Research on the Relationship Between Environmental and Economic Coupling Systems in Bohai Bay Area Based on a Vector Autoregression(VAR)Model

This study analyzed the impact of land-based contaminants and tertiary industrial structure on economic development in the selected Bohai Bay area,China.Based on panel data spanning 2011-2020,a vector autoregressive(VAR)model is used to analyze and forecast the short-run and long-run relationships between three industrial structures,pollutant discharge,and economic development.The results showed that the environmental index had a long-term cointegration relationship with the industrial structure economic index.Per capital chemical oxygen demand(PCOD)and per capita ammonia nitrogen(PNH_(3)N)had a positive impact on delta per capita GDP(dPGDP),while per capita solid waste(PSW),the secondary industry rate(SIR)and delta tertiary industry(dTIR)had a negative impact on dPGDP.The VAR model under this coupling system had stability and credibility.The impulse response results showed that the short-term effect of the coupling system on dPGDP was basically consistent with the Granger causality test results.In addition,variance decomposition was used in this study to predict the long-term impact of the coupling system in the next ten periods(i.e.,ten years).It was found that dTIR had a great impact on dPGDP,with a contribution rate as high as 74.35%in the tenth period,followed by the contribution rate of PCOD up to 3.94%,while the long-term contribution rates of PSW,SIR and PNH3N were all less than 1%.The results show that the government should support the development of the tertiary industry to maintain the vitality of economic development and prevent environmental deterioration.

On the Application of Mixed Models of Probability and Convex Set for Time-Variant Reliability Analysis

In time-variant reliability problems,there are a lot of uncertain variables from different sources.Therefore,it is important to consider these uncertainties in engineering.In addition,time-variant reliability problems typically involve a complexmultilevel nested optimization problem,which can result in an enormous amount of computation.To this end,this paper studies the time-variant reliability evaluation of structures with stochastic and bounded uncertainties using a mixed probability and convex set model.In this method,the stochastic process of a limit-state function with mixed uncertain parameters is first discretized and then converted into a timeindependent reliability problem.Further,to solve the double nested optimization problem in hybrid reliability calculation,an efficient iterative scheme is designed in standard uncertainty space to determine the most probable point(MPP).The limit state function is linearized at these points,and an innovative random variable is defined to solve the equivalent static reliability analysis model.The effectiveness of the proposed method is verified by two benchmark numerical examples and a practical engineering problem.

Temporal and spatial variation and prediction of water yield and water conservation in the Bosten Lake Basin based on the PLUS-InVEST model

To comprehensively evaluate the alterations in water ecosystem service functions within arid watersheds,this study focused on the Bosten Lake Basin,which is situated in the arid region of Northwest China.The research was based on land use/land cover(LULC),natural,socioeconomic,and accessibility data,utilizing the Patch-level Land Use Simulation(PLUS)and Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)models to dynamically assess LULC change and associated variations in water yield and water conservation.The analyses included the evaluation of contribution indices of various land use types and the investigation of driving factors that influence water yield and water conservation.The results showed that the change of LULC in the Bosten Lake Basin from 2000 to 2020 showed a trend of increasing in cultivated land and construction land,and decreasing in grassland,forest,and unused land.The unused land of all the three predicted scenarios of 2030(S1,a natural development scenario;S2,an ecological protection scenario;and S3,a cultivated land protection scenario)showed a decreasing trend.The scenarios S1 and S3 showed a trend of decreasing in grassland and increasing in cultivated land;while the scenario S2 showed a trend of decreasing in cultivated land and increasing in grassland.The water yield of the Bosten Lake Basin exhibited an initial decline followed by a slight increase from 2000 to 2020.The areas with higher water yield values were primarily located in the northern section of the basin,which is characterized by higher altitude.Water conservation demonstrated a pattern of initial decrease followed by stabilization,with the northeastern region demonstrating higher water conservation values.In the projected LULC scenarios of 2030,the estimated water yield under scenarios S1 and S3 was marginally greater than that under scenario S2;while the level of water conservation across all three scenarios remained rather consistent.The results showed that Hejing County is an important water conservation function zone,and the eastern part of the Xiaoyouledusi Basin is particularly important and should be protected.The findings of this study offer a scientific foundation for advancing sustainable development in arid watersheds and facilitating efficient water resource management.

Integrated Hydrological Modeling of the Godavari River Basin in Maharashtra Using the SWAT Model: Streamflow Simulation and Analysis

Hydrological modeling plays a crucial role in efficiently managing water resources and understanding the hydrologic behavior of watersheds. This study aims to simulate daily streamflow in the Godavari River Basin in Maharashtra using the Soil and Water Assessment Tool (SWAT). SWAT is a process-based hydrological model used to predict water balance components, sediment levels, and nutrient contamination. In this research, we used integrated remote sensing and GIS data, including Digital Elevation Models (DEM), land use and land cover (LULC) maps, soil maps, and observed precipitation and temperature data, as input for developing the SWAT model to assess surface runoff in this large river basin. The Godavari River Basin under study was divided into 25 sub-basins, comprising 151 hydrological response units categorized by unique land cover, soil, and slope characteristics using the SWAT model. The model was calibrated and validated against observed runoff data for two time periods: 2003-2006 and 2007-2010 respectively. Model performance was assessed using the Nash-Sutcliffe efficiency (NSE) and the coefficient of determination (R2). The results show the effectiveness of the SWAT2012 model, with R2 value of 0.84 during calibration and 0.86 during validation. NSE values also ranged from 0.84 during calibration to 0.85 during validation. These findings enhance our understanding of surface runoff dynamics in the Godavari River Basin under study and highlight the suit-ability of the SWAT model for this region.

A numerical model study on the spatial and temporal variabilities of dissolved oxygen in Qinzhou Bay of the northern Beibu Gulf

Oxygen facilitates the breakdown of the organic material to provide energy for life.The concentration of dissolved oxygen(DO) in the water must exceed a certain threshold to support the normal metabolism of marine organisms.Located in the northern B eibu Gulf,Qinzhou B ay receives abundant freshwater and nutrients from several rivers which significantly influence the level of the dissolved oxygen.However,the spatial-temporal variations of DO as well as the associated driving mechanisms have been rarely studied through field observations.In this study,a three-dimension al coupled physical-biogeochemical model is used to investigate the spatial and seasonal variations of the DO and the associated driving mechanisms in Qinzhou B ay.The validation against observations indicates that the model can capture the seasonal and inter-annual variability of the DO concentration with the range of 5-10 mg/L.Sensitivity experiments show that the river discharges,winds and tides play crucial roles in the seasonal variability of the DO by changing the vertical mixing and stratification of the water column and the circulation pattern.In winter,the tide and wind forces have strong effects on the DO distribution by enhancing the vertical mixing,especially near the bay mouth.In summer,the river discharges play a dominant role in the DO distribution by inhibiting the vertical water exchange and delivering more nutrients to the Bay,which increases the DO depletion and results in lower DO on the bottom of the estuary salt wedge.These findings can contribute to the preservation and management of the coastal environment in the northern Beibu Gulf.

Application of Predictive Model for Efficient Cassava (Manihot esculenta Crantz) Yield in the Face of Climate Variability in Enugu State, Nigeria

Climate variability as occasioned by conditions such as extreme rainfall and temperature, rainfall cessation, and irregular temperatures has considerable impact on crop yield and food security. This study develops a predictive model for cassava yield (Manihot esculenta Crantz) amidst climate variability in rainfed zone of Enugu State, Nigeria. This study utilized data of climate variables and tonnage of cassava yield spanning from 1971 to 2012;as well as information from a questionnaire and focus group discussion from farmers across two seasons in 2023 respectively. Regression analysis was employed to develop the predictive model equation for seasonal climate variability and cassava yield. The rainfall and temperature anomalies, decadal change in trend of cassava yield and opinion of farmers on changes in rainfall season were also computed in the study. The result shows the following relationship between cassava and all the climatic variables: R2 = 0.939;P = 0.00514;Cassava and key climatic variables: R2 = 0.560;P = 0.007. The result implies that seasonal rainfall, temperature, relative humidity, sunshine hours and radiation parameters are key climatic variables in cassava production. This is supported by computed rainfall and temperature anomalies which range from −478.5 to 517.8 mm as well as −1.2˚C to 2.3˚C over the years. The questionnaire and focus group identified that farmers experienced at one time or another, late onset of rain, early onset of rain or rainfall cessation over the years. The farmers are not particularly sure of rainfall and temperature characteristics at any point in time. The implication of the result of this study is that rainfall and temperature parameters determine the farming season and quantity of productivity. Hence, there is urgent need to address the situation through effective and quality weather forecasting network which will help stem food insecurity in the study area and Nigeria at large. The study made recommendations such as a comprehensive early warning system on climate variability incidence which can be communicated to local farmers by agro-meteorological extension officers, research on crops that can grow with little or no rain, planning irrigation scheme, and improving tree planting culture in the study area.

Review of Recent Trends in the Hybridisation of Preprocessing-Based and Parameter Optimisation-Based Hybrid Models to Forecast Univariate Streamflow

Forecasting river flow is crucial for optimal planning,management,and sustainability using freshwater resources.Many machine learning(ML)approaches have been enhanced to improve streamflow prediction.Hybrid techniques have been viewed as a viable method for enhancing the accuracy of univariate streamflow estimation when compared to standalone approaches.Current researchers have also emphasised using hybrid models to improve forecast accuracy.Accordingly,this paper conducts an updated literature review of applications of hybrid models in estimating streamflow over the last five years,summarising data preprocessing,univariate machine learning modelling strategy,advantages and disadvantages of standalone ML techniques,hybrid models,and performance metrics.This study focuses on two types of hybrid models:parameter optimisation-based hybrid models(OBH)and hybridisation of parameter optimisation-based and preprocessing-based hybridmodels(HOPH).Overall,this research supports the idea thatmeta-heuristic approaches precisely improveML techniques.It’s also one of the first efforts to comprehensively examine the efficiency of various meta-heuristic approaches(classified into four primary classes)hybridised with ML techniques.This study revealed that previous research applied swarm,evolutionary,physics,and hybrid metaheuristics with 77%,61%,12%,and 12%,respectively.Finally,there is still room for improving OBH and HOPH models by examining different data pre-processing techniques and metaheuristic algorithms.

Influence of Various Earth-Retaining Walls on the Dynamic Response Comparison Based on 3D Modeling

The present work aims to assess earthquake-induced earth-retaining(ER)wall displacement.This study is on the dynamics analysis of various earth-retaining wall designs in hollow precast concrete panels,reinforcement concrete facing panels,and gravity-type earth-retaining walls.The finite element(FE)simulations utilized a 3D plane strain condition to model full-scale ER walls and numerous nonlinear dynamics analyses.The seismic performance of differentmodels,which includes reinforcement concrete panels and gravity-type and hollowprecast concrete ER walls,was simulated and examined using the FE approach.It also displays comparative studies such as stress distribution,deflection of the wall,acceleration across the wall height,lateral wall displacement,lateral wall pressure,and backfill plastic strain.Three components of the created ER walls were found throughout this research procedure.One is a granular reinforcement backfill,while the other is a wall-facing panel and base foundation.The dynamic response effects of varied earth-retaining walls have also been studied.It was discovered that the facing panel of the model significantly impacts the earthquake-induced displacement of ER walls.The proposed analytical model’s validity has been evaluated and compared with the reinforcement concrete facing panels,gravity-type ER wall,scientifically available data,and American Association of State Highway and Transportation Officials(AASHTO)guidelines results based on FE simulation.The results of the observations indicate that the hollow prefabricated concrete ER wall is the most feasible option due to its lower displacement and high-stress distribution compared to the two types.The methodology and results of this study establish standards for future analogous investigations and professionals,particularly in light of the increasing computational capabilities of desktop computers.

An attention-based teacher-student model for multivariate short-term landslide displacement prediction incorporating weather forecast data

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property.However,traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction,while a single structural model cannot handle input sequences of different lengths at the same time.In order to solve these limitations,in this study,a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient(MIC),long short-term memory network(LSTM),and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement prediction.Through MIC,a suitable input sequence length is selected for the LSTM model.To investigate the influence of rainfall on landslides during different seasons,a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different lengths.The teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration(CMA)and Reliable Prognosis(https://rp5.ru)to improve the model’s expression capability,and the student model learns sequence information from other time series.An attention module is then designed to integrate different sequence information to derive a context vector,representing seasonal temporal attention mode.Finally,the predicted displacement is obtained through a linear layer.The proposed method demonstrates superior prediction accuracies,surpassing those of the support vector machine(SVM),LSTM,recurrent neural network(RNN),temporal convolutional network(TCN),and LSTM-Attention models.It achieves a mean absolute error(MAE)of 0.072 mm,root mean square error(RMSE)of 0.096 mm,and pearson correlation coefficients(PCCS)of 0.85.Additionally,it exhibits enhanced prediction stability and interpretability,rendering it an indispensable tool for landslide disaster prevention and mitigation.

Computational Fluid Dynamics Approach for Predicting Pipeline Response to Various Blast Scenarios: A Numerical Modeling Study

Recent industrial explosions globally have intensified the focus in mechanical engineering on designing infras-tructure systems and networks capable of withstanding blast loading.Initially centered on high-profile facilities such as embassies and petrochemical plants,this concern now extends to a wider array of infrastructures and facilities.Engineers and scholars increasingly prioritize structural safety against explosions,particularly to prevent disproportionate collapse and damage to nearby structures.Urbanization has further amplified the reliance on oil and gas pipelines,making them vital for urban life and prime targets for terrorist activities.Consequently,there is a growing imperative for computational engineering solutions to tackle blast loading on pipelines and mitigate associated risks to avert disasters.In this study,an empty pipe model was successfully validated under contact blast conditions using Abaqus software,a powerful tool in mechanical engineering for simulating blast effects on buried pipelines.Employing a Eulerian-Lagrangian computational fluid dynamics approach,the investigation extended to above-surface and below-surface blasts at standoff distances of 25 and 50 mm.Material descriptions in the numerical model relied on Abaqus’default mechanical models.Comparative analysis revealed varying pipe performance,with deformation decreasing as explosion-to-pipe distance increased.The explosion’s location relative to the pipe surface notably influenced deformation levels,a key finding highlighted in the study.Moreover,quantitative findings indicated varying ratios of plastic dissipation energy(PDE)for different blast scenarios compared to the contact blast(P0).Specifically,P1(25 mm subsurface blast)and P2(50 mm subsurface blast)showed approximately 24.07%and 14.77%of P0’s PDE,respectively,while P3(25 mm above-surface blast)and P4(50 mm above-surface blast)exhibited lower PDE values,accounting for about 18.08%and 9.67%of P0’s PDE,respectively.Utilising energy-absorbing materials such as thin coatings of ultra-high-strength concrete,metallic foams,carbon fiber-reinforced polymer wraps,and others on the pipeline to effectively mitigate blast damage is recommended.This research contributes to the advancement of mechanical engineering by providing insights and solutions crucial for enhancing the resilience and safety of underground pipelines in the face of blast events.

Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks:Climatology,Interannual Variability,and Extremes

Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.

The Impact of Model Based Offset Scaling Technique on the Amplitude Variation with Offset Responses from 3D Seismic Data Acquired from the Tano Basin, Offshore Ghana

Amplitudes have been found to be a function of incident angle and offset. Hence data required to test for amplitude variation with angle or offset needs to have its amplitudes for all offsets preserved and not stacked. Amplitude Variation with Offset (AVO)/Amplitude Variation with Angle (AVA) is necessary to account for information in the offset/angle parameter (mode converted S-wave and P-wave velocities). Since amplitudes are a function of the converted S- and P-waves, it is important to investigate the dependence of amplitudes on the elastic (P- and S-waves) parameters from the seismic data. By modelling these effects for different reservoir fluids via fluid substitution, various AVO geobody classes present along the well and in the entire seismic cube can be observed. AVO analysis was performed on one test well (Well_1) and 3D pre-stack angle gathers from the Tano Basin. The analysis involves creating a synthetic model to infer the effect of offset scaling techniques on amplitude responses in the Tano basin as compared to the effect of unscaled seismic data. The spectral balance process was performed to match the amplitude spectra of all angle stacks to that of the mid (26°) stack on the test lines. The process had an effect primarily on the far (34° - 40°) stacks. The frequency content of these stacks slightly increased to match that of the near and mid stacks. In offset scaling process, the root mean square (RMS) amplitude comparison between the synthetic and seismic suggests that the amplitude of the far traces should be reduced relative to the nears by up to 16%. However, the exact scaler values depend on the time window considered. This suggests that the amplitude scaling with offset delivered from seismic processing is only approximately correct and needs to be checked with well synthetics and adjusted accordingly prior to use for AVO studies. The AVO attribute volumes generated were better at resolving anomalies on spectrally balanced and offset scaled data than data delivered from conventional processing. A typical class II AVO anomaly is seen along the test well from the cross-plot analysis and AVO attribute cube which indicates an oil filled reservoir.

Variability of the Pacific subtropical cells under global warming in CMIP6 models

The Pacific subtropical cells(STCs)are shallow meridional overturning circulations connecting the tropics and subtropics,and are assumed to be an important driver of the tropical Pacific decadal variability.The variability of STCs under global warming is investigated using multimodal outputs from the latest phase of the Coupled Model Inter-comparison Project(CMIP6)and ocean reanalysis products.Firstly,the volume transport diagnostic analysis is employed to evaluate how coupled models and ocean reanalysis products reproduce interior STC transport.The variation of heat transport by the interior STC under the high-emissions warming scenarios is also analyzed.The results show that the multimodal-mean linear trends of the interior STC transport along 9°S and 9°N are-0.02 Sv/a and 0.04 Sv/a under global warming,respectively,which is mainly due to the combined effect of the strengthened upper oceanic stratification and the weakening of wind field.There is a compensation relationship between the interior STC and the western boundary transport in the future climate,and the compensation relationship of 9°S is more significant than that of 9°N.In addition,compared with ocean reanalysis products,the coupled models tend to underestimate the variability of the interior STC transport convergence,and thus may lose some sea surface temperature(SST)driving force,which may be the reason for the low STC-SST correlation simulated by the model.The future scenario simulation shows that the heat transport of interior STC is weakened under global warming,with a general agreement across models.

Cross Validation Based Model Averaging for Varying-Coefficient Models with Response Missing at Random

In this paper, a model averaging method is proposed for varying-coefficient models with response missing at random by establishing a weight selection criterion based on cross-validation. Under certain regularity conditions, it is proved that the proposed method is asymptotically optimal in the sense of achieving the minimum squared error.

Hybrid Dynamic Variables-Dependent Event-Triggered Fuzzy Model Predictive Control

This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.

我国牛肉价格波动影响因素研究——基于VAR模型的实证分析

为保证牛肉价格合理稳定、推动牛肉市场持续健康发展,文章基于2011年12月—2022年12月中国畜牧业月度数据,以内部传导和外部冲击为视角,选取玉米价格、生产者预期、犊牛价格、国家政策构建VAR模型,实证检验我国牛肉价格波动影响因素及程度。结果显示,玉米价格、生产者预期、犊牛价格、国家政策均可对牛肉价格造成影响。其中,玉米价格可通过内部传导机制对我国牛肉价格波动产生显著影响,且稳定贡献率在8%左右;国家政策可通过外部冲击机制对我国牛肉价格波动形成显著影响,贡献率为15%;前期牛肉价格可显著影响后期牛肉市场价格。因此,文章提出降低肉牛产业饲料成本、加大肉牛产业政府财政支持力度、完善牛肉价格波动市场监管体系的建议,以期为稳定牛肉价格、促进畜牧产业可持续发展提供参考。

基于VAR模型的生猪产业链价格波动影响因素分析

生猪产业链价格剧烈波动不仅关系消费者的切身利益,也对生猪产业长远发展具有重要影响。基于2009—2022年161个月度样本数据,文章采用VAR模型,从生猪产业链主要产品价格以及上游环节、中下游环节2个层面出发,探究生猪产业链价格波动的影响因素。结果显示:生猪产业链价格波动受自身主要产品价格的影响最大,前10期生猪产业链主要产品价格对当月生猪产业链价格波动仍具有61.835 8%的影响。从产业链上游环节观察,稻糠价格、大豆价格和小麦麸价格均能够对生猪产业链价格波动产生显著影响,前10期稻糠价格对当月生猪产业链价格波动的影响达到39.783 8%。从产业链中下游环节观察,通货膨胀水平和养殖场规模对生猪产业链价格波动的影响程度较大。研究表明,生猪产业链价格波动容易受到自身主要产品价格、稻糠价格、大豆价格、小麦麸价格、通货膨胀水平、养殖场规模等因素影响,应从完善价格波动预警机制和加强流通运行环节调控两方面着手推动生猪市场稳定发展。

财政政策、货币政策与高质量就业——基于TVP-SV-VAR模型的动态分析

更加充分、更高质量的就业是实现经济高质量发展的应有之义。文章采用中国2000—2021年的季度时间序列数据,基于时变参数向量自回归(TVP-SV-VAR)模型,实证检验财政政策与货币政策对就业质量的时变及动态影响。研究发现:落实减税降费政策、优化财政支出结构在短期和中长期均有效地促进了高质量就业。面对国际金融危机冲击时,财政政策在短期内实现了对就业质量的保障,而在长期内政策效果受到削弱,这说明高质量就业的实现并非“斯须之作”。数量型货币政策对就业质量的调节作用较为平稳,其影响具有滞后性,而价格型工具更能有效熨平外部冲击对就业质量的影响。相较货币政策,财政政策对就业质量的调控具有更强的拉动作用与抗冲击能力。

服务业进出口、OFDI和全要素生产率的动态关系研究——基于VAR模型的实证分析

研究服务业贸易、投资与服务业之间的影响关系,对提升我国服务行业国际竞争力、促进其高质量发展具有重要意义。论文使用我国2000—2021年数据,构建VAR模型对服务业进出口、服务业对外直接投资(OFDI)和全要素生产率的动态交互关系进行实证分析。研究结果显示,服务业进出口对服务业对外直接投资具有正相关关系,且长期支持效果明显;服务业进出口对OFDI、全要素生产率提升具有促进作用;在短期内,全要素生产率与OFDI之间存在双向促进作用,从长期来看则存在一定负向影响。基于实证研究结论,文中提出制定政策鼓励服务企业参与国际化竞争、发挥服务业进出口对OFDI的促进效应、提升服务企业自主创新能力等相关对策建议。

基于VAR模型的专业市场与跨境电商融合发展研究

专业市场如何应对电子商务冲击,是近二十年来各界普遍关注的现实问题。跨境电商和市场采购两种新业态新模式已成为我国外贸高质量发展的新引擎,专业市场与跨境电商的关系从冲击应对走向融合发展。浙江省义乌市是专业市场和跨境电商的改革前沿阵地。利用义乌市2008—2022年的时间序列数据,建立向量自回归模型,定量分析专业市场与跨境电商的融合发展关系。结果表明:专业市场生态体系有利于跨境电商“嵌入式”发展,专业市场的持续改革有效应对了跨境电商冲击,但两者融合发展仍缺乏互动效应和长效机制。为进一步促进两者融合发展,应搭建专业市场跨境电商平台、叠加商流和信息流带动产业转型升级、融通跨境电商进口贸易供应链和配套政策。