Background Clinical trials often report significant mobility differences between neurotypical and atypical groups, however, these analyses often do not determine which measures are capable of discriminating between groups. Additionally, indirect evidence supports the notion that some mobility impaired populations demonstrate similar mobility deficits. Thus, the current study aimed to provide a comprehensive analysis of three distinct aspects of mobility (walking, turning, and balance) to determine which variables were significantly different and were also able to discriminate between neurotypical older adults (OA) and middle-aged people with multiple sclerosis (PwMS), and between middle-aged neurotypical adults and PwMS. Methods This study recruited 21 neurotypical OA, 19 middle-aged neurotypical adults, and 30 people with relapsing remitting MS. Participants came into the laboratory on two separate occasions to complete mobility testing while wearing wireless inertial sensors. Testing included a self-selected pace two-minute walk, a series of 180˚ and 360˚ turns, and a clinical balance test capturing a total of 99 distinct mobility characteristics. We determined significant differences for gait and turning measures through univariate analyses and a series of repeated measures analysis of variance in determining significance for balance conditions and measures. In determining discrimination between groups, the Area Under the Curve (AUC) was calculated for all individual mobility measures with a threshold of 0.80, denoting excellent discrimination. Additionally, a stepwise regression of the top five AUC producing variables was performed to determine whether a combination of variables could enhance discrimination while accounting for multicollinearity. Results The results between neurotypical OA and middle-aged PwMS demonstrated significant differences for three gait and one turning variable, with no variable or combination of variables able to provide excellent discrimination between groups. Between middle-age neurotypical adults and PwMS a variety of mean and variability gait measures demonstrated significant differences between groups; however, no variable or combination of variables met discriminatory threshold. For turning, five 360˚ turn variables demonstrated significant differences and furthermore, the combination of 360˚ mean turn duration and variability of peak turn velocity were able to discriminate between groups. Finally, the majority of postural sway measures demonstrated significant group differences and the ability to discriminate between groups, particularly during more challenging balance conditions where participants stood on a compliant surface. Conclusion These results offer a comprehensive analysis of mobility differences and measures capable of discriminating between middle-age neurotypical adults and PwMS. Additionally, these results provide evidence that OA and middle-age PwMS display similar movement characteristics and thus a potential indicator of advanced aging from a mobility perspective.

Purpose The environmental impact of a product may change according to who adopts it, where it is adopted, and how it is used. Market forces are an inherent part of consequential LCA and the practice of coupling economic models with life cycle inventory data has increased in popularity. Nevertheless, the actual relationship between the price of a commodity and potential changes to its life cycle inventory has rarely been discussed explicitly. The adoption price effect refers to a change in a product's environmental impact associated with a change in price, calculated on a functional unit basis. The price of a product influences the type and quantity of incumbent product(s) it displaces. This study provides insights on when the adoption price of a product is likely to influence its life cycle inventory and also identifies conditions where adoption price is expected to have negligible effects on inventory results. Methods A switchgrass bioenergy case is used to demonstrate the adoption price effect on life cycle inventory results when introducing a new product (i.e., switchgrass) into a system with multiple incumbents (i.e., crops, hay, pasture). This study estimates the adoption price effect on nutrient emissions by coupling biogeochemical models with a simplified economic breakeven model that estimates potential switchgrass adoption. Results and discussion In this case study, high switchgrass prices correspond to nitrate emission reductions that are three times greater than low switchgrass prices (0.67 kg N reduced/Mg switchgrass vs 0.21 kg N reduced/Mg switchgrass). The large adoption price effect found within the Southeastern USA is due to the highly heterogeneous landscape in the region. There is no single dominant land use, each incumbent product has a different environmental baseline, and each is displaced at a different switchgrass price range. Meanwhile, the adoption price effect is expected to be negligible in mostly homogenous landscapes, such as the more commonly studied Corn Belt, which has a single dominant incumbent in the form of corn-soy production. In addition to the specific case study, this analysis discusses general adoption conditions likely to lead to adoption price effects when conducting consequential LCA.

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.

The maximum amount of land for growing switchgrass for ethanol is estimated for a region in the southeastern U.S. Breakeven capacities are calculated for land in row crops, hay, pasture and marginal land. Characteristics of land categories inform potential land use change impacts as well as switchgrass profitability. Variable yields within and across land categories are translated into distributions of switchgrass net revenue. Breakeven curves are generated for a range of switchgrass prices. These curves provide upper bounds for further analysis of actual switchgrass adoption in context of broader economic forces and possible policy mechanisms to minimize environmental impacts. A farm-gate price of $55Mg−1 is estimated for half of marginal and pasturelands to break even with switchgrass. At this price, only 20% of land in hay and a small fraction of row crop hectares break even. Half of hay and row crops hectares break even at approximately $90Mg−1 and $100Mg−1, respectively. At $60Mg−1, sufficient land area can profitably produce switchgrass for ethanol to displace approximately 10% of gasoline consumed in Georgia, North Carolina, and South Carolina; however, this price only indicates breakeven capacity implying that higher prices may be necessary to realize 10% displacement.

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.

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.