The advent of easily accessible technology, e-commerce, online streaming, and social networking platforms has led to massive amounts of data being stored and processed every second. The IT infrastructures needed to support this digital age consume a large amount of energy and have a negative impact on the environment. There have been several different efforts to estimate the carbon footprint of the internet, but there is no proven exact method for it. Therefore, the goals of this paper are, first—to critically review the carbon emission calculation methods and compare the results, and second—to publicize the environmental impact of our daily simple habit of internet usage. We calculated the carbon footprint of the most popular four online services (TikTok, Facebook, Netflix, and YouTube) by using top-cited methods such those from Obringer, the Shift Project, Andrae, and Hintemann and Hinterholze. When comparing the emitted carbon dioxide, the weighted average of online video streaming usage per day is 51 times more than 14 h of an airplane ride. Netflix generates the highest CO2 emissions among the four applications due to its high-resolution video delivery and its number of users.

Economic globalization (EG) accelerates very fast in Central Asia. This could cause environmental degradation, according to the environmental Kuznets curve (EKC) hypothesis. The study aims to determine how the EG of agriculture impacts environmental sustainability, and to test the EKC hypothesis on the agricultural sector in six Central Asian countries. Particularly, some main hypotheses were proposed using secondary data from Kazakhstan, Kyrgyzstan, Mongolia, Tajikistan, Turkmenistan, and Uzbekistan from 1994 to 2019. This study uses five explanatory variables: agricultural exports value (EXP), agriculture forestry and fishing value-added (AVA), the exchange rate (EXR), total natural resource rents (RENT), and external debt stocks (DEBT), while the dependent variable in this study is the CO2 emissions from on-farm energy use (EMS), temperature changes (TEMP), and forest fires (FIRE). These data are analyzed using panel data regression. As a result, AVA and RENT raise EMS; EXC raises TEMP but lowers EMS; DEBT raises TEMP but can lower FIRE. Hence, we propose recommendations to improve this condition, including a clear roadmap, enhanced partnerships, and regional and international support.

Technological advances such as smartphones, mobile applications, and online platforms have enabled a new form of economy, known as a gig economy, at a large scale, in which there is a free-market system allowing organizations (job providers) to hire independent contractors (job seeker). Unlike traditional employer and employee relationships, the gig economy creates opportunities for independent workers to seek short-term contract jobs and temporary positions. This article presents a systematic review of the literature associated with a bibliometric analysis of the global perspective of the gig economy. The study aims to present the analysis of published articles that explore the gig economy. Initially, 2297 documents were retrieved by gig economy as a keyword from Google Scholar, Scopus, and Web of Science between 2014 and 2022. After applying certain criteria, only 686 publications were selected for bibliometrics analysis. The selected articles were used to measure bibliometric indicators and evaluate the research work on the gig economy. Bibliometrics an R package for bibliometric and co-citation analysis was used to achieve the results. VOSviewer was also used to analyze the co-occurrence of the keywords. The results highlight the publication trends, top contributing authors and their countries, most cited articles, keywords, and most contributing journals to the research field.

Flexibility is perceived as a primary reason for working as a gig worker. However, there are not enough studies that investigate gig workers' motivations. Hence, this study aims to examine the motivation of white-collar gig workers using a sample size of 327 gig workers in Mongolia, a developing country’s labor market. The purpose of this study is to investigate intrinsic and extrinsic motivation factors for white-collar gig workers and their impact on behavioral intentions such as those of remote workers, freelancers, and any representatives of digital gig workers, not location-based gig workers. The primary data used in this study were collected through questionnaires from an online platform that attracts white-collar gig workers. The characteristics of this group are analyzed using Smart PLS4.0 software through the PLS-SEM technique to test the hypothesized relationships. Overall, the study contributes to an extension of current literature by understanding the white-collar digital gig workforce’s motivations and reasons behind it.