Further research

In order to advance the understanding of choice heuristics (Alemu et al., 2013; Amir and Levav, 2008; Campbell et al., 2011; Hensher et al., 2015; Leong and Hensher, 2012; Scarpa et al., 2009) it is recommended to take analysis of preference heterogeneity to a further level. Instead of developing independent analyses for each potential source of preference heterogeneity, as it was done in this study, future research should focus on developing new models which are capable of accounting for the effect of observable, latent and contextual variables simultaneously. There is an enormous potential for merging structural equations with choice models in the HCM framework proposed by (McFadden, 1986). For instance, few authors have augmented HCM by exploring the simultaneous effect of a mixture of latent influences (e.g. environmental attitudes, social

influence, social environment) on environmental preferences (Kamargianni et al., 2014; Kim et al., 2014a). Further work could also generate latent variables describing the ‘environmental context’ at the individual level which could be measured through the use of GIS-based environmental quality indicators. Using the HCM in this way would allow to further understand to which extent local clustering of WTP is driven by similarities of preferences in a specific location, or instead, is driven by the influence of the ‘local natural context’ on society’s environmental preferences.

A growing body of environmental valuation literature argues for incorporating further relevant factors while estimating contingent values (Aldrich et al., 2007; Cunha-e-Sá et al., 2012; Meyerhoff, 2006; Sauer and Fischer, 2010; Spash et al., 2009) and modelling choice behaviour (Boyce et al., 2017; Campbell et al., 2009; Czajkowski et al., 2016; Faccioli et al., 2018; Hess, 2007; Kim et al., 2014a). In order to estimate more complex models studies need large and representative samples. The majority of the empirical applications of DCE to the environment attempt to obtain samples which are representative of the population in terms of socioeconomic characteristics. However few studies are concerned about achieving the spatial representativeness of the sample (Campbell, 2007; Campbell et al., 2009; Schaafsma, 2010; Schaafsma et al., 2012). It has been suggested that not using spatially representative samples could lead to bias estimates to quantify the aggregated welfare impact (Bateman et al., 2006). However, further analysis is needed to test whether obtaining spatially representative samples also help to improve the accuracy of individual-level WTP estimates, especially in studies aiming to include the spatial dimension into the analysis of environmental choices. Another consideration to explore in future studies is if the welfare estimates and the results of second-stage analysis could be biased/different when sampling only the residents of an area. This thesis attempted to explore this in chapter x, but it was inviable since the resident subset sample for each study site is very small.

Moreover, it has to be recognised that like any other survey-based research instrument, DCE has limitations on the amount of additional information to be collected without resulting in a heavy cognitive burden for respondents. We limited our analysis to the study of three potentially relevant sources of preference heterogeneity. However, it is the intention of the researcher to use the hybrid model framework to extend the preference

heterogeneity analysis further to examine additional factors such as the temporal context (e.g. historical events), the social interaction (e.g. family and/or neighbours influence), individuals’ levels of concern for the welfare of others (e.g. altruism), and perceived environmental quality (e.g. perceived abundance). Using more complex modelling structures could provide additional insights about the effect of altruism and social motivations (Bartczak, 2015; Cooper et al., 2004; Lee and Chung, 2012) or perceived environmental quality (Cameron et al., 2011; Domínguez-Torreiro and Soliño, 2011; Kataria et al., 2012; Leggett, 2002) on the decision making process, as well as increase the understanding about ways in which these factors are linked with respondents’ socioeconomic characteristics.

More generally, two factors hamper the use of more ‘complex’ or ‘advanced’ CM techniques. First, their estimation commonly results in a substantial increase of the computational efforts as models could take several days to converge. Second, advanced choice models considerably increase coding efforts as they are not available in standard statistical packages. Czajkowski et al. (2017), have contributed to making these models available by developing a series of HCM and making the relevant Matlab code available with their publication.17 Nonetheless, future efforts could be focused on making them more readily available to choice modellers in open source statistical packages.

Applied choice experiments also have temporal and financial limitations for delivering research outputs. The financial limitations of the present analysis, for instance, restricted the ways in which stakeholders could participate throughout the design process of the DCE. Although deliberative approaches to valuation (e.g. focus groups and visioning workshops) could help to empower citizens through the democratisation of the decision making process (Brown et al., 1995; Jacobs, 1997; Kenyon et al., 2001; Lo and Spash, 2013; Sagoff, 1998; Spash, 2001; Ward, 1999), it has also been suggested that the quality, significance and the legitimacy of valuation study outcomes are often dependent on ways in which participation is framed (Carnoye and Lopes, 2015; Jacobs, 1997; Niemeyer and Spash, 2001). Deliberative environmental valuation is considered to be more useful when individuals have a direct financial relationship with the agency proposed to collect the

funds (Niemeyer and Spash, 2001). For instance, when the environmental project involves local councils to which citizens already pay taxes, or organisations to which citizens already pay bills. Moreover, it has been found that individual participatory methods are more capable of generating transparent and quantifiable data, in comparison to group-based methods which are often harder to channel directly into the policy making process (Carnoye and Lopes, 2015). Since money and time represent essential limitations to the development of environmental valuation studies, there is a further need for studies to develop cost-effectiveness analyses of using participatory approaches in the context of ES valuations.

Finally, the use of multi-case valuation studies (Christie et al., 2015; Christie and Rayment, 2012; De Valck et al., 2017; Hanley et al., 2006; Lanz and Provins, 2013; Luisetti et al., 2011; Morrison and Bennett, 2004; Shen et al., 2015) could be advantageous for the generalisability and transferability of environmental valuation study outputs (Stewart, 2012). Nonetheless, this research design could lead to the reduction of the effective sub-sample sizes and potentially decrease the significance levels of the coefficients and thus worsen results robustness. Using cross-validation of the sub-sample estimates could serve as a method to increase the validity of the obtained site-specific WTP estimates. Researchers using multi-case studies might adopt the randomisation process used in the experimental design of this research as it permits to account for the site-specific environmental preferences on the process leading to the final experimental design (Czajkowski, 2016). It is the intention of the researcher to test whether there additional benefits from this ‘site-specific' experimental designs, such as the presence of significant efficiency gains. In other regards, the use of multi-case studies increase the analysis possibilities. For instance, researchers could develop overlay analysis in GIS to identify the most preferred position of restoration projects, using the geo-referenced WTP estimates related to different study sites in addition to other relevant environmental information layers. Finally, multi-case valuation studies can be incorporated into the general practice and could be beneficial for the benefit transfer literature in two ways. First, obtaining a more scattered sample of valuation studies in the UK increase the likelihood of the study and policy location being geographically proximate, which have been found to reduce transfer errors of value transfer (Kaul et al., 2013; Spash and Vatn, 2006). Secondly, the estimates obtained from multiple-case valuation studies can be used

to test the accuracy of value transfer exercises for riverine ecosystems (Morrison and Bennett, 2004), green spaces (Perino et al., 2014) and woodland recreation (Bateman et al., 1999).