Learning to trade ethically: Knowledgeable capitalism, retailers and contested commodity chains

This paper explores what happens when corporations engage explicitly in practices of organisational learning not only to become better capitalists by generating ever more innovative ways of maintaining profitability, improving competitiveness and maximising shareholder value, but also to become more responsible corporate citizens in their business practices. In particular, I evaluate the ways in which UK food and clothing retailers are learning to develop their ethical trading programmes in response to political calls for more responsible trading. Thrift’s [Thrift, N., 2005. Knowing Capitalism. Sage, London] notion of ‘knowledgeable, or soft, capitalism’ is adopted to understand the creative and experimental ways in which retailers and their mentors (ethical consultancies, social auditors and multi-stakeholder organisations) are learning to trade ethically. Two specific examples of formal learning spaces experienced by UK food and clothing retailers are examined: (i) training courses on social auditing and (ii) corporate awareness-raising courses on ethical trade. These courses are shown to encompass various participative and affective practices of learning. And while particular limits to the success of these courses are argued to exist, ethical learning practices discussed in this paper are nonetheless suggested to play a role in the making of new, albeit moderate, forms of responsible capitalism.     

Hyper Redundancy for Accuracy Enhancement in Automated Close Range Photogrammetry

A generic network design in close range photogrammetry is one where optimal multi-ray intersection geometry is obtained with as few camera stations as practicable. Hyper redundancy is a concept whereby, once the generic network is in place, many additional images are recorded, with the beneficial impact upon object point precision being equivalent to the presence of multiple exposures at each camera position within the generic network. The effective number of images per station within a hyper redundant network might well be in the range of 10 to 20 or more. As is apparent when it is considered that a hyper redundant network may comprise hundreds of images, the concept is only applicable in practice to fully automatic vision metrology systems, where it proves to be a very effective means of enhancing measurement accuracy at the cost of minimal additional work in the image recording phase. This paper briefly reviews the network design and accuracy aspects of hyper redundancy and illustrates the technique by way of the photogrammetric measurement of surface deformation of a radio telescope of 26 m diameter. This project required an object point measurement accuracy of σ  = 0·065 mm, or 1/400 000 of the diameter of the reflector.     

UK seismology – 2002 and beyond

Alex Brisbourne reports on a one-day discussion meeting organized by SEIS-UK for seismologists to discuss current seismological research undertaken in the UK and to consider how the provision of new seismic equipment and training facility may enhance that work.     

Estimating contamination potential at waste-disposal sites using a natural tracer

Chloride is a conservative, natural tracer found in precipitation, soil water, and groundwater. The chloride mass-balance approach, long used to estimate groundwater recharge, also provides a downward flux of moisture and solute at sites where there is a potential for groundwater contamination. The flux is obtained by dividing the product of the mean annual precipitation and total annual chloride input (via precipitation and dust) by the mean soil-water chloride content. Chlorideversusdepth profiles can also be used to determine optimum depth of waste burial to minimize deterioration of waste containers. The method has been applied to three sites in arid alluvial-basin settings in New Mexico, U.S.A.: a proposed landfill, a battery recycling plant, and a hazardous-waste disposal facility. It is concluded that the method is reliable, economical, and practical. Furthermore, it can be applied at any stage in the development of a site. The chloride method should apply in any recharge area where the base of the root zone is separated from the water table by at least 3 m or so and chloride in soil water comes only from precipitation and dust.     

Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec,Canada

Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.     

Climate change hotspots mapping: what have we learned?

In the past 5 years there has been a proliferation of efforts to map climate change “hotspots” — regions that are particularly vulnerable to current or future climate impacts, and where human security may be at risk. While some are academic exercises, many are produced with the goal of drawing policy maker attention to regions that are particularly susceptible to climate impacts, either to mitigate the risk of humanitarian crises or conflicts or to target adaptation assistance. Hotspots mapping efforts address a range of issues and sectors such as vulnerable populations, humanitarian crises, conflict, agriculture and food security, and water resources. This paper offers a timely assessment of the strengths and weaknesses of current hotspots mapping approaches with the goal of improving future efforts. It also highlights regions that are anticipated, based on combinations of high exposure, high sensitivity and low adaptive capacity, to suffer significant impacts from climate change.