Qizhen Wang

Dr. Qizhen Wang


Research Focus & Metrics

Academic Career Length

6Years (2021-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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19 October 2022 in Sustainability
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Journal Article
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QWQizhen Wang
Qizhen Wang
QZQian Zhang
Qian Zhang
A large number of foreign direct investment inflows not only promote China’s economic development but also bring environmental pollution problems., Improving carbon emission efficiency and cutting carbon emissions while maintaining China’s attractiveness to foreign investment has become a topic of concern in China. Firstly, this paper measures the carbon emission efficiency of different provinces in China with the super efficiency DEA model and studies the temporal and spatial characteristics of carbon emission efficiency. Secondly, the impact of FDI on carbon emission efficiency is investigated. FDI negatively affects carbon emissions but positively affects carbon emission efficiency. In addition, the interaction term of FDI and each channel negatively affects carbon emission efficiency, indicating that each channel has a negative impact on the relationship between FDI and carbon emission efficiency. Thirdly, the results of the sub-sample analysis show that the impact of FDI on carbon emission efficiency has the feature of regional heterogeneity. Based on the results, policy implications regarding the improvement of carbon emission efficiency are proposed.

5 December 2022 in Energies
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QWQizhen Wang
Qizhen Wang
SLSuxia Liu
Suxia Liu
China’s economic development is characterized by openness, and trade and investment are important engines for promoting economic development. China’s economy is now in a transitional period, during which excessive carbon emission reduction would inevitably hinder economic development. In this context, improving carbon emission efficiency is an effective way to achieve sustainable development. This paper deals with the relationships among foreign direct investment, technological innovation and carbon emission efficiency. Our research findings include the following. First, carbon efficiency shows regional differences. East China has the highest mean value of carbon emission efficiency, followed by central China and west China over the sample period. Second, FDI exerts both direct and indirect impacts on carbon emission efficiency through technological innovation, which confirms the intermediate effect of technological innovation. Finally, sub-sample analysis indicates that the impact of FDI and technological innovation on carbon emission efficiency show regional heterogeneity. According to these findings, we offer policy recommendations as follows. The government should stimulate independent innovation, promote technological progress in renewable energy and green energy, and attract environmentally friendly foreign investment to improve carbon emission efficiency and boost green development.

QZQian Zhang
Qian Zhang
QWQizhen Wang
Qizhen Wang
The development of China’s manufacturing industry is constrained by factors such as energy and resources, and low-carbon development is arduous. Digitalization is an important method to transform and upgrade traditional industries. Based on the panel data of 13 manufacturing industries in China from 2007 to 2019, a regression model and a threshold model were used to empirically test the impact of digitalization and electricity consumption on carbon emissions. The research results were as follows: (1) The digitalization level of China’s manufacturing industry was steadily increasing; (2) The proportion of electricity consumption in China’s manufacturing industries in the total electricity consumption hardly changed from 2007 to 2019, basically maintaining at about 6.8%. The total power consumption increased by about 2.1 times. (3) From 2007 to 2019, the total carbon emissions of China’s manufacturing industry increased, but the carbon emissions of some manufacturing industries decreased. (4) There was an inverted U-shaped relationship between digitalization and carbon emissions, the higher the level of digitalization input, the greater the carbon emissions of the manufacturing industry. However, when digitalization develops to a certain extent, it will also suppress carbon emissions to a certain extent. (5) There was a significant positive correlation between electricity consumption and carbon emissions in the manufacturing industry. (6) There were double energy thresholds for the impact of labor-intensive and technology-intensive manufacturing digitalization on carbon emissions, but only a single economic threshold and scale threshold. There was a single scale threshold for capital-intensive manufacturing, and the value was −0.5352. This research provides possible countermeasures and policy recommendations for digitalization to empower the low-carbon development of China’s manufacturing industry.

13 January 2022 in Mathematical Problems in Engineering
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Journal Article
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QWQizhen Wang
Qizhen Wang
TZTong Zhao
Tong Zhao
RWRong Wang
Rong Wang
LZLing Zhang
Ling Zhang
With the continuous promotion of industrialization and urbanization, China's environmental pollution is becoming increasingly serious, which has caused considerable damage to the natural balance. Air pollution seriously harms people's physical and mental health, the ecological environment, and the social sustainable development of society. In this study, the backward trajectory model and multifractal methods were adopted to analyze air pollution in Zhengzhou. The backward trajectory analysis showed that most clusters of air pollution were from southern Hebei, eastern Shandong, and mid-western Henan, which were then transported to Zhengzhou. For the PSCF and CWT analyses, we selected four representative cities to explore how close the air pollution of Zhengzhou is to other areas on the basis of air polluted concentration. The results of several multifractal methods indicated that multifractality existed in the AQI time series of Zhengzhou and cross-correlations between Zhengzhou and each of the four cities. The widths of multifractal spectra showed that the air pollution in Zhengzhou was closest to that in Jinan, followed by Shijiazhuang, Zibo, and Luoyang. The CDFA analysis showed that carbon monoxide (CO), nitrogen dioxide (NO2), and inhalable particulate matter (PM10) had important influences on air pollution in Zhengzhou. These findings offer a useful reference for air pollution sources and their potential contributions in Zhengzhou, which can support policy makers in environmental governance and in achieving sustainable urban development.