Assessing food safety risks based on a geospatial analysis: toward a cross-regional food safety management.

BACKGROUND: Food safety risks (FSRs) are increasingly characterized by geographical complexity along with rapid urbanization, changing dietary pattern, and the modernization of the food industry. These factors pose challenges for food risk control in developing economies, more so during the global COVID-19 pandemic. The accurate assessment of risk source and transfer path is a crucial step toward enhancing cross-regional food safety management. This study aims to examine the spatial distribution, transfer path and driving factors of FSRs in China, provided with a national food safety database collected from 8.63 million batches of food sampling inspections for 33 different types of foods across 30 provinces. RESULTS: The findings reveal significant regional disparities in FSRs, which is the highest in the west with small-scale sampling inspection and the lowest in the east with intensive sampling inspection. Catering and processed foods with higher daily consumption suffer more profound FSR than agricultural products. As evidenced by the shrinking low-low agglomeration areas, the local FSRs have been effectively controlled. The high-high agglomeration areas playing positive impacts on risk control are expanding while distributed discretely. CONCLUSION: The spatial transfer of FSRs is significantly driven by multiple drivers: regulatory capacity and intensity, information disclosure, food industry, regional economy, and food consumption. Assessing FSRs based on a geospatial analysis contributes to identifying risk sources, optimizing risk management, and constructing a sustainable food safety system. © 2023 Society of Chemical Industry.

Driving Factors of Polarization on Twitter During Protests Against COVID-19 Mitigation Measures in Vienna

We conduct the analysis of the Twitter discourse related to the anti-lockdown and anti-vaccination protests during the so-called 4th wave of COVID-19 infections in Austria (particularly in Vienna). We focus on predicting users' protest activity by leveraging machine learning methods and individual driving factors such as language features of users supporting/opposing Corona protests. For evaluation of our methods we utilize novel datasets, collected from discussions about a series of protests on Twitter (40488 tweets related to 20.11.2021;7639 from 15.01.2022 – the two biggest protests as well as 192 from 22.01.2022;8412 from 11.12.2021;3945 from 11.02.2022). We clustered users via the Louvain community detection algorithm on a retweet network into pro- and anti-protest classes. We show that the number of users engaged in the discourse and the share of users classified as pro-protest are decreasing with time. We have created language-based classifiers for single tweets of the two protest sides – random forest, neural networks and a regression-based approach. To gain insights into language-related differences between clusters we also investigated variable importance for a word-list-based modeling approach. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Global patterns of phylogenetic diversity and transmission of bat coronavirus.

Bats are reservoirs for multiple coronaviruses (CoVs). However, the phylogenetic diversity and transmission of global bat-borne CoVs remain poorly understood. Here, we performed a Bayesian phylogeographic analysis based on 3,594 bat CoV RdRp gene sequences to study the phylogenetic diversity and transmission of bat-borne CoVs and the underlying driving factors. We found that host-switching events occurred more frequently for α-CoVs than for ß-CoVs, and the latter was highly constrained by bat phylogeny. Bat species in the families Molossidae, Rhinolophidae, Miniopteridae, and Vespertilionidae had larger contributions to the cross-species transmission of bat CoVs. Regions of eastern and southern Africa, southern South America, Western Europe, and Southeast Asia were more frequently involved in cross-region transmission events of bat CoVs than other regions. Phylogenetic and geographic distances were the most important factors limiting CoV transmission. Bat taxa and global geographic hotspots associated with bat CoV phylogenetic diversity were identified, and bat species richness, mean annual temperature, global agricultural cropland, and human population density were strongly correlated with the phylogenetic diversity of bat CoVs. These findings provide insight into bat CoV evolution and ecological transmission among bat taxa. The identified hotspots of bat CoV evolution and transmission will guide early warnings of bat-borne CoV zoonotic diseases.

Qualitative Analysis of System Feature Overload Impacting Discontinuous Usage from the Perspective of S-O-R (Stimulus-Organism-Response) Theory

The Social Network Site (SNS) feature continues to be developed so users can quickly spread COVID-19 information. Unfortunately, the number of features or system feature overload can impact discontinuous usage because the feature design makes it difficult for users. Discontinuous usage is a phenomenon that reduces the intensity of SNS use. This research aimed to bring a new system feature overload that leads to discontinuous usage from the perspective of human opinion and behavior. Therefore, the researchers analyzed the role of SNS feature design on discontinuous usage from the perspective of the qualitative Stimulus-Organism-Response (S-O-R) theory. This research was conducted in Indonesia, the country with the largest SNS users, on subjects affected by information overload and SNS exhaustion. The research objective was to comprehend the phenomenon's impact and the driving factors of discontinuous usage. This research had two contributions to understanding the effects of SNS feature design on user behavior. It showed a conceptual model of discontinuous usage related to the driving factors of information overload and SNS exhaustion. © 2022 ACM.

Spatial-temporal evolution and driving factors of cultivated land pressure in Shaanxi Province

In the context of the complex international situation and novel Coronavirus pandemic, the issue of food security in China has become more important. Shaanxi province is an important agricultural production base in northwest China. It is of great significance to explore and analyze the spatial-temporal evolution characteristics and driving factors of cultivated land pressure in Shaanxi Province. This study is based on the number and distribution situation of cultivated land from 1995 to 2019 in Shaanxi, the minimum area of cultivated land per capita Sminand cultivated land pressure index P measure, to integrate the traditional difference index, establish the overall differentiation measure index of GDI, reference gravity mobile model and overall differentiation measure index of GDI describe the spatial and temporal variation characteristics of cultivated land pressure in the area. The driving factors of cultivated land pressure change were analyzed and discussed by using grey correlation analysis method. The results showed that the cultivated land area in Shaanxi province decreased firstly and then increased from 1995 to 2019. The cultivated land pressure index showed a downward trend, and remained at the level of "warning pressure" for a long time, with a small variation range. The center of cultivated land pressure in Shaanxi province was located in Guanzhong area, and moved northwestward first, then southward year by year. The spatial differentiation of cultivated land pressure in Shaanxi province was not obvious before 2003, and the state was relatively stable, and the differentiation pattern became more and more obvious after 2003. Frost-free period, precipitation, grain yield per unit, fertilizer use, income of rural residents, income of urban residents, economic development level and industrial level had significant effects on cultivated land pressure in Shaanxi Province from 1995 to 2019. © 2022 Journal of Chinese Agricultural Mechanization Editorial Office. All rights reserved.

A Study on the Spatial-Temporal Evolution and Driving Factors of Non-Grain Production in China's Major Grain-Producing Provinces.

Food self-sufficiency in a large country with 1.4 billion people is very important for the Chinese government, especially in the context of COVID-19 and the Russian-Ukrainian conflict. The objective of this paper is to explore the spatial-temporal evolution and driving factors of non-grain production in thirteen major grain-producing provinces in China, which account for more than 75% of China's grain production, using 2011-2020 prefecture-level statistics. In the present study, the research methodology included GIS spatial analysis, hot spot analysis, and spatial Durbin model (SDM). The findings of this study are as follows: (1) The regions with a higher level of non-grain production were mainly concentrated in the central and western regions of Inner Mongolia, the middle and lower reaches of Yangtze River and Sichuan, while the regions with a low level of non-grain production were mainly distributed in the Northeast Plain. The regions with a higher proportion of grain production to the national total grain production were concentrated in the Northeast Plain, the North China Plain, and the Middle and Lower Yangtze River Plain of China. The hot spot regions with changes in non-grain production levels were mainly distributed in the Sichuan region and Alashan League City in Inner Mongolia, and the cold spot regions were mainly distributed in Hebei, Shandong, Henan, and other regions. (2) An analysis of the SDM indicated that the average air temperature among the natural environment factors, the ratio of the sum of gross secondary and tertiary industries to GDP, the ratio of gross primary industry to the GDP of economic development level, the urbanization rate of social development, and the difference in disposable income per capita between urban and rural residents of the urban-rural gap showed positive spatial spillover effects. The grain yield per unit of grain crop sown area of grain production resource endowment, the total population of social development, and the area sown to grain crops per capita of grain production resource endowment all showed negative spatial spillover effects. The research results of this paper can provide a reference for the country to carry out the governance of non-grain production and provide a reference for China's food security guarantee.

Evaluation and driving factors of land use economic efficiency in China's urban agglomerations under the impact of COVID-19 epidemic.

Land is an indispensable factor of production and the basic support for all social and economic activities. The COVID-19 epidemic has a great impact on China's macro-economy and land market. As a unit with a high concentration of economic entities, urban agglomeration is closely related to its land use economic efficiency. Under the impact of epidemic and the rigid constraints of the relative scarcity of land resources, improving the land use economic efficiency is crucial to the sustainable development of urban agglomerations. Taking the 10 major urban agglomerations in China as a case study, this paper constructs a theoretical and empirical analysis framework for the land use economic efficiency and its driving mechanism of urban agglomerations, and measures the land use economic efficiency of urban agglomerations from the aspects of single factor productivity and total factor productivity. The results show that the COVID-19 epidemic has a great impact on the land market of various cities in China's urban agglomerations. Whether single factor productivity or total factor productivity is used to measure land use economic efficiency of urban agglomerations, the driving effects of industrial agglomeration, industrial structure change, technological progress, and transportation infrastructure are all significant. It is necessary to take a series of measures to reform the market-oriented allocation of land elements, and improve a long-term mechanism for the smooth operation of the land market. It is necessary to improve the land use economic efficiency through a combination of industrial agglomeration, industrial structure adjustment, technological progress, and transportation infrastructure.

Spatial-Temporal Characteristics and Driving Factors of the Eco-Efficiency of Tourist Hotels in China.

At present, COVID-19 is seriously affecting the economic development of the hotel industry, and at the same time, the world is vigorously calling for "carbon emission mitigation". Under these two factors, tourist hotels are in urgent need of effective tools to balance economic and social contributions with ecological and environmental impacts. Therefore, this paper takes Chinese tourist hotels as the research object and constructs a research framework for Chinese tourist hotels by constructing a Super-SBM Non-Oriented model. We measured the economic efficiency and eco-efficiency of Chinese tourist hotels from 2000 to 2019; explored spatial-temporal evolution patterns of their income, carbon emissions, eco-efficiency, and economic efficiency through spatial hotspot analysis and center of gravity analysis; and identified the spatial agglomeration characteristics of such hotels through the econometric panel Tobit model to identify the different driving factors inside and outside the tourist hotel system. The following results were obtained: (1) the eco-efficiency of China's tourist hotels is higher than the economic efficiency, which is in line with the overall Kuznets curve theory, but the income and carbon emissions have not yet been decoupled; (2) most of China's tourist hotels are crudely developed with much room for improving the economic efficiency, and most of the provincial and regional tourist hotels are at a low-income level, but the carbon emissions are still on the increase; and (3) income, labor, carbon emissions, waste emissions, and water consumption are the internal drivers of China's tourist hotels, while industrial structure, urbanization rate, energy efficiency, and information technology are the external drivers of China's tourist hotels. The research results provide a clear path for the reduction in carbon emissions and the improvement of the eco-efficiency of Chinese tourist hotels. Under the backdrop of global climate change and the post-COVID-19 era, the research framework and conclusions provide references for countries with new economies similar to China and countries that need to quickly restore the hotel industry.

Evaluation and Countermeasures of High-Quality Development of China’s Marine Economy Based on PSO-SVM

An accurate grasp of the high-quality development of the marine economy is important for the timely adjustment of marine policies and the promotion of sustainable development of the ocean. Based on the latest development philosophy, this paper constructed the evaluation index system of high-quality development of marine economy from five dimensions including innovation, coordination, green, openness, and sharing. The particle swarm optimization (PSO) algorithm and support vector machine (SVM) model based on the entropy weight composite index were employed to evaluate the high-quality development of China’s marine economy from 2006 to 2017. The spatial and temporal distribution characteristics and dynamic evolution mechanism were revealed. The random forest model was applied to analyze the main driving factors of high-quality development of the marine economy. It was found that: (1) The high-quality development level of marine economy in Guangdong, Shandong, Jiangsu, Zhejiang, and Shanghai has always been in the forefront. The growth rate of high-quality development level of marine economy in Guangdong and Shandong was 53.69% and 37.69%, respectively. The growth rates of Fujian and Hainan were 43.46% and 33.68%, respectively. Jiangsu and Zhejiang accounted for 33.30% and 24.47%, respectively. (2) The regulation methods of the main driving factors were examined. It was necessary to adhere to innovative development and improve the marine scientific research, education, management, and service industry, in addition by optimizing and adjusting the marine industrial infrastructure and spatial layout. It is also critical to strengthen the comprehensive prevention and control of land and sea pollution and implement the total emission control of pollutants into the sea. (3) Finally, the pathway for high-quality development of marine economy was analyzed and future directions were proposed.

Literature Review and Research Prospect on the Drivers and Effects of Green Innovation

With increasing and global environmental and climate problems, green innovation has become an important means to solve the environmental crisis. With the increasing practice of green innovation in enterprises, scholars at home and abroad have discussed the drivers and effects of green innovation from different perspectives. Based on an analysis of 119 articles about the drivers and effects of green innovation in top international journals from 2006 to 2021, this paper tries to find the consistencies and contradictions of research conclusions and to explore the possible research opportunities, sorting out the main theoretical mechanisms of the existing research on the drivers and effects of green innovation, pinpointing the consistency of these theoretical perspectives in explaining the different drivers and effects of green innovation, and putting forward research prospects. The results show that the drivers of green innovation include two kinds of factors: environment and organization. The pressure of external environment and system drives enterprises to adopt green innovation practices to cater to isomorphic factors, to obtain more environmental performance, and to improve organizational legitimacy. The lack of development resources, such as knowledge and technology, within an organization drives enterprises to carry out green innovation practices and enhance organizational competitive advantage by learning and absorbing new external knowledge, new technology and other resources. In addition, resource-based view and institutional theory are two commonly used theoretical perspectives, and their theoretical logic obtains consistent support in explaining the drivers and effects of enterprise green innovation.

The stress contagion among financial markets and its determinants

The purpose of this paper is to study the spillover effects of financial stress among five important financial markets (bond, stock, foreign exchange, interbank, and real estate markets) in China, and explore the important determinants of financial stress spillover level among the markets and the impact of the Chinese stress spillover situation on the European markets. Our findings are as follows: First, there is a significant stress spillover effect among the five markets, and the total financial stress spillover index (TSSI) is very high during the global financial crisis. Generally, the stock and real estate markets are the major transmitters of stress spillover, and the interbank and bond markets are the major receivers. Second, the most macro factors have significant impacts on the financial stress spillover level among the markets, especially CPI index, the Chinese economic policy uncertainty index and VIX index. And the severity of the COVID-19 epidemic in China and the world has a significant impact on the TSSI, especially from March 2020 to August 2020. Finally, the TSSI can significantly increase the volatility of French stock market, Italian stock market and German government bond market, especially during the Sino-US trade war and the COVID-19 epidemic.

Sustainable Tourism as a Driving Factor for Competitiveness in the Olive Sector

Tourism is facing serious difficulties worldwide due to the global pandemic COVID-19, translating considerably into an industry effort to compete in the marketplace. In effect, sustainable tourism is considered to have a symbiotic relationship with competitiveness that will allow organisations to make a difference. This means that the sustainability factors are positively related to the competitiveness indicators. In this context, as the oliviculture sector faces challenges in a changing market in terms of ecological, demographic, and consumption practices changes, it is considered that sustainable tourism will enable the sector to make a difference. For, the environmental and social changes of the stakeholders enhance the promotion of sustainability to meet their needs, which in turn increases the sector's competitive advantage. In this respect, the present study was based on a literature review consolidated in a bibliometric analysis to analyse sustainable tourism as a driver of competitiveness in the oliviculture industry. For this purpose, the Scopus database was used, in which 157 full articles published until September 2021 were obtained. Based on the results, using the Bibliometrix R, it was found that research in this field has emerged in the last 20 years and focuses particularly on the terms “competitiveness”, “ecotourism” and “tourism development”. In addition, the countries with the highest scientific production and citations, the main sources of publication in this field of research, the documents with the most citations as well as the co-citations between authors were analysed. Through bibliometric analysis, it is possible to provide researchers, policy-makers and managers with a current view of the undoubted role that sustainable tourism plays in the competitiveness of the olive sector. Considering the trends, it is therefore expected to contribute bases for future strategies aimed at overcoming obstacles, overcoming challenges, and seizing opportunities for a more competitive sector. © 2022 IEEE Computer Society. All rights reserved.

Is COVID-19 pandemic a “Black Swan” event? The impact of the pandemic on the Energy Market

The recent coronavirus disease (COVID-19) pandemic outbreak affected our society greatly, offering a chance to rebuild and rethink our way of living. Energy, as a driving factor of everyday life faced an unprecedented shock. How big was this shock for both the economic and political levels? No consolidated study exists where both aspects are considered. To rethink our way of living, we should reconsider the energy policies strategies for the upcoming years. To date, such an impact has not yet been quantified using price forecasting mathematical models. We have therefore developed a methodology, to quantify the impact of COVID-19 pandemic on European energy market. This paper is addressing the following question “Is the COVID-19 pandemic a Black Swan event?”Evidently COVID-19 had significant consequences on the European energy market. Stocks suffered historical minimum prices duringthis year, with a greater impact on coal technologies than renewable ones. Moreover, stock prices return is showing unexpected fluctuations, hence resulting in incorrectly predicted price forecasts. Despite the initial shock, the energy market is returning to pre-crisis levels, with the renewable technologies leading the comeback. Based on our findings and methods, we conclude that COVID-19 pandemic was not a Black Swan event. We foresee to extend our methodology beyond European energy market and the short-term effects of the pandemic with possible application on the impact of policy makers on energy models. © ECOS 2021 - 34th International Conference on Efficency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems.

Variation of XCO2 anomaly patterns in the Middle East from OCO-2 satellite data

The anthropogenic CO2 emission is contributed to the rapid increase in CO2 concentration. In the current study the anthropogenic CO2 emission in the Middle East (ME) is investigated using 6 years column-averaged CO2 dry air mole fraction (XCO2) observation from Orbiting Carbon Observatory-2 (OCO-2) satellite. In this way, the XCO2 anomaly ((Formula presented.) XCO2) as the detrended and deseasonalized term of OCO-2XCO2 product, was computed and compared to provide the direct space-based anthropogenic CO2 emission monitoring. As a result, the high positive and negative (Formula presented.) XCO2 values have corresponded to the major sources such as oil and gas industries, and growing seasons over ME, respectively. Consequently, the Open-source Data Inventory for Anthropogenic CO2 (ODIAC) emission and the gross primary productivity (GPP) were utilized in exploring the (Formula presented.) XCO2 relation with human and natural driving factors. The results showed the capability of (Formula presented.) XCO2 maps in detecting CO2 emission fluctuations in defined periods were detectible in daily to annual periods. The simplicity and accuracy of the method in detecting the man-made and natural driving factors including the main industrial areas, megacities, or local changes due to COVID-19 pandemic or geopolitical situations as well as the vegetation absorption and biomass burning is the key point that provides the environmental managers and policymakers with valuable and accessible information to control and ultimately reduce the CO2 emission over critical regions. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Diffusion Characteristics and Driving Factors of the Smart Tourism City Policy—Event History Analysis

It is of great significance to explore the spatial-temporal characteristics and analyze the driving factors of the diffusion of smart tourism city policy, which promotes the adoption of smart tourism city policy and the sustainable development of tourism. We aimed to explore the diffusion law and influencing factors of smart tourism city so as to provide reference for the construction of smart tourism city. By employing the 249 cases in China from 2012 to 2019, we revealed the spatial-temporal characteristics and driving factors influencing the diffusion of smart tourism city policy by employing the event history analysis method. The results reveal that the diffusion of smart tourism city policy presents the typical S-shaped curve in cumulative adoptions over time. Furthermore, the diffusion of smart tourism city policy presents the spatial distribution characteristic of the Hu Line, which spreads from the eastern coastal areas to the central inland areas. Moreover, there are multiple driving sources for the diffusion of smart tourism city policy, among which economic lift force, intellectual support force, technological pull force and demand impetus force are the important driving sources for the policy diffusion.

Exploring the Spatiotemporal Evolution and Socioeconomic Determinants of PM2.5 Distribution and Its Hierarchical Management Policies in 366 Chinese Cities.

From 2013 to 2017, progress has been made by implementing the Air Pollution Prevention and Control Action Plan. Under the background of the 3 Year Action Plan to Fight Air Pollution (2018-2020), the pollution status of PM2.5, a typical air pollutant, has been the focus of continuous attention. The spatiotemporal specificity of PM2.5 pollution in the Chinese urban atmospheric environment from 2018 to 2020 can be summarized to help conclude and evaluate the phased results of the battle against air pollution, and further, contemplate the governance measures during the period of the 14th Five-Year Plan (2021-2025). Based on PM2.5 data from 2018 to 2020 and taking 366 cities across China as research objects, this study found that PM2.5 pollution has improved year by year from 2018 to 2020, and that the heavily polluted areas were southwest Xinjiang and North China. The number of cities with a PM2.5 concentration in the range of 25-35 µg/m3 increased from 34 in 2018 to 86 in 2019 and 99 in 2020. Moreover, the spatial variation of the PM2.5 gravity center was not significant. Concretely, PM2.5 pollution in 2018 was more serious in the first and fourth quarters, and the shift of the pollution's gravity center from the first quarter to the fourth quarter was small. Global autocorrelation indicated that the space was positively correlated and had strong spatial aggregation. Local Moran's I and Local Geti's G were applied to identify hotspots with a high degree of aggregation. Integrating national population density, hotspots were classified into four areas: the Beijing-Tianjin-Hebei region, the Fenwei Plain, the Yangtze River Delta, and the surrounding areas were selected as the key hotspots for further geographic weighted regression analysis in 2018. The influence degree of each factor on the average annual PM2.5 concentration declined in the following order: (1) the proportion of secondary industry in the GDP, (2) the ownership of civilian vehicles, (3) the annual grain planting area, (4) the annual average population, (5) the urban construction land area, (6) the green space area, and (7) the per capita GDP. Finally, combined with the spatiotemporal distribution of PM2.5, specific suggestions were provided for the classified key hotspots (Areas A, B, and C), to provide preliminary ideas and countermeasures for PM2.5 control in deep-water areas in the 14th Five-Year Plan.

Factors influencing Industry 4.0 adoption: IMS

Purpose>The digital transformation towards Industry 4.0 (I4.0) has become imperative for manufacturers, as it makes them more flexible, agile and responsive to customers. This study aims to identify the factors influencing the manufacturing firms’ decision to adopt I4.0 and develop a triadic conceptual model that explains this phenomenon.Design/methodology/approach>This study used a qualitative exploratory study design based on multiple case studies (n = 15) from the manufacturing industry in Malaysia by conducting face-to-face interviews. The data were analysed using NVivo. The conceptual model was developed based on grounded theory and deductive thematic analysis.Findings>Results demonstrate that driving, facilitating and impeding factors play influential roles in a firms’ decision-making to adopt I4.0. The major driving factors identified are expected benefits, market opportunities, labour problem, customer requirements, competition and quality image. Furthermore, resources, skills and support are identified as facilitating factors and getting the right people, lack of funding, lack of knowledge, technical challenges, training the operators and changing the mindset of operators to accept new digital technologies are identified as impeding factors.Research limitations/implications>Due to its qualitative design and limited sample size, the findings of this study need to be supplemented by quantitative studies for enhanced generalizability of the proposed model.Practical implications>Knowledge of the I4.0 decision factors identified would help manufacturers in their decision to invest in I4.0, as they can be applied to balancing advantages and disadvantages, understanding benefits, identifying required skills and support and which challenges to expect. For policymakers, our findings identify important aspects of the ecosystem in need of improvement and how manufacturers can be motivated to adopt I4.0.Originality/value>This study lays the theoretical groundwork for an alternative approach for conceptualizing I4.0 adoption beyond UTAUT (Unified Theory of Acceptance and Use of Technology). Integrating positive and negative factors enriches the understanding of decision-making factors for I4.0 adoption.

How has the COVID-19 Pandemic Transformed the E-Commerce Market on the Firm Level: Qualitative Insights from the Russian Market

Due to markets’ digitalization, both consumers and businesses are increasingly involved in e-commerce activities throughout the globe. Current study aims to investigate what drives and what limits firms’ integration into e-commerce activities with the focus on comparison of pre-, during and post-pandemic foci by the firms in Russian emerging market. The study is based on insights from qualitative interviews of firms’ representatives, collected in 2016 and 2021. Based on comparison of interview’s insights and content analysis, we identified the influencing factors from the firms’ perspective, further we also introduced potential consequences of the pandemic based on the respondents’ replies. One contribution of this study is to identify the limiting and the driving factors that are specific for the Russian e-commerce market. Besides, we discover some factors that can supplement the current frameworks for structuring the limiting and the driving factors of the e-commerce market. In addition to that, based on the theoretical and empirical research conducted, we can state that the factors influencing e-commerce five years ago and now are evolving rapidly and may lead to more prominent changes. © 2022, Springer Nature Switzerland AG.

Driving factors of virtual water in international grain trade: A study for belt and road countries

Water resources are distributed in the form of virtual water through international trade, which influences the water supply and consumption of each country. Therefore, it is of significance to study the driving factors of grain virtual water trade to alleviate water stress and guarantee food security. In this paper, the virtual water volume of grain crops traded between China and countries along the Belt and Road (B&R) from 2000 to 2019 was calculated, and a gravity model using panel data was applied to explore the effect of natural and socioeconomic factors on virtual water trade. The virtual water export from B&R countries to China obviously increased in the twenty years and the contributions of various crops to virtual water were more balanced. The regression results indicate that GDP and exchange rate were positively correlated with virtual water inflow, while per capital water resources, arable land, geographic distance, and population were negative factors that hindered virtual water import. The most powerful driving force for grain virtual water trade is water endowment. GDP is an important driver on importing virtual water for countries without water shortage, and a large number of local water resources will not obviously inhibit the driving force of economic strength. By comparing the contribution of factors to virtual water in the past ten years, it can be found that the contribution rate of distance decreased due to the development of transportation industry which reduced the transportation cost of exporting products. The contribution rate of GDP and exchange rate increased, because economic globalization has promoted the effect of economic factors on grain trade. Therefore, the trade structure of agricultural products should be modified based on the characteristics of virtual water flow. For countries without high economic level but water shortage, export crops with high water consumption be reasonably controlled. [Display omitted] • A gravity model was applied to explore the effect of natural and socioeconomic factors on virtual water trade. • The most powerful driving force is water endowment, which were negative factor that hindered virtual water inflow. • Economic strength is an important driver on importing virtual water for countries without water shortage. • The contribution rate of distance decreased due to the development of transportation industry and economic globalization. [ FROM AUTHOR] Copyright of Agricultural Water Management is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Spatiotemporal Pattern and Driving Factors of Cyber Fraud Crime in China

As a typical cybercrime, cyber fraud poses severe threats to civilians’ property safety and social stability. Traditional criminological theories such as routine activity theory focus mainly on the effects of individual characteristics on cybercrime victimization and ignore the impacts of macro-level environmental factors. This study aims at exploring the spatiotemporal pattern of cyber fraud crime in China and investigating the relationships between cyber fraud and environmental factors. The results showed that cyber fraud crimes were initially distributed in southeastern China and gradually spread towards the middle and northern regions;spatial autocorrelation analysis revealed that the spatial concentration trend of cyber fraud became more and more strong, and a strong distinction in cyber fraud clustering between the north and the south was identified. To further explain the formative causes of these spatial patterns, a generalized additive model (GAM) was constructed by incorporating natural and social environmental factors. The results suggested that the distribution of cyber fraud was notably affected by the regional economy and population structure. Also, the high incidence of cyber fraud crime was closely associated with a large nonagricultural population, a high proportion of tertiary industry in GDP, a large number of general college students, a longer cable length, and a large numbers of internet users.