Conference Agenda

The estimate used to value mortality risk reductions, conventionally expressed as the value of a statistical life (VSL), has long been an important parameter for regulatory analysis in developed countries. However, the application of VSL in developing countries faces challenges, due to insufficient empirical attention. We conduct a comprehensive meta-analysis of 19 primary surveys on VSL with Chinese population between 1998 and 2019, providing a unique perspective on VSL in a fast-developing society. In contrast to research conducted in developed countries, we find no evidence of publication bias. We find that the income of the population studied, the type of risk, and the magnitude of risk reduction are the most important variables affecting VSL estimates. However, the relationship between these key factors and the VSL changes as society develops. We also provide a set of VSL estimates and key parameters needed in benefit transfer, and their ranges, tailored to the settings of different economic analyses. Currently, there are no significant misconceptions about VSL in China. Therefore, it is crucial for researchers in fast-developing societies such as China to promote research and application of VSL to inform decision making.

We critically assess an almost universal Net Present Value (NPV) practice. In addition to the central NPV, analysts frequently also report multiple additional values in what is commonly referred to as NPV ‘sensitivity analysis’. This practice is often justified with reference to the future net benefits to the asset being uncertain, because the correct discounting model is difficult to identify, or for other reasons. The purpose of this paper is to explain clearly why, despite the fact that this is recommended as best practice across multiple prestigious and influential sources, the reporting of more than one NPV value either lacks sufficient theoretical support or reflects decisions taken at an inappropriate organizational level. By providing additional quantification rather than recognizing that investment decisions require qualitative managerial judgement, this practice may confuse decision-makers more than help them. We illustrate this point in relation to a number of current guidelines across the public and private sectors and with particular focus on the US Environmental Protection Agency’s latest estimates of the Social Cost of Carbon.

This derives valid estimation procedures in recreation demand models with incomplete data on trip locations. Recreation datasets will often lack details on the locations of some or most trips, and it raises concern that standard estimators could yield biased results. To address this, I derive a likelihood function that is appropriate with or without complete information on trip locations. Using Monte Carlo simulation, I compare three nested logit estimators. In an empirical application, I use data from a web-based survey of trips in Michigan to estimate a recreation demand model. Monte Carlo results and empirical results show that a convenient trip-weighting strategy that can be implemented in existing nested logit software closely approximates true values and values from a more complex structural model that fully accounts for the censored data.

Coastal lagoon habitats provide multiple ecosystem goods and services that contribute to people’s well-being. However, owing to degradation from both anthropogenic and natural causes, these areas require conservation and protection that involve considerable public investment. To help determine this investment, this study estimates the nonmarket economic value of habitat, regulation, and cultural ecosystem services (ESs) using people’s environmental attitudes and temporal preferences. Primary data were collected from 517 residents in the Veneto region through an online choice experiment survey. Respondents’ environmental attitudes and beliefs were collected using the revised New Ecological Paradigm scale and analyzed using principal component analysis. Mixed logit models were applied to evaluate the marginal willingness to pay under different scenarios. The implicit discount rate linked to the different ESs varied between -2.12% and 13.54%, showing ESs should be discounted with distinct rates. Total household indirect and nonuse values for ESs ranged from €93 to €118, according to the model used. The results suggest that people are willing to pay to improve the conservation of lagoon ESs and that the future environmental impacts are valued less than the immediate effects. Additionally, people with environmental attitudes are more likely to contribute to the protection of ESs, and unobservable factors such as motivations, attitudes, perceptions, and beliefs guide people's valuations. These findings can be used to guide policymakers in designing sound conservation policies.