This paper proposes a hybrid decision-support framework that integrates cost-benefit analysis (CBA) with participatory budgeting (PB) to inform public-sector portfolio choices. To highlight the approach, we assume that CBA has been conducted to evaluate twelve independent infrastructure and service projects, establishing their economic viability. Subsequently, PB is employed to elicit community preferences regarding the same projects, ensuring alignment with public values. The combined framework aims to reconcile technical efficiency, understood here as welfare-economic appraisal implemented through standard modelling and valuation conventions, with democratic legitimacy, captured through direct, budget-constrained citizen prioritisation. Our findings show that respondents gave the strongest average support to health care and utilities projects, with education also performing relatively well, while public transportation and road infrastructure were preferred to transport-electrification initiatives. In contrast, public projects associated with private vehicle and freight vehicle electrification attracted comparatively weak support, and public transportation electrification was supported less strongly than conventional public transport investment. More broadly, respondents tended to favour projects that delivered larger consumer-surplus and environmental benefits and projects located in, or relevant to, their own area.

Vehicle-to-grid (V2G) has been widely promoted as a mechanism for integrating electric vehicles (EVs) into electricity markets by enabling bidirectional energy flows. While much of the existing literature focuses on technical feasibility and system-level value, comparatively less attention has been paid to how EV owners perceive the private financial benefits associated with participation. This study examines how awareness and conceptual understanding of V2G are associated with EV owners’ subjective perceptions of annual electricity bill savings. Using data from a nationwide survey of 1,794 Australian EV owners, we distinguish between awareness of V2G and correct conceptualisation of the technology. We estimate a sample-selection endogenous ordered probit model that jointly accounts for awareness and potential endogeneity in correct understanding when modelling perceived savings. Results indicate that awareness and understanding are distinct phenomena and that, after accounting for awareness and shared unobserved factors, correct conceptualisation remains strongly associated with a higher propensity to report larger perceived savings. Approximate midpoint-based summaries of the predicted ordinal distributions suggest higher perceived annual savings among respondents who correctly define V2G. At the same time, overall perceived savings remain modest, with most EV owners selecting categories consistent with incremental rather than substantial bill reductions. Significant heterogeneity emerges across segments, particularly by solar panel ownership, battery storage ownership, home charging access, and age. The findings suggest that informational frictions, subjective beliefs and incomplete understanding may shape perceived private benefits of V2G, which are likely to be relevant for future participation decisions, even in a context characterised by high residential solar uptake and widespread home charging. From a policy perspective, the results underscore the importance of clear communication, contract transparency, and targeted program design that accounts for heterogeneity across user segments in scaling V2G participation. Integrating behavioural insight into energy–transport policy may therefore be critical to realising the flexibility potential of electrified mobility.

Declining revenue from traditional road funding sources, rising infrastructure costs, and the transition to electric vehicles have increased the urgency of road user charging reform as a key demand management strategy. While such schemes can improve transport system efficiency by pricing congestion and other externalities, their implementation requires careful balancing with concerns around fairness and affordability. Public acceptability therefore remains a critical constraint on policy adoption. This paper examines which policy features most strongly influence support for road user charging and how these preferences can inform the design of policies that advance efficiency, fairness, and affordability objectives. A best worst scaling approach is used to elicit the relative importance of policy features, with choices modelled using a hybrid choice framework that captures both observed preferences and underlying attitudes. Three distinct behavioural classes are identified, reflecting differing priorities related to efficiency, fairness and consistency, and broader public benefit. Across these groups, governance and institutional arrangements are central to perceived legitimacy. Features such as public ownership, not for profit operation, independent investment decision making, and transparent revenue use are strongly preferred, while more complex pricing mechanisms are viewed less favourably. The findings highlight the importance of trust, fairness, and affordability in supporting effective and acceptable reform.

Fare-free and low-fare public transport (FFPT) has shifted from a marginal idea to a visible policy in debates on transport equity, climate mitigation and cost-of-living relief. This paper reviews over 150 studies on FFPT and deep fare reductions, identified through a PRISMA-informed search of databases and grey literature. The review spans decades and covers empirical evaluations, theoretical work and policy analyses from Europe, North America, South America, Asia and Oceania. This review paper broadly addresses the main positive impacts of FFPT; key constraining factors; which groups benefit most, and under which conditions; and identifies research gaps. Across contexts, FFPT has been found to increase ridership and improve accessibility, particularly for lowincome users, students and older adults, and can strengthen social inclusion and supports framing public transport as a public good. Evidence of substantial, durable modal shift from private cars, or of large emissions reductions, is limited and context dependent. Financial sustainability, capacity pressures and unintended social effects are recurrent challenges. The few studies estimating fare elasticities find that public transport demand is price-inelastic, so pricing is only one lever shaping travel behaviour. Persistent gaps include scarce longitudinal analyses, limited environmental measurement, regional bias towards Europe and North America, weak intersectional equity perspectives and incomplete understanding of how fare reform interacts with transport, land-use and welfare systems. Overall, FFPT improves accessibility and social equity, but its environmental and behavioural impacts are modest unless combined with broader strategies of service enhancement, demand management and integrated urban planning in diverse urban contexts.

This paper examines how rising petrol prices affect weekly travel behaviour, with particular attention to modal substitution and trip suppression. The analysis draws on stated responses from 808 Queensland residents, each of whom first reported their travel behaviour for the week prior to the survey and then indicated how that behaviour would change under three hypothetical petrol price scenarios set at AUD 2.50, AUD 3.00, and AUD 3.50 per litre (noting fuel prices at the pump varied between an average of AUD 2.20 and AUD 2.53 during the survey period). Weekly trip frequencies are jointly modelled for eight travel outcomes, including car travel as driver, car travel as passenger, public transport, taxi, rideshare, cycling, walking, and avoided trips. The latter category is included within the hypothetical setting to capture the extent to which an increase in petrol prices may lead travellers to cancel or forgo trips altogether, rather than simply reallocate travel across modes. The empirical analysis is performed implementing a multivariate Generalised Poisson framework with dependence across travel alternatives introduced through a Gaussian copula. The results indicate that higher petrol prices substantially reduce car travel both as driver and as car passenger, while increasing public transport use, particularly at the higher price scenarios. However, the substitution towards public transport is only partial. A sizeable share of the adjustment instead occurs through avoided trips, suggesting that fuel price increases are more likely to suppress travel rather than simply induce a reallocation across modes. The findings further show that behavioural responses vary with socio-economic circumstances and perceived transport disadvantage, implying that the burden of higher fuel prices is unevenly distributed. Overall, the paper shows that rising petrol prices affect not only mode choice, but also the ability of individuals to maintain everyday mobility and activity participation.

In this study, we develop a novel econometric framework that allows for endogenously estimating minimum goods amounts, and their subsequent impact on individuals' multiple discrete/ continuous consumption choices. To do so, we pair a censored Tobit model (Tobin, 1958) with a Multiple Discrete Continuous Extreme Value (MDCEV) model (Bhat, 2005; 2008), with the former being employed to identify lower bounds on consumptions based upon the demographic characteristics of decision-makers. The model proposed is applied to a web-based survey designed to examine monthly expenditure decisions for the following categories: entertainment, household bills, miscellaneous costs, rent/mortgage payments, shopping, transport, childcare and other unspecified expenditure. In addition to providing information on actual expenses, recruited respondents were also asked to indicate the minimum expenditure amount they could potentially spend on the designated expenditure categories. The estimated findings suggest that allowing endogenous minimum consumption amounts within the MDCEV model results in a better understanding of the determinants driving individuals' expenditure behaviour, whilst also providing more accurate prediction both within and out of sample.

This paper proposes the use of an autoregressive spatial stochastic frontier model to measure the sales efficiency of the electric vehicle (EV) market in 88 Chinese cities for the period 2016 to 2023. In contrast to previous research on this topic, the adoption of a stochastic frontier model allows for computing the maximum level of EV sales (i.e., frontier) that each city could have potentially achieved in the timeframe under assessment given a certain set of inputs (e.g., central and local purchase subsidies, subsidies for the construction/operation of electric vehicle chargers, average petrol prices, purchase restrictions on conventional vehicles, among others). Further, the spatial-based structure of the model proposed enables the evaluation of the impact of similar policy interventions implemented in neighbouring cities on EV sales frontier estimated within the city. The empirical evidence suggests that as the provision of EV charging stations around and within the city increases, so does the maximum number of sellable electric cars. A further interesting finding is that the frontier for EV sales is positively influenced by the electric cars purchased in the previous month in neighbouring areas, revealing the presence of a strong spatial dependency. Finally, this study conducts a simulation exercise wherein three hypothetical scenarios are explored: (1) the implementation of a ten percent tax on petrol, (2) a ten percent increase in the number of public chargers available, and (3) the introduction of policies to improve the air quality of all 88 cities. The results from the simulation analysis suggests that improving the number of public charging stations by 10 percent would have resulted in the sales of nearly 41,000 EVs more across the 88 cities over eight years.

OBJECTIVE: To identify the smallest worthwhile effect (SWE) of exercise therapy for people with non-specific chronic low back pain (CLBP). DESIGN: Discrete choice experiment. METHODS: The SWE was estimated as the lowest reduction in pain that participants would consider exercising worthwhile, compared to not exercising i.e., effects due to natural history and other components (e.g., regression to the mean). We recruited English-speaking adults in Australia with non-specific CLBP to our online survey via email obtained from a registry of previous participants and advertisements on social media. We used discrete choice experiment to estimate the SWE of exercise compared to no exercise for pain intensity. We analysed the discrete choice experiment using a mixed logit model, and mitigated hypothetical bias through certainty calibration, with sensitivity analyses performed with different certainty calibration thresholds. RESULTS: Two-hundred and thirteen participants completed the survey. The mean age (±SD) was 50.7±16.5, median (IQR) pain duration 10 years (5-20), and mean pain intensity (±SD) was 5.8±2.3 on a 0-10 numerical rating scale. For people with CLBP the SWE of exercise was a between-group reduction in pain of 20%, compared to no exercise. In the sensitivity analyses, the SWE varied with different levels of certainty calibration; from 0% without certainty calibration to 60% with more extreme certainty calibration. CONCLUSION: This patient-informed threshold of clinical importance could guide the interpretation of findings from randomised trials and meta-analyses of exercise therapy compared to no exercise.

With the rapid uptake in renewable energy there are emerging risks for countries that rapidly displace baseload generation with intermittent sources. Whilst these risks can be mitigated with storage technologies, the cost to do so, is ultimately passed onto households in the form of higher electricity bills. We use a discrete choice experiment to explore some of the potential trade-offs households might be willing to consider in order to experience lower bill increases including delaying electricity infrastructure investments as well as demand-side management policies. Respondents were asked to evaluate alternative electricity contracts with lower cost increases, delayed renewable and battery storage investments and the potential imposition of consumption limits. We also explore how household risk attitudes explain differences in compensation required within a mixed logit model. Our results suggest that households which are highly risk-averse may require more compensation.

The design of stated choice surveys represents the cornerstone of current modelling efforts used to understand traveler behavior. Typically underlying such surveys are experimental designs, which are used to systematically allocate information to respondents in such a way that the data captured compliments the modelling efforts of researchers conducting the study. In this chapter, we describe the processes required to construct experimental designs for stated choice experiments, and in doing so, attempt to both demystify as well as broaden the knowledge of researchers wishing to conduct such experiments. In doing so, we present discussion of the two main types of experimental design approaches used within the literature, these being orthogonal designs and optimal designs.

In response to the Covid-19 pandemic, many countries have adopted measures to contain the spread of the virus, including mandatory quarantine for inbound travellers. This research investigates the preferences of residents of New South Wales, Australia, towards the mandatory quarantine protocol adopted in the state. Heterogeneity in individual preferences is explored by advancing the Logit Mixed Logit (LML) model defined by Train (2016). Two approaches are suggested to decompose individual heterogeneity in this framework and are applied to data collected via a stated preference experiment. The empirical findings demonstrate that on average, the community prefers returned travellers be quarantined in dedicated quarantine facilities rather than be quarantined at home or using hotels, but are mostly indifferent to how long travellers are quarantined for, and how many travellers are allowed to return to Australia. The sample do however have a preference, on average for travellers having to pay less to quarantine, meaning they wish to see greater government subsidies. However, the modelling approach demonstrates that the common use of averages potentially masks diverse preferences, and is not representative of community wants and desires, thus possibly leading to incorrect inferences about policy impacts.

This study assesses individuals' preferences for the use of forest sites for recreational purposes by means of the logit-mixed logit (LML) model. The appeal of the LML is that the analyst does not need to assume any specific functional form for the mixing distributions of random preferences. The empirical analysis generates a data-driven nonparametric representation of individuals' preference heterogeneity. We apply this approach to data collected using an unlabelled discrete choice experiment (DCE), consisting of three recreational options, two of which are in two hypothetical forest sites. Forest destinations are described by means of six attributes: forest type, signposting, hiking time, access to rivers or lakes, wildlife watch hides for visitors and cost of access. The empirical findings reveal that the signpost for each trail is the attribute for which respondents are on average willing to pay the most (6.565euro). Further evidence suggests that respondents have strong positive preferences for those forest sites that offer amenities such as wildlife watch hides and access to rivers or lakes. Finally, the histograms derived from the semi-parametric LML estimation reveal multimodality of random taste amongst respondents for different hypothetical forest sites.

INTRODUCTION Understanding the magnitude of treatment effect patients need to see to consider a treatment worthwhile is of clear clinical and research importance. Current measures of clinical importance, such as the minimum clinical important difference, are limited as they are not determined by patients, and do not reflect specific costs, risks or inconveniences of individual treatments, i.e. you could have the same MCID for surgery as for exercise. We aimed to identify the smallest worthwhile effect (SWE), a new measure of clinical importance, of exercise therapy for people with non-specific chronic low back pain (CLBP) using discrete choice experiment. METHODS The SWE was estimated as the lowest reduction in pain that participants would consider exercising worthwhile, compared to not exercising i.e., effects due to natural history and other components (e.g., regression to the mean). We recruited English-speaking adults in Australia with non-specific CLBP to our online survey via email from a registry of previous participants and advertisements on social media. We used discrete choice experiment to estimate the SWE of exercise compared to no exercise for pain intensity. We analysed the discrete choice experiment using a mixed logit model, and mitigated hypothetical bias through certainty calibration, with sensitivity analyses performed with different certainty calibration thresholds. RESULTS 213 participants completed the survey. Mean age (±SD) was 50.7±16.5, median (IQR) pain duration 10 years (5-20), and mean pain intensity (±SD) was 5.8±2.3 on a 0-10 numerical rating scale. For people with CLBP the SWE of exercise was a between-group reduction in pain of 20%, compared to no exercise. This means, for a baseline pain of 5, the SWE would be a 1/10 between-group reduction in pain. CONCLUSION This patient-informed threshold of clinical importance should guide the interpretation of findings from randomised trials and meta-analyses of exercise therapy compared to no exercise.

Subjective well-being (SWB) describes an individual's life evaluation. Direct elicitation methods for SWB via rating scales do not force individuals to trade-off among life domains, whilst best-worst scaling (BWS) approaches only provide relative measures. This paper instead offers a dual-response BWS task, where respondents nominate areas of most and least importance and satisfaction with respect to 11 SWB domains, whilst also eliciting anchoring points to obtain an absolute measure of domain satisfaction. Combining domain satisfaction and importance produces a robust measure of individual SWB, but statistically unique relative to other life satisfaction measures utilizing single- and multi-item ratings, including global satisfaction and those aggregated over SWB domains, as well as eudemonia. Surveying 2500 Australians reveals anchored-BWS improves discrimination amongst domains in terms of importance and satisfaction, illustrating its value as a diagnostic tool for SWB measurement to focus services, policy, and initiatives in areas to most impact wellbeing. This includes highlighting a major discrepancy between health satisfaction and importance, whilst also reporting that SWB is significantly lower for Indigenous, unemployed, middle-aged, males and lower income groups.

Electrification of transport is deemed by many countries worldwide as one of the key strategies to mitigate CO2 emissions, yet the availability of reliable public charging infrastructure systems represents a potential serious bottleneck to such endeavours. Existing studies exploring battery electric vehicle (BEV) charging behaviour are typically based on either non-representative samples or stated choices experiments. This paper analyses observational data from a representative sample of German BEV owners who provided information on mileage and charging activities over a timeframe of eight weeks. BEV charging patterns, related vehicles kilometres travelled (VKT) and battery charging behaviour are assessed via a multifaceted empirical framework that pairs a hazard survival-based model with a log linear regression approach. A latent class method is also employed to segment BEV owners into different charging segments. The model suggests two types of charging behaviour exist, consisting of regular and irregular chargers. Charging frequencies and patterns are found to be radically different between the two groups under study, with regular chargers estimated to charge their vehicles 1.5 times more than irregular chargers. Lastly, the framework proposed is used to explore how charging behaviour will mutate due to both technology advancements (BEV driving range improvements) and user-centric factors (VKT variations). Neither technological or user factors are predicted to substantially affect the inter-charging duration of irregular chargers, whereas both increasing BEV driving ranges and reducing VKT results in a longer elapsed time between two consecutive charges for regular chargers.

This survey study assesses respondents’ willingness to participate in noninferiority trials of antimicrobials.