Adapting the community sector for climate extremes

Abstract People experiencing poverty and inequality will be affected first and worst by the impacts of climate change to infrastructure and human settlements, including those caused by increasingly frequent and intense extreme weather events and natural disasters. They have the least capacity to cope, to adapt, to move and to recover. Community service organisations (CSOs) play a critical role in supporting individuals, families and communities experiencing poverty and inequality to build resilience and respond to adverse changes in circumstances. As such, the services they provide comprise a critical component of social infrastructure in human settlements. However, very little is understood about CSOs own vulnerability to – or their role in managing and mitigating risks to their clients and the community from – climate change impacts to physical infrastructure. The Extreme Weather, Climate Change and the Community Sector – Risks and Adaptations project examined the relationship between physical and social infrastructure (in the form of CSO service provision). Specifically, the ways in which the climate-driven failure of CSO service delivery worsens risks to the individuals and communities they serve and, on the other hand, how preparedness may reduce vulnerability to climate change and extreme weather impacts to human settlements and infrastructure.The research comprised a comprehensive and critical scoping, examination and review of existing research findings and an audit, examination and judgment-based evaluation of the current vulnerabilities and capacities of CSOs under projected climate change scenarios. It employed three key methods of consultation and data collection. A literature review examined research conducted to date in Australia and comparative countries internationally on the vulnerability and climate change adaptation needs of CSOs. A program of 10 Community Sector Professional Climate Workshops consulted over 150 CSO representatives to develop a qualitative record of extreme event and climate change risks and corresponding adaptation strategies specific to CSOs. A national survey of CSOs, which resulted in the participation of approximately 500 organisations, produced a quantitative data set about the nature of CSO vulnerability to climate change and extreme weather impacts to infrastructure, whether and how CSOs are approaching the adaptation task and key barriers to adaptation.While the methods employed and the absence of empirical data sets quantifying CSO vulnerability to climate change impacts create limitations to the evidence-base produced, findings from the research suggest that CSOs are highly vulnerable and not well prepared to respond to climate change and extreme weather impacts to physical infrastructure and that this underlying organisational vulnerability worsens the vulnerability of people experiencing poverty and inequality to climate change. However, the project results indicate that if well adapted, CSOs have the willingness, specialist skills, assets and capacity to make a major contribution to the resilience and adaptive capacity of their clients and the community more broadly (sections of which will be plunged into adversity by extreme events). Despite this willingness, the evidence presented shows that few CSOs have undertaken significant action to prepare for climate change and worsening extreme weather events. Key barriers to adaptation identified through the research are inadequate financial resources, lack of institutionalised knowledge and skills for adaptation and the belief that climate change adaptation is beyond the scope of CSOs core business. On the other hand, key indicators of organisational resilience to climate change and extreme weather impacts include: level of knowledge about extreme weather risks, past experience of an extreme weather event and organisational size.Given its size, scope and the critical role the Australian community sector plays in building client and community resilience and in assisting communities to respond to and recover from the devastating impacts of extreme weather events and natural disasters, the research identifies serious gaps in both the policy frameworks and the research base required to ensure the sector’s resilience and adaptive capacity – gaps which appear to have already had serious consequences. To address these gaps, a series of recommendations has been prepared to enable the development and implementation of a comprehensive, sector-specific adaptation and preparedness program, which includes mechanisms to institutionalise knowledge and skills, streamlined tools appropriate to the needs and capacity of a diverse range of organisations and a benchmarking system to allow progress towards resilience and preparedness to be monitored. Future research priorities for adaptation in this sector have also been identified

National innovation systems, developing countries, and the role of intermediaries: a critical review of the literature

Developed over the past three decades, the national innovation system concept (NIS) has been widely used by both scholars and policy makers to explain how interactions between a set of distinct, nationally bounded institutions supports and facilitates technological change and the emergence and diffusion of new innovations. This concept provides a framework by which developing countries can adopt for purposes of catching up. Initially conceived on structures and interactions identified in economically advanced countries, the application of the NIS concept to developing countries has been gradual and has coincided – in the NIS literature – with a move away from overly macro-interpretations to an emphasis on micro-level interactions and processes, with much of this work questioning the nation state as the most appropriate level of analysis, as well as the emergence of certain intermediary actors thought to facilitate knowledge exchange between actors and institutions. This paper reviews the NIS literature chronologically, showing how this shift in emphasis has diminished somewhat the importance of both institutions, particularly governments, and the process of institutional capacity building. In doing so, the paper suggests that more recent literature on intermediaries such as industry associations may offer valuable insights to how institutional capacity building occurs and how it might be directed, particularly in the context of developing countries where governance capacities are often lacking, contributing to less effective innovation systems, stagnant economies, and unequal development

Enabling High-Level Application Development for the Internet of Things

Application development in the Internet of Things (IoT) is challenging because it involves dealing with a wide range of related issues such as lack of separation of concerns, and lack of high-level of abstractions to address both the large scale and heterogeneity. Moreover, stakeholders involved in the application development have to address issues that can be attributed to different life-cycles phases. when developing applications. First, the application logic has to be analyzed and then separated into a set of distributed tasks for an underlying network. Then, the tasks have to be implemented for the specific hardware. Apart from handling these issues, they have to deal with other aspects of life-cycle such as changes in application requirements and deployed devices. Several approaches have been proposed in the closely related fields of wireless sensor network, ubiquitous and pervasive computing, and software engineering in general to address the above challenges. However, existing approaches only cover limited subsets of the above mentioned challenges when applied to the IoT. This paper proposes an integrated approach for addressing the above mentioned challenges. The main contributions of this paper are: (1) a development methodology that separates IoT application development into different concerns and provides a conceptual framework to develop an application, (2) a development framework that implements the development methodology to support actions of stakeholders. The development framework provides a set of modeling languages to specify each development concern and abstracts the scale and heterogeneity related complexity. It integrates code generation, task-mapping, and linking techniques to provide automation. Code generation supports the application development phase by producing a programming framework that allows stakeholders to focus on the application logic, while our mapping and linking techniques together support the deployment phase by producing device-specific code to result in a distributed system collaboratively hosted by individual devices. Our evaluation based on two realistic scenarios shows that the use of our approach improves the productivity of stakeholders involved in the application development

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

‘Smart Cities’ – Dynamic Sustainability Issues and Challenges for ‘Old World’ Economies: A Case from the United Kingdom

The rapid and dynamic rate of urbanization, particularly in emerging world economies, has resulted in a need to find sustainable ways of dealing with the excessive strains and pressures that come to bear on existing infrastructures and relationships. Increasingly during the twenty-first century policy makers have turned to technological solutions to deal with this challenge and the dynamics inherent within it. This move towards the utilization of technology to underpin infrastructure has led to the emergence of the term ‘Smart City’. Smart cities incorporate technology based solutions in their planning development and operation. This paper explores the organizational issues and challenges facing a post-industrial agglomeration in the North West of England as it attempted to become a ‘Smart City’. In particular the paper identifies and discusses the factors that posed significant challenges for the dynamic relationships residents, policymakers and public and private sector organizations and as a result aims to use these micro-level issues to inform the macro-debate and context of wider Smart City discussions. In order to achieve this, the paper develops a range of recommendations that are designed to inform Smart City design, planning and implementation strategies

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area