Exercise EEI.2: Energy Efficiency Indicators
In the exercise you have just completed, you worked on a simplified version of the IEA energy efficiency indicators template and started to familiarise yourself with energy efficiency indicators.
In this second exercise, you are given a copy of a complete energy efficiency indicators template of a fictitious country with most of the data available for the years 1990 to 2008 (and in some instances up to 2009). In this exercise, you work for the organisation that is responsible for filling in the template in this country.
- You are asked to propose one (or possibly several) solution(s) for the following issues:
a. Missing data points
a) The number of total and occupied dwellings used to be published every year by the Central Statistical Office of your country. Unfortunately, due to budget cuts, in the year 2000 the government decided to run the residential survey from which these data were obtained only every 4 years. The national statistical office does not publish any data for the intervening years. How would you estimate the missing data for the years up to 2009?
The number of dwellings is a variable that does not have sudden changes from year to year. It is thus a fair assumption to estimate the missing data points with a linear regression or using the average annual growth rate for each 4 years period.
The number of occupied dwellings can also be estimated by extrapolating the occupancy rate and applying it to the total population; then deriving total dwelling by extrapolating the vacancy rates.
b) The data for the rubber and plastic products industry is only available from 1998 onwards. Try to understand where the consumption of this industry branch was reported in the previous years and which time series you would estimate and how in order to produce consistent time series over time.
The break in series between 1997 and 1998 in the consumption of the chemical industry suggests that the consumption of the rubber and plastic products industry prior to 1998 was included together with the chemical industry. However, having consistent time series over time is key for the energy indicators analysis. We thus need to split the reported energy consumption of the chemical industry prior to 1998 between the chemical and the rubber and plastic industry. This can be done by looking at what is the share of each of these two industries out of their total consumption for the years after 1998 and apply the same ratios (or an average, or the same trends) to previous years.
If value-added is also available for these two industries, and if the trend in energy consumption between 1998 and 2008 is closely related to the share of value-added, it would be a faire assumption to consider that this relationship would hold prior to 1998.
The backcast can also be performed by estimating the energy intensity. This estimation can be made by applying the same rate of improvement between 1990 and 1998 than can be observed between 1998 and 2008.
c) The energy consumption of construction is not available from the energy balances for 2008 and 2009. How would you estimate it?
Since there is a good correlation between energy consumption and value-added of the construction industry (i.e. the energy intensity calculated as energy consumption per value added is rather constant), we can estimate the energy consumption for the two years we are missing assuming that the energy intensity would stay the same.
b. Inconsistencies
a) The reported consumption of combustible renewables and waste in the cement industry is higher than the reported consumption for the whole of the non-metallic mineral industry. How would you solve this inconsistency?
This is a rather common issue when the data for the cement industry comes from the industry association and the data for non-metallic mineral industry comes from the country’s energy balances. The cement industry makes use of non-conventional waste (such as tyres and rugs) which is often missing in the energy balances. If further data is available from the cement industry association on what is the share of non-conventional waste out of their total reported consumption of combustible renewables and waste, we can add the non-conventional waste consumption of the cement industry to the reported consumption of combustible renewables and waste of the non-metallic mineral industry. If this further information is not available, we can increase the reported consumption of combustible renewables and waste of the non-metallic mineral industry in each year to be at least equal to the consumption reported for the cement industry.
- A colleague has also informed you that there appear to be some problems with the data for the transport sector. For instance, the energy intensity of total passenger transport appears very high. Could you look at the data and identify the problems?
The energy intensity of total passenger transport (total energy consumption of passenger transport divided by total passenger kilometres), calculated in the template is about 4MJ/pkm against an IEA average of about 1.7MJ/pkm (and IEA countries ranging between 1.0 and 2.3MJ/pkm). This is due to the fact that for one of the modes (passenger cars, SUV and personal light trucks), the energy consumption is reported but there is no data for passenger-kilometres. The error is that the line for total passenger kilometres was still filled in with the sum of the data available, while this should be left as zeros, if data for the total passenger kilometres (including all modes) is unavailable.
The data in the template also had another problem. If we calculate the fuel economy of passenger cars from the energy consumption and vehicle kilometres reported, we obtain data well below the plausible range (see table below). This is often the case when the reported data for the energy and activity data have different coverage. For instance, the energy data may include all passenger cars, SUV and personal light trucks (as requested in the template) but the vehicle-kilometres may only include cars and not SUV and larger personal vehicles. This kind of issue can only be solved going back to the original sources of the data and checking the definitions of the data used.
Plausible fuel economy of road vehicles (in litres of gasoline equivalent per 100 km)OECD North America / OECD Europe / Japan / Other OECD Pacific
Low / High / Low / High / Low / High / Low / High
Cars / 9 / 15 / 7 / 12 / 6 / 11 / 8 / 14
cars gasoline / 9 / 15 / 7 / 12 / 6 / 11 / 8 / 14
cars diesel / 8 / 17 / 4 / 10 / 4 / 10 / 7 / 13
motorcycles / 3 / 8 / 2 / 7 / 2 / 7 / 3 / 8
buses / 15 / 40 / 15 / 40 / 15 / 40 / 15 / 40
freight and commercial / 20 / 45 / 20 / 45 / 20 / 45 / 20 / 45
freight and commercial gasoline / 25 / 50 / 25 / 50 / 25 / 50 / 25 / 50
freight and commercial diesel / 20 / 45 / 20 / 45 / 20 / 45 / 20 / 45