Is UK Planning a Barrier to Energy Efficient Heritage Retrofit: A Comparative Analysis of a Selection of London Boroughs

This paper is part of a larger PhD research project examining the apparent conflict in UK planning between energy efficiency and conservation for the retrofit of the thermal envelope of the existing building stock. Review of the literature shows that the UK will not meet its 2050 emission reduction target without substantial improvement to the energy performance of the thermal envelope of the existing building stock and that significantly, 40% of the existing stock has heritage status and may be exempted from Building Regulations. A review of UK policy and legislation shows that there are clear national priorities towards reducing emissions and addressing climate change, yet also shows a movement towards local decision making and control. This paper compares the current status of thirteen London Boroughs in respect to their position on thermal envelope retrofit for heritage and traditionally constructed buildings. Data collection is through ongoing surveys and interviews that compare statistical data, planning policies, sustainability and environmental priorities, and Officer decision-making. This paper finds that there is a lack of consistency in application of planning policy across Boroughs and suggests that this is a barrier to the up-take of energy efficient retrofit. Various recommendations are suggested at both national and local level which could help UK planning and planning officers deliver more energy efficient heritage retrofits.

Simulation-based optimal sensor scheduling with application to observer trajectory planning

The sensor scheduling problem can be formulated as a controlled hidden Markov model and this paper solves the problem when the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. The aim is to minimise the variance of the estimation error of the hidden state w.r.t. the action sequence. We present a novel simulation-based method that uses a stochastic gradient algorithm to find optimal actions. © 2007 Elsevier Ltd. All rights reserved.