change in the housing sector. The first theory reviewed that could potentially do this was the Punctuated Equilibrium (PE) perspective (Geels, 2010; 2011; Feeman and Perez, 1988, Freeman and Louca, 2011) The PE perspective improves upon some of the shortcomings with technological substitution models by introducing the concept of technological discontinuity (Anderson and Tushman, 1990; Geels, 2010; 2011). This acknowledges that existing dominant technologies can be in decline and acknowledges that a period of new design, competition and substitution will occur when they are being replaced with new technologies (Geels, 2010; 2011). Within the framework there is considered to be a period where incremental change is ushered in, a period where the old systems and ways of doing are replaced with the new. This creates a passage of time when new technologies ‘ferment’ and a new dominant design emerges (Anderson and Tushman, 1990; Geels, 2010; 2011). Whilst this improves upon the Technological substitution model it still does not incorporate the source of innovation or generate an understanding of where or how radical innovations develop from (Geels, 2010; 2011). As this research project is looking into change from the innovation level the PE perspective presents a number of issues in generating a deep enough understanding of the problem area from multiple perspectives. It also relies too heavily on the technological push aspects as the cause of the period of fermentation without improving the understanding of how power, policy, social practice or culture influences it (Anderson and Tushman, 1990; Geels, 2010; 2011). As such the theory was considered to be too focused on techno-economic factors to effectively elaborate and organise the key social and technical issues facing a zero carbon housing transition. Further research identified the theories which took a wider perspective on the issues facing radical socio-technical change and brought the socialisation of technology into the core of the theory, rather then in addition to market factors. One such theory studied was the Long wave theory (LWT) (Feeman and Perez, 1988, Freeman and Louca 2011). LWT improves upon the issues with the PE perspective and technological substitution theories by bringing in co-evolutionary concepts relating to new technical
these four technically and economically optimised designs a final optimised design was selected. Within the parametric analysis some of the parameters were fixed and some variable. The fixed parameters enabled a valid comparative baseline to be developed so the results were not skewed by i.e. changes to desired internal temperature. The fixed parameters were for space heating demands using an 18°C set point, hot water demand based on 5 person occupancy and 50L of hot water/ person/ day, and predicted appliance electrical consumption including lighting energy use. Other parametric factors were variable and based on the characteristics of specific elements involved with optimising the building’s energy performance including: wall and window U-value, space heating primary energy source, ventilation rate energy loss with heat recovery, heat recovery efficiency, heating and hot water system efficiency, passive solar gains and internal gains, energy generation by different renewable energy systems, renewable energy system sizing, implementation costs, build costs and running costs. Once a range of technically and economically viable systems were created the equation based modelling could be validated using a dynamic modelling tool for data for accuracy. Dynamic modelling is considered the best way to capture the complex interactions that contribute to thermal performance, demand and their uncertainties. This was achieved using TRNSYS. TRNSYS is a transient systems simulation program developed by the University of Wisconsin and has been available for over 30 years (University of Wisconsin, 2015). It is widely used for energy in building simulation. This enabled different usage and occupancy patterns to be modelled as well as more dynamic passive and solar gain modelling including solar gains, radiative heat, internal radiative gains and wall gains). TRNSYS models thermal behaviour of a building divided into different zones and models thermal demands hourly for each thermal zone (University of Wisconsin, 2015). Thus whilst TRNSYS models are less flexible to rapidly change and observe technical and economic optimisation they offer more detailed and accurate outputs. As such the final optimised design was modelled in TRNSYS to increase certainty in the thermal loads. The design parameters and building attributes of the optimised design were given to a Masters student to input. The
commercially non-viable for development. The sustainable developer considered the optimised design to hit a number of financial social objectives for local authorities which traditional developments would not meet. As such he considered the model as a low cost way to leverage unused council land assets not fit for other forms of sale or development. It was noted that if the model could be fed into council and LA carbon abatement targets he could see the potential to generate incomes for LA’s whilst reducing emissions, currently a cost to councils. By capitalising this value it was considered that it may be possible to subsidise the increased build cost of the home. Respondent SB2 (Interview). We are always looking to extract value… and add value which often leads us to look in all manner of places. Which ranges from patches of land you can bang up a few houses, which is easy, or to where you would have to stretch hard to find any opportunity to add value. Respondent SB2 (Interview): once you have the long term benefit of low bills and bit of money coming back at a domestic level there’s a way of [us] using that future promise to raise capital to subsidise the value of the house. 7.2 Market potential and demand HB1:…does the sector want a code 6 house? HB2: …Mainstream code 6. I just don’t think it’s going to happen Another critical barrier is accurately establishing demand (Byrne, 2005; Birrell & Bin, 1997; Wilkinson & Reed, 2008; Callcutt, 2007). Due to the way the development process works, this must occur at the development phase and be predicted accurately to protect investors. Demand is usually predicted using market and historical data, however, documented sales history for commercial scale zero carbon homes does not exist. Callcutt (2007) considers accurately predicting demand and managing uncertainty as essential components of successful development. Establishing if demand for a zero carbon home exists is