Challenges and opportunities to develop a smart city: A case study of Gold Coast, Australia

With the rapid growth of information and communication technologies, there is a growing interest in developing smart cities with a focus on the knowledge economy, use of sensors and mobile technologies to plan and manage cities. The proponents argue that these emerging technologies have potential application in efficiently managing the environment and infrastructure, promoting economic development and actively engaging the public, thus contributing to building safe, healthy, sustainable and resilient cities. However, are there other important elements in addition to technologies which can contribute to the creation of smart cities? What are some of the challenges and opportunities for developing a smart city? This paper aims to answer these questions by developing a conceptual framework for smart cities. The framework is then applied to the city of Gold Coast to identify challenges and opportunities for developing the city into a ‘smart city’. Gold Coast is a popular tourist city of about 600,000 populations in South East Queensland, Australia, at the southern end of the 240km long coastal conurbation that is centred by Brisbane. Recently, IBM has nominated Gold Coast as one of the three cities in Australia for its Smarter Cities Challenge Grant. The grant will provide the Gold Coast City Council with the opportunity to collaborate with a group of experts from IBM to develop strategies for enhancing its ICT arrangements for disaster response capabilities. Gold Coast, meanwhile, has potential to diversify its economy from being centred on tourism to a knowledge economy with focus on its educational institutions, investments in cultural precincts and high quality lifestyle amenities. These provide a unique opportunity for building Gold Coast as an important smart city in the region. As part of the research methodology, the paper will review relevant policies of the council. Finally, lessons will be drawn from the case study for other cities which seek to establish themselves as smart cities

A model to distribute mark-up amongst quotation component item

The outline of a proposed new unbalanced bidding model is discussed. Background is provided as regards the role of item price loading, otherwise known as unbalanced bidding. Three types of loading are described, namely those of ‘front-end loading’, ‘back-end loading’ and ‘quantity error exploitation’ (otherwise known as ‘individual rate loading’). It is proposed that one single mathematical model could embrace all three of the above types and that the aspect of risk may be addressed partially by means of using the quadratic programming techniques employed within the field of Modern Portfolio Theory. MPT is a field pioneered by Markowitz in 1959 and was developed to identify optimum portfolios of investments, typically equities. It is hypothesized that MPT presents a basis by which to distinguish Efficient Item Pricing combinations from inefficient ones and thereby provide a scientific tool by which rational contractors may optimally price a project’s items. A brief history of unbalanced bidding describes the field that was pioneered in the 1960’s by Marvin Gates and Robert Stark, as well as the subsequent contributions by the leading researchers in the field.unbalanced bidding, bidding models, item price loading, modern portfolio theory, construction industry, mathematical models, bidding strategies

Component unit pricing theory

Building contractors are often commissioned using unit price based contracts. They, nevertheless, often compete on the basis of their overall project bids and yet are paid on the basis of these projectsâ constituent item prices. If a contractor decides these prices by way of applying an uneven mark-up to their estimates of their costs, this is known as unbalanced bidding. This research provides proof and explanation that different item pricing scenarios produce different levels of reward for a contractor, whilst exposing them to different degrees of risk. The theory describes the three identified sources of these rewards as well as provides the first explanation of the risks. It has identified the three types of risk involved and provides a model by which both the rewards as well as these risks can now be measured given any item pricing scenario. The research has included a study of the mainstream microeconomic techniques of Modern Portfolio Theory, Value-at-Risk, as well as Cumulative Prospect Theory that are all suited to making decisions that involve trading-off prospective rewards against risk. These techniques are then incorporated into a model that serves to identify the one item pricing combination that will produce the optimum value of utility as will be best suited to a contractorâs risk profile. The research has included the development of software written especially for this purpose in Java so that this theory could be tested on a hypothetical project. A test produced an improvement of more than 150% on the present-value worth of the contractorâs profit from this project, if they apply this model compared to if they instead price the project in a balanced manner