Industrial agglomeration, FDI, and carbon emissions: new evidence from China's service industry.

In the context of economic servitization and low carbonization, the problem of carbon emissions in the service industry is worthy of attention. An essential channel for restraining carbon emissions from the service industry is industrial agglomeration. Based on provincial panel data from 2004 to 2021 in China, this study empirically analyzes the influence of the service industry's agglomeration on its CO2 emissions. The findings indicate that agglomeration significantly reduces the industry's carbon emissions. Next, producer services agglomeration has a significant carbon-reduction effect, whereas non-producer services agglomeration does not. Moreover, service industry agglomeration helps to restrain carbon emissions from the service industry in East China. However, it does not significantly affect carbon emissions in Central or West China. Regarding the moderating effect, foreign direct investment can enhance service industry agglomeration's carbon-reduction effect. Based on the results, relevant policy implications are provided.

Synergistic effect of air pollutant abatement on CO2 reduction in the industrial sector: evidence from China.

The synergistic abatement of air pollutants and CO2 emission in the industrial sector is crucial for China to achieve its energy conservation and emission reduction goals. However, none of the literature has conducted a systematic and in-depth research on assessing the synergistic abatement effect and identification of its driving factors for the industrial sector in China. Therefore, based on 36 industrial sub-sectors in China, we assess the synergistic effect of air pollutants on CO2 and examine the expansion mechanism of the synergistic effect. Furthermore, we explore how three driving factors, namely environmental regulation, technological progress and energy structure, affect the synergistic effect of abatement. The results indicate that, for the whole industrial sector, the synergistic effect of air pollutant abatement on CO2 reduction is significant, positively moderated by the enhancement of R&D investment, fixed asset investment and market openness. Strengthening environmental regulation, improving technological progress and optimizing energy structure could effectively promote the synergistic abatement effect. For three industrial subdivisions, the synergistic effect exists in three industrial categories, increasing R&D investment and fixed asset investment could positively moderate the synergistic effect. The three driving factors, environmental regulation, technological progress, and energy structure, could boost synergistic abatement for capital-intensive industries, but hardly for resource- and labor-intensive industries. In technology-intensive industries, only environmental regulation and technological progress could promote synergistic abatement. The findings could offer scientific support for the policymaking of the synergistic control of air pollutants emission and CO2 emission in China's industrial sector.

Impact of Manufacturing Agglomeration on the Green Innovation Efficiency-Spatial Effect Based on China's Provincial Panel Data.

Improving the efficiency of green innovation has become an urgent issue in the transformation of manufacturing industries in most developing countries within the context of increasing resource scarcity and environmental constraints. As an important feature of manufacturing development, agglomeration also plays a substantial role the promotion of technological progress and green transformation. Taking China as an example, this paper investigates the spatial impact of manufacturing agglomeration (MAGG) on green innovation efficiency (GIE). We first measure the level of MAGG and GIE in 30 provinces (autonomous regions and municipalities) in China during the period from 2010 to 2019, and then we utilize the spatial Durbin model in order to empirically test the spatial effect and heterogeneity based on theoretical analysis. The findings demonstrate that (1) the overall GIE in China has maintained a steady increase, and the level of MAGG slowly decreased from 2010 to 2019 with characteristics of obvious regional non-equilibrium and spatial correlations; (2) MAGG has a significant effect on the improvement of GIE nationally; (3) under the constraints of regional heterogeneity, the impacts of MAGG on GIE show significant differences between eastern, central and western China; (4) in terms of industry heterogeneity, high-tech MAGG can significantly enhance local GIE, while the indirect effect of non-high-tech MAGG is significantly negative. Our findings not only contribute to the advancement of studies pertaining to industry agglomeration and innovation, but also present policy implications for China and the world at large in terms of the development of high-quality and green economy.

Is air pollution politics or economics? Evidence from industrial heterogeneity.

This paper checks the asymmetrical impact of Beijing's and Shanghai's air quality (AQ) on cross-industries stock returns (SR) by using the quantile-on-quantile (QQ) regression method. The major empirical findings as shown as followings. There are heterogeneous responses from SR to AQ within the same city. Different links are discovered for Beijing and Shanghai within the same industry. Air pollution does not have political or economic properties for all industries. Our research provides useful contributions compared with past literature. First of all, we distinguish whether air pollution is political or economic. Apart from psychology and physiology, government intervention and economic expectation are also important components in interpreting the influence from AQ to SR. Second, this study adequately considers the heterogeneity of industries. Industries differently react to the identical extrinsic shock, depending on the nature of their industry. Besides, the QQ approach captures quantile-varying relationship between variables, and does not need to consider structural fracture and time lag effects. The practical significance is that investors need to focus on national industrial policies, and avoiding biased decisions in stock market from air pollution.

The impact of artificial intelligence on pollution emission intensity-evidence from China.

Artificial intelligence (AI) is a crucial component of sustainable economic development and an indicator of the next wave of technological progress. This study examines the effects and mechanisms of AI on the intensity of pollution emissions, using China as an example. Theoretical analysis demonstrates that the scale expansion effect and the technological innovation effect of AI can reduce the intensity of pollution emissions. In the meantime, AI can have a positive structural influence on reducing the intensity of pollution emissions through the upgrading of industrial structures. Therefore, we use panel data for 30 Chinese provinces from 2006 to 2019 to test the effect of AI on pollution emission intensity using a fixed effects model, employ explanatory variable substitution, endogenous analysis, regression after tailing, and eliminate related policy interference for robustness analysis. The results indicate that AI can significantly decrease the intensity of pollution emissions, with a 6.63% reduction for every 10% increase in AI utilization. We use the mediating effect model to conclude that AI can reduce the intensity of pollution emissions via the rationalization of industrial structure and advanced industrial structure, with the rationalization of industrial structure being the main mechanism. The examination of heterogeneity revealed that the implementation of AI in technology-intensive industries is an effective method for reducing the intensity of pollution emissions and that the positive impact of AI on the intensity of pollution emissions is more pronounced in the western region.

Impact of environmental regulation on green growth in China's manufacturing industry-based on the Malmquist-Luenberger index and the system GMM model.

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.

Environmental Regulation, Two-Way Foreign Direct Investment, and Green Innovation Efficiency in China's Manufacturing Industry.

This paper adopts 2009 to 2015 panel data from 27 manufacturing industries in China. A Super-SBM model is used to measure the green innovation efficiency (GIE) of China's manufacturing industry. A panel data model is then built to systematically examine the impact of environmental regulation (ER) and two-way foreign direct investment (FDI) on the GIE of China's manufacturing industry under a unified analysis framework. The results are as follows: (1) the overall level of the green innovation efficiency in China's manufacturing is low, and there is still great potential for improvement. Considering industry heterogeneity, the green innovation efficiency of patent-intensive manufacturing is significantly higher than that of non-patent-intensive manufacturing; (2) in terms of the whole manufacturing industry, ER and the interaction between ER and outward foreign direct investment (OFDI) have significantly negative effects on GIE, OFDI has significantly positive effects on GIE. (3) when considering industry heterogeneity, for patent-intensive manufacturing, ER and the interaction between ER and inward foreign direct investment (IFDI) have significantly negative effects on GIE, while IFDI has significantly positive effect on GIE. For non-patent-intensive manufacturing, ER and the interaction between ER and OFDI have significantly negative effects on GIE, while IFDI and the interaction between ER and IFDI have significantly positive effects on GIE.