Moving From Energy to Environment: Carbon Emissions
In the 1990s energy has been seen less than in the past as a scarce commodity. We recognise that our supply of energy is not running out. As a result, measures of energy use alone tell us little about the economy or about issues relating to
sustainability. Using energy data can cause undesirable side effects, or externalities. One of these problems concerns carbon emissions from fossil fuels (the dominant source of energy in most countries), which are associated with the threat of
climate change. Since climate change is certain to be a worse problem for future generations than for our own, energy use today is inexorably linked to sustainability. If we need indicators of energy use as they relate to sustainability, we must
have good indicators of how energy use (and fossil fuel use) is linked to economic and human activity. Our indicators serve this purpose.
III. The Devil is in the Decomposing: Seeing Beyond the Surface
A comparison of carbon emissions from a variety of sectors and end-uses in 1994, normalised to GDP tempts us to draw conclusions. But much more analysis must take place before we can either understand the differences among countries or explain
them. We need a way of disentangling all the factors that lie behind a particular level of carbon emissions (or any other pollutant problem).
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In our model we show how the indicator approach can be used to break down changes in carbon emissions into different underlying elements. Demand for energy services is generated from sectoral activity levels, such as value added and person-km
and the structure within each sector, such as industry mix and the mix of transport modes. The driving factors behind the activity and structure developments are GDP, population, income distribution and prices, as well as geographic factors like
climate. Energy intensity measured at end-use level represents the delivered, or final, energy needed per unit of activity. By including supply-side losses for each energy carrier and multiplying all fuels by their emission factor we calculate
the emissions resulting from each of the activities in the various sectors.
From this approach, observed changes in the end-use of energy can be separated into changes in activity changes in structure and changes in energy intensities. Changes in carbon emissions can be further decomposed into changes in fuel mix and
in supply-conversion efficiency. Hence, changes related to improved end-use energy efficiency (reductions in energy intensity) can be isolated from changes deriving from changes in other factors.
The result is an index that measures the relative impact of the varying term (energy intensity, for example) on carbon emissions. The results can literally be interpreted as "how emissions would have evolved if only one term had changed
over time and all other terms had remained the same". The benefits of this approach are that the indices are simple to obtain and the results are easy to interpret, since everything is expressed relative to a single base year.
It is important to separate the components of the carbon intensity effect (which is related to energy efficiency and fuel choices) from those related to demand by people and enterprises for energy service, since they change for different
reasons and in response to different stimuli. Polices and measures aimed at reducing emissions are most often directed towards the first category. Demand for energy service is related to welfare and economic development, e.g. industrial
production, travel activity, appliance ownership, etc., and seldom the target for energy and environmental polices. The disaggregated approach presented here allows for a better understanding of how the various components have shaped and will
shape energy and emission developments. This can help determine where policies can be most effective.
Let us now consider in a more general sense the factors behind differences in carbon emissions, some of which are relatively independent of GDP. These factors drive some of the enormous differences in emissions per unit of GDP, even among
countries with roughly similar GDP and economic structure:
- Heating degree-days vary from 900 in Australia and 1 800 in Japan to over 4 500 in Canada and Finland;
- Automobile use per capita varies from a high of 13 500 km/year in the US to a low of 3 900 km in Japan. Trucking tonne-km vary by a factor of two, and the ratio of freight hauled/GDP varies by more than a factor of two, clearly a
function of geography as well as other considerations;
- Home size varies from over 155 square metres/home (or roughly 60 square metres/capita in the US) to less than 90 square metres/home, and closer to 30 square metres/capita in Japan;
- Per capita manufacturing output varies by two to one. The manufacturing share of GDP varies from under 20% in Australia or Denmark to nearly 30% in other countries, and the share of the energy-intensive raw materials sectors varies from over
30% in Australia, the Netherlands, Finland and Sweden, to only 15% in Denmark. Similar considerations in the service sector reveal an almost two-to-one ratio of built area to population or to service-sector GDP.
- Among important driving factors, road-fuel prices vary by a factor of three, home-heating fuel prices by a factor of two, electricity prices in any sector by as much as a factor of three. GDP per capita varies by 1.75 to 1 across the
countries considered, all compared using purchasing power parity.
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Some of these factors are "natural", such as climate or geography. Others, such as those affecting the size of homes and buildings, depend on tax, housing and urban policies. Is any of these driving factors likely to relax its
apparent grip on patterns of energy use and therefore carbon emissions? We cannot say, but we estimate that these kinds of differences and the policies underlying them contribute more than half of the differences in the ratio of carbon emissions
to GDP, with the primary fuel mix, the final fuel mix, and energy intensities contributing the rest.
It is also very important to avoid confrontation among countries in the name of comparison. To some extent low energy intensities are associated with low costs. Low costs, in turn, encourage high levels of activity or structural
concentrations. Long driving distances in the US, high per capita production of aluminium in Norway and high indoor temperatures in heating-efficient Sweden are related to low fuel prices or to very high efficiencies.
The key issue for policy makers is how to repeat the achievements of the past. For countries where less carbon savings have occurred, the bad news of the past may be good news for the future. Thus there is almost no carbon left to save in the
home heating sector in Norway, but five times more per square metre and per degree-day in western Germany.
This is an edited extract of a paper presented to the 23rd Annual IAEE International Conference: Energy Market and the New Millennium: Economic, Environment, Security of Supply, Hilton Sydney, Australia, 7-10 June 2000.