ABSTRACT
Since 2022, the journal Izvestiya Rossiiskoi Akademii Nauk. Seriya Geograficheskaya is publishing special issues on important topics in geography. The comprehensive special section offered to readers' attention is another innovation of the journal. This collection has been prepared for the centennial of the International Geographical Union and its congress in Paris (July 2022). It includes seven review articles. Their English translations are simultaneously published in the journal Regional Research of Russia, no. 1 for 2022. These circumstances determine articles' genre and fundamental features. The topics of the articles were chosen so that the special section reflects areas in which the most striking results have been achieved and which are specific to Russia. The authors are leading Russian experts in their respective fields. They have tried, as far as possible, to compare the topics, methodological approaches, and research results with the world mainstream. The main focus is on the last decade, 2010–2021;when necessary, the authors also have referred to earlier publications. The authors of most of the articles pay attention to the peculiarities of spatial development that arose in 2020–2021 due to the COVID-19 pandemic. © 2022 Izvestiya Rossiiskoi Akademii Nauk. Seriya Geograficheskaya. All rights reserved.
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Under the dual pressure of "slow-burn" challenges and acute shocks, increasing economic resilience is gaining attention around the world to ensure the security and stability of economic activities. With the goal of achieving sustainable development, the China is exploring an innovative, coordinated, green, open, shared and secure development path for the regional economy. Using panel data for 241 cities at the prefecture level and above in China from 2010 to 2019, this research considers urban agglomeration planning as a quasi-natural experiment of regional integration and use a difference-in-differences method to explore the effect of regional integration on economic resilience. The results show the following. 1) Regional integration does improve economic resilience after various robustness tests. 2) The policy effect of regional integration on economic resilience varies by time, region, and urban structure. 3) Urban size structure and industrial structure are important ways in which regional integration affects economic resilience. Our findings enrich the theoretical study of the relationship between regional integration and economic resilience and provide a new path to improve regional economic resilience and achieve sustainable development.
ABSTRACT
The connection between enterprises is an important part of urban connection. Strengthening the analysis of urban functional network based on the connection between enterprises is of great significance to enrich the theoretical research of urban network. Based on the trade relationship data between listed companies and their top five customers from 2010 to 2020, this paper constructs China's urban network, and analyzes the spatio- temporal evolution characteristics of urban network based on the perspective of trade links between enterprises. The research shows that: ① From 2010 to 2020, the urban network scale shows the characteristics of first rising and then falling, and the overall network density is low, ranging from 0.014 to 0.018. The center of gravity of the network presents the trend of "S" - shaped spatial trajectory change and overall southward movement.This feature is consistent with the trend of China's economic center moving southward in recent years. The overall spatial structure of the network changes from coastal to "T" - shaped structure. This feature is consistent with the "T" strategy of China's land development. ② The network traffic is concentrated in a few node cities. The total amount of capital in and out of the top 20 cities accounts for 71.9% of the total capital flow. Beijing and Shanghai are the absolute core of the network. The provincial capitals or sub provincial cities such as Hangzhou, Wuhan, Shenzhen and Guangzhou assume the function of regional centers. Foshan, Qiqihar, Nantong and other manufacturing developed cities are important nodes. It indicates that trade links are more likely to occur in cities with high administrative levels or developed industries. ③ The Pearl River Delta has the highest network density, which is between 0.324 and 0.334. The Yangtze River Delta has the highest total trade flow, which is 78.35 billion yuan. Although the networking level of urban agglomeration in the middle reaches of the Yangtze River and Chengdu Chongqing urban agglomeration is relatively low, they have become an important force to promote the evolution of network structure. ④ The COVID-19 has had a significant impact on the trade flow and network structure of the overall network. The network associations have been further divided and reorganized. The Guangzhou Shenzhen associations have been significantly strengthened. It shows that Guangzhou and Shenzhen have a strong combination effect. The Shanghai associations have been significantly weakened. The research results have a certain reference value for promoting the construction of domestic big cycle and unified big market. © 2023 Journal of Geo-Information Science. All rights reserved.
ABSTRACT
The analysis of the daily spatial patterns of near-surface Nitrogen dioxide (NO2) concentrations can assist decision makers mitigate this common air pollutant in urban areas. However, comparative analysis of NO2 estimates in different urban agglomerations of China is limited. In this study, a new linear mixed effect model (LME) with multi-source spatiotemporal data is proposed to estimate daily NO2 concentrations at high accuracy based on the land-use regression (LUR) model and Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) products. In addition, three models for NO2 concentration estimation were evaluated and compared in four Chinese urban agglomerations from 2018 to 2020, including the COVID-19 closed management period. Each model included a unique combination of methods and satellite NO2 products: ModelⅠ: LUR model with OMI products;Model Ⅱ: LUR model with TropOMI products;Model Ⅱ: LME model with TropOMI products. The results show that the LME model outperformed the LUR model in all four urban agglomerations as the average RMSE decreased by 16.09% due to the consideration of atmospheric dispersion random effects, and using TropOMI instead of OMI products can improve the accuracy. Based on our NO2 estimations, pollution hotspots were identified, and pollution anomalies during the COVID-19 period were explored for two periods;the lockdown and revenge pollution periods. The largest NO2 pollution difference between the hotspot and non-hotspot areas occurred in the second period, especially in the heavy industrial urban agglomerations. © 2022 Elsevier Ltd
ABSTRACT
The outbreak of Coronavirus disease 2019 (COVID-19) led to the widespread stagnation of urban activities, resulting in a significant reduction in industrial pollution and traffic pollution. This affected how urban form influences air quality. This study reconsiders the influence of urban form on air quality in five urban agglomerations in China during the pandemic period. The random forest algorithm was used to quantitate the urban form-air quality relationship. The urban form was described by urban size, shape, fragmentation, compactness, and sprawl. Air quality was evaluated by the Air Quality Index (AQI) and the concentration of six pollutants (CO, O3, NO2, PM2.5, PM10, SO2). The results showed that urban fragmentation is the most important factor affecting air quality and the concentration of the six pollutants. Additionally, the relationship between urban form and air quality varies in different urban agglomerations. By analyzing the extremely important indicators affecting air pollution, the urban form-air quality relationship in Beijing-Tianjin-Hebei is rather complex. In the Chengdu-Chongqing and the Pearl River Delta, urban sprawl and urban compactness are extremely important indicators for some air pollutants, respectively. Furthermore, urban shape ranks first for some air pollutants both in the Triangle of Central China and the Yangtze River Delta. Based on the robustness test, the performance of the random forest model is better than that of the multiple linear regression (MLR) model and the extreme gradient boosting (XGBoost) model.
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The perception of sharing good and services is constantly changing around the world, especially in urban agglomerations. With any smartphone connected to the internet, at just few finger taps away, ridesharing has never been more facile for consumers. While it ensures the rapid development of the sharing economy, the collaborative consumption of transportation services raises new socio-economic and environmental challenges, making big cities the subject of the new sustainable mobility paradigm. In this context, the objective of this research was to analyze consumer preferences for shared mobility services in the case of a selection of big European cities and connect these findings with the potential of shared mobility services to shift consumption models towards a more sustainable future. Furthermore, consumer preferences were also analyzed in relation with the COVID-19 pandemic and its impact on the development potential of the sharing economy, with a focus on the transport sector. This paper was aimed at filling a literature gap by providing a comprehensive description of consumer preferences for shared mobility services with respect to the COVID-19 pandemic context. Moreover, population density and the degree of sustainable development were variables taken into consideration in this study, as well as factors specific to the consumers of shared mobility services: age, gender, nationality and other relevant characteristics of the consumer digital profile. Findings suggest divergent consumer behaviors according to the characteristics of each of the analyzed European big city: the transportation infrastructure, degree of urbanization and level of wellbeing.
ABSTRACT
The analysis of the daily spatial patterns of near-surface Nitrogen dioxide (NO2) concentrations can assist decision makers mitigate this common air pollutant in urban areas. However, comparative analysis of NO2 estimates in different urban agglomerations of China is limited. In this study, a new linear mixed effect model (LME) with multi-source spatiotemporal data is proposed to estimate daily NO2 concentrations at high accuracy based on the land-use regression (LUR) model and Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) products. In addition, three models for NO2 concentration estimation were evaluated and compared in four Chinese urban agglomerations from 2018 to 2020, including the COVID-19 closed management period. Each model included a unique combination of methods and satellite NO2 products: ModelⅠ: LUR model with OMI products;Model Ⅱ: LUR model with TropOMI products;Model Ⅱ: LME model with TropOMI products. The results show that the LME model outperformed the LUR model in all four urban agglomerations as the average RMSE decreased by 16.09% due to the consideration of atmospheric dispersion random effects, and using TropOMI instead of OMI products can improve the accuracy. Based on our NO2 estimations, pollution hotspots were identified, and pollution anomalies during the COVID-19 period were explored for two periods;the lockdown and revenge pollution periods. The largest NO2 pollution difference between the hotspot and non-hotspot areas occurred in the second period, especially in the heavy industrial urban agglomerations.
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: Since 2022, the journal Regional Research of Russia is publishing special issues on important topics in human geography, regional economics, and sociology. This special issue has been prepared for the centennial of the International Geographical Union celebrated at its Extraordinary Congress in Paris (July 2022) and includes seven review articles, commissioned and written by leading Russian experts in the respective fields. The topics of the articles were chosen so that the special issue reflects areas in which the most striking results have been achieved and which are specific to Russia. The authors have tried, as far as possible, to compare the topics, methodological approaches, and research results with the world mainstream. The main focus is on the last decade, 2010–2021;when necessary, the authors also have referred to earlier publications. The authors of most of the articles paid attention to the developments that arose in 2020–2021 due to the COVID-19 pandemic. © 2022, Pleiades Publishing, Ltd.
ABSTRACT
The outbreak of Coronavirus disease 2019 (COVID-19) led to the widespread stagnation of urban activities, resulting in a significant reduction in industrial pollution and traffic pollution. This affected how urban form influences air quality. This study reconsiders the influence of urban form on air quality in five urban agglomerations in China during the pandemic period. The random forest algorithm was used to quantitate the urban form-air quality relationship. The urban form was described by urban size, shape, fragmentation, compactness, and sprawl. Air quality was evaluated by the Air Quality Index (AQI) and the concentration of six pollutants (CO, O-3, NO2, PM2.5, PM10, SO2). The results showed that urban fragmentation is the most important factor affecting air quality and the concentration of the six pollutants. Additionally, the relationship between urban form and air quality varies in different urban agglomerations. By analyzing the extremely important indicators affecting air pollution, the urban form-air quality relationship in Beijing-Tianjin-Hebei is rather complex. In the Chengdu Chongqing and the Pearl River Delta, urban sprawl and urban compactness are extremely important indicators for some air pollutants, respectively. Furthermore, urban shape ranks first for some air pollutants both in the Triangle of Central China and the Yangtze River Delta. Based on the robustness test, the performance of the random forest model is better than that of the multiple linear regression (MLR) model and the extreme gradient boosting (XGBoost) model.
ABSTRACT
With higher densities, urban agglomerations account for the fastest rates of COVID-19 transmission. In Indonesia, one of the most rapidly urbanizing regions in the world, the national government issues overall policy on the pandemic. However, implementation is often contingent on local governments. Many policies aim to stem the spread of infection by controlling people's mobility or regulating their daily activities. Urban agglomerations are a strategic site of investigation in this light, because they consist of interconnected communities governed by various levels and jurisdictions. This paper analyzes the effects of policy interventions relative to confirmed cases in the seven major urban agglomerations in Indonesia (totaling 30 municipal/district governments). Data were collected from confirmed and fatality trends from March to mid-October 2020, which were contrasted with corresponding policies for each jurisdiction. By sorting the indicators of the spread of the pandemic and its corresponding control measures, we reach conclusions about which dimensions served to curb or trigger the surge of COVID-19 clusters. The analysis unsurprisingly shows that within each agglomeration, the main cities continue to represent the highest number of confirmed cases despite variations between them. This study also highlights two key findings. First, the effectiveness of distancing measures depends considerably on the capacity of governments to implement restrictions. For example, budget limitations resulted in uneven implementation of national mandates by decentralized authority. Facilities and services at different locations also influence our understanding of disease transmission. Second, people's ability and willingness to engage with a policy regime is contingent upon personal values or economic constraints. The study shows that viewing the spatial distribution of COVID-19 at the scale of urban agglomerations helps to explain key aspects of transmission and policy, pointing to recommendations about pursuing certain protocols. Nevertheless, key challenges remain to meet the full potential of this analytical approach, due to relatively low levels of testing and inadequate data collection measures in Indonesia.
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The article shows that the crisis caused by the COVID-19 pandemic has not lead to the emergence of fundamentally new patterns of spatial development, but has escalated its old problems. Thus, the importance of the largest cities in the country's economy has not decreased at all. On the contrary, their traditional advantages have revealed, including those connected with an increased level of education of residents. But at the same time, the most important problem of the largest cities has also aggravated, and cannot be ignored - that is strong social stratification of citizens. The development of remote employment, the scale of which will obviously be greater than pre-crisis, may lead to changes in the settlement system within urban agglomerations, but not beyond them. This is due to the potential dominance of not completely remote, but combined employment, which is necessary to ensure the transfer of implicit knowledge. But even within urban agglomerations, the possible scale of changing permanent residence due to a number of factors (climate, features of the housing stock, availability of infrastructure facilities) is very limited, the most important will be an increase in the duration of stay in dachas. This will further aggravate the existing complex of problems associated with the extremely weak consideration of the actual life of people on two houses in the activities of the authorities.
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To prevent the Corona Virus Disease 2019 (COVID-19) spreading, Chinese government takes a series of corresponding measures to restrict human mobility, including transportation lock-down and industries suspension, which significantly influenced the ambient air quality and provided vary rare time windows to assess the impacts of anthropological activities on air pollution. In this work, we divided the studied timeframe (2019/12/24-2020/2/24) into four periods and selected 88 cities from 31 representative urban agglomerations. The indicators of PM2.5/PM10 and NO2/SO2 were applied, for the first time, to analyze the changes in stoichiometric characteristics of ambient air pollutants pre-to post-COVID-19 in China. The results indicated that the ratios of NO2/SO2 presented a responding decline, especially in YRD (-5.01), YH (-3.87), and MYR (-3.84), with the sharp reduction of traffic in post-COVID-19 periods (P3-P4: 2.34 ± 0.94 m/m) comparing with pre-COVID-19 periods (P1-P2: 4.49 ± 2.03 m/m). Whereas the ratios of PM2.5/PM10 increased in P1-P3, then decreased in P4 with relatively higher levels (>0.5) in almost all urban agglomerations. Furthermore, NO2 presented a stronger association with PM2.5/PM10 variation than CO; and PM2.5 with NO2/SO2 variation than PM10. In summary, the economic structure, lockdown measures and meteorological conditions could explain the noteworthy variations in different urban agglomerations. These results would be in great help for improving air quality in the post-epidemic periods.
Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Environmental Pollutants , Air Pollutants/analysis , Air Pollution/analysis , COVID-19/epidemiology , China/epidemiology , Cities/epidemiology , Communicable Disease Control , Environmental Monitoring , Humans , Particulate Matter/analysisABSTRACT
Air pollution in India during COVID-19 lockdown, which imposed on 25th March to 31st May 2020, has brought a significant improvement in air quality. The present paper mainly focuses on the scenario of air pollution level (PM2.5, PM10, SO2, NO2 and O3) across 57 urban agglomerations (UAs) of India during lockdown. For analysis, India has been divided into six regions - Northern, Western, Central, Southern, Eastern and North-Eastern. Various spatial statistical modelling with composite air quality index (CAQI) have been utilised to examine the spatial pattern of air pollution level. The result shows that concentration of all air pollutants decreased significantly (except O3) during lockdown. The maximum decrease is the concentration of NO2 (40%) followed by PM2.5 (32%), PM10 (24%) and SO2 (18%). Among 57 UA's, only five - Panipat (1.00), Ghaziabad (0.76), Delhi (0.74), Gurugram (0.72) and Varanasi (0.71) had least improvement in air pollution level considering entire lockdown period. The outcome of this study has an immense scope to understand the regional scenario of air pollution level and to implement effective strategies for environmental sustainability.