The Creative Services Sector in Polish Cities

The creative services sector plays an important and constantly growing role in the modern economy. This publication presents the results of extensive research on the functioning of the creative sector in Polish cities, conducted on a representative sample of 287 cities located throughout Poland. The sample was good in such a way as to maintain the structure by province. The survey included questions rated on a 5-point Likert scale. The aim of the research was to study the functioning of the creative sector in Polish cities and to determine whether the active involvement of public administration in its development has a positive impact on this sector. The research was carried out on the example of a medium-sized European country, which is Poland. The original contribution of the authors of the publication is to demonstrate, on a large research sample, the existence of a positive impact of the municipal office’s activities on the creative sector for example using special funds to boost creativity sector in the city, and to ascertain the existence of a linear relationship between the city size and the level of the creative sector functioning in it.

City Size and Permanent Settlement Intention: Evidence from Rural-Urban Migrants in China

The location choice and livelihoods of rural-urban migrants are critical to the sustainable development of cities. By using data from the China Migrants Dynamic Survey (CMDS) in 2017, this paper extant the Rosen–Roback’s model by adding factors of urban social network and air pollution to the function of the individual utility of migrants. Both the Probit Model and IV estimates imply evidence of an inverse U-shaped pattern of city size and migrants’ permanent settlement in urban China. This view proves that Chinese migrants like to settle permanently in large cities, but not mega-cities, such as Beijing and Shanghai. The internal mechanism is explained by the agglomeration economies and the crowing effect brought by city size. In mega-cities, the attractiveness of the city caused by wage premium cannot offset the combined repulsive force caused by the high housing price, bad urban social network, air pollution, and health deterioration. It is worth noting that air pollution has a significant negative impact on the settlement intention of migrants, such as health conditions and precipitation. Besides, there is heterogeneity among high-skilled migrants and low-skilled migrants in different city sizes. Our findings enhance the understanding of “Escape from megacities” in China and have implications for the reform of the housing security system and the exploration of the urbanization path.

Spatial-temporal variation of CO2 emissions from private vehicle use in Japan

The transport sector is a major contributor to anthropogenic climate change through the emissions of large amounts of greenhouse gases (GHGs) from fossil fuel combustion. Private vehicles account for almost half of the transport energy demand, and are thus a major target of climate change mitigation efforts. However, emissions from private vehicles can have large variability due to various geographic, demographic and socioeconomic factors. This study aims to understand how such factors affect private vehicle emissions in Japan using a nationally representative survey of household energy consumption (n=7,370) for 2017. The results indicate a large temporal and spatial variability in private vehicle emissions. Annual emissions show three peaks associated with major holiday seasons in winter and summer. Some of the more noteworthy spatial patterns are the higher emissions in prefectures characterized by low population density and mountainous terrain. Income, city size and the fuel-saving driving behavior all have a significant effect on emissions. The results indicate the need for sub-regional and socioeconomically-sensitive mitigation efforts that reflect the very different emission patterns, and the factors affecting them. The strong effect of city size, which is often much more clear-cut than between prefectures, suggests that it is more appropriate to approach transport decarbonization in Japan at the city level.

Influence of Different Industrial Agglomeration Modes on Eco-Efficiency in China

It is a key issue for the Chinese government to improve eco-efficiency and realize green development. As a spatial organization mode of industrial labor division, industrial agglomeration has a complex impact on eco-efficiency. However, it is still debatable which industrial agglomeration modes have a positive impact on eco-efficiency. This paper employs a panel threshold model, enterprise micro-level data, and relevant economic environment data from 283 cities in China from 2004 to 2012. It tests the nonlinear effects of specialized, related diversified, and unrelated diversified agglomeration on industrial eco-efficiency. The results show that the impact of specialized and related diversified agglomeration on industrial eco-efficiency is first inhibited and then promoted. The unrelated diversified agglomeration has a significantly negative impact on industrial eco-efficiency, but the negative impact weakens when agglomeration reaches a certain level. Furthermore, the impact of the three agglomeration modes on industrial eco-efficiency depends on city size. The impact of specialized agglomeration on industrial eco-efficiency is insignificant in small- and some medium-sized cities, but it has a significant inhibitory effect on industrial eco-efficiency when the city surpasses medium size. The role of related diversified agglomeration in promoting industrial eco-efficiency is further enhanced with the growth of city size. The impact of unrelated diversified agglomeration on industrial eco-efficiency gradually changes from negative to positive, but it plays a promoting role only when the city reaches the scale of super-large and mega-cities. Finally, this paper suggests that policymakers should formulate differentiated agglomeration policies according to changes in industrial agglomeration level or city size to improve industrial eco-efficiency.

An analysis on the effects of urbanization on the local climate of three inland developing cites in East China

Most studies on the impact of China’s urbanization on local climate focus on developed coastal cities, with little attention paid to inland developing cities. In the present study, we selected three representative and neighboring developing cities (Nanchang, Jiujiang and De’an) in East China to examine, through comparative analyses, local climate changes in inland developing cities with varying sizes during the past 45 years, based on homogenized datasets (1967-2012) released by the National Ground Meteorological Station, taking local economic, demographic, etc. factors into account. Our findings are as follows: The speed of urbanization in these three inland developing cities is correlated to their respective status and sizes - the bigger the city, the faster the urbanization occurring in said city. The pace of the urbanization has a clear impact on the local temperature variability. For the past 45 years, the warming rate in Nanchang (large city) was approximately 0.27 /decade while that in Jiujiang (middle-size city) was approximately 0.23 /decade and that in De’an (small town) was approximately 0.20 /decade. The warming rate was observed to rise in line with city size. The number of high temperature days (HTDs) increased significantly in all three cities over the course of the past 45 years. During the period of 2003 to 2012, HTDs in Nanchang, Jiujiang and De’an increased by 9.8, 5.1 and 1.3 days, respectively, compared with the period of 1967-1976. The larger the city, the more significant the increase in HTDs was observed.

Generative Rationality

In this paper we contrast bounded and ecological rationality with a proposed alternative, generative rationality. Ecological approaches to rationality build on the idea of humans as “intuitive statisticians” while we argue for a more generative conception of humans as “probing organisms.” We first highlight how ecological rationality’s focus on cues and statistics is problematic for two reasons: (a) the problem of cue salience, and (b) the problem of cue novelty in teeming environments. We highlight these problems by revisiting the statistical and cue-based logic that underlies ecological rationality, by discussing its origins in the field of psychophysics (e.g., signal detection, just-noticeable-differences). We work through the most popular experiment in the ecological rationality literature—the city-size task—to illustrate how psychophysical assumptions have been linked to ecological rationality. After highlighting these problems, we contrast ecological rationality with a proposed alternative, generative rationality. Generative rationality builds on biology, in contrast to ecological rationality’s focus on statistics. We argue that in uncertain environments cues are rarely given and available for statistical processing. Environments “teem” with indefinite cues, meanings and potential objects, the salience or relevance of which is scarcely obvious based on their statistical or physical properties. We focus on organism-specificity and organism-directed probing that shapes perception and judgment. Generative rationality departs from existing bounded and ecological approaches in that cue salience is given by top-down factors rather than the bottom-up, statistical or physical properties. A central premise of generative rationality is that cues in teeming environments are noticed or recognized when they serve as cues-for-something, requiring what might be called a “cue-to-clue” transformation. Awareness toward relevant cues needs to be actively cultivated or “grown.” Thus we argue that perception might more productively be seen as the presentation of cues and objects rather than their representation. The generative approach not only applies to seemingly mundane organism (including human) interactions with their environments—as well as organism-object relationships and their embodied nature—but also has significant implications for understanding the emergence of novelty in economic and other uncertain settings. We conclude with a discussion of how our arguments link with—but modify—Herbert Simon’s popular “scissor” metaphor, as it applies to bounded rationality and its implications for decision making in uncertain, teeming environments.

Urbanization, Agglomeration, and Economic Development in South Africa

This chapter reviews the arguments and evidence for the existence of a positive relationship between urbanization and economic development in South Africa. It identifies the main tenets of agglomeration theory, which stresses the importance of city size, density, and connectivity. These ideas are applied to fundamental features of urban development, namely the triangular relationship between the location of firms, households, and transport systems. The urban premium is strengthened by government investment in urban infrastructure and supportive institutions. Contemporary South African cities are scarred by the disjointed urban structure they inherited, which undermines productivity and inclusion. Government policies towards housing, land, and transport have done little to improve the morphology of cities and harness urbanization for widely shared prosperity.

CNN-Based Spectral Super-Resolution of Panchromatic Night-Time Light Imagery: City-Size-Associated Neighborhood Effects

Data on artificial night-time light (NTL), emitted from the areas, and captured by satellites, are available at a global scale in panchromatic format. In the meantime, data on spectral properties of NTL give more information for further analysis. Such data, however, are available locally or on a commercial basis only. In our recent work, we examined several machine learning techniques, such as linear regression, kernel regression, random forest, and elastic map models, to convert the panchromatic NTL images into colored ones. We compared red, green, and blue light levels for eight geographical areas all over the world with panchromatic light intensities and characteristics of built-up extent from spatially corresponding pixels and their nearest neighbors. In the meantime, information from more distant neighboring pixels might improve the predictive power of models. In the present study, we explore this neighborhood effect using convolutional neural networks (CNN). The main outcome of our analysis is that the neighborhood effect goes in line with the geographical extent of metropolitan areas under analysis: For smaller areas, optimal input image size is smaller than for bigger ones. At that, for relatively large cities, the optimal input image size tends to differ for different colors, being on average higher for red and lower for blue lights. Compared to other machine learning techniques, CNN models emerged comparable in terms of Pearson’s correlation but showed performed better in terms of WMSE, especially for testing datasets.