The promise of smart grids

ABSTRACT

It is the promise of smart grids – their anticipated role in meeting economic, social, environmental policy objectives – that is driving action on smart grids worldwide, while the reality is rather more messy. This paper is about the implementation of smart grids in Australia, and examines the degree to which environmental and social promises have materialised (or not) within two large energy smart grid initiatives undertaken in the period 2009–2014: the federal government-sponsored Smart Grid Smart City Program and the State of Victoria’s Advanced Metering Infrastructure Program. The analysis draws on a governmentality approach to examine how the promise of smart grids has not for the most part been delivered, concentrating in particular on how new digital technologies have not “behaved” in the way originally planned. Within a governmentality framework, it is generally assumed that technologies work to support government programmes, to accomplish governance. But growing evidence points to smart grid technologies undermining the promise of smart grids. Such a finding stands at odds with the assumption in governmentality about technologies doing work in consort with rationalities of government.     

The multiple communities of low-carbon transition: an assessment of communities involved in forest carbon measurement

In recent writing on sociotechnical transitions theory communities are mostly conceived of as being local and place-based (“grassroots”). In this paper the implications for sociotechnical transitions theory of having multiple communities operating at different geographical scales, and with different objectives, are examined through a case study of low-carbon innovation in forests. The focus of analysis is the communities promoting sociotechnical innovations in the measurement of forest carbon. Innovation is being driven by the international United Nations climate policy initiative “Reducing Emissions from Deforestation and forest Degradation” (REDD+). The rise of REDD+ policy has prompted a flurry of activity in related scientific research, especially in the field of remote sensing. But other types of community are also actively positioning themselves as experts in forest carbon measurement: there are multiple communities at work, each with varying claims to innovation and expertise, from local (place-based) forest communities to international communities of foresters and forest ecologists. Recognition of the multiple communities operating within sociotechnical systems usefully draws our attention to the politics of innovation.     

Neighborhood Level Spatial Analysis of the Relationship Between Alcohol Outlet Density and Criminal Violence

Misuse of alcohol is a significant public health problem, potentially resulting in unintentional injuries, motor vehicle crashes, drownings, and, perhaps of greatest concern, serious acts of violence, including assaults, rapes, suicides, and homicides. Although previous research establishes a link between alcohol consumption increased levels of violence, studies relating the density of alcohol outlets (e.g., restaurants, bars, liquor stores) and the likelihood of violent crime have been less common. In this paper we test for such a relationship at the small area level, using data from 79 neighborhoods in the city of Minneapolis, Minnesota. We adopt a fully Bayesian point of view using Markov chain Monte Carlo (MCMC) computational methods as available in the popular and freely available WinBUGS language. Our models control for important covariates (e.g., neighborhood racial heterogeneity, age heterogeneity) and also account for spatial association in unexplained variability using conditionally autoregressive (CAR) random effects. Our results indicate a significant positive relationship between alcohol outlet density and violent crime, while also permitting easy mapping of neighborhood-level predicted and residual values, the former useful for intervention in the most at-risk neighborhoods and the latter potentially useful in identifying covariates still missing from the fixed effects portion of the model.     

Spatial PM2.5 mobile source impacts using a calibrated indicator method

Motor vehicles are major sources of fine particulate matter (PM_2.5), and the PM_2.5 from mobile vehicles is associated with adverse health effects. Traditional methods for estimating source impacts that employ receptor models are limited by the availability of observational data. To better estimate temporally and spatially resolved mobile source impacts on PM_2.5, we developed an approach based on a method that uses elemental carbon (EC), carbon monoxide (CO), and nitrogen oxide (NO_x) measurements as an indicator of mobile source impacts. We extended the original integrated mobile source indicator (IMSI) method in three aspects. First, we generated spatially resolved indicators using 24-hr average concentrations of EC, CO, and NO_x estimated at 4 km resolution by applying a method developed to fuse chemical transport model (Community Multiscale Air Quality Model [CMAQ]) simulations and observations. Second, we used spatially resolved emissions instead of county-level emissions in the IMSI formulation. Third, we spatially calibrated the unitless indicators to annually-averaged mobile source impacts estimated by the receptor model Chemical Mass Balance (CMB). Daily total mobile source impacts on PM_2.5, as well as separate gasoline and diesel vehicle impacts, were estimated at 12 km resolution from 2002 to 2008 and 4 km resolution from 2008 to 2010 for Georgia. The total mobile and separate vehicle source impacts compared well with daily CMB results, with high temporal correlation (e.g., R ranges from 0.59 to 0.88 for total mobile sources with 4 km resolution at nine locations). The total mobile source impacts had higher correlation and lower error than the separate gasoline and diesel sources when compared with observation-based CMB estimates. Overall, the enhanced approach provides spatially resolved mobile source impacts that are similar to observation-based estimates and can be used to improve assessment of health effects.

Implications: An approach is developed based on an integrated mobile source indicator method to estimate spatiotemporal PM_2.5 mobile source impacts. The approach employs three air pollutant concentration fields that are readily simulated at 4 and 12 km resolutions, and is calibrated using PM_2.5 source apportionment modeling results to generate daily mobile source impacts in the state of Georgia. The estimated source impacts can be used in investigations of traffic pollution and health.     

Development of a new scale to measure subjective career success: A mixed‐methods study

Career success is a main focus of career scholars as well as organizational stakeholders. Historically, career success has been conceptualized and measured in an objective manner, mainly as salary, rank, or number of promotions. However, the changing nature of work has also necessitated a change in the way many employees view success, adding a more subjective component. Although there has been theoretical discussion and calls to develop a comprehensive measure of subjective career success, no contemporary comprehensive quantitative measure exists. The goal of this study was to create and validate a measure of subjective career success, titled the Subjective Career Success Inventory (SCSI). The SCSI includes 24 items that address subjective career success via eight dimensions. The scale was developed and validated through four phases of data collection, beginning with interviews and focus groups, followed by item sorting tasks, then item refinement through confirmatory factor analysis, and finally convergent and discriminant validity quantitative analysis. Theoretical and practical implications are discussed. Copyright © 2015 John Wiley & Sons, Ltd.