Novel decision model for delivering sustainable infrastructure solutions – an Australian case study
Bibliographic record
Abstract
Conventional approaches to water supply and wastewater treatment in regional towns globally are failing due to population growth and resource pressure, combined with prohibitive costs of infrastructure upgrades.However, there are complexities associated with implementing sustainable infrastructure solutions, and a need to simplify the decision making process to equally compare alternatives to business-as-usual solutions.The aim of this study was to develop a model which could assist in delivering sustainable infrastructure solutions in regional towns (and elsewhere) to facilitate growth and/or reduce the burden on limited resources.The developed model (Sustainable Infrastructure Decision Model, SIDM © ) ultimately organises intelligent inputs (from expert stakeholders and quantitative calculations) systematically and holistically in order to compare relative impacts, risks, costs, and benefits of varying solutions.In this sense, it deviates both from the 'black box' designs of many other sustainability tools, which requires trust of hidden data and formulas and from heuristic approaches that often 'set up' a subjective game of bias between stakeholders.Rather, SIDM © is based on a transdisciplinary system approach which facilitates informed decisions in a transparent manner.It links water, wastewater, energy, and waste (resource flows) along with stakeholders (consumers, producers), the receiving environment (receptors), and governing systems (managers, politicians, regulators, financers).Key to the approach is the use of local context analysis as a 'design' driver, along with equal consideration of stakeholder intent, capacity, and commitment.The model also includes an economic analysis and risk-based evaluation process to ensure that the preferred solution is optimised to the environmental, social, economic, and political setting of a particular town.The SIDM © model was applied to a rapidly growing Australian township (Hopetoun) with complex resource and infrastructure constraints, which is described in this paper as a case study.Use of SIDM © resulted in an agreed decentralised solution which was approximately half of the cost of a conventional solution, with considerable water and energy savings and unanimous stakeholder support.Since this project, SIDM © has been applied to other regional towns and urban developments in Australia.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".