Exploring the Value of Infrastructure Systems and its Impacts on Decision-Making for Sustainability
Bibliographic record
Abstract
Infrastructure choices and decisions widely employ the language of value, whether to articulate what is worthwhile or to debate which principles or approaches are most appropriate to specific contexts. Infrastructure value delivery is consequential given the critical nature of these systems: they enable the mobility of people and goods and provide access to essential services such as drinking water and electricity. In this sense, infrastructure systems are the most valuable technological systems in modern society. As the world strives to achieve long-term sustainability goals, incorporating sustainability values into infrastructure decision-making becomes increasingly important. However, published conversations on value have often lacked convergence due to inconsistencies in what is meant by value and how it is measured and implemented. This dissertation bridges several gaps in the literature of value and sustainability assessment for infrastructure systems through three main research avenues: (i) providing a conceptual framework for value in academic literature; (ii) exploring how value is integrated in long-term computational modelling and multi-criteria decision-making through an example of water distribution networks; and (iii) exploring the value challenges, opportunities, and perspectives embedded in current urban infrastructure policy, namely, Circular Economy strategies for large cities. Chapter 2 reviews how the concept of value has been used to position different sustainability dimensions of infrastructure. This chapter discusses how value concepts interact in the context of infrastructure, and outlines avenues for its improved assessment. Chapter 3 implements these ideas in a modelling context by developing a dynamic computational model to evaluate the performance of long-term pipe maintenance strategies in Water Distribution Networks that allows for an improved assessment of multidimensional value while discussing the implications of diverse stakeholder perspectives. Chapter 4 discusses the value implications of urban infrastructure policy, exploring the applicability of Circular Economy (CE) policy to the management and assessment of infrastructure systems and analyzing current CE policies in six large cities from the perspective of value preservation and enhancement. Finally, Chapter 5 draws conclusions of this dissertation, summarises the key research contributions and suggests pathways for future research.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".