Innovation Hot Spots: the Case of the Computer Services Sector in the Region of Attica, Greece

Elaborating on the notion innovation hot spots, we examine the case of the computer services sector in the Region of Attica, Greece. Fast-growing, geographically and industrially clustered firms are becoming an increasingly important factor for innovation and regional development. As a result, innovation hot spots enjoy rapid growth, leading to job creation, knowledge expansion and, in the best cases, sustainable development. The most recent European Trend Chart Reports (2004 and 2005) present Greece as innovation leader in the computer services sector. Computer services are characterized by a high knowledge creation and knowledge diffusion intensity meaning that the hot spots exploiting such services position high on an innovation intensity scale. Consulting, implementation, operations management and support services enjoy similar growth since they are complementary industries forming the Attica IT innovation hot spot. The purpose of our research within this field is twofold. First, we present the conditions under which this innovation leadership has emerged and come to flourish. We argue that growth in the Region of Attica has been boosted by the Information Society Program, the Olympic Games and the necessity for modernizing Greek firms, which leads them to favor investments in new technologies. Moreover, the region presents a favorable macroeconomic environment, characterized by high rates of development, increase of consumption and investments. Second, we analyze and propose a framework for maintaining the dynamics in the region -and in innovation hot spots in general- as there is a significant risk of rise-and-fall patterns occurring, leading to former hot spots transforming into “blind spotsâ€, and core competencies developed turning into core rigidities and cultural lock-in.

Spatial patterns of knowledge-intensive business services in cities of various sizes, morphologies and economies

We compare intra-urban localization patterns of advertising and IT companies in three large Czech cities. The main aim of our analysis is an empirically-based contribution to the question to what extent do knowledge bases affect the spatial distribution of various knowledge-intensive business industries. The central research question is: To what extent is the localization of these two industries influenced by different modes of innovation/knowledge bases (symbolic vs. synthetic) and to what extent by contextual factors, such as urban size, morphology, position in the urban hierarchy and economic profile of the given city. We found that the urban contexts shape the localization patterns of advertising and IT companies more than differences in knowledge bases-both industries cluster primarily in the inner cities and urban cores. Formation of more suburban IT "scientific neighborhoods" is limited.Web of Science125art. no. 184

The evaluation of national accounting matrices with environmental accounts (NAMEA) as a methodology for carrying out a sustainability assessment of the Scottish food and drink sector

This report introduces environmental input-output (IO) accounts for Scotland as an example of a NAMEA framework. It provides an introduction to the use of basic IO multiplier methodology, which can be applied to examine pollution/waste generation and/or resource use under production and consumption accounting principles

Doing evolution in economic geography

Evolutionary approaches in economic geography face questions about the relationships between their concepts, theories, methods, politics, and policy implications. Amidst the growing but unsettled consensus that evolutionary approaches should employ plural methodologies, the aims here are, first, to identify some of the difficult issues confronting those working with different frameworks. The concerns comprise specifying and connecting research objects, subjects, and levels; handling agency and context; engaging and integrating the quantitative and the qualitative; comparing cases; and, considering politics, policy, and praxis. Second, the purpose is to articulate a distinctive geographical political economy approach, methods, and illustrative examples in addressing these issues. Bringing different views of evolution in economic geography into dialogue and disagreement renders methodological pluralism a means toward improved understanding and explanation rather than an end in itself. Confronting such thorny matters needs to be embedded in our research practices and supported by greater openness; more and better substantiation of our conceptual, theoretical, and empirical claims; enhanced critical reflection; and deeper engagement with politics, policy, and praxis

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

The spatial component of R&D networks

We study the role of geography in R&D networks by means of a quantitative, micro-geographic approach. Using a large database that covers international R&D collaborations from 1984 to 2009, we localize each actor precisely in space through its latitude and longitude. This allows us to analyze the R&D network at all geographic scales simultaneously. Our empirical results show that despite the high importance of the city level, transnational R&D collaborations at large distances are much more frequent than expected from similar networks. This provides evidence for the ambiguity of distance in economic cooperation which is also suggested by the existing literature. In addition we test whether the hypothesis of local buzz and global pipelines applies to the observed R&D network by calculating well-defined metrics from network theory.Comment: Working paper, 22 pages, 7 figure

Identifying macro-objectives for the life cycle environmental performance and resource efficiency of EU buildings

This working paper forms the main deliverable and outcome from work package A of the wider study. The aim of this working paper is to inform the identification of the most relevant macro-objectives for a building’s life cycle resource efficiency. These macro-objectives will in turn inform and set the scope for the common framework of indicators in work packages B,C and D. The first draft of this working paper was presented as the basis for discussion at the first stakeholder working group meeting, which was held in Brussels on the 16th June 2015. At that meeting the proposed boundaries, scope and coverage of the macro-objectives were discussed. Feedback from those discussions, together with follow-up written feedback, has been used in Chapters 6 and 7 of this working paper to identify a final set of macro-objectives that will be used to set the scope for the framework of indicators. In order to inform the initial proposals for discussion that were presented to stakeholders, this paper reviews existing legislation, scientific evidence, building schemes, collaborative research projects and other relevant literature. A high level scoping of environmental and resource efficiency ‘hot spots’ along the life cycle of buildings has also been carried out. Potential linkages and trade-offs between resource use, impacts along the life cycle and functional performance, with a specific focus on health and wellbeing aspects, have also been identified.JRC.B.5-Circular Economy and Industrial Leadershi

Spatial Regulation of Air Toxics Hot Spots

This paper analyzes the potential implications, in terms of net social costs and distribution of risks and abatement costs, of a policy to address the problem of air toxics “hot spots.” The policy we analyze involves regulation of air toxics sources at increasingly finer spatial resolutions. We develop a model of a decisionmaker choosing emission standards within a net cost minimization framework. Empirical application of the model to two counties in Florida demonstrates that regulation at finer resolutions could involve trade-offs between net social costs and equitable distribution of risks and, in some settings, between individual and population risks