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Air pollution is closely associated with human health and the economy. Therefore, it is important to understand variations in the spatiotemporal and sectoral emission distributions of major air pollutants and their drivers. The policies (APAPPC) promulgated by China in 2013 have also achieved remarkable results. Rate of change, trend analysis, and a geographically and temporally weighted regression model were used to study the effects of socioeconomic factors on NOx, SO2, and dust emissions in China during 2011–2017. During the study period, annual average emissions of NOx, SO2, and dust decreased by 11.45, 13.42, and 4.82 Mt (−47.64, −60.53, and −39.05%), respectively. Pollutant emissions were concentrated in North China, with Shandong and Hebei provinces exhibiting the highest NOx and SO2 and dust emissions, respectively. Pollutant emissions from the power and industrial sectors were mainly distributed in East (27.08 and 28.00%, respectively) and North China (23.57 and 20.04%, respectively), whereas emissions from the residential sector were mainly concentrated in North (22.48%) and Southwest China (20.07%). Pollutant emissions were positively correlated with electricity generation, urban population density, urban green spaces, private car ownership, the secondary industry as a share of regional GDP, and steel production and negatively correlated with disposable income and gross construction output. Per capita disposable income was the dominant driving factor.

Modeling complex spatial and temporal dependencies in multivariate time series data is crucial for traffic forecasting. Graph convolutional networks have proved to be effective in predicting multivariate time series. Although a predefined graph structure can help the model converge to good results quickly, it also limits the further improvement of the model due to its stationary state. In addition, current methods may not converge on some datasets due to the graph structure of these datasets being difficult to learn. Motivated by this, we propose a novel model named Dynamic Correlation Graph Convolutional Network (DCGCN) in this paper. The model can construct adjacency matrices from input data using a correlation coefficient; thus, dynamic correlation graph convolution is used for capturing spatial dependencies. Meanwhile, gated temporal convolution is used for modeling temporal dependencies. Finally, we performed extensive experiments to evaluate the performance of our proposed method against ten existing well-recognized baseline methods using two original and four public datasets.

This study examines the problem of modeling the joint dynamics of conditional volatility of several financial assets under an asymmetric relationship between volatility and shocks in returns (leverage effect). We propose a new multivariate asymmetric conditional heteroskedasticity model with a dynamic conditional correlation matrix (DCC-EGARCH). The proposed method allows modelling the joint dynamics of several financial assets taking into account the leverage effect in the financial markets. DCC-EGARCH model has two main advantages over previously proposed multivariate asymmetric specifications. It involves a substantially simpler optimization problem and does away with the assumption of conditional correlation time invariance. These features make the model more suitable for practical applications. To study the properties of the obtained estimators, we conducted a simulated data analysis. As a result, we found statistical evidence in favor of the developed DCC-EGARCH model compared with the symmetric DCC-GARCH process in case of considering data with the presence of the leverage effect. Further, we applied the proposed method to analyze the joint volatility of Rosneft stock returns and Brent oil prices. By estimating the DCC-EGARCH model, we found statistical evidence for both the presence of the leverage effect in the oil price data and the presence of the dynamic correlation structure between the time series, which motivates the practical application of the proposed method.

The new generation of proposed light-emitting molecules for OLEDs has raised a considerable research interest due to its exceptional feature-a negative singlet-triplet (ST) gap violating the Hund's multiplicity rule in the excited S1 and T1 states. We investigate the role of spin polarization in the mechanism of ST gap inversion. Spin polarization is associated with doubly excited determinants of certain types, whose presence in the wavefunction expansion favors the energy of the singlet state more than that of the triplet. Using a perturbation theory-based model for spin polarization, we propose a simple descriptor for prescreening of candidate molecules with negative ST gaps and prove its usefulness for heptazine-type molecules. Numerical results show that the quantitative effect of spin polarization is approximately inverse-proportional to the HOMO-LUMO exchange integral. Comparison of single- and multireference coupled- cluster predictions of ST gaps shows that the former methods provide good accuracy by correctly balancing the effects of doubly excited determinants and dynamic correlation. We also show that accurate ST gaps may be obtained using a complete active space model supplemented with dynamic correlation from multireference adiabatic connection theory.

Objective: Modelling dynamic nature of covariance of assets return almost always is a challenging area of finance. Econometrics models just pay attention to variance behavior longitudinally, however, core of numerous finance models need the analysis of the total covariance structure of returns. Method: Among the first models that analyze covariance behavior are multivariate GARCH models which were criticized for the need to estimate a large number of parameters. This paper is aimed to investigate the effect of stock return dynamic correlation structure on systematic risk, idiosyncratic risk and average stock return. To this end, a sample of 148 listed companies in Tehran Stock Exchange is examined during 2003 to 2014. GARCH framework is used for testing this claim. Results: According to the results, securities that were highly correlated with market wide risk factors in the past are likely to have low systematic risk, idiosyncratic risk and average return at present. It can be expected there is significant relationship between idiosyncrstic risk and correlation for lower turnover stock (information transparency proxy) although there is no relationship for smaller firms.

In order to investigate the sources and effects of atmospheric volatile organic compounds (VOCs) in the southeast coastal region and to formulate effective ozone control policies, 96 VOCs were measured in the southeast coastal region from September to October 2016 using an online gas chromatograph-mass spectrometer/flame ionization detector (Online-GC-MS/FID). The composition, daily trends, sources, and contribution to ozone production of 96 VOCs in the southeast coastal region were analyzed. The results of the study showed that the volume mixing ratio of alkanes was the highest, followed by aromatic hydrocarbons; the daily variation trend of alkanes and aromatic hydrocarbons was obvious, with a double-peaked feature; propane, isoprene, and aromatic hydrocarbons in the atmosphere of the southeast coastal region came from liquefied petroleum gas (LPG), natural sources, and industrial emissions, respectively. The analysis of the activity of VOCs shows that aromatic hydrocarbons and alkanes are the most important contributors to OFP in the southeast coastal region, with toluene, m-/paraxylene, and ethylene being the species that contribute the most to OFP. Therefore, the control of VOC emissions from anthropogenic sources is a priority for ozone pollution control in the southeast coastal region in the future.

The stochastic characteristics of wind power and photovoltaic (PV) make the resource allocation of power system difficult. Therefore, it is necessary to consider the correlation between the power generation of wind and PV power stations to avoid resource waste and guarantee system power supply, while the traditional correlation analysis method cannot accurately describe the multiscale and time-varying characteristics of the correlation. In this article, based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the time series of the wind and PV power generation was decomposed into multiscale components. Moreover, the time-dependent intrinsic correlation (TDIC) is introduced to excavate the local correlation of the power generation time series under the framework of a multi-time scale, the dynamic change of a correlation is captured by analyzing the TDIC plots. The analysis shows that the strength and nature of the association between wind and PV vary with time scales and time spells, reflecting rich, dynamic characteristics. The correlation variation of different scale components in local time is of great significance to power system operation, planning, and resource optimal allocation.

This study examines the static and dynamic correlations in the ASEAN equity markets. The importance of this research appears from the fact that practitioners can get the benefit if their investments yield the same or higher returns given lower or the same risk in their portfolio. Firstly, this advantage comes from including the assets that decrease volatility of the portfolio. Hence, the correlation between the ASEAN markets should be examined. Secondly, co-movements in market realizations may increase global financial instability. Its existence is important for international investors, financial institutions, and policy makers. The study locates the relationship between ASEAN and its major trading partners, including Japanese, US, and UK markets, in order to find more rational results. This study utilizes alternative multivariate GARCH forms to provide useful information on the dynamic evolution and implications of return volatilities. The results show that the volatilities of all the equity markets under study are persistent over time. The estimates from VEC model indicate that the movements of the US and UK equity market returns have some degree of influence on several of the ASEAN equity markets. The results imply that, first, most of the developing ASEAN equity markets work by its own information with small relation to the developed world. Second, it is still convincing to state that investing in ASEAN equity markets should provide investors a better mean-variance portfolio. And, third, buy-and-hold strategy seems to be more beneficial than readjusting the ASEAN equities portfolio.

The planning of the park-level integrated energy system (PIES) is a long-term process, it faces risk caused by the volatility of energy prices including the natural gas price (NGP) and the electricity price (EP). The uncertainties of them are complex and have time-varying correlation, which influence the results of risk losses evaluation. To resist the future risk and guide the planning of PIES, a risk assessment method considering the uncertainty and dynamic correlation of energy prices is proposed in this paper. Considering the heteroscedasticity effect of NGP and EP, the time-varying marginal distribution models of them are established using the generalized autoregressive conditional heteroscedasticity (GARCH) model. Subsequently, resorting to the dynamic Copula function and dynamic conditional correlation (DCC) model, the dynamic joint distribution of NGP and EP is established, which describes the dynamic correlation of NGP and EP. On this basis, the Monte Carlo method is adopted to generate scenarios of NGP and EP to simulate possible situations. Then, the conditional value at risk (CVaR) is introduced as the risk measure to quantify the risk losses for the PIES planning schemes. Simulation results verified the effectiveness of the proposed method, which can present different correlation features of energy prices in different time periods and beneficial to evaluate the risk losses. And the utilization of multi-energy complementary supply and distributed renewable energy can improve the risk-resistant ability of the PIES.

Airports, as integral components of the global aviation industry, experience dynamic changes in air passenger traffic load, which are also related to the trends of local urban development and airspace restrictions of nearby airports. Using time-series data from 2006 to 2019, this study comprehensively applied the autoregressive distributed lag and vector auto-regression mode approaches to identify causal relationships between the urban development factors, including GDP, population, tourism industry, industrial structure, etc., of Tianjin city (China); the Tianjin airport passenger flow; and the Beijing Capital airport's airspace restriction factor, namely, airport aircraft sorties. The results show that the growth of Tianjin city's GDP, primary industry and disposable income per capita was accompanied by a long-term decline in the passenger flow at the Tianjin airport. In addition, increased aircraft sorties of Tianjin airport, as well as the growth of primary, secondary and tertiary industries in Tianjin city, led to a short-term decline in passenger flow at the Tianjin airport. In general, there is variability in the long- and short-term impacts of urban economic structure on airport passenger flow, and this variability applies to other airports. The increased aircraft sorties at the Beijing Capital airport had a short-term positive impact on passenger flow at the Tianjin airport but resulted in a long-term decline of the latter's aircraft sorties. This phenomenon indicates that there is interaction between airports and that this influence varies depending on the competition and cooperation mechanisms between airports. The findings of this study are considered instrumental in guiding the competitive and cooperative strategies of nearby airports and predicting the coupled trends of the airport and urban development.

This paper investigates the level of pairwise dynamic correlations between prices of four agricultural commodities - corn, wheat soybean and barley that are traded in Novi Sad commodity exchange market. We use DCC-GARCH model, which is specially designed for this type or research. The results of the estimated dynamic conditional correlations show that low and positive correlation exist between all the pairs of the selected agricultural commodities, where the highest correlation is recorded between wheat and barley (24%), corn-barley pair follows (20%), while all other dynamic correlations are below 20%. The results indicate that price movements of the selected agricultural cereals are independent, which means that price discovery of one agricultural commodity does not provide information about the price of another agricultural commodity. Therefore, our results strongly suggest that traders in this market do not rely on the price co-movements between particular agricultural assets when they plan their selling or buying strategies, but to analyze fundamental macroeconomic factors.

This article investigates the reaction of integrated financial markets to the COVID-19 pandemic. Using a dynamic conditional correlation model on Euronext Stock Exchange, I find that the integration of financial markets can act as a buffer against negative shocks such as COVID-19. However, this benefit begins to fade as the number of deaths from COVID-19 increases. In this case, a negative relationship with the dynamic correlation is found in some pairs of member countries.

As China’s economy and the U.S. economy have shown a definite interaction, there is considerable interest in studying the correlation between the Chinese stock market and the US financial markets. This paper uses an Asymmetric Dynamic Conditional Correlation (ADCC)-GARCH to investigate the correlation between the Shanghai Composite Index (SHCI) and the U.S. financial markets, including SP500, NASDAQ, and US dollar indexes. The empirical results show that the time-varying daily and the lag-one correlation between China and the US stock markets have different performances during global events and national events. Compared with the complicated effect of negative events on the correlation of the stock market, SHCI and USD are negatively correlated with higher negative correlation during the global negative events. In addition, we found Chinese investors are more contagious to the news than American investors, indicating that the Chinese government’s policy are more indicated to Chinese investors. Finally, some policy suggestions are provided, and are beneficial to risk prevention and control, and investment.

Multi-agent reinforcement learning approaches can be roughly classified into two categories. One is the agent-based approach which can be implemented in real distributed systems, though most approaches of this type cannot provide meaningful theoretical verifications. The other can be seen as the more formalized approach, which can provide theoretical results. However, most of current algorithms usually require unrealistic global communication, which makes them impractical for real distributed systems. In this article, we propose a dynamic correlation matrix based multi-agent reinforcement learning approach where the meta-parameters are evolved using an evolutionary algorithm. We believe that our approach is able to fill the gap between the two kinds of traditional multi-agent reinforcement learning approaches by providing both agent-level implementation and system-level convergence verification. The basic idea of this approach is that agents learn not only from local environmental feedback, i.e., their own experiences and rewards, but also from other agents’ experiences. In this way, the agents’ learning speed can be increased significantly. The performance of the proposed algorithm is demonstrated on a number of application scenarios, including blackjack games, urban traffic control systems and multi-robot foraging.

With the economic globalization, it is important to accurately estimate and predict the correlation between global stock markets for the efficient asset allocation around the globe. While practitioners may resort to various GARCH-type models for correlation forecasting, the forecasting accuracy is still not very satisfactory. For the first time, this paper introduces a deep learning method into the research of stock market correlation. A hybrid DCDNN model is developed based on the RDNN (recurrent deep neural network) and DCC-GARCH models. Deep learning is then devised to forecast the prediction error of the DCC-GARCH model in order to improve the prediction accuracy of stock market correlation. An autoencoder is also introduced in the empirical study to extract useful features of the stock index data. Then, the correlations among the stock markets in China, Hong Kong, the United States and Europe are predicted and tested. We show that the accuracy of the DCDNN model is significantly higher than that of the DCC-GARCH model. The results indicate that the introduction of deep learning can help improve the efficacy of existing correlation forecasting methods.

We discuss the interplay between the nondynamic and dynamic electron correlation in excited states from the perspective of the suppression of dynamic correlation (SDC) and enhancement of dynamic correlation (EDC) effects. We reveal that there exists a connection between the ionic character of a wave function and EDC. Following this finding we introduce a quantitative measure of ionicity based solely on local functions without referring to valence bond models. The ability to recognize both the SDC and EDC regions underlies the presented method, named CASΠDFT, combining complete active space (CAS) wave function and density functional theory (DFT) via the on-top pair density (Π) function. We extend this approach to excited states by devising an improved representation of the EDC effect in the correlation functional. The generalized CASΠDFT uses different DFT functionals for ground and excited states. Numerical demonstration for singlet π → π* excitations shows that CASΠDFT offers satisfactory accuracy at a fraction of the cost of the ab initio approaches.

Based on a logistic map and Feigenbaum map, we proposed a logistic Feigenbaum non-linear cross-coupled hyperchaotic map (LF-NCHM) model. Experimental verification showed that the system is a hyperchaotic system. Compared with the existing cross-coupled mapping, LF-NCHM demonstrated a wider hyperchaotic range, better ergodicity and richer dynamic behavior. A hyperchaotic sequence with the same number of image pixels was generated by LF-NCHM, and a novel image-encryption algorithm with permutation that is dynamically related to plaintext pixels was proposed. In the scrambling stage, the position of the first scrambled pixel was related to the sum of the plaintext pixel values, and the positions of the remaining scrambled pixels were related to the pixel values after the previous scrambling. The scrambling operation also had a certain diffusion effect. In the diffusion phase, using the same chaotic sequence as in the scrambling stage increased the usage rate of the hyperchaotic sequence and improved the calculation efficiency of the algorithm. A large number of experimental simulations and cryptanalyses were performed, and the results proved that the algorithm had outstanding security and extremely high encryption efficiency. In addition, LF-NCHM could effectively resist statistical analysis attacks, differential attacks and chosen-plaintext attacks.

Recent studies have demonstrated the superior performance of introducing ``scan-wise" contrast labels into contrastive learning for multi-organ segmentation on multi-phase computed tomography (CT). However, such scan-wise labels are limited: (1) a coarse classification, which could not capture the fine-grained ``organ-wise" contrast variations across all organs; (2) the label (i.e., contrast phase) is typically manually provided, which is error-prone and may introduce manual biases of defining phases. In this paper, we propose a novel data-driven contrastive loss function that adapts the similar/dissimilar contrast relationship between samples in each minibatch at organ-level. Specifically, as variable levels of contrast exist between organs, we hypothesis that the contrast differences in the organ-level can bring additional context for defining representations in the latent space. An organ-wise contrast correlation matrix is computed with mean organ intensities under one-hot attention maps. The goal of adapting the organ-driven correlation matrix is to model variable levels of feature separability at different phases. We evaluate our proposed approach on multi-organ segmentation with both non-contrast CT (NCCT) datasets and the MICCAI 2015 BTCV Challenge contrast-enhance CT (CECT) datasets. Compared to the state-of-the-art approaches, our proposed contrastive loss yields a substantial and significant improvement of 1.41% (from 0.923 to 0.936, p-value$, 11 pages

Interactions between biological molecules in a cell are tightly coordinated and often highly dynamic. As a result of these varying signaling activities, changes in gene co-expression patterns could often be observed. The advancements in next-generation sequencing technologies bring new statistical challenges for studying these dynamic changes of gene co-expression. In recent years, methods have been developed to examine genomic information from individual cells. Single-cell RNA sequencing (scRNA-seq) data are count-based, and often exhibit characteristics such as over-dispersion and zero-inflation. To explore the dynamic dependence structure in scRNA-seq data and other zero-inflated count data, new approaches are needed. In this paper, we consider over-dispersion and zero-inflation in count outcomes and propose a ZEro-inflated Negative binomial dynamic COrrelation model (ZENCO). The observed count data are modeled as a mixture of two components: success amplifications and dropout events in ZENCO. A latent variable is incorporated into ZENCO in order to model the covariate-dependent correlation structure. We conduct simulation studies to evaluate the performance of our proposed method and to compare it with existing approaches. We also illustrate the implementation of our proposed approach using scRNA-seq data from a study of minimal residual disease in melanoma.

U-Shaped Steel Sheet Piles (USSSP) have buckling and bending problems during construction. In this research, the structural measure of stiffened plates applied along the longitudinal direction of USSSP is analyzed and proposed. A theoretical calculation method of stiffening effect is proposed based on the dynamic correlation coefficient method and the formula for the critical buckling load of locally stiffened USSSP is derived. A parametric analysis of the number, width, and arrangement scheme of the stiffened plates on the USSSP with different lengths is examined. The empirical formula derived for the total stiffening area and the critical load of the axial compression members of the USSSP is compared with practical results from an engineering case study. The results show a quadratic function under the condition of arranging the different number of stiffened plates at equal intervals, which is accurate in deriving the critical load values USSSP after local stiffening, and provide a theoretical basis for the optimization design of local reinforcement of USSSP axial compression members in engineering projects.

Wireless sensor network enhances the classic features of wireless communication with cognitive capabilities for efficient spectrum usage. This work focuses on the dynamic correlation between the secondary users (SUs) based on their statistical behaviour while performing the cooperative communication in cognitive radio sensor network. The proposed approach addresses the problem of uneven and repetitive communication between the SUs in a cooperative communication scenario. The authors’ objective is to use a novel approach based on the Gaussian copula theory and advanced particle swarm optimisation algorithm to analyse the dependencies of time-varying spectrum sensing behaviour of multiple SUs. Here, time delay in prediction reduces due to the analysis of the dynamic correlation between the time delay in spectrum sensing results for the same set of channels. The simulation results show the performance of the proposed approach outperforming the other well-known techniques.

This study analyzes the spillover effect and dynamic correlation of the China-US bean futures markets and discusses the relationship between the dynamic correlation of the bean futures price index and investor sentiment. First, the spillover effect of the China-US bean futures markets is analyzed through the BEKK-GARCH model. Then, the DCC-GARCH model is used for obtaining the dynamic correlation coefficients of the China-US bean futures markets. Next, the principal component analysis method is chosen to construct a comprehensive investor sentiment index. Lastly, the dynamic impacts between the change in investor sentiment and the correlation of the China-US bean futures price index are discussed through the ensemble empirical mode decomposition and impulse response analysis. The results show that the spillover effect of different degrees and directions exists between the China-US bean futures markets, and the dynamic correlation coefficients among different bean futures are also different. Besides, a certain degree of interactions exists between the high-frequency and low-frequency components of the comprehensive investor sentiment index and the dynamic correlations of bean futures price indexes.

Investigation of the cardiac rhythm in clinical practice makes wide use of 24-hour ECG monitoring. One method for analysis of long-term ECG signal recordings involves representation of the signal as a correlation rhythmogram. Analysis of scattergrams yields statistical data whose processing gives parameters with little information value. These indicators cannot describe dynamic changes in R-R intervals, which contain additional information on the nature of the heartbeat. Our study developed a system for representing signals from long-term ECG traces in the form of dynamic correlation rhythmograms. The algorithm allows changes in heartbeat dynamics over 24 h (or more) to be visualized in less than 1 min. Using the developed system as a tool for visualizing dynamic information on R-R intervals, specialists can extract unique information on the nature of the heartbeat and classify cardiac disorders with greater accuracy.

A variety of personality determinants and emotional states have been linked to individual differences in risk preferences. However, most variables reported in the literature were investigated independently and tested without repeated longitudinal measurements. To understand the interaction between personality traits, personality states and risk preferences, we will perform a longitudinal examination of emotional states, a variety of personality traits, and risk preferences on two separate discovery and confirmatory cohorts. We will use the preliminary findings from the discovery cohort to form specific testing regression models and seek to replicate the results on the confirmatory cohort.

Affected by the air leakage, abandoned coal mine goaf is prone to spontaneous combustion, resulting in environmental pollution and geological disasters. Based on previous studies, this paper further conducted long-term, multi-measurements, and continuous measurements of the spontaneous combustion state in the abandoned mine goaf in the eastern part of the Haizhou Open-pit Mine. We evaluated the correlation between the surface CO2 flux and the changes in underground fire areas, the scope and evolution trend of the fire areas, and the distribution and change laws of surface CO2 fluxes. The results show that there is a significant positive correlation between the surface CO2 flux and soil temperature. The surface CO2 flux value reflects the CO2 emission intensity of goaf. The high surface CO2 flux in the test area showed month-wise expansion and increase. The CO2 emission gradually increases with the months, and the annual CO2 emission in the experimental area is approximately 7017 t.

The increase in carbon emissions year by year poses a severe challenge to the high-quality development and sustainability of China’s economy. How to reduce the intensity of carbon emissions has become a prominent issue to promote green growth. Based on the provincial panel data from 2011 to 2020, this paper uses Exploratory Spatial Data Analysis (ESDA), the spatial econometric model and intermediary effect test as analysis methods. The following results are drawn. Firstly, China’s industrial structure distortion index shows a downward trend. The industrial structure distortion index is the highest in the west of China, followed by the middle of China and is the lowest in the east of China. Secondly, the distortion of the industrial structure will not only lead to the increase in local carbon emission intensity but also produce reverse spillover to adjacent areas. Thirdly, the results of intermediary effect analysis show that industrial structure distortion can affect the transmission mechanism of carbon emission intensity by affecting two-way FDI. This paper has a profound practical significance for promoting the process of industrial upgrading by insisting on developing foreign trade to achieve carbon emission reduction. The main innovation of this paper is to put forward the concept of industrial structure distortion and bring it into a unified research framework with two-way FDI and carbon emission intensity.

In this paper, industrial structure distortion, two-way FDI and carbon emission intensity are brought into a unified research framework, and based on China's panel data from 2011 to 2020, empirical tests are conducted employing Exploratory Spatial Data Analysis (ESDA), spatial econometric model and intermediary effect test. The results show the following. Firstly, China's industrial structure distortion index shows a downward trend. The industrial structure distortion index is the highest in the west, followed by the middle, and the lowest in the East. Secondly, the relationship between carbon emission intensity and economic development shows a "decoupling" effect and keeps decreasing year by year. The spatial disparity is remarkable, showing the pattern of "the east leading, the middle catching up and the west lagging ". At the provincial level, except in Xinjiang province, the carbon emission intensity of other provinces showed different degrees of decline. In terms of spatial distribution, the polarization characteristics of carbon emission intensity are significant, and the traditional spatial distribution pattern has been broken. Thirdly, there is a positive spatial correlation between China's industrial structure distortion, two-way FDI and carbon emission intensity. The distortion of industrial structure will not only lead to the increase of local carbon emission intensity but also produce reverse spillover to adjacent areas. IFDI and OFDI provide a strong driving force for the decline of carbon emission intensity. IFDI promotes the decline of carbon emission intensity in adjacent areas, while OFDI will increase the carbon emission intensity in surrounding areas. The interaction of IFDI and OFDI can significantly reduce the carbon emission intensity of local and adjacent areas. Fourthly, the results of intermediary effect analysis show that two-way FDI is the two channels of industrial structure distortion affecting carbon emission intensity. Industrial structure distortion can affect the transmission mechanism of carbon emission intensity by affecting two-way FDI.

This research investigates the dynamic correlation between China’s policy uncertainty and the crude oil markets (i.e. domestic and international markets) using monthly time-series data from May 200...

In this paper, we study the valuation of vulnerable European options incorporating the reduced-form approach, which models the credit default of the counterparty. We provide an analytical pricing model in which the components of the state processes, including the dynamics of the underlying asset value and the intensity process corresponding to the default event, are cross-exciting and they could facilitate the description of complex structure of events dependence. To illustrate how our model works, we present an application when the state variables follow specific affine jump-diffusion processes. Semi-analytical pricing formulae are obtained through a system of matrix Riccati equations. The derived formula can be implemented numerically, and we give numerical analysis to investigate the impact of the dynamic correlation between jump risk of the underlying asset value and default risk of the counterparty.

This study employs mainly the Bayesian DCC-MGARCH model and frequency connectedness methods to respectively examine the dynamic correlation and volatility spillover among the green bond, clean energy, and fossil fuel markets using daily data from 30 June 2014 to 18 October 2021. Three findings arose from our results: First, the green bond market has a weak negative correlation with the fossil fuel (WTI oil, Brent oil, natural gas, heating oil, and gasoline) and clean energy markets, which means that green bonds play a critical hedging role against fossil fuel and clean energy. Second, the green bond and clean energy are net volatility receivers from WTI crude oil and heating oil for the short term, indicating that investors and policymakers need to pay attention to the WTI oil volatility spillover risk when promoting green bonds and clean energy. Third, the correlation and volatility spillover from WTI crude oil to green bonds and clean energy is stronger than that of Brent oil, which implies that investors and policymakers need to consider the price movements of WTI crude oil more than Brent oil when investing in the green bond market. In summary, our conclusion is that investors should be aware that green bond investing addresses the two-pronged investment strategy of (i) risk diversification and (ii) carbon mitigation. Thus, this study can provide essential information for energy investors and policymakers to achieve sustainable investment.

This paper studies the volatility spillover and dynamic correlation between EU emission allowance (EUA) prices and energy prices by considering three energy commodities, including oil, gas, and coal. The asymmetric BEKK model is employed for multi-phase analysis of EU ETS, yet only a little empirical evidence backing up the existence of volatility spillover between EU ETS and energy markets, i.e., the establishments of the EU ETS may not effectively limitation and influence energy markets. The time-varying conditional correlation between EUA and each of energy prices is analyzed. The dynamic correlation shows there is a relatively stable, positive correlation between the EUA and Brent oil, natural gas. However, modeling the dynamics correlation also suggests that the correlation between the EUA and the natural gas, coal became weaker and more volatile since second and third phases, especially after the Global Financial Crisis in 2008, which may indicate that the demand reduction in emission allowances caused by the economic slowdown far exceeds the reduction in the annual restraint of EU ETS.

This paper aims to explore the dynamic relationships between the crude oil price (shocks) and investor sentiment. Specifically, this paper utilizes web crawler to construct Chinese investor sentiment index. The structural vector autoregression (SVAR) model is then used to decompose the crude oil price shocks into three types of oil price shocks. Finally, the wavelet coherence analysis (WTC) is employed to study the dynamic correlation between crude oil price (shocks) and investor sentiment in the time and frequency domain, and their asymmetric dynamic correlation under different trends of crude oil price. Using data from February 2013 to June 2021, our empirical results suggest the heterogeneous dynamic correlations and lead-lag relationships exist between crude oil price (shocks) and investor sentiment over different time and frequency domains. In addition, there are asymmetric dynamic correlations and lead–lag relationships between crude oil price (shocks) and investor sentiment under different trends of crude oil price.

PurposeThe findings would help in designing useful and relevant hedging strategies against geopolitical risks (GPRs), which are rampant in the Gulf Cooperation Council (GCC) region.Design/methodology/approachThis study focuses on the regional and global costs of GPRs for businesses in the Gulf region.FindingsThe results of the analysis show that the time-varying conditional correlation between the stock returns of the GCC countries and the Saudi Arabian geopolitical risk is consistently negative, suggesting that the Saudi Arabian geopolitical risk hurts the GCC stock markets, thus underscoring the importance of studying regional GPRs.Originality/valueThe contribution of this paper is twofold: First, it uses a newly geopolitical risk index that includes recent geopolitical events not included in the Caldara and Iacoviello (2018) index. In addition to war threats and acts, terrorist threats and acts and nuclear threats, the authors consider global trade tensions (GTTs), Saudi Arabia's geopolitical risk and OPEC news mainly related to OPEC oil production levels. Second, it assesses whether Saudi Arabia, which is the largest economy in the region and the main global oil exporter, is really a risk exporter to the rest of the GCC countries.

Purpose: This study assessed the correlation between corneal sensitivity and subjective symptoms in patients with chronic corneal ulcers before and after treatment. Methods: Corneal sensitivity was measured with Cochet-Bonnet aesthesiometry in twenty patients with mean age of53 years SD±20.65. Impression cytology was used to assess conjunctivalization, inflammation and keratinization. The epithelial integrity and stromal reaction were determined by anterior segment OCT. Treatment included either conservative measures or surgical ocular surface reconstruction. A questionnaire on subjective symptoms was implemented in order to obtain comparable data about their severity and effect on everyday life. Results: Mean corneal aesthesiometry was 143.03±81.68 mm/grms before treatment and 72.48±72.80 mm/grms on follow-up (p=0.001). An inverse correlation between age and corneal sensitivity was observed before treatment (p=0.002), but not after (p=0.102). Anterior segment OCT on follow-up demonstrated epithelialized corneal surface, reduced stromal reaction and scar tissue preventing further melting. All tested subjective symptoms demonstrated significant change after treatment except driving. Spearman’s analysis demonstrated a correlation between corneal sensitivity and pain (p=0.033), hyperemia (p=0.009) and driving (p=0.043). The change in corneal sensitivity correlated with tearing reduction (p=0.046) and prolonged tolerance to phone use (p=0.044). Conclusions: Cochet-Bonnet aesthesiometry is a useful diagnostic tool for evaluating corneal nerve function and monitoring the effect of treatment.

To explore the core mechanisms of green synergistic development in China in the context of transition, this study takes 30 Chinese provinces and cities as a sample. Additionally, it is the first study to include digital finance, green finance, and ecological governance in the same research framework, and it innovatively explores the internal linkages between them from the perspective of systemic coupling and dynamic correlation. The study finds that the levels of digital finance, green finance, and ecological governance at the provincial level in China are all increasing to a certain extent. The coupling coordination degree between the three shows a development trend first rising and then falling in a transitional stage between barely coordinated and high-quality coordination, with developed regions having a higher coupling coordination degree, and less developed regions having a lower coupling coordination degree. From the dynamic correlation perspective, digital finance, green finance, and ecological governance all have a positive self-reinforcing effect, with the interaction effects being positively significant, except for the non-significant contribution of green finance to the level of digital finance and ecological governance. This study makes a series of theoretical suggestions that can optimize the transition path based on the reality of China's green development.

This study examines the dependence and the dynamic conditional correlation among Islamic and Conventional Insurances and Stock market with Qatar and Abu Dhabi. The main objective of this article is to study how the dynamics of correlations between the major return series evolved from January, 2006 to August, 2018. To this end, we adopt a dynamic conditional correlation (DCC) model into a multivariate GARCH with symmetric effect and the dynamic conditional correlation (DCC) model into a multivariate GJR-GARCH with asymmetric framework. Empirical results indicate the evidence of time-varying co-movement, a high and low persistence of the conditional correlation in the short run.

The impact of capital deepening on total factor productivity (TFP) is a significant and controversial issue. Based on the calculation of relevant indicators, this study adopts a Bayesian time-varying parameter model, Bayesian quantile regression, and adaptive Bayesian quantile models for in-depth statistical analysis. TFP was found to have a complex non-linear structure, and physical and human capital deepening indicators show a significant upward trend. The deepening of physical capital has a negative impact on TFP, while the deepening of human capital has a positive impact. In the capital deepening structure, the level of TFP has been improved and its structure optimized. Primary human and non-production physical capital deepening has no significant effect on TFP, while secondary human capital deepening has some significant effects on TFP. Tertiary and productive human capital deepening of TFP present two different forms of significant effect: the influence coefficient of the former declines in the increasing quantile and the change is larger, while the latter has a stable negative impact. The results of this study provide insights in terms of the improvement of China’s productivity.

This paper uses the law of one price (LOP) and the DCC-GARCH method based on ten-day price sequences. The findings indicate that, compared with the international refined oil markets with mature market-oriented pricing mechanisms, only in the Chinese market do gasoline and diesel prices meet the LOP. This finding shows that, in the context of the gradual integration of the global refined oil market, the international level of China's refined oil price is still quite low. The price reforms pertaining to China's refined oil products still need to be pushed forward in the direction of marketization and internationalization.