scholarly journals Spatio-Temporal Impact of Global Migration on Carbon Transfers Based on Complex Network and Stepwise Regression Analysis

2022 ◽  
Vol 14 (2) ◽  
pp. 844
Author(s):  
Cuixia Gao ◽  
Ying Zhong ◽  
Isaac Adjei Mensah ◽  
Simin Tao ◽  
Yuyang He
Keyword(s):  
Complex Network ◽  
Stepwise Regression ◽  
Economic Globalization ◽  
Migration Flow ◽  
Global Migration ◽  
Female Migrants ◽  
Regression Approach ◽  
Energy Environment ◽  
Spatio Temporal ◽  
The Impact

Considering the advancement of economic globalization, the reasons for migration together with the lifestyles of migrants will change the use of energy, environment of origin and destination. This study therefore explores the patterns of global trade-induced carbon emission transfers using “center-of-gravity” and complex network analysis. We further investigate the determinants of carbon transfers by integrating the impact of population migration through the STIRPAT framework for 64 countries over the period 2005–2015 using the stepwise regression approach. Our results unveil that higher levels of migration flow induce higher carbon flow. Specifically, every 1% increase in migration, triggers carbon transfers to increase within the range of 0.118%−0.124%. The rising impact of migration cannot be ignored, even though the coefficients were not so high. Besides, for both male and female migrants, their impact on carbon transfers generated by the intermediate products were higher than those generated by the final products. However, the influence is more obvious in male migrants. With the aim of dividing the sample of countries into three income groups, the results generally show that the impacts of migration vary across levels of income. Therefore, the environmental pressure caused by immigration should be considered by destination countries in the formulating of migration policies. On the other hand, origin countries should take some responsibility for carbon emissions according to their development characteristics.

Narrative, Intertext and Allusion in the Qur'anic Presentation of Job

1999 ◽  
Vol 1 (1) ◽  
pp. 1-25 ◽  
Author(s):  
A. H. Johns
Keyword(s):  
Key Words ◽  
Complex Network ◽  
Key Words And Phrases ◽  
Divine Revelation ◽  
Western Tradition ◽  
Islamic Tradition ◽  
The Impact ◽  
The Way

Job (Ayyūb) is a byword for patience in the Islamic tradition, notwithstanding only six Qur'anic verses are devoted to him, four in Ṣād (vv.41-4), and two in al-Anbiyā' (vv.83-4), and he is mentioned on only two other occasions, in al-Ancām (v.84) and al-Nisā' (v.163). In relation to the space devoted to him, he could be accounted a ‘lesser’ prophet, nevertheless his significance in the Qur'an is unambiguous. The impact he makes is achieved in a number of ways. One is through the elaborate intertext transmitted from the Companions and Followers, and recorded in the exegetic tradition. Another is the way in which his role and charisma are highlighted by the prophets in whose company he is presented, and the shifting emphases of each of the sūras in which he appears. Yet another is the wider context created by these sūras in which key words and phrases actualize a complex network of echoes and resonances that elicit internal and transsūra associations focusing attention on him from various perspectives. The effectiveness of this presentation of him derives from the linguistic genius of the Qur'an which by this means triggers a vivid encounter with aspects of the rhythm of divine revelation no less direct than that of visual iconography in the Western Tradition.

scholarly journals Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 307
Author(s):  
Chi Zhang ◽  
Naixia Mou ◽  
Jiqiang Niu ◽  
Lingxian Zhang ◽  
Feng Liu
Keyword(s):  
Tibetan Plateau ◽  
Snow Cover ◽  
Snow Depth ◽  
Feedback Effect ◽  
Effect Of Temperature ◽  
The Tibetan Plateau ◽  
Reanalysis Dataset ◽  
Spatio Temporal ◽  
The Impact ◽  
Main Factor

Changes in snow cover over the Tibetan Plateau (TP) have a significant impact on agriculture, hydrology, and ecological environment of surrounding areas. This study investigates the spatio-temporal pattern of snow depth (SD) and snow cover days (SCD), as well as the impact of temperature and precipitation on snow cover over TP from 1979 to 2018 by using the ERA5 reanalysis dataset, and uses the Mann–Kendall test for significance. The results indicate that (1) the average annual SD and SCD in the southern and western edge areas of TP are relatively high, reaching 10 cm and 120 d or more, respectively. (2) In the past 40 years, SD (s = 0.04 cm decade−1, p = 0.81) and SCD (s = −2.3 d decade−1, p = 0.10) over TP did not change significantly. (3) The positive feedback effect of precipitation is the main factor affecting SD, while the negative feedback effect of temperature is the main factor affecting SCD. This study improves the understanding of snow cover change and is conducive to the further study of climate change on TP.

scholarly journals Spatio-Temporal Change of Land Use in a Coastal Reclamation Area: A Complex Network Approach

2021 ◽  
Vol 13 (16) ◽  
pp. 8690
Author(s):  
Caiyao Xu ◽  
Lijie Pu ◽  
Fanbin Kong ◽  
Bowei Li
Keyword(s):  
Land Use ◽  
Complex Network ◽  
Coastal Wetland ◽  
Eastern China ◽  
Wetland Ecosystems ◽  
Ecological Protection ◽  
Land Use Types ◽  
Coastal Reclamation ◽  
Reclamation Area ◽  
Spatio Temporal

Coastal ecological protection and restoration projects aimed to restore and recover the ecological environment of coastal wetland with high-intensity human reclamation activity, while the integrity of the coastal wetland system with human reclamation activity and the ability of individual land use types to control the overall system were not fully considered. In this study, a six-stage land use conversion network was constructed by using a complex network model to analyze coastal land use dynamic changes in the coastal reclamation area located in eastern China from 1977 to 2016. The results showed that land use types had gradually transformed from being dominated by natural types to artificial types, and the speed of transformation was accelerating. The proportion of un-reclaimed area decreased from 93% in 1977 to 46% in 2007, and finally fell to 8% in 2014 and 2016. Tidal flat and halophytic vegetation were the main output land use types, while cropland, woodland and aquaculture pond were the main input land use types. Cropland had the highest value of betweenness centrality, which played a key role in land use change from 1992 to 2014. The land use system of the coastal reclamation area was the most stable in 2002–2007, followed by 1984–1992, and the most unstable in 2007–2014. The Chinese and local government should carry out some measures to improve the land use in coastal wetland ecosystems, including the allocation and integration of land use for production space, living space, and ecological space, and develop multi-functionality of land use to realize the coastal high-quality development and coastal ecological protection and restoration.

scholarly journals Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1432
Author(s):  
Xwégnon Ghislain Agoua ◽  
Robin Girard ◽  
Georges Kariniotakis
Keyword(s):  
Satellite Images ◽  
Forecast Accuracy ◽  
Absolute Error ◽  
Data Sources ◽  
Test Case ◽  
Temporal Data ◽  
Multiple Sources ◽  
Forecasting Error ◽  
Spatio Temporal ◽  
The Impact

The efficient integration of photovoltaic (PV) production in energy systems is conditioned by the capacity to anticipate its variability, that is, the capacity to provide accurate forecasts. From the classical forecasting methods in the state of the art dealing with a single power plant, the focus has moved in recent years to spatio-temporal approaches, where geographically dispersed data are used as input to improve forecasts of a site for the horizons up to 6 h ahead. These spatio-temporal approaches provide different performances according to the data sources available but the question of the impact of each source on the actual forecasting performance is still not evaluated. In this paper, we propose a flexible spatio-temporal model to generate PV production forecasts for horizons up to 6 h ahead and we use this model to evaluate the effect of different spatial and temporal data sources on the accuracy of the forecasts. The sources considered are measurements from neighboring PV plants, local meteorological stations, Numerical Weather Predictions, and satellite images. The evaluation of the performance is carried out using a real-world test case featuring a high number of 136 PV plants. The forecasting error has been evaluated for each data source using the Mean Absolute Error and Root Mean Square Error. The results show that neighboring PV plants help to achieve around 10% reduction in forecasting error for the first three hours, followed by satellite images which help to gain an additional 3% all over the horizons up to 6 h ahead. The NWP data show no improvement for horizons up to 6 h but is essential for greater horizons.

scholarly journals Detecting Tourism Typologies of Regional Destinations Based on Their Spatio-Temporal and Socioeconomic Performance: A Correlation-Based Complex Network Approach for the Case of Greece

2021 ◽  
Vol 2 (1) ◽  
pp. 113-139
Author(s):  
Dimitrios Tsiotas ◽  
Thomas Krabokoukis ◽  
Serafeim Polyzos
Keyword(s):  
Network Analysis ◽  
Complex Network ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Spatial Dimension ◽  
Economic Dimension ◽  
Complex Network Analysis ◽  
Time Period ◽  
Time Patterns ◽  
Spatio Temporal

Within the context that tourism-seasonality is a composite phenomenon described by temporal, geographical, and socio-economic aspects, this article develops a multilevel method for studying time patterns of tourism-seasonality in conjunction with its spatial dimension and socio-economic dimension. The study aims to classify the temporal patterns of seasonality into regional groups and to configure distinguishable seasonal profiles facilitating tourism policy and development. The study applies a multilevel pattern recognition approach incorporating time-series assessment, correlation, and complex network analysis based on community detection with the use of the modularity optimization algorithm, on data of overnight-stays recorded for the time-period 1998–2018. The analysis reveals four groups of seasonality, which are described by distinct seasonal, geographical, and socio-economic profiles. Overall, the analysis supports multidisciplinary and synthetic research in the modeling of tourism research and promotes complex network analysis in the study of socio-economic systems, by providing insights into the physical conceptualization that the community detection based on the modularity optimization algorithm can enjoy to the real-world applications.

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