Russian energy efficiency accounting system

For a full discussion on policy list, see Bashmakov and Bashmakov (2012) and Bashmakov (2013).

Cahill and Ó Gallachóir (2011) demonstrated how physical and economic output data when fully available may be jointly used in decomposition analysis to reflect the interplay of both energy efficiency indicators based on physical units and those based on value added.

\( \mathrm{EPI}=\frac{{\displaystyle \sum_{i=1}^n{E}_i^T}}{{\displaystyle \sum_{i=1}^n{A}_i^T\times {I}_i^0}}=\frac{1}{{\displaystyle \sum_{i=1}^n{w}^T\times \frac{I_i^T}{I_i^o}}} \)

Canada’s Office of Energy Efficiency calls this factor “service level,” but calling it “amenities,” “equipment,” “well-being,” or “comfort” factor might be more accurate.

As GDP is expressed in constant prices, the GDP energy intensity evolution was hardly affected by oil and gas price growth in those years.

It is not really energy price dynamics that matters to consumers, but the share of energy costs in the overall income as shown in Bashmakov (2004b, 2007).

Specific energy consumption for space heating is usually defined as the ratio of energy consumption per 1 m² per degree-day of the heating season. Degree-days for the whole of the Russian Federation were assessed as the average for 20 Russian regions.

Voigt et al. (2014) conducted a decomposition analysis for Russia (among 40 countries) over the 1995–2007 time horizon. Total energy use was split to 34 economic activity sectors. Impacts of only two factors—structural changes and technological improvements (using as proxy energy intensity per unit of value added)—were assessed. The study concluded that Russia’s energy intensity decline over 1995–2007 was mostly driven by structural shifts, which is very much in line with this paper conclusion. The approach used accounts shifts towards lower energy-intensive products within every value added sectors as energy efficiency improvement, while the approach applied in this paper accounts them as structural shifts. Voigt et al. estimates of the technological factor contribution are higher comparing with our findings due to the following: (a) smaller number of sectors used in decomposition; (b) less factors considered, and (c) different approach to energy use split and energy efficiency indicators evaluation. Comparison of results illustrates that using product, works, and services energy use split along with more factors in the decomposition analysis allows for better reflection of technological progress impact on GDP energy intensity evolution.

Those statistical forms are developed by Rosstat (http://​www.​gks.​ru), but very limited amount of information from those forms is published.

Very much like in the European Union