Blee, 2001). This assessment system of public acceptance of nuclear power uses a comprehensive evaluation method based on a series of questions, and is more objective and therefore accurate. Furthermore, the assessment system can highlight the main factors and their relative impacts, providing good guidance on ways to improve the public acceptance of nuclear power. 5.1. Indices of the public acceptance of nuclear power To obtain the index of public acceptance for the year 2006 as an example, we firstly got the aij values from the survey of year 2006, shown in Table 5. Putting the aij into the assessment model, we get: Z2006 =
8 5
wj
j=1
2837
vi × aij
i=1
= 0.2 × (1 × 49.9 + 0.8 × 35.7 + 0.6 × 12.3 + 0.4 × 1.4 + 0.2 × 0.7)
vi × aij
86.54 70.34 73.3 71.62 43.12 54.94 77.06 78.42
influential in the public acceptance of nuclear power. These factors should be emphasized in communications with the general public. 5.3. Status of factors affecting public acceptance
5
In the assessment model, the value of i=1 vi × aij highlights the quantitative description of each of factors affecting public acceptance, as shown in Table 6. We can see that at present in China, the public’s opinions on all the benefits of nuclear power, the trust in government and experts and their judgment on risks of the existing operating nuclear power plant are quite favorable, but the familiarity and knowledge of nuclear power is relatively poor. We should therefore give much more attention to extending the knowledge about nuclear power to the general public, to improve their familiarity. 6. Conclusions
+0.1 × (1 × 20.8 + 0.8 × 27.3 + 0.6 × 38.2 + 0.4 × 10.2 + 0.2 × 3.5) +0.15 × (1 × 24.2 + 0.8 × 33.3 + 0.6 × 29.9 + 0.4 × 10.1 + 0.2 × 2.4) +0.15 × (1 × 8.2 + 0.8 × 50.5 + 0.6 × 33.7 + 0.4 × 6.4 + 0.2 × 1.2) +0.1 × (1 × 2.3 + 0.8 × 9.6 + 0.6 × 24.3 + 0.4 × 29.1 + 0.2 × 34.6) +0.1 × (1 × 0.5 + 0.8 × 7 + 0.6 × 64.1 + 0.4 × 23.5 + 0.2 × 4.9) +0.1 × (1 × 27.2 + 0.8 × 35.9 + 0.6 × 15.6 + 0.4 × 15.7 + 0.2 × 5.5) +0.1 × (1 × 28.3 + 0.8 × 45.8 + 0.6 × 17.9 + 0.4 × 5.9 + 0.2 × 1.9) = 71.43
This value is the index of the public acceptance of nuclear power for year 2006. In the same way, we can get the indices for the years 2002–2005; they are Z2002 = 72.96, Z2003 = 68.92, Z2004 = 70.74, Z2005 = 69.34. The trend is shown in Fig. 2. We can see that public acceptance of nuclear power in China is quite good and being maintained at a relatively high level. 5.2. Important factors for communicating with the public During the process of assessment, from unitary coefficients in Table 4, we discover that the opinions on the benefit to power supply, the safety judgment on nuclear power plants, the trust in experts and benefit to environment protection each have bigger coefficients than the others, meaning that these factors are more
Based on the survey and statistical theory, the assessment system was established to quantitatively describe the public acceptance of nuclear power that was previously assumed, having only a qualitative character. This paper proposes a tool to describe the degree of the public opinion more comprehensively while simply. For “comprehensively”, a series of questions are used, instead of only one or two questions like “do you support the nuclear power?” For “simply”, an assessment system is constructed to draw an index. Five indices of the public acceptance of nuclear power were calculated, to describe the present situation and trends of public acceptance of nuclear power in China. This provides useful references for governmental agencies, the nuclear industry and investors for when they must make important decisions. During the process of quantitative assessment, the main factors affecting the public acceptance of nuclear power in China were derived, pertinently providing suggestions to improve the public’s attitude. The objective of this paper is to highlight the position of the general public in China, on a national basis. The methodology could also be applied to another country, or to local people in specific areas, to allow comparisons between different countries or regions. Alternative assessment systems could also be developed. Moreover, the methodology used in this paper could also be used to study the public acceptance of gene-modified food, etc. References
Fig. 2. Indices of public acceptance over 5 years.
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