Shelie A. Miller

Minimizing Land Use and Nitrogen Intensity of Bioenergy

The environmental impacts of bioenergy products have received a great deal of attention. Life cycle analysis (LCA) is a widely accepted method to quantify the environmental impacts of products. Conducting comprehensive LCAs for every possible bioenergy alternative is difficult because of the sheer magnitude of potential biomass sources and energy end products. The scopes of LCAs are often simplified to compare multiple products on the basis of greenhouse gas emissions and net energy balances, and may neglect equally important considerations such as nitrogen and land use. This study determines the most desirable energy crops on the basis of nitrogen and land use. The theoretical minimum nitrogen and land use requirements of fourteen bioenergy feedstocks are evaluated. These results can help prioritize certain feedstock crops for more in-depth life cycle analyses and can be used to inform policies on dedicated energy crops. The results of the study indicate that sugar cane has the best nitrogen and land use profile of the analyzed feedstocks. Sugar cane is the largest contributor to bioenergy production worldwide and is an effective policy choice from a nutrient and land use perspective. Conversely, soybeans and rapeseed are the least effective biomass sources with respect to land use and nitrogen requirements, yet these crops are also used to meet biofuel production targets worldwide. These results indicate current energy policies either do not consider or undervalue nitrogen and land use impacts, which could lead to unsustainable recommendations. Interestingly, when both nitrogen and land intensity are taken into account, reasonably small differences are seen between the remainder of the analyzed feedstocks, indicating an inherent trade-off between energy yield and nitrogen impacts.

A Stochastic Approach to Model Dynamic Systems in Life Cycle Assessment

This article presents a framework to evaluate emerging systems in life cycle assessment (LCA). Current LCA methods are effective for established systems; however, lack of data often inhibits robust analysis of future products or processes that may benefit the most from life cycle information. In many cases the life cycle inventory (LCI) of a system can change depending on its development pathway. Modeling emerging systems allows insights into probable trends and a greater understanding of the effect of future scenarios on LCA results. The proposed framework uses Bayesian probabilities to model technology adoption. The method presents a unique approach to modeling system evolution and can be used independently or within the context of an agent‐based model (ABM). LCA can be made more robust and dynamic by using this framework to couple scenario modeling with life cycle data, analyzing the effect of decision‐making patterns over time. Potential uses include examining the changing urban metabolism of growing cities, understanding the development of renewable energy technologies, identifying transformations in material flows over space and time, and forecasting industrial networks for developing products. A switchgrass‐to‐energy case demonstrates the approach.

Framework for analyzing transformative technologies in life cycle assessment

Emerging products and technologies pose unique challenges for the life cycle assessment (LCA) community, given the lack of data and inherent uncertainties regarding their development. An emerging technology that has the potential to be transformative and effect broad-scale change within society, as well as the underpinning assumptions associated with its life cycle, is particularly difficult to analyze. Despite the associated challenges, LCA methods must be developed for transformative technologies. The greatest improvement potential occurs at the early phases of technology development; therefore, prospective LCA results can be used to anticipate potential unintended consequences and develop design pathways that lead to preferential outcomes. This paper identifies and categorizes ten factors that influence the LCA results of transformative technologies in order to provide a formal structure for determining appropriate factors for inclusion within an LCA. Appropriate factors for an analysis should be selected according to the overall research questions of the study and are applicable to both attributional and consequential approaches to LCA.

Applying Industrial Symbiosis to Smallholder Farms

The study of industrial symbiosis (IS) has largely focused on the exchange of energy and materials among industrial processes in an effort to increase value and reduce environmental impact. Agricultural systems, particularly those located in developing countries, can benefit from the principles of IS. Relatively few studies have analyzed the potential benefits of integrated material and energy flows in smallholder farming, even though these systems are considered essential to the worlds food supply and poverty reduction. Although the concepts can be applied to virtually any system, the study of industrial symbiosis has traditionally focused on industrialized systems in developed countries. The research presented here applies the principles of IS to smallholder farms using optimization techniques to maximize farm output while minimizing wastes. Our research links IS to the growing field of integrated farming research (IFR), which seeks to create new technologies that increase the production of farms by viewing the farm as a system. Bridging these fields enriches the potential for robust research outcomes in both areas and fills a current knowledge gap. IS benefits from exploring new applications and increasing its penetration into the developing world. IFR benefits from established IS tools to create alternate pathways for increased output based on symbiotic relationships. A small farming system in Liberia, West Africa, is used as a case study. System integration of individual unit processes shows increased productivity and decreased waste. The results of this analysis indicate that there are unrealized opportunities for IS in developing countries, and integration of IS techniques into smallholder farming operations has the potential for impacting sustainable development.

Land use and geospatial aspects in life cycle assessment of renewable energy

Renewable energy systems such as wind, solar and biomass are significantly more land intensive than traditional fossil fuels. Moreover, their environmental implications are highly geographically heterogeneous. Consequently, they present a significant challenge to existing life cycle assessment techniques. Four specific issues are identified in this paper: determining changes in land use due to increased production of renewable energy, characterizing land use impacts, understanding geographic variability in inventory data, and modeling energy distribution effects. This paper reviews the extent of recent research activity in each of these areas as it applies to wind, solar, bioenergy or life cycle assessment in general. Some areas, such as land use needed for distribution of wind or solar energy, have received little or no research activity, despite an increased level of concern in political or policy arenas. This deficiency will be addressed in a new National Science Foundation workshop planned for September 2009 in Boston MA.

Developing LCA techniques for emerging systems: Game theory, agent modeling as prediction tools

Although Life Cycle Assessment (LCA) has become an important tool in the context of environmental and industrial analysis, its limitations keep it from achieving wider acceptance. One limitation is its inability to forecast. LCA can present environmental impacts of established processes but cannot do so for emerging processes or developing products. We propose two different techniques as an addition to the traditional LCA to address these processes and products. In this paper, we compare the tools proposed, assess the limitations inherent in each technique, and finally formulate recommendations of systems where each technique can best serve the forecasting needs of an LCA. We also propose further work towards creating a functioning Predictive Dynamic Life Cycle Assessment that can provide insightful information on emerging situations in general.

Potential for Integrating Diffusion of Innovation Principles into Life Cycle Assessment of Emerging Technologies

Life cycle assessment (LCA) measures cradle-to-grave environmental impacts of a product. To assess impacts of an emerging technology, LCA should be coupled with additional methods that estimate how that technology might be deployed. The extent and manner that an emerging technology diffuses throughout a region shapes the magnitude and type of environmental impacts. Diffusion of innovation is an established field of research that analyzes the adoption of new innovations, and its principles can be used to construct scenario models that enhance LCA of emerging technologies. Integrating diffusion modeling techniques with an LCA of emerging technology can provide estimates for the extent of market penetration, the displacement of existing systems, and the rate of adoption. Two general perspectives of application are macro-level diffusion models that use a function of time to represent adoption, and microlevel diffusion models that simulate adoption through interactions of individuals. Incorporating diffusion of innovation concepts complement existing methods within LCA to inform proactive environmental management of emerging technologies.

Planning the development of electricity grids in developing countries: An initial approach using Agent Based Models

This paper presents a proof of concept model for the electrification process of developing countries. Considering the general needs and characteristics in those countries is important as they share factors far from the developed world situation. However, enough flexibility is included to allow implementation in individual countries with unique circumstances. To achieve a robust yet flexible model, Agent Based Modeling is utilized. The model is built in the NetLogo architecture, which combines user interphases and geographic information extensions for adaptability. The result is a tool that addresses a gap in the literature. Although models for developing countries exist, they fail to adequately capture the characteristics of their energy sectors. Agent Based Modeling allows the modeling of the electrification process through dynamic agents that do not focus on historical data or the normal development path followed by developed countries. The models strength is in the ease of scenario building so that policy makers and researchers can see the impact of decisions on the overall process. This also allows the stakeholders to quickly and easily seek the paths desired based on their objectives and identify the catalysts needed for those results.

The Environmental Impact of Autonomous Vehicles Depends on Adoption Patterns.

Autonomous vehicles (AVs) have the potential to transform our transportation system. The forces that will influence the environmental impacts of large-scale AV adoption are identified to help determine necessary future research directions. It is too early to determine which of these forces will dominate the system and dictate whether AV adoption will result in net reductions or increases in greenhouse gas (GHG) emissions. The environmental research community must develop a better understanding of the disruptive forces of AVs to help develop a strategy to reduce transportation emissions. Particular emphasis is needed regarding how AVs will be adopted and used, as these patterns may ultimately dictate the environmental impacts of AVs. Without better integration of engineering, social science, and planning disciplines to model future adoption scenarios, important opportunities to steer markets toward sustainable outcomes will be lost.

Comparative Human Toxicity Impact of Electricity Produced from Shale Gas and Coal

The human toxicity impact (HTI) of electricity produced from shale gas is lower than the HTI of electricity produced from coal, with 90% confidence using a Monte Carlo Analysis. Two different impact assessment methods estimate the HTI of shale gas electricity to be 1-2 orders of magnitude less than the HTI of coal electricity (0.016-0.024 DALY/GWh versus 0.69-1.7 DALY/GWh). Further, an implausible shale gas scenario where all fracturing fluid and untreated produced water is discharged directly to surface water throughout the lifetime of a well also has a lower HTI than coal electricity. Particulate matter dominates the HTI for both systems, representing a much larger contribution to the overall toxicity burden than VOCs or any aquatic emission. Aquatic emissions can become larger contributors to the HTI when waste products are inadequately disposed or there are significant infrastructure or equipment failures. Large uncertainty and lack of exposure data prevent a full risk assessment; however, the results of this analysis provide a comparison of relative toxicity, which can be used to identify target areas for improvement and assess potential trade-offs with other environmental impacts.