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Globalization

Globalization is the process by which the world is becoming increasingly interconnected and interdependent through the spread of ideas, goods, services, and technologies across national and regional boundaries.
This multifaceted phenomenon involves the integration of economies, cultures, political systems, and social structures on a global scale.
Globalization has far-reaching impacts on various aspects of human life, including eonomic development, cultural exchange, environmental sustainability, and social justice.
Researchers and policymakers must navigate the complexiteis of globalization to address emerging challenges and harness its potential benefits for the betterment of humankind.

Most cited protocols related to «Globalization»

1
Temperature-Driven Dengue Epidemic Potential
Vector parameters and their dependence on temperature were based on studies on A. aegypti for various virus serotypes in different regions of the world. Inconsistencies in methods and different errors in data processing and data fitting are to be expected. In addition, due to limited information this study did not distinguish between different virus serotypes and virus titers (dosages) that can affect the parameters [26] (link). Furthermore, we extended the daily biting rate from the low temperature limit of 21°C down to 12.4°C. This extension was based on the fact that the measured varies slowly with T as shown in equation (2) in the observed range (21°C≤T≤32°C, p = 0.05) in Thailand and shows an even flatter linear increase in Puerto Rico ( ). We expect that our extension would not substantially affect . The exponential fitting of n in equation (5) with three constants based on experimental data is not unique because the temperature range is less than one order of magnitude. Other relationships, such as polynomials, might work as well. We chose the exponential function because it has been used in other modeling of n in malaria-carrying mosquitoes [8] (link). Within the range of temperature used in this estimation of n, the is not likely to be affected by the fitting equations used.
When including DTR, we chose the simple sinusoidal function instead of the Parton-Logan function or other more sophisticated temperature variations [11] (link) so as to match the monthly data on T and DTR. DTR from the present CRU data were used for projected climate change. This might be reasonable because the uncertainty of the future projected temperature is large and the error introduced by DTR is less important.
Finally, our results provide insights into the potential role of temperature and DTR on dengue but do not provide projections of numbers of actual cases because transmission requires the following four conditions: 1) susceptible humans, 2) abundant vector, 3) virus introduction, and 4) conducive weather/climate. Here we consider only one, the role of temperature, and assume that the other conditions are already met. This method can overestimate the dengue epidemic potential for areas where there are no humans, vectors, or viruses. Thus, it is called epidemic potential and not risk. Reported case mapping might be closer to reality, but this provides limited insights into how changing conditions could affect future disease burdens.
Mosquitoes are not inert, and they actively avoid extremes of temperatures by seeking out microenvironments that buffer extreme ambient temperature. A. aegypti in particular is tightly tied to, and highly buffered by, humans and the land use associated with the urbanization and transport of people and goods that have increased with globalization. The natural history of dengue is complex and involves the interplay of many factors such as climate, ecology, vector biology, and human drivers that are influenced by demographic and societal changes, socioeconomic conditions, human behavior, etc. Therefore, the true dengue risk in a specific area might be quite different from our estimation based on the vectorial capacity and the influence of climate. However, as a first approximation, this study improves our current understanding of dengue epidemic potential. Our approach is based on evidence from the scientific literature on transmission dependencies on weather and climate and synthesizes many research studies on vector parameters. It provides a basis for the improvement of dengue modeling based on weather and climate data, and it provides one possibility for how the dengue transmission potential could change as the global climate continues to change.
Liu-Helmersson J., Stenlund H., Wilder-Smith A, & Rocklöv J. (2014). Vectorial Capacity of Aedes aegypti: Effects of Temperature and Implications for Global Dengue Epidemic Potential. PLoS ONE, 9(3), e89783.
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Publication 2014
Climate Climate Change Cloning Vectors Cold Temperature Culicidae Dengue Fever Epidemics Globalization Homo sapiens Malaria Sinusoidal Beds Transmission, Communicable Disease Urbanization Virus
2
Rice Seedling Cold-Stress Response
RNA samples were obtained from a time course experiment on rice seedlings exposed to cold-stress [15 ]. During the time-course experiment, 7 clones were randomly selected from those showing a higher magnitude of increase or decrease (more than 1.5-fold) from microarray experiments that were normalized with globalization, and 7 clones were selected by a totally random manner. For those clones, northern blotting analyses were performed with the same RNA batch that was used for probing microarray experiments. Radioactivity of detected bands on probed membranes was measured using the BAS system (Fuji). For each band on an image, the signal intensity was detected as the sum of signal values in pixels within the band. The background was estimated based on the average intensities of the electrophoresis lane but excluding the band itself. The relative signal of a band was calculated by subtracting the background from the intensity data. Each signal datum was normalized by creating ratios to the control samples. For 4 clones out of 14 clones, northern analyses could not detect the signals.
, & Konishi T. (2004). Three-parameter lognormal distribution ubiquitously found in cDNA microarray data and its application to parametric data treatment. BMC Bioinformatics, 5, 5.
Publication 2004
Clone Cells Cold Shock Stress Electrophoresis Globalization Microarray Analysis Northern Blotting Oryza sativa Radioactivity Seedlings Tissue, Membrane
3
Analyzing Environmental and Globalization Indicators
Data for EPI are derived from the Yale Center for Environmental Law and Policy and the Center for International Earth Science Information Network at Columbia University, (Website of the Yale Center for Environmental Law & Policy: https://epi.envirocenter.yale.edu/epi-downloads. (accessed on 2 March 2021) Website of the Center for International Earth Science Information Network: https://sedac.ciesin.columbia.edu/data/collection/epi. (accessed on 21 February 2021) while data for Global are provided by the KOF Swiss Economic Institute. (https://kof.ethz.ch/en/forecasts-and-indicators/indicators/kof-globalisation-index.html (accessed on 22 February 2021) Data for Democ are obtained from Bjørnskov and Rode (2019) [61 (link)], while data for GI are derived from the Organization for Economic Co-operation and Development (OECD), and the data for the other variables are provided by the World Bank. We merge all data together based on the country and year; after deleting the missing values, we obtain an unbalanced panel data covering 148 countries from 2001 to 2018. All variables are taken into their natural logarithms by plus 1, except for Democ and Forest.
Table 2 provides the basic descriptive statistics of these variables. For EPI, the minimum, and maximum are 2.843, and 4.520, respectively; while the mean and standard deviation (S.D) are 4.110 and 0.312, respectively, suggesting that the environmental performance fluctuates less among such sample countries. Next, we pay attention to globalization: the min, mean, median and max of Global are 3.282, 4.133, 4.160 and 4.522, respectively, while the S.D is 0.256. For other variables, the mean and median of GDP are 8.663 and 8.704, with an S.D of 1.486, suggesting that the economic performance varies among these countries.
Wang Q.J., Geng Y, & Xia X.Q. (2021). Revisited Globalization’s Impact on Total Environment: Evidence Based on Overall Environmental Performance Index. International Journal of Environmental Research and Public Health, 18(21), 11419.
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Publication 2021
Attention Forests Globalization
4
Evaluating Globalization's Impact on Environmental Performance
As suggested by previous studies, panel estimation is more valid than time series and cross-section estimation, since it includes the two dimensions of time and individual which can improve the efficiency and offer more information about individuals’ dynamic progress, as well as solving the potential problems caused by missing variables (Wen et al., 2016; [1 (link)] Wang et al., 2021 [54 (link)]).
In line with Wen et al. (2016) [1 (link)], we also conduct empirical testing for the impact of globalization on environmental performance using system GMM estimation, which can control the lag term of EPI, meaning to include the dynamic progress of EPI, which is given below: EPIit=α1EPIi,t−1+β1Globalit+β’X+ui+ut+εit,
where i=1,2,3…N  is the dimension of individuals and t=1,2,3…T is the time dimension. EPI is the environmental performance, while EPIi,t−1 is the first lag of it, to test the dynamic progress of environmental performance; Global is the variable of globalization, and the other terms are similar to those in Equation (1). X represents the control variables, β stands for the corresponding coefficient, ui and ut capture the individual and time fixed effects, respectively; i=1,2,3…N  stand for the individual country; t=1,2,3…T refers to the year; and εit is the error term.
Wang Q.J., Geng Y, & Xia X.Q. (2021). Revisited Globalization’s Impact on Total Environment: Evidence Based on Overall Environmental Performance Index. International Journal of Environmental Research and Public Health, 18(21), 11419.
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Publication 2021
Globalization
5
Drivers of Language Extinction Risk
The range size and speaker population size of each language were estimated based on information from the Ethnologue, 16th edition [25 ], which represents the most authoritative and only globally comprehensive source of basic information about languages and their speakers. The data were assembled in a Geographical Information System by Global Mapping International as the WLMS database [33 ], providing georeferenced polygons showing their geographical range, associated with information on speaker population size. Languages that are given as points or have no known location/population size were excluded, leaving 6359 (92% of the known 6909 languages) and 6569 (95%) languages in the analysis for range size and population size, respectively. The total area (km2) of all the polygons for each language was defined as the range size, and the latest estimate of the total number of mother-tongue speakers in the polygon attributes as the speaker population size.
Speaker growth rates were estimated using the index of linguistic diversity (ILD) database [34 ], updated with the Ethnologue, 16th edition; this database provides information on temporal changes in the speaker population size (i.e. estimates of speaker population size and survey years) between 1949 and 2005 for 1500 languages selected at random from the Ethnologue. The ILD database is currently the only global database with information on changes in the population size of languages. To estimate speaker growth rate, we selected languages with at least three records of speaker population size, including at least one non-zero record. This resulted in 649 languages, including 24 languages that have become extinct after 1949, to be analysed for their speaker growth rate. This sample size represents approximately 9% of all known languages but the languages included are well scattered across the globe, roughly following the pattern of distribution for all the languages (see electronic supplementary material, figures S1 and S2). The biases in range size and speaker population size between the 649 languages and all available languages in the ILD and WLMS databases were also very small (see the electronic supplementary material, figure S3, for more detail). Thus, we expect the effect of using the sample of 649 languages for drawing conclusions to be minimal. The level of intergenerational transmission in each language was derived from the Atlas of the World's languages in danger [15 ] (see the electronic supplementary material, appendix B for more detail).
Data on potential drivers of extinction risk were derived from different global data sources (electronic supplementary material, appendix C). Since records used for estimating speaker growth rates were mostly collected between 1978 and 2000 (see the electronic supplementary material, figure S4), we used data sources from this period as much as possible. Though information on gross domestic product (GDP) and globalization was only available at the country level, the obtained data fit the purpose of this analysis, given that the economic status and degree of globalization of a country, not of each speaker, are expected to cause language shifts through educational developments [19 ] and the economic benefits of speaking national and global languages [17 ]. Language richness in each cell was defined as the total number of languages whose geographical range overlaps that cell, based on the WLMS database. The land area of a latitudinal band was calculated as the sum of the land area of all grid cells within the same latitude at the 2° resolution.
Amano T., Sandel B., Eager H., Bulteau E., Svenning J.C., Dalsgaard B., Rahbek C., Davies R.G, & Sutherland W.J. (2014). Global distribution and drivers of language extinction risk. Proceedings of the Royal Society B: Biological Sciences, 281(1793), 20141574.
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Publication 2014
Cells Extinction, Psychological Globalization Grid Cells Mothers Tongue Transmission, Communicable Disease

Most recents protocols related to «Globalization»

1
Analyzing Trade Resilience in Global Network

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Publication 2023
Emergencies Eye Globalization
2
Income Inequality Determinants in Emerging Markets
We extracted the remaining control variables from the 2019 KOF index of globalization, the Center for Systemic Peace, Major Episodes of Political Violence (1946–2018), and the World Bank database. We have used GDP per capita (current US$) because Kuznets’s (1955 ) seminal paper argued that as countries develop, income disparity first increases, peaks, and then decreases with time. Next, we include domestic credit to the private sector by banks10 (percent of GDP) because bank finance is the most dominant source of external funds for small and medium enterprises (SMEs) in developing countries (Beck et al., 2008 (link)). Thus, the banking sector significantly impacts firm-level innovation and helps inventors increase their incomes. The next variable is general government’s final consumption expenditure (% of GDP), to control for the effect of government size in each country. Empirical studies find that government size is associated with an increase in inequality, in emerging markets (Round & Odedokun, 2004 (link); Anyanwu, 2011 ; Anyanwu et al., 2016 (link)). Next, we include civil war in our analysis because emerging markets are normally characterized by civil conflict due to rapid economic growth, poverty, and emerging institutions. Moreover, due to disruptions in product and factor markets, increased transaction costs, reduction in social spending, and the general disruption in economic activity, civil conflicts can adversely impact income inequality (Bircan et al., 2017 (link)).
Next, we include the globalization index because the countries in our paper had pursued economic liberalization policies during the study period. The globalization index measures globalization along the economic, social, and political dimensions as well as foreign direct investment, FDI, as a percentage of GDP. Empirical results regarding FDI and inequality are mixed. The International Monetary Fund (2007 ) finds that financial globalization especially FDI is associated with higher inequality in developing countries. While Jaumotte et al. (2013 (link)) find that trade globalization reduces income inequality. Claus et al. (2012 ) also find that globalization has a positive impact on inequality. We include inflation because of its relationship with inequality as discussed by Albanesi (2007 (link)) and Siami-Namini and Hudson (2019 (link)). We end up with a balanced panel of twenty-four countries over 24 years (1995–2018).
Eyisi N., Ichoku H.E, & Kanwal A. (2023). Front-end Innovation and Top Income Inequality: Evidence from Emerging Markets. Journal of the Knowledge Economy.
Publication 2023
Globalization Head Inventors Private Sector
3
Informal Settlements in Peri-Urban Woldia, Ethiopia

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Publication 2023
Earthquakes Face Genets Globalization Imprinting (Psychology) Landslides Obstetric Delivery Urban Population
4
Industry 4.0 and Korean MNEs' GVCs
In the present study, we focus on Industry 4.0 and the GVCs of South Korean (hereafter, Korean) MNEs for three reasons. First, Korea is one of the leaders in country-level innovation, making Industry 4.0 particularly salient among Korean MNEs. The Korean government and presidential committee of Industry 4.0 have prioritized the preparedness and competitiveness of Korea’s Industry 4.0 efforts. Furthermore, Korea’s leading information and communication technology companies, such as Samsung, LG, and SK, have made huge investments in Industry 4.0 capabilities. These efforts are reflected in some notable rankings: Korea ranked first on the Bloomberg Global Innovation Index in 2019 and 2021, second for R&D intensity and value-added manufacturing, and fourth for high-tech density. In 2019, Korea tied with Japan for third place in the world’s high-tech rankings (including Industry 4.0) compiled by The Nikkei (2020 ), the holding company of The Financial Times.
Second, Korean MNEs have extensive experience with subsidiary-level sales and purchases within their GVC networks “due to their accumulated age and diverse foreign direct investments (FDIs) across all realms of the globe” (Lee, Jiménez, Yang, & Song, 2020 (link): 451). Historically, Korean MNEs have concentrated on the advantages offered by both internal value chains and GVCs, yet their organizational strategies have recently expanded to include external suppliers and customers within their GVC networks.5 Thus, Korean MNEs leverage tangible and intangible resources through both domestic and foreign suppliers and customers to obtain “upgraded technological and process management skills,” “distinctive capabilities,” and “internationalization knowledge” (Lee et al., 2020 (link): 452).
Finally, in 2019, Korea’s FDI outflow of USD 36 billion was the 9th largest in the world, according to the World Investment Report 2020 by the United Nations Conference on Trade and Development (UNCTAD, 2020 ). All of the above suggest that Korea is an ideal country for the study of Industry 4.0 and its impact on MNEs’ globalization.
Lee J.Y., Kim D., Choi B, & Jiménez A. (2023). Early evidence on how Industry 4.0 reshapes MNEs’ global value chains: The role of value creation versus value capturing by headquarters and foreign subsidiaries. Journal of International Business Studies, 54(4), 599-630.
Publication 2023
Conferences Eye Globalization Koreans
5
Modeling Factors Influencing COVID-19 Spread

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Publication 2023
A-factor (Streptomyces) COVID 19 factor A General Practitioners Globalization Head Labor Force Physicians

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More about "Globalization"

Globalization is the process of worldwide integration and interconnectedness, driven by the dissemination of ideas, goods, services, and technologies across national and regional boundaries.
This multifaceted phenomenon involves the blending of economies, cultures, political systems, and social structures on a global scale.
Globalisation has far-reaching impacts on various aspects of human life, including economic development, cultural exchange, environmental sustainability, and social justice.
Researchers and policymakers must navigate the complexities of this global integration to address emerging challenges and harness its potential benefits for the betterment of humankind.
This process is facilitated by advancements in transportation, communication, and information technologies, enabling the seamless flow of capital, labor, and products around the world.
The impacts of globalization can be observed in diverse sectors, from the spread of multinational corporations and the integration of financial markets to the exchange of cultural practices and the coordination of environmental policies.
Key subtopics include international trade, foreign direct investment, outsourcing, migration, cultural homogenization, and global governance.
Synonyms for globalization include worldwide integration, internationalization, and global interconnectedness.
Related terms include Metformin hydrochloride (a medication used to treat type 2 diabetes), Formic acid (a naturally occurring organic acid), HPLC-grade acetonitrile (a common solvent used in high-performance liquid chromatography), Dextran sulfate sodium salt (DSS, a polysaccharide used in research), Buchi Rotavapor R-144 (a laboratory equipment for solvent evaporation), FBS (fetal bovine serum, a supplement used in cell culture), FreeZone 1 (a laboratory freeze dryer), Lipopolysaccharide (LPS, a component of bacterial cell walls), and Sorvall Legend Micro 17 (a centrifuge used in research laboratories).
The Milli-Q water purification system is also relevant, as it provides high-quality water for various research and analytical applications.
By incorporating these related terms, the content becomes more comprehensive and informative for readers interested in the broader context of globalization.