Are Some Deaths Worse Than Others? The Effect of 'Labelling' on People's Perceptions

This paper sets out to explore the extent to which perceptions regarding the 'badness' of different types of deaths differ according to how those deaths are 'labelled' in the elicitation procedure. In particular, we are interested in whether responses to 'contextual' questions - where the specific context in which the deaths occur is known - differ from 'generic' questions - where the context is unknown. Further, we set out to test whether sensitivity to the numbers of deaths differs across the 'generic' and 'contextual' versions of the questions. We uncover evidence to suggest that both the perceived 'badness' of different types of deaths and sensitivity to the numbers of deaths may differ according to whether 'generic' or 'contextual' descriptions are used. Qualitative data suggested two reasons why responses to 'generic' and 'contextual' questions differed: firstly, some influential variables were omitted from the 'generic' descriptions and secondly, certain variables were interpreted somewhat differently once the context had been identified. The implications of our findings for 'generic' questions, such as those commonly used in health economics (for example, the EQ 5D), are discussed.Preferences, Context effects, Affect heuristic

Valuing risk reductions: Testing for range biases in payment card and random card sorting methods

Concerns have been raised that the payment card (PC) format widely used in contingent valuation surveys of health treatments and risk reductions is subject to range bias. In response recent surveys have adopted an alternative random card sorting (RCS) approach - though this approach's susceptibility to range bias has not yet been formally tested. This study addressed this gap and showed, somewhat unexpectedly, that the RCS procedure was no less vulnerable to range bias than the PC method for eliciting both monetary values of health risk reductions and non-monetary estimates of death rates. Conclusions for future research initiatives are drawn

The effect of dynamic proximity cues on counterfactual plausibility

Previous research has found that people consult closeness or proximity cues when they evaluate the plausibility or likelihood of a counterfactual alternative to reality. In this paper we asked whether the plausibility of counterfactuals extends to dynamic proximity cues that signal a sense of propensity or acceleration in the lead-up to an outcome. Subjects gambled on obtaining either three heads or three tails from three coin-flips. When they lost the gamble they thought it was more likely that they could have won if they had lost on the third coin-flip that was revealed rather than the first or second coin-flip. We discuss how the sense of propensity was raised prior to the revelation of the final decisive losing coin-flip which created a perception of psychological momentum towards winning. Moreover, the consequence of this propensity effect was to positively bias perceptions of the likelihood of the counterfactual winning outcome

The effect of dynamic proximity cues on counterfactual plausibility

Previous research has found that people consult closeness or proximity cues when they evaluate the plausibility or likelihood of a counterfactual alternative to reality. In this paper we asked whether the plausibility of counterfactuals extends to dynamic proximity cues that signal a sense of propensity or acceleration in the lead-up to an outcome. Subjects gambled on obtaining either three heads or three tails from three coin-flips. When they lost the gamble they thought it was more likely that they could have won if they had lost on the third coin-flip that was revealed rather than the first or second coin-flip. We discuss how the sense of propensity was raised prior to the revelation of the final decisive losing coin-flip which created a perception of psychological momentum towards winning. Moreover, the consequence of this propensity effect was to positively bias perceptions of the likelihood of the counterfactual winning outcome

Are Some Deaths Worse Than Others? Results from a Discrete Choice Experiment

Previous research has shown that people wish a premium to be placed on the prevention of certain types of deaths as they perceive those deaths as 'worse' than others. The research reported in this paper is an attempt to quantify such a 'bad death' premium via a discrete choice experiment (DCE). The four underlying attributes included were: the age of the victim, who was most to blame for the death, the severity of the victim's pain and suffering in the period leading up to death, and the duration of the victim's pain and suffering in the period leading up to death. In addition, a fifth attribute - number of deaths - was included in order to provide a quantitative scale against which to measure the "bad death premium". The results show that each of the 4 underlying attributes did matter to respondents in determining whether deaths were worse than others, but also uncovered marked insensitivity to variations in the number of those deaths. The implication of our findings for the use of quantitative variables in DCEs is discussed.Discrete choice experiment, Value of preventing a fatality, Relative weights, Insensitivity

Exploring differences between TTO and direct choice in the valuation of health states

There is recent interest in using Discrete Choice Experiments (DCEs) to derive health state utility values and results can differ from Time Trade Off (TTO). Clearly DCE is 'choice-based' whereas TTO is generally considered to be a 'matching' task. We explore whether procedural adaptations to the TTO -which make the method more closely resemble a DCE -makes TTO and choice converge. In particular, we test whether making the matching procedure in TTO less 'transparent' to the respondent reduces disparities between TTO and choice. We designed an interactive survey that was hosted on the internet and 2022 interviews were achieved in the UK. We found a marked divergence between TTO and choice, but this was not related to the 'transparency' of the TTO procedure. We conclude that a combination of insensitivity in the TTO (however conducted) and factors other than differences in utility affecting choices is driving the divergence

The global methane budget 2000–2017

Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4 yr−1 (range 550–594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4 yr−1 or ∼ 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions; range 336–376 Tg CH4 yr−1 or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is 29 Tg CH4 yr−1 larger than our estimate for the previous decade (2000–2009), and 24 Tg CH4 yr−1 larger than the one reported in the previous budget for 2003–2012 (Saunois et al., 2016). Since 2012, global CH4 emissions have been tracking the warmest scenarios assessed by the Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost 30 % larger global emissions (737 Tg CH4 yr−1, range 594–881) than top-down inversion methods. Indeed, bottom-up estimates for natural sources such as natural wetlands, other inland water systems, and geological sources are higher than top-down estimates. The atmospheric constraints on the top-down budget suggest that at least some of these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric observation-based emissions indicates a predominance of tropical emissions (∼ 65 % of the global budget, < 30∘ N) compared to mid-latitudes (∼ 30 %, 30–60∘ N) and high northern latitudes (∼ 4 %, 60–90∘ N). The most important source of uncertainty in the methane budget is attributable to natural emissions, especially those from wetlands and other inland waters. Some of our global source estimates are smaller than those in previously published budgets (Saunois et al., 2016; Kirschke et al., 2013). In particular wetland emissions are about 35 Tg CH4 yr−1 lower due to improved partition wetlands and other inland waters. Emissions from geological sources and wild animals are also found to be smaller by 7 Tg CH4 yr−1 by 8 Tg CH4 yr−1, respectively. However, the overall discrepancy between bottom-up and top-down estimates has been reduced by only 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methane budget include (i) a global, high-resolution map of water-saturated soils and inundated areas emitting methane based on a robust classification of different types of emitting habitats; (ii) further development of process-based models for inland-water emissions; (iii) intensification of methane observations at local scales (e.g., FLUXNET-CH4 measurements) and urban-scale monitoring to constrain bottom-up land surface models, and at regional scales (surface networks and satellites) to constrain atmospheric inversions; (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions; and (v) development of a 3D variational inversion system using isotopic and/or co-emitted species such as ethane to improve source partitioning