Projecting greenhouse gas (GHG) emissions is important because it helps us to assess how much the energy and GHG policies in place help to achieve targets set by law. For example, if we apply California’s AB32 target to the San Diego region, we would have to achieve the 1990 GHG emissions level (approximately 29 million metric tons CO2e) in 2020. To assess whether the policies we are enacting and implementing today can hope to achieve this target, we need a projection that tells us where we would be without these policies. Figure 1 shows the regional GHG emissions and a projection made in 2008 based on economic and demographic forecasts of the region (see San Diego County Greenhouse Gas Inventory September 2008 at http://www.sandiego.edu/law/centers/epic/reports-papers/reports.php). According to this projection, we would have needed to avoid about 14 million metric tons CO2e in 2020 to achieve the 1990 level.
Figure 1 San Diego region’s greenhouse gas emissions trends and 2008 BAU projection
The start year of the projection is the one with actual data and is usually called the baseline year. There are several approaches to projecting emissions, which can lead to confusion regarding terminology and meaning of projections. Projections can be calculated in at least three different ways:
a) Based on actual emissions to date and projected using historical trend analysis and/or economic and demographic forecasts. In this case, the future effects of existing policy affecting energy or greenhouse gases are not considered. This is the case in the projection shown in Figure 1, which starts in 2008. Another example would be if a projection that starts in 2012 in California but does not include the future effects of the Renewables Portfolio Standard, RPS (http://www.cpuc.ca.gov/PUC/energy/Renewables/).
b) Based on actual emissions to date and projected to include the future effects of the policies in place in the starting year. Using the example above, such a projection would include the effects of the RPS in any future year.
c) Based on actual emissions to date and then projected to include the future effects of energy and GHG-related implemented and planned. In this case, not only the effects of the existing 33% RPS but also the potential effects of the Governor’s Executive Order B-30-15 (http://gov.ca.gov/news.php?id=18938) with the goal of reaching 50% RPS would be included.
These projections may be called a “business-as-usual” (BAU), baseline or a base case projection. The Intergovernmental Panel on Climate Change (IPCC) defines a “business-as-usual” baseline case as the level of emissions that would result if future development trends follow those of the past and no changes in policies take place. Given the potential for confusion around projected GHG emissions levels, it is important to know what lies beneath the projection.
Dependence of GHG Emissions Projections on State Forecasts
Greenhouse gas (GHG) emissions arise from 14 categories in our region. For the overall inventory in any year, we estimate emissions from each category separately and then add them up to get an overall emission value for that year. Figure 2 shows estimated 2012 emissions for the San Diego region by category.
Similarly, we estimate future GHG emissions by projecting each category of emissions separately, and then combine individual category projections to produce an overall GHG projection. What the projection looks like depends on what the underlying category projections look like and how they have been updated since the last inventory. It is important to note that neither the baseline year (1990) emissions nor the targets (for 2020 for example) change.
Let us examine how projections of individual categories affect the overall projections for GHGs in the San Diego region. We focus only on the largest contributors to total emissions: on-road transportation, and electricity and natural gas end-use. These categories are projected separately on the basis of state forecasts for each region or service territory.
On-Road Transportation GHG Emissions Projections
The on-road transportation CO2 emissions projection for the region is developed using data from the California Air Resources Board’s state pollutant emissions model called EMFAC, which provides vehicle tailpipe emissions on a regional, county, metropolitan planning organization (MPO) or air basin basis. It does not directly provide fuel use or fuel use projections. Instead, the model is based both on miles traveled (regional) and emissions rates (regional) and is updated every 3-4 years. The miles traveled are provided by the MPO, in San Diego this is the San Diego Association of Governments (SANDAG). Projections of miles traveled depend on the regional population, housing and jobs growth, which are projected by SANDAG. The regional population, housing and jobs growth forecasts are updated in “Series” every 3-4 years. In the San Diego region, Series 11, 12 and 13 were updates from 2006, 2010 and 2013, respectively. Series forecasts may include the effects of local policies to reduce VMT.
When the state EMFAC model is updated, it includes not only the Series update from the MPO but also the future effects of only those state policies implemented, such as fuel economy standards, which affect tailpipe emissions. EMFAC updates do not include state policies that may affect VMT, or the amount of driving.
As a result, the state forecast for on-road transportation GHG emissions is not simply type a) as described above but is more like type b).
Electricity and Natural Gas Forecasts
Unlike on-road transportation emissions forecasts, the state California Energy Commission (CEC) forecasts electricity and natural gas demand for utility service territories. In 2014, the CEC forecast demand for natural gas from 2014-2024 and for electricity from 2015-2025. The main policy measures accounted for in the CEC forecasts are as follows:
- Energy efficiency savings expected through the 2013-2014 utility program cycle
- Title 24 building standards savings expected through the latest 2013 update
- Federal appliance standards savings through 1992 and state Title 20 appliance standards savings through 1982, except for certain specific appliance standards updated through 2011.
Importantly, because the CEC forecasts demand, it only accounts for state policies that results in demand reduction, and NOT for the effect of the RPS, which only has effects on GHG emissions.
Combining Emissions from Individual Categories to Project “BAU” Total GHG Emissions
Once projections are completed for each emissions category, they are combined to develop an overall total “BAU” emissions projection. Figure 3 shows three “BAU” emissions projections (colored solid lines with x) for the San Diego region.
Figure 3 Projections for greenhouse gas emissions, San Diego region.
- The red line projection was already shown in Figure 1. The inventory year was 2008 and the projection started in 2009. (The two spikes in 2003 and 2007 represent actual estimated emissions from large regional wildfire events). With this projection, we needed about 14 MMT CO2e reduction to reach the 2020 target (29 MMT).
- The blue line projection from 2011 reflects the effects of the economic recession of 2008, therefore starts at a lower level than the red line. With this projection, we would have needed about 8 MMT CO2e reduction to reach the 2020 target (29 MMT).
- The green line projection is based on SANDAG’s most recent Series 13 demographic and economic growth forecast incorporated into EMFAC2011, as well as the CEC forecasts discussed previously. This projection shows a decreasing GHG emissions growth rate and trend through 2050 compared with either the red or blue line. With this projection, we now need about 7 MMT CO2e reduction to reach the 2020 target.
However, in 2014, EMFAC was revised to include significant future GHG reductions expected from the following mandates in place in 2012:
- California’s Advanced Clean Cars Program, which includes the latest Federal Corporate Average Fuel Economy Standards, the Pavley tailpipe emissions standards (2012 – 2016 model years) and the Low Emissions Vehicles LEV III GHG regulation (2017 – 2025 model years).
- Up to 15.4% of sales of electric vehicles, held constant from 2025 to 2050
- California’s heavy-duty aerodynamics regulation.
As a result, the latest “BAU” projection of overall GHG emissions now includes the effects of the EMFAC 2014 revisions in comparison with the previous green line projection (Figure 4). As before, the state electricity and natural gas state forecasts have not changed and do NOT include the effects of the policies in place expected to lead to GHG reductions in this category, such as the Renewable Portfolio Standard.
Figure 4 Another “BAU” projection, San Diego region.
Therefore, the latest “BAU” purple line overall total emissions projection in Figure 4 now reflects the very different approaches to forecasting emissions in the two major sectors. In the transportation sector, the state emissions forecast reflects the emissions effects through 2035 of mandates in place in 2012. In contrast, the state forecasts for electricity and natural gas are for demand and do not reflect future expected GHG emissions reductions from state GHG reduction policies in this category beyond demand reduction policies listed above. We now appear to need about 4 MMT CO2 e to achieve the target in 2020 but the projection does not include GHG reduction effects of the the major state electricity and natural gas GHG reduction policies.
There are several observations to be made from this situation:
- There is no standard definition of “Business-as-Usual” GHG emissions projections.
- Our overall regional GHG projection depends on state forecasts produced for different purposes. Electricity and natural gas use are forecast for demand planning, on-road transportation is forecast for criteria pollution emissions compliance purposes. Regional GHG projections are used to estimate GHG reductions needed to achieve state-mandated GHG targets in a given year.
- Care is warranted in drawing conclusions from “BAU” projections as just a cursory look at the overall medium to long-term GHG projection that is based on different updates to each category will appear to unfairly bias the GHG reduction benefits towards particular categories. In the above example (Figure 4), the purple line projection compared with the green line reflects the future GHG emissions reductions effects due only to the major emission-reducing on-road transportation policies.
- We need to know clearly what goes into the underlying category GHG emissions projections, else we may over (double count) or underestimate the additional GHG reductions we need to achieve for any target year. Understanding the policies included in the main state forecasts is imperative to the development of energy and GHG policy at the local level.
 Working Group III: Mitigation, s. 22.214.171.124 Baseline Scenario Concepts, at http://www.ipcc.ch/ipccreports/tar/wg3/index.php?idp=286
 Emission FACtors model was developed and EPA-approved for the purpose of estimating criteria pollutant on-road vehicle tailpipe emissions in California. It also provides tailpipe carbon dioxide emissions. EMFAC CO2 results have been used for many years by regions and cities for GHG planning purposes, such as in the city land use planning model, CALEEMOD, see http://www.caleemod.com/.