Effective urban planning is increasingly affected and governed by the current information society. This paper argues that the so-called third data revolution creates unprecedented challenges for sustainable city policy in the digital age. Three types of data revolutions are distinguished in this paper. The nowadays popular notion of smart cities may be helpful to enhance the cognitive quality of urban policy-making, but leads also to new issues on complex data handling in the modern age of social media and digital information systems. The various issues at stake are outlined in this paper, while this study is concluded with a sketch of the contours of a so-called smart city policy fly-wheel, so as to demonstrate that the urban fabric–also in a smart city context–is always work in progress, with a view to the achievement of sustainable development goals. Highlights The birth of the smart cities concept originates from the third data revolution. Sustainable city policy calls for smart data management. In the ‘New Urban World’ systematic data warehousing is needed. The tension between closed administrative city borders and open data systems can be bridged. The attainment of sustainable development goals (SDGs) presupposes smart city policy. 

We study aspects of economic growth in a stylized smart city with two distinct features. First, the modeled inhabitants of this city are smart because they possess skills. Using the language of Richard Florida, these inhabitants comprise the city's creative class and hence they possess creative capital. Second, the city is smart because it uses information and communication technologies (ICTs) and we model one specific kind of ICT use. In this setting, we first derive expressions for three growth related metrics. Second, we use these metrics to show that the economy of smart city A converges to a balanced growth path (BGP). Third, we compute the growth rate of output per effective creative capital unit on this BGP. Fourth, we study how heterogeneity in initial conditions affects outcomes on the BGP by introducing a second smart city B into the analysis. At time t = 0 two key savings rates in city A are twice as large as in city B. We compute the ratio of the BGP value of income per effective creative capital unit in city A to its value in city B. Finally, we compute the ratio of the BGP value of skills per effective creative capital unit in city A to its value in city B.

We study aspects of economic growth in a region that is creative in the sense of Richard Florida. We model creativity by supposing that the region under study has two sectors. The first sector uses physical capital {K(t) } and trained workers {A(t) W(t) } to produce creative capital {R(t) }. The second sector uses physical and creative capital to produce a final consumption good {Q(t) }. In this setting, we accomplish four tasks. First, we derive the equations of motion for physical capital per trained worker (k) and creative capital per trained worker (r). Second, we find combinations of k and r for which k˙ = r˙ = 0. Third, we investigate whether the economy of our creative region has a balanced growth path (BGP). Finally, assuming that our region is initially on a BGP, we study the impact of a permanent increase in the savings rate (s) on the trajectory of output per worker.

This study addresses the strategic governance challenges of modern smart cities from the new viewpoint of big data management. It seeks to develop and highlight a systematic methodological framework for handling multivariate big data in a smart urban decision support context in the 'New Urban World', so as to enhance the cities' competitive performance through the design and development of operational urban management principles and strategies. The specific aim of this paper is to provide the critical and basic cornerstones for an applicable interactive dashboard architecture as a supporting tool in a structured process of innovative city strategies and consequent enhanced socio-economic performance. A core element in the present paper is formed by a smart urban dashboard system that acts as an interactive navigation tool supporting operational choices of all stakeholders involved. This dashboard is able to integrate complex and ever-changing big data bases serving as 'signposts' of city intelligence (or i-city smartness) for daily or strategic decisions of all urban stakeholders. This study thus outlines successively the concept of smart i-cities in our 'urban century', the great potential of digital technology for managing big data in governing i-cities, and the foundations of an urban dashboard on the basis of the so-called Pentagon model as a policy strategy vehicle. Starting from extensive data on a broad set of global cities, the potential of this approach is exemplified by means of an illustrative application of a smart urban dashboard for the city of Stockholm.

Older population cohorts above the age of 65 are growing disproportionally to younger cohorts generally, and more specifically at the level of subnational regions, although this general trend is heterogeneous across regions (Guerin et al. 2015 and UNICEF 2015). At the same time that aging is on the increase, another demographic process, migration, is transforming or moulting qualitatively and itself scaling up. Today, many migration movements are episodic and thus more unstable than in the past (e.g., from famines, war, political turbulence and natural disasters), as illustrated by the growth of return, circular and temporary or chain migration. In fact, at the same time these growing and emerging types of migration are occurring the distinction between migration and commuting is blurring, e.g., those who are commuting across continents and intercontinentally might also be viewed as temporary migrants. As with aging, there are great differentials in the manifestation of these processes across places, regions and nations and in turn in their impact on regional labor markets. This chapter introduces the theme of the book and outlines its aims, scope and structure 

We study whether pessimism and optimism about the future by forward looking agents provides a rationale for pensions. We first distinguish between an agent’s true and pessimistic preferences and then analyze whether this agent’s level of saving depends on the pessimism parameter (π) and how welfare measured by the agent’s true preferences depends on π. Next, we examine whether it is possible for pessimism to increase the agent’s true utility and then show that this kind of pessimism does not provide a rationale for pensions. Moving on to optimism, we study the need for a pay-as-you-go (PAYG) pension system when the agent is optimistic about the generosity of the PAYG system. This optimism is modeled with a parameter (ω). In this setting, we first study the impact that an increase in ω has on the agent’s saving and then examine whether this agent’s welfare increases or decreases in ω. Finally, we show that this kind of optimism does not justify the presence of the PAYG pension system.

This chapter aims to provide an advanced assessment methodology for sustainable national energy-environment-economic efficiency strategies, based on an extended Data Envelopment Analysis (DEA). The use of novel efficiency-improving approaches based on DEA originates from the so-called Distance Friction Minimisation (DFM) method. To design a feasible improvement strategy for low-efficiency DMUs, we develop a Target-Oriented (TO) DFM model. However, the TO-DFM model does not incorporate a time-series concept in setting the target improvement level. In this chapter, we develop a new model from a blend of the TO-DFM and a Time-Series (TS) approach that incorporates the time-series concept and a stepwise target score to achieve a final target-efficiency score in order to generate a more realistic efficiency-improving projection. In the empirical analysis, Super-Efficiency DEA is used in our comparative study on the efficiency assessment of energy-environment-economic targets for the Asian countries, for which we employ appropriate data sets from the years 2003 to 2012. Hence, the above-mentioned TS-TO-DFM model is able to analyse realistic circumstances and determine the requirements for an operational sustainability strategy for efficiency improvement in inefficient Asian countries.

The bottom line of welfare on our planet and its people is not only dependent on traditional economic measures, but also on knowledge and education and - last but not least - on human health. Human health is a critical factor for the welfare and prosperity of society. Many parameters appear to play a role in a health equation, even though the empirical measurement of health is fraught with many conceptual and empirical problems. As a consequence, we observe many disparities in empirical health conditions in a heterogeneous society; an appropriate definition and measurement of 'good health' are far from easy. Next to health disparities caused by a heterogeneity among the population, there is also an important geographical component in the spread of health patterns of the population as a result of differences in environmental quality-of-life, spatial density, quality of and access to health care facilities, and social stress conditions. From this perspective, geography matters in the field of human health. Although geographic differences in health conditions are not the only reason for people to reside or stay in a certain place, they are certainly an important decision parameter, often in combination with wellness conditions and environmental quality conditions. The aim of the present paper is to provide an overview of the literature on the geography of health and wellness, while the study is concluded with some lessons for research and policy. Purpose: The aim of the present paper is to provide an overview of the literature on the geography of health and wellness. Methodology/Approach: Literature review. We will outline the geography of human health, through a concise literature survey of the geographical patterns in human health outcomes to address the general research findings on spatial differences in health in relation to urban-rural patterns of life. Findings: The measurement of human health is fraught with many difficulties, as it is often not clear whether a correction is made for supply factors (such as health care facilities) or for individual characteristics of the people concerned (such as age or gender). In the social-medical research literature this has led to an increasing popularity of meta-analytic methods. Research Limitation/implication: Meta-analysis may be seen as a collection of quantitative research techniques that aim at providing a synthesis of previously undertaken impact studies in a given field. Clearly, and ideally, both the response and the moderator variables would have to be identical, but in reality this is not the case. Besides, the quality of the research may be difficult (often reflected in the quality of the journal in which the results are published), while also the contextual conditions may be completely different (such as physical-geographical conditions or socio-economic or poverty conditions). This makes the results of meta-analytic studies somewhat ambiguous, but nevertheless it is a valuable method that may shed more light on the determinants of health outcomes. There is clearly a case for more detailed spatial data on individual health situations. There may be a self-selection (or sorting) mechanism in the locational decisions of households so that there is a need for a more systematic data collection and analysis in this area. Originality/Value of paper: The paper aims to unravel the various forces that determine human health, in particular from the spatial perspective of places of residence.

The purpose of this introductory chapter is twofold. First, the chapter provides an overview of some key economic themes in contemporary times and the spectacular rise of Asia. Next, the chapter provides an overview of the twelve remaining chapters that comprise this book. These chapters address one or more research questions on four topics. These four topics are (i) poverty, (ii) energy and climate change, (iii) money and macroeconomics, and (iv) ageing, education, production, and the internet.