Novel decision model for delivering sustainable infrastructure solutions – an Australian case study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».