The value of disappearing beaches: A hedonic pricing model with endogenous beach width

Beach nourishment is a policy used to rebuild eroding beaches with sand dredged from other locations. Previous studies indicate that beach width positively affects coastal property values, but these studies ignore the dynamic features of beaches and the feedback that nourishment has on shoreline retreat. We correct for the resulting attenuation and endogeneity bias in a hedonic property value model by instrumenting for beach width using spatially varying coastal geological features. We find that the beach width coefficient is nearly five times larger than the OLS estimate, suggesting that beach width is a much larger portion of property value than previously thought. We use the empirical results to parameterize a dynamic optimization model of beach nourishment decisions and show that the predicted interval between nourishment projects is closer to what we observe in the data when we use the estimate from the instrumental variables model rather than OLS. As coastal communities adapt to climate change, we find that the long-term net value of coastal residential property can fall by as much as 52% when erosion rate triples and cost of nourishment sand quadruples.     

Quantifying ecological thresholds from response surfaces

Ecological thresholds are abrupt changes of ecological state. While an ecological threshold is a widely accepted concept, most empirical methods detect them in time or across geographic space. Although useful, these approaches do not quantify the direct drivers of threshold response. Causal understanding of thresholds detected empirically requires their investigation in a multi-factor domain containing the direct drivers (often referred to as state space). Here, we present an approach to quantify thresholds from response surfaces modeled empirically in state space. We present two indices of shape attributes measured from response surfaces. The response surfaces are built using a regression method in state space. The indices are threshold strength (T) and diagonality (D). We use 48 simulated response surfaces of different shapes to test the efficacy of the indices in 3D. Our results show that T is sensitive to the steepness of the transition from one state to the next, with various forms of abrupt, centralized thresholds yielding the highest values among the simulated surfaces. D represents the orientation of the response surface or the simultaneous influence of more than one predictor in eliciting the response gradient. Strongly diagonal surfaces have the most diagonal surface area demonstrated by sharply undulating diagonal surfaces. Given that the success of T and D requires a regression method to accurately capture any shape of complex data structure, we also test the accuracy of empirical regression methods known to be tractable with complex data. We test classification and regression trees (CART), Random Forest, and non-parametric multiplicative regression (NPMR) for binary and continuous responses. We use the 48 simulated response surfaces to test the methods, and we find that prediction accuracy depends on both the T and D of the simulated data for each method. We choose the most accurate method among those we test for capturing any shape of response surface from real data, NPMR. Finally, we use NPMR to build response surfaces and quantify T and D from real ecological data sets. We demonstrate how measuring threshold strength and diagonality from multi-factor response surfaces can advance ecology.     

Anticipation and tracking of pulsed resources drive population dynamics in eastern chipmunks

Pulsed systems are characterized by boom and bust cycles of resource production that are expected to cascade through multiple trophic levels. Many of the consumers within pulsed resource systems have specific adaptations to cope with these cycles that may serve to either amplify or dampen their community-wide consequences. We monitored a seed predator, the eastern chipmunk (Tamias striatus), in an American beech (Fagus grandifolia) dominated forest, and used capture-mark-recapture analyses to estimate chipmunk vital rates and relate them to interannual variation in beech seed production. The summer activity and reproduction of adults anticipated autumn beech production, with high activity and intense reproduction occurring in summers prior to beech masts. Chipmunks also reproduced every spring following a beech mast. However, adult survival was independent of beech production. In contrast, juvenile survival was lower in years of mast failure than in years of mast production, but their activity was consistently high and independent of beech production. Population growth was strongly affected by the number of juveniles and therefore by beech seed production, which explains nearly 70% of variation in population growth. Our results suggest that a combination of resource-dependent reproduction and variable activity levels associated with anticipation and response to resource pulses allows consumers to buffer potential deleterious effects of low food abundance on their survival.     

Can home gardens scale up into movements for social change? The role of home gardens in providing food security and community change in San Jose,California

Urban policymakers and sustainable food activists have identified urban agriculture as an important strategy for confronting a host of urban problems, including food insecurity, health disparities, access to urban green space and community economic revitalisation. Much recent work on urban agriculture has examined community and school gardens, but little research has been undertaken on home gardens as a solution to urban problems. This article examines a home-gardening programme in San Jose, California, La Mesa Verde, asking whether some of the benefits found in community gardens can be found in home gardens. Specifically, we look at financial, health and community benefits, examining the potential of home gardens to become forces for broader social change. We ask whether gardens can become agents of cultural preservation, self-determination, particularly for recent immigrants who use these spaces to build identities and work towards collective action and self-determination.     

Climate change and sustainable development: realizing the opportunity

Manifold linkages exist between climate change and sustainable development. Although these are starting to receive attention in the climate exchange literature, the focus has typically been on examining sustainable development through a climate change lens, rather than vice versa. And there has been little systematic examination of how these linkages may be fostered in practice. This paper examines climate change through a sustainable development lens. To illustrate how this might change the approach to climate change issues, it reports on the findings of a panel of business, local government, and academic representatives in British Columbia, Canada, who were appointed to advise the provincial government on climate change policy. The panel found that sustainable development may offer a significantly more fruitful way to pursue climate policy goals than climate policy itself. The paper discusses subsequent climate change developments in the province and makes suggestions as how best to pursue such a sustainability approach in British Columbia and other jurisdictions.     

A land use regression model for predicting ambient concentrations of nitrogen dioxide in Hamilton, Ontario, Canada

This paper reports on the development of a land use regression (LUR) model for predicting the intraurban variation of traffic-related air pollution in Hamilton, Ontario, Canada, an industrial city at the western end of Lake Ontario. Although land use regression has been increasingly used to characterize exposure gradients within cities, research to date has yet to test whether this method can produce reliable estimates in an industrialized location. Ambient concentrations of nitrogen dioxide (NO2) were measured for a 2-week period in October 2002 at > 100 locations across the city and subsequently at 30 of these locations in May 2004 to assess seasonal effects. Predictor variables were derived for land use types, transportation, demography, and physical geography using geographic information systems. The LUR model explained 76% of the variation in NO2. Traffic density, proximity to a highway, and industrial land use were all positively correlated with NO2 concentrations, whereas open land use and distance from the lake were negatively correlated with NO2. Locations downwind of a major highway resulted in higher NO2 levels. Cross-validation of the results confirmed model stability over different seasons. Our findings demonstrate that land use regression can effectively predict NO2 variation at the intraurban scale in an industrial setting. Models predicting exposure within smaller areas may lead to improved detection of health effects in epidemiologic studies.