What drives the green transformation of enterprises? A case of carbon emissions trading pilot policy in China.

As an important policy instrument to achieve greenhouse gas emission reduction, carbon emissions trading has also promoted the green transformation of enterprises while achieving carbon reduction targets. This study uses the implementation of the Chinese carbon emissions trading pilot policy (CETPP) as a quasi-natural experiment and analyzes the impacts of the CETPP on the green transformation of enterprises with the difference-in-differences (DID) method based on a sample of 297 listed Chinese A-share high-energy-consuming enterprises. The result findings show that CETPP can significantly promote the green transformation of enterprises. The heterogeneity analysis also reveals that CETPP has differential effects on enterprises belonging to different industries, which is caused by the fact that enterprises in different industries differ significantly in their green transformation paths and modes. Moreover, CETPP has a significant facilitating effect on the green transformation of non-state-owned enterprises compared to state-owned enterprises. Finally, marketization and enterprise social responsibility are two major mechanisms for the CETPP to promote the green transformation of enterprises. Our findings reveal that policymakers should further deepen the dynamic management of carbon emission allowances and guide enterprises to actively undertake social responsibility, thus leveraging the market regulation mechanism to promote the green transformation of enterprises.

When will China's industrial carbon emissions peak? Evidence from machine learning.

The manufacture of products in the industrial sector is the principal source of carbon emissions. To slow the progression of global warming and advance low-carbon economic development, it is essential to develop methods for accurately predicting carbon emissions from industrial sources and imposing reasonable controls on those emissions. We select a support vector machine to predict industrial carbon emissions from 2021 to 2040 by comparing the predictive power of the BP (backpropagation) neural network and the support vector machine. To reduce noise in the input variables for BP neural network and support vector machine models, we use a random forest technique to filter the factors affecting industrial carbon emissions. The statistical results suggest that BP's neural network is insufficiently adaptable to small sample sizes, has a relatively high error rate, and produces inconsistent predictions of industrial carbon emissions. The support vector machine produces excellent fitting results for tiny sample data, with projected values of industrial carbon dioxide emissions that are astonishingly close to the actual values. In 2030, carbon emissions from the industrial sector will have reached their maximum level.

Does green finance improve carbon emission efficiency? Experimental evidence from China.

As a noteworthy initiative of financial supply-side reform to precisely support the green development system, can green finance (GF) help achieve the dual goals of "carbon peaking" and "carbon neutrality"? Using data from China's provincial panel between 2007 and 2019, this paper measured the green finance index by the entropy method and the carbon emission efficiency (CEE) with carbon emission as the non-desired output by the Super-SBM model. Then, the influence of GF on CEE was empirically investigated by the dynamic panel model and the spatial Durbin model. The findings show that GF can significantly improve CEE and has a positive spillover impact on CEE in provinces with close economic ties; the upgrading of the industrial structure is a key mediator in the transmission of GF to CEE; and regional heterogeneity analysis finds that GF notably improves CEE in eastern, high development levels of economic and GF regions. The research can offer some theoretical and empirical references for green finance to contribute to low-carbon economic growth.

Can new energy demonstration city policy reduce carbon emissions? A quasi-natural experiment from China.

Against achieving carbon peaking by 2030 and carbon neutrality by 2060 context in China, the new energy demonstration city policy (NEDCP) has a crucial function to perform in promoting resource utilization efficiency, building the green development policy system, and facilitating carbon emission reduction. However, existing research has rarely investigated the contribution of NEDCP on carbon reduction. To investigate the policy effect of NEDCP, the differences-in-differences (DID) model is introduced to quantify the influence of NEDCP on carbon reduction, taking a statistical sample of 285 Chinese cities over the period 2005-2017 on the basis of exploring the intrinsic mechanism of NEDCP on carbon emissions. The statistical results reveal that NEDCP significantly inhibits carbon emissions. NEDCP's dampening impact on carbon reduction is more pronounced in the eastern area but not in other areas. City size and resource endowment heterogeneity results suggest that NEDCP significantly inhibits the output of carbon emissions in non-resource-based and large cities but insignificantly in resource-based and small- and medium-sized cities. Finally, we conclude that policy-makers should not only broaden the scope of NEDCP implementation continuously but also design relevant policy combination tools following the basic characteristics of each city to provide institutional guarantees for achieving carbon emission reduction.

Can carbon emission trading pilot policy drive industrial structure low-carbon restructuring: new evidence from China.

Industrial structure low-carbon restructuring is an essential channel to accelerate China's economic growth and fulfilling carbon emission reduction goals. Whether carbon emission trading pilot policy, as an influential carbon reduction instrument, fosters industrial structure low-carbon restructuring is of major significance to green economic development. This paper empirically investigates the shock of the carbon emission trading pilot policy on industrial structure low-carbon restructuring using the differences-in-differences (DID) and synthetic control method (SCM). Statistics reveal that sectors with low carbon productivity, such as electricity, steam, and hot water production and supply, ferrous metal smelting and pressing, etc., and sectors with high carbon productivity, such as electrical equipment and machinery, electronics and telecommunication equipment, etc. The industrial structure did not develop a stable trend of change before the 12th Five-Year Plan, but a stable trend of low-carbon restructuring emerged after such a period. Carbon emission trading pilot policy significantly facilitates industrial structural low-carbon restructuring. Carbon emission trading pilot policy inhibits energy-intensive industries in the industrial sector significantly, which promotes industrial structure low-carbon restructuring. Therefore, policymakers need to develop a nationwide carbon emission trading market that includes more industries to guide production factors to industrial sectors with high carbon productivity for industrial restructuring and dual carbon goals.

The impact of local government competition and green technology innovation on economic low-carbon transition: new insights from China.

The government-led Chinese economic development system determines that local government competition is a significant factor affecting the economic low-carbon transition. Driving an economic development mode with green technology innovation as the core is the critical path to realizing an economic low-carbon transition. Consequently, it is of significant practical relevance to investigate the impact of local government competition and green technology innovation on the economic low-carbon transition under the government-led Chinese economic development system. This paper systematically explores the nexus between green technology innovation and economic low-carbon transition in terms of local government competition perspective using the system generalized method of moments, panel threshold model, and geographically weighted regression on the basis of a dataset of 30 provincial administrative areas in China from a period of 2006-2019. The results indicate that green technology innovation significantly promotes the economic low-carbon transition. Local government competition not only significantly dampens the economic low-carbon transition but also considerably inhibits the positive effect of green technology innovation on the economic low-carbon transition. A significant N-shaped association is evident between green technology innovation and the economic low-carbon transition when green technology innovation is applied as a threshold, while such association is insignificant when local government competition is used as a threshold. Compared with high-competition intensity areas, green technology innovation promotes economic low-carbon transition weaker in low- competition intensity areas, while local government competition inhibits economic low-carbon transition stronger. However, local government competition significantly inhibits the positive effect of green technology innovation on the economic low-carbon transition in low-competition intensity areas, while insignificant in high-competition intensity areas. The geographically weighted regression technique as a whole also verified the above results. Therefore, policymakers should not only increase research and development investment in green technologies, but also develop a regionally linked low-carbon emission reduction system to avoid ineffective competition among governments to facilitate the earlier fulfillment of the "dual carbon" goal.

Diagnosis of children with attention-deficit/hyperactivity disorder (ADHD) comorbid autistic traits (ATs) by applying quantitative magnetic resonance imaging techniques.

Objective: To explore the feasibility of applying quantitative magnetic resonance imaging techniques for the diagnosis of children with attention-deficit/hyperactivity disorder (ADHD) comorbid autistic traits (ATs). Methods: A prospective study was performed by selecting 56 children aged 4-5 years with ADHD-ATs as the study group and 53 sex- and age-matched children with ADHD without ATs as the control group. All children underwent magnetic resonance scans with enhanced T2*- weighted magnetic resonance angiography (ESWAN), 3D-PCASL, and 3D-T1 sequences. Iron content and cerebral blood flow parameters were obtained via subsequent software processing, and the parameter values in particular brain regions in both groups were compared and analyzed to determine the characteristics of these parameters in children with ADHD-ATs. Results: Iron content and cerebral blood flow in the frontal lobe, temporal lobe, hippocampus, and caudate nucleus of children with ADHD-ATs were lower than those of children with ADHD without ATs (p < 0.05). Iron content and CBF values in the frontal lobe, temporal lobe and caudate nucleus could distinguish children with ADHD-ATs from those without ATs (AUC > 0.5, p < 0.05). Conclusions: Quantitative magnetic resonance techniques could distinguish children with ADHD-ATs. Trial registration: This study protocol was registered at the Chinese clinical trial registry (ChiCTR2100046616).

Does government intervention affect CO2 emission reduction effect of producer service agglomeration? Empirical analysis based on spatial Durbin model and dynamic threshold model.

Achieving carbon peak and carbon neutrality is an inherent requirement for countries to promote green recovery and transformation of the global economy after the COVID-19 pandemic. As "a smoke-free industry," producer services agglomeration (PSA) may have significant impacts on CO2 emission reduction. Therefore, based on the nightlight data to calculate the CO2 emissions of 268 cities in China from 2005 to 2017, this study deeply explores the impact and transmission mechanism of PSA on CO2 emissions by constructing dynamic spatial Durbin model and intermediary effect model. Furthermore, the dynamic threshold model is used to analyze the nonlinear characteristics between PSA and CO2 emissions under different degrees of government intervention. The results reveal that: (1) Generally, China's CO2 emissions are path-dependent in the time dimension, showing a "snowball effect." PSA significantly inhibits CO2 emissions, but heterogeneous influences exist in different regions, time nodes, and sub-industries; (2) PSA can indirectly curb CO2 emissions through economies of scale, technological innovation, and industrial structure upgrading. (3) The impact of PSA on China's CO2 emissions has an obvious double threshold effect under different degree of government intervention. Accordingly, the Chinese government should increase the support for producer services, dynamically adjust industrial policies, take a moderate intervention, and strengthen market-oriented reform to reduce CO2 emissions. This study opens up a new path for the low-carbon economic development and environmental sustainability, and also fills in the theoretical gaps on these issues. The findings and implications will offer instructive guideline for early achieving carbon peak and carbon neutrality.

Application of Three-Dimensional Pseudocontinuous Arterial Spin Labeling Perfusion Imaging in the Brains of Children With Autism.

Objective: To explore the application of three-dimensional pseudocontinuous arterial spin labeling (3D-PCASL) perfusion imaging in the brains of children with autism and to understand the characteristics of cerebral blood perfusion in children with autism. Methods: A total of 320 children with autism (160 men and 160 women) aged between 2 and 18 years and 320 age- and sex-matched healthy children participated in the study. All children were scanned by 3.0 T magnetic resonance axial T1 fluid-attenuated inversion recovery (FLAIR), T2 FLAIR, 3D-T1, and 3D-PCASL sequences. After postprocessing, cerebral blood flow (CBF) values in each brain region of children with autism and healthy children at the same age were compared and analyzed. Furthermore, CBF characteristics in each brain region of autistic children at various ages were determined. Results: The CBF values of the frontal lobe, hippocampus, temporal lobe, and caudate nucleus of children with autism are lower than those of healthy children (P < 0.05). Additionally, as the ages of children with autism increase, the number of brain regions with decreased CBF values gradually increases. A receiver operating characteristic (ROC) analysis results show that the CBF values of the frontal lobe, hippocampus, temporal lobe, and caudate nucleus can distinguish children with autism [area under the ROC curve (AUC) > 0.05, P < 0.05]. Conclusion: The 3D-PCASL shows lower brain CBF values in children with autism. Clinical Trial Registration: www.ClinicalTrials.gov, identifier: ChiCTR2000034356.

Quantitative susceptibility mapping shows lower brain iron content in children with attention-deficit hyperactivity disorder.

To investigate the feasibility of quantitative susceptibility mapping in children with attention-deficit hyperactivity disorder (ADHD), 53 children with ADHD aged 5-16 years were prospectively selected as the study group and 49 healthy children matched with age and gender were selected as the control group. All children underwent magnetic resonance imaging conventional sequence, 3D-T1, and enhanced T2*-weighted magnetic resonance angiography (ESWAN) sequence scanning. The iron content of brain regions was obtained through software postprocessing, and the iron content of brain regions of children with ADHD and healthy children was compared and analyzed to find out the characteristics of the iron content of brain regions of children with ADHD. The iron content in frontal lobe, globus pallidus, caudate nucleus, substantia nigra, putamen, and hippocampus of children with ADHD was lower than that of healthy children (p < .05). There was no significant difference in the content of iron in the left and right brain regions of children with ADHD (p > .05). The volume of frontal lobe and hippocampus of children with ADHD was lower than that of healthy children (p < .05). Iron content in brain areas such as globus pallidus, caudate nucleus, hippocampus, and putamen could distinguish children with ADHD (Area under curve [AUC] > 0.5, p < .05). Quantitative susceptibility mapping showed decreased iron content in some brain regions of children with ADHD.

Analysis of the mechanism of the impact of internet development on green economic growth: evidence from 269 prefecture cities in China.

As the digital economy develops rapidly and the network information technology advances, new development models represented by the network economy have emerged, which have a crucial impact on green economic growth. However, the relevant previous studies lacked the role of analyzing the direct and indirect effects of internet development on green economic growth at the prefecture-level city level. For this purpose, this paper aims to examine the intrinsic mechanism of the impact of internet development on green economic growth and provide empirical support for cities and regions in China to increase internet construction. Furthermore, the mixed model (EBM), which includes both radial and non-radial distance functions, is applied to calculate the green economic growth index. Fixed effect model and mediation effect model are also employed to test influence mechanisms of the internet development on green economic growth using panel data of 269 prefecture-level cities in China from 2004 to 2019. The statistical results reveal that internet development has contributed significantly to green economic growth. When the internet development level increases by 1 unit, the green economic growth level increases by an average of 5.0372 units. However, regional heterogeneity is evident between internet development and green economic growth, that is, the promoting effect of internet development on green economic growth is gradually enhanced from the eastern region to the western region. We also find that internet development guides industrial structure upgrading improves environmental quality and accelerates enterprise innovation, which indirectly contributes to green economic growth. And internet development mainly achieves green economic growth through enterprise innovation. Based on the above findings, we concluded that policymakers should not only strengthen the guiding role of social actors to promote the stable development of the internet industry, but also foster the construction of the three models of "internet+industry integration," "internet+environmental governance," and "internet+enterprise innovation" to promote green economic growth.

Assessing the impact of energy internet and energy misallocation on carbon emissions: new insights from China.

With the deterioration of environmental quality caused by fossil energy use, the research on energy internet and energy misallocation is of critical relevance to achieve low-carbon sustainable development. However, we find that the relevant research that analyzes energy internet and energy misallocation on carbon emissions under the same framework is ignored. For this purpose, the generalized method of moments (GMM), panel threshold model, and spatial analysis (deviation ellipse, hotspot analysis, and geographically and temporally weighted regression (GTWR)) model were applied to investigate the impact of energy internet and energy misallocation on carbon emissions using panel data of 30 provinces in China from 2004 to 2018. The major statistical results include the following: (1) energy misallocation significantly contributes to carbon emissions, while energy internet inhibits carbon emissions. Energy internet can negatively moderate the positive effect of energy misallocation on carbon emissions. (2) The effect of energy misallocation on carbon emissions reveals an inverted "U-shaped" characteristic of first promoting and later inhibiting, but the inhibiting effect is insignificant. Moreover, the marginal effect of energy misallocation on carbon emissions decreases when the energy internet crosses the second thresholds consecutively, while the marginal effect of the energy internet on carbon emissions shows an inverted "N" shape. (3) Compared with the under-allocated regions, the promotion effect of energy misallocation on carbon emissions and the inhibitory effect of energy internet on carbon emissions are stronger in the over-allocated regions, while the energy internet has a more significant negative moderating effect on energy misallocation. (4) The gravity center of China's carbon emissions gradually shifts to the northwest with time. The longitude of the gravity center (east-west direction) changes greatly, while the latitude of the gravity center (north-south direction) changes less. Besides, the carbon emission hotspot regions centered on Shanxi spread to the neighboring provinces, which form a high-high agglomeration region, and the cold spot region dominated by Qinghai, Guangxi, and Guangdong forms low-low agglomeration characteristics. Finally, the GTWR model shows that the impact of energy internet and energy misallocation on carbon emissions shows significant hierarchical, banded, or block-like characteristics in spatial distribution.

Does Low-Carbon City Pilot Policy Alleviate Urban Haze Pollution? Empirical Evidence from a Quasi-Natural Experiment in China.

As a comprehensive environmental regulation, the low-carbon city pilot policy (LCCP) may have an impact on haze pollution. The evaluation of the effectiveness of LCCP on haze pollution is greatly significant for air pollution prevention and control. Taking LCCP as the starting point, in this study we constructed DID, PSM-DID, and intermediary effect models to empirically test the impact and mechanism of LCCP on haze pollution, based on the panel data of 271 cities in China from 2005 to 2018. The findings show that (1) LCCP has significantly reduced the urban haze pollution, and the average annual concentration of PM2.5 in pilot cities decreased by 14.29%. (2) LCCP can inhibit haze pollution by promoting technological innovation, upgrading the industrial structure, and reducing energy consumption. Among these impacts, the effect of technological innovation is the strongest, followed by industrial structure, and energy consumption. (3) LCCP has significantly curbed the haze pollution of non-resource dependent cities, Eastern cities, and large cities, but exerted little impact on resource-dependent cities, Central and Western regions, and small and medium-sized cities. (4) LCCP has a spatial spillover effect. It can inhibit the haze pollution of adjacent cities through demonstration and warning effects. This study enriches the relevant research on LCCP and provides empirical support and policy enlightenment for pollution reduction.

Energy structure, digital economy, and carbon emissions: evidence from China.

As a new production factor, digitalization plays a vital role in society, economy, and the environment. Based on the expanded STIRPAT model, this paper empirically tests the impact of energy structure and digital economy on carbon emissions by panel data from 2011 to 2017 in 30 provinces of China. The results show that the energy structure mainly based on coal has a significant driving effect on carbon emissions. Compared with non-resource-based provinces, the increase of energy structure dominated by coal has a greater effect on carbon emission in resource-based provinces. It is worth noting that this kind of influence has a greater impact on the central region of China, followed by the western region and the eastern region. Besides, the digital economy has a significant moderating effect. With the development of digital economy, the impact of coal-based energy structure on carbon emissions is gradually decreasing. This effect is more significant in non-resource-based provinces and eastern China, but not significant in resource-based cities and central and western China.

Does environmental decentralization exacerbate China's carbon emissions? Evidence based on dynamic threshold effect analysis.

As an institutional influential factor of environmental quality, environmental decentralization may play an important role in carbon emission control. Given China's rapid economic development and increasing environmental pollution, this study aims to investigate how environmental decentralization affects China's carbon emissions. Environmental decentralization indicators are reconstructed from the perspective of dynamic changes in the number of staff in environmental protection agencies on the basis of China's 30 provinces from 2005 to 2016. Spatial panel and dynamic threshold models are employed to investigate the impacts of environmental decentralization on carbon emissions. Results indicate that a remarkably positive spatial correlation is found in regional carbon emissions in China, and environmental decentralization has a positive impact on carbon emissions, suggesting that China's current environmental decentralization system may be unconducive to carbon emission control. Taking different environmental decentralization types as threshold variables, results from the dynamic threshold panel model show that environmental administrative decentralization and environmental monitoring decentralization have a positive threshold promoting effect on carbon emissions, whereas environmental supervision decentralization has a negative threshold inhibiting effect on carbon emissions. Therefore, the environmental administrative and monitoring power of local governments must be appropriately reduced and an environmental management mechanism must be developed for joint prevention and control to reduce carbon emissions.