“Yearning for it” and “no turning back”—A study on Chinese international high school students’ motivations in the context of the COVID-19 outbreak

This paper studies the macro situation of studying abroad in the context of the COVID-19 outbreak and the motivations of Chinese international high school students studying abroad. The research shows that from the macro situation, the “push” growth is caused by the epidemic situation, racial discrimination, tightening employment and immigration policies, online teaching, and other reasons, while the “pull” growth is caused by the good control of the epidemic situation and the sense of belonging and security for students in China; however, from the perspective of individual choice, most international high school students still insist on studying abroad, “demand effect” and “cost effect” can explain their motivations of studying abroad. The motivation of those students going abroad unshakably mainly shows “demand effect.” They tend to satisfy the differentiated demand of personal development through studying abroad and believe that this demand is more difficult to obtain in home country. While the motivation of those students going abroad reluctantly mainly shows “cost effect.” The cost of early investment including not only monetary expenditures but also behavioral choices in K-12 education leads to their continued investment.

A Study on Improving Customer Value Based on the Effect of Word of Mouth

The overlapping effect originates from an extension of Mendel’s law in genetics, where one of the interactions between non-alleles is called additive effect. It is more applied in studies on overlapping brand niches in marketing today, with relatively few researches on continuous customer value creation characterised by user adhesion and need matching. Based on the need matching and user adhesion that are features of the mobile Internet era, this article proposes a model for continuous customer value creation based on overlapping marketing. According to grounded theory, this article extracts three types of factors—demand effect, user effect, and overlapping marketing—that affect continuous customer value creation in the smart terminal business. From the perspective of service perception, this research explores how overlapping marketing affects product requirement matching and user adhesion based on a survey of 491 participants, and validates the theoretical model and hypotheses. It is found that overlapping marketing can effectively enhance need matching, improve user adhesion and increase customer value. This research not only addresses the confusion regarding need matching and user adhesion in the communications market, but also reveals how the smart terminal business affects continuous customer value creation in the era of the mobile Internet through overlapping marketing, combined with need matching and user adhesion.

Structural emission reduction of China's industrial system and energy system: An input-output analysis

The global greenhouse effect caused by excessive energy CO 2 emissions has seriously affected the sustainable development of the society, and energy consumption and production mainly come from industrial system and energy system. This paper used the structural decomposition analysis (SDA) and the input-output analysis to study the structural emission reduction of China's industrial and energy systems in 2007-2015. The results showed that: (1) From the analysis of structural factors, the final demand effect was the main factor to promote the growth of energy CO 2 emissions, and the energy intensity effect played a weak role in promoting the growth of energy CO 2 emissions. (2) From the perspective of energy systems, the emission reduction effect of blast furnace gas, raw coal, refinery dry gas and natural gas is obvious, while that of crude oil, gasoline, fuel oil and kerosene is not obvious. (3) From the perspective of China's industrial systems, the tertiary industry played a major role in the final demand effect, followed by secondary industries and the primary industry in turn. Finally, this paper provided a theoretical basis and realistic guiding route for the accurate and efficient emissions reduction of energy system and China's industrial system.

Examining the Driving Factors of Urban Residential Carbon Intensity Using the LMDI Method: Evidence from China’s County-Level Cities

Improving carbon efficiency and reducing carbon intensity are effective means of mitigating climate change. Carbon emissions due to urban residential energy consumption have increased significantly; however, there is a lack of research on urban residential carbon intensity. This paper examines the spatiotemporal variation of carbon intensity in the residential sector during 2001–2015, and then identifies the causes of the variation by utilizing the logarithmic mean Divisia index (LMDI) with the help of Microsoft Excel 2016 for 620 county-level cities in 30 Chinese provinces. The results show that high carbon intensity is mainly found in large cities, such as Beijing, Tianjin, and Shanghai. However, these cities showed a downward trend in carbon intensity. In terms of influencing factors, the energy consumption per capita, urban sprawl, and land demand are the three most influential factors in determining the changes in carbon intensity. The effect of energy consumption per capita mainly increases the carbon intensity, and its impact is higher in the municipal districts of provincial capital cities than in other types of cities. Similarly, the urban sprawl effect also promotes increases in carbon intensity, and a higher degree of influence appears in large cities. However, as urban expansion plateaus, the effect of urban sprawl decreases. The land-demand effect reduces the carbon intensity, and the degree of influence of the land-demand effect on carbon intensity is also clearly stronger in big cities. Our findings show that lowering the energy consumption per capita and optimizing the land-use structure are a reasonable direction of efforts, and the effects of differences in influencing factors should be paid more attention to reduce carbon intensity.

Research on driving factors of China’s carbon emissions based on structural decomposition

After further processing the input-output tables of 2007, 2012 and 2017, the carbon emissions are decomposed into four driving factors: energy intensity effect, Leontief technology effect, final demand structure effect and final total demand effect through IO-SDA model. The results show that the energy intensity effect has a significant negative effect, which is the main factor to promote the reduction of carbon emissions. The Leontief technical effect and the final total demand effect are positive effects, and the total final demand effect is the main factor leading to the increase in carbon emissions, and the effect of the final demand structure effect is not significant. In addition, the results of the influence coefficient and the inductance coefficient show that: metal smelting and rolling manufacturing, petroleum processing and coking and nuclear fuel processing, coal mining and processing, and oil and gas mining and processing industries are high-energy-consuming industries, but the status of the basic industry makes it possible to formulate energy-saving policies only in terms of technological progress.

Dynamic evolution analysis of the factors driving the growth of energy-related CO2 emissions in China: An input-output analysis

In recent years, the global greenhouse effect caused by excessive energy-related carbon emissions has attracted more and more attention. In this paper, we studied the dynamic evolution of factors driving China's energy-related CO2 emissions growth from 2007 to 2015 by using energy consumption method and input-output analysis and used the IO-SDA model to decompose the energy carbon emissions. Within the research interval, the results showed that (1) on the energy supply-side, the high carbon energy represented by raw coal was still the main factor to promote the growth of energy-related CO2 emissions. However, the optimization of energy consumption structure is conducive to reducing emissions. Specifically, the high carbon energy represented by raw coal exhibited a downward trend in promoting the increment of energy-related CO2 emissions, while the clean energy represented by natural gas showed an upward trend in promoting the increment of CO2 emissions. It is worth noting that there is still a lot of room for optimization of China’s energy consumption structure to reduce emissions. (2) On the energy demand-side, the final demand effect is the main driving force of the growth of carbon emissions from fossil energy. Among them, the secondary industry plays a major role in the final demand effect. The "high carbonization" of the final product reflects the characteristics of China's high energy input in the process of industrialization. At the same time, since the carbon emission efficiency of the tertiary industry and the primary industry is better than that of the secondary industry, actively optimizing the industrial structure is conducive to slowing down the growth of carbon emission brought by the demand effect. (3) The input structure effect is the main restraining factor for the growth of energy carbon emissions, while the energy intensity effect has a slight driving effect on the growth of energy carbon emissions. The results show that China's "extensive" economic growth model has been effectively reversed, but the optimization of fossil energy utilization efficiency is still not obvious, and there is still a large space to curb carbon emissions by improving fossil energy utilization efficiency in the future.