Heating costs

In 2010, my heating costs exceeded my advance payments by about 25%. This motivated me to decompose the costs to see what drove the changes. Here is the result:

The numbers refer to Euros. Read von right to left: 2010 was a cold year (+102EUR), but gas consumption in this house was relatively low (–89EUR). Also, running costs and gas price were below previous year’s value (–49EUR). The main driver (+183EUR) for the increased costs was my share on the total costs. In 2009, I had to pay 1.7% of the total costs, which rose to 2.3% in 2010. This means my landlord assumes I used more energy than the other tennants in 2010. I doubt that, and one aspect to support my doubts is that the values of the two heat meters in my living room were nearly at the same value, while I used only one heater.

Thus I believe it was a measurement error. I am not going to complain about this, but will check next measurements very carefully.

Not a direct result of the decomposition, but also interesting is the (temperature adjusted) enery use per square meter, which was in 2010 by about 110 kWh/m2, which is nearly average.

Here’s the R code to produce the chart:

Regional differences on what drives CO2 emissions

If you are investigating the change of CO2 emissions, then you might ask: Where do the changes occur? Well here is the answer.

The staircase plots show the contributing factors to CO2 emissions for each continent. population refers to population effects, gdp_pcap refers to income per capita, energy_intensity refers to energy used per dollar added value, and carbon intensity refers to CO2 emissions per energy unit. The numbers are Gt CO2 and come, not very carefully curated, from Worldbank.

As you can see, europe’s emissions stay at nearly the same level, mostly because there is no population effect. On the other hand, Asia’s economic rise seems to explain much of the change of CO2 emissions in the time from 1995 to 2007.

Here is how to produce the chart:

Index decomposition with R

Few days ago, I finally finished a small package ida. It enables you to analyse contributions of underlying factors to the change in an aggregate, using methods based on index number theory. These methods have become popular by, but are not restricted to, investigating the change of CO2 emissions.

Here is a chart that shows what the change of population, welfare, efficiency and fuel substitution contributed to CO2 emissions:

The numbers refer to Gt CO2. The data comes from Worldbank, however we treated missing values rather uncautious here. So the result may or may not be valid. However, it puts the efforts in perspective: Clearly the reduction of the energy use per GDP has not been capable to compensate the additional emissions from population growth and income per capita growth. The carbon intensity, i.e. the emissions per energy unit, remained nearly unchanged.

Here is how to produce the chart: