Smart Development in Smart Communities

Gilberto Antonelli, Giuseppe Cappiello

• Publisher: Routledge
• Year: 2016
• Print ISBN: 9781138189041
• eBook ISBN: 9781317281252
• Edition: 1
• Topic: Business
• Subtopic: Sviluppo di business
• Index: Business

The concept of smart cities has become one of the most significant new lines of thinking to emerge in the social sciences in recent years, both from the research and policy angles. To date, the focus in smart specialization has been on what regions as a whole can do to bring about innovation, but it hasn't necessarily addressed the role cities play within the field. This book aims to address that gap, drawing together a team of leading contributors, to illustrate this process with particular focus on cities.

Smart Development in Smart Communities discusses the cross-fertilization between smart specialization and cities in fostering smart development and its interactions with the macro-, micro- and meso-economic framework, from both a theoretical and applied perspective. Specific topics covered by the book include: human capital formation and utilization; centralized/decentralized industrial policies; innovation policies; collective learning; and the role of public utilities in sustaining smart development processes.

This book tackles some of the most important questions that must be faced when investigating how structural change and innovation processes are shaping local and global economic development. It will be of interest to academics and researchers in the area of Development Economics, Urban Studies and Public Management.

Part I
Conceptualizing the idea of smart development

I.1 Smart specialization

1 Issues and challenges for smart specialisation

Riccardo Leoncini

1 Introduction

The concept of smart specialisation is universally attributed to the Knowledge for Growth (K4G) Expert Group created in March 2005 by EU Commissioner for Research, Janez Potoc?nik. This group of scholars produced a vast array of contributions on the themes of innovation and knowledge, which constituted the theoretical and methodological base for the subsequent rich debate on the concept of smart specialisation strategy (S3 if we adopt the acronym proposed by the European Commission to refer to the smart specialisation strategy).¹

The K4G Group produced a series of policy briefs on the themes of education, technology and research to help overcome the deficit that EU has with regard to the other world areas in fields such as R&D, education, training and lifelong learning strategies, university–industry links, processes of technological diffusion, and the globalisation of R&D.

According to the proponents, the concept of S3 can be defined as “an entrepreneurial process of discovery that can reveal what a country or region does best in terms of science and technology. That is, we are suggesting a learning process to discover the research and innovation domains in which a region can hope to excel” (Foray et al., 2009, p. 2).

The idea of S3 seems to emerge from a quite extended and well-known literature to which the proponents seem to ‘naturally’ refer, that is that of a process of development based on the evolutionary principles, self-centred, endogenous, and bottom-up. These characteristics should favour the growth of regions and/or countries that are not necessarily technological leaders. In particular, the bottom-up process is linked to the well-known concepts developed by the evolutionary/institutionalist approach, such as entrepreneurship, technological learning, and local and tacit knowledge. They seem to be useful to discover areas in which there exist technological opportunities for future specialisations that can be developed from the material and immaterial resources locally available.

More recently, in the framework of the studies on Cyber-Physical Systems, Big Data or Cloud Computing and of the debate on national industrial strategies,² also the notion of ‘smart factory’ has been introduced. This debate can be seen as one of the follow-ups³ of S3.

The analysis of the concept of smartness allows for the individuation of some crucial elements such as, for instance, the centrality of technological innovation and, in particular, the increasing potentialities of the information and communication technologies (ICT), both in terms of the competitiveness of local economic systems and of the sustainability of their techno-economic development. In turn, from the technological domain, it is possible to thread some other connected elements such as the role of networks, so widely analysed by the literature, that transcend the material infrastructure (such as for instance, the ICT) to encompass the immaterial ones linked to knowledge-intensive business services (the so-called KIBS), to organisation of knowledge, to cultural activities.

Another element is the reference to the knowledge and its transferability: on the one side, S3 implies the generation of knowledge spillovers on a local/regional basis; on the other side, the urban environment is considered to be an ideal locus for the interactions among ‘peers’, such as those belonging to the creative class (Florida, 2002). Among the determinants of comparative (or absolute) advantages, some are particularly important, such as the attraction and the training of human capital. Human capital and highly skilled labour have thus a new role, as they are the main elements to be able to participate in innovative processes. The increasing role of professions with high cognitive content and their localisation all contribute to enhancing the spillover processes, that is they contribute to generate complementarities between demographic dimension of the urban area and productivity, specific competences of ‘high-level’ professionals, and new techniques of production. However, this creates a tension between two attributes (‘smart’ and ‘inclusive’) of the economic growth that the European Commission put forward because of the likely wage discriminations that this could produce to the disadvantage of less skilled workers. This implies the necessity of institutional solutions focused on a higher level of participation for all the economic actors in order to better guide the process.

Another element to underline is that according to this approach, it is not the intensity of investments in science and technology (S&T) to insure a competitive advantage, but rather the cross-fertilisation of the results of research efforts and the development of applications allowing adaptation on a large scale of the General Purpose Technologies (GPT). Hence, the emphasis is on the transversality of second-level applications rather than on the race for the first-level ones. The logical corollary is that applications must be regional (but possibly cross-national) and intersectoral and facilitate the diffusion/adoption of these technologies through the creation and/or the highlighting of complementarity and transversal elements (such as, but not only, ICT).

A final and fundamental characteristic of smart projects is their bottom-up nature.⁴ This is why smart technologies can model themselves on the basis of the vocation of each single component of the system and of the community involved. The bottom-up approach is to be seen especially in its leaning on elements such as listening, participation, co-design, and diffusion and exchange of information. This leads to the effective collaboration of the agents involved and also decreases the strong competitive forces that otherwise the market would put on agents. This leads to the problem of governance, which in this case is to be meant as a multi-centric system made of a plurality of agents, based on efficacy of regulation, on high degrees of freedom for private agents, and on accountability. On these themes, however, the wide literature on smartness still lacks a satisfying analysis of the institutional framework that smart specialisation policies badly need.

2 Smart specialisation

Smart specialisation defines industrial growth as a self-centred process mainly based on innovation, on the utilisation of regional potentialities, and on the ensuing sectoral diversification of local economic systems, starting from their scientific and technological vocation. Within an evolutionary approach, this result is obtained through a dynamic process of entrepreneurial discovery (Foray et al., 2009), joined to the creation of local knowledge, which must be original and coherent to the so-called ‘pertinent specialisations’ of the region (David et al., 2013). In this way, the process of knowledge creation can exploit the existing capabilities and can generate complementarities with other regions, close either geographically and/or sectorally.

The process of discovery is delegated to the economic agents involved in the production and transfer of the knowledge and of the factors incorporating it (in both codified and tacit forms). Moreover, it assumes different characteristics depending on the fact that it can be in regions that can be either leader (or core) or follower (or adapter). In the former, investments are channelled towards the introduction of General Purpose Technologies (GPT), which are thus partially endogenous. In the latter, investments are more oriented towards the ‘co-invention of applications’, that is, sectoral applications of GPT in one or more areas of interests beneficiary of S3.

The diversification does not exclude traditional sectors (such as tourism, fishing, clothes, etc.) that can anyway embark in innovative and transformative trends, whenever prerequisites at the local level are found. In this way, firms in follower regions can redefine the markets within which they operate and re-locate themselves within them with an increased competitive capacity. Moreover, firms can increase the (private and social) returns of the inventions they use, in this way incentivising also the innovative activities of the leader regions.

For this purpose, S3 presupposes the generation of knowledge spillovers on a regional basis. In so doing, it outdoes the concept of national system of innovation and attributes to a properly defined set of actors (researchers, entrepreneurs, suppliers, users) the function of selecting the knowledge-intensive areas where smart strategies must focus. This happens through the recognition of technological and/or market niches that can be exploited (McCann and Ortega-Argiles, 2011).

In this context, the applications of ICTs are relevant because they are susceptible to trigger the growth in productivity necessary for the process of co-invention of applications and to diffuse innovation to other sectors different from the originating one. This would increase the returns on innovative investments.

EU endorsed this approach; S3 strategies are defined as paths of specialisations aimed at favouring the strengthening of existing industries at the regional level and at favouring the diversification of the paths of innovation and growth on an intra-regional basis. To accomplish this task, the concept of related variety (Boschma, 2005) was adopted. According to this concept, the competitive advantage of a certain region comes from building a certain degree of variety into its industries. However, this must be correlated in terms of competences through a process of diversification of the exiting specialisations. On an inter-regional basis, it is necessary to enhance connectivity between leader regions, where industries operate in high-intensity knowledge sectors, and follower regions, specialised in sectors active in the co-invention of applications. Complementarities can thus be generated either within the same region or between different areas (Foray et al., 2012) depending on the spatial, cognitive, and sectoral proximity characterising them.

Smart specialised regions reinforce the capacity to apprehend and innovate, thanks to the potentialities of the peculiar territorial context, and they are enriched by both internal and external economies obtained by means of S3. The idea is that of channelling material and immaterial resources towards economic activities, showing a potential inter-regional and, whenever possible, global competitive advantage. This presupposes a coherent match between investments in knowledge and human capital, on the one side, and the production specialisation of the local economic systems, on the other (Camagni and Capello, 2012).

On the basis of the suggestions contained in the agenda for growth of the Europe 2020 Strategy, and of the classifications of innovative regions elaborated by OECD (2011), McCann and Ortega-Argiles (2011) highlight three particular factors contributing to the specification of the concept of S3: (1) embeddedness, (2) relatedness, and (3) connectivity.

The notion of embeddedness refers to the need to develop these processes of S3 within a specific socio-economic realm, characterised by a local labour market and a certain sectoral composition. As a consequence, smart processes of development need the presence of both sectors and co-invention of applications of relevant dimension and the presence of a sufficiently skilled labour force.

The notion of relatedness refers to the need to follow strategies of specialised differentiation, oriented towards the development of technologies that are relatively close to those already existing in that particular regional context (‘major local embedded industries’). They have already a dimensional scale that is big enough to undertake such strategies. The aim of this strategy seems to be that of favouring, in a painless way in terms of jobs and consistency of the industrial structure, the absorption of idiosyncratic shocks that are external to the region.

The notion of connectivity introduces the need to promote growth processes that involve sectors connected to others external to the region, with the aim of exploiting knowledge spillovers and, more generally, all the transactions and flows between the regional economic context and the external economies.

This definition of smartness presupposes the empirical analysis of the quantitative and qualitative endowment of production factors of the region and its capacity to increase its knowledge stock. In turn, this involves the recognition of the main factors of competitiveness and of the bottlenecks, through a process of discovery. This allows concentrating resources and private and public investments on the fundamental priorities, avoiding uniformity and duplications of investments (in innovation), with respect to the neighbouring regions.

In this way, regions can value proximity in a model that builds relationships betwee...

Table of contents

• Cover
• Title
• Copyright
• Contents
• List of figures
• List of tables
• About the contributors
• Foreword
• Introduction
• Part I Conceptualizing the idea of smart development
• Part II Measuring and applying smart local development
• Part III Governing smart local development
• Conclusion
• Author index
• Subject index