It should not come as news that the real estate sector is a large user of energy, with significant carbon emissions as a result. Some quick stats: 60% of EU and 73% US electricity consumption is in commercial and residential buildings, and 38% of total US carbon emissions stem from buildings.
Real estate owners, occupiers, and investors are increasingly setting targets to reduce the carbon emissions from their real estate holdings. They do so for many reasons, including cost reduction (“doing well”), environmental stewardship (“doing good”), and environmental regulation that is increasingly targeted towards buildings (e.g. mandatory disclosure in many US cities, minimum energy performance requirements for commercial buildings in the UK and the Netherlands, etc). Examples abound:
- Unibail-Rodamco (Europe’s largest listed property company) announced a target to reduce carbon emissions by 50% from 2016 to 2030;
- PGGM Investments (in the top-10 of largest pension funds in the world) requires its investments to reduce carbon emissions by 50% between 2015 and 2020 (!).
- Etc. (there are many many more example to list, these are some of the recently announced, highly publicized targets).
Now, the carbon footprint of a building is the product of its energy consumption and the carbon intensity of the grid that supplies the the building’s energy. That is an important notion: unlike the energy consumption, which purely depends on the building and its location (i.e. weather), carbon footprint is heavily influenced by the electricity grid’s carbon intensity, which in turn is determined by country or state energy policies. The carbon footprint of an energy efficient building can thus be relatively high if it is connected to a grid that uses electricity mainly generated from coal combustion. On the other hand, if a building’s electricity is generated from hydropower, its carbon footprint will be relatively minimal, even though the building can be a significant user of energy.
To put carbon emission targets into proper perspective, it is therefore important to understand the carbon intensity of the grid. Note that owners and/or occupiers can always decide to buy carbon offsets (a cheap trick), or to procure renewable energy more directly (see for example, this article about “Amazon Brings on Five New Solar Projects to Power Its Cloud”). Abstracting from such idiosyncratic decisions by individual building owners and occupiers, the average carbon intensity of a statewide or national grid, as well as the development of such intensity over time, provides important insight into the ability to reduce carbon emissions in building portfolios not from actual building improvements, but rather from “improvements” in electricity generation.
GeoPhy has collected detailed information on the current and historical carbon intensity of the grid in more than 54 countries. This data serves as input into the GeoPhy Carbon Model, where the carbon emissions of more than 105 million buildings are analyzed and tracked over time. To understand the feasibility of carbon targets, and the role of the grid in achieving such targets, we recently did an analysis of 32 countries, where the electricity grid’s carbon intensity is calculated by using the country’s electricity mix and applying each electricity source’s carbon intensity to that mix.
The result is a timeline of each country’s electricity grid carbon intensity in gCO2eq/kWh from 1967 until 2014, with an additional forecast for the 2015-2020 period. This forecast is based on the EU Reference Scenario 2016 and the International Energy Outlook 2016. In the table below, we show the carbon intensity of the electricity grid at 5 points in time (2020, 2014, 2004, 1994 and 1984), ranking the 32 countries from highest carbon intensity to lowest carbon intensity (note that we included the US national average here, masking variation across states).
Source: 1984-2914 data from Worldbank. 2020 predictions from EU Reference Scenario 2016 and the International Energy Outlook 2016. * indicates that 2020 predictions are based on statistical modeling, in the absence of publicly available forecasts.
According to the analysis, the countries with the highest grid carbon intensity are characterized by high dependency on coal as the main source of electricity generation. This includes Australia (in 2014, 83% of the electricity was generated from coal and natural gas), Poland (86%), Israel (98%), and Japan (73%).
The five snapshots of time in the table show significant variation in carbon intensity over time, with more detailed time series graphs for each country providing the year-by-year trend (download country-by-country data here). For example, the figure below shows that the carbon intensity of Japan’s grid was showing a decline until 2011, when the Fukushima nuclear disaster occurred. Nuclear electricity generation was mostly phased out by 2012, and the grid’s carbon intensity has since been rising again, due to reliance on natural gas as the main electricity fuel source.
Countries with the lowest carbon grid intensity in the analysis are Iceland, Norway, Switzerland and Sweden, where the main electricity generation sources are either hydropower (96% of Norway’s electricity generation) or a combination of the lower intensity sources (renewables and nuclear energy).
Countries showing impressive reduction in the carbon intensity of their grid include France, where the grid’s intensity fell from 603gCO2eq/kWh in the late 1970s to 51gCO2eq/kWh in 2014. This decline is mostly driven by the increase in nuclear energy. Another country showing a steady decline in grid carbon intensity is Germany (see figure below), where the decreased dependence on coal from 77% in 1967 to 45% in 2014 and embracing renewable electricity generation, resulted in cutting the grid’s carbon emissions by 40%.
Looking at predictions until 2020, the average carbon intensity of the grid is expected to decrease by 7%. For the most progressive countries, this is as high as 48% (UK and Denmark), whereas other countries are actually expected to increase their carbon emissions per kWh of electricity produced (e.g. Austria, Israel, Italy). Note also that these predictions do not include uncertainties such as economic shocks, disasters such as Fukushima, as well as the implications of intermittency stemming from the renewable energy flooding the grid.
Both the historical numbers and the carbon intensity forecast provide some important lessons for real estate investors, companies and governments setting reduction targets -- the “greening” of the grid should not be an excuse for inaction, but it will get many companies and real estate investors a long way to reaching their carbon reduction goals! One way to more directly measure the achievements of firms would be to measure energy intensity rather than carbon intensity -- this reflect real energy consumption of the building, rather than procurement of renewable energy, buying carbon offsets, or changing carbon intensity of the grid. An interesting example is a recent announcement by TH Real Estate (a large investment manager), which aims to reduce its energy intensity in the real estate portfolio by 30% from 2015 to 2030.