On the importance of pro-environmental organizational climate for employee green behavior

In their call to action, Ones and Dilchert(2012) discuss several possible individual and some contextual determinants of employee green behavior that await examination by industrial and organizational I–O) psychologists. Although these authors briefly mentioned organizational climate, specifically ethical climate, as a potentially relevant predictor of green behaviors, they mostly emphasized the role of individual difference characteristics and traditional job performance determinants such as knowledge, skills, abilities, and other person factors (KSAOs).

An intraindividual perspective on pro-environmental behaviors at work

Ones and Dilchert (2012) highlight the importance of examining workplace environmental sustainability at the interindividual (or between-person) and organizational levels. In this commentary, we aim to extend these authors’ framework by focusing on pro-environmental behaviors and their potential predictors at the intraindividual, or within-person, level. To this end, we will first describe the intraindividual perspective, its benefits, and the diary study methodology often used to operationalize this perspective. Secondly, we will share how the intraindividual perspective was useful in an empirical study we conducted on multilevel relationships among employees’ pro-environmental attitude, daily affect, and daily proenvironmental behaviors. Finally, we will discuss a number of possible limitations of the diary study methodology, ways to Correspondence concerning this article should be addressed to Megan J. Bissing-Olson. E-mail: m.bissing-olson@uq.edu.au Address: School of Psychology, The University of Queensland, Brisbane, Queensland 4072, Australia overcome them, and directions for future research.

Pro-Environmental Organizational Culture and Climate

This chapter reviews the concepts of organizational culture and climate and applies them to environmental sustainability. Though culture and climate are often used interchangeably, the chapter identifies key distinctions between them and highlights how they can complement one another. The two concepts are used to discuss how the organizational context for environmental sustainability, and employee perceptions thereof, influence individual pro-environmental behavior. Organizational climate is integrated with a dynamic model of organizational culture to describe how pro-environmental cultures and climates emerge. The chapter also highlights how organizations with different motivations can create pro-environmental cultures and climates. The chapter uses the Sierra Nevada Brewing Company as an archetype of an organization with a pro-environmental culture and climate. In the course of the discussion, the chapter nominates several imperatives for research and recommendations for practice.

Assessing the combined effect of asbestos exposure and smoking on lung cancer: a Bayesian approach

We review the literature on the combined association between lung cancer and two environmental exposures, asbestos exposure and smoking, and explore a Bayesian approach to assess evidence of interaction between the exposures. The meta-analysis combines separate indices of additive and multiplicative relationships and multivariate relative risk estimates. By making inferences on posterior probabilities we can explore both the form and strength of interaction. This analysis may be more informative than providing evidence to support one relation over another on the basis of statistical significance. Overall, we find evidence for a more than additive and less than multiplicative relation.

Adaptive frequency scaled wavelet packet decomposition for frog call classification

Environmental changes have put great pressure on biological systems leading to the rapid decline of biodiversity. To monitor this change and protect biodiversity, animal vocalizations have been widely explored by the aid of deploying acoustic sensors in the field. Consequently, large volumes of acoustic data are collected. However, traditional manual methods that require ecologists to physically visit sites to collect biodiversity data are both costly and time consuming. Therefore it is essential to develop new semi-automated and automated methods to identify species in automated audio recordings. In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification. After syllable segmentation, the advertisement call of each frog syllable is represented by a spectral peak track, from which track duration, dominant frequency and oscillation rate are calculated. Then, a k-means clustering algorithm is applied to the dominant frequency, and the centroids of clustering results are used to generate the frequency scale for wavelet packet decomposition (WPD). Next, a new feature set named adaptive frequency scaled wavelet packet decomposition sub-band cepstral coefficients is extracted by performing WPD on the windowed frog calls. Furthermore, the statistics of all feature vectors over each windowed signal are calculated for producing the final feature set. Finally, two well-known classifiers, a k-nearest neighbour classifier and a support vector machine classifier, are used for classification. In our experiments, we use two different datasets from Queensland, Australia (18 frog species from commercial recordings and field recordings of 8 frog species from James Cook University recordings). The weighted classification accuracy with our proposed method is 99.5% and 97.4% for 18 frog species and 8 frog species respectively, which outperforms all other comparable methods.

Interfuel substitution in Australia: A way forward to achieve environmental sustainability

This paper examines the possibilities for interfuel substitution in Australia in view of the need to shift towards a cleaner mix of fuels and technologies to meet future energy demand and environmental goals. The translog cost function is estimated for the aggregate economy, the manufacturing sector and its subsectors, and the electricity generation subsector. The advantages of this work over previous literature relating to the Australian case are that it uses relatively recent data, focuses on energy-intensive subsectors and estimates the Morishima elasticities of substitution. The empirical evidence shown herein indicates weak-form substitutability between different energy types, and higher possibilities for substitution at lower levels of aggregation, compared with the aggregate economy. For the electricity generation subsector, which is at the centre of the CO2 emissions problem in Australia, significant but weak substitutability exists between coal and gas when the price of coal changes. A higher substitution possibility exists between coal and oil in this subsector. The evidence for the own- and cross-price elasticities, together with the results for fuel efficiencies, indicates that a large increase in relative prices could be justified to further stimulate the market for low-emission technologies.

Factors influencing hospital readmission and length of stay of Vietnamese patients with type 2 diabetes and cardiac disease

Background: Increased hospital readmission and longer stays in the hospital for patients with type 2 diabetes and cardiac disease can result in higher healthcare costs and heavier individual burden. Thus, knowledge of the characteristics and predictive factors for Vietnamese patients with type 2 diabetes and cardiac disease, at high risk of hospital readmission and longer stays in the hospital, could provide a better understanding on how to develop an effective care plan aimed at improving patient outcomes. However, information about factors influencing hospital readmission and length of stay of patients with type 2 diabetes and cardiac disease in Vietnam is limited. Aim: This study examined factors influencing hospital readmission and length of stay of Vietnamese patients with both type 2 diabetes and cardiac disease. Methods: An exploratory prospective study design was conducted on 209 patients with type 2 diabetes and cardiac disease in Vietnam. Data were collected from patient charts and patients' responses to self-administered questionnaires. Descriptive statistics, bivariate correlation, logistic and multiple regression were used to analyse the data. Results: The hospital readmission rate was 12.0% among patients with both type 2 diabetes and cardiac disease. The average length of stay in the hospital was 9.37 days. Older age (OR= 1.11, p< .05), increased duration of type 2 diabetes (OR= 1.22, p< .05), less engagement in stretching/strengthening exercise behaviours (OR= .93, p< .001) and in communication with physician (OR= .21, p< .001) were significant predictors of 30-dayhospital readmission. Increased number of additional co-morbidities (β= .33, p< .001) was a significant predictor of longer stays in the hospital. High levels of cognitive symptom management (β= .40, p< .001) significantly predicted longer stays in the hospital, indicating that the more patients practiced cognitive symptom management, the longer the stay in hospital. Conclusions: This study provides some evidence of factors influencing hospital readmission and length of stay and argues that this information may have significant implications for clinical practice in order to improve patients' health outcomes. However, the findings of this study related to the targeted hospital only. Additionally, the investigation of environmental factors is recommended for future research as these factors are important components contributing to the research model.

Preventing infections through cleaner hospitals (PITCH): An environmental cleaning bundle

Translating the numerous lengthy cleaning standards and guidelines into meaningful and sustained improvements in cleaning practice is challenging. This research hypothesized that an evidence based cleaning bundle would improve cleaning performance, knowledge and attitudes, and ultimately reduces healthcare associated infections (HAI) in a way that is value for money. A bundle is a small, straightforward set of evidence based practices, that when performed collectively and reliably, improves patient outcomes.

Environmental applications of inorganic-organic clays for recalcitrant organic pollutants removal: Bisphenol A

Bisphenol-A (BPA) adsorption onto inorganic-organic clays (IOCs) was investigated. For this purpose, IOCs synthesised using octadecyltrimethylammonium bromide (ODTMA, organic modifier) and hydroxy aluminium (Al13, inorganic modifier) were used. Three intercalation methods were employed with varying ODTMA concentration in the synthesis of IOCs. Molecular interactions of clay surfaces with ODTMA and Al13 and their arrangements within the interlayers were determined using Fourier transform infrared spectroscopy (FTIR). Surface area and porous structure of IOCs were determined by applying Brunauer, Emmett, and Teller (BET) method to N2 adsorption-desorption isotherms. Surface area decreased upon ODTMA intercalation while it increased with Al13 pillaring. As a result, BET specific surface area of IOCs was considerably higher than those of organoclays. Initial concentration of BPA, contact time and adsorbent dose significantly affected BPA adsorption into IOCs. Pseudo-second order kinetics model is the best fit for BPA adsorption into IOCs. Both Langmuir and Freundlich adsorption isotherms were applicable for BPA adsorption (R2 > 0.91) for IOCs. Langmuir maximum adsorption capacity for IOCs was as high as 109.89 mg g‒1 and it was closely related to the loaded ODTMA amount into the clay. Hydrophobic interactions between long alkyl chains of ODTMA and BPA are responsible for BPA adsorption into IOCs.

Transfunctional living walls-designing living walls for environmental and social benefits

Greater attention is being directed towards incorporating greenery into the built environment as increasing global urbanisation drives the search for sustainable urbanism. This research takes a parametric approach to studying living wall dynamics using three methods to cover a diversity of design parameters and performance criteria. The findings led to a functional typology for living walls based on a range of design, context and performance parameters wider than previously identified. Such parametric studies offer valuable insights into 'transfunctional' living walls for homes, schools and public spaces.

Predicting the temporal response of seagrass meadows to dredging using Dynamic Bayesian Networks

Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.

Content-based birdcall retrieval from environmental audio

This research investigates techniques to analyse long duration acoustic recordings to help ecologists monitor birdcall activities. It designs a generalized algorithm to identify a broad range of bird species. It allows ecologists to search for arbitrary birdcalls of interest, rather than restricting them to just a very limited number of species on which the recogniser is trained. The algorithm can help ecologists find sounds of interest more efficiently by filtering out large volumes of unwanted sounds and only focusing on birdcalls.

Using Bayesian networks to model surveillance in complex plant and animal health systems

In this chapter we consider biosecurity surveillance as part of a complex system comprising many different biological, environmental and human factors and their interactions. Modelling and analysis of surveillance strategies should take into account these complexities, and also facilitate the use and integration of the many types of different information that can provide insight into the system as a whole. After a brief discussion of a range of options, we focus on Bayesian networks for representing such complex systems. We summarize the features of Bayesian networks and describe these in the context of surveillance.

Aircraft trajectory clustering techniques using circular statistics

This paper presents a statistical aircraft trajectory clustering approach aimed at discriminating between typical manned and expected unmanned traffic patterns. First, a resampled version of each trajectory is modelled using a mixture of Von Mises distributions (circular statistics). Second, the remodelled trajectories are globally aligned using tools from bioinformatics. Third, the alignment scores are used to cluster the trajectories using an iterative k-medoids approach and an appropriate distance function. The approach is then evaluated using synthetically generated unmanned aircraft flights combined with real air traffic position reports taken over a sector of Northern Queensland, Australia. Results suggest that the technique is useful in distinguishing between expected unmanned and manned aircraft traffic behaviour, as well as identifying some common conventional air traffic patterns.

Social and environmental reporting practices of organisations operating in, or sourcing products from, a developing country : evidence from Bangladesh

This research consists of a broad study in three parts of the social and environmental reporting practices of organisations operating in or sourcing products from a developing country, in this case Bangladesh. The first part of this study explores the social and environmental disclosure practices of the Bangladesh Garment Manufacturers and Exporters Association (BGMEA), the body responsible for organising the activities of 4,200 entities involved in the export of garments from Bangladesh. By way of interview, this part documents the opinions of numerous senior executives from the BGMEA with regard to any changes in the degree of social and environmental pressures since 1985. Utilising a complementary theoretical perspective that includes legitimacy theory, stakeholder theory and institutional theory this part then performs an analysis of the BGMEA's annual reports (1987-2005), t o explore the link between the perceived pressures and changes entailed therein and the social and environmental disclosure practices of the BGMEA across the period of analysis. The results show that the disclosure practices of BGMEA appear to be directly driven by the changing expectations of multinational buying companies- the group deemed to be the most powerful stakeholder group. This section is the first known study to interview managers from a large organisation in a developing country about shifting stakeholder expectations and then to link these changing expectations to annual report disclosures across an extended period of analysis. The findings then directly lead to the second major part of this thesis which investigates the social and environmental disclosure practices of two major multinational buying companies: Nike and H&M. Adopting a joint consideration of legitimacy theory and media agenda setting theory, this second part investigates the linkage between negative media attention and positive corporate social and environmental disclosures over a 19 year period. The results support the view that for those industry-related social and environmental issues that attract the greatest amount of negative media attention, these companies react by providing positive social and environmental disclosures. The results were particularly significant in relation to labour practices in developing countries-the issue that attracts the greatest amount of negative media attention for the companies in question. While the second part demonstrates that the media influences particular disclosure practices, the third part of the thesis shows what drives the media. Based on the speculation provided in the second part, the third part tests the proposition that the media is an important ally of NGOs in their quest to influence change in corporate accountabilities. Through the use of interviews, the results of this part of the study provide evidence to support previously untested perspectives about NGOs' utilisation of the m edia. The results reveal that NGOs use the media because the media is responsible for creating real changes in the operations and disclosure policies of organisations sourcing products from Bangladesh. The various pressures impacting the activities of organisations operating in or sourcing products from developing countries constitutes a fascinating area of investigation, and it is hoped that this study will motivate further research in this area.