{"title":"Collaborative Network Topologies in Spatial Economies","authors":"Shaun Lichter, T. Friesz, C. Griffin, Amir Bagherzadeh","doi":"10.1007/s11067-022-09564-x","DOIUrl":"https://doi.org/10.1007/s11067-022-09564-x","url":null,"abstract":"","PeriodicalId":54733,"journal":{"name":"Networks & Spatial Economics","volume":"22 1","pages":"439 - 459"},"PeriodicalIF":2.4,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46522614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Circular Economy Model of Economic Growth with Circular and Cumulative Causation and Trade","authors":"K. Donaghy","doi":"10.1007/s11067-022-09559-8","DOIUrl":"https://doi.org/10.1007/s11067-022-09559-8","url":null,"abstract":"","PeriodicalId":54733,"journal":{"name":"Networks & Spatial Economics","volume":"22 1","pages":"461 - 488"},"PeriodicalIF":2.4,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41945987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Targeted Advertising in the Public Transit Network Using Smart Card Data","authors":"H. Faroqi, M. Mesbah, Jiwon Kim, A. Khodaii","doi":"10.1007/s11067-022-09558-9","DOIUrl":"https://doi.org/10.1007/s11067-022-09558-9","url":null,"abstract":"","PeriodicalId":54733,"journal":{"name":"Networks & Spatial Economics","volume":"22 1","pages":"97 - 124"},"PeriodicalIF":2.4,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41882966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Time Evolution of City Distributions in Germany","authors":"K. Ikeda, M. Osawa, Y. Takayama","doi":"10.1007/s11067-021-09557-2","DOIUrl":"https://doi.org/10.1007/s11067-021-09557-2","url":null,"abstract":"","PeriodicalId":54733,"journal":{"name":"Networks & Spatial Economics","volume":"22 1","pages":"125 - 151"},"PeriodicalIF":2.4,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"52593497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Granular Local Search Matheuristic for a Heterogeneous Fleet Vehicle Routing Problem with Stochastic Travel Times","authors":"Ramon Faganello Fachini, V. Armentano, F. Toledo","doi":"10.1007/s11067-021-09553-6","DOIUrl":"https://doi.org/10.1007/s11067-021-09553-6","url":null,"abstract":"","PeriodicalId":54733,"journal":{"name":"Networks & Spatial Economics","volume":"22 1","pages":"33 - 64"},"PeriodicalIF":2.4,"publicationDate":"2022-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49527099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article analyzes the issues of energy cooperation between Russia and China in connection with the ‘green transition’ in China, China reaching the peak level of emissions in 2030 and its transition to carbon neutrality by 2060. In the foreseeable future, the key area of energy cooperation between the two countries will be the gas sector, with natural gas is being considered as a ‘transitional’ fuel on the way from coal to renewable energy sources. The Chinese economy is actively moving to the use of gas in the energy and residential sectors. At the same time, considering the scale of the Chinese economy, coal will be in demand for a long time, since technological and economic reasons make it difficult to abandon this raw material quickly in favor of less carbon-intensive types of energy resources. Against this background, the Russian fuel and energy industry can avail of the contradictory trends in the energy sector of China – the existing desire for development with low-carbohydrate emissions and current significant volumes of coal generation. This creates a stable basis for the development of bilateral energy cooperation for the upcoming decades. Russia and China have different views on low-carbon development, which is dictated by the different role of energy resources in the economy of each country. China seeks self-sufficiency in supply and therefore purposefully follows the path of the ‘green transition’, while Russia proceeds from the relative duration of the era of non-renewable energy resources. For this reason, ‘green’ projects in Russia are still more related to environmental care within the framework of individual projects ‘on the ground’, and not with a systematic movement towards decarbonization of the energy industry
{"title":"The Fuel and Energy Industry of China and Russia in the Context of the Transition to the Low-Carbon Development Trajectory","authors":"V. Kryukov, Y. Kryukov","doi":"10.14530/se.2022.3.141-167","DOIUrl":"https://doi.org/10.14530/se.2022.3.141-167","url":null,"abstract":"The article analyzes the issues of energy cooperation between Russia and China in connection with the ‘green transition’ in China, China reaching the peak level of emissions in 2030 and its transition to carbon neutrality by 2060. In the foreseeable future, the key area of energy cooperation between the two countries will be the gas sector, with natural gas is being considered as a ‘transitional’ fuel on the way from coal to renewable energy sources. The Chinese economy is actively moving to the use of gas in the energy and residential sectors. At the same time, considering the scale of the Chinese economy, coal will be in demand for a long time, since technological and economic reasons make it difficult to abandon this raw material quickly in favor of less carbon-intensive types of energy resources. Against this background, the Russian fuel and energy industry can avail of the contradictory trends in the energy sector of China – the existing desire for development with low-carbohydrate emissions and current significant volumes of coal generation. This creates a stable basis for the development of bilateral energy cooperation for the upcoming decades. Russia and China have different views on low-carbon development, which is dictated by the different role of energy resources in the economy of each country. China seeks self-sufficiency in supply and therefore purposefully follows the path of the ‘green transition’, while Russia proceeds from the relative duration of the era of non-renewable energy resources. For this reason, ‘green’ projects in Russia are still more related to environmental care within the framework of individual projects ‘on the ground’, and not with a systematic movement towards decarbonization of the energy industry","PeriodicalId":54733,"journal":{"name":"Networks & Spatial Economics","volume":"32 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88647608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Spatial Analysis of Socio-Economic Systems: Genesis and Current State","authors":"Viacheslav Seliverstov","doi":"10.14530/se.2022.1.192-198","DOIUrl":"https://doi.org/10.14530/se.2022.1.192-198","url":null,"abstract":"","PeriodicalId":54733,"journal":{"name":"Networks & Spatial Economics","volume":"3 2","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72390147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article provides a comparative analysis of the existing spatial structure of urban settlement of the two largest agglomerations of our country – Moscow and St. Petersburg. Quantitative (dynamics of the number of settlements with the status of a city, dynamics of the population of cities, the number of houses under construction and the total area of residential units under construction) and qualitative (analysis of the automobile and railway networks) indicators are used. The following scientific methods are used in the work: statistical, geostructural, cartographic modeling, grouping method. 2002–2021 the two agglomerations are characterized by positive trends in population growth, which are associated with migration growth. The supporting framework of the Moscow agglomeration is represented by a uniform radial-circular type with a mixed monopolycentric model with a dominant core and the presence of large sub-centers, while the St. Petersburg agglomeration is a coastal type with a pronounced monocentric model and exclusive dominance of the core. On the basis of the existing supporting frames, to determine the stages of development of two agglomerations, a spatial typology of cities was carried out depending on the degree of their remoteness from the corresponding central points (nuclei). As a result, five orders (belts) with averaged transport availability isochrones were identified. Transformation processes of staged agglomeration development proceed most intensively within the entire Moscow agglomeration, which is at the stage of suburbanization. In the St. Petersburg agglomeration, the most active urbanized zone are cities located at distances of up to 50 km from the central point of the core, and it is characterized by a transition from the urbanization stage to the suburbanization stage
{"title":"Comparative Analysis of the Spatial Structures of the Moscow and St. Petersburg Agglomerations","authors":"D. Olifir","doi":"10.14530/se.2022.1.073-100","DOIUrl":"https://doi.org/10.14530/se.2022.1.073-100","url":null,"abstract":"The article provides a comparative analysis of the existing spatial structure of urban settlement of the two largest agglomerations of our country – Moscow and St. Petersburg. Quantitative (dynamics of the number of settlements with the status of a city, dynamics of the population of cities, the number of houses under construction and the total area of residential units under construction) and qualitative (analysis of the automobile and railway networks) indicators are used. The following scientific methods are used in the work: statistical, geostructural, cartographic modeling, grouping method. 2002–2021 the two agglomerations are characterized by positive trends in population growth, which are associated with migration growth. The supporting framework of the Moscow agglomeration is represented by a uniform radial-circular type with a mixed monopolycentric model with a dominant core and the presence of large sub-centers, while the St. Petersburg agglomeration is a coastal type with a pronounced monocentric model and exclusive dominance of the core. On the basis of the existing supporting frames, to determine the stages of development of two agglomerations, a spatial typology of cities was carried out depending on the degree of their remoteness from the corresponding central points (nuclei). As a result, five orders (belts) with averaged transport availability isochrones were identified. Transformation processes of staged agglomeration development proceed most intensively within the entire Moscow agglomeration, which is at the stage of suburbanization. In the St. Petersburg agglomeration, the most active urbanized zone are cities located at distances of up to 50 km from the central point of the core, and it is characterized by a transition from the urbanization stage to the suburbanization stage","PeriodicalId":54733,"journal":{"name":"Networks & Spatial Economics","volume":"15 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77982720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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