CONTROLES PRENATALES

As explained previously, all environmental valuation methods have different capacities for calculating the welfare impact of environmental goods and services. A considerable

effort in the environmental valuation literature has been focused on improving SP techniques to address methodological criticism levelled against them. The need for improving what was considered to be a well-established method for eliciting environmental preferences, i.e. the CV, became more relevant after the accumulation of evidence revealing significant problems related to it (Hausman, 2012; Kahneman and Knetsch, 1992).

Despite their popularity in marketing and transport research areas (Anderson et al., 1985; Louviere and Hensher, 1983; Louviere and Woodworth, 1983), survey-based methodologies utilising choice modelling (CM) approaches did not become an attractive alternative in the environmental research literature until the 1990s. That is, following the first application to natural resources by Adamowicz et al. (1994). Environmental valuation literature then started to argue for the ‘adequacy’ and ‘superiority’ of one variant of the CM approach, known as ‘discrete choice experiments’ to study society’s preference-based values (see Hanley et al., 2001).

The DCE conceptual base relies on Lancaster’s economic theory of value (Lancaster, 1966) and the random utility maximisation (RUM) theory (McFadden, 1973). The former theory explains an individual’s utility derived from the consumption of a good as the composite of utilities associated with the characteristics or attributes of this good. The latter theory proposes the inclusion of random elements in the utility model (RUM models) to allow for the estimation of choice probabilities (McFadden, 2001).

In environmental applications of RUM models, an individual’s utility for an alternative 𝑖

depends on a deterministic component and a random component 𝑈𝑖 = 𝑉𝑖+ 𝜀𝑖 = 𝛽 𝑥𝑖 +

𝜀_𝑖, where 𝑥_𝑖 is a vector of attributes describing the environmental management option and

𝛽 is a vector of coefficients that explain the relative importance of the attributes to individuals. Individuals are assumed to select the management options which provides

them with more utility. Therefore, the probability of choosing alternative 𝑖 over 𝑗

is P(𝑖 𝑐ℎ𝑜𝑠𝑒𝑛) = 𝑃(𝑉_𝑖 + 𝜀_𝑖 > 𝑉_𝑗+ 𝜀_𝑗; ∀𝑗 ∈ 𝐶). The error term 𝜀 reflect researchers’ inability to observe all the factors influencing respondents’ choices for environmental management (McFadden, 1973), and randomness in choice on the part of respondents.

Further details of the different approaches used to estimate RUM models can be found in the sections 4.2, 5.2 and 6.2 of this thesis.

In a DCE, respondents are repeatedly asked to elicit their most preferred option when facing repetitive hypothetical choice scenarios of goods described in terms of their attributes and variations on their levels. The answers are used to estimate the model which predicts choice probabilities on the basis of an individual’s willingness to trade between attributes. As the attributes are commonly used to define the characteristics of environmental goods or environmental policies, and one of the attributes usually represents the cost of this alternative, marginal values of a unitary change in any one of the attributes can be computed. The marginal WTP estimates are calculated with the ratio of the attribute coefficient to the estimate of the marginal utility of income (Train, 2009a).

The WTP estimate values, not only reflect individuals’ potential monetary contribution but can also be interpreted as their ‘behavioural intentions’ (Bateman et al., 2003; Pouta and Rekola, 2001). Behaviour literature suggests that ‘behavioural intentions’ precede explicit behaviour and therefore are relevant to study for understanding and predicting social behaviour (Ajzen, 1985; Ajzen and Fishbein, 1980).

The application of DCE for assessing environmental preference-based values has practical and estimation advantages. Regarding the practical advantages, it is considered that the DCE technique is a realistic way of collecting preferences as it emulates real market situations where respondents are required to choose among alternative goods (Louviere et al., 2010). Second, the careful selection and design of attributes allow for increasing their credibility/viability and consequentially improve choice scenario realism (Hess and Rose, 2009). Third, DCE is considered to be a cost-efficient technique for measuring use along with non-use values (Birol and Koundouri, 2008; Hanley et al., 2001, 1998). The cost-efficiency of DCE is explained by their capacity to extract additional policy-relevant information such as the total and marginal values of several attributes (Hanley et al., 1998), as well as their capacity to derive multiple responses from each person surveyed (Hanley et al., 2001).

Regarding the estimation advantages, we can mention two relevant ones. First, the panel nature of the choice data permits to test the validity and consistency of respondent’s

answers throughout the repeated sampling of individuals (Boxall et al., 1996). Second, the DCE is considered to be a more robust method to avoid collinearity among attributes (Hanley et al., 1998), reduce strategical bias (Birol and Koundouri, 2008) and lessen ethical protests “as the choice context can be less ‘stark’ than direct elicitation of

willingness to pay” (Hanley et al., 2001, p. 451).

On the other hand, the main criticism of using DCE for studying the decision heuristics

relate to: i) the hypothetical bias caused by its ‘fictitious’ nature (Gómez-Baggethun and

Ruiz-Pérez, 2011; Murphy et al., 2005), ii) the insensitivity of WTP estimate values to the scope or scale of attributes (Boyle et al., 1994; Kahneman and Knetsch, 1992), and iii) the effects of imposing a ‘cognitive burden’ to respondents (Swait and Adamowicz, 1996; Tversky and Shafir, 1992).

The problems mentioned above are not exclusive to this SP technique, and in fact, are partially mitigated because of the repetitive and additive nature of DCE (Foster and Mourato, 2003; Hanley et al., 2001). Furthermore, careful piloting of the design and modelling process help to alleviate these issues. Hence, the use of focus groups can help

to define choice attributes, whereas pilot surveys are useful for pre-testing the survey and

choice cards (Hoyos, 2010).

In their systematic review of the SP published literature, Mahieu et al. (2014) found that the probability for an article to use DCE during the years 2004-2013 is higher in comparison to the CV technique. Their analysis also revealed that this probability is relatively small in environmental studies when compared to studies from other research disciplines such as agriculture, health and transport. Their findings suggest that even though there is a growing popularity of this method, the use of DCE is still contested in the environmental literature. This could be partly explained by the ongoing debate in the literature around its capacity to account for the pluralism of values (Gómez-Baggethun and Ruiz-Pérez, 2011; Kumar and Kumar, 2008), as well as its ability to realistically represent and predict individuals’ behavioural decision making process (Moshe Ben- Akiva et al., 1999; Spangenberg and Settele, 2010).

Simplifying the complexity of the decision heuristic is inevitable in any model, as they

reviews the studies using the DCE to value estuarine ES, as well as the discrete choice literature exploring the sources of preference heterogeneity (section 2.5 and 2.6). In reviewing the valuation literature, we identified an emerging trend which claims for the use of more flexible modelling approaches which acknowledge the complexity of the human decision making process. Choice models have started to adapt to the study of intangible goods such as the estuarine ES, and have started to use behaviourally realistic

structures for analysing preference-based assigned values. Thus, our research is in line

with this novel body of literature as it contributes to revealing additional layers of preference heterogeneity.