Contribution of the internet towards sustainable development through its economic growth, social capital and environmental effects

Spectacular growth in the use of the Internet has revolutionised many aspects of nations and human lives, including the key pillars of sustainable development such as economic, social and environmental aspects, among others. However, such phenomenal growth in the use of this enabling technology has also led to different forms of social inequalities, popularly known as ‘digital divide’. However, it is not merely the access divide that haunts the digital landscapes of the world today. With the rapid diffusion of the Internet technology, other forms of divide resulting from various factors such as age, education, speed and e-skills are emerging as potential threats to achieving the expected benefits from this general purpose technology. Empirical literature on the effects of the Internet support the view that digital divide potentially hampers the positive effects of the Internet. Currently, this is the central focus of the debate with regards to the potential economic, social and environmental effects of the Internet and the burning question is whether the Internet significantly impacts these three key parameters of sustainable development. This thesis seeks to answer this question through economic growth, social capital and environmental effects of the Internet – in the context of Organization of Economic Cooperation and Development (OECD) countries and in Australia, in particular. To accomplish this aim, this study is guided by four research questions: i) Does Internet use affect economic growth in OECD countries, and in Australia, in particular? ii) Does Internet use affect social capital in OECD countries and in Australia and regional Australia, in particular? iii) Does Internet use have any effect on electricity consumption in OECD countries, and in Australia, in particular? and iv) Does Internet use have any effect on CO2 emissions in OECD countries, and in Australia, in particular? In order to addressing these research questions, this study uses panel macro data for OECD countries, annual time series macro data for Australia, and quantitative survey data from regional Australia. Secondary data are obtained from the World Development Indicators Database of the World Bank. Data on social capital are gathered from the World Values Survey. An advanced panel data econometric estimation technique – the Pooled Mean Group (PMG) regression technique – is applied for panel data analysis, while the Autoregressive Distributed Lag (ARDL) model is used for analysis of time series data. Summated scale method is applied to quantify the social capital variable and multivariate regression technique is employed to examine the Internet–social capital nexus at a regional level. This PhD by publication thesis consists of seven chapters. The Introduction and Conclusions are presented in Chapter one and Chapter seven, respectively. A total of nine research outputs delivered by this research are presented in the remaining five chapters. Research question one is addressed in paper one and paper two. Research question two is addressed in papers three, four and five. Papers six and seven deal with research question three while research question four is addressed in papers eight and nine. Paper one and paper two examine economic growth effects of the Internet for OECD countries and for Australia respectively. In addition to enriching the existing literature on Internet-growth association, these two papers make a contribution by identifying the weaknesses of previous studies. Findings suggest that the Internet stimulates economic growth both for the panel of OECD countries and for Australia as well. Internet use data is analysed for the first time for Australia in paper two. To address research question two, the potential of the Internet in generating social capital is examined in papers three, four and five. Findings from both OECD panel and Australian time series investigations indicate that the Internet reduces social capital in the long run, while it slightly enhances social capital in the short run. Paper five analyses survey data to explore the relationship between the Internet and social capital in regional Australia. The survey data was collected from the Western Downs Region of Queensland. The social capital variable was constructed from five theoretically supported and statistically tested dimensions of social capital concept using summated scale method. These dimensions are; bonding social capital, bridging social capital, trust, neighbourhood effects and community engagement. This is believed to be a novel contribution to the existing literature on social capital measurement which suffers from intense debate on the topic. This paper also provides a conceptual framework on Internet-social capital relationship that may be a useful guideline for similar studies in future in regional context. The key finding indicates a positive relationship between Internet use and social capital implying that Internet-enabled network connectivity stimulates social capital in regional Australia. Research questions three and four deal with the environmental effects of the Internet. Research question three is addressed in papers six and seven – these papers investigate the effect of Internet use on electricity consumption for a panel of OECD countries and for Australia, respectively. In both studies, the Internet is found to cause an increase in electricity consumption. Such findings enforced the development of research question four, which investigates the CO2 emissions effect of the Internet. This is addressed in papers eight and nine. Both investigations found that Internet use does not have any significant effect on CO2 emissions. In other words, the growth in Internet use is still environmentally sustainable for these countries. All of papers six, seven, eight and nine are believed to make important empirical contributions to the literature on the environmental effects of the Internet. The findings from these studies are expected to provide stimuli for future researchers to examine such effects for other regions and countries. The conceptual framework of this study is believed to be a contribution by itself as it studies the effects of the Internet in all three key aspects of sustainable development (economic, social and environmental). Also, the massive literature review of all the three areas will enable future researchers identify research gaps in a relatively easier way for further investigations. This study offers a number of policy recommendations. To ensure expected economic benefits from Internet use, it is recommended in paper one and paper two that demand-side issues – such as education and skills – need more attention from policymakers responsible for framing and revising digital divide policies. Despite mixed findings on the Internet-social capital relationship from papers three, four and five, the inclusion of the social capital issue in digital divide policy should not be ruled out in the process of ensuring long-run success in addressing the digital divide. To achieve energy efficiency gains from the Internet and to exploit its emissions abatement potential, ‘green Internet’ and ‘Internet for green’ are strongly recommended in papers six, seven, eight and nine in order to combat future negative environmental effects of this technology. Finally, the overall findings from the investigations undertaken by this thesis confirm that the growth in the use of the Internet contributes towards sustainable development for the OECD countries as well as for Australia in particular

Fiscal forecasting: lessons from the literature and challenges

While fiscal forecasting and monitoring has its roots in the accountability of governments for the use of public funds in democracies, the Stability and Growth Pact has significantly increased interest in budgetary forecasts in Europe, where they play a key role in the EU multilateral budgetary surveillance. In view of the increased prominence and sensitivity of budgetary forecasts, which may lead to them being influenced by strategic and political factors, this paper discusses the main issues and challenges in the field of fiscal forecasting from a practitioner’s perspective and places them in the context of the related literature. JEL Classification: H6, E62, C53Fiscal policies, forecasting, government budget, monitoring

Quantifying the effects of modelling choices on hospital efficiency measures: A meta-regression analysis

It has often been argued that the results of efficiency analyses in health care are influenced by the modelling choices made by the researchers involved. In this paper we use meta-regression analysis in an attempt to quantify the degree to which modelling factors influence efficiency estimates. The data set is derived from 253 estimated models reported in 95 empirical analyses of hospital efficiency in the 22-year period from 1987 to 2008. A meta-regression model is used to investigate the degree to which differences in mean efficiency estimates can be explained by factors such as: sample size; dimension (number of variables); parametric versus non-parametric method; returns to scale (RTS) assumptions; functional form; error distributional form; input versus output orientation; cost versus technical efficiency measure; and cross-sectional versus panel data. Sample size, dimension and RTS are found to have statistically significant effects at the 1% level. Sample size has a negative (and diminishing) effect on efficiency; dimension has a positive (and diminishing) effect; while the imposition of constant returns to scale has a negative effect. These results can be used in improving the policy relevance of the empirical results produced by hospital efficiency studies.

Towards highly informative learning analytics

Among various trending topics that can be investigated in the field of educational technology, there is a clear and high demand for using artificial intelligence (AI) and educational data to improve the whole learning and teaching cycle. This spans from collecting and estimating the prior knowledge of learners for a certain subject to the actual learning process and its assessment. AI in education cuts across almost all educational technology disciplines and is key to many other technological innovations for educational institutions. The use of data to inform decision-making in education and training is not new, but the scope and scale of its potential impact on teaching and learning have silently increased by orders of magnitude over the last few years. The release of ChatGPT was another driver to finally make everyone aware of the potential effects of AI technology in the digital education system of today. We are now at a stage where data can be automatically harvested at previously unimagined levels of granularity and variety. Analysis of these data with AI has the potential to provide evidence-based insights into learners’ abilities and patterns of behaviour that, in turn, can provide crucial action points to guide curriculum and course design, personalised assistance, generate assessments, and the development of new educational offerings. AI in education has many connected research communities like Artificial Intelligence in Education (AIED), Educational Data Mining (EDM), or Learning Analytics (LA). LA is the term that is used for research, studies, and applications that try to understand and support the behaviour of learners based on large sets of collected data

World Wide Web? A closer look at the transnational online public discourse on climate change

This dissertations pursues three research objectives: (1) map how transnational the online public discourse on the global phenomenon of climate change is, (2) understand the role of the (trans)nationalized online public discourses on climate change in today’s hybrid media system, and (3) find, implement, and validate computational methods to study public discourses across different political and language spaces. Devoted to these objectives, the three articles included in this thesis produced the following results: (1) The public discourse on climate change is transnationalized to a considerable degree. First, the same topics define the issue in the countries studied. However, some of the topics are of different importance to the actors in these countries. Second, the discourses in the countries are shaped by both domestic and foreign actors. However, the scope of transnationalization is restricted to countries of the Global North, with a clear bias towards the United States. The Global South is thus a blind spot. (2) For the studied case of Germany, there is no evidence for continuous resonance among climate change skeptics’ online communication and legacy media. However, there are occasions of selective resonance when climate change skeptics manage to exploit specific events to push their perspectives and positions onto the mass media’s agenda. The influence of the transnational skeptical counter-movement on German mainstream discourse is therefore limited. (3) The combination of machine translation and topic models is a great option when it comes to the automated analysis of large multilingual corpora. Regardless of whether full texts or only the vocabulary of a corpus is translated, the approach produces reliable and robust results. Moreover, the analysis of transnational discourse convergence has shown that machine translation and topic models can also be used for comparative research

A New Paradigm for Proactive Self-Healing in Future Self-Organizing Mobile Cellular Networks

Mobile cellular network operators spend nearly a quarter of their revenue on network management and maintenance. Remarkably, a significant proportion of that budget is spent on resolving outages that degrade or disrupt cellular services. Historically, operators have mainly relied on human expertise to identify, diagnose and resolve such outages while also compensating for them in the short-term. However, with ambitious quality of experience expectations from 5th generation and beyond mobile cellular networks spurring research towards technologies such as ultra-dense heterogeneous networks and millimeter wave spectrum utilization, discovering and compensating coverage lapses in future networks will be a major challenge. Numerous studies have explored heuristic, analytical and machine learning-based solutions to autonomously detect, diagnose and compensate cell outages in legacy mobile cellular networks, a branch of research known as self-healing. This dissertation focuses on self-healing techniques for future mobile cellular networks, with special focus on outage detection and avoidance components of self-healing. Network outages can be classified into two primary types: 1) full and 2) partial. Full outages result from failed soft or hard components of network entities while partial outages are generally a consequence of parametric misconfiguration. To this end, chapter 2 of this dissertation is dedicated to a detailed survey of research on detecting, diagnosing and compensating full outages as well as a detailed analysis of studies on proactive outage avoidance schemes and their challenges. A key observation from the analysis of the state-of-the-art outage detection techniques is their dependence on full network coverage data, susceptibility to noise or randomness in the data and inability to characterize outages in both spacial domain and temporal domain. To overcome these limitations, chapters 3 and 4 present two unique and novel outage detection techniques. Chapter 3 presents an outage detection technique based on entropy field decomposition which combines information field theory and entropy spectrum pathways theory and is robust to noise variance. Chapter 4 presents a deep learning neural network algorithm which is robust to data sparsity and compares it with entropy field decomposition and other state-of-the-art machine learning-based outage detection algorithms including support vector machines, K-means clustering, independent component analysis and deep auto-encoders. Based on the insights obtained regarding the impact of partial outages, chapter 5 presents a complete framework for 5th generation and beyond mobile cellular networks that is designed to avoid partial outages caused by parametric misconfiguration. The power of the proposed framework is demonstrated by leveraging it to design a solution that tackles one of the most common problems associated with ultra-dense heterogeneous networks, namely imbalanced load among small and macro cells, and poor resource utilization as a consequence. The optimization problem is formulated as a function of two hard parameters namely antenna tilt and transmit power, and a soft parameter, cell individual offset, that affect the coverage, capacity and load directly. The resulting solution is a combination of the otherwise conflicting coverage and capacity optimization and load balancing self-organizing network functions

Economic activity and climate change

In this paper,we surve yrecent econometric contributions t omeasure the relationship between economic activity and climate change.Due to the critical relevance of these effectsfor the well-being of future generations,there is an explosion of publications devoted to measuring this relationship and its main channels.The relation between economic activity andclimate change is complex with the possibility of causality running in both directions. Starting from economic activity,the channels that relate economic activity and climate changeare energy consumption and the consequent pollution. Hence, we first describe the main econometric contributions about the interactions between economic activity and energy consumption, moving then to describing the contributions on the interactions between economicactivity and pollution. Finally, we look at the main results on the relationship between climate change and economic activity. An important consequence of climate change is the increasing occurrence of extreme weather phenomena. Therefore,we also survey contributions on the economice effects of catastrophic climate phenomena

Computational modelling of the effect of side chain chemistry on the micro-structure and electrolyte interactions of mixed transport polymers

As we scale up our use of energy storage facilities to meet the demands of the future, the prob- lems associated with current energy storage technologies will grow to unacceptable levels. In this work I explore how we can develop high performing polymers for use as cathode materials in energy storage devices operating with aqueous electrolytes. Energy storage devices using these materials have the potential for low cost production and safe operation. Through a combination of atomistic simulation methods, this thesis relates aspects of the polymer chemistry to their microstructural properties, and subsequently to their ability to operate successfully as electrodes.Open Acces