In the theory of urban development, the evolutionary perspective is becoming dominant. Cities are understood as complex systems shaped by bottom-up processes with outcomes that are hard to foresee and plan for. This perspective is strengthened by the current turn towards smart cities and the intensive use of digital technologies to optimize urban ecosystems. This paper extends the evolutionary thinking and emerging dynamics of cities to smart city planning. It is based on recent efforts for a smart city strategy in Thessaloniki that enhances the economic, environmental, and social sustainability of the city. Taking advantage of opportunities offered by the IBM Smarter Cities Challenge, the Rockefeller 100 Resilient Cities, the World Bank, and the EU Horizon 2020 Program, Thessaloniki shaped a strategy for an inclusive economy, resilient infrastructure, participatory governance, and open data. This process, however, does not have the usual features of planning. It reveals the complex dimension of smart city planning as a synthesis of technologies, user engagement, and windows of opportunity, which are fuzzy at the start of the planning process. The evolutionary features of cities, which until now were ascribed to the working of markets, are now shaping the institutional aspects of planning for smart cities.

Smart Cities from an Evolutionary Perspective: Frame of Reference

Masdar City is a landmark in twenty-first-century urban development as it is the first zero carbon city, opening up an era of technology-led sustainability and green growth. But, is Masdar a city? According to The Guardian (Goldenberg, 2016) only 300 people so far live on the site and all are students at the Institute of Science and Technology. In fact, Masdar is actually a group of buildings, a large physical complex; more an engineering construct than a city. It will become a city in the future, when people and human activities, culture, institutions, and behaviors give purpose and use to infrastructures and buildings. Masdar will evolve into a city, as all cities do; they evolve and become cities rather than being constructed as cities from scratch. This idea of “cities becoming cities” rather than “cities planned as cities” is a core premise of evolutionary thinking about urban development. Cities are extremely complex and chaotic systems; many forces work simultaneously in their making and even small variations in the outcome interact and produce huge changes in results. Economic and political forces create numerous constraints on cities, yet there is room for genuine development that is not bound by deterministic conditions. Evolutionary thinking holds a preeminent position in urban and regional development theory. Cities and regions offer resources that are actualized by selective mechanisms that drive change and growth. Lambooy (2002) argues that urban regions offer effective contexts for development through an evolutionary process where cognitive, innovative, and organizational competencies are influenced by a selection environment composed of institutions, markets, and spatial structure. This environment drives the choice between alternative planning ideas and designs for new investments in city services and infrastructures. Here there is an analogy to the way Nelson and Winter (1977) have described innovation as a purposive, but inherently stochastic activity, which is guided by an external selection environment that determines how different technologies are selected and change over time. The innovation selection environment is shaped by market and nonmarket forces, consumer preferences, investment, and imitation processes, as well as political and regulatory control over firms. Simmie and Martin (2010) widen this understanding of how innovation in cities is produced, connecting the development of cities and regions to four conceptual frameworks that offer an evolutionary account of resilience and adaptation: (1) generalized Darwinism which places emphasis on variety, novelty, and selection; (2) path dependence theory that underlines historical continuity “lock-in” and new path creation; (3) complexity theory with its emphasis on self-organization, bifurcations, and adaptive growth; and (4) panarchy that links resilience and “adaptive cycles.” Boschma (2004) points out the uniqueness of urban and regional growth paths from an evolutionary perspective, since the competitiveness of a region depends on intangible, non-tradable assets resting on a knowledge base embedded in the region’s specific institutional setting. Transferring growth models from one region to another is questionable as there is no “optimal” development model, and new successful trajectories and developmental paths emerge spontaneously and unexpectedly in space. Bettencourt et al. (2010) argue that agglomeration non-linearities connect most urban socioeconomic indicators with population size, making larger cities centers of innovation, wealth, and crime. They find that local urban dynamics display long-term memory, so cities under- or out-perform their size expectation and maintain such advantage for decades.