Statistics aid the identification, measurement and then promotion of energy efficient behaviours and lifestyles
19 May 2014
As an energy resource, energy efficiency has the unique potential to contribute simultaneously to long-term energy security, economic growth, and even improved health and well-being; in particular it is a key means to cut energy consumption and greenhouse gas emissions. Critical to achieving those gains is not just the adoption of more efficient devices but also the identification, measurement and then promotion of behaviours and lifestyles that limit or reduce energy consumption.
Energy-smart behaviours, choices and practices are critical to unlocking energy savings and ensuring that these savings persist into the future. Lifestyles make up a significant part of the large gap between potential and actual levels of efficiency and are the key to reducing the even larger gap between consumer attitudes and actual actions. Conversely, negative behavioural factors can cancel out energy savings achieved from technical improvements.
Energy efficiency indicators help explain how economic and human activity interacts with energy use, but often cannot predict the variation in overall energy consumption or quantify the impact of individual variables or factors on it. So, selecting and developing indicators is only the first step in analysing the energy situation. Each indicator has its own purpose, but also limitations in what it can explain. Providing an accurate picture requires a set of several indicators. The lack of sufficient data is currently the biggest problem in developing energy efficiency indicators.
Decomposition distinguishes among effects
One of the most important issues from an energy policy perspective is to what extent improvements in energy efficiency have been responsible for the changes in final energy intensity in different countries. To fully understand that, indicators must distinguish among different impacts: the effect of changes in activity, economic structure and other factors that influence energy demand, and the result of changes in energy intensity – or output divided by energy – which are a proxy for technical energy efficiency. The IEA makes this distinction by using a decomposition approach in its analysis.
Decomposition, or factorisation, analysis quantifies the impacts of different driving forces or factors on energy consumption in a sector or country, identifying the largest sources of potential reduction and priority areas for development of energy efficiency policies.
Decomposition of energy end-use trends often distinguishes among three main components affecting energy consumption: aggregate activity, sectoral structure and energy intensity.
But if more detailed data are available, the decomposition can be extended to investigate the specific drivers of energy consumption. For example, rather than just using the number of dwellings as an activity variable when analysing energy efficiency in the residential sector, if data on both floor areas and number of dwellings are available, then the influence of both number of dwellings and dwelling size can be monitored.
Therefore a key issue with decomposition analysis is the choice of activity variable, as this selection will determine what energy consumption drivers can be measured and tracked. The activity variable should be chosen from easily available data and be linked directly to stated policy and programme objectives.
Both physical activity variables (e.g. production in tonnes) and activity value (value added in monetary terms) are valuable for measuring energy efficiency, and both should be tracked on a consistent basis.
Manuals on collecting more and better data
The need to collect more data and develop more robust indicators was a key message from last year’s inaugural Energy Efficiency Market Report, which focused significantly on indicators analysis. The IEA developed the manual Energy Efficiency Indicators: Essentials for Policy Making and is preparing a companion publication, Energy Efficiency Indicators: Fundamentals on Statistics, to detail how to collect additional data that facilitate creation of the most appropriate and reliable indicators to enable reliable international comparisons.
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