Partial attribute attendance in environmental choice experiments: A comparative case study between Guangzhou (China) and Brussels (Belgium).

Attribute non-attendance (ANA) in discrete choice experiment (DCE) exercises has attracted increasing, yet limited, scholarly attention. This paper attempts to investigate ANA in a comparative case study, with a focus on its patterns and their association with socioeconomic, behavioral and perceptual factors, as well as its impacts on willingness-to-pay (WTP) estimates. We deploy a four-level polytomous scale (always, often, seldom, and never considered) for respondents to state their various degrees of attribute attendance (SANA) in an identical DCE questionnaire about urban river restoration initiatives in two global cities with contrast socioeconomic contexts, yet similar request for restoring polluted and modified urban rivers, Guangzhou (south China) and Brussels (Belgium). The survey results reveal the existence of large proportions of partial attendance in two sampled cities. We use an extended mixed logit model, which incorporates separate parameters delineating each attribute's different attendance groups, to estimate respondents' average WTP values. We find that accounting for SANA could improve the goodness-of-fit of the model and affect the magnitude of mean WTP estimates. Respondents' attribute attendance level pertaining to various attributes is mainly associated with their perceived importance of urban rivers' ecosystem services, but may not be necessarily correlated with the strength of their preference for corresponding attributes as indicated by the mean WTP estimates. Whether this discontinuity between respondents' stated ANA levels and WTP estimates within Guangzhou sample questions the ability of DCEs to generate unbiased welfare estimation and policy guidance in developing countries calls for further studies.

Natural capital approaches for the optimal design of policies for nature recovery.

By embedding a spatially explicit ecosystem services modelling tool within a policy simulator we examine the insights that natural capital analysis can bring to the design of policies for nature recovery. Our study is illustrated through a case example of policies incentivising the establishment of new natural habitat in England. We find that a policy mirroring the current practice of offering payments per hectare of habitat creation fails to break even, delivering less value in improved flows of ecosystem services than public money spent and only 26% of that which is theoretically achievable. Using optimization methods, we discover that progressively more efficient outcomes are delivered by policies that optimally price activities (34%), quantities of environmental change (55%) and ecosystem service value flows (81%). Further, we show that additionally attaining targets for unmonetized ecosystem services (in our case, biodiversity) demands trade-offs in delivery of monetized services. For some policy instruments it is not even possible to achieve the targets. Finally, we establish that extending policy instruments to offer payments for unmonetized services delivers target-achieving and value-maximizing policy designs. Our findings reveal that policy design is of first-order importance in determining the efficiency and efficacy of programmes pursuing nature recovery. This article is part of the theme issue 'Bringing nature into decision-making'.

Bringing the vertical dimension into a planar multilevel autoregressive model: A city-level hedonic analysis of homebuyers' utilities and urban river attributes.

Throughout history, urban rivers have been regarded as valuable natural elements that satisfy various human needs and affect where people reside. With the increasing expansion of modern cities along the vertical dimension, how urban rivers affect housing values and homebuyers' purchasing decisions in a 3-D context has attracted a significant amount of attention from researchers, environmental practitioners, urban planners, and policymakers. In this paper, we attempt to estimate how homebuyers' utilities are affected by various river attributes and their interactions using the vibrant high-rise apartment housing market in Guangzhou (south China) as a case study. An appropriate 3-D weights matrix is identified using ex ante Monte Carlo simulation combined with ex post validation on the basis of information criteria. By using the identified 3-D spatial weights scheme in a multilevel autoregressive modelling framework, an intricate combination of multidimensional spatial heterogeneity and spatial dependence can be sufficiently accounted for. Our analytical results reveal that river view and riverfront location are considered as negative utilities by Guangzhou's homebuyers, showing the significant negative impacts of river pollution. Yet, the proximity to urban rivers is regarded as a positive utility, revealing that homebuyers enjoy a sense of being close to nature and an emotional bond with traditional water culture. The black-odorous river water itself devalues apartment prices and adds the negative utilities of river view and riverfront location. Riparian greening would command a price premium, as well as mitigate the negative utilities of river view and riverfront location. Although the availability of walking paths and sitting benches along river stretches is generally regarded as a positive utility, it may worsen the negative impact of river view, but enhance the positive impact of river proximity. These results provide deeper managerial insights into how different river attributes influence apartment buyers' utilities and thus help environmental managers (in collaboration with housing developers) design urban river restoration initiatives so as to create pleasant and attractive neighbourhoods for prospective homebuyers.