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Frontiers of Environmental Science & Engineering

ISSN 2095-2201

ISSN 2095-221X(Online)

CN 10-1013/X

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2018 Impact Factor: 3.883

Front Envir Sci Eng    2012, Vol. 6 Issue (6) : 860-868    https://doi.org/10.1007/s11783-012-0467-y
RESEARCH ARTICLE
The influencing factors of the WTP for the risk reduction of chemical industry accidents in China
Lei HUANG, Zhijuan SHAO, Weiliang BAO, Bailing DUAN, Jun BI, Zengwei YUAN()
State Key Laboratory of Pollution Control & Resource Reuse, School of Environment, Nanjing University, Nanjing 210093, China
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Abstract

To explore the factors that influence respondents’ willingness to pay (WTP) for the risk reduction of chemical industry accidents, a questionnaire survey combined with contingent valuation and psychometric paradigm methods were conducted in the city of Yancheng, Jiangsu Province, China. Both traditional socioeconomic variables and perceived characteristics of the hazards were considered in this study, and a Tobit model was used to find the factors influencing WTP under three risk reduction scenarios. The results showed that three demographic characteristics, age, gender, and income, significantly affected the WTP for chemical risk reduction. In addition, three extracted public risk perception factors, effect, knowledge, and trust, also strongly affected the WTP. The mean WTP value increased as the magnitude of the risk reduction increased. The number of factors influencing the WTP decreased as the reduction level improved, and only the effect factor had a significant influence on the WTP for a higher level (80%) of risk reduction. The cost for chemical safety management of Yancheng was calculated, and the optimized risk reduction level was determined. These findings can assist governments and policy makers to formulate suitable strategies for risk control, to reach target groups of people to develop effective communication, and to provide specific references for the best investment for the security of local residents.

Keywords risk perception      willingness to pay      contingent valuation method      risk management school of the environment     
Corresponding Author(s): YUAN Zengwei,Email:yuanzw@nju.edu.cn   
Issue Date: 01 December 2012
 Cite this article:   
Lei HUANG,Zhijuan SHAO,Weiliang BAO, et al. The influencing factors of the WTP for the risk reduction of chemical industry accidents in China[J]. Front Envir Sci Eng, 2012, 6(6): 860-868.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-012-0467-y
https://academic.hep.com.cn/fese/EN/Y2012/V6/I6/860
variablessymboldefinition and unit
risk reduction classification factorrisk 120% risk reduction, the control variable to the classification variables
risk 250% risk reduction
risk 380% risk reduction
gendergendermale=1, female=2
educationeducationcontinuous variable, unit: year
ageagecontinuous variable, unit: year
incomeincomecontinuous variable, in 5 ranks, unit: ten thousand CNY
risk perception factor I, effectfactor Icontinuous variable
risk perception factor II, knowledge factor IIcontinuous variable
risk perception factor III, trustfactor IIIcontinuous variable,
Tab.1  Definition of Tobit regression variables
demographic characteristicpercent/%scenario 1(20% risk reduction)scenario 2(50% risk reduction)scenario 3(80% risk reduction)
mean/CNYwilling to pay/%mean /CNYwilling to pay /%mean /CNYwilling to pay /%
gendermale46.4062.3161.7079.2962.70103.7362.70
female53.6057.9365.90126.2567.40162.8567.80
educationprimary school and level below4.101.0025.001.2525.001.2525.00
middle school40.3018.0856.0025.7656.0039.2256.00
high school29.0067.0656.50122.5557.40172.9657.40
college23.4068.2469.80124.9171.70153.7972.10
graduate3.2067.7281.8091.3681.80138.6481.80
age18-2917.0102.3568.60233.5770.80266.1771.10
30-3927.5025.3159.1041.7759.1051.3759.10
40-4932.7026.6555.6034.0755.6041.2055.60
50-5920.407.3642.9012.1442.9014.2842.90
Over 602.400.7521.801.2521.801.2521.80
monthly income/CNYBelow1 thousand14.7026.9356.5839.1658.6554.2358.65
1-2 thousands26.7042.8363.30126.5764.80120.9065.60
2-4 thousands28.4055.8171.30102.5371.30147.0371.30
4-8 thousands16.50101.7762.30148.4562.30241.9562.30
Over 8 thousands13.7087.5163.49106.4065.04128.7365.04
Tab.2  The main statistical characterization of WTP
Fig.1  Average score of public risk perception to chemical accident
correlationbenefittrustexposureknowledgefamiliaritydreadacceptability instantcontrollability
benefit1.00
trust-0.021.00
exposure-0.380.15***1.00
knowledge-0.190.10**0.12**1.00
familiarity-0.02-0.04-0.010.201.00
dread-0.300.09**0.330.19-0.09**1.00
acceptability-0.12**0.040.190.10**-0.13**0.371.00
instant0.27-0.03-0.220.07*0.16-0.13**-0.181.00
controllability-0.09**0.08*0.20-0.10**-0.060.08**0.22-0.211.00
Tab.3  Results of the correlation analysis of nine risk perception variables
factor Ifactor IIfactor III
benefit0.640.220.18
public exposure0.690.120.24
dread0.670.160.39
acceptability0.57-0.150.49
instant0.510.400.27
controllability0.400.39-0.26
knowledge 0.250.730.15
familiarity-0.150.630.22
trust0.020.180.59
Tab.4  Results of the maximum likelihood factor analysis
variableCoeff.
constant-202.20
risk 2-102.40
risk 3-38.20
gender81.00
age-9.30
education46.50
income21.70
Factor I43.00
Factor II42.00
Factor III60.20
log likelihood-737.27
pseudo R20.15
n458
Tab.5  Results of the Tobit model analysis
WTPscenario 120% reductionscenario 250% reductionscenario 380% reduction
factor I37.38***62.01**82.06**
factor II9.4454.0060.18
factor III30.19**49.1383.50
gender18.5698.44129.92
age-4.70***-10.67**-14.57
education20.20**47.0753.25
income17.45**18.0630.74
constant-77.83-224.71-267.47
log likelihood-630.32-743.49-754.78
pseudo R20.180.150.15
Tab.6  Results of the Tobit regression for three levels of risk reduction
scenario 120% reductionscenario 250% reductionscenario 380% reduction
mean9.3616.8019.78
median0.791.581.58
minimum bid000
maximum bid315.21945.661576.09
standard deviation200.12495.31644.1
95% confidence interval6.47-12.249.66-23.9310.45-29.01
n458458458
Tab.7  Descriptive statistics of the WTP for chemical risk reduction (US$, each person/month)
Fig.2  The relationship between the WTP and the magnitude of risk reduction
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