A systematic review of the energy and climate impacts of teleworking

Information and communication technologies (ICTs) increasingly enable employees to work from home and other locations (‘teleworking’). This study explores the extent to which teleworking reduces the need to travel to work and the consequent impacts on economy-wide energy consumption. Methods/Design: The paper provides a systematic review of the current state of knowledge of the energy impacts of teleworking. This includes the energy savings from reduced commuter travel and the indirect impacts on energy consumption associated with changes in non-work travel and home energy consumption. The aim is to identify the conditions under which teleworking leads to a net reduction in economy-wide energy consumption, and the circumstances where benefits may be outweighed by unintended impacts. The paper synthesises the results of 39 empirical studies, identified through a comprehensive search of 9,000 published articles. Review results/Synthesis: Twenty six of the 39 studies suggest that teleworking reduces energy use, and only eight studies suggest that teleworking increases, or has a neutral impact on energy use. However, differences in the methodology, scope and assumptions of the different studies make it difficult to estimate ‘average’ energy savings. The main source of savings is the reduced distance travelled for commuting, potentially with an additional contribution from lower office energy consumption. However, the more rigorous studies that include a wider range of impacts (e.g. non-work travel or home energy use) generally find smaller savings. Discussion: Despite the generally positive verdict on teleworking as an energy-saving practice, there are numerous uncertainties and ambiguities about its actual or potential benefits. These relate to the extent to which teleworking may lead to unpredictable increases in non-work travel and home energy use that may outweigh the gains from reduced work travel. The available evidence suggests that economy-wide energy savings are typically modest, and in many circumstances could be negative or non-existent

Modelling Australian Domestic Tourism Demand: A Panel Data Approach.

This study estimates the income and tourism price elasticities of demand for Australian domestic tourism using a panel data approach. Given that about 76% of total tourism revenue in Australia is generated by domestic tourism, it is worthwhile examining whether changes in Australian households income and the prices of domestic travel can influence the demand for domestic travel. The research employs a panel data approach. This method has been widely employed in the literature on international tourism demand, but thus far, has not appeared in the context of the domestic tourism demand literature. The model used for this study is panel Three-Stage Least Square (3SLS). The data employed are based on quarterly time-series from 1999 to 2007 across seven Australian States. The paper reveals some notable results. First, the income elasticity for domestic visiting friends and relatives (VFR) trips in Australia is negative, implying that Australian households will not choose to travel domestically when there is an increase in household income. Second, the national income variables are positively correlated with domestic business tourism demand, indicating that the demand is strongly responsive to changes in Australia s economic conditions. Third, an increase in the current prices of domestic travel can cause the demand for domestic trips to fall in the next one or two quarters ahead. Finally, the coefficients for lagged dependent variables are negative, indicating perhaps, that trips are made on a periodic basis.Domestic tourism demand, Australia, Panel data Acknowledgements: We are grateful to the School of Accounting, Finance and Economics, Faculty of Business and Law, Edith Cowan University for providing travel funding to present this paper in the 18th World IMACS/MODSIM Congress. The second author would like to thank Sustainable Tourism Cooperative Research Centre (STCRC) for financial supports to produce this paper as part of her PhD thesis.

Predicting and Evaluating Software Model Growth in the Automotive Industry

The size of a software artifact influences the software quality and impacts the development process. In industry, when software size exceeds certain thresholds, memory errors accumulate and development tools might not be able to cope anymore, resulting in a lengthy program start up times, failing builds, or memory problems at unpredictable times. Thus, foreseeing critical growth in software modules meets a high demand in industrial practice. Predicting the time when the size grows to the level where maintenance is needed prevents unexpected efforts and helps to spot problematic artifacts before they become critical. Although the amount of prediction approaches in literature is vast, it is unclear how well they fit with prerequisites and expectations from practice. In this paper, we perform an industrial case study at an automotive manufacturer to explore applicability and usability of prediction approaches in practice. In a first step, we collect the most relevant prediction approaches from literature, including both, approaches using statistics and machine learning. Furthermore, we elicit expectations towards predictions from practitioners using a survey and stakeholder workshops. At the same time, we measure software size of 48 software artifacts by mining four years of revision history, resulting in 4,547 data points. In the last step, we assess the applicability of state-of-the-art prediction approaches using the collected data by systematically analyzing how well they fulfill the practitioners' expectations. Our main contribution is a comparison of commonly used prediction approaches in a real world industrial setting while considering stakeholder expectations. We show that the approaches provide significantly different results regarding prediction accuracy and that the statistical approaches fit our data best

Dynamic Time-Dependent Route Planning in Road Networks with User Preferences

There has been tremendous progress in algorithmic methods for computing driving directions on road networks. Most of that work focuses on time-independent route planning, where it is assumed that the cost on each arc is constant per query. In practice, the current traffic situation significantly influences the travel time on large parts of the road network, and it changes over the day. One can distinguish between traffic congestion that can be predicted using historical traffic data, and congestion due to unpredictable events, e.g., accidents. In this work, we study the \emph{dynamic and time-dependent} route planning problem, which takes both prediction (based on historical data) and live traffic into account. To this end, we propose a practical algorithm that, while robust to user preferences, is able to integrate global changes of the time-dependent metric~(e.g., due to traffic updates or user restrictions) faster than previous approaches, while allowing subsequent queries that enable interactive applications

Taming the Business Cycles in Commercial Aviation: Trade-space analysis of strategic alternatives using simulation modeling

We investigate the effectiveness of strategic alternatives that are designed to dampen the cyclicality manifest in the commercial aviation related industries. The constituent enterprises of the commercial aviation system exhibit managerial and operational independence and have diverse value functions that often viewed the enterprises to view their competition as a zero-sum game. We argue that this need not always be the case; in the commercial aviation system both airline and airframe manufacturers constituents would benefit from a steadier influx of aircraft that counters the current situation that is characterized by relatively stable demand growth rate for air travel while airline profitability and aircraft ordering fluctuate intensely. In order to identify and evaluate the symbiotic potential, we use a system dynamics model of commercial aviation. After testing several individual strategic alternatives, we find that capacity management is key to cycle moderation for non-collusive strategies. Comparing faster aircraft deliveries to semi-fixed production schedules among other alternatives shows only the latter alternative to be Pareto efficient