Are Labour Markets Necessarily Local? Spatiality, Segmentation and Scale

This paper draws on recent debates about scale to approach the geography of labour markets from a dynamic perspective sensitive to the spatiality and scale of labour market restructuring. Its exploration of labour market reconfigurations after the collapse of a major firm (Ansett Airlines) raises questions about geography’s faith in the inherently ‘local’ constitution of labour markets. Through an examination of the job reallocation process after redundancy, the paper suggests that multiple labour markets use and articulate scale in different ways. It argues that labour market rescaling processes are enacted at the critical moment of recruitment, where social networks, personal aspirations and employer preferences combine to shape workers’ destinations

Statistical characterisation of bio-aerosol background in an urban environment

In this paper we statistically characterise the bio-aerosol background in an urban environment. To do this we measure concentration levels of naturally occurring microbiological material in the atmosphere over a two month period. Naturally occurring bioaerosols can be considered as noise, as they mask the presence of signals coming from biological material of interest (such as an intentionally released biological agent). Analysis of this 'biobackground' was undertaken in the 1-10 um size range and a 3-9% contribution was found to be biological in origin - values which are in good agreement with other studies reported in the literature. A model based on the physics of turbulent mixing and dispersion was developed and validated against this analysis. The Gamma distribution (the basis of our model) is shown to comply with the scaling laws of the concentration moments of our data, which enables us to universally characterise both biological and non-biological material in the atmosphere. An application of this model is proposed to build a framework for the development of novel algorithms for bio-aerosol detection and rapid characterisation.Comment: 14 Pages, 8 Figure

Ancillary human health benefits of improved air quality resulting from climate change mitigation

<p>Abstract</p> <p>Background</p> <p>Greenhouse gas (GHG) mitigation policies can provide ancillary benefits in terms of short-term improvements in air quality and associated health benefits. Several studies have analyzed the ancillary impacts of GHG policies for a variety of locations, pollutants, and policies. In this paper we review the existing evidence on ancillary health benefits relating to air pollution from various GHG strategies and provide a framework for such analysis.</p> <p>Methods</p> <p>We evaluate techniques used in different stages of such research for estimation of: (1) changes in air pollutant concentrations; (2) avoided adverse health endpoints; and (3) economic valuation of health consequences. The limitations and merits of various methods are examined. Finally, we conclude with recommendations for ancillary benefits analysis and related research gaps in the relevant disciplines.</p> <p>Results</p> <p>We found that to date most assessments have focused their analysis more heavily on one aspect of the framework (e.g., economic analysis). While a wide range of methods was applied to various policies and regions, results from multiple studies provide strong evidence that the short-term public health and economic benefits of ancillary benefits related to GHG mitigation strategies are substantial. Further, results of these analyses are likely to be underestimates because there are a number of important unquantified health and economic endpoints.</p> <p>Conclusion</p> <p>Remaining challenges include integrating the understanding of the relative toxicity of particulate matter by components or sources, developing better estimates of public health and environmental impacts on selected sub-populations, and devising new methods for evaluating heretofore unquantified and non-monetized benefits.</p

Influence of travel time variability on train station choice for park-and-rider users

© 2017 The Authors. Published by Elsevier B.V. It is increasingly recognised that park and ride (PnR) is an efficient travel mode joining private car with public transport system for providing low carbon emissions and better social equity. Departure train stations, as a transfer point of the travel mode, are paid more attention by commuters. This paper presents non-linear multinomial logit station choice models for understanding train station choice under travel time unreliability. A research framework about station choice under uncertainty is established based on discrete choice theory, cumulative prospect theory and mean-variance approach. Four weighting functions were tested for the station choice model. The data used to capture PnR users' choice behaviour under uncertainty was collected based on a stated preference experiment designed for D-efficiency and the travel time to the station was obtained from revealed preference data. The results showed that the non-linear MNL model with GE risky weighting function fits the data best. From the model, the respondents' attitude towards travel time variability was identified as risk averse. In addition, PnR users who have experienced greater travel time variations could tend to be more risk averse towards their station choice under travel time variability than those who have experienced less travel time variations