The E3MG research programme is led by Annela Anger and Jean-Francois Mercure, in collaboration with Terry Barker and with colleagues at Cambridge Econometrics and the Tyndall Centre
4CMR evolved from the ground-breaking work of Dr Terry Barker and colleagues in creating coupled energy-economy-environment (E3) models applied to technology change and climate change mitigation. This resulted in first UK-specific, then EU-specific, then global versions of the model, culminating in the E3 Model, Global (or E3MG). As with most of the modelling at 4CMR, E3MG is macroeconomic, examining not only the impact of policies on specific sectors of economic activity but the impact on the overall performance of the economy, including consideration of net social welfare and equity.
Why do we examine all of the regions of the world simultaneously? Our answer is two-fold. First, the economies of the world are highly inter-connected, with imports and exports of one region affecting economic performance in another. Equally important, however, is our commitment to examining pathways that bring much needed economic development to the "bottom of the pyramid", the majority of the world's population that lives in poverty.
The Mathematical Bits
At the heart of our modelling is a set of "time step" equations that follow the economic indicators year-by-year through the various Economic Activities in the figure below and to the left. These time step equations are of the form:
Amount in this year = Amount in last year + Amount of change in this year
Amount in next year = Amount in this year + Amount of change in next year
and so on through the entire period of time over which we are producing projections.
How do we find the part of these equations shown as "Amount of change in X year"? These are obtained from econometric data measuring how an amount such as Aggregate Energy Demand has changed in the past, and how that change depends on a number of key economic variables. From these econometric data, we produce equations of the form:
Amount of change in a year = a*V1 + b*V2...
where V1, V2,...are Variables on which the "Amount of change in a year" depends; and a, b,...are Parameters that relate these Variables to the "Amount of change in a year". We in turn find as these Parameters from other econometric data, locating the best numerical value of each parameter needed to explain those data on past performance of the economic system. Lastly, for many of the Variables, there is a lag between when the variable changes, and when this is reflected in "Amount of change in a year". This is because in reality, it takes some time for a "shock" to the economic system to begin to drive changes in that system. For example, even though the price of a fuel might change rapidly to make it more competitive with other fuels, businesses can't make the change immediately because the new fuel might require replacing the equipment that generates energy.
Once we have calculated a measure of performance such as Aggregate Energy Demand in a year, we convert that into emissions of air pollutants through the use of Emissions Factors, with units of amount of emissions in a year per unit of fuel use or Aggregate Energy Demand. Our primary interest is in carbon dioxide, but we also look at the other greenhouse gases of methane (CH4), N2O, PFCs, SF6 and HFCs, as well as NOx, CO, SO2, CFCs, VFCs and PM-10. Our interest in the latter pollutants is both because they are regulated, and hence might be subject to controls apart from concerns over climate change, and because their reduction might be a co-benefit of reducing greenhouse gas emissions. For example, many policies that reduce carbon dioxide will also reduce PM-10 emissions, and this reduction in PM-10 emissions brings reductions in respiratory and cardiovascular diseases, in turn reducing burdens on health care and lost productivity of workers. This reduction is a co-benefit of what was initially only a climate change mitigation strategy.
The Result
A year-by-year estimate of economic performance and emissions, allowing us to develop historical reconstructions of how emissions have changed over time in the past and to explain these changes (similar to what the climate scientists have done for temperature and atmospheric carbon dioxide), and project out to the future to assess how different policies and pathways of technology change might affect these emissions.
Pathways to Climate Change Mitigation
The modelling we perform helps decision-makers examine scenarios of the effects of a variety of changes in the world's economy as it decarbonises, whether driven by policies or market conditions. Some of the most relevant examples of the questions we consider are:
- How rapidly will the global economy decarbonise if the price of carbon is set at X?
- What must be the price of carbon if we want the global economy to reduce carbon emissions by Y% by 2050?
- What will be the impact on global carbon emissions and economic performance if the regional economies move to electricity and if power production uses carbon capture and storage?
- What will be the impact on global carbon emissions and economic performance if a sector such as Housing achieves ambitious targets for energy efficiency?
- How globally competitive will a given regional economy remain if it were to unilaterally impose carbon charges on the use of carbon-based fuel?
These and the other questions we ask help the world understand the implications of alternative pathways to decarbonisation of the global economy, and to identify policies, instruments and practices that move the world most effectively along those pathways.
