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Financial agglomeration and green economic growth have become a trend in global financial and economic development. This paper analyzes the impact mechanism of financial agglomeration on green economic growth from two angles: growth promotion and energy conservation/emission reduction. Based on the Slacks Based Model-Data Envelopment Analysis (SBM-DEA) of undesired outputs, the green economic growth efficiency of 30 regions in China from 2008 to 2017 was measured. The study uses a spatial measurement model and finds that financial agglomeration has a significant positive effect on green economic growth. The specific manifestation is that with a 1% increase in the level of financial agglomeration, the productivity of green total factor increases by about 0.1837% and 0.0964% green economic growth in neighboring areas. Further analysis shows that this improvement in green total factor productivity is mainly achieved through technological advancement that promotes coordinated economic growth, energy saving and emission reduction.
Yu Qian; Jun Liu; Jeffrey Yi-Lin Forrest. Impact of financial agglomeration on regional green economic growth: evidence from China. Journal of Environmental Planning and Management 2021, 1 -26.
AMA StyleYu Qian, Jun Liu, Jeffrey Yi-Lin Forrest. Impact of financial agglomeration on regional green economic growth: evidence from China. Journal of Environmental Planning and Management. 2021; ():1-26.
Chicago/Turabian StyleYu Qian; Jun Liu; Jeffrey Yi-Lin Forrest. 2021. "Impact of financial agglomeration on regional green economic growth: evidence from China." Journal of Environmental Planning and Management , no. : 1-26.
Urban governance is an important cornerstone in the modernization of a national governance system. The establishment of smart cities driven by digitalization will be a vital way to promote economic green and sustainable growth. By using the data of 274 prefecture-level cities in China from 2004 to 2017, we study the impact of smart city policy on economic green growth and the underlying mechanism of the impact. It is shown that the establishment of smart cities has significantly promoted the green growth of China's economy. This conclusion is further confirmed by using exogenous geographic data as instrumental variables and robustness tests, such as the quasi-experimental method of Difference in Difference with Propensity Score Matching (PAM-DID). The mechanism test shows that promoting economic growth, reducing per unit GDP energy consumption, and lowering waste emissions represent three ways for smart cities to promote green economic growth. The heterogeneity test shows that smart city policy has an obvious promotional effect on the economic green growth of both large cities and non-resource-based cities. This paper is expected to provide a reference for the urban development and economic transformation of emerging economies.
Yu Qian; Jun Liu; Zhonghua Cheng; Jeffrey Yi-Lin Forrest. Does the smart city policy promote the green growth of the urban economy? Evidence from China. Environmental Science and Pollution Research 2021, 1 .
AMA StyleYu Qian, Jun Liu, Zhonghua Cheng, Jeffrey Yi-Lin Forrest. Does the smart city policy promote the green growth of the urban economy? Evidence from China. Environmental Science and Pollution Research. 2021; ():1.
Chicago/Turabian StyleYu Qian; Jun Liu; Zhonghua Cheng; Jeffrey Yi-Lin Forrest. 2021. "Does the smart city policy promote the green growth of the urban economy? Evidence from China." Environmental Science and Pollution Research , no. : 1.
Based on the pilot projects of intelligent manufacturing of the Ministry of Industry and Information Technology of the People’s Republic of China and the annual data reported by listed companies, this paper studies the effect of China's intelligent policy on the performance of listed manufacturing companies by using the panel data of relevant enterprises from 2011 to 2017, as well as the mechanism of impact. Our empirical tests, using the difference-in-difference method, shows that intelligent policy can significantly improve the economic performance of manufacturing enterprises by guiding enterprises to optimize their intelligent management, strengthening investment in intelligent equipment and promoting collaborative manufacturing. Further empirical tests show that the impact of intelligent policy on economic performance is different in time. In the later selected pilot enterprises, the impact of intelligent policy on their economic performance is more significant; there is regional heterogeneity in the effects of intelligent policy: in regions with low intelligence, the positive impact of intelligent policy on the economic performance of manufacturing industry is more significant. Based on these conclusions, relevant intelligent policy suggestions are put forward.
Jun Liu; Yuan-Jun Yang; Ya-Ru Cao; Jeffrey Yi-Lin Forrest. Stimulating effects of intelligent policy on the performance of listed manufacturing companies in China. Journal of Policy Modeling 2021, 43, 558 -573.
AMA StyleJun Liu, Yuan-Jun Yang, Ya-Ru Cao, Jeffrey Yi-Lin Forrest. Stimulating effects of intelligent policy on the performance of listed manufacturing companies in China. Journal of Policy Modeling. 2021; 43 (3):558-573.
Chicago/Turabian StyleJun Liu; Yuan-Jun Yang; Ya-Ru Cao; Jeffrey Yi-Lin Forrest. 2021. "Stimulating effects of intelligent policy on the performance of listed manufacturing companies in China." Journal of Policy Modeling 43, no. 3: 558-573.
Artificial Intelligence (AI) is becoming the engine of a new round of technological revolution and industrial transformation; as such, it has attracted much attention of scholars in recent years. Surprisingly, scarce studies have shed lights on the effects of AI on the environment, especially with respect to carbon intensity. Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, we use Chinese industrial sector data from 2005 to 2016 to investigate how AI affects carbon intensity. The empirical results show that AI, as measured separately by the adoption of robotics by industry and the number of academic AI-related papers, significantly reduces carbon intensity. The results remain robust after addressing endogenous issues. We find that there are both stages and industrial heterogeneity in the effects of AI on carbon intensity. AI had a more decrease effect on carbon intensity during the 12th Five-Year Plan than the 11th. Compared with capital-intensive industries, AI tends to have a more decrease effect on carbon intensity in the labor-intensive and tech-intensive industries. To enlarge the effects of AI on reducing carbon intensity, the government should promote the development and application of AI and implement differentiated policies in line with the industry characteristics.
Jun Liu; Liang Liu; Yu Qian; Shunfeng Song. The effect of artificial intelligence on carbon intensity: Evidence from China's industrial sector. Socio-Economic Planning Sciences 2021, 101002 .
AMA StyleJun Liu, Liang Liu, Yu Qian, Shunfeng Song. The effect of artificial intelligence on carbon intensity: Evidence from China's industrial sector. Socio-Economic Planning Sciences. 2021; ():101002.
Chicago/Turabian StyleJun Liu; Liang Liu; Yu Qian; Shunfeng Song. 2021. "The effect of artificial intelligence on carbon intensity: Evidence from China's industrial sector." Socio-Economic Planning Sciences , no. : 101002.
Green growth in manufacturing is critical to the sustainable development of manufacturing, and environmental regulations can help ensure green growth. The impact of environmental regulations on China’s manufacturing industry sectors is investigated to further green development in manufacturing. Using panel data for manufacturing industry sectors from 2008 to 2015, the Malmquist-Luenberger index model is employed to calculate green growth efficiency and an econometric model is constructed to measure the impact of environmental regulations on green growth. By using the system generalized method of moments (system GMM) model and other panel estimation models to generate regression results, it is found that environmental regulation exhibits a U-shaped nonlinear influence on green growth; as the intensity of environmental regulations increases, there is an initial inhibiting effect followed a positive impact on green growth in the manufacturing industry. Once environmental regulation intensity reaches a certain level, it mainly promotes green growth through technological progress. Further findings include the following: impacts of environmental regulation on green growth are heterogeneous across industries, and effects (e.g. U-shaped impacts) are most significant among high-energy industries, high-pollution industries, and medium-pollution industries.
Yaru Cao; Jun Liu; Yu Yu; Guo Wei. Impact of environmental regulation on green growth in China’s manufacturing industry–based on the Malmquist-Luenberger index and the system GMM model. Environmental Science and Pollution Research 2020, 27, 41928 -41945.
AMA StyleYaru Cao, Jun Liu, Yu Yu, Guo Wei. Impact of environmental regulation on green growth in China’s manufacturing industry–based on the Malmquist-Luenberger index and the system GMM model. Environmental Science and Pollution Research. 2020; 27 (33):41928-41945.
Chicago/Turabian StyleYaru Cao; Jun Liu; Yu Yu; Guo Wei. 2020. "Impact of environmental regulation on green growth in China’s manufacturing industry–based on the Malmquist-Luenberger index and the system GMM model." Environmental Science and Pollution Research 27, no. 33: 41928-41945.
This paper analyzes the impact of artificial intelligence (AI) on technological innovation through logic reasoning and empirical modeling. Based on the industrial robot data provided by the International Federation of Robotics (IFR) and the panel data of China's 14 manufacturing sectors from 2008 to 2017, this paper empirically analyzes the impact of AI on technological innovation. Our analysis shows that the mechanism of how AI affects technological innovation is that the former promotes technological innovation through accelerating knowledge creation and technology spillover, improving learning and absorptive capacities, while increasing R&D and talent investment. Our empirical results indicate that under the condition of controlling intensity of R&D investment, FDI, ownership structure, technical imitation, AI significantly promotes technological innovation. And the impact of AI on technological innovation experiences sector heterogeneity: AI has more significant impact on the technological innovation of low-tech sectors. The higher the level of AI, the greater its impact on technological innovation. Based on our established conclusions, we provide corresponding suggestions and recommendations for managerial decision-making.
Jun Liu; Huihong Chang; Jeffrey Yi-Lin Forrest; Baohua Yang. Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors. Technological Forecasting and Social Change 2020, 158, 120142 .
AMA StyleJun Liu, Huihong Chang, Jeffrey Yi-Lin Forrest, Baohua Yang. Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors. Technological Forecasting and Social Change. 2020; 158 ():120142.
Chicago/Turabian StyleJun Liu; Huihong Chang; Jeffrey Yi-Lin Forrest; Baohua Yang. 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors." Technological Forecasting and Social Change 158, no. : 120142.
Meteorological disasters have become a global challenge due to the increased prevalence and severity, and China is among the most affected countries. In this paper, based on a randomized survey in China, the authors employed a structural equation model to study the influencing factors of public participation in meteorological disaster prevention and mitigation (MDPM). It is found that the behavior of the government has a significant positive influence, with an influencing coefficient of 0.494 on the public’s willingness to participate in MDPM. The degree of community involvement also has a significant positive influence on the public’s willingness, with an influencing coefficient of 0.636. The public’s attention to meteorological events and ability to participate have less impact on their participation in MDPM, with coefficients of 0.057 and 0.075, respectively. The information acquisition has a significant negative impact, with an influencing coefficient of −0.084. There is a strong positive covariation between community participation and governmental behavior, with a covariance coefficient of 0.27, indicating that the two factors promote each other and together boost the public’s willingness to participate in MDPM.
Rongrong Duan; Jun Liu; Changkai Wang; Guo Wei. Influencing Factors of Public Participation in Meteorological Disaster Prevention and Mitigation. Sustainability 2020, 12, 3108 .
AMA StyleRongrong Duan, Jun Liu, Changkai Wang, Guo Wei. Influencing Factors of Public Participation in Meteorological Disaster Prevention and Mitigation. Sustainability. 2020; 12 (8):3108.
Chicago/Turabian StyleRongrong Duan; Jun Liu; Changkai Wang; Guo Wei. 2020. "Influencing Factors of Public Participation in Meteorological Disaster Prevention and Mitigation." Sustainability 12, no. 8: 3108.
Based on panel data on 285 Chinese cities from 2003 to 2012, we use a dynamic spatial panel model to empirically analyze the effect of manufacturing agglomeration on haze pollution. The results show that when economic development levels, population, technological levels, industrial structure, transportation, foreign direct investment, and greening levels are stable, manufacturing agglomeration significantly aggravates haze pollution. However, region-specific analysis reveals that the effects of manufacturing agglomeration on inter-regional haze pollution depends on the region: the effect of manufacturing agglomeration on haze pollution is the largest in the Western region, followed by the Central region, and is the least in the Eastern region. Based on the above conclusions, we put forward several specific suggestions, such as giving full play to the technology and knowledge spillover effects of manufacturing agglomeration, guiding manufacturing agglomerations in a scientific and rational way, accelerating the transformation and upgrading of manufacturing industries in agglomeration regions.
Jun Liu; Yuhui Zhao; Zhonghua Cheng; Huiming Zhang. The Effect of Manufacturing Agglomeration on Haze Pollution in China. International Journal of Environmental Research and Public Health 2018, 15, 2490 .
AMA StyleJun Liu, Yuhui Zhao, Zhonghua Cheng, Huiming Zhang. The Effect of Manufacturing Agglomeration on Haze Pollution in China. International Journal of Environmental Research and Public Health. 2018; 15 (11):2490.
Chicago/Turabian StyleJun Liu; Yuhui Zhao; Zhonghua Cheng; Huiming Zhang. 2018. "The Effect of Manufacturing Agglomeration on Haze Pollution in China." International Journal of Environmental Research and Public Health 15, no. 11: 2490.