Double Threshold Research Articles

How does digital economy drive the high‐quality development of regional manufacturing?

AbstractDigital technologies promote economic progress. The digital economy drives the development of manufacturing. This paper explores the impact of the digital economy on the high‐quality development of manufacturing using panel data from the Yangtze River Economic Belt in China. Employing the entropy method, we first measure the level of the digital economy and the high‐quality development of manufacturing. Then, we divide the digital economy into three dimensions: the digital foundation, the digital application, and the digital innovation, and investigate how each dimension influences the high‐quality development of manufacturing. Results show that: (1) Both the digital economy level and the high‐quality development level of manufacturing exhibit steady growth, while the overall value of the Yangtze River Economic Belt stays low. (2) Three dimensions of the digital economy positively affect the high‐quality development of manufacturing, with the most noticeable effect of the digital innovation, followed by the digital application and the digital foundation. (3) Threshold effect tests demonstrate that both the digital foundation and the digital application exhibit a double threshold effect on the high‐quality development of the manufacturing, but the digital innovation has a single threshold effect. (4) Last but not the least, the digital foundation positively affects the high‐quality development of the manufacturing in downstream and upstream regions, but less apparently influences midstream regions by the heterogeneity analysis. Additionally, both the digital application and the digital innovation have significant effects on the high‐quality development of the manufacturing across all regions.

A Multi-Active and Multi-Passive Sensor Fusion Algorithm for Multi-Target Tracking in Dense Group Clutter Environments

Multi-target tracking (MTT) of multi-active and multi-passive sensor (MAMPS) systems in dense group clutter environments is facing significant challenges in measurement fusion. Due to the difference in measurement information characteristics in MAMPS fusion, it is difficult to effectively correlate and fuse different types of sensors’ measurements, leading to difficulty in taking full advantage of various types of sensors to improve target tracking accuracy. To this end, we present a novel MAMPS fusion algorithm, which is based on centralized measurement association fusion (MAF) and distributed deep neural network (DNN) track fusion, named the MAMPS-MAF-DNN algorithm. Firstly, to reduce the impact of the dense group clutter, a clutter pre-processing algorithm is elaborated, which combines the advantages of the CFDP (cluster by finding density peaks) and double threshold screening algorithms. Then, for the single-active and multi-passive sensor (SAMPS) system, a centralized MAF algorithm based on angle information is developed, called the SAMPS-MAF algorithm. Finally, the SAMPS-MAF algorithm is extended to the MAMPS system within the DNN framework, and the complete MAMPS-MAF-DNN algorithm is proposed. Experimental results indicate that, compared to the existing MAF and covariance intersection (CI) fusion algorithms, the proposed MAMPS-MAF-DNN algorithm can fully combine the advantages of multi-active and multi-passive sensors, efficiently reduce the computational complexity, and obviously improve the tracking accuracy.

The government's environmental attention and corporate green innovation: A threshold analysis and quantile regression approach.

Based on an analysis of 643 listed firms in clean technology sectors, this study explores the nonlinear impact of the government's environmental attention (GEA) on firms' green innovation by exploiting threshold and quantile regression techniques on Stata 17. We show that a double threshold exists when the level of the GEA is 51 or 104, above which the positive impact of the GEA on green innovation in cleantech firms significantly diminishes. The results from the quantile regression further indicate that cleantech firms receive almost no benefits from the GEA at lower levels of green innovation. Thus, policy-makers designing environmental policies should consider that the marginal benefit of environmental attention on green innovation wanes beyond certain levels, especially for firms that lack sufficient enthusiasm for innovation.

Innovative talent agglomeration, spatial spillover effects and regional innovation performance-Analyzing the threshold effect of government support.

As the primary source of innovation in science and technology, the gathering of creative talent plays a significant role in fostering regional innovation and providing the impetus needed to realize the Chinese people's aspiration of a great national rejuvenation. Using panel data from 31 provinces over a 12-year period between 2009 and 2020, the spatial Durbin model is constructed to examine the spatial spillover impact of talent agglomeration on the advancement of regional innovation performance, and the panel threshold model is identified and set up to consider whether the nonlinear effect between talent agglomeration and regional achievements in innovation is significant. The analysis demonstrates that: talent pooling has a non-linear effect on the level of innovation performance development, within a certain scale, talent pooling produces an increasing marginal contribution to innovation performance, but after exceeding the limit, it produces a diminishing marginal contribution; the double threshold effect of talent pooling on regional innovation performance is more significant, and government support as a moderating variable confirms that there is a structural mutation between talent pooling and innovation capability. The structural mutation, within a certain threshold range, plays the role of efficient promotion, providing reference for improving regional innovation level.

Wi-Fi sensing gesture control algorithm based on semi-supervised generative adversarial network

A Wi-Fi-sensing gesture control system for smart homes has been developed based on a theoretical investigation of the Fresnel region sensing model, addressing the need for non-contact gesture control in household environments. The system collects channel state information (CSI) related to gestures from Wi-Fi signals transmitted and received by network cards within a specific area. The collected data undergoes preprocessing to eliminate environmental interference, allowing for the extraction of complete gesture sets. Dynamic feature extraction is then performed, followed by the identification of unknown gestures using pattern recognition techniques. An improved dynamic double threshold gesture interception algorithm is introduced, achieving a gesture interception accuracy of 98.20%. Furthermore, dynamic feature extraction is enhanced using the Gramian Angular Summation Field (GASF) transform, which converts CSI data into GASF graphs for more effective gesture recognition. An enhanced generative adversarial network (GAN) algorithm with an embedded classifier is employed to classify unknown gestures, enabling the simultaneous recognition of multiple gestures. A semi-supervised learning algorithm designed to perform well even with limited labeled data demonstrates high performance in cross-scene gesture recognition. Compared to traditional fully-supervised algorithms like linear discriminant analysis (LDA), Light Gradient Boosting Machine (LightGBM), and support vector machine (SVM), the semi-supervised GAN algorithm achieves an average accuracy of 95.67%, significantly outperforming LDA (58.20%), LightGBM (78.20%), and SVM (75.67%). In conclusion, this novel algorithm maintains an accuracy of over 94% across various scenarios, offering both faster training times and superior accuracy, even with minimal labeled data.

Urban–Rural Integration and Agricultural Technology Innovation: Evidence from China

Urban and rural relations are important for the sustainable development of a nation or region. Urban and rural integration (URI), as a conceptual framework and strategic tool for managing urban–rural relations, is supposed to play a role in linking urban and rural socio-economic systems to promote the two-way flow of resources, facilitate spatial restructuring and functional transformation, enhance resource allocation efficiency, and shorten the gap between urban and rural areas. This article takes agriculture technological innovation in China as an example, and discuss whether and how URI can promote agricultural technological innovation (ATI). Based on a dataset of 288 prefecture-level cities spanning the years from 1999 to 2018, this article analyzes the mechanism and effect of URI on the development of ATI by using the negative binomial regression model and other models based on measuring the level of URI in the municipal area. The main conclusions are as follows. (1) URI positively promotes the development of ATI and exhibits significant heterogeneity across various dimensions, regions, and agricultural production areas. (2) The mechanism analysis shows that governance systems and mature markets positively moderate the impact of URI on promoting ATI, which also exhibits heterogeneous characteristics across various regions. (3) The impact of URI on the development of ATI exhibits a double threshold effect, and its marginal impact on the development of ATI increases with the deepening of URI. Moreover, the threshold effect of URI demonstrates significant heterogeneity. In central and urban areas, after surpassing the threshold value, the influence of URI on agricultural technological innovation becomes greater. Therefore, it is crucial to continuously deepen the development of URI, smooth the channels of factor flow, enhance resource sharing, break down the urban and rural barriers, and create a new engine for ATI development.

Deciphering the complex interplay: Heterogeneous, threshold, and mediation effects of trade openness on CO2 emissions in Africa.

Despite having barely anything to do with the issue of CO2 emissions, Africa has been experiencing more severe climate change and its adverse effects than most other regions of the globe. However, the issue of CO2 emissions and its adverse effects has received relatively little attention in the African research arena. To this end, the present research assesses the effect of trade openness on the CO2 emissions utilizing panel data from 46 African countries spanning 2000 through 2022. To account for the possible heterogeneity and nonlinearity, the panel quantile regression and threshold methods were employed. Moreover, this study investigates the key mediating effects of the channel. The empirical findings show that greater trade openness is associated with significantly higher CO2 emission, additionally; it demonstrates that the influence is heterogeneous across different CO2 emission quantiles in African countries. Besides the result from the double threshold model reveals a complex, nonlinear relationship between trade openness and CO2 emissions in Africa. Moreover, the findings divulge that openness to trade indirectly reduces CO2 emissions through the substitution and technology channels whereas it indirectly increases carbon dioxide production via the economic track. Therefore, it is vital to promote the use of renewable energy, effectively leverage the knowledge spillover effects of trade to decrease energy intensity and formulate pertinent policies aimed at curbing carbon emissions and addressing the imminent threat of climate change in Africa. Besides, the nonlinear and heterogeneous effects of trade openness on CO2 emissions suggest that policies and interventions related to the impact of trade openness on CO2 emissions should consider the current level of carbon dioxide emissions.

Assessing Watershed Flood Resilience Based on a Grid-Scale System Performance Curve That Considers Double Thresholds

Enhancing flood resilience has become crucial for watershed flood prevention. However, current methods for quantifying resilience often exhibit coarse spatiotemporal granularity, leading to insufficient precision in watershed resilience assessments and hindering the accurate implementation of resilience enhancement measures. This study proposes a watershed flood resilience assessment method based on a system performance curve that considers thresholds of inundation depth and duration. A nested one- and two-dimensional coupled hydrodynamic model, spanning two spatial scales, was utilized to simulate flood processes in plain river network areas with detailed and complex hydraulic connections. The proposed framework was applied to the Hangjiahu area (Taihu Basin, China). The results indicated that the overall trend of resilience curves across different underlying surfaces initially decreased and then increase, with a significant decline observed within 20–50 h. The resilience of paddy fields and forests was the highest, while that of drylands and grasslands was the lowest, but the former had less recovery ability than the latter. The resilience of urban systems sharply declined within the first 40 h and showed no signs of recovery, with the curve remaining at a low level. In some regions, the flood tolerance depth and duration for all land use types exceeded the upper threshold. The resilience of the western part of the Hangjiahu area was higher than that of other regions, whereas the resilience of the southern region was lower compared to the northern region. The terrain and tolerance thresholds of inundation depth were the main factors affecting watershed flood resilience. The findings of this study provide a basis for a deeper understanding of the spatiotemporal evolution patterns of flood resilience and for precisely guiding the implementation and management of flood resilience enhancement projects in the watershed.

CO Dissociation Induced by 1 keV/u Ar2+ Ion

CO is one of the important molecules in dense molecular clouds, and its dissociation induced by cosmic ray heavy ions is a fundamental process for molecular breaking up and rearrangement in astronomical networks. Extensive laboratory simulations are required to understand molecular evolution in astrophysical contexts. Here, we investigate the CO dissociation induced by 1 keV/u Ar2+ using cold target recoil ion momentum spectroscopy. Kinetic energy release for double electron capture Ar2++CO→Ar0+C++O+ and transfer ionization Ar2++CO→Ar++C++O++e− was obtained. The dissociation mechanisms are attributed to different KER distributions. The autoionization process is identified below the CO2+ double ionization threshold.

ESG performance and green innovation of Chinese enterprises: Based on the perspective of financing constraints

Sustainable development needs to rely on green innovation. Enterprise ESG performance rating to alleviate the information asymmetry between enterprises and investors, for the green innovation provides development opportunities. However, there is a lack of research on the mechanism of the two based on the perspective of financing constraints. Therefore, considering the theories of sustainable development, green innovation, financing constraints and stakeholders, the basic mechanism and non-linear mechanism of ESG and green innovation are explored, aiming to provide reference for playing the role of ESG performance in promoting green innovation. The results show that better ESG performance can significantly promote the output of green innovation, and this promoting effect exists for both high-end green innovation and low-end green innovation, and its main channel is to alleviate financing constraints. In addition, this incentive exists a double threshold of digital finance and a single threshold of enterprise scale. Perfect digital finance and larger enterprise scale will strengthen the incentive effect of enterprise ESG performance on green innovation output. Considering financing constraints, this study further clarifies the relationship between enterprises ESG performance and green innovation, enriching the theoretical framework of green innovation and offering a new pathway for advancing green innovation among Chinese enterprises.

Effects and mechanisms of digitalization to enhance the resilience of the hog breeding industry: evidence from China

IntroductionWith the rapid development of digital agriculture, digitalization has gradually become a key factor affecting the resilience of China’s pig farming industry.MethodsFrom both the test results and the theoretical point of view, the individual fixed-effect model is more suitable for this paper’s study. Therefore, based on the panel data of 31 provinces in China from 2011 to 2022, this study constructs an individual fixed-effect model to examine how digitization affects the resilience of China’s pig farming industry.Results and discussionThe findings indicated that digitization significantly enhanced the resilience of China’s hog breeding industry, especially in potential growth areas. Improving breeding technology and scale is a crucial avenue for digitalization to bolster the industry’s resilience. Digitalization exerts a double threshold effect on the industry’s resilience, with its promotional impact only realized within a specific range. The article proposes policy suggestions, such as strengthening infrastructure construction and giving play to regional advantages.

The impact of artificial intelligence on the energy transition: The role of regulatory quality as a guardrail, not a wall

In recent years, the economic impact and environmental contribution of Artificial Intelligence (AI) have gradually become a new focus in academia. This study uses a panel data sample of 50 countries to explore the impact of AI on energy transition (ET), aiming to fill an important research gap. The results highlight several critical insights. First, AI has had a significant positive impact on facilitating the ET. This conclusion still holds after a series of robustness tests. Second, AI positively affects ET by promoting renewable energy technology innovation and upgrading the electricity structure, resulting in both technological and structural effects. Third, the impact of AI on ET is non-linear. Threshold effect models show that AI impacts ET differently at various levels of regulation quality (RQ), exhibiting a double threshold effect. AI hinders ET when RQ is lower than the first threshold value. When RQ is in the second range, AI significantly facilitates ET. However, when RQ exceeds the second threshold value, AI hinders ET again. These findings provide insights into the mechanisms of AI's impact on ET and emphasize that an appropriate level of regulation is crucial for AI to facilitate ET. Finally, this study analyzes heterogeneity and offers targeted policy recommendations.

Algorithm for Processing X-ray Images Using Fuzzy Logic

Introduction. To improve the diagnostics of knee joint diseases, it is necessary to enhance the quality of processing radiographic images, i.e., to provide experts with more accurate information for pathology analysis. The objective of the study is to demonstrate the capabilities of fuzzy logic in improving the algorithm for determining reference lines and knee flexion angles. This requires a program that analyzes X-ray images. The methods known today, described in scientific and applied literature, are not sufficiently automated. In some cases, orthopedists and surgeons have to manually refine images and adjust lines. This gap is filled by the presented work. The algorithm developed by the author is described. It does not involve human participation and automatically identifies the lines and angles of knee flexion. Based on the result issued by the system, the doctor can, firstly, judge the presence of pathology. Secondly, the information provided by the program allows for more accurate planning, performing operations, and prescribing therapy.Materials and Methods. Images from two X-ray machines operating in Al-Basel Hospital (Latakia, Syria) were used. The Python language was used for the software implementation of the algorithm. The solution was tested on 500 patients at Al-Basel Hospital. The results generated by the new system and previous versions of X-ray image processing programs were compared.Results. An algorithm for constructing reference lines and angles for processing knee joint X-ray images is created, described, and implemented in practice. The capabilities of fuzzy logic in automating double threshold detection when identifying bone boundaries in images are shown. The operation of an improved Gaussian filter designed for processing X-ray images is described. The modified method of knee bone X-ray analysis includes the development of an algorithm for automatic detection of structures and anomalies in knee joints, determination and measurement of anatomical parameters, assessment of the degree of damage, etc. The method for determining the contour boundaries on radiographs combined the Canny detector, the watershed algorithm, and fuzzy logic. The program has been implemented in medical practice and shows 98% accuracy, spending less than 20 seconds to process the image.Discussion and Conclusion. The new system provides high accuracy, acceptable efficiency, and does not require manual correction of images. Experts are now able to identify subtle indicators of disorders. In addition, the new method makes it possible to understand complex cases when several factors are combined, indicating potential pathology. Widespread implementation of the method will improve the quality of medical services in orthopedics. Scientific research in this direction should be continued to expand the set of strategies for the treatment of diseases of the musculoskeletal system. It is necessary to create solutions with absolute accuracy, higher processing efficiency, as well as methods suitable for analyzing other joints.

A designated private set based trapdoor authentication scheme for privacy preserving trust management in decentralized systems

Authentication is crucial for network system security, relying on methods such as passwords, ID cards, biometrics, and behavioral characteristics. The conventional centralized authentication may lead to potential performance bottlenecks and privacy risks such as key exposure, single point of failure. Decentralized authentication systems using cryptographic techniques aim to address these issues but often tradeoff between flexibility and communication efficiency. In this paper we propose a new cryptographic concept called designated private set-based trapdoor authentication (DPSBTA) for flexible and efficient trust management in decentralized systems. DPSBTA eliminates the need for a trusted authority, with users’ access privileges defined by their private sets. During the authentication process, each server can designate an element set and only if a user holds adequate elements which are contained in the designated set can he obtains a credential from the server. The key features of DPSBTA include: decentralized trapdoor authentication management, without a trusted authority, conducted in a double threshold manner; privacy preservation, as servers do not know users’ element holdings or credential generation; round-optimal communication, with only two rounds of interaction between users and servers. We present the generic construction, security models, and concrete algorithms with correctness proof. The theoretical proof and the performance evaluations demonstrate the tangible security and high efficacy of the proposed DPSBTA.

A new denoising method of ship-radiated noise: Improved variational mode decomposition coupled with fractional order entropy double threshold criterion

A new denoising method of ship-radiated noise: Improved variational mode decomposition coupled with fractional order entropy double threshold criterion

Environmental Regulation, Factor Marketisation Allocation and Carbon Emissions Performance: Empirical Evidence from Resource-Based Cities in China

Resource-based cities had an irreplaceable role in the process of the economic miracle in China. Advancing such cities’ carbon emissions reduction is a crucial aspect of the country’s steady realisation of the dual carbon peak and neutrality strategy. The reasonable implementation of environmental regulation and the efficiency of factor marketisation allocation are the key links for resource-based cities to improve carbon emissions performance, break the resource curse and reduce carbon emissions. Based on this, this study centres on the driving relationship between environmental regulation, the efficiency of factor marketisation allocation and carbon emissions performance as the core research problem. This study takes the panel data of 116 resource-based cities in China from 2006 to 2020 as the research sample; the non-radial meta-frontier total factor carbon emissions performance index is selected as the measurement index of carbon emission performance of resource-based cities based on the applicability analysis of the model. This study explores the characteristics of regional heterogeneity and type heterogeneity of carbon emissions performance driven by environmental regulation under the moderating effect of the efficiency of factor marketisation allocation and further explores the threshold effect, aiming to clarify the driving relationship between the three. The findings reveal that the driving effect of environmental regulation intensity on carbon emissions performance exhibits a fluctuating upward trend, the effect transformed by compliance cost and innovation compensation. The efficiency of factor marketisation allocation has a double threshold superposition effect on carbon emissions performance fluctuation that is driven by environmental regulation, indicating that market and government effectiveness can operate together to improve the carbon emissions performance. Based on these results, this study proposes countermeasures and suggestions for improving carbon emissions performance using environmental regulation and the efficiency of factor marketisation allocation.

Towards sustainable development: Can green digital finance become an accelerator for reducing pollution and carbon emissions in China?

As a fusion of financial ''digitalization'' and ''greening'', green digital finance is crucial for ecological quality enhancement. Based on panel data from 285 cities in China spanning 2011 to 2021, this study aims to examine the synergistic impact of Green Digital Finance (GDFO) on Pollution and Carbon Reduction (PCR). The two-way fixed effect model, moderated effect model, and threshold model are employed to study the direct, transmission, and non-linear effects of GDFO on PCR. Results indicate that GDFO significantly enhances PCR in urban areas, and this conclusion remains valid after several robustness tests. Mechanically, encouraging green technology innovation, promoting industrial structure upgrading, and enhancing financial supervision are effective channels for strengthening the impact of GDFO on PCR. At varying levels of green digital finance and financial supervision, the marginal impact of GDFO on PCR exhibits double threshold characteristics in the form of "U-shaped" and "inverted U-shaped". Furthermore, the impact of GDFO on PCR is more pronounced in the eastern and western regions, resource cities, and cities with a high level of financial development, excellent digital infrastructure, and robust environmental regulation. This study offers theoretical support and empirical evidence for deepening the GDFO to promote PCR at the city level.

Can Ecological Protection Affect High-Quality Forestry Development?—A Case Study of China

As the global ecological environment faces serious challenges, ecological protection has attracted global attention. As a basic industry of the national economy, China’s total forestry output value is 8.04 trillion RMB in 2022. This study aims to assess the impact of ecological protection on high-quality forestry development and find its mechanism by using provincial panel data from 2010 to 2021 in China. The variables of environmental regulation and urbanization level were introduced. The benchmark regression model, mediation effect model and threshold effect model were employed for empirical analysis. The results show that: (1) The estimated ecological protection coefficient is 0.146. The ecological protection level significantly promotes high-quality forestry development; (2) The effect is more significant in the western region and the area with higher forest coverage. The estimated coefficients are 1.392 and 0.745. It is less affected by the marginal effect of the ecological protection level in the east; (3) The estimated environmental regulation coefficient is −0.021. Ecological protection promotes high-quality forestry development by reducing environmental regulations; (4) The impact increases with the level of urbanization. The p-value of the double threshold is 0.073. Therefore, policy recommendations are proposed to strengthen ecological protection and promote high-quality forestry development.

Comparison of acute physiological responses between one long and two short sessions of moderate-intensity training in endurance athletes.

To compare acute physiological responses and perceived training stress between one long and two short time- and intensity-matched sessions of moderate-intensity training in endurance athletes. Fourteen male endurance athletes (VO2max: 69.2 ± 4.2mL·min-1·kg-1) performed one 6 × 10-min interval session (SINGLE) and two 3 × 10-min interval sessions interspersed with 6.5h recovery (DOUBLE) of moderate-intensity training on two separate days, while running in the laboratory, using a counterbalanced cross-over trial. The two training days were separated into a first part/session (interval stage 1-3) and second part/session (interval stage 4-6). Respiratory variables, heart rate (HR), blood lactate concentrations (BLa), and rating of perceived exertion (RPE) were collected during sessions, whereas supine heart rate (HR) was assessed in a 60-min recovery period following sessions. Measures of perceived training stress (1-10) were assessed in the morning of the subsequent day. HR, Bla, and RPE increased in the second compared to first part of SINGLE (168 ± 7 vs. 173 ± 7bpm, 2.60 ± 0.75 vs. 3.01 ± 0.81mmol·L-1, and 13.4 ± 1.0 vs. 14.8 ± 1.1-point, respectively, all p < 0.05). HR and Bla decreased in the second compared to first session of DOUBLE (171 ± 9 vs. 166 ± 9bpm and 2.72 ± 0.96 vs. 2.14 ± 0.65mmol·L-1, respectively, both p < 0.05). SINGLE revealed higher supine HR in the recovery period following sessions (65.4 ± 2.5 vs. 60.7 ± 2.5bpm p < 0.05), session RPE (sRPE, 7.0 ± 1.0 vs. 6.0 ± 1.3-point, p = .001) and sRPE training load (929 ± 112 vs. 743 ± 98, p < 0.001) compared to DOUBLE. In the subsequent morning, increased levels of perceived fatigue and muscle soreness were observed following SINGLE compared to DOUBLE (7.0 ± 2.5 vs. 8.0 ± 1.0-point, p = .049 and 6.0 ± 2.5 vs. 7.0 ± 2.5-point, p = .002, respectively). One long moderate-intensity training session was associated with a duration-dependent "drift" in physiological responses compared to two short time- and intensity-matched sessions, thereby suggesting a higher overall training stimulus. Simultaneously, the lower cost of the two shorter sessions indicates that such organization could allow more accumulated time at this intensity. Overall, these findings serve as a starting point to better understand the pros and cons of organizing moderate-intensity training as one long versus shorter sessions performed more frequently (e.g., as "double threshold training") in endurance athletes.

Does digital economy promote regional green innovation? An empirical study based on the transmission effect and threshold effect of marketization

Exploring the green innovation effect of the digital economy under the condition of socialist market economy with Chinese characteristics is of great contemporary value. In this paper, we incorporate digital economy development, marketization and green innovation into a unified analytical framework. Based on the theoretical analysis of the intrinsic logical relationship among the three, a panel data of 31 provinces in mainland China from 2011 to 2021 is employed to empirically test the effect and mechanism of digital economy development on green innovation under the condition of marketization. The results show that digital economy development is effective in improving the level of regional green innovation; Marketization is an important transmission mechanism for digital economy to release the effectiveness of green innovation. Besides, marketization has a significant double threshold effect on green innovation driven by digital economy. To promote the synergistic development of China's digital economy, market-oriented reform and green innovation in the future, we should focus on promoting the construction of an “enterprise-public-government” community for digital economy and improving the mechanism of market-oriented green innovation.