Recuento bajo microscopio.…

The REWSS model, built on a basic LCA of an emerging technology and existing data, puts energy and water supplies into the same systemic framework and approach to infrastructure. With the model in Python, the code is usable by any computer, but a visual interface would be valuable for a widespread release. Maintenance of the model would include updating and appending supply sources and modes as more information becomes available. This is particularly true for water-related classes. Automation of regional data collection through the use of increasingly available government databases would increase the easily available regions.

There are several possible improvements to the modeling approaches, mostly around expanding included impact categories or refining system boundaries and included classes. Many of these could be the subject of future research. Key immprovements are as follows:

1. Water quality is a key impact that is not included in any form. For states that cool power plants with open-loop cooling, or states with significant amounts of fertilizer runoff or salts, simply calculating water consumption for energy is insufficient. In addition, energy will often be used to remove pollutants, adding a further reason to investigate including water quality metrics. No single metric has emerged in LCA as a measure of water quality – this researcher suggests that a measure of energy required to bring water to a defined standard of quality be used – and this lack requires either additional data collection for multiple impact

categories or patience while the scientific community standardizes. A second issue is that water quality is less additive over multiple watersheds, making it more difficult to place within the scope of this work.

2. Transportation is currently implemented as the total energy consumption, but this obscures both easy measures of vehicle efficiency and different types of transportation. A better approach, albeit one with additional difficult data collection requirements at the regional level, would be to split transportation into personal mobility (person-miles) and freight transport (ton-miles). Treating transportaiton as two separate classes would allow for more appropriate technology pathways, and the use of increasing efficiencies rather than decreasing capacity factor for tracking effectiveness.

3. More explicit accounting for energy or water usage on the demand side would provide information that may be beyond the intended use of this model, but be of use to users. A key example is the use of energy for heating water in buildings. These two aspects are technically present in current calculations – some fraction of water demand will be heated, and some fraction of heat demanded is for water – but establishing regional parameters for these connections, while data-intensive, would add key links to discussing the regional WEN.

4. Demand-side changes were briefly discussed for all three case studies, but not explicitly modeled. REWSS can easily consider a scenario with alternate demand

demand, capacity changes, and the use of certain sources, as well as to expand the options available for modeling demand-side changes.

5. The model is currently dominated by a linear framework that responds to static scenarios, but a key set of important planning questions involves the best path or the minimum tradeoffs – say, the lowest GHG emissions possible while increasing cost by <10%, or lowest water consumption while meeting GHG emission goals. These questions could be investigated by pairing the median, rather than MCA-enabled, REWSS model with an optimization algorithm. Once an optimal path has been determined, the standard MCA-enabled model can be run to assess uncertainty.

6. Regional life-cycle impacts are important, but final results would be improved by inclusion of a built-in assessment of feasibility for the region in question. This feasibility would necessarily include available regional precipitation, renewable energy potential, and changes in non-renewable resource stocks. The primary barrier to inclusion is again data availability and the highly regional nature of feasibility.

7. From a technical perspective, Python is a versatile and reasonably platform- independent language, but long-term storage of LCA and regional data should be done using a database rather than in code. Ideally, the database would be publicly visible, with the ability for other researchers to submit edits, and linked with a web-based interface for the REWSS model – another property doable with Python. The would make the information more accessible and help in updating LCA data as new options for various sources become available or are better

examined. This approach would also make the addition of new options or sources simpler for end users.

It is the belief of this researcher that the work presented in this dissertation provides new information on a key emerging energy source, a functioning combining many data sets and topics into a holistic calculation, and new insights into the future impacts for the three regions examined. The central REWSS model is made available for other users to glean insights into their own regions, with expansion upon its basic approach encouraged to highlight new sources or interconnections between the WEN. There is much that can be learned from combining region, life-cycle impacts, water, energy, and scenarios, and this tool can be a core part of many future projects. There are far too many accessible questions to investigate in the course of a single degree, and future readers are encouraged to make use of REWSS for their own purposes.