Objectives: Increased risk of autism with the increment of urbanization has been documented in developed countries. However, very few studies in developing countries focused on this topic. By using Chinese nationally representative large dataset, we investigated the association between urbanicity and autism among children aged 0-17 years in China. Also, we analyzed whether there existed a sexually dimorphic effect on this association. Methods: Data from the Second National Sample Survey on Disability (SNSSD) was used in this study, and 616,940 children was selected for analysis. Autism was measured by experienced psychiatrists according to The International Statistical Classification of Diseases and Related Health Problems 10th Revision Symptom Checklist for Mental Disorders. Logistic regression models allowing for multiple demographic and socioeconomic covariates were used to evaluate the association between the level of urbanization and autism in children. Results: Compared with children in low level of urbanization areas, those in high urbanization level areas was 2.12 (95%CI: 1.28, 3.49) times more likely to develop autism, and 1.85(95%CI: 1.21, 2.84) times for those in moderate level of urbanization areas. Stratified analyses found that all observed associations were only in male children, not in female children. Conclusions: Our findings suggested that higher level of urbanicity was associated with higher risk of autism in children. This association was only present in male children, not in female children.

Introduction

Urbanization is a major socio-ecological change and progress in human society, mainly taking place at a massive scale in low- and middle-income countries (Lederbogen et al., 2011). Currently, 55% humans live in urban areas, and this proportion will increase to 68% by 2050, with close to 90% of the increase occurring in Asia and Africa regions (Nations, 2018). Although urbanicity generates wealthy, productivity, improved sanitary conditions and better health care services, it also results in stressful social environment, greater social disparities and serious pollution(Dye, 2008; Lederbogen et al., 2011; Li et al., 2016). These factors related to both health benefits and risks, but mental health is negatively affected (Lederbogen et al., 2011). Recently, urbanization has been well-established to increase the risk of mental health problems, which strongly implicated in mood and anxiety disorders, depression and psychosis (McKenzie, Murray, & Booth, 2013; Sundquist, Frank, & Sundquist, 2004; Vassos, Pedersen, Murray, Collier, & Lewis, 2012). Autism is a neurodevelopmental disorder characterized by social interaction and communication impairments and the restricted and repetitive behaviors (M. B. J. E. c. Lauritsen & psychiatry, 2013).

As human development and urbanization expand across the landscape, increasing numbers of streams are threatened with impairment from disturbance and stresses associated with land use changes. In this investigation, a Bayesian Network (BN) with an expert-informed model structure was developed to predict stream vulnerability to urbanization across a range of biophysical conditions. Primary factors affecting vulnerability were stream buffers, colonization connectivity, agriculture, watershed area, and sand/gravel aquifers. On a scale from 0 to 100 (lowest to highest probability), BN model vulnerability scores ranged from a minimum of 20 to a maximum of 87.5 across the 23,554 stream catchments in our statewide study area. Catchment vulnerability scores were linked with predictions of land development suitability from a second BN model in order to map the locations of streams at risk of impairment from projected future urbanization in two large watersheds in Maine, USA. Our BN synthesis identified 5% of the streams that are at risk based on two assessment criteria: (1) their catchments have projected future impervious cover (IC) levels greater than 6% and (2) the stream catchments have predicted vulnerability scores in the highest quartile of the BN model probability distribution. These at-risk streams represent priority targets for proactive monitoring, management, and conservation efforts to avoid future degradation and expensive restoration costs. This study laid the conceptual groundwork for using BN spatial models to identify streams that are not only vulnerable to urbanization, but are also located in catchments classified with a high probability of development suitability and future urbanization.

Introduction

An undeveloped forested watershed can tolerate only a limited amount of urbanization and human development activity before symptoms of stress and degradation begin to appear in downstream aquatic ecosystems. However, the response of streams to anthropogenic land use changes can vary as a function of watershed biophysical conditions that influence resistance or resilience properties of the coupled catchment and stream system (Alberti and Marzluff 2004; McCluney et al., 2014; Utz et al., 2016). In general, one would expect the streams at highest risk of impairment from development to be those with watershed characteristics that confer low resistance or high vulnerability to changing land use conditions or urbanization. Here, resistance refers to the ability of an ecosystem to resist change and to maintain structure and function despite increased exposure to stressors (Pearsons and Li 1992; Vieira, Clements, Guevara, & Jacobs, 2004). Conversely, vulnerability describes the sensitivity of a system to a stress and the degree to which the system will experience harm due to exposure to a stressor or perturbation (Besaw et al., 2009; Turner et al., 2003). Resilience describes the ability of a system to recover from disturbance or stress. Under authority of the federal Clean Water Act (CWA) and state water quality standards, the U.S. Environmental Protection Agency (US EPA) and state regulatory agencies endeavor to sustain healthy aquatic resources and to restore the chemical, physical, and biological integrity of waters that have been impaired by urbanization, non-point pollution, or other stressors. In Maine, the Department of Environmental Protection (Maine DEP) monitors the health of streams and determines if they attain water quality standards and criteria associated with four state-defined statutory classes (Courtemanch, Davies, & Laverty, 1989; Danielson et al., 2012; Davies, Drummond, Courtemanch, Tsomides, & Danielson, 2016). If a stream does not attain water quality standards or criteria associated with its designated class, then it may be listed as impaired in the CWA 303(d) inventory of impaired waters (Maine DEP, 2012a). Unfortunately, the economic cost of restoring impaired streams can be substantial − as one example, ongoing restoration of the impaired Long Creek ecosystem in Portland, Maine is projected to cost $14 million (FB Environmental Associates 2009).

In the past three decades, China has experienced vigorous urbanization. However, the economy-centered urbanization caused social issues and is being replaced by the new-type people-centered urbanization. Technology innovation plays a key role in urbanization. Previous studies examine the relationship between technology innovation and the traditional economy-centered urbanization. But the relationship between technology innovation and the new-type people-centered urbanization remains unknown. As such, this paper develops a comprehensive evaluation index system for technology innovation and the new-type urbanization based on the data from Shaanxi province between 2000 and 2014. A variation coefficient method is applied to determine the weight of each index. Then a model is proposed and tested. At the end, conclusions are reached based on the results of the tests on the model. Recommendations for dealing with technology innovation and the new-type urbanization are provided.

Introduction

Urbanization is an integral element of rapid income growth and industrialization throughout the world (Fan, 2017; Feng and Xu, 1992, Feng, 1993, Feng and Xu, 2000; Henderson et al., 2009). According to United Nations (2010), 70% of the world’s population will live in cities in 2030. Technology innovation plays an important role in the process of urbanization. For example, urbanization in the U.K. began at the time when the first technological revolution occurred. During the second and the third technological revolutions, urbanization processed quickly in the U.S. The continuous introduction of foreign technologies and continuous innovation from 1950s to 1970s changed Japan into a science, technology and economic power. The urbanization rate in Japan rose quickly from 33% to 70% in this period. In recent years, industry development has been driven by advanced ICT including IoT (Internet of Things) (Gürdür and Asplund, 2018; Kim, 2017; Li et al., 2015, Li et al., 2018; Oliverio, 2018; Wang et al., 2016; Viriyasitavat and Martin, 2017). Because manufacturing and service production are more efficient when undertaken in urbanized areas, urbanization is critical to the success of modernization (Henderson et al., 2009). Urbanization changes population distribution, production mode, life style, and ecological environment. China’s urbanization has been a notable global event (Li, 2013, 2017; Li and Zhou, 2013). In the past three decades, market reform and globalization drove dramatic growth and structural changes in the Chinese economy, society, and spatial organization (Lin and Yi, 2011). As economy grows, China is experiencing a massive rural-urban migration and expansion of cities and towns (Fan, 2008; Henderson et al., 2009; Hsing, 2010; Lin, 2009; McGee et al., 2007). In 2013, China’s urbanization rate rose to 53.7%, with an urban resident population of 770 million (Wang et al., 2015). However, compared with developed countries which have an average urbanization rate of 80%, the urbanization rate in China is still low. Rapid urbanization has put significant stress on city infrastructure because it may lead to a reduced per-capita access to subsistence resources in urban areas (Homer-Dixon, 1999; Homer-Dixon and Blitt, 1998). According to Buhaug and Urdal (2013), rapid urbanization can seriously constrain local governments’ ability to provide basic public services, including employment, education, housing, electricity, water, sanitation, healthcare, enforcement of law and order. In order to reduce costs, improve efficiencies, and deliver the quality of life, cities rely more on information and communications technology and new working practices (Naphade et al., 2011). As Nam and Pardo (2011a) point out, the smart city approach is emerging as a way to solve tangled and wicked problems inherited in the rapid urbanization (Nam & Pardo, 2011).

The political changes that Serbia underwent in 2000, followed by the shift to a democratic system of governance and a market economy, as well as the country’s renewed openness toward Europe and the world, all brought about fundamental changes to the framework in which spatial development action can be taken. The new socio-economic context altered the understanding of the key pillars of sustainable development and led to changes in the notion of spatial intervention and, consequently, in professional approaches to spatial and urban planning. This left the field wide open for the creation of new knowledge and the education of a cadre of professionals able to rise to the complex new challenges posed in practice in a country undergoing transition. Although a new framework for professional action was created, the basic preconditions for the new planning system to operate remained absent, and consequently, essential changes to the actual practice of urban planning did not occur. An important aspect of this issue is the traditional urban planning education (UPE) within the framework of architecture studies. This means that urban planning practice is traditionally rooted in technical disciplines and oriented toward engineering knowledge and does not recognise the need to incorporate learning from social and environmental sciences. The paper generally deals with the role of academia in improving urban planning in Serbia through the establishment of an innovative master’s degree programme in line with the requirements of contemporary global planning practices and key competences for sustainability. It particularly addresses the issue of how the production of final master’s degree projects, organized as an integrated platform for dialogue, can enhance the capacities of the broader professional community, placing specific emphasis on re-examination of the role of the profession in spatial development, the reach of its actions and outcomes that ought to be produced. The teaching process focused on the education of a new generation of experts enabled to act in a new socio-economic context, and the essential creation of preconditions for change to the planning paradigm within the community of practitioners, government officials, local communities and teachers. The paper will first discuss the concept of UPE for sustainable development (UPESD) in relation to the contemporary understanding of UPE and the requirements of education for sustainable development (ESD) and will then contextualise this concept to the conditions that prevail in post-social transitional countries (PSTCs). This will be followed by a presentation of the pedagogical model (PM) for UPESD tailored to the conditions of PSTCs and an examination of the results of its implementation through a comparative overview of the processes of producing of final master’s degree projects by three generations of students. Finally, the results of the application of the PM will be discussed, and general conclusions will be drawn.

مقاله سال 2016

China’s economic momentum has spiked aggressively in recent years, and the country continues to play a major role in the process of economic globalization. Following such rapid development, environmental damage has become a severe problem and the availability of natural resources has sharply declined. The Chinese Government has increased investment in energy infrastructure construction and pushed for new technologies to supply power more effectively. Energy resources are materials and natural processes that have the ability to provide widespread power under present socioeconomic conditions. Coal and petroleum are two relevant modern energy resources.

Although production efficiency has greatly improved since the Industrial Revolution, the main resources for developing machine production, such as iron ore, coal and petroleum, are still in short supply. Due to its energy-intensive economy, China has the highest national annual energy consumption (Statistical Review of World Energy, 2015). From 2012 to 2013, China’s annual energy consumption increased by 15.4%, to 3.75 billion tons of standard coal equivalent (TCE), of which coal, oil and natural gas accounted for 68%, 19% and 4.4%, respectively (China Statistical Yearbook, 2015). In 2012, China’s energy consumption per 10,000 Yuan GDP was 0.76 TCE, a 6% decrease compared with 2010. This is a result of China recently advocating the development of renewable and clean energy while decreasing the use of nonrenewable energy. It is important to note that many people continue to use less efficient energy sources, such as coal and oil, both of which provide little energy for pollution they create. Despite this, alternative energies with high efficiency and low environmental impact are also becoming more prevalent, including geothermal and hydropower systems, nuclear power, wind power and natural gas.

China’s rapid economic growth and associated high energyconsumption in both imply huge social costs, including water pollution, health and safety problems, poor air quality, and resource shortages on both local and global scales (He et al., 2002). For example, Song et al. (2015a) demonstrated that the economic scale effect significantly explained the increased carbon emissions in the Yangtze River Delta region. Among 500 developed cities in China, fewer than five achieve the World Health Organization standards for air quality. China contains some of the most polluted places in the world. The main reason for these problems is the emission of sulfur dioxide, nitrogen oxides, and smoke from heavy chemical industries, coal plants and the overexploitation of natural resources.