Modelling greenhouse gas emissions for Australian residential building construction

International pressure to reduce greenhouse gas emissions has forced many countries to look beyond 'demand side' measures. Several industry sectors are examining indirect requirements for energy and other resources that involve significant greenhouse gas emissions. The operation of buildings is responsible for approximately one quarter of greenhouse gas emissions in Australia. Moreover, he construction process consumes vast quantities of raw materials and complex goods and services each year. Each of the processes required for the provision of these products requires energy, and most of this is fossil fuel based. A national model of greenhouse gas emissions is required for residential building construction, to indicate where emissions reduction strategies should focus. A disaggregated input-output model is developed for the Australian residential building construction sector, and recommendations are made about how this model can be used in the development of policies of emissions mitigation for both the sector and individual residential buildings.

Understanding cange in residential property markets: Mapping residency of employment data

The changing nature of residential housing markets is due to a large number of influences, although some have a larger effect than others do on house values. Whilst it is extremely difficult to completely disaggregate all influencing factors, it is possible to highlight areas that have a strong relationship with property – one of these is residency of employment. This research investigates these links between residential housing markets as measured by the level of house prices and residency of employment as measured by industry sector employment. It focuses on Local Government Areas in the State of Victoria, Australia and examines change over a ten year period between 1991 and 2001 using census and house price information. It is supported by data sourced from the Australian Bureau of Statistics and the Victorian Government’s Valuer General’s Office. The analysis also considers changes in these employment sectors from Australia’s overall perspective, as well as comparison with changes in Victoria’s overall residency of employment trends. It is assisted by a spatial representation of three 'shift-share' components and property values with the support of a geographical information system (GIS).

Investigating residential property markets using employment data

The changing nature of residential housing markets is due to a large number of influences, although some factors have a larger effect than others on house values. Whilst it is extremely difficult to completely disaggregate all influencing factors, it is possible to highlight areas that have a strong relationship with property – one of these is employment. Due to the growing importance between housing affordability and the capacity to meet the cost of living the form of regular mortgage repayments or rent, there are clear links between the cost of housing and the ability to pay for the housing product.

The research investigates the links between residential housing markets as measured by the level of house prices and employment as measured by industry sector employment. It focuses on Local Government Areas in the State of Victoria, Australia and examines change over a ten year period between 1991 and 2001 using census and house price information. It is supported by data sourced from the Australian Bureau of Statistics and the Victorian Government’s Valuer General’s Office. The analysis also considers changes in these employment sectors from Australia’s overall perspective, as well as comparison with changes in Victoria’s overall employment trends. It is assisted by a spatial representation of three shiftshare components and property values with the assistance of a geographical information system (GIS).

Understanding property cycles in a residential market

Purpose – This paper aims to review property cycle theory and the relevance of the larger body of knowledge about cycles with reference to the housing market. It also aims to highlight the lack of research into property cycles in the residential sector on a suburb or smaller region basis, as well as the potential for increased knowledge about cycles to assist to avoid housing stress.

Design/methodology/approach – The paper conducts a literature review of previous cycle research and encourages the use of cycle theory. It discusses the established body of knowledge about business cycles and the office market sector, as well as investigating levels of housing affordability and how detailed knowledge about property cycles can assist to decrease housing affordability in residential areas, which will eventually experience a downturn.

Findings – It is argued that an increased level of certainty about cycle behaviour in particular suburbs will give households a higher level of confidence when considering whether and when to enter the market. Property cycle research has the potential to assist low-income homeowners to better understand the characteristics of cycles and associated risks in each residential.

Research limitations/implications – This is a conceptual paper and has conducted a review of cycle research and housing affordability in certain countries. Some areas or countries may be affected to varying degrees by property cycles and levels of housing affordability.

Practical implications – In extended periods of high volatility it is argued that a better understanding of housing cycles will allow more homeowners to avoid negative equity and the stress associated with repossessions. Property cycles are unavoidable although there is typically relatively little information available in the open market about the timing and amplitude of cycles in individual areas.

Originality/value – This paper is unique as it highlights the potential for property cycles to be used to avoid housing stress in the residential market. Traditionally cycle research is used to increase returns and avoid downturns in the office and/or business sectors.

Entrepreneurship in the neighborhood:shifting patterns of economic activities in residential neighborhoods in five dutch cities

Cities are oftentimes seen as undergoing a process of "emergence" in the "new economy." However, this process has largely remained empirically underdetermined. This article examines the intra-city geography of emerging businesses in newly dominant sectors of the urban economy. The change in dominant sectors coincides with a shift towards small- and medium-sized businesses, creating new economic opportunities for urban residential areas. The residential neighborhood is introduced as a place where supply and demand side drivers operate to attract or limit such new economic activity. Allen Scott's perspective of the cognitive-cultural economy is used to analyze which neighborhoods are flourishing sites of the cognitive-cultural sectors. His perspective on industries that are on the rise in urban environments and their growth potential proves very valuable. Social demographic characteristics on the level of the neighborhood are used as predictors of the composition of the local economy. The analyses show that in particular wealthy, gentrified neighborhoods are more prone than others to becoming "hubs" of the cognitive-cultural economy. However, disadvantaged neighborhoods may under certain conditions serve as incubators for business start-ups as they offer low-rent office spaces. This has important consequences for their future economic growth potential as well as the distribution of successful businesses in the city. © 2013 Urban Affairs Association.

Utilizing “Big Data” to Improve the Hotel Sector’s Energy Efficiency: Lessons from Recent Economics Research

Hotel chains have access to a treasure trove of “big data” on individual hotels’ monthly electricity and water consumption. Benchmarked comparisons of hotels within a specific chain create the opportunity to cost-effectively improve the environmental performance of specific hotels. This paper describes a simple approach for using such data to achieve the joint goals of reducing operating expenditure and achieving broad sustainability goals. In recent years, energy economists have used such “big data” to generate insights about the energy consumption of the residential, commercial, and industrial sectors. Lessons from these studies are directly applicable for the hotel sector. A hotel’s administrative data provide a “laboratory” for conducting random control trials to establish what works in enhancing hotel energy efficiency.

A pilot study on the effect of indoor particle sources on indoor particle concentration in residential houses

Characterization of indoor particle sources from 14 residential houses in Brisbane, Australia, was performed. The approximation of PM2.5 and the submicrometre particle number concentrations were measured simultaneously for more than 48 h in the kitchen of all the houses by using a photometer (DustTrak) and a condensation particle counter (CPC), respectively. From the real time indoor particle concentration data and a diary of indoor activities, the indoor particle sources were identified. The study found that among the indoor activities recorded in this study, frying, grilling, stove use, toasting, cooking pizza, smoking, candle vaporizing eucalyptus oil and fan heater use, could elevate the indoor particle number concentration levels by more than five times. The indoor approximation of PM2.5 concentrations could be close to 90 times, 30 times and three times higher than the background levels during grilling, frying and smoking, respectively.

Indoor exposure to submicrometer particles and PM2.5 in residential houses in Brisbane, Australia

As part of a large study investigating indoor air in residential houses in Brisbane, Australia, the purpose of this work was to quantify indoor exposure to submicrometer particles and PM2.5 for the inhabitants of 14 houses. Particle concentrations were measured simultaneously for more than 48 hours in the kitchens of all the houses by using a condensation particle counter (CPC) and a photometer (DustTrak). The occupants of the houses were asked to fill in a diary, noting the time and duration of any activity occurring throughout the house during measurement, as well as their presence or absence from home. From the time series concentration data and the information about indoor activities, exposure to the inhabitants of the houses was calculated for the entire time they spent at home as well as during indoor activities resulting in particle generation. The results show that the highest median concentration level occurred during cooking periods for both particle number concentration (47.5´103 particles cm-3) and PM2.5 concentration (13.4 mg m-3). The highest residential exposure period was the sleeping period for both particle number exposure (31%) and PM2.5 exposure (45.6%). The percentage of the average residential particle exposure level in total 24h particle exposure level was approximating 70% for both particle number and PM2.5 exposure.