Energy Policy Scenarios to 2050
1.4 Mathematical Process
The heart of this study is a qualitative assessment of how policies and measures can meet emerging challenges and achieve outcomes closer to the 3 A's than would otherwise be the case.
To provide the regional groups with a consistency check on their internal thinking, a mathematical simulation model of the energy sector was used. This provided quantitative baseline data to compare with the qualitative regional output, and iterations were made with the regional groups to identify potential inconsistencies and disagreements. In many cases, the model was altered to account for these inconsistencies, and its results were thus strengthened.
The model is a global simulation model for the energy sector with a year-by-year recursive simulation and partial equilibrium framework, endogenous international energy prices, and lagged adjustments of supply and demand by region. It has a hierarchical structure of interconnected regional and national sub-models (e.g., individual countries and sub-regions).
In brief, economic (GDP) and population growth rates are captured with assumptions about annual rates of growth for each region and sub-region. Technology trends and energy prices at the consumer level are the other two main drivers of energy demand. Energy intensity is calculated on the basis of energy demand and GDP projections to capture energy efficiency trends. Primary energy mix is derived either directly through assumptions about public and private investments, or through assumptions on parameters reflecting the necessary market conditions for these investments to be made by private decision makers. Greenhouse gas emissions result from projections for energy demand, fuel mix, and carbon capture and storage. Supply-demand tensions are addressed through exogenous inputs related to business development conditions. In the case of oil and gas, supply-demand tensions reflect the development of business conditions worldwide that push international prices up or down.
Inconsistencies between model projections and study group analyses fell into three general categories: the need for more precision in the qualitative storylines, the need to modify the quantitative assumptions in the model based on input from the regional study, and the need by one study region to accommodate developments in another region that affect global development and prices.
It is important to note that (1) the model is top-down, (2) this version of the model is aimed at projections (likely outcomes), and (3) it is aimed to a large extent at showing the need to reduce carbon emissions from energy use. The study group, on the other hand, was by definition looking at plausible (realistic) storylines (bottom-up) with a number of drivers in play.
For example, study group members questioned assumptions in the model about whether the Middle East can or will supply oil at the projected levels, whether China will decrease its coal use to the levels assumed in the simulation, and whether North America can produce oil at a level high enough to become a net exporter. This is not to imply that the model is wrong and the study group right, only that they come from different perspectives. Nevertheless, the results from the model do show, after iterations with the study group and modifications to identify key indicators and scenario constraints, the quantitative effect of different scenarios on the achievement of the 3 A's and on the qualitative elements identified in Section 3. The trends about achieving the 3 A's are addressed in Section 4.