Household change at the food-energy-water nexus: expanding social behavioral science perspectives
Bibliographic record
Abstract
This dissertation contributes to social and behavioral science perspectives that push forward vital energy transitions in the face of climate change. In its three analytical chapters, this dissertation achieves three central objectives: 1) accumulates findings on household behavior at the food-energy-water nexus across disciplines, 2) identifies social behavioral drivers of household green technology purchase, and 3) expands the focus of consumption research beyond the individual to consider how household social dynamics shape food, energy, and water use in the home.Systematically reviewing published FEW intervention literature, Chapter 2 proposes a typology that characterizes household food, energy, and water conservation interventions as active, passive, or structural, and household-specific or non-specific, illustrating six distinct categories: information, tailored information, action, gamification, policy/price change, and material/technological provision. The review reveals four lessons for future intervention research: household non-specific information and tailored information appear to be more effective when used together, the impacts of feedback are reinforced when contact with participants is persistent, price-based interventions are often ineffective, and material/technology provision has proven very effective in a limited number of studies.Chapter 3 explores social and psychological determinants of green purchasing behavior in the US and Canada, motivated by the importance of efficient technology adoption to reach national emissions goals. This analysis establishes a causal chain from values to environmental concern to green lifestyle orientation, or the perception of importance of environmental action to one’s overall lifestyle, which predicts green purchase intentions for lightbulbs, appliances, and vehicles. Income also impacts purchase intentions in both US and Canadian samples, illustrating the pervasiveness of consumer lock-in that has potential to significantly slow green technology adoption. These findings stress the importance of exploring pro-environmental behavior not in isolation, but as interconnected with broader lifestyle circumstances. Chapter 4 tests the effects of various household social dynamics on a variety of pro-environmental actions, in response to a “unit of analysis” problem, where intervention research often targets individuals despite much resource consumption happening in the context of multi-person households. Multiple linear regression models demonstrate that positive household dynamics, including enhancing and norming behaviors, predict variance in pro-environmental actions in the household. In addition, individual and household levels of environmental awareness predict variance in both positive and negative household social dynamics. These results support qualitative research efforts that emphasize the importance of household social dynamics to resource consumption, providing avenues for future quantitative research to take a practice-based approach.Using multiple methods and drawing from a variety of theoretical bodies, this dissertation contributes to household behavior change literature that bridges disciplinary boundaries in the social sciences, providing paths forward for individual and household-level mitigation efforts.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".