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With the in-depth implementation of the “Belt and Road” initiative (BRI), the investment patterns between Belt and Road countries have also become more complicated. The impact of this complex investment network on regional economic development is also growing. To reveal the complexity of this investment pattern, and to better promote the sustainable development of the region’s economy, this paper used the complex network method to study the foreign direct investment (FDI) network of 50 countries along the Belt and Road from 2003 to 2017, revealing its structural and behavioral characteristics and evolution process. The results showed that the imbalance of the investment network structure is outstanding, and preferential selection behavior is obvious. The Central and Eastern European countries show significant clustering behavior. In addition, the network evolved slowly and followed the “Pareto rule” in the early stages of its evolution. The BRI was a turning point in the evolution process. On this basis, the quadratic assignment procedure (QAP) regression analysis method was used to further study the factors affecting the formation process of this investment pattern. It found that economic development level, geographical distance, and bilateral trade were the main influencing factors. Among them, bilateral trade had the greatest impact on the pattern of network.

With the in-depth implementation of the “Belt and Road” initiative (BRI), the investment patterns between Belt and Road countries have also become more complicated. The impact of this complex investment network on regional economic development is also growing. To reveal the complexity of this investment pattern, and to better promote the sustainable development of the region’s economy, this paper used the complex network method to study the foreign direct investment (FDI) network of 50 countries along the Belt and Road from 2003 to 2017, revealing its structural and behavioral characteristics and evolution process. The results showed that the imbalance of the investment network structure is outstanding, and preferential selection behavior is obvious. The Central and Eastern European countries show significant clustering behavior. In addition, the network evolved slowly and followed the “Pareto rule” in the early stages of its evolution. The BRI was a turning point in the evolution process. On this basis, the quadratic assignment procedure (QAP) regression analysis method was used to further study the factors affecting the formation process of this investment pattern. It found that economic development level, geographical distance, and bilateral trade were the main influencing factors. Among them, bilateral trade had the greatest impact on the pattern of network.

In about 15 years, there is likely to be urban air mobility (UAM) in larger cities across the globe. Air taxis will provide on-demand transportation for individual needs. They will also connect important transportation nodes, such as airports and city centers, as well as providing quick transfers between train stations or a convenient option for crossing rivers and lakes. It is hoped that UAM will help meet today’s political targets of sustainability and decarbonization. However, there are certain threats that could impede the sustainable and thus successful introduction of UAM to our cities, with noise being a prominent limitation. This paper argues that citizens have to be viewed as stakeholders in urban air transportation, regardless of whether they or not intend to use it, and that a concept of resident participatory noise sensing (PNS) will be beneficial to the implementation of UAM. Web-based services and smartphones facilitate the access and updating of current information about local noise distributions, thus enabling them to be used to foster UAM in smart cities.

In about 15 years, there is likely to be urban air mobility (UAM) in larger cities across the globe. Air taxis will provide on-demand transportation for individual needs. They will also connect important transportation nodes, such as airports and city centers, as well as providing quick transfers between train stations or a convenient option for crossing rivers and lakes. It is hoped that UAM will help meet today’s political targets of sustainability and decarbonization. However, there are certain threats that could impede the sustainable and thus successful introduction of UAM to our cities, with noise being a prominent limitation. This paper argues that citizens have to be viewed as stakeholders in urban air transportation, regardless of whether they or not intend to use it, and that a concept of resident participatory noise sensing (PNS) will be beneficial to the implementation of UAM. Web-based services and smartphones facilitate the access and updating of current information about local noise distributions, thus enabling them to be used to foster UAM in smart cities.

When it comes to pollution, we do not usually think about the clothes we wear, but the clothing industry is really endangering our planet. The market economy has transferred the decision-making power of the garment industry from enterprises to consumers. To make the fashion industry sustainable, in addition to technological innovation, it is also necessary to conduct research on the service objects of the industry. Consumer clothing preference research is an important part of the sustainable development of the clothing industry, and it will also have an impact on environmental and design sustainability. Hence, a psychophysical experiment based on solid color shirts is carried out to analyze people’s perceptions and preferences concerning fashion colors, including the aesthetic differences and similarities between males and females, and establish a hierarchical feed-forward model of color preferences relating to solid color shirts. Firstly, 480 colors of solid shirts from different clothing brands were collected, and the mean shift clustering algorithm was used to classify them into 19 clusters in the CIELAB color space. Secondly, another 22 solid colors, combined with the 19 colors of the cluster centers, formed a solid color scheme. Thirdly, 41 solid male and female shirts and fabrics were simulated as stimuli in three dimensions, and they were presented on a calibrated computer display. The simulations were assessed by 34 observers (consisting of 17 males and 17 females) in terms of 11 semantic scales, including cold/warm, heavy/light, passive/active, dirty/clean, tense/relaxed, plain/gaudy, traditional/modern, masculine/feminine, slim-look/fat-look, hard-to-match/easy-to-match, and dislike/tike. The experimental results demonstrated that the hard-to-match/easy-to-match response was found to be highly correlated with dislike/like. Furthermore, the response of the females concerning hard-to-match/easy-to-match had a strong correlation with two adjective pairs (plain/gaudy and slim-look/fat-look), while that of the males also had a strong correlation with two adjective pairs (plain/ gaudy and masculine/feminine). Finally, a hierarchical feed-forward model of aesthetic perception for solid color shirts was established to predict the shirt preference degree. These findings could be used to develop a more robust and comprehensive theory of fashion color preferences and provide a reference for the design of solid color shirts. A more comprehensive color preference theory is not only an effective tool to solve the problem of pollution in the clothing industry, but also an important theoretical basis for the “sustainable design” of clothing, which is of great significance to the sustainable development of the clothing industry.

When it comes to pollution, we do not usually think about the clothes we wear, but the clothing industry is really endangering our planet. The market economy has transferred the decision-making power of the garment industry from enterprises to consumers. To make the fashion industry sustainable, in addition to technological innovation, it is also necessary to conduct research on the service objects of the industry. Consumer clothing preference research is an important part of the sustainable development of the clothing industry, and it will also have an impact on environmental and design sustainability. Hence, a psychophysical experiment based on solid color shirts is carried out to analyze people’s perceptions and preferences concerning fashion colors, including the aesthetic differences and similarities between males and females, and establish a hierarchical feed-forward model of color preferences relating to solid color shirts. Firstly, 480 colors of solid shirts from different clothing brands were collected, and the mean shift clustering algorithm was used to classify them into 19 clusters in the CIELAB color space. Secondly, another 22 solid colors, combined with the 19 colors of the cluster centers, formed a solid color scheme. Thirdly, 41 solid male and female shirts and fabrics were simulated as stimuli in three dimensions, and they were presented on a calibrated computer display. The simulations were assessed by 34 observers (consisting of 17 males and 17 females) in terms of 11 semantic scales, including cold/warm, heavy/light, passive/active, dirty/clean, tense/relaxed, plain/gaudy, traditional/modern, masculine/feminine, slim-look/fat-look, hard-to-match/easy-to-match, and dislike/tike. The experimental results demonstrated that the hard-to-match/easy-to-match response was found to be highly correlated with dislike/like. Furthermore, the response of the females concerning hard-to-match/easy-to-match had a strong correlation with two adjective pairs (plain/gaudy and slim-look/fat-look), while that of the males also had a strong correlation with two adjective pairs (plain/ gaudy and masculine/feminine). Finally, a hierarchical feed-forward model of aesthetic perception for solid color shirts was established to predict the shirt preference degree. These findings could be used to develop a more robust and comprehensive theory of fashion color preferences and provide a reference for the design of solid color shirts. A more comprehensive color preference theory is not only an effective tool to solve the problem of pollution in the clothing industry, but also an important theoretical basis for the “sustainable design” of clothing, which is of great significance to the sustainable development of the clothing industry.

Abstract With the development of China’s economy, the total area of public buildings is continuously increasing, and higher energy consumption in this field is gradually becoming prominent. In order to improve this situation, China has been working on energy savings in public buildings since 2007. Energy-saving models and energy policies have become the lifeline of sustainable energy development. Furthermore, the energy-saving renovation of public buildings, as a key project, has attracted the attention of governments and scholars. In the report of the 18th National Congress of the Communist Party of China, energy conservation and emission reduction were given a prominent place. The 13th Five-Year Plan (2016-2020) aims to further promote China’s energy conservation work. On the one hand, administrative and legal measures should be taken to strengthen energy conservation. On the other hand, it is necessary to speed up the establishment of a long-term market-based energy-saving mechanism and to rely on market forces to remove various obstacles to energy conservation. Energy performance contracting (EPC) is a practical and effective method applied in energy conservation. Therefore, this research aims to put forward a relatively perfect energy-saving risk management system for EPC public building projects based on analysis of the factors influencing investment risks of energy-saving projects and the use of quantifiable risk management models, thus providing some theoretical support for the risk decision-making of energy service companies.