Double Threshold Research Articles

Spatial nonlinear effects of urban vitality under the constraints of development intensity and functional diversity

Urban vitality is the comprehensive characterization of the activities and processes in the city, which is influenced not only by environmental factors but also by the vitality of surrounding areas. Thus, the spatial effect of urban vitality can be understood under the constraint of the built environment. Taking Xiamen Island as a case, multisource big data are used to measure urban vitality, development intensity, and functional diversity. Spatial weight matrices with different distances are constructed. Through the spatial autoregressive threshold model, the threshold values of development intensity and functional diversity are estimated, and the spatial nonlinear effects of urban vitality based on different spatial weight matrices are analyzed. Results show that: (1) There is a significant spatial spillover effect of urban vitality. The spatial spillover effect is influenced by the built environment conditions and shows the characteristics of nonlinear growth with the change in development intensity and functional diversity; (2) the spatial spillover effect of urban vitality is significantly enhanced with the expansion of influence distance; (3) The development intensity has double thresholds. With the improvement of development intensity, the spatial spillover effect of urban vitality becomes stronger and more significant. The functional diversity has a significant single threshold value. The discussion of functional diversity should be carried out on the premise of density. The conclusion provides a theoretical basis for the optimization of urban vitality and built environment.

Enhancing fine retinal vessel segmentation: Morphological reconstruction and double thresholds filtering strategy.

Eye diseases such as diabetic retinopathy are progressive with various changes in the retinal vessels, and it is difficult to analyze the disease for future treatment. There are many computerized algorithms implemented for retinal vessel segmentation, but the tiny vessels drop off, impacting the performance of the overall algorithms. This research work contains the new image processing techniques such as enhancement filters, coherence filters and binary thresholding techniques to handle the different color retinal fundus image problems to achieve a vessel image that is well-segmented, and the proposed algorithm has improved performance over existing work. Our developed technique incorporates morphological techniques to address the center light reflex issue. Additionally, to effectively resolve the problem of insufficient and varying contrast, our developed technique employs homomorphic methods and Wiener filtering. Coherent filters are used to address the coherence issue of the retina vessels, and then a double thresholding technique is applied with image reconstruction to achieve a correctly segmented vessel image. The results of our developed technique were evaluated using the STARE and DRIVE datasets and it achieves an accuracy of about 0.96 and a sensitivity of 0.81. The performance obtained from our proposed method proved the capability of the method which can be used by ophthalmology experts to diagnose ocular abnormalities and recommended for further treatment.

The role of green finance in reducing agricultural non-point source pollution—an empirical analysis from China

IntroductionGreen finance plays a crucial role in driving sustainable development and has the potential to effectively reduce pollution emissions, thereby positively impacting the environment. However, in the agricultural sector, China, unlike developed countries, primarily relies on a small-peasant economy, and the green financial system is not well-developed. As a result, the specific emission reduction effects and mechanisms of green finance on agricultural non-point source pollution (ANSP) remain unclear. The objective of our research is to explore the internal mechanisms through which green finance influences ANSP, with the aim of providing valuable policy insights to the government and promoting the green transformation of agriculture for enhanced food security.MethodsThis study employs an empirical analysis of green finance on ANSP using provincial panel data from China spanning the years 2005 to 2020. By utilizing robust data and applying empirical analysis, we can derive scientifically credible conclusions. We introduce a relative indicator to assess the trend of ANSP and investigate the pathways through which green finance operates using heterogeneity analysis, intermediary effect evaluation, and threshold effect analysis.Results and DiscussionThe empirical findings reveal the following insights: (1) While green finance demonstrates a significant reduction effect on ANSP, this effect varies across different regions. Specifically, the impact of green finance on ANSP reduction is more pronounced in areas characterized by strong comprehensive agricultural strength, high levels of economic development, and predominantly focused on plantation agriculture. (2) From a mechanistic standpoint, green finance substantially diminishes ANSP by facilitating agricultural scaling and promoting the adoption of green technologies. (3) The threshold effect analysis demonstrates that the mitigation effect of green finance on ANSP exhibits non-linear characteristics, with a double threshold effect observed. As the level of green finance development increases, the mitigation effect is further enhanced.ConclusionIn conclusion, the appropriate implementation of green finance can effectively enhance the agricultural environment and ensure food security. Considering the heterogeneity of the role of green finance and the presence of threshold values, it is crucial for the government to tailor green finance policies according to local conditions. This research not only expands on previous studies but also offers valuable insights for the government in formulating green finance policies. Furthermore, it provides a viable pathway for reducing ANSP while serving as a reference for other developing countries aiming to establish green agriculture and sustain food system security.

The Impact of the Rural–Urban Migration of Chinese Farmers on the Use of Rural Homesteads: A Threshold Model Analysis

China’s urbanisation process is unique compared to that of other developed economies in that while the rural population is migrating to the cities in large numbers, the area of rural homestead use also continues to increase. This research uses macro data and a threshold model to further analyse this phenomenon of “farmers leaving while rural homestead increasing”. Specifically, we focus on the mechanisms of action, development patterns and regional differences in the impact of the rate of rural–urban migration (RRUM) on the rate of increase in the area of rural homesteads (IARH), and discuss the spatial spillover effects of the impact between the two. The results of the research show that: (1) There is an “inverted U-shaped” double threshold effect on the impact of RRUM on IARH. (2) Rural population density and regional urban–rural income disparity are used as threshold variables, respectively, resulting in a sudden change in the relationship between RRUM and IARH. (3) The threshold effect of RRUM on IARH mainly exists in the central and western regions, non-minority nationality areas, non-provincial capital cities and non-resource-based cities. (4) The RRUM can not only directly affect the local IARH, but also indirectly affect the surrounding areas through spatial spillover effects. Our research provides critical insights for policy makers on the reform of the rural homestead system and urbanisation development strategies in different regions.

The nonlinear effect of new urbanization on water pollutant emissions: Empirical analysis based on the panel threshold model

Rapid urbanization has led to a significant increase in water consumption and wastewater discharge. Balancing the relationship between urbanization development and water pollutants emissions is crucial for the sustainable development of the country. Given the uneven regional economic development and resource distribution in China, exploring the relationship between new urbanization and water pollution emissions cannot be limited to a single perspective such as population urbanization. This study developed a comprehensive evaluation index system for new urbanization level. Based on data from 30 provincial-level regions in China from 2006 to 2020, a Panel Threshold Regression Model (PTRM) was used to explore the nonlinear relationship between the new urbanization level and water pollution discharge. The research results show that China's new urbanization level (NUBL) and its subsystems, including population urbanization (P-NUBL), economic urbanization (E-NUBL), and spatial urbanization (SP-NUBL), all have a double threshold effect on chemical oxygen demand (COD) emissions. The promoting effect of NUBL and E-NUBL on COD emissions gradually increased in the later stage of the study. P-NUBL and SP-NUBL show a trend of inhibiting COD emissions after crossing the dual threshold values. Social urbanization (S-NUBL) and ecological urbanization (EL-NUBL) had no threshold effect, but they also had a promoting effect on COD emissions. In addition, the speed of new urbanization in eastern China was significantly faster than that in central and western China, with provinces such as Beijing, Shanghai, and Jiangsu being the first to enter the high threshold stage. The central region began to gradually enter the middle threshold stage, but provinces such as Hebei, Henan, and Anhui are still in the high pollution and high emission stage. The level of new urbanization in western China is relatively low, and future development should prioritize economic construction. Provinces with high thresholds and low water pollution emissions still need to be developed. The results of this study have important implications for promoting the harmonious development of water-saving and sustainable urban development in China.

RED WoLF hybrid energy storage system: Algorithm case study and green competition between storage heaters and heat pump

Green house gases reduction is critical in current climate emergency and was declared as major target by United Nations. This manuscript proposes the progressive adaptive recursive multi threshold control strategy for hybrid energy storage system that combines thermal storage reservoirs, heat pumps, storage heaters, photovoltaic array and a battery. The newest control strategy is tested in numerical experiment against primal dual simplex optimisation method as benchmark and previous iterations of RED WoLF threshold approaches. The proposed algorithm allows improvement in reduction of CO2 emissions by 9% comparatively to RED WoLF double threshold approach and by 26% comparatively to RED WoLF single threshold approach. Besides, the proposed technique is at least 100 times faster than linear optimisation, making the algorithm applicable to edge systems. The proposed method is later tested in numerical experiment on two measured datasets from Luxembourg school and office, equipped with batteries and ground source heat pumps. The system allows the reduction of CO2 emission and improvement of self-consumption, size reduction of the photovoltaic array installed at the facilities by at least by half as well as substituting battery storage by thermal storage, reducing the initial investment to the system. Intriguingly, despite 3.6 times difference in efficiency between heat pumps and storage heaters, the system equipped with latter have potential to achieve similar performance in carbon reduction, suggesting that energy storage have more prominent carbon reduction effect, than the power consumption, making cheaper systems with storage heaters a possible alternative to heat pumps.

A Direct Drive Sensorless Starting Scheme Based on Triple Current Threshold Method in Switched Reluctance Machines for Low-Cost Current Sampling Applications

This paper proposes a new direct drive sensorless starting scheme in switched reluctance machines (SRMs). Firstly, the performances in terms of accuracy, injected current pulse value, negative torque, total torque, and torque ripple of different current threshold methods are comprehensively compared, which is rarely reported in the literature. On this basis, with the advantages of high accuracy, low injected current, largest starting torque ability, and lowest torque ripple, the outgoing phase injection double current threshold (OPI-DCT) method is selected as the best candidate. To solve the problem that the phase current cannot be detected under the demagnetization state in typical low-cost resistor-based current sampling applications, a triple current threshold method is proposed, whose first two thresholds represent the turn-on and turn-off instants and the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> threshold indicates that the demagnetization current has returned to zero. To extend the speed range of the proposed method, an analytical method combined with a look-up table searching method is proposed to accurately predict the current threshold value, which can realize injection as soon as the demagnetization current returns to zero. Finally, both steady state and starting experiments under different load conditions verify the effectiveness and accuracy of the proposed method.

Financing constraints and impact on corporate performance growth: Study in China listed logistics enterprises.

Based on 2010-2019 Chinese logistics listed companies as research samples, the paper used the binary Logit model measuring degree of financing constraints. The Kernel density function and Markov chain model are used to forecast China listed companies financing logistics dynamic constraints and business performance growth. Furthermore the stock of knowledge was chosen as a threshold variable to explore the impact of financing constraints on corporate performance growth of listed logistics enterprises. We find that the degree of financing constraints of logistics enterprises in our country has not been significantly eased. Corporate performance has not changed significantly and there are no obvious spatial gap and polarization with the passage of time. The impact of financing constraints on the corporate performance growth of logistics enterprises in China has a double threshold effect of knowledge stock, and has an inhibitory effect that first increases and then decreases. This is because in the short term, the investment of knowledge stock by enterprises can crowd out more corporate liquidity, and in the long run, it is related to the conversion rate of the knowledge stock itself. Because of the uneven regional distribution of resources and differences in the degree of economic development, there is a growing disincentive effect in central China as the stock of knowledge accumulates.

"Could Financial Deepening Be the Solution to the Carbon Emission Problem? Empirical Evidence from CEE Countries"

"Financial deepening has increased in recent de cades in CEE countries that have transitioned from a centrally planned economy to a market economy. However, its impact on carbon emissions is con troversial. Although the determinants of emissions have been frequently examined in line with the EU’s goal of net zero carbon emissions, the empirical lit erature on the effects of financial deepening is insuf ficient. In that context, this study aims to investigate the impact of financial depth on the level of carbon emissions in CEE counties. A panel threshold regres sion model was carried out for a set of 11 countries, from 1995 to 2018. The main findings confirmed the existence of a double threshold effect. While in the low financial depth regime, financial deepening increases carbon emissions, in the medium regime it reduces them. In the high regime, however, no sta tistically significant effects were observed regarding the effects of the financial deepening. Moreover, emissions are reduced with taxes. None of the countries in the sample has financial depth index value between threshold values, according to the last three years’ averages. The findings argue that financial deepening will not be an adequate solution to reducing emissions and point to the importance of public tax policies."

Green finance, the low-carbon energy transition, and environmental pollution: evidence from China.

With 2008-2020 China's provincial panel data as the research object, we use the entropy weight method to compute green finance and pollution, and we empirically study the scope of the spillover effects and threshold impacts of green finance on environmental pollution using the spatial Durbin model and threshold regression model. The findings are as follows: first, under different spatial weight matrices, green finance has a spatial spillover effect on pollution. And the effect boundary is approximately 500km. Second, with the low-carbon energy transition, technological progress, and green finance as threshold variables, there are a single threshold, single threshold, and double threshold, respectively, that have significant threshold effects. Third, the spillover and threshold effects of green finance on pollution differ by region. The indirect effect is negative in the eastern region and positive in the central and western regions. There is a single threshold effect of low-carbon energy transition and green finance in the western region, a single threshold effect of green finance in the central region, and a single threshold effect of technological progress in the eastern region. On this basis, we put forward. specific policy recommendations.

Large-Scale Two-Sample Comparison of Support Sets

ABSTRACT Two-sample multiple testing has a wide range of applications. Most of the literature considers simultaneous tests of equality of parameters. The article takes a different perspective and investigates the null hypotheses that the two support sets are equal. This formulation of the testing problem is motivated by the fact that in many applications where the two parameter vectors being compared are both sparse, one might be more concerned about the detection of differential sparsity structures rather than the difference in parameter magnitudes. Focusing on this type of problem, we develop a general approach, which adapts the newly proposed symmetry data aggregation tool combined with a novel double thresholding (DT) filter. The DT filter first constructs a sequence of pairs of ranking statistics that fulfill global symmetry properties and then chooses two data-driven thresholds along the ranking to simultaneously control the False Discovery Rate (FDR) and maximize the number of rejections. Several applications of the methodology are given including high-dimensional linear models and Gaussian graphical models. We show that the proposed method is able to asymptotically control the FDR and have power guarantee under certain conditions. Numerical results confirm the effectiveness and robustness of DT in FDR control and detection ability. Supplementary materials for this article are available online.

A novel voxel-particle energy approach to predict 3D microscopic fracture surface of porous geomaterials and fracture permeability modeling

Characterizing and predicting 3D microscopic fractures are rarely reported due to the challengeable difficulty to determine its spatial trajectories. In this study, a double threshold segmentation algorithm is proposed to obtain 3D microstructure models of porous geomaterials by X-ray computing tomography (CT) images. A novel voxel-particle energy (VPE) approach is proposed to predict 3D fracture characteristics, and fracture permeability modeling is conducted. Results show that excellent agreements are found between the experimental and numerical results, showing the accuracy of proposed VPE method. As dynamic viscosity increases, microscopic fracture permeability increases. Microscopic fracture permeability decreases with increasing input pressure, and it increases with increasing voxel scale. The proposed VPE method provides an effective tool to predict 3D cracks and its spatial trajectories, which is helpful for the deep resource and underground space development.

Digital economy and carbon dioxide emissions: Examining the role of threshold variables

Considering that previous literature has mainly focused on the impact of the digital economy (DE) on environmental degradation, ignoring the role of natural resources, this study uses two key factors (natural resource rent and anticorruption regulation) as threshold variables to reveal the effect of natural resources on the association between DE and carbon dioxide (CO2) emissions. In doing so, the study covers 97 countries, uses annual data between 2003 and 2019, and applies a panel threshold model. The outcomes present that the influence of the DE on CO2 emissions has a single-threshold effect (i.e., there is an inverted U-shaped link between the DE and CO2 emissions) when natural resource rent is the threshold variable. Specifically, the DE significantly increases CO2 emissions when the natural resource rent is at a low-to-medium level, but the DE suppresses CO2 emissions growth when natural resource rent exceeds the threshold. Moreover, the DE drives overall CO2 emissions growth when anticorruption regulation is the threshold variable and there are double thresholds for its impact on CO2 emissions. Specifically, a rise in anticorruption regulation initially exacerbates the contribution of DE impact on CO2 emissions and then weakens it over time. Based on the results, the study proposes various implications, such as formulating a DE development strategy, considering natural resources in the development of the DE, and strengthening anti-corruption efforts in the field of environmental protection.

The heterogeneity impact of factor market distortions on GTFP: from the view of resource-based cities.

Improving the efficiency of factor allocation, breaking the resource curse, and achieving high-quality economic development are an urgent concern in resource-based cities. Using the panel data of 116 prefecture-level resource-based cities in China from 2005 to 2020, this paper constructs the data envelopment analysis-Malmquist index model to measure the level of high-quality economic development; to construct a resource misallocation growth accounting model based on the total production function to measure the distortion coefficient, the threshold panel model is employed to explore the threshold effect of factor market distortion on high-quality economic development. The results yielded three important findings: (1) The green total factor productivity of resource-based cities in China presents spatial heterogeneity and type heterogeneity. (2) Factor market distortion gradient standards are proposed innovatively, and the factor market distortion level shows the spatial heterogeneity and type heterogeneity. (3) The influence of factor market distortion on high-quality economic development in resource-based cities presents a double threshold effect. According to the above research conclusions, this paper gives several policy recommendations to promote the factor marketization allocation and high-quality economic development of resource-based cities.

Spectrum sensing using 16-QAM and 32-QAM modulation techniques at different signal-to-noise ratio: a performance analysis

&lt;p&gt;Spectrum sensing techniques are implemented for effective use of spectrum resources in the cognitive-radio. In this research work attempt has been made for the performance of energy detector with cooperative spectrum sensing using double dynamic threshold on the MATLAB software. Additive white gaussian noise is used and the frequency range between 54 MHz to 862 MHz is considered with wideband. Findings in receiver operative curve have been observed to analyze probability-of-detection (𝑃𝑑) under different values of probability-of-false alarm (𝑃𝑓) condition and diverse ranges of signal to noise ratio with different number of samples of input signal. Presence and absence of primary user has been marked by using a hypothetical model based on Neyman pearson approach. From the results, it has been observed that more the number of sample values, better is the probability-of-detection (𝑃𝑑) value as achieved for 32-QAM signal as compared to 16-QAM signal. Also, better results have been witnessed at -9db signal to noise ratio value as compared to -15db and -20db. This work provides almost 10% of enhancement in the probability of detection at -9db signal to noise ratio for 16-QAM modulated signal as compared to the existing model where the implementation of energy detector spectrum sensing technique through simulink model.&lt;/p&gt;

Time Synchronization and Signal Detection in Non-Orthogonal Unicast and Broadcast Networks

The next generation digital TV (DTV) broadcast, namely Advanced Television System Committee (ATSC) 3.0, relies on substantial single frequency networks (SFN), each of which requires a studio to transmitter link (STL) to transmit data from the broadcast gateway (BG), leading to significant construction costs. Therefore, the wireless in-band distribution link (IDL) technology is proposed, which uses layered-division multiplexing (LDM) to transmit the STL data via wireless links from the BG to the SFN transmitters within the same spectrum as the broadcast services. In this paper, we propose the time synchronization and signal detection algorithm for wireless IDL systems with both unicast and broadcast signals. We introduce two multiplexing schemes, N-LDM and 3-LDM, and demonstrate their superiority over traditional frequency division multiplexing (FDM). To overcome the challenge of multi-access interference and synchronization errors caused by multipath, we develop a unique word (UW) design algorithm for transmitted signals and a double threshold correlation detection algorithm (DTCDA) that outperforms traditional ones. We employ a second threshold to enhance detection performance at low signal-to-noise ratios, and utilizes successive interference cancellation (SIC) to mitigate multiple-access interference. More importantly, our approach requires no knowledge of the unicast user number or channel state information. Simulation results validate the effectiveness of the proposed scheme.

Capture threshold of bipolar and unipolar pacing of left ventricle via coronary sinus branch: longitudinal study.

The aim of this paper is to first monitor the changes in the capture threshold of endovascularly placed leads for left ventricle pacing, second to compare the pacing configurations, and third to verify the effect of Steroid elution for endovascular leads. The study included 202 consecutive single centre patients implanted with the Quartet™ lead (St. Jude Medical). The capture threshold and related lead parameters were tested during implantation, on the day of the patient's discharge, and 3, 9, and 15 months after implantation. The electrical energy corresponding to the threshold values for inducing ventricular contraction was recorded for subgroups of patients with bipolar and pseudo-unipolar pacing vectors and electrodes equipped with and without a slow-eluting steroids. The best setting for the resynchronization effect was generally chosen. Capture threshold was taken as a selection criterion only if there were multiple options with (expected) similar resynchronization effect. The measurements showed that the ratio of threshold energies of UNI vs. BI was 5× higher (p < 0.001) at implantation. At the end of the follow-up, it dropped to 2.6 (p = 0.012). The steroid effect in BI vectors was caused by a double capture threshold in the NSE group compared to the SE group (p < 0.001), increased by approximately 2.5 times (p < 0.001). The study concludes that after a larger initial increase in the capture threshold, the leads showed a gradual increase in the entire set. As a result, the bipolar threshold energies increase, and the pseudo-unipolar energies decrease. Since bipolar vectors require a significantly lower pacing energy, battery life of the implanted device would improve. When evaluating the steroid elution of bipolar vectors, we observe a significant positive effect of a gradual increase of the threshold energy.

The Relationship between Urbanization and Consumption Upgrading of Rural Residents under the Sustainable Development: An Empirical Study Based on Mediation Effect and Threshold Effect

With the rapid development of urbanization, the overall consumption level of rural residents has been improved accordingly in China, and the consumption structure has gradually become perfect. However, in the process of urbanization, it is necessary to handle some practical problems in order to realize the sustainable development of consumption in rural areas. Under the concept of sustainable consumption, urbanization has become a new engine that drives rural residents’ consumption in China. The digital economy has injected new vitality to expand domestic demand as well. Therefore, whether consumption upgrading in rural areas is affected by urbanization and the digital economy is a topic worth studying. To discuss the relationship between urbanization, the digital economy and consumption levels in rural areas, this paper uses an intermediary effect model to test how urbanization promotes the consumption upgrading in rural regions with the digital economy as the intermediary variable and explores the threshold characteristics of urbanization. The results are as follows: urbanization significantly promotes the rural residents’ consumption upgrading, and there is regional heterogeneity. It improves the consumption optimization in the eastern and central regions, but not in the western region. The digital economy intermediates the relationship between urbanization and consumption optimization. Moreover, urbanization has a double threshold, and with the increase of the threshold value, the marginal effect of urbanization on rural residents’ consumption upgrading also increases gradually. China should seize the opportunities of the development of urbanization and the digital economy, constantly raise farmer income, and narrow the income gap between urban and rural area, so as to promote the sustainable development of consumption in rural areas.

The Impact of Financial Development on Industrial Upgrading Based on the Analysis of Intermediation Effect and Threshold Effect

Accelerating industrial upgrading is essential for sustainable development. This paper aims to study how financial development affects industrial transformation and upgrading. First, the financial development index (fin) and the industrial upgrading index (htec, indu) are built using the entropy value approach, which is based on panel data from 30 provinces and cities in China from 2010 to 2020; second, using government intervention as the threshold variable, a fixed effects model and a threshold-effects model are utilized to empirically examine the non-linear link between financial development and industrial sophistication; and finally, the mechanism of financial development on industrial upgrading is examined using the mediating effect model, with science and technology innovation serving as the mediating variable. The study found that financial development has a positive contribution to high-tech industries (htecs). There is a U-shaped non-linear relationship between financial development and industrial advancement (indu). Finance has a stronger effect on promoting industrial upgrading via the intermediary role of science and technology innovation (tec). There is a significant double threshold effect between finance and industrial upgrading. Based on this, this paper puts forward countermeasure suggestions from the perspectives of financial development and scientific and technological innovation. It provides a basis for decision making to realize China’s industrial upgrading and helps in sustainable development in the economy and society.

How will industrial collaborative agglomeration affect the efficiency of regional green development?

The integrated development of various industries in China is essential for promoting long-term sustainable development and achieving carbon neutrality. In this study, we analyze panel data from 30 Chinese provinces (excluding Tibet, Hong Kong, Macao, and Taiwan) covering the period between 2005 and 2020 to investigate the impact of collaborative agglomeration between productive service and manufacturing industries on green development efficiency. We utilize a mediation effect model to examine the role of technological innovation in driving the relationship between industrial collaborative agglomeration and regional green development efficiency. Our findings reveal that the collaborative agglomeration of the productive service and manufacturing industries has a significant positive effect on improving regional green development efficiency. We also identify a non-linear relationship, indicating a double threshold effect. Technological innovation plays an important role in facilitating industrial collaborative agglomeration and promoting the efficiency of regional green development. Moreover, our results demonstrate significant regional heterogeneity in the impact of industrial collaborative agglomeration on regional green development efficiency. Based on these findings, we propose several policy recommendations to achieve high-quality regional economic development, including improving the quality of industrial synergy and agglomeration between regions, strengthening the intermediary promotion role of technological innovation, and enhancing regional green productivity.