
Abstract
Climate change has been receiving wide attention from the international community. The
UK, like many other countries, has set its target to achieve a 12.5% reduction in levels of six
greenhouse gases (GHG) by 2010, relative to 1990 levels. In order to gain a complete
understanding of the costs and benefits of GHG reductions, the public’s willingness-to-
pay (WTP) must be considered. In this study the preference and WTP for GHG emissions
reductions involving one of the major UK water companies’ customers were investigated
using a Bayesian mixed logit analysis of survey-based choice experiment data. Advanced
econometric techniques were employed to accommodate indifference and multi-modality
in preferences and WTPs across various customer segments. Results show that average per
unit WTP to avoid increased GHG emissions is larger than that for efforts to reduce them. In
addition, customers are less likely to be indifferent to the former compared to the latter; this
is particularly true when GHG emission reductions are relatively small. In this case a
relatively large proportion of customers are found to be indifferent with a near-zero per
unit WTP. On the other hand, as reductions in GHG emissions increase more customers
become sensitized as reflected by the smaller proportion of customers with a near-zero per
unit WTP. The implications of these results as related to cost-effective investment planning
aimed at mitigating climate change risks are further discussed.
2012 Elsevier Ltd. All rights reserved.
1. Introduction
Climate change strategy drives the transition to a low carbon economy, creating opportunities and risks to which businesses must respond to succeed. International policies such as the Kyoto Protocol are major drivers behind the EU and UK emissions targets and behind legislations that shape thebusiness environment. The UK adopted the Climate Change Act (2008), which sets a target for the UK to reduce carbon emissions to 80% below 1990 levels by 2050 (Department of
Trade and Industry, 2003). It also sets an interim target of a 34% reduction by 2020 (with the potential to increase this to a 42% cut given an international agreement) and established the
concept of carbon budgets. It also targets to achieve a 12.5% reduction in levels of six greenhouse gases (GHG’s) by 2010, relative to 1990 levels (Tompkins et al., 2010). In the UK, the
water industry is made up of 12 water and sewage service providers and 14 water suppliers delivering more than 17 billion litres of water per day to domestic and commercial customers, the majority of which is then processed before being returned to the environment. As a result of these processes, the water industry produces about 4 million tonnes of CO2 (Carbon Trust, 2011). Out of the UK total GHG emissions, water and wastewater utility companies generate close to 0.8%. This contribution increases to 5.5% if GHG emissions from household water heating is included (Reffold et al., 2008). In this respect, the UK water industry faces difficult choices during investment planning as it needs to meet regulatory demands as well as its customers’ needs. An obvious interest for these water companies is thus to maximize benefits and revenues on their investments which may involve trade-offs between the various aspects of the service delivered, such as water quality, security of supply, and environmental steward-ship. In order to justify investment, it is important to acquire accurate information regarding attitude and associated benefits emerging from the mitigation of various risks linked to climate change ensuing from the operations of water companies and other related industries (e.g., emissions from water and sewage treatment plants, emissions from water heating systems, aging fleet). There is a wealth of literature examining public percep-tions of climate change. According to a UK survey, only 1% of the English public are not familiar with terms like ‘climate change’, ‘global warming’, or ‘greenhouse effect’; in fact, most people claim that they are familiar with the main causes of climate change and are concerned about it (DEFRA, 2002). In addition, the bulk of literature has focused on climate change mitigation and socio-technological transitions relating to energy policy (Foxon and Pearson, 2008; Jacobsson and Bergek, 2004; Smith et al., 2005). Evidence suggests that societal shifts occur as a result of a set of simultaneous changes in technology, practices, regulation, industrial networks, infra-structure and culture (Geels, 2002). However, many factors come to play when considering mitigating climate change through reduction in GHG emission levels. One of these factors is societal benefits, as often elicited in stated preference surveys by measures of willingness-to-pay (WTP), for improvements to existing services that may contribute to such mitigations. Several empirical studies, have demonstrated the public concern surrounding GHG emissions, and have shown a positive WTP for strategies aimed at reducing GHG emissions. Valuation studies conducted in a variety of countries and contexts (Brouwer et al., 2008; Carson et al., 2010; Lee and Cameron, 2008; MacKerron et al., 2009; Viscusi and Zeckhau- ser, 2006) all estimated a positive and often substantial WTP among the public for strategies aimed at reducing GHG emissions and attendant climate change risks. Stated preference studies of GHG emissions reduction have been important for demonstrating public support for mitigat-ing climate change, and for providing information on the effective design of policies to this end. Such studies reflect the uncertainties and misconceptions inherent in the debate on the role of GHG emissions in global warming, rainfall irregularities, and other symptoms of climate change (Viscusi and Zeckhauser, 2006). These uncertainties and misconcep-tions are demonstrated through the diversity of public opinion and support for GHG emissions reduction strategies. This has driven some studies to account for preference heterogeneity (i.e. the variation of preference intensities across members of the population) in the modelling of WTP for GHG mitigation (Carson et al., 2010; Fleischer and Sternberg, 2006; Layton and Brown, 2000). However, the assumption of joint normality adopted by such mixed logit (MXL) applications, whether applied to parameters governing GHG emissions or other service factors, could be unrealistic. A case in point is the price parameter and the need for it to be strictly negative, something which cannot be achieved with unbounded normal distributions. As a result, distributions of price parameters always end up with a share of the population counter-intuitively preferring higher prices. Moreover, since the normal distribution includes zero, the calculation of WTP becomes intractable with the price parameter being in the denominator. For these reasons, price parameters have been usually either fixed, or assigned alternative distributions such as lognormal (Revelt and Train, 1998), uniform and con-strained triangular distributions (Hensher and Greene, 2003; Revelt and Train, 1999). The problem is even more general than the need to impose sign constraints on the price parameters. Indeed as far as risk attitudes are concerned, levels of ‘dislike’ of higher risks of climate change are expected to be strictly positive when evaluated against a lower- or no-risk baseline level, which means that the distribution of attitudes would be bounded by zero at the lower end. Moreover, indifference towards risk of climate change might be prevalent among a large proportion of customers, especially where risk levels are low, while other customer groups may have a significant dislike of risk, in which case the distribution of risk parameters would be bimodal or diffuse. To the best of our knowledge, such distributional assumptions remain largely ignored by the stated preference literature dealing with climate change risks, if indeed preference heterogeneity is accounted for at all. This is rather surprising given the evidence on perceived individual and social barriers to taking action to address climate change, despite widespread agreement that people have a responsi-bility to do so (Lorenzoni et al., 2007). A recent exception to this pattern is Carson et al. (2010) who examined key determinants of public preferences toward climate change policy in Australia using a discrete choice experiment. Policy attributes included: (1) the starting date of an emissions trading program (2010 vs. 2012); (2) how to redistribute revenue generated by the policy option (reducing the Goods and Service Tax (GST) vs. give it to low to low-income households and seniors); (3) whether to invest some of the policy revenue in R&D (invest in R&D vs. lower taxes); (4) whether initially to exempt the transport sector (yes vs. no);
and (5) whether energy-intensive industries should receive special treatment (yes vs. no). Carson et al. (2010) employ an original orthogonal main effects design, combined with its mirror image or ‘‘fold-over’’ and subjected to some further modifications, resulted in a highly balanced design of 16 pairs of policy options. Respondents were then asked to choose the option they preferred most and their choices were recorded. The highly balanced nature of the design enabled the authors to employ a simple non-parametric alternative to the computationally intensive mixed logit model to analyze respondents’ stated choices. Attribute strengths of preference and their heterogeneity were recovered merely by counting the number of times particular levels were chosen and constructing their resultant frequency distributions. To this end, strengths of preference for attribute levels chosen as frequently as their opposites (i.e. eight times each) were normalized to 0, signifying actual indifference to it. Hence strengths of preference ranged from 8 (level never chosen) to 8 (level always chosen). Results indicated significant heterogeneity in the public’s preferences towards climate change policy attributes. Moreover, indifference and multi-modality was prevailed in more than one policy attribute. For example, a sizeable fraction of respondents were indifferent to the starting date of the emissions
trading scheme (with the largest mode at zero), with the number of respondents favouring a 2010 start outnumbering those preferring a 2015 start (with another mode at 8). Likewise with investing 20% of the revenue generated by the climate change policy to R&D, whereby a sizeable fraction was indifferent between it and lowering taxes, while another supported it. Moreover, tri-modality was observed with respect to the revenue redistribution attribute, where the largest fraction was indifferent between lowering GST and giving it to low-income households and the second and third roughly equally split between supporting the former and latter option, respectively. In this study, we investigate similar patterns of indifference and multi-modality in water customers’ preferences for reductions of greenhouse gas emissions associated with the operations of Veolia Water Central (VWC), a UK water company, and their WTP for such mitigations. Sources of GHG emissions associated with the water company include water treatment and distribution, wastewater collection and treatment, as well as other running operations related to transportation and administration. This study presents and discusses an application of Train and Sonnier’s (2005) bounded hierarchical Bayes (HB) mixed logit model to analyze data from a choice experiment (CE) on customers’ preferences for improvements in water service levels; reduced GHG emissions among others. In comparison to Carson et al. (2010), our data directly gauge attitudes to quantitative reductions of GHG emissions, rather than to policies implemented to achieve emission reduction targets. Hence it can be safely assumed
that such attitudes will be in the same positive direction for customers and will only differ in scale, where some would be indifferent with near-zero WTPs and others sensitive with positive and significant WTPs. In contrast, respondents may hold opposite views to given policies in Carson et al. (2010), varying between support and opposition to the proposed
policy.
In distributional terms, we can herein justify imposing bounds on preference parameter distribution, so as to restrict attitudes and WTP to be on one side of zero. Therefore, this study departs from the restrictive assumption of joint normality of preference parameters by using bounded and flexible distributions of tastes that are based on simple transformations of the normal distribution with zero at one end. This approach has been applied by Rigby and Burton
(2006) and Rigby et al. (2009) to model consumer perceptions and demand for genetically modified (GM) foods, showing that consumers are polarized into two segments: one strongly averse to purchasing GM food while another is indifferent. Balcombe et al. (2009) explored various bounded specifications to model consumers’ WTP for reduced and no-pesticide food.
As regards risk attitudes governing the safety of the water service, Chalak and Reid (2010) have also implemented this approach to modelling customers’ attitudes to risks of water
safety failure. They reported that indifference or nearindifference to risk is prevalent among a sizeable segment of the population, especially where risk levels are relatively low, and that recent experience of water safety events considerably reduces the extent of this indifference. Thus, they concluded that awareness campaigns realistically mimicking the trauma associated with such events would sensitize customers to such lower-risk events, much like TV ads warning against the dangers of drunk-driving by means of graphic representations of car accidents and their aftermaths. Similar evidence of indifference to risks of climate change
among a segment of water customers can be noted in this study.
2. The hierarchical Bayes mixed logit model
Since the development of simulation methods and their integration into relatively user-friendly software packages in the mid-1990s, the popularity of the mixed logit model was boosted and the number of applications of this computationally intensive model was dramatically increased (Hensher and Greene, 2003). The mixed logit model is one of many discrete choice models that aim to relax many of the constraining assumptions imposed by the standard conditional logit specification, most notably the independence from irrelevant alternatives (IIA) and taste homogeneity assumptions (Hensher and Greene, 2003; Train, 2009). This model assumesa continuity of preferences over some range of values following a distribution that is commonly assumed to have a parametric form of known properties. Most commonly, the normal distribution is conveniently chosen to this end. In this model, a person n faces a choice among J alternatives in each of T time periods. The utility derived from alternative j andperiod t (Unjt) would be:



where T is any of the transformations of normals proposed above.
According to Train and Sonnier (2005) and Rigby and Burton (2006), lognormal distributions have relatively thick tails which may yield unrealistically large WTP estimates. Moreover, log-normal distributions assign zero probability to null taste parameters and WTPs and hence do not accommodate indifference. In practice, a model with log-normal distributed parameters yielded unreasonably large parameter estimates and was consequently ignored. In the normal censored distribution, on the other hand, a mass of tastes is around zero with the density above zero being normally distributed, hence accommodating indifference in tastes. Finally, the SB distribution
allows a distribution of tastes between l and u. in this application, l was fixed at zero while u was determined in an iterative process to maximize model fit as measured by loglikelihood at convergence. This distribution is flexible enough to accommodate distributions that look like the lognormal albeit with a thinner tail, but also the possibility of flat plateaus between drop-offs on both sides, and even bi-modality. It is worth noting that the model parameters can be easily specified with full correlation under any combination of parameters distributions, since the correlation among the elements of b simply induces correlation among the elements of l, c or j.
3. Empirical study—customers’ preferences for greenhouse gas reductions
This study is based on a choice experiment (CE) survey undertaken for Veolia Water Central (VWC) – formerly known as Three Valleys Water – which serves 1.2 million households in Southeast England, as part of their periodic business plan submission to Ofwat, the England and Wales economic regulator of the water industry, for the price review cycle ending in 2009 (referred to as Price Review 2009, or PR09). Such submissions by water and sewerage companies in England and Wales help Ofwat’s to set limits on the prices water and sewerage companies can charge to their customers. In their submissions, water and sewerage companies need to demonstrate that their investments are consonant with their customers’ preferences and WTP’s for proposed improvements. Hence this study was commissioned by VWC with theaim is to gauge customers’ priorities for investment in their water service. The approach of using customer preference surveys for investment planning in the water industry is not new, with surveys carried out by Thames Water in Price Review 1999 (PR99) and more recently by Yorkshire Water at Price Review 2004 (PR04) (Willis et al., 2005). Respondents were presented with a series of ten choice scenarios consisting each of three options; a fixed status quo option characterizing the current situation and two generically constructed options. These options were described in terms ‘service attributes’. Extensive focus groups conducted with customers throughout the VWC region were used to help select the service attributes that were most relevant to the customers, as well as their most appropriate metrics and level ranges. The final list of attributes included:River water levels (RIVER), with two levels: a status quo levelequivalent to ‘no change’ with generally low flow of riversthroughout the summer months and drying up of somesmaller rivers, and an ‘enhanced service’ level where lowflows and some drying of small rivers will occur in at most 1-2 summer months;Water saved through further water efficiency measures(SAVED), ranging from a status quo level of no furtherefficiency measures to a maximum improvement levelequivalent to the water used by 300,000 household in a year,or, 25% use reduction;Number of tests of water quality failing to meet standards(TESTS), ranging from a deterioration level of 100 in 100,000to a maximum improvement of 5 in 100,000; and Annual greenhouse gas emissions (EMISS), the attribute ofinterest, ranging from a deterioration (1) level equivalent to130,000 cars doing average yearly mileage to a maximumimprovement (+2) level equal to 15,000 cars doing averageyearly mileage.A full account of the attributes and their levels is presented in Table 1. Levels of 0 characterize the current level or status quo; 1 deterioration relative to the status quo, and +1, +2 and +3 improvements relative to the status quo. It was ensured that for each attribute, the range of levels cover realistic situations, as advised by VWC. As to the associated changes in annualbills that would accompany improvements/deteriorations in attribute levels, 8 rather than 4 levels were defined: 2 decreases in annual bill (decrease by £15 or £5), no change in annual bill (i.e., status quo), and 5 increases in annual bill (increase by £5, £10, £15, £20 or £35). An example choice card is presented in Table 2.

A Db—optimal design (Ferrini and Scarpa, 2007) was obtained for the CE in which parameter mean estimates and their standard errors from a preliminary pilot survey of 50 respondents were used to update the design for the main data collection phase. A total of 120 choice sets were thus designed and blocked into twelve groups of ten sets each. Each respondent was faced with a randomly assigned block of choice sets. The survey targeted a representative sample of 245 households served by VWC. This included the 50 interviews from the pilot phase since virtually no modifications were made to the pilot survey instrument before it went into the main data collection phase. Interviews were conducted face-to-face, in the home and using computer-aided personal interviewing (CAPI) scripts. Interviewing took place in October and November 2007. The sample was geographically representative down to the parish level (based on the UK’s 2001 population census). In addition, the sample was also representative of the company’s customer base in terms of age, gender and socio-economic grade, in addition to geography.
4. Results
Three HB mixed logit models with fully correlated parameters were estimated, differing with respect to the distributional specification of parameters: (1) normal, (2) censored normal and (3) SB. Econometric estimation was implemented in MATLAB R2009a using code written by Kenneth Train and available on his home page (http://elsa.berkeley.edu/Software/abstracts/train1006mxlhb.html). Levels were dummy coded such that they measured attitudes towards each level relative to its status quo baseline level (i.e. events that were assigned levels of zero in Table 1). We accounted for full correlation among parameters since each of the RIVER, SAVED, TESTS and EMISS parameter sets pertained to quantitative levels of the same attribute and are hence governed by similar perceptions. We follow the terminology of Train and Sonnier (2005) by referring to underlying parameter estimates as bn and their censored normal or SB transforms as partworths. A partworth estimate indicates the marginal utility of a given attribute; that is, the utility derived from a unit increase in the provision of a good, or reduction in the emission of a ‘bad’, such as GHGs. A positive partworth indicates the actual preference for the good in question, and a negative partworth reflects the dislike of the bad in question. As for yearly bill increase/decrease (BILL), it was treated as continuous such that its partworth represented a per-unit value. The BILL parameter was fixed in order to avoid unreasonably high WTP values for customers with virtually null BILL parameters. The signs of BILL and deterioration (1) levels of the TESTS and EMISS were reversed since censored normal and SB distributed partworths can only be positive. This ensures that the partworth estimates are necessarily negative. An error component (s) to account for the correlation among the two generic options other than status quo was also estimated (with a normal distribution centered around zero). This error component is particularly useful where utilities associated with generic options are more prone to noise given the fact that they are ‘imagined’, as opposed to the ‘experi-enced’ status quo situation. Indeed all three models returned highly significant estimates of the variance of s ( p-value < 0.01).In the three models, means and variances of the bn’s were estimated by making 2000 draws of b and the diagonal elements of V. In terms of model fit, the model with SB distribution of parameters yielded the best model fit with a simulated log-likelihood function lower than that given by models with normal and censored-normal distributions ð2091:3; 2094:9 and 2105:3; respectivelyÞ thus supporting the decision to impose sign restrictions on partworth esti-mates. It further suggests that shares of respondents who ‘dislike’ improvements (i.e. negative parameters) or ‘prefer’ deterioration (i.e. positive parameters are) in the normal distribution model are an artefact of the specification. To illustrate, such shares take implausibly high values such as 33% who ‘prefer’ to see an increase of GHG emission equivalent to 65,000 cars doing average yearly mileage (GHG = 1), or 43%who ‘dislike’ enhanced river water levels (RIVER = +1). Further, we interpret the fact that higher log-likelihood is attained by the model with SB distribution of parameters compared to censored normal as evidence in support of imposing upper bounds to parameter distributions. Henceforth discussions and comparisons will be limited to the SB model.



