Table XX: Examples of key internal and external factors impacting on BAU External factors Internal factors Electricity grid Grid factor changes Energy The energy used per carbon factor year on year due to a intensity of XX is likely to change factor variety of external service over time, due to applied to factors outside of the provision changing nature of convert units of organisation’s control services and also kWh of including relative changing efficiency of electricity of different fuels for equipment. This is one consumed to a power generation. of the areas that are figure of carbon Over a longer period particularly difficult to dioxide of time, the grid factor model at this point in equivalents changes due to energy time and it is likely emitted. policy and the relative that this will require contribution of further studies to different fuels and effectively model sources of generation e.g. renewables. The aim of energy policy is to reduce the carbon intensity of the grid and this will have a large effect on the overall footprint The need for the Over time Population Estate growth/changing organisation’s services changes organisations build demographics depends on both the and decommission size and structure of estate in order to the population served meet the requirements and therefore changes of population served. to this population may The more this can be need to be modelled with real incorporated data e.g. known floor areas or energy efficiency data, the more accurate this forecast can be. Why is it important to model BAU? As carbon management has become more sophisticated, organisations have become aware that the use of a single annual percentage growth to represent BAU is not necessarily accurate. Furthermore, by failing to model BAU, carbon managers cannot clearly demonstrate progress against targets and value for money for the carbon management programme, which in turn has a knock-on effect on internal investment. If an organisation’s BAU carbon footprint is actually increasing faster than anticipated, the efforts of the carbon management team would be underestimated – this is especially true when the measured footprint appears to be flatlining or even increasing and senior managers might question the impact of investment; however a more accurate model of the BAU could show that without the efforts of the CM team, the footprint would have risen even more steeply.
Table XX: Examples of key internal and external factors impacting on BAU External factors Internal factors Electricity grid carbon factor factor applied to convert units of kWh of electricity consumed to a figure of carbon dioxide equivalents emitted. Grid factor changes year on year due to a variety of external factors outside of the organisation’s control including relative of different fuels for power generation. Over a longer period of time, the grid factor changes due to energy policy and the relative contribution of different fuels and sources of generation e.g. renewables. The aim of energy policy is to reduce the carbon intensity of the grid and this will have a large effect on the overall footprint Energy intensity of service provision The energy used per XX is likely to change over time, due to changing nature of services and also changing efficiency of equipment. This is one of the areas that are particularly difficult to model at this point in time and it is likely that this will require further studies to effectively model Population growth/changing demographics The need for the organisation’s services depends on both the size and structure of the population served and therefore changes to this population may need to be incorporated Estate changes Over time organisations build and decommission estate in order to meet the requirements of population served. The more this can be modelled with real data e.g. known floor areas or energy efficiency data, the more accurate this forecast can be. Why is it important to model BAU? As carbon management has become more sophisticated, organisations have become aware that the use of a single annual percentage growth to represent BAU is not necessarily accurate. Furthermore, by failing to model BAU, carbon managers cannot clearly demonstrate progress against targets and value for money for the carbon management programme, which in turn has a knock-on effect on internal investment. If an organisation’s BAU carbon footprint is actually increasing faster than anticipated, the efforts of the carbon management team would be underestimated – this is especially true when the measured footprint appears to be flatlining or even increasing and senior managers might question the impact of investment; however a more accurate model of the BAU could show that without the efforts of the CM team, the footprint would have risen even more steeply.
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