This study presents valuations of components of marine natural capital that have hitherto been overlooked by the valuation literature. Using a discrete choice experiment, it values a set of ecosystem services linked to seabed natural capital in the UK section of the North Sea. The study focuses on offshore seabed habitats, using Good Environmental Status as a measure of seabed health, thus linking directly to management targets under the EU Marine Strategy Framework Directive. It considers how changing pressures on seabed habitats could affect marine industries and other ecosystems through trade-offs with (1) the contribution that exploitation of these habitats makes to the maritime cultural heritage; and (2) changes to the health of seabird populations. For seabed habitats and seabirds, the elicited values mainly represent non-use values for changes in the condition of natural capital assets. For maritime cultural heritage the valuation refers to the changed provision of this cultural ecosystem service. Results show that the public in England hold significant, strongly correlated, values for changes in the condition of offshore seabeds and seabird populations. Projected losses in maritime cultural heritage are found to lead to expected welfare decreases. Implications of these findings for marine planning and decision-making are discussed.
The existing empirical evidence shows that both contingent valuation and discrete choice experiment (DCE) methods are susceptible to various ordering effects. However, very few studies have analysed attribute-ordering effects in DCEs, and no study has investigated their potential influence on information-processing strategies, such as attribute non-attendance (ANA). This paper tests for attribute-ordering effects and examines whether the order of attributes describing the alternatives affects respondents’ propensity to attend to or ignore an attribute. A split-sample approach is used, where one sample received a DCE version in which the positions of the first and last non-monetary attributes are switched across the sequence of choice tasks compared with the other sample. The results show that attribute order does not affect welfare estimates in a significant way under the standard assumption of full attribute attendance, thus rejecting the notion of procedural bias. However, the welfare estimates for the attributes whose order was reversed and the share of respondents who ignored them differ significantly between the two attribute-ordering treatments once ANA behaviour is accounted for in the estimated choice models. These results highlight the important role of information-processing strategies in the design and evaluation of DCEs.