Abstract

۱٫ Introduction

۲٫ Literature review

۳٫ Empirical framework

۴٫ Empirical findings

۵٫ Conclusion and discussion

Disclosure statement

Notes on contributors

References

Abstract

     The aim of the present study is to reveal the causal effects of Smart City policies on urban innovation. Using the panel data harvested from 103 cities in China, the constructed sample was analysed rigorously based on the combination of the strength of Propensity Score Matching (PSM) and Difference-in-Differences (DID). As suggested by the empirical results, Smart City policies indeed positively and significantly impact urban innovation . Besides, whether policy effects vary with the regional location and city scale is explored; the results reveal that the impact is significantly positive only for the megacities as well as cities in central China.

Introduction

     Since the late 1990s, smart city projects have been rolled out globally. According to Deloitte Touche Tohmatsu’s report (Deloitte, 2018), the constructions of over 1,000 smart cities have been launched worldwide with 500 policy pilot areas in China, which noticeably outclasses Europe’s 90 of the second place. The mentioned projects are capable of not only enhancing the intelligence of specific socio-economic aspects of daily life, but also presenting a considerable number of other benefits, which cover fostering a competitive economy (Giffinger & Haindl, 2010), boosting more feasible governance (Nam & Pardo, 2011), expediting the innovation process (Paskaleva, 2011; Schaffers et al., 2012), advancing social capital (Hodgkinson, 2011), protecting diversity and individuality (Lind, 2012), etc. Nevertheless, some have argued that the potentials or benefits of smart cities have been overestimated; they considered that smart cities may not be as ‘smart’ as their name suggests (Hollands, 2008). Though considerable capitals have been invested in the advancement of smart cities, smart cities have not achieved their original goals (Yigitcanlar & Lee, 2014). Numerous risks also exist in the application of smart city strategies, e.g., the digital divide resulting from unequal use of ICT (Coe et al., 2001), the replication of technology solutions (Townsend et al., 2010), the overlook of city’s requirements and priorities (Caragliu & Del Bo, 2019), the slow progression attributed to budgetary issues and inadequate planning (Shwayri, 2013), etc.

Conclusion and discussion

     The present paper aims to evidence whether Smart City policies make cities more innovative – in the case of Chinese cities. To accurately estimate the policy effect, Propensity Score Matching (PSM) and Difference-in-Differences (DID) are integrated to eliminate some biases that might affect the effectiveness of this study. To be specific, PSM technique is adopted to construct the matched control group that possesses similar observable city characteristics with treatment group. Subsequently, DID approach is employed to estimate the differences of those groups’ treatment effects before and after the policy implementation. Besides, our results comply with several existing studies (Caragliu & Del Bo, 2019; Veeckman & Graaf, 2014), revealing that Smart City policies indeed stimulate urban innovation. Though innovative is not a major objective of implementation of Smart City policies, our findings have demonstrated a positive spill-over effect of the policies on urban innovation. Compared with similar non-pilot cities, pilot cities of smart city policy are granted more patents.