Intelligent manufacturing, green technological innovation and environmental pollution

Shen, Yang; Zhang, Xiuwu

doi:10.1016/j.jik.2023.100384

Cited by 67 publications

(18 citation statements)

References 76 publications

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“…Furthermore, historical emphasis on pursuing total GDP growth in regional development, often at the expense of improving living standards, has been a persistent issue. This is especially true when following an extensive economic growth model characterized by "high investment, high pollution, high consumption," which has resulted in an ecological system under "overcapacity", particularly evident in ecological fragility and severe environmental pollution (Shen and Zhang, 2023). This has significantly hindered improvements in ecological well-being for the populace.…”

Section: Discussionmentioning

confidence: 99%

Evaluation and obstacle analysis of high-quality development in Yellow River Basin and Yangtze River Economic Belt, China

Yang,

Feng,

Chen

2023

Humanit Soc Sci Commun

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Based on China’s new development philosophy, and following the idea of “assessing the overall levels firstly, then anchoring the subsystems contributions and finally identifying the obstacle factors”, an evaluation system of high-quality development (HQD) levels is constructed from five subsystems of innovative development (ID), coordinated development (CD), green development (GD), open development (OD) and shared development (SD). In this paper, the entropy weight TOPSIS model is used to measure the HQD levels of 19 provincial units located in the Yellow River Basin (YRB) and the Yangtze River Economic Belt (YREB) in 2010–2019, and with the assistance of the Obstacle Degree Model, the obstacle factors affecting the HQD of both are depicted. The results show that: (1) between 2010 and 2019, the HQD indices in YRB and YREB have been on an increasing trend, and the subsystems indices all show a trend of “GD > CD > SD > ID > OD”, but the overall levels of HQD in both is not high, and there is still much room for improvement. (2) YREB has a significantly higher HQD level than YRB. From 2010 to 2019, the average values of the HQD indices in YRB and YREB were 0.2886 and 0.4073, respectively, but the index in YRB improves at a faster pace than YREB, and the gap of HQD levels between the two show a trend of continuous reduction towards regional balance and coordination. (3) The analysis of obstacle factors shows that while OD and ID are the main obstacles to HQD in both basins, the impact of both on the resistance to HQD in both basins is on a decreasing trend. GD is another bottleneck for the HQD of YRB, and the pressure on GD in the middle and upper reaches of the provincial units in the basin is increasing, and the obstacles are on a rising trend.

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Section: Discussionmentioning

confidence: 99%

Evaluation and obstacle analysis of high-quality development in Yellow River Basin and Yangtze River Economic Belt, China

Yang,

Feng,

Chen

2023

Humanit Soc Sci Commun

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“…Given that the industrial sector is a major source of energy consumption and carbon emissions, this study used industrial robot installation density to measure the level of digital technology development at the provincial level. According to the research concepts of the existing literature (Acemoglu and Restrepo, 2020;Caselli et al, 2021;Chen et al, 2022;Shen and Zhang, 2023) is the robot installation density of each year and industry level.…”

Section: Core Explanatory Variablementioning

confidence: 99%

Assessing the Impact of Digital Technologies on Energy Efficiency: The Role of OFDI and Virtual Agglomeration

Yang,

Mengyu,

Xiuwu

2024

Journal of Resources and Ecology

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Improving energy efficiency is crucial for achieving the carbon peaking and carbon neutrality goals. The digital economy, which is characterized by big data, artificial intelligence, the internet of things, and a new generation of mobile Internet, has quietly penetrated all aspects of the economy and society, profoundly changing the means of production and lives of human beings. Digital technologies have great potential to improve the global energy system's security, productivity, efficiency, and sustainability. Based on the panel data of 30 provinces in mainland China from 2006 to 2021, this study divided energy efficiency into total and single factor energy efficiency. The two-way fixed-effect model and the Driscol-Kraay method were used to adjust the standard error test in order to examine the impact of digital technology represented by industrial robots on energy efficiency and its path mechanism. Studies have shown that digital technology can significantly improve total factor energy efficiency and reduce energy intensity per unit of GDP. This conclusion was found to be still valid after the robustness test using feasible generalized least squares, time-varying difference in difference and fixed effect space Durbin model. The results of the mechanism test show that digital technology can improve energy efficiency by increasing the degree of industrial virtual agglomeration and the channels of foreign direct investment. This paper provides a valuable discussion on how information technology advances can improve energy efficiency in the era of the digital economy. The conclusions will help relevant market players to formulate policies and measures and corporate strategies to improve energy efficiency. At the same time, it also deepens the theoretical understanding and mechanism path of digital technology's impact on energy consumption.

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“…Compared with other estimation methods, this method can obtain consistent standard errors in the control of heteroscedasticity and autocorrelation. When the time dimension is gradually increased, the standard errors are robust to the general form of sectional correlation and time correlation (Driscoll and Kraay, 1998;Shen et al, 2023). According to research H1, bidirectional fixed effect (TWFE) is used to fit Equations 1, 2 to obtain the results in Table 4.…”

Section: Baseline Regressionmentioning

confidence: 99%

“…Critical tasks in the pilot zone include big data system innovation, data resource sharing and opening up, big data innovation and application, big data industry cluster development, overall development of big data infrastructure, data center integration and utilization, and big data exchange and cooperation. Promoting the use and sharing of data resources is the primary task of establishing big data pilot zones (Shen et al, 2023;Guo et al, 2023). Based on the construction list and experiment Time of the national big data comprehensive experimental zone approved by the National Development and Reform Commission, the Ministry of Industry and Information Technology, and the Cyberspace Administration of the CPC Central Committee, this paper constructs proxy variables of DT by using the interaction terms of time and place virtual variables and then uses time-varying DID to estimate.…”

Section: Replace the Core Explanatory Variablementioning

confidence: 99%

“…Governments worldwide are actively embracing the revolution and driving digital transformation across industries (Müller et al, 2019;Saha et al, 2022;Shen and Zhang, 2023). The IEA's report on the Digital Transformation of Energy points out that digital technologies will provide new solutions to significant problems facing the development of the energy industry and make production and operation models more efficient.…”

Section: Introductionmentioning

confidence: 99%

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Technology-driven energy revolution: the impact of digital technology on energy efficiency and its mechanism

Wu,

Han,

Shen

2023

Front. Energy Res.

Self Cite

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Introduction: Improving energy efficiency is significant for achieving carbon emission reduction and promoting the transformation of green economic development. In the sustainable development framework set out in the 2030 Agenda for Sustainable Development, Goal 7.3 explicitly aims to double the global rate of energy efficiency improvement by 2030. The rapid development of digital technology, along with its universality and penetrative characteristics, has provide a feasible solution for improving energy efficiency and environmental conditions. However, the theoretical understanding of the impact and underlying logic of digital technology on energy efficiency remains unclear.Methods: Based on the panel data of 30 provinces in China from 2006 to 2021, this paper adopts econometric methods, including two-way fixed effect, instrumental variable method, and Driscoll-Kraay standard error. It investigates the influence of digital technology on energy efficiency and its internal mechanism from single factor and all factor levels.Result: The results show that Digital technology, represented by industrial robots, significantly improves energy efficiency, whether measured by the energy consumption intensity of GDP or the total-factor energy efficiency estimated using the SBM-GML model. The results still hold even after conducting endogeneity tests and robustness tests. Digital technology can improve energy efficiency by increasing virtual industrial agglomeration and promoting outward foreign direct investment.Discussion: In addition to promoting the theoretical understanding of the impact of digital technology on energy efficiency and exploring its mechanism, this paper also provides empirical evidence for policy makers and enterprises to formulate effective measures and strategies to improve energy efficiency under the background of digital economy.

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Intelligent manufacturing, green technological innovation and environmental pollution

Cited by 67 publications

References 76 publications

Evaluation and obstacle analysis of high-quality development in Yellow River Basin and Yangtze River Economic Belt, China

Evaluation and obstacle analysis of high-quality development in Yellow River Basin and Yangtze River Economic Belt, China

Assessing the Impact of Digital Technologies on Energy Efficiency: The Role of OFDI and Virtual Agglomeration

Technology-driven energy revolution: the impact of digital technology on energy efficiency and its mechanism

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