Interaction between Higher Education Outputs and Industrial Structure Evolution: Evidence from Hubei Province, China

Dan, He; Zheng, Manxin; Cheng, Wei; Lau, Yui‐yip; Yin, Qingmei

doi:10.3390/su11102923

Cited by 18 publications

(15 citation statements)

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“…The former ndings of industry structure and CO2 emissions are also con rmed in studies using China as a context (Zhou, Zhang & Li, 2013;Zhang, Liu, Zhang & Tan, 2014;Wang, Wu, Sun, Shi, Sun &Zhang, 2019;Guan, Meng, Reiner, Zhang, Shan & Mi et al, 2018), as well as technological level and CO2 emissions (Ang, 2009;Wang, Zeng & Liu, 2019;Yunfeng & Laike, 2010). Some scholars suggest that education plays a key role in the evolution of industry structure and technological progress (Keep, 2012;He, Zheng, Cheng, Lau & Cheng, 2019;Hansmann, 2012;Atkinson & Mayo, 2010;Adams & Demaiter, 2018). Following this reasoning, education should also play an important role in reducing CO2 emissions.…”

Section: Literature Reviewmentioning

confidence: 86%

The Impacts of Non-Renewable Energy Consumption and Education Expenditure on CO2 Emission Intensity of Real GDP in China

Deng

Liang

2021

Preprint

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BackgroundWith the economic development, China has become the world's largest CO2 emitter. Given that climate warming has increasingly become the focus of the international community, Chinese government committed to reducing its CO2 emission intensity substantially. Prior studies find that the evolution of economic structure and technological progress can reduce CO2 emissions, but lack of considering CO2 emissions and output as a whole. In addition, the role of education expenditure is relatively overlooked. This paper contributes to the literature by examining the link of CO2 emission intensity, non-renewable energy consumption and education expenditure in China during 1971-2014. ResultsWe use the ARDL approach and find that in the long run, every 1% increase in non-renewable energy consumption results in a 0.92% increase in CO2 intensity, while every 1% increase in operational education expenditure reduces the CO2 intensity by 0.86%. In the short term, 36% of the deviation from the long run equilibrium is corrected in the next period.ConclusionsWe draw out two important conclusions and make important policy recommendations. First and foremost, as long as the increase in operational educational expenditure exceeds the increase in non-renewable energy consumption, CO2 intensity of real GDP will decrease in the long run. This means that in the development stage when economic activities are still highly dependent on non-renewable energy sources, the Chinese government should continue to vigorously increase expenditures on public education. Second, the increase in non-renewable energy consumption will result in an increase in CO2 intensity of real GDP. Therefore, gradually increasing the proportion of clean energy consumption in the energy nexus is another powerful starting point for China to achieve its goal of reducing CO2 intensity of real GDP.JEL ClassificationC32. I2. Q4. Q53. Q56.

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Section: Literature Reviewmentioning

confidence: 86%

The Impacts of Non-Renewable Energy Consumption and Education Expenditure on CO2 Emission Intensity of Real GDP in China

Deng

Liang

2021

Preprint

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“…Moreover, it creates a perfect match between the demands of maritime industry development and the supply of human capital. In the future, we may suggest some methods to facilitate the knowledge transfer from academic institutions to the maritime industry by generating collaboration networks, conducting private-public development programs and scholarly activities, and setting up specialized units [10].…”

Section: Discussionmentioning

confidence: 99%

“…Human capital theory reinforces the relationship between education and improved productivity [9]. He (2019) [10] expected that the tertiary students would generate a large pool of innovative human capital in the future. With investments in higher education, it is expected that the industry will evolve from low value-added exportation to high value-added exportation.…”

Section: Introductionmentioning

confidence: 99%

“…Although the maritime industry is one of the economic upholders globally, a critical issue makes vulnerable the sound development of the maritime industry: a talent shortage and a lack of young generations motivated to join the labor market over the next decades [8]. This is a time in which we need to revisit maritime programs and adjust the settings of higher education institutions in response to the transformations of the industrial structure [10].…”

Section: Introductionmentioning

confidence: 99%

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Maritime Undergraduate Students: Career Expectations and Choices

et al. 2021

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The following study presents an inquiry into understanding the motivating factors of students to enroll in a maritime university and to further continue with a career in the maritime industry. By collecting data from 378 undergraduate students enrolled in various maritime programs (navigation, electromechanics, electrical engineering, and economic engineering in transport), we aimed to better understand the profiles of students, their information sources, interest, and the prospects of associated programs where they are enrolled. As such, this study seeks to enable educators and industry practitioners to better understand the educational and career paths chosen by undergraduates in the maritime field. It can align the students’ expectations with program delivery. We examined students’ perceptions and assessments according to the program they are enrolled in. Taking into account the fact that there is a world low attraction for maritime careers, the results of this study are useful for maritime education and training (MET) providers during the design and marketing campaign of the educational program to attract students. Additionally, the findings are useful for public administration and the Ministry of Education’s analysis of expanding educational and research programs, as well as for Ministry of Labour forecasting. Employers from the maritime industry can find useful the main motives for which a graduate would choose to work in this sector, business field, or a related business. Moreover, industry practitioners and academia can expand the study at a larger scale, comprising more countries and taking into account national and regional characteristics.

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“…Due to constant digital transformation and innovation, the demand for an immensely skilled workforce with new skill sets is increasing [21]. Regarding the expansion of technology-based knowledge and the advent of the global economy, education is directly responsible for the evolution of industrial structures [22]. Therefore, the importance of on-demand educational training and lifelong learning activities is beyond doubt as the reskilling of the workforce facilitates the ability of people to cope with future job creation.…”

Section: Plos Onementioning

confidence: 99%

Regional development potentials of Industry 4.0: Open data indicators of the Industry 4.0+ model

2021

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This paper aims to identify the regional potential of Industry 4.0 (I4.0). Although the regional background of a company significantly determines how the concept of I4.0 can be introduced, the regional aspects of digital transformation are often neglected with regard to the analysis of I4.0 readiness. Based on the analysis of the I4.0 readiness models, the external regional success factors of the implementation of I4.0 solutions are determined. An I4.0+ (regional Industry 4.0) readiness model, a specific indicator system is developed to foster medium-term regional I4.0 readiness analysis and foresight planning. The indicator system is based on three types of data sources: (1) open governmental data; (2) alternative metrics like the number of I4.0-related publications and patent applications; and (3) the number of news stories related to economic and industrial development. The indicators are aggregated to the statistical regions (NUTS 2), and their relationships analyzed using the Sum of Ranking Differences (SRD) and Promethee II methods. The developed I4.0+ readiness index correlates with regional economic, innovation and competitiveness indexes, which indicates the importance of boosting regional I4.0 readiness.

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Interaction between Higher Education Outputs and Industrial Structure Evolution: Evidence from Hubei Province, China

Cited by 18 publications

References 50 publications

The Impacts of Non-Renewable Energy Consumption and Education Expenditure on CO2 Emission Intensity of Real GDP in China

The Impacts of Non-Renewable Energy Consumption and Education Expenditure on CO2 Emission Intensity of Real GDP in China

Maritime Undergraduate Students: Career Expectations and Choices

Regional development potentials of Industry 4.0: Open data indicators of the Industry 4.0+ model

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