Short-term Component Research Articles

Spatiotemporal Characterization and Driving Factors of Fine Particulate Matter and Its Chemical Components in the Huaihe River Basin

According to the data sets of fine particulate matter （PM2.5） and its components in 35 cities in the Huaihe River Basin from 2015 to 2021, the temporal and spatial distribution patterns of pollutants were analyzed. The influence of meteorological factors on PM2.5 concentrations was examined using a random forest model. The original series of PM2.5, sulfate （SO42-）, nitrate （NO3-）, ammonium salt （NH4+）, organic matter （OM）, and black carbon （BC） were rebuilt using KZ （Kolmogorov-Zurbenko） filtering and multiple linear regression （MLR） to quantify the effects of meteorological conditions. The results demonstrated that from 2015 to 2021, the declining rates of PM2.5, SO42-, NO3-, NH4+, OM, and BC in the Huaihe River Basin were 4.71, 0.99, 1.05, 0.77, 1.01, and 0.19 μg·（m3·a）-1, respectively. The high mass concentrations of PM2.5 and its components were concentrated in the central and western regions of the HRB, whereas those in coastal and southern cities were lower. The variance contributions of the short-term, seasonal, and long-term components of PM2.5 to the original PM2.5 sequences in 35 cities were 51.6%, 35.9%, and 7.0%, respectively. The PM2.5 in coastal cities were more affected by the short-term components. The meteorological conditions were unfavorable for PM2.5 reduction in the HRB from 2015 to 2018, whereas the meteorological conditions supported the PM2.5 decrease from 2019 to 2021. From 2015 to 2021, the contribution rates of meteorological conditions to the long-term component reductions of PM2.5, SO42-, NO3-, NH4+, OM, and BC were 28.3%, 29.1%, 31.0%, 29.3%, 27.8%, and 28.6%, respectively. The contribution rates of meteorological conditions to the long-term PM2.5 reduction were 43.4%, 25.6%, 25.5%, and 20.6% in the HRB cities in Anhui, Shandong, Jiangsu, and Henan Provinces, respectively. With the decrease in PM2.5 concentration in the HRB, the sulfur oxidation rate （SOR） increased significantly, while the nitrogen oxide oxidation rate （NOR） changed little.

Predictive Model for O3 in Shanghai Based on the KZ Filtering Technique and LSTM

In this study, a Kolmogorov-Zurbenko （KZ） filter was proposed to decompose the original ozone （O3） sequence to improve the accuracy of ozone long-term series prediction and select relevant meteorological features. Furthermore, the enhanced maximal minimal redundancy （mRMR） feature selection technique was combined with the support vector regression （SVR） approach to select the most illuminating meteorological features. Subsequently, from May to August 2023, during high ozone concentration periods, a long short-term memory network （LSTM） was utilized to assess and predict high ozone concentration periods at the monitoring stations of Jingan （urban area）, Pudong-Chuansha （suburban area）, and Dianshan Lake （suburban area） in Shanghai. The results showed that pressure, temperature, humidity, boundary layer height, and wind direction were the best combinations of O3 baseline and short-term components, as chosen by feature screening. The R2 values for Jingan Station, Pudong-Chuansha Station, and Dianshan Lake Station were 0.86, 0.83, and 0.85, respectively. The RMSE values were 18.26, 18.74, and 20.02 μg·m-3, respectively. These findings suggest that decomposing the original O3 sequence improved the prediction accuracy of ozone concentrations. Additionally, as indicated by the R2 and RMSE values found for every monitoring station, feature screening preserved the model's predictive performance.

The cerebellum acts as the analog to the medial temporal lobe for sensorimotor memory

The cerebellum is critical for sensorimotor learning. The specific contribution that it makes, however, remains unclear. Inspired by the classic finding that for declarative memories, medial temporal lobe (MTL) structures provide a gateway to the formation of long-term memory but are not required for short-term memory, we hypothesized that for sensorimotor memories, the cerebellum may play an analogous role. Here, we studied the sensorimotor learning of individuals with severe ataxia from cerebellar degeneration. We dissected the memories they formed during sensorimotor learning into a short-term temporally-volatile component, that decays rapidly with a time constant of just 15 to 20 s and thus cannot lead to long-term retention, and a longer-term temporally-persistent component that is stable for 60 s or more and leads to long-term retention. Remarkably, we find that these individuals display dramatically reduced levels of temporally-persistent sensorimotor memory, despite spared and even elevated levels of temporally-volatile sensorimotor memory. In particular, we find both impairment that systematically worsens with memory window duration over shorter memory windows (<12 s) and near-complete impairment of memory maintenance over longer memory windows (>25 s). This dissociation uncovers a unique role for the cerebellum as a gateway for the formation of long-term but not short-term sensorimotor memories, mirroring the role of the MTL for declarative memories. It thus reveals the existence of distinct neural substrates for short-term and long-term sensorimotor memory, and it explains both the trial-to-trial differences identified in this study and long-standing study-to-study differences in the effects of cerebellar damage on sensorimotor learning ability.

The method of forming ensembles of complex signals based on multi-scale decomposition of time intervals at different levels of detail

This article addresses the challenges of forming signal ensembles in dynamic cognitive radio environments, focusing on the limitations of traditional methods. The study proposes a new approach based on multiscale time interval decomposition, which allows for the creation of signal ensembles at varying levels of temporal detail. The key innovation of this method is its ability to improve signal reproduction accuracy, reduce inter-symbol and inter-channel interference, and enhance the overall efficiency of data processing. The method's adaptability to changing environmental conditions is also a core advantage, enabling better use of bandwidth and reduced transmission delay. Through the decomposition of time intervals into coarse, intermediate, and fine levels, this method allows for the detailed analysis of short-term, medium-term, and long-term signal components. Experimental results demonstrate the superiority of this approach in terms of signal recovery accuracy, noise resilience, and processing speed. These findings highlight the potential for optimizing signal processing in cognitive radio networks, particularly in environments with high levels of noise and interference. Future research aims to integrate machine learning algorithms to further enhance adaptability in real-time scenarios.

Predicting improvement in biofeedback gait training using short-term spectral features from minimum foot clearance data.

Stroke rehabilitation interventions require multiple training sessions and repeated assessments to evaluate the improvements from training. Biofeedback-based treadmill training often involves 10 or more sessions to determine its effectiveness. The training and assessment process incurs time, labor, and cost to determine whether the training produces positive outcomes. Predicting the effectiveness of gait training based on baseline minimum foot clearance (MFC) data would be highly beneficial, potentially saving resources, costs, and patient time. This work proposes novel features using the Short-term Fourier Transform (STFT)-based magnitude spectrum of MFC data to predict the effectiveness of biofeedback training. This approach enables tracking non-stationary dynamics and capturing stride-to-stride MFC value fluctuations, providing a compact representation for efficient processing compared to time-domain analysis alone. The proposed STFT-based features outperform existing wavelet, histogram, and Poincaré-based features with a maximum accuracy of 95%, F1 score of 96%, sensitivity of 93.33% and specificity of 100%. The proposed features are also statistically significant (p 0.001) compared to the descriptive statistical features extracted from the MFC series and the tone and entropy features extracted from the MFC percentage index series. The study found that short-term spectral components and the windowed mean value (DC value) possess predictive capabilities regarding the success of biofeedback training. The higher spectral amplitude and lower variance in the lower frequency zone indicate lower chances of improvement, while the lower spectral amplitude and higher variance indicate higher chances of improvement.

Spatial-temporal Variations and Driving Factors of Surface Ozone over the Qinghai-Xizang Plateau from 2015 to 2021

The spatial-temporal distribution pattern of surface O3 over the Qinghai-Xizang Plateau （QXP） was analyzed based on air quality monitoring data and meteorological data from 12 cities on the QXP from 2015 to 2021. Kolmogorov-Zurbenko （KZ） filtering was employed to separate the original O3-8h series into components at different time scales. Then, multiple linear regression of meteorological variables was used to quantitatively isolate the effects of meteorology and emissions. The results revealed that the annual mass concentrations of surface O3-8h from 2015 to 2021 in 12 cities over the QXP ranged from 78.7 to 156.7 μg·m-3, and the exceedance rates of O3 mass concentrations （National Air Quality Standard of grade II） ranged from 0.7%-1.5%. The monthly O3-8h mass concentration displayed a single-peak inverted "V"-shape and a multi-peak "M"-shape. The maximum monthly concentration of O3 occurred in April to July, and valleys occurred in July, September, and December. The short-term, seasonal, and long-term components of O3-8hdecomposed by KZ filtering contributed 29.6%, 51.4%, and 9.1% to the total variance in the original O3 sequence in 12 cities, respectively. From the whole region, the meteorological conditions were unfavorable for O3 reduction on the QXP from 2015 to 2017, which made the long-term component of O3 increase by 0.2-2.1 μg·m-3. The meteorological conditions were favorable for O3-8h reduction from 2018 to 2021, which led to the long-term component of O3-8h decrease by 0.4-1.1 μg·m-3. The meteorological conditions increased the long-term component of O3-8h in Ngari, Lhasa, Naqu, Nyingchi, Qamdo, Haixi, and Xining, with an average contribution of 30.1%. The meteorological conditions decreased the long-term component of O3-8h in Shigatse and Golog, with contributions of 359.0% and 56.5%, respectively. The increase in the long-term component of O3-8h in Ngari, Shigatse, Nagqu, Haixi, and Xining could be due to the rapid decrease in the long-term component of PM2.5 （4.04 μg·（m3·a）-1）.

A cloud-based medical device for predicting cardiac risk in suspected coronary artery disease: a rapid review and conceptual economic model.

The CaRi-Heart® device estimates risk of 8-year cardiac death, using a prognostic model, which includes perivascular fat attenuation index, atherosclerotic plaque burden and clinical risk factors. To provide an Early Value Assessment of the potential of CaRi-Heart Risk to be an effective and cost-effective adjunctive investigation for assessment of cardiac risk, in people with stable chest pain/suspected coronary artery disease, undergoing computed tomography coronary angiography. This assessment includes conceptual modelling which explores the structure and evidence about parameters required for model development, but not development of a full executable cost-effectiveness model. Twenty-four databases, including MEDLINE, MEDLINE In-Process and EMBASE, were searched from inception to October 2022. Review methods followed published guidelines. Study quality was assessed using Prediction model Risk Of Bias ASsessment Tool. Results were summarised by research question: prognostic performance; prevalence of risk categories; clinical effects; costs of CaRi-Heart. Exploratory searches were conducted to inform conceptual cost-effectiveness modelling. The only included study indicated that CaRi-Heart Risk may be predictive of 8 years cardiac death. The hazard ratio, per unit increase in CaRi-Heart Risk, adjusted for smoking, hypercholesterolaemia, hypertension, diabetes mellitus, Duke index, presence of high-risk plaque features and epicardial adipose tissue volume, was 1.04 (95% confidence interval 1.03 to 1.06) in the model validation cohort. Based on Prediction model Risk Of Bias ASsessment Tool, this study was rated as having high risk of bias and high concerns regarding its applicability to the decision problem specified for this Early Value Assessment. We did not identify any studies that reported information about the clinical effects or costs of using CaRi-Heart to assess cardiac risk. Exploratory searches, conducted to inform the conceptual cost-effectiveness modelling, indicated that there is a deficiency with respect to evidence about the effects of changing existing treatments or introducing new treatments, based on assessment of cardiac risk (by any method), or on measures of vascular inflammation (e.g. fat attenuation index). A de novo conceptual decision-analytic model that could be used to inform an early assessment of the cost effectiveness of CaRi-Heart is described. A combination of a short-term diagnostic model component and a long-term model component that evaluates the downstream consequences is anticipated to capture the diagnosis and the progression of coronary artery disease. The rapid review methods and pragmatic additional searches used to inform this Early Value Assessment mean that, although areas of potential uncertainty have been described, we cannot definitively state where there are evidence gaps. The evidence about the clinical utility of CaRi-Heart Risk is underdeveloped and has considerable limitations, both in terms of risk of bias and applicability to United Kingdom clinical practice. There is some evidence that CaRi-Heart Risk may be predictive of 8-year risk of cardiac death, for patients undergoing computed tomography coronary angiography for suspected coronary artery disease. However, whether and to what extent CaRi-Heart represents an improvement relative to current standard of care remains uncertain. The evaluation of the CaRi-Heart device is ongoing and currently available data are insufficient to fully inform the cost-effectiveness modelling. A large (n=15,000) ongoing study, NCT05169333, the Oxford risk factors and non-invasive imaging study, with an estimated completion date of February 2030, may address some of the uncertainties identified in this Early Value Assessment. This study is registered as PROSPERO CRD42022366496. This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135672) and is published in full in Health Technology Assessment; Vol. 28, No. 31. See the NIHR Funding and Awards website for further award information.

Trend Analysis and Spatial Source Attribution of Surface Ozone in Chaozhou, China

Surface ozone (O3), a critical air pollutant, poses significant challenges in urban environments, as exemplified by the city of Chaozhou in southeastern China. This study employs a novel combination of trend analysis and spatial source attribution techniques to evaluate the long-term dynamics of surface ozone and identify its sources. Utilizing the Kolmogorov–Zurbenko (KZ) filter and percentile regression, we analyzed the temporal trends of daily maximum 8 h moving average ozone (MDA8 O3) concentrations from 2014 to 2023. Our analysis revealed a general long-term downward trend in MDA8 O3 values alongside notable monthly fluctuations, with peak concentrations typically occurring in October and April. Additionally, the percentile regression analysis demonstrated a significant downward trend in MDA8 O3 concentrations across nearly all percentiles, with larger decline rates at higher percentiles, highlighting the effectiveness of local and regional O3 management strategies in Chaozhou. The changes in MDA8 O3 concentrations were mainly influenced by the short-term component, contributing 62.2%, while the contribution of the long-term fraction is relatively small. This suggests a significant influence of immediate meteorological conditions and transient pollution events on local O3 levels. To further elucidate the origins of high O3 concentrations, trajectory cluster analysis, trajectory sector analysis (TSA), and potential source contribution function (PSCF) analysis were conducted. The trajectory cluster analysis revealed that the northeast air mass was the main transport air mass in Chaozhou during the study period, accounting for 39.1% of occurrences. The northeast cluster C with medium-distance trajectories corresponds to higher concentration of O3, which may be the main transport pathway of O3 pollution in Chaozhou. TSA corroborates these findings, with northeast sectors 1, 2, and 3 accounting for 50.3% of trajectory residence time and contributing 52.2% to O₃ levels in Chaozhou. PSCF results further indicate potential high O₃ sources from the northeast, especially in autumn. This comprehensive analysis suggests that Chaozhou’s elevated O3 levels are influenced by both regional transport from the northeast and local emissions. These findings offer crucial insights into the temporal dynamics of surface O3 in Chaozhou, paving the way for more effective and targeted air quality management strategies.

Forecasting methods in Greek coastal shipping: The case of Southwest Crete

The aim of this paper is to exact the most effective model at capturing the seasonal and short-term components of passenger traffic in Southwest Crete coastal shipping. There has been no similar effort in the past. The passenger traffic forecast is crucial for the public and private sector, as it is necessary for decision making. In our analysis we considered the six largest ports of Southwest Crete. The seasonal repeated fluctuations and the quarterly observations made Winter’s triple exponential smoothing, time series decomposition, simple seasonal model, seasonal ARIMA model and Lis’ simplistic forecast suitable for our case. The results showed that in four of the six ports the Winters’ method is best adapted. The port of Gavdos adapts better to the decomposition method and the port of Sougia to Li’s method. No port led, through the seasonal ARIMA models or simple seasonal model, to better results. In most cases, traffic trend did not change over time, the seasonal component significantly affected the time series, and the time series smoothing was strong.

Translation, cultural adaptation, and validation of Numerical Pain Rating Scale and Global Rating of Change in Tibetan musculoskeletal trauma patients

Tibetan-speaking patients seeking care in predominantly Mandarin-speaking healthcare settings frequently face communication barriers, leading to potential disparities and difficulties in accessing care. To address this issue, we translated, culturally adapted, and validated the Numerical Pain Rating Scale (NPRS) and the Global Rating of Change (GRoC) into Tibetan (NPRS-Tib and GRoC-Tib), aiming to facilitate cross-linguistic and cross-cultural interactions while examining potential challenges in the adaptation process. Using standard translation-backward translation methods, expert review, pilot testing, and validation through a cross-sectional study with a short-term longitudinal component, we engaged 100 Tibetan patients with musculoskeletal trauma for psychometric validation, including 37 women (aged 22–60 years, mean age 39.1 years). The NPRS-Tib and GRoC-Tib exhibited outstanding psychometric properties, with an Intraclass Correlation Coefficient (ICC) of 0.983 for NPRS-Tib indicating superb test–retest reliability, and expert review confirming good content validity for both instruments. A Spearman's correlation coefficient (Rho) of -0.261 (P = 0.0087) revealed a significant, albeit weak, correlation between changes in NPRS-Tib scores and GRoC-Tib scores. The adaptation process also presented notable challenges, including translation discrepancies from translators' diverse backgrounds and levels of expertise, ambiguity in scale options, and the lack of established tools for criterion validity assessment in Tibetan.

Economic policy uncertainty and volatility of corporate bond credit spread: Evidence from China and the United States

In the prevailing situation of economic instability, it is customary for governments to enact economic policies aimed at influencing the decision-making processes of corporations. Nevertheless, the uncertainty surrounding economic policies inevitably impacts the capital structure of firms, consequently giving rise to fluctuations in corporate bond credit spreads through various transmission channels. Therefore, this study examines the impact of economic policy uncertainty (EPU) on the volatility of corporate bond credit spread in China and the United States (U.S.) from January 2014 to July 2021. Developing a one-sided filtering single-factor and multi-factor GARCH-MIDAS model in which monthly EPU and daily corporate bond credit spread are used as variables with different frequencies, the total volatility of corporate bond credit spread is decomposed into long- and short-term volatility components. Utilising the EPU data, encompassing indices delineating EPU in the U.S. (Baker et al., 2016) and China (Huang and Luk, 2020), alongside the corporate bond credit spreads in both the Chinese and American markets, we find that the level and volatility of EPU positively impact the long-term volatility of corporate bond credit spread with different ratings. Moreover, the EPU level contributes to more than 12% to the total volatility of corporate bond credit spread in the Chinese samples and more than 4.31% in the U.S. samples. While the variance of EPU contributes to over 5.19% to the total volatility of corporate bond credit spread in the U.S. samples, which is more than that of the Chinese samples.

Do carbon prices spill over to inflation? Multiscale evidence from China

The urgent need to reduce carbon emissions causes great concerns about rising inflation. Through measuring the interconnectedness in the carbon-inflation system consisting of four representative carbon pilots, this study demonstrates strong spillover effects between carbon returns and inflation in China, mainly driven by short-term components. The net spillovers from inflation are usually positive in the short-term, indicating that it is a net information transmitter. By contrast, inflation switches roles between information transmitter and receiver in the long-term. The net pairwise spillovers between carbon returns and inflation differ across pilots, showing heterogeneity in the carbon-inflation system. Guangdong and Shenzhen pilots exhibit significant net spillover effects on inflation, particularly in the short-term. Finally, important events exert significant impacts on the spillover effects between carbon returns and inflation, but do not necessarily enhance the overall connectedness. Overall, the multiscale interplay of the carbon-inflation system reveals an early warning of the transmission risk from carbon costs to inflation and provides useful implications for risk management at different time horizons.

Quantitative impacts of meteorology and emissions on the long-term trend of O3 in the Yangtze River Delta (YRD), China from 2015 to 2022

Extensive spatiotemporal analyses of long-trend surface ozone in the Yangtze River Delta (YRD) region and its meteorology-related and emission-related have not been systematically analyzed. In this study, by using 8-year-long (2015–2022) surface ozone observation data, we attempted to reveal the variation of multiple timescale components using the Kolmogorov–Zurbenko filter, and the effects of meteorology and emissions were quantitatively isolated using multiple linear regression with meteorological variables. The results showed that the short-term, seasonal, and long-term components accounted for daily maximum 8-hr average O3 (O3–8 hr) concentration, 46.4%, 45.9%, and 1.0%, respectively. The meteorological impacts account for an average of 71.8% of O3–8 hr, and the YRD's eastern and northern sections are meteorology-sensitive areas. Based on statistical analysis technology with empirical orthogonal function, the contribution of meteorology, local emission, and transport in the long-term component of O3–8 hr were 0.21%, 0.12%, and 0.6%, respectively. The spatiotemporal analysis indicated that a distinct decreasing spatial pattern could be observed from coastal cities towards the northwest, influenced by the monsoon and synoptic conditions. The central urban agglomeration north and south of the YRD was particularly susceptible to local pollution. Among the cities studied, Shanghai, Anqing, and Xuancheng, located at similar latitudes, were significantly impacted by atmospheric transmission—the contribution of Shanghai, the maximum accounting for 3.6%.

Analisis laba jangka pendek pada UD Trikora Manado

Short-term profit is the income a company receives in a performance measure during a certain time. Short-term profit components consist of break-even point, margin of safety, contribution margin, shut-down point and degree of operating leverage. The aim of this research is to determine short-term profit analysis at UD Trikora Manado. This type of research uses a qualitative descriptive method with qualitative data and quantitative data. The data source used is primary data with interview and documentation data collection. The results of this research show that the break-even point shows that UD Trikora is above the break-even point, the margin of safety shows that UD Trikora has still reached the safe limit. Contribution margin can cover fixed costs and make a profit. Shut down point shows that UD Trikora is above Shut down point so the company is still worth continuing and degree of operating leverage shows that if the company experiences a 1% increase in sales, then the company's opportunity to make a profit in 2021 and 2022 is equal to 1.18%.

Analysis of the Trend Characteristics of Air Pollutants in the Fenwei Plain Based on the KZ Filter

In order to improve air quality, China has implemented a series of the most stringent control measures ever in recent years. Quantitatively analyzing the contribution of emissions to the trend change in air pollutants is an essential scientific basis for verifying the effectiveness of air pollution control. We based our study on the air quality online monitoring data and meteorological element data of 11 cities in the Fenwei Plain from 2018 to 2022. We quantitatively investigated the changing patterns of NO2, O3, PM10, and PM2.5 and their influencing factors in the major cities of the Fenwei Plain by using the KZ filtering and MLR modeling analysis methods. The results show the following: (1) The long-term fractions of NO2, PM10, and PM2.5 in the Fenwei Plain decreased by 10.5, 33.1, and 17.1 μg·m−3, with decreases of 25.8%, 29%, and 28.8%, respectively, from 2018 to 2022. The long-term fractions of O3 showed the characteristics of decreasing and then increasing, with 2020 as the dividing line. (2) The short-term components of NO2, PM10, and PM2.5 contributed the most to the total variance, with the proportion of short-term components ranging from 34.7% to 69.8%, 53% to 73%, and 43% to 58%, respectively. The seasonal components of O3 contributed the most to the total variance, with the proportion of short-term components ranging from 54% to 70.7%. (3) Most cities in the Fenwei Plain had unfavorable meteorological conditions with regard to NO2, PM10, and PM2.5 in 2018–2020 and favorable meteorological conditions in terms of NO2, PM10, and PM2.5 in 2020–2022. O3 showed different characteristics from the other three pollutants. Most cities in the Fenwei Plain had meteorological conditions in 2018–2019 that were unfavorable for improving O3 levels. In 2019–2021, meteorological conditions were favorable for improving O3 levels, while in 2021–2022, meteorological conditions were unfavorable for improving O3 levels.

Stock Returns and Risks: An Empirical Assessment Using Modified CAPM Approach

Modern portfolio theory assists stakeholders in financial markets to optimize their portfolios to attain expected return at a specified level of risk and uncertainty. However, the conventional capital asset pricing model (CAPM), proposed by Sharpe (1964) and Lintner (1965), fails to accurately estimate the expected rate of return of stock equities due to the presence of external uncertainties, and rapid changes in macroeconomic dynamics. Therefore, this study has adopted a modified CAPM that incorporates additional factors besides beta to more accurately estimate the expected return of stock equities. It highlights these exposures by using time-varying beta CAPM, applied to the cement industry of Pakistan for the period from January 2016 to March 2022. The results show that the expected rate of return is very much close to risk free rate of return in the cement industry of Pakistan. Furthermore, the findings of the study confirm that the prediction of expected rate of return depends on variations in estimation techniques and data availability. Additionally, by introducing the concept of a reference portfolio and a market portfolio, the modified CAPM represents an improvement over conventional method. Moreover, by breaking down the beta deviations into long-term and short-term components, this new method allows for precise computation and simplifies the prediction of expected returns in both equity and portfolio investments. The findings of this study will be beneficial for investors, financial analyst, and portfolio-managers in the decision-making about portfolio investments.

Exchange rate volatility predictability: A new insight from climate policy uncertainty

Against the background of growing concern about climate change, this study attempts to examine the impact of US climate policy uncertainty on exchange rate volatility. Specifically, this paper explores whether climate policy changes and their extreme observations, as well as the short-term asymmetry of exchange rate volatility, can help predict future exchange rate volatilities. The in-sample estimation results show that the uncertainty of US climate policy significantly affects the volatility of the US exchange rate, and extreme positive shocks of climate policy uncertainty have a greater impact on the exchange rate than extreme negative shocks of the same magnitude. In addition, we find significant asymmetries in the short-term components of exchange rate volatility, especially in the USD-CNY exchange rate and the USD-EUR exchange rate. Multiple out-of-sample forecasting tests show that models incorporating extreme values of climate policy uncertainty exhibit superior forecasting performance. Notably, the predictive performance of these models tends to be stronger during periods of low volatility and relatively weak during periods of high volatility. This paper provides valuable insights for stakeholders to make informed decisions and optimize strategies in the face of uncertain climate policies.

Assessment of the impact of government response measures on the spread of COVID-19: panel data evidence for 50 countries

This article investigates the impact of Government’s response measures to new COVID-19 cases. This is proxied by a stringency index of 50 countries from April 2020 to March 2022. Our World in Data COVID-19 dataset was utilised employing several panel econometrics methods. This article fills a gap in empirical research by employing a range of econometric methods over an extended period and countries. The article provides the basis for the formulation and implementation of government response measures or policies to other major public health events that may occur in the future. The research found a positive association between Government’s response measures and new cases initially and in the long term and a significant short-term error correction component. This indicates an incremental and repeatable implementation approach, proposing a cyclical or lagged relationship where response measures reach a level of stringency after some time, then induce a decline in new cases. The timing, speed, and stringency of implementation of the government policy response measures are crucial in our understanding of the relationship or link between the government policy response measures and new COVID-19 cases.

Policy uncertainty, geopolitical risks and China’s carbon neutralization

In the present study, it was explored how the volatility of the carbon neutrality concept index (CNCI) was affected by China economic policy uncertainty (CEPU) index, climate policy uncertainty (CPU) index, and geopolitical risk (GPR) index. According to the research of Amendola et al. the GARCH-MIDAS model was improved by introducing the realized kernel volatility of China stock market into the short-term volatility component. On this basis, the GARCH-RKV-MIDAS model was constructed. Meanwhile, both GARCH-MIDAS and GARCH-RKV-MIDAS models were applied to identify the influencing factors for CNCI volatility during the period between January 2018 and June 2022, with CNCI predicted. According to the research results, both the CPU index and the GPR index exert a significant effect on the long-term volatility of CNCI, despite no significant difference made by the CEPU index to the long-term volatility of CNCI. As for the prediction of CNCI volatility, the GARCH-RKV-MIDAS model clearly outperforms the GARCH-MIDAS model. Moreover, the CPU index outperforms the GPR index and the CEPU index in predicting the volatility of the CNCI.

A New Combined Prediction Model for Ultra-Short-Term Wind Power Based on Variational Mode Decomposition and Gradient Boosting Regression Tree

Wind power is an essential component of renewable energy. It enables the conservation of conventional energy sources such as coal and oil while reducing greenhouse gas emissions. To address the stochastic and intermittent nature of ultra-short-term wind power, a combined prediction model based on variational mode decomposition (VMD) and gradient boosting regression tree (GBRT) is proposed. Firstly, VMD is utilized to decompose the original wind power signal into three meaningful components: the long-term component, the short-term component, and the randomness component. Secondly, based on the characteristics of these three components, a support vector machine (SVM) is selected to predict the long-term and short-term components, while gated recurrent unit-long short-term memory (GRU-LSTM) is employed to predict the randomness component. Particle swarm optimization (PSO) is utilized to optimize the structural parameters of the SVM and GRU-LSTM combination for enhanced prediction accuracy. Additionally, a GBRT model is employed to predict the residuals. Finally, the rolling predicted values of the three components and residuals are aggregated. A deep learning framework using TensorFlow 2.0 has been built on the Python platform, and a dataset measured from a wind farm has been utilized for learning and prediction. The comparative analysis reveals that the proposed model exhibits superior short-term wind power prediction performance, with a mean squared error, mean absolute error, and coefficient of determination of 0.0244, 0.1185, and 0.9821, respectively.