The environmental sustainability of Australia's private rental housing stock
Notice bibliographique
Résumé
This project contributes to present debates about the sustainability of Australian cities by focusing attention on the opportunities for and barriers to improving the environmental sustainability of Australia's private rental housing stock. The Australian Government, in partnership with state and territory and local governments, is currently committed to delivering a 60 per cent cut in carbon emissions from 1990 levels by 2050. At the household level, this has translated into a commitment to improving the energy efficiency of residential housing stock and to assisting residential households to reduce their resource consumption (DEWHA 2008). While early research and policy initiatives in Australia have been directed towards the construction industry and new homes, less attention has been granted to the existing dwelling stock, including private rental housing. In contrast, private rental housing has been the focus of policy and research attention in the United Kingdom and Europe, and to a lesser extent Canada and the US (See positioning paper www.ahuri.edu.au/publications download/40560-pp, pp.10-15). This research project addresses this gap. Improvements in the environmental sustainability of Australia's private rental housing offers advantages for the community in terms of achieving substantial reductions in emissions from Australia's residential sector, as well as potential long-term economic benefits for landlords and improved health and well-being of tenants. However, improving the environmental sustainability of private rental housing poses unique policy challenges. Of central concern is the 'principal-agent' or 'split incentive' problem. While the landlord (or the principal) is generally responsible for purchasing the energy-using facilities in the home, the tenant (or the agent) is generally responsible for the payment of recurrent energy bills (GCCR 2008, p.456). This situation potentially discourages landlords from investing in the infrastructure required in order to protect private rental tenants, particularly low-income tenants, from rising energy and water costs. The role of the 'split incentive' and other potential barriers, such as cost and lack of information, in constraining property adaptation is examined through quantitative modelling work and consultation with stakeholders and private rental investors. A summary of the five research questions and the major findings is provided in Table 1 below. (Table presented.) The project has generated an array of findings; some of which clarify key issues surrounding sustainable home improvement in the private rental sector, and some of which beg further research questions. The quantitative modelling work demonstrated that low-income private rental households are vulnerable to rising energy costs. While private renters have lower energy use than owner occupiers, they must put aside a similar percentage of their disposable income in order to meet higher energy bills under the CPRS. Related to this issue, is the question of whether private renter households are able to adapt to higher energy prices by exercising choice in the marketplace by opting for more sustainable properties, as well as the extent to which the private dwelling in which they live is likely to undergo energy and water saving improvements. The available quantitative data does not support the hypothesis that there is a 'split incentive' in place that results in private rental households paying higher bills than owner occupiers; indeed owner-occupiers pay more for energy, even when a range of assumed explanatory variables such as gross household income, household size and dwelling type are held constant. In contrast, consultation with private rental investors revealed that even among a group of investors who were relatively supportive of environmental measures, the major barriers to adopting energy and water saving measures were viewed as cost and a lack of financial incentive to act. Investors raised the issue of the split incentive, particularly in relation to large cost items such as solar panels and hot water systems. In addition, they did not envisage that they would be able to recoup costs through higher rental yields. Further, investors noted that record low vacancy rates meant that they had little incentive to upgrade properties to attract tenants. These results are puzzling and they emphasise the need for more adequate quantitative data on household energy and water consumption and the condition of Australia's housing stock, including information about key infrastructure items such as space heating and cooling systems and installation of solar technologies. Interestingly, the quantitative modelling work and qualitative consultation with stakeholders and investors suggests that barriers to advancing Australia's private rental stock might not be as insurmountable as first presumed. Indeed, there are aspects of Australia's private rental market that suggest some flexibility and capacity for sustainable home improvement. In particular, there are established incentives available through the taxation system to encourage investment in dwellings. In general, these were not seen by investors as sufficiently generous for them to act, but they did see some scope for the acceleration of depreciation schedules and the introduction of complementary rebates and measures such as land tax relief. The high churn of properties in and out of the private rental market also acted as a driver for sustainable home improvements among the investors consulted. This characteristic of the Australian market can not in itself deliver comprehensive change across the sector, but it raises the prospect of sustainable properties entering the property market at the higher end. Moreover, the profile of private rental investors is important. There are many investors who had not anticipated owning rental properties, but who have entered the market in order to support themselves in retirement. These investors are dependent on the income generated by their rental properties, but they also hold a range of views and values in relation to environmental and social issues. Our program review, as well as our consultation with stakeholders and investors, emphasises that different segments of the private rental market require different policy settings and interventions in order to overcome major barriers, create well-targeted incentives, and tap into existing motivations and drivers among investors.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,003 | 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 | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».