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Input/Output analysis - Shortcuts to life cycle data?

14. A Dynamic Life-cycle Approach

Thomas Gloria, Tufts University

14.1 Introduction

This research presents and applies a dynamic distributed activities life-cycle emissions methodology based on the philosophy of Structural Economics and the constructs of temporal economic Input-Output (IO) interindustry modeling. Structural Economics provides a framework that allows for the integration of ecological economic concerns with environmental engineering concerns in a system-wide perspective that is suitable for comparing the implications of alternative future courses of actions.

The Structural Economic philosophy is implemented using a Sequential Interindustry Model (SIM) approach. SIM considers the time consuming nature of the production process and the corresponding timing of industry input. In essence, SIM unravels the ‘whirlpools’ of interindustry relationships, providing an empirical approach to investigate transient behaviour of finite economic activities.

A case study examining the environmental implications of introducing Fuel Cell Electric Vehicles into the U.S. Economy is presented to demonstrate the methodology. To simplify the example presented, the case study is streamlined by examining scenarios of life-cycle CO₂ emission trends over a time period of twenty years. Scenarios are developed that embrace the constructs of experimental design to achieve rigorous results.

14.2 Dynamic Sequentional Interindustry Modeling

The model applied in this research was the Sequential Interindustry Model (SIM). SIM is based on the Leontief static model where production is augmented by the specifics of production chronology. In SIM, sequences are important. SIM considers the time consuming nature of the production process and the corresponding timing of industry input. In general, the emphasis of SIM is on modeling interindustrial and intertemporal production activities in order to examine transient processes associated by final demand.

A simple example of baking bread is presented. In Figure 14-1, each batch of bread is defined by the time of the start of production t, to the end of production, s . The pass-through time for one batch of bread production is indicated by h_j, where in this example, j is the bread making industry. This pass-through time is determined by the longest application period, h_ij, which indicates the time input is received by a supplying industry to the time of product completion.

In order to fully describe total production, activities required to produce the input to bread-making are also to be considered. The production of yeast associated with the production of bread making is then illustrated by superimposing the two production sequences as shown in Figure 14.2 A true total cross-sectional output production figure would include the activities necessary to produce the input of sugar, salt, flour and fuel for heat.

Figure 14.1  Look here!
Multiple batches of bread making in SIM

In this simple example it is assumed that the processes do not change with time – they are time-invariant. Interesting issues arise when introducing the dynamics of technological change and changes in final demand. For example, in the production of baking bread one could account for a technological innovation that reduces bread rising time by half, shortening the pass-through time to four intervals. The transient associated with the adoption of this innovation would be described by concurrent use of both technologies at prescribed distribution levels.

14.3 A Life-cycle Approach

For life-cycle SIM the emphasis to capture activities expands to include the entire life-cycle of a commodity. In contrast to traditional IO-modeling, in life-cycle IO-modeling there are multiple input distributed over a commodity's entire life-cycle – not just within the interval of production, but also within the intervals of use and end-of-life. Moreover, there are multiple outputs distributed over the entire life-cycle of a commodity – multiple outputs within production as well as multiple outputs beyond production. For example, a simplified life-cycle SIM schema depicting the sequence of the life-cycle stages of automobiles distributed over time as shown in Figure 14.3. Within each interval, t, several model year vehicles are either being produced, in the early stages of their use phase, the later stages of the use phase or at end-of-life (EOL) disposition which includes dismantling of parts, reuse and recycling activities. In SIM, the sequence of the life-cycle of the vehicles by model year is preserved. The model captures the implementation of improving production techniques and fuel economy for each model year.

Figure 14.2 Look here!
The superposition of yeast production and bread making production.

The estimate of the life-span of the automobiles depicted in Figure 14.3 is based on an expected value of operational life to be 9 years. A more accurate Life-cycle SIM schema would include the stochastic representation of the life-cycle of the population of automobiles for each model year. This would have a profound effect regarding the period of influence attributed to a specific model year. For example, the operational life of an automobile is described by a Weibull distribution. An automobile model year with an expected value of nine years would have a significant proportion of the vehicle still in operation at 12 or 13 years. Thus, within a single interval certain automobiles continue to be used while others reach their end-of-life (EOL) disposition state. Over the life span of a population of vehicles of a model year the ratio of the automobiles that continue as part of the consumption activities to the ones that have reached EOL declines in a manner consistent with its Weibull distribution.

Figure 14.3 Look here!
SIM schema of the life-cycle of the automobile.

To accommodate for this new expanded structure of economic activities, a technical matrix that describes distributed multiple input and output activities in an enhanced SIM model was developed by redefining the Make and Use table of the static IO-model. The Make table captures all commodities produced by a given industry and the Use table captures all commodities consumed by a given industry. The combination of the two tables can lead to analyses that have multiple inputs and outputs. For example, industries are allowed to produce more than one product and commodities are allowed to have multiples of the same input distributed over time.

The distinction of the expanded make and use tables is their representation of data stores of all possible industry and commodity activities in time. Moreover, there is a temporal link between the make and use matrices. That is, the set of distributed make and use tables as pairs represent all the recipes for production, both input and output that transform final demand stimulus into economic activity.

Typical ecological IOA examines the interindustry activities associated with the production of a commodity. Social accounting expands the analysis by examining the various categories of consumers – by geography, income, behaviour, age, or gender. A more appropriate analysis in this application of environmental Life Cycle Assessment (LCA), is the clustering of the production and consumption of commodities by the associated activities to fulfil a "functional unit". For example it is more appropriate to define the functional unit of personal transportation is satisfied by the purchase of an automobile that was produced within interval s . The functional unit perspective facilitates the representation of alternatives. The LCA is not just of an automobile, it is an LCA study of personal travel.

14.4 Model Example

As an illustration of the methodology presented, the following case study seeks to answer the question:

Production interindustry relationships were developed from existing Bureau of Economic Analysis (BEA) Make and Use tables at Two-Digit SIC level. The Two-Digit industry and commodity sectors were aggregated and disaggregated to form the 19 industry and commodity sectors defined in Table 14-1. The "Rest of the Economy" (ROE) sectors were included to provide context within the analysis.

Table 14.1
Interindustry and Commodity Sectors Defined.

Table14.3
Table of Contrasts for Vehicle and Fuel Purchases (in MMTCE).

The experimental design utilised for the analysis of the case study allowed for the systematic creation of scenarios based on chosen factors that then led to immediate interpretation of primary effects and any interactive effects. The scenarios generated were based on using a simple 2^f experimental design, where f is equal to the number of factors represented at two levels, low (-) and high (+). Scenarios, also known as treatments, were then derived that represented all possible combinations of the levels of the factors. For this case study, 6 factors were chosen which resulted in the analysis of potentially 2⁶ or 64 treatments. An exhaustive experiment that included all possible treatments allowed for the analysis of interactive effects. Techniques of fractional experimental design were then used to reduce the number of treatments without compromising analytical content. In the case study, the number treatments were reduced 50%, from 64 treatments to 2^6-1 or 32 treatments. The end result of the experimental design was then a table of contrasts. The table of contrasts allowed for the immediate recognition of important factors, their main effects, their interactive effects and their rank order. This method allowed for a concise quantitative tabulation that was transparent to the complex interactions of the system simulated.

14.5 Notable Observations

The simulation of the experiment normalised to vehicle production and use examined the introduction of a promising technology, FCEVs that would supplant an existing technology, ICEVs, through a 25% market penetration of new purchases. Although, the production and disposition of the two products' technologies were virtually the same and assumed to be identical statically, the emerging FCEV technology is far superior to the old regarding use phase characteristics. FCEVs emit 50% the amount of CO₂ when compared to the average ICE passenger vehicle and 40% for ICE light trucks. However, the static analysis does not provide insight regarding emission reductions when normalised to the present and future fleet of vehicles in use. By expanding the level of normalisation, the system becomes one that is dynamic.

The emissions generated by satisfying the functional unit of personal transportation over a period of 20 years involved changes in the production and use of the automobile. This involved the dynamics of:

These five areas of dynamics ultimately varied the demand for fuel both in the production of vehicles and their use over the period of analysis. Therefore, fuel consumption was dynamically determined, where future fuel demand and eventual consumption was based on the previous purchases of vehicles.

The insights revealed by simulating these dynamic factors were of the following:

First, the benefit of emissions reduction by introducing FCEVs was significant but not of a magnitude suggested by an apparent static analysis of a 50% reduction in emissions per vehicle mile travelled. FCEVs initially entered the market in year 2004 and emission reductions after 6 years on the market were only 2%. Despite 16 years of aggressive 25% market penetration, the final interval of simulation resulted in a maximum reduction of 13.4%. This difference from the static observation was mainly due to the relatively small number of FCEVs in operation compared to the rest of the fleet. FCEVs accounted for 21.5% of the vehicles in operation at the final interval.

Second, near the end of the simulation period, within the last 5 years, several interactive effects between the FCEV penetration and other factors became apparent: The more VMTs, the greater the benefit of FCEVs to reduce emissions. Conversely, the fewer VMTs the less the benefit of FCEVs to reduce emissions. However, overall increasing VMTs results in significant increases in emissions, i.e. increases in the reduction of emissions were only realised when there was overall more consumption. The market penetration of FCEVs reduced the benefits realised by fuel economy of light trucks, however, the FCEVs reduce emissions by a greater amount, hence net emission reduction was greater. The business cycle reduced the effectiveness of FCEVs to decrease emissions. This occurred for two reasons. First, the recession period of the business cycle indirectly reduces total VMTs, which as stated above also decreases the effectiveness of FCEVs to reduce emissions. Second, because during the recession period of the business cycle purchases of all vehicles are decreased, fewer FCEVs were in operation, which also decreased the effectiveness of FCEVs to reduce emissions.

Third, in addition to FCEV market penetration, other direct effects observed include the magnitude of reductions due to the combined improvements in fuel economy of ICE passenger vehicles and light trucks. In the last interval of the simulation, the improvements in their fuel economy led to reductions equivalent to those of FCEVs. However, as shown in Figure 14.4, the cumulative amount of reductions due to ICE fuel economy improvements over the period of analysis occurred sooner. Changes in their fuel economy benefited reductions in emissions much earlier than reductions due to FCEV operation. The existing market penetration of ICE passenger vehicles and light trucks is much greater (75%) than the market penetration of FCEVs. As long as ICEVs remain the predominate vehicle purchased, slight changes in the fuel economy of new vehicles will result in relatively larger reductions in emissions.

Figure14.4:
Cumulative CO₂ Emission Reductions.

Last, despite the limited boundary of this experiment to include only production related to vehicle manufacturing and fuel refinement, emissions reduction due to gains in energy efficiency were significant. This is due to the large contribution of GHGs in the generation of electricity in the utilities sector. Electricity used directly in the production of vehicles and their respective fuels, and any indirect effects contributed to the reduction in emissions.

In the expanded normalisation experiment, the rest of the economy was included in the simulation of vehicle production and use. The magnitudes of the quantitative results were surprising. As in the first experiment, the innovative technology, FCEVs were assessed to be environmentally superior in their reduction of CO₂ emissions when compared statically to similar vehicles. Plausible economic conditions were simulated to favour the introduction of the technology. Marginal improvements were made to competing technologies to gain insight as to the magnitude of their potential improvements. Within the larger context of the U.S. economy, the percentage reduction was 3%. This reduction was realised after considerable duration in the market with favourable characteristics of an aggressive market penetration and a far superior fuel economy, twice that of existing vehicles.

The normalisation to the rest of the economy illustrates the contextual importance of the 3% reduction of emissions. The U.S. economy becomes 1.8% more energy efficient each year. This slight trend in efficiency accounted for an 8.3% reduction in CO₂ emissions for the interval of year 2020. This is a reduction of nearly three times that of the 50% gain in efficiency by the new transportation technology of FCEVs. This disparity of influence is based on the magnitude and interdependence simulated and captured by the Life-cycle SIM model. Small reductions in fuel purchases by all industries, both directly and indirectly, lead to significant total reductions in emissions.

A further analytical observation regarding insight gained is the relevance to the principles of economic lock-in, that is, present and future purchases are a function of past purchases and production activities. The principle can be observed in two ways. First, based on previous purchases of vehicles the system described is inflexible to fuel purchases of the future. Simply, if there are no methanol fuel cell vehicles purchased today, there will be no consumption of methanol in the future. More precisely, there is a relationship of purchases of methanol and gasoline based on the several aspects of the current vehicles in operation. The vehicles in operation as demonstrated are directly related to the vehicles purchased. Second, because the system is inflexible, inefficiencies occur. In this case, although methanol fuel cell vehicles may be the overall superior technology, economically and environmentally, there still remains a large consumption of gasoline dictated by the previous purchases of automobiles that can only operate by combustion of gasoline.

14.6 Concluding Remarks

The conventional approach to LCA has traditionally been a static engineering or technical exercise with little concern towards social, economic and temporal aspects. In this paper a method to expand the LCA methodology to address these shortcomings was presented. This research developed and applied a dynamic life-cycle emissions methodology based on the philosophy of Structural Economics and the Sequential Interindustry Model (SIM). Specifically, this research examined:

In conclusion, the dynamic simulation gives insight to the dynamics of a new technology and its diffusion into a dynamic economy and its ultimate change in resources used and emissions to the environment. Even though simplifications were made, insights beyond a static analysis are significant. The rate of introduction of a new technology and its effect on emissions reduction for an interval time can be determined. Further, cumulative emissions over the period of analysis can be simulated. This is especially important to estimate actual impact to the environment. The effects of the dynamics of the economic system and the relevance to the introduction of the new technology can be examined. Through experimental design, insight is gained regarding those interactive factors that counteract the new technology benefits, those factors that enhance them, and equally as important those factors that are insignificant. Moreover, policy decisions can be assisted regarding:

Historically, the dimension of time in LCA has been ignored or assumed to be infinite. The research in this study indicates that there is much to be gained through dynamic analyses. Although the constructs of LCA remain the same (Goal and Scope, LCI, LCIA and interpretation), their depth and breadth in a dynamic context are vastly more complex. Therefore, there is a need for discussion and consensus in the open literature regarding the philosophy and constructs of dynamic LCA.

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