Scientific methodological paper on the questionnaire preparation including willingness to pay estimates

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1 Scientific methdlgical paper n the questinnaire preparatin including willingness t pay estimates Leni Avatane, Valeri Gatta, Yuri Kazepv, Michela Maine, Pal Plidri Vittri Sergi, Eva Valeri.

2 Wrk package 4: Deliverable nr.: 4.2 Lead partner: Authrs: Nature: Disseminatin level: PP Status: Pilt Study: Discrete Chice Analysis Partner 1 (Uniurb) Leni Avatane, Valeri Gatta, Yuri Kazepv, Michela Maine, Pal Plidri Vittri Sergi, Eva Valeri Reprt Final Reprt Date: 17 Octber

3 Acknwledgements The research n which this paper is based, was financially supprted by Eurpean Unin under the 7th Framewrk Prgramme; Theme: ENV [ENV Mbilising envirnmental knwledge fr plicy and sciety Grant agreement: (Prject Title: SEFIRA). T be cited as Leni Avatane, Valeri Gatta, Yuri Kazepv, Michela Maine, Pal Plidri Vittri Sergi, Eva Valeri (2014) Pilt Study: Discrete Chice Analysis FP7 Crdinatin Prject Sefira, Urbin: University f Urbin Carl B.. The views expressed in this reprt are the sle respnsibility f the authrs and d nt necessarily reflect the views f Eurpean Cmmissin. Leni Avatane, Valeri Gatta, Yuri Kazepv, Michela Maine, Pal Plidri Vittri Sergi, Eva Valeri 3

4 Cntents Acknwledgements... 3 Intrductin... 5 Pilt research plan Questinnaire prject and methdlgy Cnclusins Acrnyms References Appendix

5 Intrductin The SEFIRA prject aims at crdinating trans-disciplinary scientific and sciecnmic resurces at the Eurpean scale, in rder t supprt the review and implementatin f the air quality legislatin, imprving its effectiveness and acceptability. Air quality plicies, individuals behaviurs and chices will be analysed in their sci-ecnmic cntext that will be the fcus f the WP4 pilt study. The actual debate n air quality measures fcuses mainly n the technical r nntechnical nature f plicy instruments. Plicies that belng t the set f nn-technical measures seem t ffer the scpe fr assessing acceptability t the general public cmpared with technical measures, in that they ften imply mre significant changes in the lifestyles f individuals. They als ften ffer different ways f analysing a prblem and therefre embdy chices and trade-ffs. This standard classificatin is relevant but if the research fcus is abut plicy acceptability mre attentin shuld be paid t understand hw plicy efficacy is related t behaviural cmpnents that determine plicy acceptability. It fllws that understanding the crrelatin between behaviural cmpnents and acceptability drivers may becme significant in the design f successful air quality plicies. Hwever, currently, the majrity f air quality imprvements are due t the implementatin f technical measures, usually analysed by IAMs, whse public acceptability level as well as scial perceptin f the plicy effectiveness are rather unknwn. Behaviural changes (e.g. changes in lifestyles, legal traffic restrictins, pllutin taxes, emissin trading systems, etc.) are nt internalised in the ptimisatin prcess f mst widespread IAMs. On the cntrary, such changes are reflected by alternative exgenus scenaris f the driving frces. The GAINS 1 mdel, run by the Internatinal Institute fr Applied System Analysis (IIASA) that is ne f the partners f the Sefira Crdinatin Prject, has been develped as a tl t identify emissin cntrl strategies that achieve given targets n air quality and greenhuse gas emissins at least csts. It cnsiders measures fr the full range f precursr emissins that cause negative effects n human health via the expsure f fine 1 5

6 particles and grund-level zne, damage t vegetatin via excess depsitin f acidifying and eutrphying cmpunds, as well as the six greenhuse gases cnsidered in the Kyt prtcl. In additin, it als cnsiders hw specific mitigatin measures simultaneusly influence different pllutants. Thereby, GAINS allws fr a cmprehensive and cmbined analysis f air pllutin and climate change mitigatin strategies, which reveals imprtant synergies and trade-ffs between these plicy areas. An imprtant aspect is that ften it is usually nt sufficient chsing nly the mst cst-efficient measure, and therefre, the secnd mst cst-efficient measure is als chsen, and s n, adding up measures/plicies until the desired reductin level has been reached. Hwever, it is nt pssible t take mre advanced emissin reductin measures int accunt. Mrever, a type f measures used t estimate GAINS mdel regards behaviural changes aimed t reduce anthrpgenic driving frces that generate pllutin withut internalizing such behaviural respnses, but reflecting nly such changes thrugh alternative exgenus scenaris f the driving frces. On this pint a specific fcus n the study f users behaviur tward the acceptance (r rejectin) f selected air quality measures/plicies culd be a useful tl in rder t identify which measures are mst accepted (prviding als segmentatin analysis by sci-ecnmic aspects f users) and therefre ptentially the mst respected and adpted. This aspect is imprtant in particular fr thse plicies whse impact depends n the users preferences and behaviur. After having dealt with the main factrs affecting plicy acceptability, a distinctin between technical and nn-technical measures has been develped in the deliverable D 4.1 at sectin 2.1 Classifying technical and nn-technical measures using a behaviural apprach. The increasing imprtance f nn-technical measures has been reflected in the questinnaire preparatin that is fcusing mre n the persnal attitudes that n specific technical measures alnes. Attentin paid by plicy makers and experts in better understanding the sci-ecnmic and individual implicatins f nn-structural measures, is substantially increasing. This implies that future air quality plicies might g twards an increased use f measures requiring the ppulatin t be actively part f a plicy prcess whse aim is t imprve air quality thrugh the participatin and invlvement f lcal cmmunities. The level f cmmunity participatin and sharing has t be extensively assessed, described and studied thrugh the use f new methdlgies and interdisciplinary appraches. 6

7 Fr this reasn, we suggest that a pilt analysis based n discrete chice experiments (DCEs) might be an apprpriate tl t investigate the acceptability f envirnmental plicy measures. DCEs, in fact, allw eliciting individual preferences fr ptential new air quality plicy, analysing their ex-ante acceptability. Discrete chice mdels (DCMs) are a rbust and widespread quantitative methdlgy fr the study f individual preferences. Requiring respndents t make individuals trade-ffs between varius plicy drivers (attributes), DCMs allw t estimate the respndent sensitivity tward each plicy characteristics and their relevant weight. This apprach is mre useful than the wide used Likert scales in which respndents specify their level f agreement (r disagreement) n a symmetric agree-disagree scale fr a series f statements, whse range nly captures the intensity f their feelings fr a given item. The literature in different fields recgnise that simply asking human beings t rate/chse their preferred item frm a list will generally yield n mre infrmatin than the fact that human beings want all the benefits and refuse the csts. Translating the previus sentence in a generic mdel utput, it will tell us that, fr instance, respndents prefer beautiful and high perfrmance cars at n cst. A DCE, instead, detects trade-ffs between attributes characterising tw r mre chice ptins. In ther wrds, they give infrmatin n the weight that each attribute (and each attribute-level) has n respndents chices. Therefre, a DCE invlves a data cllectin thrugh the cnstructin f hypthetical chice experiments in an applied survey. Several steps, have been described in detail by D.4.1 2, ranging frm the prblem definitin, the experimental design and questinnaire setting and administratin t mdel estimatin and interpretatin. One f the mst imprtant is the definitin f the chice set elements: alternatives, attributes, attributes-levels and their range, because the DCM utput mainly depends n these elements. The SEFIRA WP4 pilt survey fresees a chice experiment made up by tw unlabelled plicies (chice alternatives), described by selected attributes. The jint cperatin amng SEFIRA partners allwed us t discuss and identify a set f apprpriate attributes t include in the DCE pilt survey. The questinnaire will be tested befre f its use and a useful number f attributes will be selected in rder t achieve the best infrmatin quality. Mrever, in the DCE the scial aspects related t individual chices will be taken int cnsideratins (e.g. scial dilemma, etc.) and sci-ecnmic data f respndents will be used in rder t perfrm segmentatin analysis, highlighting 2 Fr further details n the methdlgy steps see: Valeri et al. 2014, p 28. 7

8 sci-ecnmic differences in the air quality acceptability acrss the selected cuntries in the pilt. The DCE that has been develped aiming at prviding infrmatin useful t be integrated within the GAINS mdel, run by the SEFIRA prject member IIASA. In fact, DCMs results are expected t prvide additinal relevant behaviural infrmatin that can be ptentially integrated int GAINS scenaris selecting and testing the mst apprpriate air quality plicies. Within this framewrk, the fllwing crucial aspects, representing the basis fr the present reprt, have been investigated and clarified in reprt 4.1. (Valeri et al. 2014): a) The standard envirnmental literature makes a distinctin between technical and nn-technical measures t imprve air quality. b) There is an urgent need t understand and prperly analyse nn-technical measures. c) IAMs take int cnsideratin behaviural measures nly as exgenus items in the scenari analysis. d) SEFIRA fcuses n the rle f individual behaviur fr successful plicies. e) The acceptability is crucial fr the implementatin and effectiveness f plicies. f) The understanding f trade-ffs amng the varius acceptability drivers is a pririty fr the plicy making prcess. g) There is well established DCM literature n plicy acceptability. h) DCMs fcus n identifying the underlying influences n an individual s chice behaviur, estimating the attribute s trade-ffs. i) DCMs are based n the ecnmic cnsumer thery and the principle f randm utility maximizatin; j) DCMs in SEFIRA are used t understand the rle f selected acceptability drivers/attributes cncerning air quality plicies. In this reprt we will give an in-depth descriptin f the practical elements fr cnstructing and develping the DCE pilt survey. In particular, we illustrate and mtivate the methdlgical chices made in the research plan with respect t: i) the 8

9 selectin f the attributes characterising alternative plicy measures; ii) the number and range f the attribute levels; iii) the experimental design useful fr defining the mst apprpriate attribute levels cmbinatins t be presented t the respndents in their chice tasks; iv) the questinnaire structure, the type f survey administratin and the sampling strategy fr data acquisitin purpse; v) data mdelling and utput evaluatin. The details f the questinnaire cnfiguratin are als shwn explaining the reasns fr including specific questins. In the final appendix bth the Italian draft versin that will be used fr a pilt pre-test and a beta English versin fr explanatin purpses are attached. Thse tls will be tested and imprved by ur partners in rder t be ready fr the next steps f the DCE. Finally, the next steps, required fr achieving the SEFIRA WP4 targets, are pinted ut. 9

10 Pilt research plan Every plicy implies a certain level f scial utility depending n its characteristics. If we assume that individual preferences are captured by utility functins, the higher is the utility level f a plicy, the greater is the prbability that an individual chses/prefers that plicy. Stated preference methds refer t a family f techniques which fresees interviewing individuals cncerning their preferences regarding a set f different ptins t estimate utility functins. The ptins are descriptins f gds r services which differentiate fr the characteristics they hld. They mainly deal with hypthetical situatins made up adhc by the researcher. By their nature, these methds require purpse-designed surveys fr their cllectin f data. Such methds are, by far, the mst used t measure the tradeffs cncerning preferences. They are based n a decmpsitin mechanism: in fact, preferences are btained and brken dwn int as many partial preferences as there are. A preference can be expressed in three different ways: respndent may give a rank between ptins (n metric valuatin), they may rate a set f alternatives, r they may chse the best ptin. The latter is less infrmative but easier and faster fr individuals than the ther tasks and it is the ne that they make in reality, by cmparing a set f situatins and selecting ne. Furthermre, this methd des nt require any assumptins t be made abut rder r cardinality measurement (Luviere, 1988). A pilt research based n DCEs needs a series f planning phases t be develped. High quality infrmatin are gathered if a careful and scrupulus initial wrk is dne (Gatta, 2006). Mre in detail, the fllwing steps are required: 1) prblem characterizatin; 2) selectin f attributes; 3) assignment f levels; 4) experimental design; 5) questinnaire structure and administratin; 6) sampling; 7) mdel implementatin; 8) results interpretatin. In what fllws, the varius steps are described: Prblem characterizatin First f all, the research questin has t be set s t take the mst apprpriate decisin when implementing a DCE survey. In ur case, the main bjective is t investigate peple acceptability twards specific plicy drivers belnging t air quality measures thus 10

11 btaining a weight f their imprtance in specific urban areas in the EU. All the questins that need t be answered in each stage have t be strictly linked t the gal f the study. Selectin f attributes This stage invlves identifying the relevant attributes characterising an hypthetical air quality plicy measure. This is usually dne thrugh literature reviews and fcus grup discussin. The acceptability cncept and the main drivers by which it is influenced have been defined in the deliverable D 4.1. In the literature there is a wide list f drivers affecting the plicy acceptability frm an individual perspective. There are a number f factrs that are generally knwn as having a majr rle in the acceptability (r nnacceptability) f a measure and mainly related t the prblem perceptin the scial nrms, the knwledge abut ptins, the perceived effectiveness and efficiency, equity and fairness f a measure, and sci-ecnmic and system characteristics. Hwever, there are many different acceptability drivers are, and sme chices are needed in rder t select thse drivers t be included in the WP4 pilt DCE. In fact, when the number f attributes rises the burden f infrmatin that respndents have t prcess befre chsing increases. In this specific cntext, the cperatin between the SEFIRA partners frm the University f Urbin (UNIURB), the Internatinal Institute fr Applied System Analysis (IIASA) the King s Cllege f Lndn (KINGS), the Pragma market research cmpany (PRAGMA) allwed us t narrw the full list f drivers (arund #30). First, a mnetary cst is included t allw the estimatin f willingness t pay measures. Anther relevant aspect is the level f persnal engagement (hw peple are willing t accept changes in their individual life style) related t mbility behaviur and eating habits. The time required fr the plicy t give its beneficial effects n air quality and its impact in terms f premature deaths are als cnsidered. Assignment f levels The attribute levels shuld be realistic (practically-achievable) and span the range ver which ne expects respndents t have preferences. Mrever, the levels shuld be chsen s that respndents appreciate attribute variatins. The number f levels used has an impact n the experimental design. In fact, the larger the number f levels per attribute, the larger the experimental design will be, requiring a mre cmplex structure as well as sample size increasing in rder t btain statistically significant results. In ur case, 3-11

12 levels per attribute is cnsidered the mst apprpriate strategy t balance technical needs and decisin-making perspective (having the pssibility t detect ptential nn-linear effects f attribute variatins n utility). Attribute levels are designed using either abslute values r percentage terms. The latter case is applied when an attribute is pivted arund either a self-reprted value stated by respndents (i.e. mbility behavir and eating habits) r a fixed value representing an average measure fr Eurpe (i.e. premature deaths caused by atmspheric pllutin in 2013) helping t frame the chice scenari accrding t the actual experience. Hwever, levels are always displayed with abslute values (percentage variatins are calculated) t facilitate respndents in their chsing prcess. In mre detail, the attribute levels are reprted belw: Attribute levels selected Level 1 Level 2 Level 3 Level 4 Per capita annual cst n individual cst Decrease required in the use f car/mtrcycle N changes 25% less 50% less Decrease required in the cnsumptin f red meat and/r dairy prducts N changes 25% less 50% less Reductin f premature deaths 10% less 20% less 50% less Reductin f the PM 2.5 Level -50% -100% Tempral hrizn f the plicy 1 year 5 years 15 years Distributin f the cst f the plicy Wh pllutes mre pays mre Pr peple pay less Experimental design Statistical design thery is used t cmbine the levels f the attributes int a number f alternatives t be presented t respndents. The ttal number f ptins is a functin bth f the number f attributes and the number f attribute levels. An experimental design is, de fact, a matrix f values cntaining the levels f the attributes that will cnstitute DCEs. The analyst has t ptimize the allcatin f the attribute levels t the design matrix given his research gals. The pssible designs that can be implemented are: full factrial, fractinal factrial and efficient. Several design 12

13 prperties (e.g. rthgnality, level balance, minimal verlap) are cnsidered as criteria t generate a specific design (Rse and Bliemer, 2004; Huber and Zwerina, 1996). In ur case, a fractinal factrial design is chsen. This type f experimental design prvides the means t select subsets f the ttal set f pssible alternatives in a statistically efficient manner. A full factrial design (each pssible cmbinatin f all the attribute levels) wuld imply an impracticably large number f cmbinatins t be evaluated (i.e. 3x3x3x3x3=243). An efficient design, requiring an a priri knwledge abut the parameters t be estimated (nt present), wuld imply a very cmplex multistage apprach nt in line with the budget set. The attribute levels are cmbined int plicy ptins and five binary chice sets per interview are cnstructed. T allw fr a rich variatin in the cmbinatin f attribute levels, a blcking strategy is adpted preparing different versins f the survey frm. Questinnaire structure and administratin Generally, a questinnaire fr DCEs includes additinal sectins t the chice tasks in rder t btain mre infrmatin n the sample interviewed useful fr prfiling and segmenting them. An in-depth descriptin f the questinnaire is reprted in the next paragraph. The main survey mdes are: CATI (cmputer-assisted telephne interviews); CAPI (cmputer-assisted persnal interviews); CAWI (cmputer-assisted web interviews). Data cllectin methds have their prs/cns and differ in terms f cst, quality, quantity and time needed t gather the data, respnse rate and sample cntrl, flexibility and degree f cmplexity. The CAWI methd has been chsen mainly fr its multiple advantages: it has been cnsidered mre cnsistent with DCEs than CATI, since it allws the use f visual aids and gives respndents all the time they need t make their chices; als, being mre cst-effective, it allws t interview larger sample sizes and hence t increase the rbustness f the statistical analysis. 13

14 Sampling Sampling is the prcess f selecting a relatively small grup f peple frm a specific universe (ppulatin) t be surveyed. Sampling invlves several specific decisins. First, defining the target ppulatin is extremely imprtant in rder t accurately seize whm the study bjects are and where they are lcated (sampling frame). In ur case, given the research bjectives and the plicy drivers selected, the target ppulatin is defined as active peple wh bth use car/mtrcycle fr their mbility and eat red meat and/r dairy prducts, even ccasinally. Our sample will be natinally representative reflecting the distributin f ppulatin 18+ in terms f age and gender. The sample will be representative at NUTS level 3 s that it will be able t cnsider bth rural and urban areas. The questinnaire will be prgrammed by Pragma s EDP team using the NIPO sftware which ffers the capability t build highly sphisticated and prfessinal questinnaires (cmplex ruting, rtatin, randmizatin and dynamic texts) and tp grant data integrity and accuracy. The sample will be prvided by an access panel (Cint). Mdel implementatin After administering the survey, DCEs data need t be apprpriately rganized s t be analyzed via specific ecnmetric sftware (e.g. BIOGEME, NLOGIT). Generally, a data set is generated where each respndent prduces n rws, ne fr each chice made. Quality cntrl prcedures are used t detect pssible incmplete data recrds, utliers r incrrect entries. Data analysis allws t btain infrmatin abut the relative imprtance f the attributes thrugh the estimatin f attribute cefficients using discrete chice mdels (Hensher et al., 2005). The mst ppular is the Multinmial lgit mdel, which is derived frm the assumptin that the errr terms f the utility functins are independent and identically Gumbel distributed. The standard estimatin technique fr this kind f prblem (Maximum Likelihd) estimates that set f cefficients which, when inserted int the deterministic part f the utility functin, maximizes the jint prbability acrss all the bservatins f the chices actually made. Multinmial lgit mdel is characterized by imprtant advantages (e.g. simplicity in estimatin, mdel s clsed-frm specificatin, accessible and easy t use packaged 14

15 estimatin sftware) and relevant drawbacks mainly linked t the assumptin f preference hmgeneity acrss respndents (McFadden, 1974) where the estimated parameters represent the marginal utility f each attribute variatin. Mre flexible mdels can be used in rder t test fr preference hetergeneity (Marcucci and Gatta, 2012), incrprating taste hetergeneity via the systematic cmpnent f utility and relying n the assumptin f either cntinuus r discrete mixture structure (i.e. mixed lgit and latent class mdel), and t integrate latent cnstructs int the decisin making prcess (i.e. hybrid chice mdels). A selectin f the mst knwn DCM applicatins has been prvided in D p. 24. Results interpretatin Validity and reliability tests f the ecnmetric results are needed. Gdness-f-fit is investigated via lg-likelihd rati tests, verall mdel explanatry pwer (Pseud R- square index), predictin success tables. Discrete chice mdels allw us t btain estimates f the weight f each attribute. It is imprtant, frm an interpretatin pint f view, t check the statistical significance f the attribute cefficients as well as the cnsistency f their signs with ecnmic thery. Fundamental infrmatin regarding the apprpriateness f varius plicies can be btained by calculating: i) direct elasticity (i.e. percentage change in the prbability f chsing a particular alternative in the chice set with respect t a given percentage change in an attribute f the same alternative); ii) crss elasticity (i.e. percentage change in the prbability f chsing a particular alternative in the chice set with respect t a given percentage change in an attribute f a cmpeting alternative); iii) marginal rates f substitutin (i.e. willingness t pay fr each attribute). In particular, the behaviural interpretatin f the utput is linked t the willingness t pay measures. In a chice mdeling framewrk, where utility is assumed t be linear, additive functin in the attributes each assciated with a single taste weight, the willingness t pay pint estimate fr a given attribute can be btained dividing its marginal cefficient by that f cst estimates f the amunt f mney an individual is willing t pay (r willing t accept) t btain sme benefit (r avid sme cst) frm a specific actin/plicy. 15

16 16 Plicy implicatins are discussed via willingness t pay estimates and scenari simulatins. The intent is t perfrm analyses and scenari simulatins that prvide decisin makers with ex-ante evaluatins f the mst likely reactin t alternative air quality plicies.

17 Questinnaire prject and methdlgy In this sectin the different cmpnents f the questinnaire are illustrated, while in the appendix the final questinnaire fr the test pilt is reprted. There are three sectins: User prfiling ; Chice experiment ; Persnal attitudes. Sectin 1 User prfiling In this sectin specific infrmatin regarding sci-ecnmic status are inquired. In mre detail, standard questins are psed: age, gender, educatin, emplyment, marital status, husehld cmpsitin and family incme. Mbility behavir and eating habits are als investigated. In particular, the fllwing tw specific questins will be the base fr the custmized levels displayed in the chice tasks (sectin 2 f the questinnaire): 1) Hw many days weekly d yu use car/mtrcycle? ; 2) Hw many days weekly d yu eat red meat/dairy prducts?. Sci-ecnmic infrmatin is useful fr bth prfiling the sample interviewed and detecting pssible different tastes. In fact, preference hetergeneity can be investigated by interacting sci-ecnmic variables with the attributes characterizing chice ptins r, alternatively, by estimating different mdels fr subsets f data. Sectin 2 Chice experiment In the secnd part f the questinnaire, five chice experiments are presented t the respndents. Befre asking peple t make a cmpensatry evaluatin amng the alternative ptins in a chice task, an intrductry sectin is shwn. This is fundamental t give respndent an verall view f the chice cntext prviding the mst imprtant infrmatin thus crrectly filling the questinnaire. In particular, the cntext f air quality plicies is described alng with the specific definitin f the key terms as well as the attributes characterizing the alternatives. 17

18 A graphical chice task is displayed belw: Example f a chice task Characteristics Plicy 1 Plicy 2 Individual annual cst 50 eur per year 25 eur per year Reductin required in the use f car/mtrcycle Reductin required in the cnsumptin f beef, lamb, prk, hrse meat and/r dairy prducts Optin 1 Reductin f premature deaths Optin 2 Reductin f PM 2.5 level N reductin required 5 days less per mnth less premature deaths per year At the level set by the WHO guidelines 10 days less per mnth 10 days less per mnth less premature deaths per year 50% abve the threshld set by the WHO guidelines Optin 1 Tempral hrizn f the plicy 1 year 5 years Optin 2 Distributin f the cst f the plicy Wh pllutes mre, pays mre Pr peple pay less Which plicy d yu chse? Plicy 1 Plicy 2 Nne f these tw (If nne f these tw). If yu were cmpelled t chse ne f thse plicies, which ne wuld yu prefer? Plicy 1 Plicy 2 Respndent are asked t cmpare the tw alternatives and select the ne prviding the highest utility. They are als allwed t indicate whether bth hypthetical plicies are unacceptable. 18

19 Sectin 3 Persnal attitudes In the last sectin, attitudinal data are cnsidered. The cnsideratin f latent factrs linked t persnal attitudes and mtivatins is a plus f the pilt research. In fact, the rle psychlgical factrs play in the chice prcess might be relevant. Accunting fr bth ecnmetric and psychmetric aspects int behaviral chice mdels imprves results realism. The questinnaire includes specific items derived frm psychlgical theries f deliberate chice, such as Thery Planned Behavir (Ajzen, 1991) and its extensins, with a 5-pint Likert scale (ranging frm I d nt agree t I fully agree ): Envirnmental perceptin (e.g. Envirnmental prtectin will prvide a better wrld fr me and my children) Prblem awareness (e.g. Currently I live in a highly plluted city) Persnal nrm (e.g. It is my wn respnsibility t undertake actins t preserve the envirnment) Scial Netwrk and technlgy (e.g. Scial netwrks are imprtant fr increasing scial invlvement in envirnmental issues) Scial trust (e.g. I usually trust peple arund me) Health awareness (e.g. I m aware that atmspheric pllutin is dangerus fr living beings) In additin, ther valuable infrmatin is requested regarding: i) the plicy pririties fr imprving air quality; ii) the cncept f fairness; iii) the sectr that shuld mainly bear the ecnmic and rganising cst f a new set f plicies. The intent is t pssibly test, develp and extend the discrete chice framewrk thrugh hybrid chice mdels (Dazian and Blduc, 2013) which substantially reflect the integratin f psychmetric mdels in the field f discrete chice micr-ecnmetrics. An explicit cnsideratin f psychlgical factrs, in fact, might lead t a mre behavirally realistic representatin f the chice prcess and, cnsequently, better explanatry pwer. This methdlgical framewrk will help t indirectly pint ut the imprtance that peple award t different factrs that characterize air quality plicies. 19

20 Cnclusins This reprt presents the tl resulting frm the methdlgical and theretical reflectins described in the previus reprt (D. 4.1) reprting the fregrund fr the applicatin f the DCE methd. The selected tls will be first tested thrugh a small pilt DCE. Such an experiment will enable us t assess the technical aspects f the methdlgy and its adequacy t investigate the ptential f the methd in increasing ur knwledge f specific plicy drivers (attributes) that belng t air quality plicies and that culd influence their degree f acceptability. The results will als help Pragma imprving the final versin f the questinnaire t be translated int the natinal languages f the selected cuntries. In the appendix the beta versin f the questinnaire. Once the tl has been tested, DCEs will allw us nt nly t have a specific cefficient measuring the weight (r the imprtance) f the selected plicy acceptability drivers, but it will allw us als t detect the trade-ffs that individuals make between the different levels. In fact, the DCE will be implemented fcusing n the study f users behaviur tward the acceptance (r rejectin) f selected air quality measures/plicies. The results f the experiment will identify which measures are mst accepted and therefre ptentially the mst respected and chsen. This aspect is imprtant in particular fr thse plicies whse impact depends n the users preferences and behaviur. The next steps needed t reach the SEFIRA WP4 bjectives are the fllwing: a) Carry ut a first test pilt fr checking all the critical elements in the questinnaire. The test will be carried ut thrugh 500 CAWI interviews in Italy, and it will be finalised t check the structure and cntent f the questinnaire, in particular the attribute levels, the mde f cllectin and the adequateness f the target in the light f the survey gals, as well as the respnse rate (cmpleted interviews, interrupted, etc...) and the length f interview. b) Analyse the test pilt data and validate the questinnaire. c) Implement the DCE thrugh a survey using CAWI methdlgies in the five Eurpean cuntries selected fr the SEFIRA prject (UK, Sweden, Germany, Pland, Italy). The survey will be managed by the SEFIRA Partner PRAGMA. The questinnaires will be translated in the cuntries languages by prfessinal translatrs and reviewed by each partner f the SEFIRA Cnsrtium. Then, Pragma will prepare the CAWI survey in each language. 20