A test of policy labels in environmental choice modelling studies

Blamey, Russell K.; Bennett, Jeff; Louviere, Jordan J.; Morrison, Michael A.; Rolfe, John

doi:10.1016/s0921-8009(99)00101-9

Cited by 137 publications

(89 citation statements)

References 25 publications

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“…Each alternative is described by a set of characteristics, known as attributes (Blamey et al, 2000;Mangham et al, 2009), which take on different levels. Choices between the alternatives reveal respondents' implicit trade-offs between attribute levels .…”

Section: Methodology: Choice Experimentsmentioning

confidence: 99%

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Investigating fishers’ preferences for the design of marine Payments for Environmental Services schemes

Barr

Mourato

2014

Ecological Economics

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We determine the effects of various management restrictions on adoption rates of marine Payments for Environmental Services (PES) schemes. Choice experiments are used in order to determine how fisher participation rates change under different marine PES programme designs. Various designs, with differing restriction rates, show different rates of adoption. However, fishers show a high utility loss associated with any move away from the current management situation, irrespective of restriction levels. This indicates that PES scheme costs may be high and creating an enabling environment could be important to reducing perceived losses, as could investment into conditional in-kind compensation mechanisms. The paper also shows choice experiments to be a useful tool in marine PES design.

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Section: Methodology: Choice Experimentsmentioning

confidence: 99%

“…This choice path is illustrated in Figure 2. This implies that the ratio of the choice probabilities for any two alternatives would be affected by the addition or removal of one set of alternatives and as such violates the IIA assumption, rendering the CLM inappropriate (Blamey et al 2000).…”

Section: Ce Analysismentioning

confidence: 99%

Investigating fishers’ preferences for the design of marine Payments for Environmental Services schemes

Barr

Mourato

2014

Ecological Economics

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“…An emerging literature suggests that consumer awareness changes in response to environmental information (Desvousges et al, 1992;Blamey et al, 2000;Loureiro, 2003;Loureiro and Lotade, 2005;Leire and Thidell, 2005). Existing research reports customers purchase of green power (Bird and Swezey, 2003) and positive willingness to pay (WTP) for green energy electricity premia (Byrnes et al, 1999;Ethier et al, 2000;Gossling et al, 2005;Zarnikau, 2003;Bergmann et al, 2006).…”

Section: Rps and Mgpomentioning

confidence: 99%

U.S. state policies for renewable energy: Context and effectiveness

Delmas¹,

2011

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a b s t r a c tOver the past decade, state policies on renewable energy have been on the rise in the U.S., providing states with various options for encouraging the generation of renewable electricity. Two promising policies, the Renewable Portfolio Standard (RPS) and the Mandatory Green Power Option (MGPO), have been implemented in many states but the evidence about their effectiveness is mixed. In this paper, we argue that recognizing the natural, social, and policy context under which MGPO and RPS are adopted is necessary in order to measure their true effectiveness. This is because the context rather than the policy might lead to positive outcomes and there is the possibility for sample bias. When controlling for the context in which the policies are implemented, we find that RPS has a negative impact on investments in renewable capacity. However, we find that investor owned utilities seem to respond more positively to RPS mandates than publicly owned utilities. By contrast, MGPO appears to have a significant effect on installed renewable capacity for all utilities regardless of the context in which it is implemented. IntroductionAddressing climate change has become an important priority for the U.S. administration. In the U.S., greenhouse gas emissions come primarily from the combustion of fossil fuels in energy use. Energy related carbon dioxide emissions, resulting from the combustion of petroleum, coal, and natural gas, represented 82% of total U.S. anthropogenic greenhouse gas emissions in 2006. 1 While the U.S. generates 3% of its electricity from renewable resources, 2 other developed countries such as Denmark, Germany, Spain, and New Zealand generate 25%, 20%, 16%, and 8% of their electricity from renewables, respectively. 3 In addition, all of the European Union member states have aggressive renewable energy percentages they plan to reach by 2020, while the U.S. has no federally defined targets for renewable generation. 4 While there are current debates about the implementation of a federal renewable policy, U.S. states have taken a leading role in establishing renewable energy policies since the late 1990s. These include Renewable Portfolio Standards, the requirement to sell green products, disclosure policies, and subsidies. Analyzing the effectiveness of state renewable policies can be helpful to inform the current policy debate at the federal level.Recent research has started to look at the effectiveness of state policies on the generation of renewable electricity but has found mixed results. While some studies have found positive results (Bird et al., 2005;Menz and Vachon, 2006;Zarnikau, 2003;Yin and Powers, 2010), others have found no significant results (Carley, 2009).Furthermore, most of these studies provide little information on the natural and institutional conditions under which state renewable policies are adopted, which could have an impact on their effectiveness. For example, the relationship between installed renewable capacity and a state's resource endowment (e.g., sun, biomass...

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“…CM originates from conjoint analysis, information integration theory in psychology [6] and discrete choice theory in economics/econometrics [58,59]. It applies a choice experiment approach using a variety of instruments (e.g., pencil and paper, computer aided personal survey instrument (CAPI), internet-based survey) to indirectly elicit attribute values based on either ranking or rating of products described by a number of attributes in several labelled or unlabelled choice sets [18,41,47]. Subsequently, via statistical techniques, the analysis will derive a value for each of these attributes and thus express the relative preferences among vehicle attributes [29,79].…”

Section: Preference Valuation Studiesmentioning

confidence: 99%