Smart innovative cities: The impact of Smart City policies on urban innovation

Highlights

• In this paper we look at the urban innovation impact of Smart City (SC) policies.

• Data cover Smart City features for 309 European metropolitan areas, SC policy intensity, and urban innovation.

• PSM estimates suggest that cities engaging in SC policies above the EU average also tend to patent more.

• This effect is stronger for high-tech patents, while decreases for more narrowly defined technological classes.

• This result suggests possible technological spillovers from technologies directly involved in Smart City policies.

Abstract

Smart City policies have attracted relevant attention and funding over the last few years. While the time seems now ripe to conclude that such policies have a positive impact on urban economic growth, the picture is much less clear when looking at the microfoundations of this effect.

In this paper we look at the urban innovation impact of Smart City policies. In fact, typical Smart City projects imply the involvement not only of major multinational corporations, along with local public authorities, but also of local companies, typically with the aim to translate general technological solutions to the local needs.

A new data set collected for these analyses comprises data on Smart City features for 309 European metropolitan areas, Smart City policy intensity, and urban innovation outputs. The latter are proxied by calculating total patent applications to the European Patent Office between 2008 and 2013. Patent counts also include technologically narrower classes, namely high-tech, ICT, and specific Smart City technologies patent applications.

Propensity Score Matching estimates suggest that cities engaging in Smart City policies above the EU average also tend to patent more intensively. This effect is stronger for high-tech patents, while decreases for more narrowly defined technological classes. This last result suggests possible technological spillovers from technologies directly involved in Smart City policies.

Introduction

Smart City policies have attracted relevant attention and funding over the last few years. While the time seems now ripe to conclude that such policies have a positive impact on urban economic growth, the picture is not as clear when looking at the microfoundations of this effect. In fact, while statistical evidence does suggest the existence of a positive association between the implementation of Smart City policies and urban economic performance (Caragliu and Del Bo, 2018a), how this exactly comes about is much less clear.

One possible channel for Smart City policies to exert a positive impact on economic performance and growth is through fostering urban innovation. In fact, Smart City projects are often the result of a strategic interaction between major multinational corporations heavily investing in these technologies, and municipal and regional authorities seeking to enhance local performance by means of adapting such technologies to the local needs. While the latter seek to maximize public value creation (Anthopoulos et al., 2016), cities also resort to private investors both as additional means of financing as well as a way to enact public investment strategies (Galati, 2017).

The literature on Smart Cities stresses the need for local context conditions for fully reaping the benefits of large investments in high-tech solutions (Caragliu et al., 2011). It therefore comes as no surprise if technologies that are conceived for a vast audience need to be translated, with the involvement of local actors, to the specific context where they are deployed.

In GSMA (2013), several examples of local-global partnerships have been documented. For instance, over the last few years the municipality of San Francisco has started a pilot project called “SFpark” to collect, with mobile sensors, information on parking space availability throughout the city to be distributed to drivers by means of a dedicated app. Moreover, the app also prices available parking spots on the basis of present demand and supply conditions. While sensors used for identifying parking availability have been provided by Fybr, a Saint Louis-based company with international clients (https://fybr.com; at the time the project began, StreetSmart Technologies), three local public agencies, viz. the City of San Francisco, San Francisco Municipal Transportation Agency (SFMTA) and the San Francisco County Transportation Authority (SFCTA), have been involved in the deployment of the sensors and in monitoring the project's performance (UPADE, 2014).

Another example also discussed in GSMA (2013) is the municipality of Busan's (South Korea) partnership with Cisco and KT to promote an App Development Centre to co-create Smart City services by means of start-ups. A cloud-based mobile app development platform has helped establishing over the first year since inception 13 start-ups, which yielded a grand total of 70 new apps, with total revenues equal to 2.2 million USD and revenues from online sales equal to 42,000 USD.

Other celebrated examples of effective public-private partnerships for delivering innovative Smart City technologies are also discussed in the literature on European case studies. For instance, Amsterdam's Climate Street initiative has the aim to transform a traditional retail street, Utrechtsestraat, into a sustainable shopping area by optimizing the street's stores' energy and logistics management, along with the related public services. Smart meters constantly monitor demand and supply of energy, and grids also constantly measure how full public trashcans are, so that waste collection only takes place when needed. These combined actions have allowed the city of Amsterdam reduce annual CO² emissions from 3400 tons in 2010 to 1276 tons in 2012 (GSMA, 2013).

These examples provide a rich background against which our empirical approach can be usefully tested. Urban innovation, therefore, seems at first glance to be a potentially relevant channel of Smart City policy impact, and this paper sheds light on its extent. A new data set collected for these analyses comprises data on Smart City features for 309 European metropolitan areas, Smart City policy intensity, and urban innovation outputs. The latter are proxied by calculating total patent applications to the European Patent Office on the basis of the OECD RegPat data base. Patent counts also include technologically narrower classes, namely high-tech, ICT, and specific Smart City technologies patent applications.¹

Estimates are built on Propensity Score Matching (henceforth, PSM), in order to uncover causation links. Results suggest that cities engaging in Smart City policies above the EU average also tend to patent more intensively. This effect is stronger for high-tech patents, while it decreases for more narrowly defined technological classes.

In order to understand the impact of Smart City policies on urban innovation, we move as follows. In Section 2 we critically summarize two main strands of literature within which our empirical work is framed. In Section 2.1 we briefly discuss the burgeoning literature on cities as innovation hubs. In Section 2.2, instead, we deal with the much less prominent, but growing, literature on Smart City policies. In Section 3 we describe the identification strategy adopted for testing the main assumption of the paper. Data and details on the Propensity Score procedure are discussed in Section 4, while Section 5 presents the main empirical findings of the paper. Finally, Section 6 concludes by drawing the main policy lessons, as well as by highlighting future developments in this promising line of research.