Bibliographic record
Abstract
The water-energy nexus refers to the relationship between water and energy, wherein each one needs the other. This thesis examines that part of the water-energy nexus concerned with water needed for energy production, conversion, and utilization. There has been limited focus on assessing the life cycle water footprints of energy pathways. Such an approach assesses the water requirement for the various unit operations in energy pathways from fuel extraction to its final energy form. A study of the life cycle water footprints of different energy pathways with a focus on minimizing water use could help in policy formation and investment decisions. The main objective of this research is to establish a benchmark for water demand coefficients for energy pathways based on a complete life cycle. The focus is on the assessment of different energy pathways and development of life cycle water demand coefficients through comprehensive modeling. The research includes the evaluation of energy pathways based on both conventional and non-conventional sources of energy, and the energy sources assessed are coal, natural gas, oil, biomass, wind, solar, hydroelectricity, nuclear, and geothermal. The initial focus is on power production. The conversion efficiency of power generation is correlated to developed water demand coefficients to study the effect of a power plant’s performance on water use. Coal-based power generation has high water use compared to gas-fired power generation due to differences both in conversion efficiency and the unit operations of fuel extraction. Biomass-based power generation has the highest water demand coefficients over the complete life cycle and wind has the lowest. This study found complete life cycle water consumption coefficients for power generation for coal transported by conventional means to be 0.96 – 3.21 L/kWh and 0.07 – 2.57 L/kWh for gas-fired power plants. Excluding biomass and hydroelectricity pathways, non-conventional energy technology has complete life cycle water consumption coefficients of 0.005 – 4.39 L/kWh. The corresponding range for biomass pathways is 259.6 – 1164.01 L/kWh. Throughout the complete life cycle of a transportation fuel produced from the oil sands in Alberta 2.08 – 4.19 volume of water per volume of oil are consumed, and the corresponding fuel from crude oil extracted from five selected oil fields in North America consumes 1.71 – 8.25 volume of fresh water per volume of oil. The water demand coefficients developed in this study could be used in making decision regarding selection of water efficient pathways.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".