An overview of domestic energy models and a new approach with agent based modelling

Government targets for CO2 reductions are being progressively tightened, the Climate Change Act set the UK target as an 80% reduction by 2050 on 1990 figures. The residential sector accounts for about 30% of emissions. This paper discusses current modelling techniques in the residential sector: principally top-down and bottom-up. Top-down models work on a macro-economic basis and can be used to consider large scale economic changes; bottom-up models are detail rich to model technological changes. Bottom-up models demonstrate what is technically possible. However, there are differences between the technical potential and what is likely given the limited economic rationality of the typical householder. This paper recommends research to better understand individuals’ behaviour. Such research needs to include actual choices, stated preferences and opinion research to allow a detailed understanding of the individual end user. This increased understanding can then be used in an agent based model (ABM). In an ABM, agents are used to model real world actors and can be given a rule set intended to emulate the actions and behaviours of real people. This can help in understanding how new technologies diffuse. In this way a degree of micro-economic realism can be added to domestic carbon modelling. Such a model should then be of use for both forward projections of CO2 and to analyse the cost effectiveness of various policy measures.

Modelling of Johne's disease control options in beef cattle: a decision support approach

Johne's disease in cattle is a contagious wasting disease caused by Mycobacterium avium subspecies paratuberculosis (MAP). Johne's infection is characterised by a long subclinical phase and can therefore go undetected for long periods of time during which substantial production losses can occur. The protracted nature of Johne's infection therefore presents a challenge for both veterinarians and farmers when discussing control options due to a paucity of information and limited test performance when screening for the disease. The objectives were to model Johne's control decisions in suckler beef cattle using a decision support approach, thus implying equal focus on ‘end user’ (veterinarian) participation whilst still focusing on the technical disease modelling aspects during the decision support model development. The model shows how Johne's disease is likely to affect a herd over time both in terms of physical and financial impacts. In addition, the model simulates the effect on production from two different Johne's control strategies; herd management measures and test and cull measures. The article also provides and discusses results from a sensitivity analysis to assess the effects on production from improving the currently available test performance. Output from running the model shows that a combination of management improvements to reduce routes of infection and testing and culling to remove infected and infectious animals is likely to be the least-cost control strategy.

Multiple-project discount rates for cost-benefit analysis in construction projects: A formal risk model for microgeneration renewable energy technologies

The traditional economic approach for appraising the costs and benefits of construction project Net Present Values involves the calculation of net returns for each investment option under different discount rates. An alternative approach consists of multiple-project discount rates based on risk modelling. The example of a portfolio of microgeneration renewable energy technology (MRET) is presented to demonstrate that risks and future available budget for re-investment can be taken into account when setting discount rates for construction project specifications in presence of uncertainty. A formal demonstration is carried out through a reversed intertemporal approach of applied general equilibrium. It is demonstrated that risk and the estimated available budget for future re-investment can be included in the simultaneous assessment of the costs and benefits of multiple projects.

Assessing the business risk of technology obsolescence through enterprise modelling

The problem of technology obsolescence in information intensive businesses (software and hardware no longer being supported and replaced by improved and different solutions) and a cost constrained market can severely increase costs and operational, and ultimately reputation risk. Although many businesses recognise technological obsolescence, the pervasive nature of technology often means they have little information to identify the risk and location of pending obsolescence and little money to apply to the solution. This paper presents a low cost structured method to identify obsolete software and the risk of their obsolescence where the structure of a business and its supporting IT resources can be captured, modelled, analysed and the risk to the business of technology obsolescence identified to enable remedial action using qualified obsolescence information. The technique is based on a structured modelling approach using enterprise architecture models and a heatmap algorithm to highlight high risk obsolescent elements. The method has been tested and applied in practice in two consulting studies carried out by Capgemini involving three UK police forces. However the generic technique could be applied to any industry based on plans to improve it using ontology framework methods. This paper contains details of enterprise architecture meta-models and related modelling.

Modelling the resistance of Enterprise Architecture adoption: linking strategic level of Enterprise Architecture to organisational changes and change resistance

During the last few years Enterprise Architecture (EA) has received increasing attention among industry and academia. By adopting EA, organisations may gain a number of benefits such as better decision making,increased revenues and cost reduction, and alignment of business and IT. However, EA adoption has been found to be difficult. In this paper a model to explain resistance during EA adoption process (REAP) is introduced and validated. The model reveals relationships between strategic level of EA, resulting organisational changes, and sources of resistance. By utilising REAP model, organisations may anticipate and prepare for the organisational change resistance during EA adoption.