Jose F. Alfaro

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.

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.

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.

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.

Solid waste management index for Brazilian Higher Education Institutions

This paper presents the Solid Waste Management Index (SWaMI) for Higher Education Institutions (HEIs). The main objectives are to present how SWaMI was developed, to apply the Index in three different universities in Brazil and one in United States, to statistically compare the results and to present an analysis of these HEIs under the SWaMI assessment dimensions perspective. The SwaMI fills a gap regarding a specific waste management tool for HEIs decision makers, considering the responsibility of educating and training future leaders and the need to insert the sustainable waste management discussion in its end activities. Criteria were selected through literature review and divided in dimensions, further weighted according to their significance in waste management. These weights were discussed and stipulated based on expert opinion using the Budget Allocation Process (BAP) weighting method. The individual indexes for each dimension were further combined into a composite index through the Linear Aggregation Method. Main findings shows that when comparisons were deployed between HEIs, no statistical significance was noticed when the means were compared between universities using ANOVA with Tukey test. Nevertheless, when comparing each dimension within each HEI, there was significant difference between the Policy and Management dimension and the other three dimensions of the evaluation criteria at USP. Researchers concluded that the SWaMI provides decision makers with graphic results concerning HEIs solid waste management situation, hence, it allows the creation of a baseline data on how the current system works, pointing out the dimensions that present the greatest weakness allowing to perform benchmarking between buildings, institutes, and even between HEIs.