To what extent does air pollution affect happiness? The case of the Jinchuan mining area, China

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    ralllu, is considered. The main nding is that both types of perceived risk negatively ande's happiness, although in absolute terms, the total perceived risk effect is less than

    are family size, age, proximity to the pollution source,work environment and current health condition. Perceivedrisk due to intensity of exposure is inuenced by environmental knowledge and proximity to the pollutionsource; perceived risk of hazard by ability, environmental knowledge, family size, family health experienceand proximity to the pollution source. Environmental knowledge is found to be a function of age, ability andwork environment. On the basis of the ndings, we conclude that reducing air pollution is an important policy

    nd Castanas, 2008).measure well-being. Itoverall quality of lifeably with life satisfac-

    Ecological Economics 99 (2014) 8899

    Contents lists available at ScienceDirect

    Ecological E

    j ourna l homepage: www.e lseduction (Unsworth and Ormrod, 1982). Generally speaking, poor local airconditions tend to make people less happy. This also applies to the

    tion (Haybron, 2007). In 1967, psychologist Warner (Wilson, 1967)introduced the notion of happiness and presented a broad review of its

    meaning. Since Wilson's review, happiness has been widely studied byincreasingly attracted public and private attention. The main reason isthat polluted air not only affects people's health (Brunekreef andHolgate, 2002; Peters et al., 2001), but also has detrimental effects on res-idential property values (Foell and Green, 1990), and on agricultural pro-

    like eye irritation (Bernstein et al., 2004; Kampa aSince the 1970s, happiness has been used to

    denotes an individual's evaluation of her or his(Veenhoven, 1999). The term is used interchangegrowth. However, the rapid growth has also resulted in unprecedentedincreases in energy consumption and emissions of air pollutants withwide ranging global, national and local effects (Brunekreef and Holgate,2002; Hao et al., 2007; Wei, 2008).

    This paper focuses on local impacts of air pollution a topic that has

    serious environmental problems, especially air pollution. The mainpollutants of Jinchuan's air include suspended particles, sulfur dioxide,chlorine gas and carbon dioxide (Huang et al., 2009; Li and Zhao, 2004;Wei, 2008; Xiao, 2003). The rst three pollutants contribute to illnessessuch as cancer, asthma, and bronchitis, and less serious health problems We gratefully acknowledge the comments and sugreviewers and W.Chen that have helped us a lot to imppaper. The usual disclaimer applies. Corresponding author.

    E-mail addresses: (Z. Li), (H(J. Xue).

    0921-8009/$ see front matter. Crown Copyright 2014, China has become thengine of global economic

    largest nickel resources in China. Mining and smelting industries domi-nate the local economy and substantially contribute to the economicdevelopment of the city. However, the two industries also produceAs a result of its rapid economic developmesecond largest economy in the world and an eJEL Classication:D60Q53

    Keywords:HappinessMoney measures of well-beingPerceived riskObjective riskEnvironmental knowledgeStructural equation modelMiningAir pollutionChina

    1. Introductionmeasure to ameliorate happiness. As environmental knowledge is an important determinant of perceived risk,reduction policies should be accompanied by disclosure of the state of air quality.

    Crown Copyright 2014 Published by Elsevier B.V. All rights reserved.

    Jinchuan mining area, Gansu province, China. The Jinchuan area has theAvailable online 5 February 2014the (positive) effect of ability, measured by income and education. Other important determinants of happinessAccepted 27 December 2013imity to the pollution sourcesignicantly inuence peoplAnalysis

    To what extent does air pollution affect haJinchuan mining area, China

    Zhengtao Li a,, Henk Folmer a,b, Jianhong Xue b

    a Department of Economic Geography, Faculty of Spatial Sciences, University of Groningen, Lanb Department of Agricultural Economics, College of Economics and Management, Northwest A&

    a b s t r a c ta r t i c l e i n f o

    Article history:Received 23 February 2013

    This paper presents a structu(i) intensity of exposure to pogestions by three anonymousrove previous versions of the

    . Folmer),

    Published by Elsevier B.V. All rightsiness? The case of the

    en 1, 9747 AD Groningen, The Netherlandsniversity, 3 Taicheng Road, Yangling, Shaanxi 712100, China

    equation model of happiness, as inuenced by inter alia perceived risk due toted air, and (ii) hazard of pollutants. In addition, objective riskmeasured as prox-


    v ie r .com/ locate /eco leconpsychologists (Argyle, 1987; Diener et al., 1999; Haybron, 2007; Sarasonet al., 1990; Schkade and Kahneman, 1998). This literature deals withindividual valuation and subjective views of the quality of life (Dieneret al., 1999).

    In economics, the concept of happiness was introduced by Easterlin(1974) who analyzed the US data and found that people with higher


  • 89Z. Li et al. / Ecological Economics 99 (2014) 8899incomes are more likely to report being happy than people with lowerincomes. He, and subsequent authors such as Welsch (2002, 2006,2007, 2009), have argued that individual well-being can be measureddirectly with happiness data. Operationally, happiness is measured bythe answers given by people to questions such as On a scale from oneto ten, where one is worst conceivable and ten is best conceivable,how satised are you nowadays with your life? (Van Praag andBaarsma, 2005).

    The traditional economic yardsticks to the measurement of well-being, such as equivalent and compensating variation, are money mea-sures derived from the notion that individuals maximize utility under abudget constraint (Suzanne and Lynne, 2005; Varian, 1992). Despitetheirwidespread use, there is consensus among a growing group of econ-omists that the traditional money measures of well-being are subject tofallacies (Gowdy, 2004; Rehdanz and Maddison, 2008; Welsch, 2009).Specically, they ignore the fact that individuals are not merely actingto maximize utility under an income constraint (Folmer, 2009 and therefernces therein); nor do they fully cover the relevant dimensions ofwell-being (Folmer, 2009; Sumner, 2006). In particular, they fail toaccount for psychological and sociological aspects (Ferrer-i-Carbonelland Gowdy, 2007; Folmer and Johansson-Stenman, 2011; Kahnemanand Sugden, 2005; McGillivray, 2007; Welsch, 2006, 2007, 2009). To llthe gap between the narrowly denedmoneymeasures and amore com-prehensive notion of well-being, the notion of happiness has been intro-duced into the environmental economics literature (see inter aliaWelsch (2002, 2006, 2007, 2009)).

    The happiness economics literature does not purport to replacemoney measures of welfare but, rather, to complement them withbroader notions of well-being (Graham, 2005). Measurements of happi-ness are based on surveys in which respondents are invited to valuetheir welfare in terms of its various dimensions including income, familyrelationships, own and family health condition, public goods such as thequality of schools, health care, safety and accessibility, and environmentalquality. Happiness analysis thus relies on amore comprehensive notion ofwell-being than conventional economic money measures. Consequently,it allows estimating and comparing the importance and weights of thevarious dimensions of well-being, rather than the mere tradeoff betweenenvironmental quality and income, as is typical in conventional valuationstudies. It thus directly highlights the role of non-income factors thataffect well-being (Ferrer-i-Carbonell and Gowdy, 2007; Luechinger andRaschky, 2009; Van Praag and Baarsma, 2005).

    Although the happiness approach is relatively new in environmentaleconomics, a number of studies have been conducted to explain thedifference in people's happiness as a function of ambient environ-mental quality. Van Praag and Baarsma (2005) conducted a postal surveyamong the population livingwithin a radius of 50 km aroundAmsterdamSchiphol Airport to analyze how people's happiness was inuenced byaircraft noise. They found that noise has a signicant and negative inu-ence on happiness. Rehdanz and Maddison (2005) analyzed a panel ofsixty-seven countries in a bid to explain differences in happiness as aresult of temperature and precipitation. Their study indicates that climatevariables have a highly signicant effect on country-wide happiness.Brereton et al. (2008) analyzed Irish data disaggregated at the individualand local levels to show that amenities such as climate, environmentaland urban conditions, have a direct impact on happiness. Luechingerand Raschky (2009) applied the happiness approach to estimate andmonetize utility losses caused by oods in seventeen OECD countriesbetween 1973 and 2004. Their results show a negative and signicantimpact of oods on happiness.

    There is also a literature on the relationship between air pollution andhappiness. Levinson (2012) used the General Social Survey (GSS), whichasked respondents in various U.S. locations how happy theywere. Subse-quently, he matched the happiness data with the Environmental Protec-tion Agency's Air Quality System (AQS) data. He found that people, whowere interviewed on days when air pollution was worse than the local

    seasonal average, reported relatively low levels of happiness. Ferreiraet al. (2013) analyzed the relationship between air quality and subjectivewell-being in Europe. They found a robust negative impact of SO2 concen-trations on self-reported life satisfaction. Welsch (2002, 2006, 2007,2009) also explored the relationship between air pollution and happinessamong European countries and found that air pollution has a statisticallysignicant negative impact on happiness. Rehdanz andMaddison (2008)analyzed differences in happiness in terms of environmental quality withdata drawn from the German Socio-Economic Panel (GSEP) and foundthat severe local air pollution signicantly reduces people's happiness.Using a similar approach, Luechinger (2010) and Luechinger andRaschky (2009) also found a negative effect of air pollution on happiness.

    The happiness studies mentioned above are commonly based onexpert or objective risks. However, analyses that are solely based onobjective risk may fail to accurately capture its impact on happiness.One reason is that objective risk is a measure that does not account forsocio-psychological conditions in particular, perception. In fact, allobjective measures of risk (and of other states of one's environmentincluding the natural environment) are processed and transformed byperception. Consequently, it is the latter that impacts on mental condi-tions such as happiness (Braman et al., 2005; Davis, 2000; Elias andShiftan, 2012; Menon et al., 2008). Specically, individuals with differentbackgrounds are likely to perceive the sameobjective level of air pollutiondifferently. For example, peoplewho have suffered from an illness relatedto air pollution are likely tohave a different perceived risk level, comparedwith those who have not suffered such an illness. In a similar vein, envi-ronmental knowledge is likely to affect perception and thus happiness.Hence, although perceived risk is affected by objective risk, both kindsof risk are likely to differ not only because of personal experiences butalso because of such issues as imperfect information or lack of condencein ofcial information sources. Note that in this paper we consider notonly subjective risk as a determinant of happiness but also objectiverisk, as (crudely) measured by air quality in one's residential area andbywork environment. These latter two variables are also used as determi-nants of perceived risk (see Sections 3 and 5).

    Laboratory experiments have frequently indicated that individualstend to under-estimate high-risk events and over-estimate small-riskevents, which is an illustration of the fact that perceived risk differsfrom objective risk (Ellsberg, 1961; Riddel and Shaw, 2006; Shaw andWoodward, 2008). The laboratory outcomes have been conrmed byVan Praag and Baarsma (2005). They found that perceived noise ismore adequate in predicting individual happiness than objective mea-sures. A similar result was obtained by Rehdanz and Maddison (2008)who estimated the differences in happiness in terms of perceived air pol-lution in residential areas. Ferrer-i-Carbonell and Gowdy (2007)examined the relationship between happiness and attitudes regardingozone pollution with data from the British Household Panel Survey.They found that concern about ozonepollution signicantly andnegative-ly impacts on an individual's happiness.

    This paper examines the impact of perceived health risk due to air pol-lution on happiness which is furthermoremodeled as a function of socio-economic variables such as age and income, and of environmental knowl-edge. It also takes into account that environmental knowledge and per-ceived risk are endogenous and may interact.

    The paper is organized as follows. Section 2 outlines the conceptualmodel and Section 3 describes the methodology. Section 4 presentsthe empirical results and Section 5 the main conclusions and policyrecommendations.

    2. Conceptual Model

    We assume that individual i's happiness can be represented by thefollowing happiness function:

    HAPi HAP Xi; PRi 1where HAPi denotes happiness, Xi is a set of individual characteristics

  • Exogenous variables correlated with Environmental Knowledge Exogenous variables correlated with Happiness

    AgeWork environment AbilityAge

    Endogenous variables

    Environmental knowledge

    HPerceived risk




    90 Z. Li et al. / Ecological Economics 99 (2014) 8899(specied below) and PRi is perceived air pollution risk. Furthermore,we assume that PRi is functionally related to Xi, HAPi and environmentalknowledge (EKi) which, in turn, is a function of Xi and PRi. Formally:

    PRi PR Xi;HAPi; EKi 2

    EKi EK Xi;PRi : 3

    The above HappinessPerceived RiskEnvironmental Knowledge (HPE) model is presented in Fig. 1. The expected signs of the impacts arepresented in Table 1.

    Several of the relationships in Fig. 1 and Table 1 are well-known orintuitively clear. We therefore only present a brief discussion of the lessfamiliar aspects of the conceptual model.

    The main dependent variable is happiness (abbreviated HAP). It ismeasured as follows. First, following Van Praag and Baarsma (2005),respondents are asked how satised they are currently with their livesas a whole. Second, a list of various dimensions of life is presented tothem and they are asked how much pleasure and joy they get from

    Exogenous variables correlated with Perceived Ri


    Family health experience

    Family size


    Fig. 1. The HappinessPerceived RiskEeach of them. The dimensions include nancial situation, work situation,living in Jinchuan and interpersonal relationships. The respondents areasked to answer the questions on 10 point scales, where 1 is worst con-ceivable and 10 is best conceivable. The questions used to measureHAP are presented in Table 3.

    Perceived risk (PR) is dened as an individual's judgment or assess-ment of hazards or dangers that might pose immediate or long-termthreats to their health and well-being (Adeola, 2007). Perceived risk isassumed to have a negative impact on a respondent's happiness. This

    Table 1The expected signs of the relationships in the HPE model.

    Eq. (1) Eq. (2) Eq. (3)


    Happiness (HAP) ()Perceived risk (PR) () (+)Environmental knowledge (EK) (+/)Family size (FS) () ()Current health condition (CHC) (+)Age (AGE) (+) (+/)Ability (+) (+) (+)Family health experience (FHE) (+)Proximity to the pollution source (PPS) (+/) ()Work environment (WE) () (+)hypothesis is supported by Van Praag and Baarsma (2005), who foundthat perceivednoise negatively inuences people's happiness. In addition,Rehdanz andMaddison (2008) studied the impact of perceived air pollu-tion on happiness in Germany and found that higher perceived air pollu-tion signicantly diminishes happiness. Perceived risk ismeasured by theset of items presented in Table 5.

    We also assume a reverse effect from happiness on perceived risk.Direnfeld and Roberts (2006) point out that happiness tends to triggerrecall of positive information which leads to optimistic assessments;that is, higher happiness levels result in lower risk perception. Forexample, Foo (2011) examined how emotion inuences risk perceptionsof Singaporeans and found that happiness negatively affects perceivedrisk.

    We dene environmental knowledge (EK) as a body of knowledge ofan individual or group of people relating to their environment (Johnson,1998). We hypothesize that environmental knowledge impacts onpeople's perceived risk. The sign of the impact can be positive or negative.Wallquist et al. (2010) examined the impact of knowledge of CarbonDioxide Capture and Storage (CCS) on perceived risk and found that


    Family sizeappiness

    Current health condition

    Work environment

    ity to the on source

    Proximity to the pollution source

    onmental Knowledge (HPE) model.more knowledge eased people's concerns. On the other hand, Klerckand Sweeney (2007) investigated the effect of people's knowledge onperceived risk associated with GM food in Australia and found a positiverelationship. Because of these opposite outcomes, we do not a priori spec-ify the expected sign of the impact for this case study. It is an empiricalmatter.

    We also hypothesize a reverse effect from perceived risk on environ-mental knowledge. That is, high perceived risk will induce people tocollect more and better information about the risk (Osberghaus andReif, 2010). In a similar vein, behavior aimed at avoiding or mitigatingrisks will encourage people to acquire more environmental knowledgein general and of the perceived risks in particular (Laird et al., 2003).We expect a positive impact. The questions used tomeasure environmen-tal knowledge are presented in Table 4.

    Regarding the exogenous variables of the HPE model, we postulatethat income has a positive impact on happiness (Easterlin, 2001; Rojas,2006; Siahpush et al., 2005; Smyth and Qian, 2008; Welsch, 2006).Income in this paper is the monthly net income of the household1

    to which the respondent belongs. We distinguish ten classes (see

    1 We take family as a communitarian group where resources, particularly nancial re-sources, are pooled. Rojas (2007) points out that household income is a better predictorof a person's happiness than personal income in a communitarian family.

  • Max Mean SD


    ditiif thcard

    91Z. Li et al. / Ecological Economics 99 (2014) 8899Table 2). We also assume a positive effect of education on happiness(Chen, 2010; Stevenson and Wolfers, 2008). Education is measured asthe respondent's highest education level achieved (see Table 2). Follow-ing inter alia Abreu and Lins (2010), Cottrell (2003), Khan et al. (2013),Kim (2009), Ndunda and Mungatana (2013), Starr (2009), Straughanand Roberts (1999), and Suzanne and Lynne (2005), we postulate thatindividuals with medium or high incomes have higher level of environ-mental knowledge and perceived risk than lower income individuals.Note that income and education are endogenous in that they depend onability. To account for this, we take both variables as observed indicators(i.e. functions) of the latent variable ability (see Sections 3 and 4 for a def-inition of a latent variable andabrief discussionof the simultaneous use oflatent and observed variables in one model framework).

    Because older people can better regulate emotions than youngerindividuals, we hypothesize a positive impact of age on happiness

    Table 2Descriptive statistics for the observed exogenous variables.

    Variables Min

    Age (AGE) 21Family size (FS) 1Current health condition (CHC) 1Family health experience (FHE) 0

    Education (EDU)Primary schoolMiddle schoolHigh schoolVocational school,Bachelor's degreeMaster's degree

    Proximity to the pollution source (PPS)Nearby smelting plants, severe air pollution (SAP)Medium air pollution (MAP)Far away from smelting plants, light air pollution (LAP, reference case)

    Work environment (WE)Non-JMC employee (reference case)Miners or smelter workers of JMC (MS)JMC employee, but not miner or smelter worker (NMS)

    Note: Family size: number of family members living in the same house. Current health con4 = good, 3 = no good, no bad, 2 = bad, 1 = very bad). Family health experience: 1cardiovascular diseases (e.g., hypertension, heart attack, chest pain, arrhythmia and myopneumonia, asthma, and lung cancer), 0 otherwise.(Cheng et al., 2011; Inglehart, 1990; Kahneman and Krueger, 2006;Labouvie-Vief and Blanchard-Fields, 1982). The impact of age on environ-mental knowledge is ambiguous. On the one hand, young people tend tohave more recent information on (inter alia) environmental issues andrisks than older generations (Abreu and Lins, 2010). On the other hand,older individuals have more experience with the environmental prob-lems in Jinchuan. We assume that family size negatively impacts on hap-piness. The reason is that in larger families the household's materialresources are shared by a larger number of people (Blanchower andOswald, 2004; Keister, 2004; Van Praag and Baarsma, 2005). We alsoinclude family size in the perceived risk equation because a larger familymay possess more information to assess risk. Hence, the expected impactis negative (Ajetomobi and Binuomote, 2006; Amaefula et al., 2012; Xuet al., 2010).

    A respondent's current health condition, measured by self-evaluation(see Table 2), is expected to positively impact on happiness (Graham,2008, 2009). This assumption is supported by Bickerstaff (2004) whopoints out that people's understanding of polluted air is embedded indaily life through their own, and their familymembers' health experience.Hence, we postulate a positive impact of family health experience on per-ceived risk (see also Howell et al., 2003; Bickerstaff and Walker, 1999;Kahneman and Krueger, 2006). We measure family health experienceby means of a dichotomous variable which takes the value 1, if therespondent or one or more of his or her family members have been hos-pitalized for cardiovascular diseases or respiratory diseases, and 0otherwise.Tait et al. (1989) and Dravigne et al. (2008) argue that the harsherpeople's work environment is, the unhappier they will be. Thus, wehypothesize that a harsh work environment (i.e. in the mine or insmelters of the Jinchuan mining company, denoted JMC) negativelyinuences people's happiness. We also assume that people working inthemining company have better knowledge of Jinchuan's environmen-tal issues than non-JMC employees because JMC is the culprit ofJinchuan's environmental issues (see Juang et al., 2010; Arcury et al.,2002, for similar arguments). We distinguish three work environmentclasses (see Table 2).

    In addition to subjective risk, we also take objective risk into account,asmeasured by proximity to the pollution source (smelting plants). Sincepollution is subject to distance decay, we postulate that objective riskdecreases along with distance to the pollution source. The same appliesto other nuisances associated with the smelting plants, such as noise

    78 44.11 11.46 2.95 0.785 3.68 0.851 0.33 0.48

    Household net income (IN) (CNY per month) %% 10002000 4.70%0% 20003000 15.30%0% 30004000 18.30%0% 40005000 19.10%0% 50006000 20.90%% 60007000 13.00%

    70008000 3.70%0% 80009000 1.80%0% 900010,000 1.10%0% More than 10,000 2.00%


    on: respondent's self-evaluation of his/her own current health condition (5 = very good,e respondent or one or more of his/her family members have been hospitalized forial infraction) or respiratory diseases (e.g., upper respiratory tract infection, bronchitis,and reek. Moreover, people may get used to (Maderthaner et al., 1978),or take measures to reduce such nuisances. Hence, the further one livesaway from the pollution source, the lower is the suffering; that is a posi-tive happiness effect. On the other hand, since the air is seriously polluted,house prices and rents are lower in the areas close to the smelting plants2

    (see also Bookwalter andDalenberg (2010)who found a similar effect forSouth Africa). Moreover, the best medical facilities, shopping areas andschools of Jinchuan are located in the heavily polluted area (see Fig. 2).The outcome of these opposing effects on happiness is uncertain. Weexpect that proximity to the pollution source negatively inuences per-ceived risk. The reason is that respondents who live further away fromthe smelting plants are less exposed to air pollution than those who livenearby (Bickerstaff and Walker, 2001; Combest-Friedman et al., 2012;Riddel and Shaw, 2006). We distinguish three distance categories (seeTable 2).

    3. Methodology

    The conceptual model (Fig. 1 and Table 1) contains both latentvariables (happiness, perceived risk, environmental knowledge) andobserved variables (e.g. age and family size). Latent variables (or

    2 Housing allocation is to a very limited extent based on supply and demand. Rather, it isthe local government and themining company that allot relatively cheap housing to theiremployees. These houses are mainly located near the company in heavily polluted areas.This information is based on discussions with local ofcials and administrators.

  • 92 Z. Li et al. / Ecological Economics 99 (2014) 8899theoretical constructs) refer to those phenomena that are supposed toexist but cannot be directly observed (Oud and Folmer, 2008). However,they can be measured by observed variables or indicators. For example,the theoretical notion of happiness is measured by questions aboutpeople's satisfactionwith their nancial condition, interpersonal relation-ships, working condition and other dimensions of life (Table 3).

    A structural equation model (SEM) allows simultaneous use of bothlatent and observed variables within one framework (Bollen, 1998;Jreskog and Srbom, 1996). A SEM is made up of two components; viz.the measurement model and the structural model.3 The measurementmodels relate the latent variables to their indicators, as follows4:

    y y 4

    x x 53 It is possible to include means and intercepts into the system. However, we delete

    them here because in the application we standardize all latent variables.4 Note that directly observed variables can be conveniently included in the system by

    dening an identity relationship between an observed variable and the corresponding la-tent variable.

    Fig. 2.Heavily, moderately and lightly polluted areas of the Jinchuanmining area. Note: the domnorth-west during winter.Source: JEQMR (2011) and Wei (2008).where is an m 1 vector of endogenous latent variables, a n 1vector of exogenous latent variables, y a p 1 vector of endogenousobserved variables, and x a q 1 vector of exogenous observed variables.y is a (p m)matrix that species the relationships (loadings) betweenthe endogenous observed variables y and the endogenous latent variableswhile x is a (q n) matrix with the loadings of the observed variablesx on the exogenous variables . and are the measurement errors of yand x, respectively. The covariance matrices of and are (p p)and (q q), respectively.

    The structural model species the relationships between the exoge-nous and the endogenous latent variables, and the relationships amongthe latent endogenous variables mutually:

    B 6

    where B is an (m m) matrix that contains the structural relationshipsamong the latent endogenous variables, an (m n) matrix of theimpacts of the exogenous latent variables on the endogenous latentvariables and a random (m 1) vector of errors with covariancematrix(m m). The covariance matrix of is(n n).

    inantwind directions are from the east and south-east during summer and fromwest and

  • A prerequisite for estimation of a SEM is that it is identied. Onecondition for identication of a SEM is that all latent variables havebeen assigned measurement scales. This can be done by xing one mea-surement coefcient for each latent variable, usually at 1, or by xingthe variances of the latent variables, usually also at 1. In the applicationbelow,we apply the lattermethod. In addition to xing themeasurementscales, the order and rank conditions need to bemet for identication. Thelatter may be tedious to check. However, the software programs LISREL 8andOpenMx give indications of identication problems by evaluating theinformationmatrix at theminimum of the tting function. If the estimat-ed information matrix is singular, the model is not identied (Silvey,1975). Jreskog (1981) shows that the rank of the information matrixindicates which parameters are not identied.

    Estimation of a SEM is commonly conducted by minimizing thedistance between the observed covariance matrix and the theoreticalcovariancematrixwhich is a function of the unknownmodel parameters.Several of the variables in the HPEmodel, particularly the indicators of

    non-normal variables are analyzed as interval or normal variables, thecommonly used estimators, particularly maximum likelihood, may leadto distorted parameter estimates, incorrect chi-square goodness of tmeasure and incorrect standard errors. When some or all of the observedvariables are ordinal or binary and the distribution of some variables isskewed, WLS based on polychoric correlations should be applied. TheWLS tting function reads

    FWLS W1 7

    where is a vector of the elements in the lower (or upper) half, includingthediagonal, of thematrix of polychoric correlations. is the vector of cor-responding elements of the theoretical matrix which is a function of themodel parameters (i.e., factor loadings, structural model parameters, var-iances and covariances of and of the errors). The vector() denotes therestrictions imposed on the population polychoric correlationmatrix.W1 is a positive-deniteweightmatrix. Browne (1982, 1984) showed that if





    Table 3Frequency distribution of the scores of the indicators of happiness.

    Indicators Questions Score

    1 2 3 4 5 6 7 8 9 10

    How satised are you withHAP1 Your current life as a whole? 4.60% 0.00% 0.50% 2.10% 9.70% 15.80% 19.20% 35.20% 12.70% 0.00%HAP2 Your interpersonal relationships? 0.00% 0.10% 1.20% 2.10% 8.50% 17.10% 19.50% 35.20% 9.95% 6.20%HAP3 Your nancial condition? 0.20% 1.40% 3.40% 9.10% 16.20% 20.10% 18.50% 19.30% 6.90% 3.20%HAP4 Your work situation? 1.50% 1.30% 2.30% 7.50% 20.90% 23.60% 23.30% 15.00% 3.60% 0.90%HAP5 Living in Jinchuan? 0.70% 0.80% 2.40% 3.70% 13.90% 20.60% 23.30% 24.10% 5.70% 4.90%

    93Z. Li et al. / Ecological Economics 99 (2014) 8899happiness, perceived risk and environmental knowledge (see Tables 3, 4and 5), are ordinal or dichotomous. Moreover, since a small minority ofthe respondents disagree with the statements relating to the environ-mental issues faced by the Jinchuan population, the indicators of environ-mental knowledge are skewed and thus highly non-normal. Flora andCurran (2004), Jreskog and Srbom (1993, 1996), Poon and Lee(1987), and Satorra and Muthn (1995) argue that when ordinal or

    Table 4Frequency distribution of the scores of the indicators of environmental knowledge.

    Indicators Questions Strongly agree Agr

    Jinchuan suffers fromEK1 Air pollution 82.40% 15.4EK2 Industrial solid waste 63.60% 23.3EK3 Water pollution 51.00% 30.6

    Environmental issues in Jinchuan are mainly caused byEK4 Local industrial activities 76.40% 16.8

    The main air pollutants in Jinchuan areEK5 Sulfur dioxide 65.20% 17.1EK6 Suspended particles 48.50% 27.5EK7 Carbon dioxide 29.70% 28.2EK8 Chlorine gas 68.10% 17.4Table 5Frequency distribution of the scores of the indicators of perceived risk.

    Indicators Question Score (days)

    PR1 PAPL 0 1 21.80% 17.60% 37.90%

    Indicator Questions Strongly agree Agree

    Jinchuan's air pollution increases the possibility of suffering fromPR2 Respiratory illnesses 75.90% 20.00%PR3 Cardiovascular illnesses 46.90% 28.10%PR4 Lung cancer 53.10% 30.50%PR5 Death 42.30% 30.90%W1 is the correct weight matrix, FWLS is an asymptotically efcientestimator of the parameters, standard errors and chi-square overall teststatistic (for an overview of the main advantages of the use of SEM, seeamong others Folmer and Oud (2008)).

    Estimation of a SEM can be done by a variety of software packages ofwhich LISREL 8 (Jreskog and Srbom, 1996) and OpenMx (in R) areprobably best known. The packages include various test statistics, notably

    Neither agree nor disagree Disagree Strongly disagree

    0.80% 0.80% 0.70%13.10% 2.60% 0.70%11.90% 5.60% 0.90%

    4.80% 1.60% 0.40%

    15.50% 1.70% 0.50%21.30% 2.10% 0.50%29.00% 8.50% 4.60%11.00% 2.80% 0.80%3 4 5 6 724.20% 9.40% 4.60% 1.70% 2.60%

    Neither agree nor disagree Disagree Strongly disagree

    3.00% 0.80% 0.30%20.90% 2.50% 1.60%13.40% 2.30% 0.80%19.70% 4.80% 2.40%

  • z-statistics for individual parameters, R-squared for structural and mea-surement equations, and overall goodness of t statistics. The packages

    94 Z. Li et al. / Ecological Economics 99 (2014) 8899can also be used to obtain modication indices, which make suggestionsabout model improvement by freeing xed or constrained parameters.

    The conceptual model presented in Fig. 1, in terms of Eqs. (4)(6),reads as follows5:

    Measurement models








    1;1 0 02;1 0 0

    5;1 0 00 6;2 00 7;2 0 0 10;2 00 0 11;30 0 12;3 0 0 18;3















    11 0 0 0 0 0 0 0 021 0 0 0 0 0 0 0 00 1 0 0 0 0 0 0 00 0 1 0 0 0 0 0 00 0 0 1 0 0 0 0 00 0 0 0 1 0 0 0 00 0 0 0 0 1 0 0 00 0 0 0 0 0 1 0 00 0 0 0 0 0 0 1 00 0 0 0 0 0 0 0 1










    The structural model reads




    0 12 021 0 230 32 0






    11 12 13 14 0 16 17 18 1921 0 23 0 25 26 27 0 031 32 0 0 0 0 0 38 39










    4. Empirical Results

    4.1. The Survey

    Data was obtained by a survey in the city of Jinchuan. The total popu-lation of Jinchuan is 204,000 (in 2010) and the total number of house-holds is 60,400, distributed over seventeen communities. The number ofhouseholds varies per community from 1126 to 6454.

    A two-step stratied random sampling procedure was applied. First,following Wei (2008) and JEQMR (2011), the city of Jinchuan area wasdivided into three sub-areas based on distance from the pollution source,i.e. the level of air pollution: (i) heavily polluted, (ii) moderately polluted,

    5 See Table 2 for a denition of the labels in Eqs. (8)(10).and (iii) lightly polluted (Fig. 2). The number of communities varies persub-area from four to six. Secondly, the number of interviewees in eachcommunity was randomly selected in proportion to its total size. Perhundred households we randomly selected 12 households which gavea sample size of 800.

    Because the questionnaire was long (eight pages) and complex, theinterviews were face-to-face rather than by telephone or mail. Prior tothe survey, a pilot survey was held to test the draft questionnaire. Thequestionnaire was adjusted, corrected and re-worded, according to theresults of the pilot survey. The interviewers were selected from a groupof college students at Gansu Non-ferrous Metallurgy College in Jinchuan.Understanding the environmental issues of Jinchuan and the locallanguage were two selection criteria. The interviewers were trained inorder to acquaint them with the questionnaire and in communicationwith local inhabitants.

    Interviewees were family heads of Jinchuan, usually the husbands. Allinterviewees were holding Jinchuan hukouwhich means that they arepermanent Jinchuan inhabitants who had lived in the area for at leastten years. The survey was carried out in August, 2012.

    4.2. Descriptive Statistics

    In total, 800 interviews were held of which 41 (5.12%) were incom-plete. There was no evidence of non-random dropout. Descriptive statis-tics are presented in Tables 25.

    Fig. 2 presents the lightly, moderately and heavily polluted areas,together with the location of schools, medical facilities and shoppingareas. The prevailing winds for the period 20062010 are east andsouth-east during summer and west and north-west during winter.Fig. 2 indicates that air pollution decays by distance from the smeltingplants and that the facilities are concentrated in the heavily pollutedareas.

    Table 3 shows the scores of the indicators of happiness. For the over-all indicator (HAP1) and interpersonal relationships (HAP2), a vastmajority of the respondents (more than 80%) are moderately to highlysatised (scores 6 or higher) while only a rather small minority areunsatised to highly dissatised (a score of 5 or lower). Regardingwork situation (HAP3) and nancial situation (HAP4), nearly onethird of the respondents are moderately to severely dissatised (ascore of 5 or lower). Table 3 also indicates that a majority of the respon-dents (78.6%) rate living in Jinchuan (HAP5) at 6 or higher.We concludethat most of the respondents are satised with living in Jinchuan,though a substantial minority is dissatised with some aspects.

    The respondents' environmental knowledge was tested by eightindicators which can be divided into two categories (see Table 4). Therst category (EK1EK4) is about general environmental issues in theJinchuan area while the second (EK5EK8) relates to specic air pollut-ants. For each indicator a ve point scale was used. Table 4 shows thatmost of the respondents agree or strongly agree that air pollution(97.8%), industrial solid wastes (86.9%) and water pollution (81.6%) areenvironmental issues in the Jinchuan area.Moreover, 93.2% either strong-ly agree or agree that Jinchuan's environmental issues are caused by localindustrial activities. Finally, themajority of the respondents either strong-ly agree or agree that chlorine gas (85.5%), sulfur dioxide (82.3%),suspended particles (76%) and carbon dioxide (57.9%) are the main airpollutants.

    Note that all four gasses aremainpollutants of smelters (Barcan, 2002;Mylona, 1996; Tamaki et al., 2002;Worrell et al., 2001). It follows that itsresidents are well informed about Jinchuan's air pollution.

    Perception of the risks of air pollution is measured by ve indicators(Table 5). The rst indicator (PAPL) relates to perceived health risk dueto exposure: what is the average number of days per week in whichJinchuan's air was heavily polluted during the past year? Table 5 showsthat the majority (62.1%) answered 2 or 3 days indicating perceptionof medium exposure. This result stands in contrast to the outcomes of

    the other four indicators, i.e. judgment of health risks caused by air

  • meet their critical values indicating that it has a satisfactoryt. In addition,a comparison of Table 6 and Table A.1 shows that the revisions of the ini-tialmodel, particular the deletion of the insignicant variables, are correctbecause of no deterioration in the overall goodness of t statistics. Partic-ularly, GFI and AGFI are equal in the initial and nalmodels, the 2/DF hasimproved, and SRMR and RMSEA have slightly deteriorated. These out-comes support the nal model which we now discuss in detail.

    Table 7 presents the loadings, standard errors and R2s for each nalmeasurement equation. The R2 or reliability of an indicator is the per-centage of the variance of the indicator explained by the underlyinglatent variable. For happiness, HAP4 is the most reliable indicator withR2 = 0.31 and HAP5 the least with R2 = 0.12. This means that happi-

    PR4 0.61 0.03 0.38PR5 0.56 0.03 0.32

    Environmentalknowledge (EK)

    EK1 0.51 0.04 0.26EK2 0.47 0.03 0.22EK3 0.39 0.04 0.15EK4 0.47 0.04 0.22EK5 0.56 0.03 0.31EK5 0.47 0.03 0.22EK7 0.32 0.03 0.10EK8 0.41 0.04 0.17

    Ability (AB) Education 0.41 0.04 0.17Income 0.52 0.04 0.27

    Table 6Overall goodness of t measures.

    Fit index Final model Critical value

    2/DF 2.60 b3Goodness-of-t index (GFI) 0.97 N0.95Adjusted goodness-of-t index (AGFI) 0.96 N0.95Root mean square error of approximation (RMSEA) 0.045 b0.05Standardized root mean square residual (SRMR) 0.028 b0.08

    95Z. Li et al. / Ecological Economics 99 (2014) 8899pollution (PR2PR5). For each indicator, a ve point scale ranging fromstrongly agree to strongly disagree was used. Table 5 indicates thatthe majority of respondents agree or strongly agree that air pollutionincreases the possibility of suffering from respiratory illnesses (95.9%),lung cancer (83.6%), cardiovascular illnesses (75%) and death (73.1%).

    The respondents' risk perceptions are in line with scientic evidence.Particularly, according to Winder (2001) and Das and Blanc (1993),exposure to chlorine gas may result in nasal irritation, sore throat,coughing, respiratory distress with airway constriction and accumulationof uid in the lungs (pulmonary edema). In addition, cardiovascularcollapse may occur after severe exposure. Sulfur dioxide can react withother compounds in the atmosphere to form small particles which canpenetrate deeply into the lung's sensitive parts and lead to, or worsenrespiratory disease, for example, emphysema and bronchitis. These parti-cles also can aggravate existingheart disease, resulting into increasedhos-pitalization and premature death (Devalia et al., 1994; Nadel et al., 1965).Suspended particles can get deep into the lungs and cause serious healthproblems including premature death for people with heart or lung dis-ease, nonfatal heart attacks, irregular heartbeat, aggravated asthma,decreased lung function, and increased respiratory symptoms, such as ir-ritation of the airways, coughing or difcult breathing (Pope et al., 1995;Seaton et al., 1995). From this overview of the objective risks of the vari-ous pollutants and the corresponding risk perceptions in Table 5 it followsthat the residents arewell informed about the risks of themain air pollut-ants in the Jinchuan area.

    4.3. The Estimated SEM

    Before going into detail, we point out that the estimated coefcientsare standardized or beta coefcients. A standardized coefcient measuresthe standard deviation change in the dependent variable due to a stan-dard deviation increase in an explanatory variable. The use of beta coef-cients renders the scales of the regressors irrelevant and makes theestimated effects directly comparable.

    As a rst step, we estimated the full conceptualmodel (denoted initialmodel) presented in Eqs. (8)(10). The estimated measurement model(see Appendix A Table A.2) showed that PR1, i.e. perception of the riskdue to frequency of occurrence of serious air pollution in Jinchuan, hadextremely low reliability (0.03). Factor analysis indicated that this wasdue to the fact that it measures a different dimension of perceived riskthan the other four indicators (PR2PR5). While PR1measures perceivedrisk due to intensity of exposure, PR2PR5measure the perceived risk dueto hazard of pollutants. Therefore, we decided to split the latent variableperceived health risk into two latent variables: PRL1whichmeasures per-ceived risk due to exposure, and PRL2 which measures the perceivedhealth risks of the main air pollutants. The nal measurement model ispresented in Table 7 which shows that there are no indicators withextremely low reliabilities. The structuralmodelwas adjusted accordinglyin that there are two latent perceived health risk variables.

    In the initial structural model with PRL1 and PRL2 (Appendix ATable A.3) the impact of happiness on the two perceived latent healthrisk variables was negligible and insignicant. Therefore, these relation-ships were deleted from the structural model. In addition, several of theexogenous explanatory variables were highly insignicant. We deletedthem one by one in a stepwise backward elimination procedure startingwith the variable with the largest p-value. The nal structural model ispresented in Table 8.

    Before discussing themain relationships in the nalmodel, we discussthe overall goodness of t, presented in Table 6. Several overall goodnessof t indices of SEMs have been developed. Themost widely reported arethe 2/DF (DF denoting degrees of freedom), the goodness-of-t statistic(GFI), the adjusted goodness-of-t index (AGFI), the standardized rootmean square residual (SRMR) and the root mean square errorof approximation (RMSEA) (see Bentler and Bonett, 1980; Jreskogand Srbom, 1993; Tabachnick and Fidell, 2007; Byrne, 2013). Table 6

    indicates that all the overall goodness of t indices of the nal modelness is better measured by HAP4 than by HAP5. From Table 7 it followsthat all coefcients are signicant and have satisfactory reliabilities.Moreover, the coefcients of the indicators of the latent variables happi-ness, environmental knowledge and ability are virtually identical in ini-tial and nal models. Finally, note that in the measurement model thelatent variables are purged of their measurement errors which reduceattenuation in the structural model. For details we refer to, amongothers, Jreskog (1973, 1977, 1981) and Bollen (1989).

    Table 8 shows the estimated coefcients, standard errors, and R2s ofthe nal structural model. We rst discuss the signs of the relationshipsand next the magnitudes of the effects. The results indicate that bothperceived risk due to intensity of exposure (PRL1) and perceived riskdue to hazard of pollutants (PRL2) signicantly and negatively affecthappiness, as hypothesized. Furthermore, environmental knowledgehas a positive and signicant impact on both latent perceived risk vari-ables. However, in contrast to the assumption in the conceptual model,the perceived risk variables did not signicantly impact on environmen-tal knowledge in the initial structural model (see Appendix A,Table A.3). A possible explanation is that the suffocating and pungentodor of sulfur dioxide and chlorine gas, which are the main directlyobservable air pollutants in the Jinchuan area is sufcient evidence for

    Table 7The nal measurement model.

    Latent variables Indicator Coefcient Standard error R2

    Happiness (HAP) HAP1 0.52 0.03 0.27HAP2 0.42 0.03 0.17HAP3 0.53 0.03 0.28HAP4 0.56 0.03 0.31HAP5 0.35 0.03 0.12

    Perceived risk due to intensityof exposure (PRL1)

    PR1 1.00 1.00

    Perceived risk due to hazardof pollutants (PRL2)

    PR2 0.56 0.04 0.31PR3 0.52 0.03 0.27

  • 96 Z. Li et al. / Ecological Economics 99 (2014) 8899Table 8The nal structural model (standardized coefcients).

    Explanatory variables HAP PRL1 PRL2 EK

    Perceived risk due to intensityof exposure (PRL1)


    Perceived risk due to hazardof pollutants (PRL2)


    Environmental knowledge (EK) 0.16 0.69

    (0.04) (0.09)Family size (FS) 0.11 0.09

    (0.03) (0.03)Age (AGE) 0.14 0.06

    (0.02) (0.03)Family health experience (FHE) 0.06

    (0.03)Current health condition (CHC) 0.17

    (0.02)Ability (AB) 0.52 0.10 0.35

    (0.10) (0.08) (0.07)Medium air pollution (MAP) 0.10 0.08 0.06

    (0.04) (0.03) (0.04)Serious air pollution (SAP) 0.10 0.18 0.05

    (0.04) (0.03) (0.04)JMC employee, but not mineror smelter worker (NMS)

    0.25 0.06(0.05) (0.05)

    Miners and smelter workersof JMC (MS)

    0.26 0.12(0.05) (0.06)

    R2 0.29 0.05 0.53 0.14

    Proximity to the pollution source (PPS) is represented by two dummy variables (1) SAP(nearby smelting plants, severe air pollution) and (2) MAP (medium polluted area). Thereference category is LAP (far away from pollution source, light air pollution). Workenvironment (WE) is represented by two dummy variables: MS (miners and smelterworkers of the Jinchuan mining company) and NMS (people who are JMC employees,but not miners or smelter workers). The reference category is non-JMC employee.the inhabitants' risk perception. The persistence of the odor rendersfurther knowledge acquisition redundant. We deleted the relationshipin the nal structural model.

    Regarding the exogenous variables, in line with expectation and com-mon knowledge, current health condition has a positive and signicantimpact on happiness. As hypothesized in the conceptual model, familysize signicantly and negatively inuences happiness, possibly becausethe larger the family, the smaller the amount of the available resourcesto each family member (Keister, 2004). Family size also has a negativeimpact on perceived risk due to hazard of pollutants (PRL2), possiblybecause of riskdilution; i.e. a bigger family has a larger capability to absorbrisk (Ajetomobi and Binuomote, 2006; Amaefula et al., 2012). The impactof family size on perceived risk due to intensity of exposure (PRL1) wasnegligible and insignicant.

    Ability, as measured by income and education positively and signi-cantly inuences happiness because it enlarges people's optionsto satisfy their needs and empowers them to be the driver of their owndestiny. The positive impact on environmental knowledge and perceivedrisk due to hazard of pollutants (PRL2) derives from the fact that individ-uals with more ability can better master information. That is, they canacquire better understanding of the nature of environmental issuesincluding those in Jinchuan, and make a better judgment of health riskcaused by the main air pollutants in the Jinchuan area. Age positivelyand signicantly inuences happiness because when people movethrough adulthood, they acquire life experience which allows them tobetter regulate their emotions, particularly to maximize the positive andminimize the negative effects of events and situations (Seo and Barrett,2007). The positive effect of age on environmental knowledge showsthat the experience effect dominates the more recent knowledge effect.Apparently, the long spell of living in the area has led to better knowledge

    Note: Standard errors in parenthesis. 10%.

    5%. 1%.about Jinchuan'smining and smelting industries and related environmen-tal issues.

    As expected, family health experience positively and signicantlyinuences people's perceived risk due to hazard of pollutants (PRL2).Apparently, family health experience tends to raise awareness andincrease anxiety. Proximity to the smelting plants, as measured by thedummies SAP and MAP, has positive impacts on happiness. Apparently,the benets of living close to the smelting plants (cheap housing priceand rents and good facilities) outweigh the negative health effects ofreek and noise. The dummies also positively and signicantly inuencepeople's perceived risk due to intensity of exposure (PRL1). Theirimpact on perceived risk due to hazard of pollutants (PRL2) is positive,though marginally signicant. Work environment, as measured by thedummies NMS and MS, negatively inuences happiness. Moreover, theimpacts for thosewhowork in themine or smelting plants aremore neg-ative than for the other employees of JMC. This indicates that a workenvironment without direct exposure to pollution and other hardships(as for non-JMC employees) improves people's happiness. Theworkplaceis, after all, a place where people spend a substantial part of their lives. Aspredicted, peopleworking in themining companyhave better knowledgeof Jinchuan's environmental issues than non-JMC employees.

    In addition todirect effects, the variables discussed in Table 8 alsohaveindirect effects. An indirect effect of a variable is its effect on an endoge-nous variable through intervening endogenous variables (Jreskog andSrbom, 1996). The total effect is the sumof the direct and indirect effects.Table 9 presents the indirect and the total standardized effects of allvariables on all endogenous variables in the nal HPE model.

    Table 9 shows that ability has the largest positive total effect (0.44)on happiness, followed by current health condition and age with totaleffects of 0.17 and 0.13, respectively. Perceived risk due to intensity ofexposure (PRL1) and perceived risk due to hazard of pollutants (PRL2)negatively and signicantly inuence happiness with total effects of0.11 and0.21, respectively. In absolute value, the two perceivedrisk variables together have the next largest impact on happiness(0.32). Although they have nodirect effect on happiness, environmentalknowledge and family health experience negatively and signicantlyimpact on happiness through the two perceived risk variables with atotal effect of0.16 and0.01, respectively. The total effect of familysize is also negative:0.09. The positive total effects of living inmoder-ately (MAP) and heavily (SAP) polluteddistricts aremainly directed andderived from lower house prices and better facilities in these districtsthan in districts with less pollution. Miners and smelter workers (MS)and other JMC workers (NMS) have the largest negative total effectson happiness:0.26 and0.28, respectively.

    Environmental knowledge and living inmoderately (MAP) and heavi-ly (SAP) polluted districts are three most important determinants of per-ceived risk due to intensity of exposure (PRL1) with total effects of 0.16,0.08 and 0.18, respectively. Ability and age also positively and signicant-ly inuence perceived risk due to intensity of exposure with total effectsof 0.06 and 0.01. Miners and smelter workers (MS) and other JMCworkers (NMS) indirectly and positively inuence PRL1 with total effectsof 0.01 and 0.02, respectively. The total effect of NMS, however, is insignif-icant. Environmental knowledgehas the largest positive total effect (0.69)on perceived risk due to hazard of pollutants (PRL2), followed by ability(0.34). The total effects of the other variables are very small (in absolutevalue b0.1). Ability is the most important determinant of environmentalknowledge with a total effect of 0.35. The total impacts of the othervariables are small.

    5. Summary and Conclusion

    Theneo-classical approach tomeasure (environmental)well-beingbymeans of money measures derived from utility maximization under abudget constraint, has been criticized for not fully capturing the relevantdimensions of well-being and insufciently accounting for psychological

    and sociological aspects. The notion of happiness has been introduced to

  • Total effects


    0.11(0.02)0.21(0.04)0.16 0.16 0.69

    (0.02) (0.02) (0.01) (0.02) (0.03)0.01 0.06

    97Z. Li et al. / Ecological Economics 99 (2014) 8899capture broader dimensions of human life and to allow for comparison oftheir relative importance. It thus complements the conventional money

    Table 9Indirect and total standardized effects (nal model).

    Variables Indirect effects

    HAP PRL1

    Perceived risk due to intensity of exposure (PRL1)

    Perceived risk due to hazard of pollutants (PRL2)

    Environmental knowledge (EK) 0.16(0.04)

    Family size (FS) 0.02

    (0.01)Age (AGE) 0.01 0.01

    (0.00) (0.01)Family health experience (FHE) 0.01

    (0.01)Current health condition (CHC)

    Ability 0.08 0.06(0.03) (0.02)

    Medium air pollution (MAP) 0.02(0.01)

    Serious air pollution (SAP) 0.03(0.01)

    JMC employee, but not miner or smelter worker (NMS) 0.01 0.01(0.01) (0.01)

    Miners and smelter workers of JMC (MS) 0.02 0.02(0.01) (0.01)

    Note: Standard errors in parenthesis. 10%.

    5%. 1%.measures based on constrained utility maximization.The main focus of the paper is the impact of health risk on happiness.

    Based on recent socio-psychological and economic literature, we arguethat the commonly used objective measures of environmental risk needto be supplemented with subjective notions, particularly perception.Both objective and subjective measures have been considered here toestimate the impact of (inter alia) perceived risk of air pollution onhappiness, based on a cross-sectional data set of 759 households in theJinchuan mining area, China. For this purpose, a four-equation structuralequation model (SEM) is estimated.

    The results of this paper support and extend Ferrer-i-Carbonell andGowdy (2007) ndings that environmental quality affects happiness. Itsmain outcome is that the more health risks an individual perceives(both in terms of exposure and hazardous pollutants), the less happyshe or he is. Apart from perceived risk, ability, age, proximity to thepollution source, work environment and current health condition areimportant determinants of happiness.

    Another important nding is that perceived health risk consists of twotypes: perceived risk due to intensity of exposure measured as theaverage number of days per week of severe pollution, and perceivedhealth risk due to hazard of pollutants. The estimation procedure,structural equation model with latent variables, turned out to beinstrumental in identifying the two types. The rst type is systematicallyinuenced by environmental knowledge and proximity to the pollutionsource; the second by environmental knowledge, family size, familyhealth experience, ability, and proximity to the pollution source. Finally,environmental knowledge was found to be a function of age, ability andwork environment.

    The ndings of this paper imply that improving air quality is animportant policy measure to improve happiness in the Jinchuan area.Perception of its health risks, particularly of respiratory andcardiovascular illnesses including cancer and premature death, has thelargest total negative effect on happiness and the next largest in absolutevalue. The results also show that risk perception is inuenced by environ-

    (0.01) (0.03)0.17

    (0.02)0.24 0.44 0.06 0.34 0.35

    (0.07) (0.09) (0.02) (0.10) (0.07)0.08 0.08 0.06(0.04) (0.03) (0.04)0.07 0.18 0.05(0.04) (0.03) (0.04)

    0.04 0.26 0.01 0.04 0.06(0.05) (0.07) (0.01) (0.05) (0.05)0.08 0.28 0.02 0.08 0.12(0.04) (0.05) (0.01) (0.04) (0.04)(0.04) (0.04) (0.09)0.09 0.09(0.03) (0.03)

    0.04 0.13 0.01 0.04 0.06mental knowledge. Therefore, improvement of air pollution should bewidely communicated. For that purpose, a new environmental manage-ment institution could be created in which the government, scienticinstitutions, the mining and smelting company and citizen organizationswould participate. The institution should respond to public concernsand stimulate dialog and cooperation between the participants.

    The present study can be extended in several ways as it only focuseson the relationship between air pollution and happiness. However,apart from air pollution, water pollution and solid waste are alsoenvironmental issues caused by mining and smelting in the Jinchuanarea with associated health risks. It would be interesting to analyze howoverall environmental degradation affects people's happiness. In addition,it would be interesting and important, both from a theoretical and policypoint of view, to further develop the notions of happiness, perceived riskand environmental knowledge and to test their indicators, similarly to thedevelopment and measurement of theoretical notions like intelligence inpsychology.

    Appendix A. The initial model

    Table A.1Overall goodness of t measures.

    Fit index Initial model Critical value

    2/DF 2.75 b3Goodness-of-t index (GFI) 0.97 N0.95Adjusted goodness-of-t index (AGFI) 0.96 N0.95Root mean square error of approximation (RMSEA) 0.048 b0.05Standardized root mean square residual (SRMR) 0.029 b0.08

  • 98 Z. Li et al. / Ecological Economics 99 (2014) 8899Table A.2Measurement models.

    Latent variables Indicators Coefcient Standard errors R2

    Happiness (HAP) HAP1 0.52 0.03 0.27HAP2 0.42 0.03 0.18HAP3 0.52 0.03 0.27HAP4 0.56 0.03 0.31HAP5 0.34 0.03 0.12

    Perceived risk (PR) PR1 0.16 0.03 0.03PR2 0.56 0.06 0.31PR3 0.52 0.05 0.27PR4 0.61 0.05 0.37PR5 0.56 0.05 0.31

    Environmentalknowledge (EK)

    EK1 0.51 0.07 0.26EK2 0.46 0.07 0.21EK3 0.39 0.06 0.15EK4 0.47 0.07 0.22References

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    Environmental knowledge (EK) 0.48

    (0.30)Family size (FS) 0.12 0.09

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    0.25 0.04(0.05) (0.05)

    Miners and smelter workers of JMC (MS) 0.26 0.09(0.05) (0.05)

    R2 0.28 0.32 0.21

    Note: Standard errors in parenthesis. 10%.

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    5. Summary and ConclusionAppendix A. The initial modelReferences


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