China's Urban Future
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Multi-Scale Analysis of Green Space for Human Settlement Sustainability in Urban Areas of the Inner Mongolia Plateau, China
sustainability Article Multi-Scale Analysis of Green Space for Human Settlement Sustainability in Urban Areas of the Inner Mongolia Plateau, China Wenfeng Chi 1,2, Jing Jia 1,2, Tao Pan 3,4,5,* , Liang Jin 1,2 and Xiulian Bai 1,2 1 College of resources and Environmental Economics, Inner Mongolia University of Finance and Economics, Inner Mongolia, Hohhot 010070, China; [email protected] (W.C.); [email protected] (J.J.); [email protected] (L.J.); [email protected] (X.B.) 2 Resource Utilization and Environmental Protection Coordinated Development Academician Expert Workstation in the North of China, Inner Mongolia University of Finance and Economics, Inner Mongolia, Hohhot 010070, China 3 College of Geography and Tourism, Qufu Normal University, Shandong, Rizhao 276826, China 4 Department of Geography, Ghent University, 9000 Ghent, Belgium 5 Land Research Center of Qufu Normal University, Shandong, Rizhao 276826, China * Correspondence: [email protected]; Tel.: +86-1834-604-6488 Received: 19 July 2020; Accepted: 18 August 2020; Published: 21 August 2020 Abstract: Green space in intra-urban regions plays a significant role in improving the human habitat environment and regulating the ecosystem service in the Inner Mongolian Plateau of China, the environmental barrier region of North China. However, a lack of multi-scale studies on intra-urban green space limits our knowledge of human settlement environments in this region. In this study, a synergistic methodology, including the main process of linear spectral decomposition, vegetation-soil-impervious surface area model, and artificial digital technology, was established to generate a multi-scale of green space (i.e., 15-m resolution intra-urban green components and 0.5-m resolution park region) and investigate multi-scale green space characteristics as well as its ecological service in 12 central cities of the Inner Mongolian Plateau. -
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sustainability Article Efficiency Loss and Intensification Potential of Urban Industrial Land Use in Three Major Urban Agglomerations in China Xiangdong Wang 1,2,3,* , Xiaoqiang Shen 1,2 and Tao Pei 3 1 College of Management, Lanzhou University, Lanzhou 730000, China; [email protected] 2 Institute for Studies in County Economy Development, Lanzhou University, Lanzhou 730000, China 3 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; [email protected] * Correspondence: [email protected] Received: 24 December 2019; Accepted: 20 February 2020; Published: 22 February 2020 Abstract: In recent decades, efficiency and intensification have emerged as hot topics within urban industrial land use (UILU) studies in China. However, the measurement and analysis of UILU efficiency and intensification are not accurate and in-depth enough. The study of UILU efficiency loss and intensification potential and their relationship is still lacking, and the application of parametric methods with clearer causal mechanisms is insufficient. This paper argued that the intensification potential of UILU could be defined as the amount of saved land or output growth resulting from reduced efficiency loss of UILU. Accordingly, we constructed quantitative models for measuring and evaluating the intensification potential of UILU, using the stochastic frontier analysis (SFA) method to calculate efficiency loss in three major urban agglomerations (38 cities) in China. Our results revealed a large scale and an expanding trend in the efficiency loss and intensification potential of UILU in three major urban agglomerations in China. From 2003 to 2016, the annual efficiency loss of UILU was 31.56%, the annual land-saving potential was 979.98 km2, and the annual output growth potential was 8775.23 billion Yuan (referring to the constant price for 2003). -
Changchun–Harbin Expressway Project
Performance Evaluation Report Project Number: PPE : PRC 30389 Loan Numbers: 1641/1642 December 2006 People’s Republic of China: Changchun–Harbin Expressway Project Operations Evaluation Department CURRENCY EQUIVALENTS Currency Unit – yuan (CNY) At Appraisal At Project Completion At Operations Evaluation (July 1998) (August 2004) (December 2006) CNY1.00 = $0.1208 $0.1232 $0.1277 $1.00 = CNY8.28 CNY8.12 CNY7.83 ABBREVIATIONS AADT – annual average daily traffic ADB – Asian Development Bank CDB – China Development Bank DMF – design and monitoring framework EIA – environmental impact assessment EIRR – economic internal rate of return FIRR – financial internal rate of return GDP – gross domestic product ha – hectare HHEC – Heilongjiang Hashuang Expressway Corporation HPCD – Heilongjiang Provincial Communications Department ICB – international competitive bidding JPCD – Jilin Provincial Communications Department JPEC – Jilin Provincial Expressway Corporation MOC – Ministry of Communications NTHS – national trunk highway system O&M – operations and maintenance OEM – Operations Evaluation Mission PCD – provincial communication department PCR – project completion report PPTA – project preparatory technical assistance PRC – People’s Republic of China RRP – report and recommendation of the President TA – technical assistance VOC – vehicle operating cost NOTE In this report, “$” refers to US dollars. Keywords asian development bank, development effectiveness, expressways, people’s republic of china, performance evaluation, heilongjiang province, jilin province, transport Director Ramesh Adhikari, Operations Evaluation Division 2, OED Team leader Marco Gatti, Senior Evaluation Specialist, OED Team members Vivien Buhat-Ramos, Evaluation Officer, OED Anna Silverio, Operations Evaluation Assistant, OED Irene Garganta, Operations Evaluation Assistant, OED Operations Evaluation Department, PE-696 CONTENTS Page BASIC DATA v EXECUTIVE SUMMARY vii MAPS xi I. INTRODUCTION 1 A. -
The First Real-Estate Development by Japanese Developers in Changchun, Jilin Province, China Marubeni Coporation and Mitsubishi Jisho Residence Co., Ltd
July 18, 2013 Marubeni Corporation Mitsubishi Jisho Residence Co., Ltd. The First Real-Estate Development by Japanese Developers in Changchun, Jilin Province, China Marubeni Coporation and Mitsubishi Jisho Residence Co., Ltd. set off the Joint Development –“Changchun Jingyue Project (Tentative)” <Perspective of the project> Marubeni Corporation (“Marubeni”) and Mitsubishi Jisho Residence Co., Ltd. (“Mitsubishi Jisho Residence”), as the first Japanese developers, plan to implement a real-estate development project with Jilin Weifeng Industry Co., Ltd. (“Weifeng”), a local Chinese developer, in Changchun, China. This project, as our first project in Changchun, with an area of 130,000 square meters, is located in Changchun Jingyue National High-tech Industrial Development Zone (“Jingyue DZ”), concentrating on Town House and Residential. The Project Company, Changchun Top Chance Property Development Co., Ltd. (“Changchun Top Chance”) owned by Marubeni (40%), Weifeng (35%) and Mitsubishi Jisho Residence (25%), has started the construction for the release this coming fall. Changchun is the capital of Jilin Province, also a core city in the northeastern part of China, with a population of 7,620,000. It is administered as one of 15 sub-provincial cities which are independent and equivalent to provinces. Having a solid industrial basis including automobile manufacturing as typified by FAW (First Automotive Works) Group, along with manufacturing transportation facilities and processing agricultural products, Changchun is continuing double digit economic growth, which is higher than the national average. Jingyue DZ is a national-level development zone approved by the State Council in August, 2012, with an area of 479 square kilometers, of which about half of the area, 243 square kilometers, consists of forest and a lake. -
Appendix 1: Rank of China's 338 Prefecture-Level Cities
Appendix 1: Rank of China’s 338 Prefecture-Level Cities © The Author(s) 2018 149 Y. Zheng, K. Deng, State Failure and Distorted Urbanisation in Post-Mao’s China, 1993–2012, Palgrave Studies in Economic History, https://doi.org/10.1007/978-3-319-92168-6 150 First-tier cities (4) Beijing Shanghai Guangzhou Shenzhen First-tier cities-to-be (15) Chengdu Hangzhou Wuhan Nanjing Chongqing Tianjin Suzhou苏州 Appendix Rank 1: of China’s 338 Prefecture-Level Cities Xi’an Changsha Shenyang Qingdao Zhengzhou Dalian Dongguan Ningbo Second-tier cities (30) Xiamen Fuzhou福州 Wuxi Hefei Kunming Harbin Jinan Foshan Changchun Wenzhou Shijiazhuang Nanning Changzhou Quanzhou Nanchang Guiyang Taiyuan Jinhua Zhuhai Huizhou Xuzhou Yantai Jiaxing Nantong Urumqi Shaoxing Zhongshan Taizhou Lanzhou Haikou Third-tier cities (70) Weifang Baoding Zhenjiang Yangzhou Guilin Tangshan Sanya Huhehot Langfang Luoyang Weihai Yangcheng Linyi Jiangmen Taizhou Zhangzhou Handan Jining Wuhu Zibo Yinchuan Liuzhou Mianyang Zhanjiang Anshan Huzhou Shantou Nanping Ganzhou Daqing Yichang Baotou Xianyang Qinhuangdao Lianyungang Zhuzhou Putian Jilin Huai’an Zhaoqing Ningde Hengyang Dandong Lijiang Jieyang Sanming Zhoushan Xiaogan Qiqihar Jiujiang Longyan Cangzhou Fushun Xiangyang Shangrao Yingkou Bengbu Lishui Yueyang Qingyuan Jingzhou Taian Quzhou Panjin Dongying Nanyang Ma’anshan Nanchong Xining Yanbian prefecture Fourth-tier cities (90) Leshan Xiangtan Zunyi Suqian Xinxiang Xinyang Chuzhou Jinzhou Chaozhou Huanggang Kaifeng Deyang Dezhou Meizhou Ordos Xingtai Maoming Jingdezhen Shaoguan -
Supplement of Modeling Diurnal Variation of Surface PM2.5
Supplement of Atmos. Chem. Phys., 20, 2839–2863, 2020 https://doi.org/10.5194/acp-20-2839-2020-supplement © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License. Supplement of Modeling diurnal variation of surface PM2:5 concentrations over East China with WRF-Chem: impacts from boundary-layer mixing and anthropogenic emission Qiuyan Du et al. Correspondence to: Chun Zhao ([email protected]) The copyright of individual parts of the supplement might differ from the CC BY 4.0 License. Supporting materials for “Modeling diurnal variation of surface PM2.5 concentration over East China with WRF-Chem: Impacts from boundary layer mixing and anthropogenic emission” Figure S1. Spatial distribution of peak diurnal index of surface PM2.5 concentrations in the four months from experiments CTL1, CTL2, and CTL3. The observations are shown as the color filled circles. The observations at the stations within one city are averaged and shown as one circle as they are too close to be shown distinctly. Figure S2. Comparison between monthly mean surface PM2.5 concentrations and diurnal index of surface PM2.5 concentrations at each observational site over the YRD region of East China (within black box of Fig. 1a) for April and October from observations and experiments CTL1, CTL2, and CTL3. Figure S3a. Relative contribution (normalized by monthly mean surface PM2.5 concentrations for each month) to surface PM2.5 concentrations every 3-hour from individual process (transport, emission, dry and wet deposition, PBL mixing, chemical production/loss) averaged over Nanjing(a) for January, April, July, and October of 2018 from experiments CTL1, CTL2, and CTL3. -
Changsha:Gateway to Inland China
0 ︱Changsha: Gateway to Inland China Changsha Gateway to Inland China Changsha Investment Environment Report 2013 0 1 ︱ Changsha: Gateway to Inland China Changsha Changsha is a central link between the coastal areas and inland China ■ Changsha is the capital as well as the economic, political and cultural centre of Hunan province. It is also one of the largest cities in central China(a) ■ Changsha is located at the intersection of three major national high- speed railways: Beijing-Guangzhou railway, Shanghai-Kunming railway (to commence in 2014) and Chongqing-Xiamen railway (scheduled to start construction before 2016) ■ As one of China’s 17 major regional logistics hubs, Changsha offers convenient access to China’s coastal areas; Hong Kong is reachable by a 1.5-hour flight or a 3-hour ride by CRH (China Railways High-speed) Changsha is well connected to inland China and the world economy(b) Domestic trade (total retail Total value of imports and CNY 245.5 billion USD 8.7 billion sales of consumer goods) exports Value of foreign direct Total value of logistics goods CNY 2 trillion, 19.3% investment and y-o-y USD 3.0 billion, 14.4% and y-o-y growth rate growth rate Total number of domestic Number of Fortune 500 79.9 million, 34.7% tourists and y-o-y growth rate companies with direct 49 investment in Changsha Notes: (a) Central China area includes Hunan Province, Hubei Province, Jiangxi Province, Anhui Province, Henan Province and Shanxi Province (b) Figures come from 2012 statistics Sources: Changsha Bureau of Commerce; Changsha 2012 National Economic and Social Development Report © 2013 KPMG Advisory (China) Limited, a wholly foreign owned enterprise in China and a member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative ("KPMG International"), a Swiss entity. -
Bay to Bay: China's Greater Bay Area Plan and Its Synergies for US And
June 2021 Bay to Bay China’s Greater Bay Area Plan and Its Synergies for US and San Francisco Bay Area Business Acknowledgments Contents This report was prepared by the Bay Area Council Economic Institute for the Hong Kong Trade Executive Summary ...................................................1 Development Council (HKTDC). Sean Randolph, Senior Director at the Institute, led the analysis with support from Overview ...................................................................5 Niels Erich, a consultant to the Institute who co-authored Historic Significance ................................................... 6 the paper. The Economic Institute is grateful for the valuable information and insights provided by a number Cooperative Goals ..................................................... 7 of subject matter experts who shared their views: Louis CHAPTER 1 Chan (Assistant Principal Economist, Global Research, China’s Trade Portal and Laboratory for Innovation ...9 Hong Kong Trade Development Council); Gary Reischel GBA Core Cities ....................................................... 10 (Founding Managing Partner, Qiming Venture Partners); Peter Fuhrman (CEO, China First Capital); Robbie Tian GBA Key Node Cities............................................... 12 (Director, International Cooperation Group, Shanghai Regional Development Strategy .............................. 13 Institute of Science and Technology Policy); Peijun Duan (Visiting Scholar, Fairbank Center for Chinese Studies Connecting the Dots .............................................. -
Deciphering the Spatial Structures of City Networks in the Economic Zone of the West Side of the Taiwan Strait Through the Lens of Functional and Innovation Networks
sustainability Article Deciphering the Spatial Structures of City Networks in the Economic Zone of the West Side of the Taiwan Strait through the Lens of Functional and Innovation Networks Yan Ma * and Feng Xue School of Architecture and Urban-Rural Planning, Fuzhou University, Fuzhou 350108, Fujian, China; [email protected] * Correspondence: [email protected] Received: 17 April 2019; Accepted: 21 May 2019; Published: 24 May 2019 Abstract: Globalization and the spread of information have made city networks more complex. The existing research on city network structures has usually focused on discussions of regional integration. With the development of interconnections among cities, however, the characterization of city network structures on a regional scale is limited in the ability to capture a network’s complexity. To improve this characterization, this study focused on network structures at both regional and local scales. Through the lens of function and innovation, we characterized the city network structure of the Economic Zone of the West Side of the Taiwan Strait through a social network analysis and a Fast Unfolding Community Detection algorithm. We found a significant imbalance in the innovation cooperation among cities in the region. When considering people flow, a multilevel spatial network structure had taken shape. Among cities with strong centrality, Xiamen, Fuzhou, and Whenzhou had a significant spillover effect, which meant the region was depolarizing. Quanzhou and Ganzhou had a significant siphon effect, which was unsustainable. Generally, urbanization in small and midsize cities was common. These findings provide support for government policy making. Keywords: city network; spatial organization; people flows; innovation network 1. -
Reliability Optimization of a Railway Network
sustainability Article Reliability Optimization of a Railway Network Xuelei Meng 1,2,*, Yahui Wang 3, Limin Jia 2 and Lei Li 4 1 School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China 2 State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China; [email protected] 3 School of Foreign Languages, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China; [email protected] 4 Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Province, Zhejiang Normal University, Jinhua 321004, Zhejiang, China; [email protected] * Correspondence: [email protected] Received: 7 October 2020; Accepted: 14 November 2020; Published: 24 November 2020 Abstract: With the increase of the railway operating mileage, the railway network is becoming more and more complicated. We expect to build more railway lines to offer the possibility to offer more high quality service for the passengers, while the investment is often limited. Therefore, it is very important to decide the pairs of cities to add new railway lines under the condition of limited construction investment in order to optimize the railway line network to maximize the reliability of the railway network to deal with the railway passenger transport task under emergency conditions. In this paper, we firstly define the reliability of the railway networks based on probability theory by analyzing three minor cases. Then we construct a reliability optimization model for the railway network to solve the problem, expecting to enhance the railway network with the limited investment. The goal is to make an optimal decision when choosing where to add new railway lines to maximize the reliability of the whole railway network, taking the construction investment as the main constraint, which is turned to the building mileage limit. -
Choosing Entry Mode to Mainland China
東海管理評論【特刊】 民國一百年,第十二卷,第一期,71-120 Choosing Entry Mode to Mainland China Joung -Yol Lin*, Batchuluun AMRITA** Abstract The Economic Cooperation Framework Agreements (ECFA) is an agreement between the Republic of China (Taiwan) and People’s Republic of China (Mainland China), which was signed on June 29, 2010. The ECFA can have a far reaching impact on bilateral businesses relationship of the two parties, further strengthening the financial infrastructure and enhancing financial stability. Currently, thirteen Taiwanese banks meet the capital adequacy or stake acquisition requirements in the mainland China; on the contrary, five mainland Chinese banks meet the capital adequacy and operation experience requirements for opening a representative office in Taiwan. Consequently, a merger of banks and related options between the two regions are under discussion. In a review of the ECFA and other reports, Taiwanese banks will be able to progress further on the banking business in the mainland market within 2 years. However, there are still many uncertainties and questions concerning bank characteristics after ECFA; such as competitive position, market efficiency, long term returns and dimensional stability. This paper investigates theoretical and empirical studies and application of PESTEL analysis on the major factors in the macro environment of China. Specific attention is made in regards to the securities, banking and insurance aspects. The vital finding of this study is investigation of the entry mode strategy for the Chinese market with a long –term vision to foster into global competition. Finally, in order to intensify the competitive advantage, this paper explores a viable model for Taiwanese Banks to structure their products and services upon. -
Reconstructing the Evolutionary History of Chinese Dialects
Reconstructing the Evolutionary History of Chinese Dialects Esra Erdem Institute of Information Systems, Vienna University of Technology, Vienna, Austria [email protected] Feng Wang Department of Chinese Language and Literature, Peking University, Beijing, China [email protected] Evolutionary relations between languages based on their shared characteristics can be represented as a phylogeny --- a tree where the leaves represent the extant languages, the internal vertices represent the ancestral languages, and the edges represent the genetic relations between the languages. On the other hand, languages not only inherit characteristics from their ancestors but also sometimes borrow them from other languages. Such borrowings can be represented by additional non-tree edges, turning a phylogeny into a phylogenetic network. With this motivation, we reconstruct the evolutionary history of languages in two steps: first we compute a plausible phylogeny with a minimal number of incompatible characters, and then we turn this phylogeny into a perfect phylogenetic network, by adding a small number of lateral edges, so that all characters are compatible with the network. For both steps, to formulate the problems and to solve them, we use answer set programming --- a new form of declarative programming. This method has been successfully applied to reconstruct the evolutionary history of Indo-European languages. In the following we summarize its application to reconstruct the evolutionary history of Old Chinese and the following 23 Chinese dialects: Guangzhou, Liancheng, Meixian, Taiwan, Xiamen, Zhangping, Fuzhou, Nanchang, Anyi, Shuangfeng, Changsha, Beijing, Yuci, Taiyuan, Ningxia, Chengdu, Yingshan, Wuhan, Ningbo, Suzhou, Shangai 1, Shangai 2, Wenzhou. We have started with a dataset consisting of 200 lexical characters (the Swadesh wordlist), each with 1--24 states.