In order to convey the results of our industrial ecology research to broader audiences, the Green Design Institute research group at Carnegie Mellon University offers the Green Design Apprenticeship for local high school students. The Green Design Apprenticeship introduces participants to industrial ecology concepts and how they intersect with engineering. The content of the program has evolved to include the topics of life cycle assessment, energy and water resources, transportation, and the built environment. The program has resulted in exposing a new generation of scholars to industrial ecology and has also benefited the research of graduate students involved with the program. The process of developing the instructional materials for younger, novice students based on complex industrial ecology research was a challenging task requiring thoughtful and iterative planning. Through the development and delivery of the program, we have experienced awareness of where our own research fits into the larger industrial ecology scope, have improved our communication of complex industrial ecology concepts into simple terms, and have gained valuable insight for engaging students in our teaching.

There is consensus on the importance of upstream methane (CH) emissions to the life cycle greenhouse gas (GHG) footprint of natural gas systems, but inconsistencies among recent studies explain why some researchers calculate a CH emission rate of less than 1% whereas others calculate a CH emission rate as high as 10%. These inconsistencies arise from differences in data collection methods, data collection time frames, and system boundaries. This analysis focuses on system boundary inconsistencies. Our results show that the calculated CH emission rate can increase nearly fourfold not by changing the magnitude of any particular emission source, but by merely changing the portions of the supply chain that are included within the system boundary. Our calculated CH emission rate for extraction through pipeline transmission is 1.2% for current practices. Our model allows us to identify GHG contributors in the upstream supply chain, but also allows us to tie upstream findings to complete life cycle scenarios. If applied to the life cycles of power systems and assessed in terms of cumulative radiative forcing, the upstream CH emission rate can be as high as 3.2% before the GHG impacts from natural gas power exceed those from coal power at any point during a 100-year time frame.

The Clean Air Act in the United States identifies diesel-powered motor vehicles, including transit buses, as significant sources of several criteria pollutants that contribute to ground-level ozone formation or smog. The effects of air pollution in urban areas are often more significant due to congestion and can lead to respiratory and cardiovascular health impacts. Life cycle assessment (LCA) has been utilized in the literature to compare conventional gasoline-powered passenger cars with various types of electric and hybrid-powered alternatives, however, no similarly detailed studies exist for mass transit buses. LCA results from this study indicate that the use phase, consisting of diesel production/combustion for the conventional bus and electricity generation for the electric bus, dominates most impact categories; however, the effects of battery production are significant for global warming, carcinogens, ozone depletion, and eco-toxicity. There is a clear connection between the mix of power-generation technologies and the preference for the diesel or electric bus. With the existing U.S. average grid, there is a strong preference for the conventional diesel bus over the electric bus when considering global warming impacts alone. Policy makers must consider regional variations in the electricity grid prior to recommending the use of battery electric buses to reduce carbon dioxide (CO) emissions. This study found that the electric bus was preferable in only eight states, including Washington and Oregon. Improvements in battery technology reduce the life cycle impacts from the electric bus, but the electricity grid makeup is the dominant variable.

The electric power industry plays a critical role in the economy and the environment, and it is important to examine the economic, environmental, and policy implications of current and future power generation scenarios. However, the tools that exist to perform the life cycle assessments are either too complex or too aggregated to be useful for these types of activities. In this work, we build upon the framework of existing input-output (I-O) models by adding data about the electric power industry and disaggregating this single sector into additional sectors, each representing a specific portion of electric power industry operations. For each of these disaggregated sectors, we create a process-specific supply chain and a set of emission factors that allow calculation of the environmental effects of that sector's output. This new model allows a much better fit for scenarios requiring more specificity than is possible with the current I-O model.

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The mix of electricity consumed in any stage in the life cycle of a product, process, or industrial sector has a significant effect on the associated inventory of emissions and environmental impacts because of large differences in the power generation method used. Fossil-fuel-fired or nuclear-centralized steam generators; large-scale and small-scale hydroelectric power; and renewable options, such as geothermal, wind, and solar power, each have a unique set of issues that can change the results of a life cycle assessment. This article shows greenhouse gas emissions estimates for electricity purchase for different scenarios using U.S. average electricity mix, state mixes, state mixes including imports, and a sector-specific mix to show how different these results can be. We find that greenhouse gases for certain sectors and scenarios can change by more than 100%. Knowing this, practitioners should exercise caution or at least account for the uncertainty associated with mix choice.

Economic input-output life cycle assessment (IO-LCA) models allow for quick estimation of economy-wide greenhouse gas (GHG) emissions associated with goods and services. IO-LCA models are usually built using economic accounts and differ from most process-based models in their use of economic transactions, rather than physical flows, as the drivers of supply-chain GHG emissions. GHG emissions estimates associated with input supply chains are influenced by the price paid by consumers when the relative prices between individual consumers are different. We investigate the significance of the allocation of GHG emissions based on monetary versus physical units by carrying out a case study of the U.S. electricity sector. We create parallel monetary and mixed-unit IO-LCA models using the 2007 Benchmark Accounts of the U.S. economy and sector specific prices for different end users of electricity. This approach is well suited for electricity generation because electricity consumption contributes a significant share of emissions for most processes, and the range of prices paid by electricity consumers allows us to explore the effects of price on allocation of emissions. We find that, in general, monetary input-output models assign fewer emissions per kilowatt to electricity used by industrial sectors than to electricity used by households and service sectors, attributable to the relatively higher prices paid by households and service sectors. This fact introduces a challenging question of what is the best basis for allocating the emissions from electricity generation given the different uses of electricity by consumers and the wide variability of electricity pricing.

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