899 resultados para UK,


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Because of the importance and potential usefulness of construction market statistics to firms and government, consistency between different sources of data is examined with a view to building a predictive model of construction output using construction data alone. However, a comparison of Department of Trade and Industry (DTI) and Office for National Statistics (ONS) series shows that the correlation coefcient (used as a measure of consistency) of the DTI output and DTI orders data and the correlation coefficient of the DTI output and ONS output data are low. It is not possible to derive a predictive model of DTI output based on DTI orders data alone. The question arises whether or not an alternative independent source of data may be used to predict DTI output data. Independent data produced by Emap Glenigan (EG), based on planning applications, potentially offers such a source of information. The EG data records the value of planning applications and their planned start and finish dates. However, as this data is ex ante and is not correlated with DTI output it is not possible to use this data to describe the volume of actual construction output. Nor is it possible to use the EG planning data to predict DTI construc-tion orders data. Further consideration of the issues raised reveal that it is not practically possible to develop a consistent predictive model of construction output using construction statistics gathered at different stages in the development process.

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As a method of procuring the services of the built environment, performance-based contracting (PBC) seeks to link the building supplier to longer term commitments than has traditionally been the case in the construction sector. By rewarding the building producer according to the way that building or structure delivers the users' requirements, rather than according to a list of assembled parts, a number of additional risks are taken by contractors, including fitness for purpose, costs and briefing. The extent to which contractors recognize these risks and their methods of dealing with them vary considerably and are influenced by their attitudes towards risk. As the risks associated with PBC are seen as large, uninsurable, and vulnerable to changing client requirements, the majority of respondents would reject the use of PBC as a method of contracting. Nevertheless, PBC may be used under particular conditions, where rewards are deemed sufficient to compensate for the additional risk to the contractor of undertaking work on the basis of a stream of payments paid over the life of a structure depending on the satisfactory performance of the building or as part of a private finance initiative.

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The UK industry has been criticised for being slow to adopt construction process innovations. Research shows that the idiosyncrasies of participants, their roles in the system and the contextual differences between sections of the industry make this a highly complex problem. There is considerable evidence that informal social networks play a key role in diffusion of innovations. The aim is to identify informal communication networks of project participants and the role these play in the diffusion of construction innovations. The characteristics of this network will be analysed in order to understand how they can be used to accelerate innovation diffusion within and between projects. Social Network Analysis is used to determine informal communication routes. Control and experiment case study projects are used within two different organizations. This allows informal communication routes concerning innovations to be mapped, whilst testing if the informal routes can facilitate diffusion. Analysis will focus upon understanding the combination of informal strong and weak ties, and how these impede or facilitate the diffusion of the innovation. Initial work suggests the presence of an informal communication network. Actors within this informal network, and the organization's management are unaware of its' existence and their informal roles within it. Thus, the network remains an untapped medium regarding innovation diffusion. It is proposed that successful innovation diffusion is dependent upon understanding informal strong and weak ties, at project, organization and industry level.

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Attempts to reduce the energy consumed in UK homes have met with limited success. One reason for this is a lack of understanding of how people interact with domestic technology – heating systems, lights, electrical equipment and so forth. Attaining such an understanding is hampered by a chronic shortage of detailed energy use data matched to descriptions of the house, the occupants, the internal conditions and the installed services and appliances. Without such information it is impossible to produce transparent and valid models for understanding and predicting energy use. The Carbon Reduction in Buildings (CaRB) consortium of five UK universities plans to develop socio-technical models of energy use, underpinned by a flow of data from a longitudinal monitoring campaign involving several hundred UK homes. This paper outlines the models proposed, the preliminary monitoring work and the structure of the proposed longitudinal study.

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This paper examines the life cycle GHG emissions from existing UK pulverized coal power plants. The life cycle of the electricity Generation plant includes construction, operation and decommissioning. The operation phase is extended to upstream and downstream processes. Upstream processes include the mining and transport of coal including methane leakage and the production and transport of limestone and ammonia, which are necessary for flue gas clean up. Downstream processes, on the other hand, include waste disposal and the recovery of land used for surface mining. The methodology used is material based process analysis that allows calculation of the total emissions for each process involved. A simple model for predicting the energy and material requirements of the power plant is developed. Preliminary calculations reveal that for a typical UK coal fired plant, the life cycle emissions amount to 990 g CO2-e/kWh of electricity generated, which compares well with previous UK studies. The majority of these emissions result from direct fuel combustion (882 g/kWh 89%) with methane leakage from mining operations accounting for 60% of indirect emissions. In total, mining operations (including methane leakage) account for 67.4% of indirect emissions, while limestone and other material production and transport account for 31.5%. The methodology developed is also applied to a typical IGCC power plant. It is found that IGCC life cycle emissions are 15% less than those from PC power plants. Furthermore, upon investigating the influence of power plant parameters on life cycle emissions, it is determined that, while the effect of changing the load factor is negligible, increasing efficiency from 35% to 38% can reduce emissions by 7.6%. The current study is funded by the UK National Environment Research Council (NERC) and is undertaken as part of the UK Carbon Capture and Storage Consortium (UKCCSC). Future work will investigate the life cycle emissions from other power generation technologies with and without carbon capture and storage. The current paper reveals that it might be possible that, when CCS is employed. the emissions during generation decrease to a level where the emissions from upstream processes (i.e. coal production and transport) become dominant, and so, the life cycle efficiency of the CCS system can be significantly reduced. The location of coal, coal composition and mining method are important in determining the overall impacts. In addition to studying the net emissions from CCS systems, future work will also investigate the feasibility and technoeconomics of these systems as a means of carbon abatement.

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Since the first PFI hospital was established in 1994, many debates centred on the value for money and risk transfer in PFIs. Little concern is shown with PFI hospitals’ performance in delivering healthcare. Exploratory research was carried out to compare PFI with non‐PFI hospital performance. Five performance indicators were analysed to compare differences between PFI and non‐PFI hospitals, namely the length of waiting, the length of stay, MRSA infection rate, C difficile infection rate and patient experience. Data was collected from various government bodies. The results show that only some indexes measuring patient experience emerge statistically significant. This leads to a conclusion that PFI hospitals may not perform better than non‐PFI hospitals but they are not worse than non‐PFI hospitals in the delivery of services. However, future research needs to pay attention to reliability and validity of data sets currently available to undertake comparison.