4. Decomposition of Overall Changes
An important consideration for policymakers is the aggregate impact of changes over time. Policies that focus solely on carbon intensities of vehicles and not on systematic changes in travel volume and mode may overlook important shifts that offset or even overcome the savings from lower vehicle carbon intensities. For example, even though the intensity of trucking, the dominant component of both freight and transport emissions, declined from 1960 to 1973, the
aggregate carbon intensity of freight actually increased. Because car travel, air travel and truck freight are the most carbon intensive modes, shifts in their relative importance can reinforce or offset changes in individual intensities. On the other hand, transport policies that take advantage of improvements in the travel or freight system that either raise vehicle utilization or promote shifts to less carbon intensive modes can give fuel and CO
2 savings at no change in technology. To understand the overall effect on emissions that is reflected in mode shift, intensity shifts, change in fuel mix, and the overall level of travel or freight, we provide additional decomposition techniques. While the foregoing descriptive analysis reveals what lies behind aggregate changes, more powerful decomposition techniques yield greater insights about the past and the future [
21].
The starting point of this decomposition is the
ASIF equation, developed to understand and decompose components that multiply to yield a given output or input [
23].
A represents total transport activity in passenger-km (or tonne-km for freight), S is the modal shares (in % of total passenger or tonne-km carried by each mode k), I is the fuel intensity of each mode, in energy use per passenger (or tonne-) km k using fuel or energy source j, and F is the carbon content of each fuel k used in mode j.
I depends both on the vehicle energy intensity, V (in energy per vehicle-km), and vehicle utilization, L in passengers or tonnes. I has two subscripts, one for mode j (travel or freight) and one for fuel type k. This reflects the fact that the fuel intensities of vehicles, travel, or freight may be a function of the fuel itself.
F expresses the carbon content of a given fuel
k used for a given mode
j. For simplicity it is assumed that fuels are fully combusted, so their carbon contents are given by the Intergovernmental Panel on Climate Change (IPCC). In more sophisticated formulations life-cycle analysis accounts for the CO
2 released not only in combustion but in preparation of the fuel and for large transit systems construction of infrastructure [
24]. This analysis does count the primary energy and emissions associated with electricity use for traction. For the U.S. this is small and essentially limited to some Amtrak and intercity and urban rail services and trolley buses, overall tiny compared to the diesel fuel used by buses and railroads.
With this formulation the decomposition asks how much changes in A, S, I and F combine to yield a change in G over time. Note that the “ASIF” identity summarizes at the most aggregate level how different components of carbon emissions have changed.
The simplest approach asks the question “how much did total emissions change over any period because of a change in a single factor from a given base year?” This “all else equal” technique is called a Laspeyres decomposition [
25]. This approach is computationally simple but can leave large residuals—the product of each change does not yield the total change because of cross terms. A more sophisticated technique uses the Log Mean Divisia Index or LMDI [
26]. This approach has the advantage of using a rolling baseline and allocating the cross terms that appear when all of the components of the
ASIF identity have changed over time. Ang [
26] argues that LMDI decomposition indices have significant advantages over other decomposition techniques [
27]. However, LMDI is computationally challenging and in many cases simpler techniques such as the Laspeyres decomposition produce similar results.
In this paper we produce a set of indices using both techniques partly for comparative purposes. We use 1990 as a base year for the Laspeyres decomposition, as present CO
2 negotiations use that same year for a base. Since LMDI does not have fixed weights, a base year is not necessary.
Figure 10 and
Figure 11 present, at the end of this section, present the LMDI results for different years normalizing 1990 to 100 so that comparisons from that date may be easily made.
Table 1 provides the same information in a little more detail. Each index in table one measures the overall change in emissions, owing to changes in an
ASIF factor, with the 1990 levels fixed as 100%. Where these indices are falling, changes in the corresponding factor can be understood as contributing to a decline in emissions.
Table 1.
Decomposition of Changes in Carbon Emissions from Travel and Freight, 1960–2008.
Table 1.
Decomposition of Changes in Carbon Emissions from Travel and Freight, 1960–2008.
| LMDI Index | Laspeyres |
---|
| 1960 | 1970 | 1973 | 1980 | 1990 = 100% | 2008 | 1960–2008 | 1960–2008 |
Travel |
Actual | 43.17% | 76.58% | 89.57% | 87.99% | 100.0% | 126.49% | 310.72% | 317.8% |
Activity | 45.51% | 68.70% | 75.75% | 77.81% | 100.0% | 148.95% | 340.05% | 326.0% |
Mode Shift | 93.6% | 97.25% | 97.50% | 98.01% | 100.0% | 99.22% | 106.33% | 103.6% |
Vehicle Use | 83.78% | 81.83% | 85.65% | 93.89% | 100.0% | 95.17% | 113.94% | 84.9% |
Fuel Intensity | 128.61% | 144.02% | 142.39% | 123.86% | 100.0% | 88.53% | 69.77% |
Fuel Mix | 98.17% | 97.10% | 99.25% | 99.08% | 100.0% | 101.54% | 103.48% | 105.1% |
Carb. Content | 100% | 100% | 100% | 100% | 100% | 100% | 100% | |
Summary |
Pkm/GDP | 117.1% | 128.9% | 123.5% | 106.79% | 100.0% | 88.4% | 75.5% | |
Emissions/GDP | 115.7% | 144.6% | 146.7% | 121.25% | 100.0% | 79.8% | 68.9% | |
FREIGHT |
Actual | 43.56% | 57.40% | 68.30% | 82.05% | 100.0% | 130.50% | 302.24% | 335.3% |
Activity | 51.21% | 67.04% | 74.11% | 91.17% | 100.0% | 130.54% | 264.90% | 295.6% |
Mode Shares | 78.63% | 84.38% | 89.93% | 86.38% | 100.0% | 109.71% | 142.13% | 142.7% |
Fuel Intensity | 113.43% | 107.27% | 107.86% | 109.25 | 100.0% | 84.99% | 72.26% | 79.9% |
Fuel Mix | 95.29% | 94.72% | 95.13% | 95.49% | 100.0% | 107.17% | 111.21% | 103.4% |
Carbon Content | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% | |
Summary |
Tonne-km/GDP | 134.9% | 121.4% | 118.1% | 122.53% | 100.0% | 85.8% | 64.8% | |
Emissions/GDP | 117.1% | 108.23% | 111.9% | 113.06% | 100.0% | 85.2% | 72.7% | |
Table 1 also compares the overall change from 1960 to 2008 for both Laspeyres and LMDI methods. As is evident they are quite similar. Note that for the passenger sector a simpler Laspeyres decomposition was used, merging the Vehicle Use (Vkm/Pkm) and Fuel Intensity (Energy/Vkm) indices. The relevant comparison here is of the product of the two LMDI indices (which yields an overall change of 79.70%) with the Laspeyres estimate of 84.9%. In general the comparison suggests that the much simpler Laspeyres decomposition can yield most of the qualitative conclusions we reach from the more involved LMDI technique. That said, for the remainder of this discussion we refer to LMDI decomposition outputs.
Figure 10.
LMDI decomposition results for the passenger sector.
Figure 10.
LMDI decomposition results for the passenger sector.
Figure 11.
LMDI decomposition results for freight.
Figure 11.
LMDI decomposition results for freight.
This decomposition technique can be used to project future trends [
28], and reveal how much each of the multiplicative factors in
ASIF formula can be altered away from their current trends to lead to lower CO
2 emissions. Similarly, the approach supports a “back-casting” exercise whereby rough targets for emission in a future year can be compared to emissions levels today to scope out ranges of change in each of the formula’s components that together might bring the U.S. from the present levels to proposed future levels.
Most obviously, the results in
Table 1 show that overall emission levels increased due to higher travel volume. This is reflected in the “activity” term, which increased steadily from 1960, and continued rising through all four benchmark years, albeit significantly more slowly after 1990. This increase was strongly led by the increase in the absolute levels of car and then air travel. The same occurred in freight, with trucking leading much of the growth in overall freight. Travel and freight activity on each mode grew at different rates, giving rise to mode-shift within the sector as a whole, rather than simply shifts between the types of transport. However, the absolute levels of urban transit and intercity rail dropped for most of the period, indicating real shifts in modes from these to car or, for intercity rail, to air. These shifts raised emissions.
Overall the effect of the structural mode shifts within the passenger sector has been small (as observed in line 3). Transit and rail lost a small share to cars, but their share of travel in 1960 was so small that the impact of the shift on emissions was minor. Cars lost significant share to air travel, which now accounts for some 11% of all passenger km Americans travel at home. But by 1990, the base year in the calculation above, the intensity of flying was close to that of car travel, so that shift had only a small impact.
For freight the impact of mode shifts has been much larger. Trucking is much more fuel and CO
2 intensive than rail or ship (
Figure 6) and its share rose significantly, from around 25% of all tonne-kilometers hauled in 1960 to almost 45% by 2008. This accounts for the 1960 “mode share” index lying at less than 79% of its 1990 value in 1960. The implied increase to 1990 continued almost unabated through 2008, when the mode share index reached 110% of its 1990 value. One reason for the big mode shift 1990–2008 was a near collapse of water-borne freight, whose overall level fell by nearly 33% from its 1990 level. This was largely due to the fall in oil shipments from Alaska to mainland USA with the decline in oil production there. The volume of freight decreased relative to GDP, but not quite as rapidly as that of travel, and the overall emission relative to GDP for freight fell less than did freight volume.
To some degree the increase in emissions attributed to activity and mode shifts has been offset by improvements in both fuel intensity and the intensity of vehicle utilization. In the passenger sector, fuel intensity indices went from over 128% of 1990 values in 1960 to less than 89% by 2008. This index measures the energy used per vehicle km traveled and is therefore closely linked to the technological energy efficiency of passenger transport. At the same time vehicle utilization rose from about 84% of its 1990 value in 1960 through the early 1990s until beginning to fall to about 95% of the 1990 value in 2008. Vehicle utilization is indicated by the inverse of the ratio of passenger kilometers to vehicle kilometers. The decline in index values in recent years was caused by an increase in the number of passengers sharing vehicles (principally air, urban rail and bus), as well as an end to the longer-term decline in vehicle occupancy of cars. Overall, this meant lower emissions for the same number of passenger kilometers. That is, if more people use the same number of vehicles, emissions fall compared to constant utilization, hence the value of the index falls.
For freight as well, efficiencies have improved as indicated by the intensity index going from over 113% of the 1990 value in 1960 to about 85% in 2008. The intensity indicator here captures the effects on emissions of changes in energy used per tonne km in the freight sector.
Shifts in fuels had little impact on carbon intensities in the passenger sector. This is seen by the fact that the fuel mix index varies from 98.17% of 1990 values in 1960 to 101.54% in 2008. The small impact arises because oil products—gasoline, diesel, jet fuel and marine or rail diesel—dominate. All release similar amounts of CO2 when burned relative to the energy they contain. Perhaps in the future were fuel shifts to electricity increase (and be accompanied by an increase in renewable generation), we might see this factor playing a greater role.
For freight there is a slightly larger influence of fuel mix changes, with this index alone contributing to a slow increase in emissions over the last five decades (from about 95% of the 1990 levels in 1960 to about 107% in 2008).
To summarize, although significant gains have been achieved in fuel intensity for both passenger travel and freight, it is not sufficient to offset the leading factor in that contributed to an overall increase in emissions, namely travel activity. In order to gain the bold reductions in emissions required, policies must address not only the fuel intensity of travel modes, but travel volume as well. Thus policies such as smart growth plans, pay-as-you drive insurance and congestion fees are increasingly more significant in addressing emissions from travel.
In Freight increases in emissions can be attributed to changes in mode shifts and fuel mix, on top of activity. As the importation of finished goods is on the rise as well as the transport of consumer package goods, fresh foods, and high value items like electronics, the increase in the use of trucking is likely to continue [
29], maintaining the aforementioned trends. Transport reforms can address these trends by shifting some trucking fees to variable costs based on actual km driven and applying congestion pricing to encourage trucking firms to reduce distances per shipment or tonne-km.
While the ASIF decomposition provides a strong analysis tool to identify the necessary policies to address transport emissions, experience suggests that achieving such policies may be challenging. The next section further discusses the impact of past regulation in the context the ASIF formula.
5. The Impact of Regulation
The ASIF decomposition can shed light on the effectiveness of transport policies, by understanding what components regulation has addressed and what components have been neglected. A prevailing national policy approach is supply-side regulation, early on dominated by CAFÉ—Corporate Average Fuel Economy standards instituted by congress in 1975, and more recently characterized by biofuel production subsidies.
In the context of the
ASIF formula, fuel economy standards affect vehicle energy intensity (
I). The
ASIF decomposition makes it apparent that the gains obtained through fuel economy standards can be largely offset by increases in travel volume (
A) and modal share (
S). In fact, since VKT per capita had increased almost forty percent over its 1973 level by 1990, and nearly sixty percent by 2007, and GDP per capita—a driver of both VKT and oil use—increased even more, the lack of growth in oil use per capita through 2008 is a sign that CAFE standards had a strong effect [
11]. CAFÉ standards provoked producers to produce more fuel efficient cars than otherwise would be demanded in the market with short-term gasoline price swings. Despite the effectiveness of CAFÉ standards to slow the pace of rising emissions levels, regulating fuel efficiency is a necessary but an insufficient step to achieve actual reduction in emissions.
A more recent trend in national transport regulation is both supply-side and demand-side subsidies. On the supply-side, subsidies for biofuels and other fuel alternatives were provided with the justification of reducing carbon emissions. Whether biofuels provide any carbon savings is still a contested issue, but in terms of the ASIF decomposition, such policies affect the F component (carbon content), where A and S components remain the large drivers of change for aggregate fuel use and carbon emissions.
On the demand side, subsidies for hybrid purchases and “cash for clunkers” programs are examples of targeted policies that affect new vehicles fleet fuel efficiency (the I component of ASIF formula). But these programs achieve questionable relative gains for their high costs to the public.
A recent addition to policy discussions has been Feebates, or bonus/malus [
30,
31]. The idea was proposed many decades ago in California [
30]. New vehicles emitting less than a certain balance point of emissions (which could be the “standard”) receive a rebate on new purchase price, proportional to the amount by which they lie below the standard, while cars over that standard value are taxed on top of the price. The balance point can correspond to the sales-weighted standard or other value, and can be reduced over time. The steepness of the slope of taxation or rebate per gram/CO
2 can also be varied. Preliminary results from France [
31] suggest a measurable effect. Since this program was introduced, new light duty vehicle CO
2/km in France went from fourth lowest to lowest in EU, and many other countries have developed such programs recently [
30]. The overall impact of such policy design will be seen as an acceleration of the decline in intensity (
I) for car travel, assuming vehicle occupancy is constant.
A final point that is frequently overlooked but has tremendous impact on actual effectiveness of transport policy is the regulatory context in the U.S. in general and in transport in particular. The size and fragmentation of U.S. transport sector makes it particularly challenging to regulate. On the consumer level, millions of decision makers make daily choices that have a cumulative effect on global GHG emissions. Additionally, regional, state and federal governments share duties of taxing, funding and building transport infrastructure. Different agencies within each governing body are in charge of different components. For example, National Highway Traffic Safety Administration sets the CAFÉ standards, while Environmental Protection Agency has to set air pollution standards. 2010 marks the first year where these two agencies are cooperating to obtain complimenting fuel efficiency standards.
Since market supply in transportation is a relatively concentrated market (12 automobile producers supply nearly all cars sold [
32]), most regulation has been targeted at production. But this phenomenon has not made legislation any easier, since automobile manufacturers have been using their political and economic influence to contest regulation in courts, leading to prolonged periods between actual regulation and implementation. A prevalent outcome of court litigation is the adoption of lenient rules that appease plaintiffs. A notable example in transportation is the provision CAFÉ credits for installation of vehicle safety features. The result of these political and structural challenges is apparent in the legislation that is finally adopted by Congress. Laws are often vague or specify goals without specifying the methods to obtain them, such as specifying a fuel efficiency standard by certain year without specifying the requirements from automobile makers. In addition, the actual laws that are adopted are those who are likely not to be contested by the public or strong market players, thus subsidies are much more prevalent than taxes, many times at the cost of efficiency.
Given these challenges, an important consideration is that according to [
33], congestion and traffic accidents have greater social costs per mile in comparison to the costs of environmental externalities. Thus, future regulation that can effectively address congestion and traffic volume of all traffic modes, will have significant co-benefits on emissions as well. Currently, this may be the easier route to take in order to affect future carbon emissions in the U.S., since price signals are grossly absent from U.S. policy scene. Paradoxically, given the enormous weight put upon administrative and judicial rulings that take years to promulgate, price signals in the U.S. are even more significant if steady reduction in CO
2 emissions is to be made.