81 research outputs found

    Intervening to change behaviour and save energy in the workplace: a systematic review of available evidence

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    Workplaces worldwide are a major source of carbon emissions and changing energy use behaviour in these environments has the capacity for large carbon savings. This paper reviews and synthesises empirical evidence to identify what types of behaviour change intervention are most successful at saving energy in an office-type workplace. We draw on the field of health-related behaviour change interventions and adopt the Behaviour Change Wheel (Michie et al., 2014) as a framework through which to assess the success of the interventions reviewed here (n = 22 studies). We find that interventions creating social and physical opportunities for employees to save energy are the most successful i.e. which constitute Enablement (including direct support and greater control to employees), Environmental Restructuring (particularly automated and retrofitted technologies) and Modelling (various forms of social influence). The communal nature of most workplaces demands scrutiny to understand the effect of social influences. We provide recommendations for future research, including the need to consider forms of intervention not yet researched; Coercion, Restriction, and Training. We conclude by calling for further, well evaluated, energy saving behavioural interventions in a variety of workplaces to identify those techniques which offer the greatest success in saving energy and thus reducing carbon emissions

    Defining complementary tools to the IVI. The Infrastructure Degradation Index (IDI) and the Infrastructure Histogram (HI)

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    [EN] The Infrastructure Value Index (IVI) is quickly becoming a standard as a valuable tool to quickly assess the state of urban water infrastructure. However, its simple nature (as a single metric) can mask some valuable information and lead to erroneous conclusions. This paper introduces two complementary tools to IVI: The Infrastructure Degradation Index (IDI) and the Infrastructure Histogram (HI). The IDI is focused on time (compared to the IVI, focused on value), represents an intuitive concept and behaves in a linear way. The joint analysis of IVI and IDI provides results in a more complete understanding of the state of the assets, while maintaining the simplicity of the tools. The Infrastructure Histogram allows for a full evaluation of the infrastructure state and provides a detailed picture of network age compared to its expected life, as well as an order of magnitude of the required investments in the following years.Cabrera Rochera, E.; Estruch-Juan, ME.; Gomez Selles, E.; Del Teso-March, R. (2019). Defining complementary tools to the IVI. The Infrastructure Degradation Index (IDI) and the Infrastructure Histogram (HI). Urban Water Journal. 16(5):343-352. https://doi.org/10.1080/1573062X.2019.1669195S343352165Alegre, H., Vitorino, D., & Coelho, S. (2014). Infrastructure Value Index: A Powerful Modelling Tool for Combined Long-term Planning of Linear and Vertical Assets. Procedia Engineering, 89, 1428-1436. doi:10.1016/j.proeng.2014.11.469Amaral, R., Alegre, H., & Matos, J. S. (2016). A service-oriented approach to assessing the infrastructure value index. Water Science and Technology, 74(2), 542-548. doi:10.2166/wst.2016.250Aware-p.org. 2014. “AWARE-P/Software.” Accessed 25 November 2018. http://www.aware-p.org/np4/software/Baseform. 2018. “Baseform.” Accessed 24 November 2018. https://baseform.com/np4/productCanal de Isabel II Gestión. 2012. Normas Para Redes de Abastecimiento. [Standards for Water Supply Networks.]. https://www.canalgestion.es/es/galeria_ficheros/pie/normativa/normativa/Normas_redes_abastecimiento2012_CYIIG.pdfFrost, and Sullivan. 2011. “Western European Water and Wastewater Utilities Market.” https://store.frost.com/western-european-water-and-wastewater-utilities-market.html#section1Leitão, J. P., Coelho, S. T., Alegre, H., Cardoso, M. A., Silva, M. S., Ramalho, P., … Carriço, N. (2014). Moving urban water infrastructure asset management from science into practice. Urban Water Journal, 13(2), 133-141. doi:10.1080/1573062x.2014.939092Marchionni, V., Cabral, M., Amado, C., & Covas, D. (2016). Estimating Water Supply Infrastructure Cost Using Regression Techniques. Journal of Water Resources Planning and Management, 142(4), 04016003. doi:10.1061/(asce)wr.1943-5452.0000627Marchionni, V., Lopes, N., Mamouros, L., & Covas, D. (2014). Modelling Sewer Systems Costs with Multiple Linear Regression. Water Resources Management, 28(13), 4415-4431. doi:10.1007/s11269-014-0759-zPulido-Velazquez, M., Cabrera Marcet, E., & Garrido Colmenero, A. (2014). Economía del agua y gestión de recursos hídricos. Ingeniería del agua, 18(1), 95. doi:10.4995/ia.2014.3160Rokstad, M. M., Ugarelli, R. M., & Sægrov, S. (2015). Improving data collection strategies and infrastructure asset management tool utilisation through cost benefit considerations. Urban Water Journal, 13(7), 710-726. doi:10.1080/1573062x.2015.102469

    Expanded Quality Management Using Information Power (EQUIP): Protocol for a Quasi-experimental Study to Improve Maternal and Newborn Health in Tanzania and Uganda.

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    Maternal and newborn mortality remain unacceptably high in sub-Saharan Africa. Tanzania and Uganda are committed to reduce maternal and newborn mortality, but progress has been limited and many essential interventions are unavailable in primary and referral facilities. Quality management has the potential to overcome low implementation levels by assisting teams of health workers and others finding local solutions to problems in delivering quality care and the underutilization of health services by the community. Existing evidence of the effect of quality management on health worker performance in these contexts has important limitations, and the feasibility of expanding quality management to the community level is unknown. We aim to assess quality management at the district, facility, and community levels, supported by information from high-quality, continuous surveys, and report effects of the quality management intervention on the utilization and quality of services in Tanzania and Uganda. In Uganda and Tanzania, the Expanded Quality Management Using Information Power (EQUIP) intervention is implemented in one intervention district and evaluated using a plausibility design with one non-randomly selected comparison district. The quality management approach is based on the collaborative model for improvement, in which groups of quality improvement teams test new implementation strategies (change ideas) and periodically meet to share results and identify the best strategies. The teams use locally-generated community and health facility data to monitor improvements. In addition, data from continuous health facility and household surveys are used to guide prioritization and decision making by quality improvement teams as well as for evaluation of the intervention. These data include input, process, output, coverage, implementation practice, and client satisfaction indicators in both intervention and comparison districts. Thus, intervention districts receive quality management and continuous surveys, and comparison districts-only continuous surveys. EQUIP is a district-scale, proof-of-concept study that evaluates a quality management approach for maternal and newborn health including communities, health facilities, and district health managers, supported by high-quality data from independent continuous household and health facility surveys. The study will generate robust evidence about the effectiveness of quality management and will inform future nationwide implementation approaches for health system strengthening in low-resource settings
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