In this paper, we investigate the nexus between urban air pollution and residents’ preferences for greenspace. We first provide a theoretical discussion on three potential mechanisms that link these two environmental issues. To start with, where people choose to locate in a city, as reflected by their exposure to air pollution, may indicate their preferences for greenspace through a residential sorting effect: residents of heavily polluted neighbourhoods may have a lower appreciation of environmental amenities in general, including greenspace. Further, air pollution may have direct implications for the use value of greenspace. On the one hand, people tend to reduce outdoor activities in severe pollution as an avoidance behaviour, which may lead to reduced visits to and hence lower use value of greenspace. On the other hand, residents of severely polluted areas may derive additional benefits from greenspace, as trees are able to enhance air quality. To empirically test these mechanisms, we undertook choice experiment surveys in Beijing to elicit the public’s willingness to pay (WTP) for greenspace. We purposefully valued three types of greenspace: a neighbourhood park near respondents' homes; a city park in central Beijing; and a national park in an outlying location. We use realtime pollution data to help explain the spatial and temporal variation in WTP, whilst controlling for other possible influencing factors. Neighbourhood parks are likely to provide direct air purification services for communities nearby, and our results indeed suggest that respondents exposed to higher levels of annual pollution are willing to pay more for an additional neighbourhood park. In contrast, WTP for the city park and national park is more likely to be linked with pollution levels via the residential sorting and reduced visits mechanisms. Yet our results find no evidence for such connections.
In this paper, we investigate the use of geographically weighted choice models for modelling spatially clustered preferences. We argue that this is a useful way of generating highly‐detailed spatial maps of willingness to pay for environmental conservation, given the costs of collecting data. The data used in this study come from a discrete choice experiment survey of public preferences for the implementation of a new national forest management and protection programme in Poland. We combine these with high‐resolution spatial data related to local forest characteristics. Using locally estimated discrete choice models we obtain location‐specific estimates of willingness to pay (WTP). Variation in these estimates is explained by characteristics of the forests close to where respondents live. These results are compared with those obtained from a more typical, two stage procedure which uses Bayesian posterior means of the mixed logit model random parameters to calculate location‐specific estimates of WTP. We find that there are indeed strong spatial patterns to the benefits of changes to the management to national forests. People living in areas with more species‐rich forests and those living nearer bigger areas of mixed forests have significantly different WTP values than those living in other locations. This kind of information potentially enables a better distributional analysis of the gains and losses from changes to natural resource management, and better targeting of investments in forest quality.
The paper investigates the spatial heterogeneity of public's preferences for the implementation of a new country-wide forest management and protection program in Poland. Spatial econometric methods and high resolution geographical information system data related to forest characteristics are used to explain the variation in individual-specific willingness to pay (WTP) values, derived from a discrete choice experiment study. We find that respondents' WTP is higher the closer they live to their nearest forest, and the scarcer forests are in the area where they live. Interestingly, the higher the ecological value of forests in respondents' area, the more people prefer extending areas of national forest protection. We also investigate spatial patterns in individual-specific WTP scores and in latent class membership probabilities, finding that preferences are indeed spatially clustered. We argue that this clustering should be taken into account in forest management and policy-making.