Search

Currently, the rapid development of China's social economy has put forward higher requirements for the power supply chain, and the research on the power material price prediction model helps to ensure the adaptability and flexibility of the supply chain and provides assistance to the management of power materials. However, the time discontinuity of power material procurement data, the existing research methods to meet the demand for forecasting data in discontinuous time, this paper starts from the raw materials of electric power equipment, using Granger causality test to screen the factors affecting the price of raw materials and the lead time, and predicts the price of raw materials through the two models of linear regression and Random Forest, and calculates the numerical relationship between the raw material price and the purchase price of electric power materials, so as to utilize the predicted price of raw materials to forecast the price of electric power materials and to predict the price of electric power materials. Finally, it is found that the linear regression model is more favorable for the prediction of power equipment procurement price, and it predicts the future procurement price of 110kV power cables, and provides procurement strategies based on the prediction results. [ABSTRACT FROM AUTHOR]

To evaluate the potential impact of coronavirus disease 2019 (COVID-19) and vaccinations on otologic diseases, including facial nerve paralysis (including Ramsay Hunt syndrome), vestibular neuritis, sudden sensorineural hearing loss, and Meniere's disease. In this retrospective study, we conducted a time-series analysis employing a causal impact algorithm on a large-scale inpatient database in Japan. We compared the actual number of hospitalized patients with otologic diseases to two predictions: one without any covariates and another with a covariate accounting for the reduction in the number of hospitalized patients due to lockdown measures. Additionally, we performed Granger causality tests to ensure the robustness of our findings. No significant increase was noted in the number of hospitalized patients with otologic diseases following the onset of the COVID-19 pandemic in the causal impact analysis. Similarly, no notable surge was observed in hospitalizations for these diseases following the introduction of the COVID-19 vaccine. The Granger causality tests results aligned with the causal impact analysis findings. Our findings indicate that COVID-19 and vaccinations had minimal discernible effects on hospitalization of patients with otologic diseases, suggesting that otologic diseases may not be significantly impacted by COVID-19 and vaccinations, which could have implications for public health policies and the allocation of healthcare resources during a pandemic. Further research and monitoring of long-term effects are warranted to validate these findings and guide healthcare decision-making. [ABSTRACT FROM AUTHOR]

Inferring causal effects between variables when utilizing observational data is challenging due to confounding factors not controlled through a randomized experiment. Propensity score matching can decrease confounding in observational studies and offers insights about potential causal effects of prophylactic management interventions such as vaccinations. The objective of this study was to determine potential causality and impact of vaccination with an Escherichia coli J5 bacterin on the productive performance of dairy cows applying propensity score matching techniques to farm-recorded (e.g., observational) data. Traits of interest included 305-d milk yield (MY305), 305-d fat yield (FY305), 305-d protein yield (PY305), and somatic cell score (SCS). Records from 6,418 lactations generated by 5,121 animals were available for the analysis. Vaccination status of each animal was obtained from producer-recorded information. Confounding variables considered were herd-year-season groups (56 levels), parity (5 levels: 1, 2, 3, 4, and ≥5), and genetic quartile groups (4 levels: top 25% through bottom 25%) derived from genetic predictions for MY305, FY305, PY305, and SCS, as well as for the genetic susceptibility to mastitis. A logistic regression model was applied to estimate the propensity score (PS) for each cow. Subsequently, PS values were used to form pairs of animals (1 vaccinated with 1 unvaccinated control), depending on their PS similarities (difference in PS values of cows within a match required to be <20% of 1 standard deviation of the logit of PS). After the matching process, 2,091 pairs of animals (4,182 records) remained available to infer the causal effects of vaccinating dairy cows with the E. coli J5 bacterin. Causal effects estimation was performed using 2 approaches: simple matching and a bias-corrected matching. According to the PS methodology, causal effects of vaccinating dairy cows with a J5 bacterin on their productive performance were identified for MY305. The simple matched estimator suggested that vaccinated cows produced 163.89 kg more milk over an entire lactation when compared with nonvaccinated counterparts, whereas the bias-corrected estimator suggested that such increment in milk production was of 150.48 kg. Conversely, no causal effects of immunizing dairy cows with a J5 bacterin were identified for FY305, PY305, or SCS. In conclusion, the utilization of PS matching techniques applied to farm-recorded data was feasible and allowed us to identify that vaccination with an E. coli J5 bacterin relates to an overall milk production increment without compromising milk quality. [ABSTRACT FROM AUTHOR]

A considerable body of earlier literature has analysed the determinants of road safety outcomes at the aggregate level. A sub-set of that literature has sought to assess the effect of road expenditure, both investment and maintenance spending, on road safety. The results have been inconclusive and, in part, counterintuitive. To clarify that relationship, this paper undertakes a bivariate analysis using European panel data. Three main results emerge. First, there is only limited evidence of longer-term relationships (common stochastic trends) among different measures of the variables. Second, causality tests on stationary variables suggest that the direction of causality depends on how the variables are measured. Third, unrestricted Vector Autoregression (VAR) analyses suggest that there is a positive relationship from some measures of road safety outcomes to road infrastructure investment, and that an increase in one real measure of road infrastructure investment reduces fatalities permanently, if with a lag. While not modelling the true and complex data generating processes, these bivariate analyses help address the salient empirical issues emerging from earlier literature, and the results provide a detailed description of the relationship between road safety and infrastructure expenditure. • Road safety is a major public policy issue and priority. • Earlier analyses of road expenditure and road safety have been inconclusive. • Variable measurement, causality and time series properties require attention. • Some measures of road expenditure boost road safety and vice versa. [ABSTRACT FROM AUTHOR]

This study, for the first time in Taiwan, uses the mixed frequency vector autoregressive (MF-VAR) model in the high-frequency dataset to investigate the causal association between economic progress and carbon dioxide emissions over time periods spanning from 1970Q1-2019Q4. This study also includes primary energy consumption as a control variable for analysis since the bias of missing variables could lead to misleading results. In accordance with the forecast error variance decomposition and the Granger causality test, our empirical findings imply that the MF-VAR model has more explanatory power than the traditional VAR model. The empirical LF-VAR model results demonstrate a reciprocal and causal association between economic progress and carbon dioxide (CO2) emissions is supported by evidence; primary energy consumption significantly influences economic progress. Additionally, MF-VAR model results also indicate, in Taiwan, a reciprocal and causal association between economic progress and carbon dioxide emissions as well as between primary energy consumption and economic progress. Based on the finding of the results, this study suggests policy implications to the government that an effective energy strategy should be addressed along with the plan for economic development in Taiwan. [ABSTRACT FROM AUTHOR]

Over the past decade, population growth and industrialization have been the driving force behind the increase in greenhouse gas emissions. The implementation of modern technologies capable of producing energy from renewable sources is insufficient to meet the demand for energy, which is experiencing a strong growth trend. In the last decade, it has been possible to observe a decrease in the use of oil and a significant increase in renewables, the inconsistency and unmanageability of which make it impossible to balance supply and demand. However, these have a positive impact not only on the environment, but also on the development in the economic field and employment. This study aims to examine the current relationship between economic growth and renewable energy consumption, for the period 1990–2019, on the territory of Finland, through a Granger causality test. The variables used for this work are renewable energy consumption, GDP, trade, and gross capital formation. The results are significant and lead to important policy implications; in fact, the analysis shows a feedback effect between renewable energy consumption and economic growth, and, through the Granger causality test, it is shown how renewable energy consumption can affect the rate of future growth. • Relationship between economic growth and renewable energy in Finland is analyzed. • Granger causality test is developed. • The renewable energy consumption can affect the rate of future growth. [ABSTRACT FROM AUTHOR]

Since 2015, the United Kingdom (UK) has increased its investment in transportation infrastructure to compensate for years of underinvestment in comparison with peer countries. It is of critical importance to evaluate the effect of these recent investments, as well as historical ones, on economic development, both theoretically and empirically, to provide guidance on future infrastructure investment. However, few research looks into such policy adjustment on economic growth in the UK. To fill this gap, this paper investigates the relationship between transport infrastructure development and economic growth in the UK from different time spans. Principal component analysis (PCA) is used to construct a comprehensive measure of transport infrastructure development. This paper then applies Vector Error Correction Model (VECM) to investigate both long-run and short-run relationships between transport infrastructure development and economic growth from 1970 to 2017 in the UK. Empirical results suggest that transportation infrastructure has a long-run promotive effect on economic development. However, in the short run, this effect turns out to be significantly negative. The analysis of this paper indicates differentiated roles that the UK's transport infrastructure played in economic growth, which should be considered in future policy design of achieving economic sustainability in the UK. • This paper investigates the relationship between transport infrastructure development and economic growth in the UK. • Transportation infrastructure has a long-run promotive effect on economic development. • In the short run, transportation infrastructure fails to promote economic development. [ABSTRACT FROM AUTHOR]

The corruption-economic growth nexus and the relationship between innovation and corruption have long been extensively studied. While individual issues have been empirically analysed, two effects have not yet been sufficiently researched: (1) the overall effect of innovation on the corruption-growth relationship and (2) the direction of causality between these variables. The present article used data from 109 countries over the 2010–2018 period and focused on the macro level. We employed Granger causality tests for panel data (Dumitrescu–Hurlin) to determine the direction of causation among variables. We also employed nonstationary panel techniques with Fully Modified Ordinary Least Square (FM-OLS) to assess stationarity and long-run relationships. After investigating the direct impact of corruption on economic growth, we then examined the indirect effect through different transmission channels, including innovation. The results were tested and found robust in subcategories based on the developmental and regional levels. Finally, this study analysed the impact of innovation on the relationship between corruption and economic growth. The results indicated a robust negative relationship between corruption and economic growth and corruption and innovation. The findings also revealed a positive relationship between innovation and economic growth and a bi-directional causality between the variables. Furthermore, innovation was found to reduce the harmful effects of corruption on economic growth, mainly in developed countries. [ABSTRACT FROM AUTHOR]

The 12th Sustainable Development Goals (SDGs) highlighted the importance of ensuring sustainable consumption and production patterns. Researchers have found that energy subsidies may encourage overconsumption, misuse, and moral hazard problems. Hence, there is a need to rationalize inefficient energy subsidies. Consequently, the Malaysian government has been implementing a subsidy reformation program to improve energy efficiency and reduce fiscal burdens. This study aims to identify and compare the impacts of energy subsidies and oil prices on energy consumption across the agricultural, industrial and service sectors in Malaysia. It is critical for policymakers to better understand the role of energy subsidies in influencing energy consumption, especially the magnitude to which energy subsidies influence energy consumption, in order to structure a subsidy reform strategy which effectively addresses the energy overconsumption issue. In this study, a time series dataset covering the years of 1978 through 2018 is analyzed. The ARDL approach and Granger causality test were employed to determine meaningful statistical evidence and revealed four important findings. Firstly, energy subsidies have a positive relationship with energy consumption. Secondly, oil prices have a negative relationship with energy consumption and energy subsidies significantly interact with oil prices to affect energy consumption. This significant positive moderating effect suggests that energy subsidies may counteract the negative impact of oil prices on energy consumption. Thirdly, output has the greatest (positive) impact on energy consumption. And finally, energy subsidies drive higher outputs in the agricultural sector. [ABSTRACT FROM AUTHOR]

The Rutting prediction model is an essential element of efficient pavement management systems. Accuracy of commonly used predictive model necessitates knowledge of the input parameters that was incorporated and local calibration of the model coefficients. In this paper, a novel rutting prediction model based on multivariate transfer entropy and graph neural networks is proposed for incorporating a limited number of observable inputs, which can accommodate with sufficient prediction performance and generalization to a variety of complex pavement design structure data. The multivariate transfer entropy based graph representation is able to find the significant causality between variables and rutting. The influence factor analysis results confirm the high influence of temperature and vehicle axle load. Several experiments are set up on the Research Institute of Highway Ministry of Transport track (RIOHTrack) dataset for the comparison between the proposed model and the state-of-art prediction models. The result demonstrates that the proposed model is more accurate and robust compared to existing methods on the rutting prediction task. [ABSTRACT FROM AUTHOR]

In the wake of the COVID19 pandemic, what is crucial for the continued promotion of ecological civilization construction and sustainable green development in China is to achieve the dynamic and balanced development between pollution reduction, carbon emission reduction, and economic growth. Therefore, the indexes of CO 2 emissions, pollution reduction intensity, and high-quality economic development are quantified in this study by using the panel data related to 26 cities in the Yangtze River Delta urban agglomeration(YRDUA) from 2010 to 2020. Furthermore, kernel density analysis, GIS spatial analysis, and other methods are employed to demonstrate the spatial and temporal patterns of pollution reduction intensity, CO 2 emission levels, and the characteristics of high-quality economic development in the YRDUA. In order to reveal the dynamic relationship between pollution reduction, carbon emission reduction, and high-quality economic development, a panel vector autoregressive model (PAVR) was established and the system generalized method of moments was used to estimate the model coefficients. Additionally, Granger causality tests, impulse response analysis, and variance decomposition were conducted. There is a fluctuating upward trend shown by the overall level of CO 2 emissions in the YRDUA, with significant spatial nonuniformity exhibited in carbon emissions. Despite the self-promotion mechanism playing a more significant role in the high-quality economic development in the YRDUA and its three regions, the impact of pollutant and carbon emissions on high-quality economic development is quite limited. A significant promoting effect is exhibited in those high and moderately high-quality economic development regions, while nonlinear effects are observed in those high and moderately high-quality economic development regions. As for the interactive relationships, the three factors are correlated with each other to a more significant extent in those high and moderately high-quality economic development regions, which contributes to mutual influence and causal relationship. However, there is no effective interactive mechanism observed among the three factors in the whole area and low-quality economic development regions, with high-quality economic development playing a major role in environmental pollution and carbon emission reduction. • The Yangtze River Delta urban agglomeration is divided into three categories. • The economic self-promotion mechanism is notably significant. • Pollutant and carbon emissions have a very limited impact on economic development. • High economic activity significantly contributes to pollutant and carbon emissions. • Different types of cities have different modes of operation. [ABSTRACT FROM AUTHOR]

Carbon emissions inequality (CE-I) represents a significant obstacle to achieving sustainable development and social equity, highlighting the unequal distribution of environmental burdens and benefits among nations. To address this issue effectively, it is important to consider how institutional factors and economic conditions contribute to these disparities. This paper investigates the asymmetric impact of institutional quality (IQ) and income on CE-I, employing a Quantile-on-Quantile Regression (QQR) approach applied to six major global emitters—China, the United States, Germany, India, Russia, and Japan—from 1995 to 2019. The QQR method enables a detailed examination of how these relationships vary across different quantiles of the data, providing insights beyond those of traditional regression models. The findings reveal a non-linear relationship in which improvements in IQ generally decrease CE-I, particularly in contexts of weak governance; however, diminishing returns are observed at higher IQ levels. Income exhibits an inverted U-shaped effect on CE-I, where economic growth initially increases emissions inequality but moderates it at higher income levels. Robustness checks employing quantile Granger causality tests, quantile regression, and Kernel-based regularized least squares demonstrate that both IQ and income levels play significant roles in shaping CE-I patterns. Based on these findings, specific policy actions are recommended: in contexts of weak governance, policies should prioritize strengthening IQ to reduce CE-I, while in high-income countries, efforts should focus on promoting equitable income distribution to manage the growth-inequality relationship. Targeted interventions are essential to ensuring both environmental sustainability and social justice in reducing CE-I. [Display omitted] • Examines how institutional quality and income affect carbon inequality. • Uses a quantile-on-quantile analysis on data from six major global emitters. • Better institutions reduce carbon inequality, effect weakens at higher quantiles. • Income's impact on carbon inequality often shows an inverted U-shaped curve. [ABSTRACT FROM AUTHOR]

The Baltic Sea highly susceptible to the proliferation of Phytoplankton blooms. Present work examines the long-term trend and spatio-temporal variability of satellite derived chlorophyll concentration (Chl a) in the Baltic Sea during the period 2004–2021. Furthermore, the influence of water quality and meteorological parameters on Baltic Sea primary productivity has been analyzed using robust Generalized Additive Models (GAM) and Granger Causality statistical test. Statistically significant increasing trend in chlorophyll a concentration is observed in the Baltic Sea at the 95 % confidence interval. GAM model reveals that the most significant controlling factor is sea surface temperature (p < 0.0001), followed by nitrate and phosphate. Both GAM and Granger Causality tests confirm that water quality parameters are the major drivers in limiting the growth of Phytoplankton blooms in the Baltic Sea. Summer bloom shows in-phase relationship with Sea surface temperature and out of phase relationship with the phosphate nutrient. • Increasing trend of Chlorophyll a concentration in the Baltic Sea results from increased nutrients loads and warmer temperature. • Sea surface temperature acts as most significant predictor that limits the growth of phytoplankton blooms. • Water quality parameters are the potential causal drivers that promotes the growth of phytoplankton blooms in the Baltic Sea. • Summer bloom shows in-phase relationship with Sea surface temperature and out of phase relationship with the phosphate nutrient. [ABSTRACT FROM AUTHOR]

Changes in rainfall, groundwater levels, and reservoir water levels exacerbate the deformation of water-involved landslides, accelerating the transition from landslide evolution to extinction. Extracting the destruction patterns of landslides from extensive monitoring data, and understanding their overall deformation mechanisms are crucial for geological hazard prevention and control. Herein, we took the Jiuxianping landslide in the Three Gorges Reservoir area as an example and proposed a deformation mechanism analysis model for water-related landslides based on monitoring data mining techniques. Using Granger causality testing, the study analyzes the spatiotemporal characteristics of GPS displacement data from three different profiles, which confirms that Jiuxianping exhibits a traction destruction mode. By comparing GPS displacement data and their Granger causality relationships across different profiles, we reveal that segmented sliding features of the landslide's front, middle, and trailing during its evolution. Furthermore, the impact intensity of triggering factors (rainfall and reservoir water level changes) on landslide displacement was identified. Based on GPS displacement data from profiles II–II′, an empirical mode decomposition–long short-term memory-regression model (EMD-LSTM-regression) is developed for multisource prediction of landslide displacements. The Shapley additive explanations algorithm is used to analyze the influence of rainfall and reservoir water level changes on periodic displacements at different positions of the landslide. Owing to the large area of the Jiuxianping landslide, the response to triggering factors varies across different locations. In the context of global warming and frequent extreme weather events, these findings offer important insights for preventing and mitigating water-related landslides in the Three Gorges Reservoir area, while also providing new perspectives for the analysis of global water-involved landslide deformation. • Use the Granger causality test to analyze landslide displacement monitoring data. • The differences in deformation characteristics of the three profiles of the Jiuxianping landslide are related to the number of slip-resisting segments. • The deformation in the middle and trailing parts of the landslide is mainly controlled by the deformation at the front edge of the landslide. • The deformation at different locations of the landslide responds to triggering factors to varying degrees. [ABSTRACT FROM AUTHOR]

This study explores the nexus between electricity consumption, fossil fuel use, renewable energy adoption, population growth, GDP, and environmental pollution in Indonesia, incorporating open innovation dynamics as a novel dimension of analysis. The study uncovers significant long-term relationships between the variables using time series data from 2000 to 2023 and applying econometric techniques such as cointegration analysis, Granger causality tests, and Vector Error Correction Models (VECM). The results indicate that electricity consumption drives economic growth and CO₂ emissions, while fossil fuel dependency significantly contributes to environmental pollution. Renewable energy adoption, though growing, is insufficient to offset the environmental damage caused by fossil fuel use. The study also identifies the potential of renewable energy to mitigate pollution levels, though challenges such as technological barriers and high initial investment costs remain. Population growth intensifies energy demand and pollution, underscoring the necessity for sustainable energy policies. Additionally, the research highlights the role of open innovation, particularly in renewable energy, mobile payment platforms, and collective intelligence, in addressing Indonesia's energy-environment challenges. Policy recommendations emphasize the need for more substantial incentives for renewable energy, the regulation of fossil fuel consumption, and the development of an open innovation ecosystem that engages both the public and private sectors. [ABSTRACT FROM AUTHOR]

The shift to renewable energy is essential for addressing climate change and reducing reliance on fossil fuels. This paper analyzes the relationship between patent applications for renewable energy technologies and clean energy production in 45 countries from 2000 to 2019. Using a System Generalized Method of Moments Panel Vector Autoregressive (GMM-PVAR) model, Impulse Response Functions (IRFs), and Granger causality tests, the findings show that a shock to renewable energy patents does not immediately affect clean energy production. However, a temporary decline occurs in the second period, followed by gradual increases that stabilize in the long run. This suggests a delayed but sustained impact of innovation on energy generation due to the time required for technology implementation and infrastructure adaptation. Bidirectional causality between patents and energy production reveals a feedback loop, where innovation drives energy development, and increased energy production fosters further advancements. Additionally, GDP per capita, foreign direct investment, political stability, and trade openness significantly affect clean energy production. Political stability positively influences long-term renewable energy generation, while GDP and trade openness show short-term fluctuations before stabilizing. These findings highlight the evolving nature of the renewable energy sector and the critical role of innovation and economic factors in driving growth. [Display omitted] • The effect of patents on renewable energy in 45 countries (2000–2019) was analyzed. • Patents have no immediate impact on renewable energy but show long-term effects. • There is a bidirectional causality between patents and renewable energy production. • Renewable energy improves political stability by reducing fossil fuel dependence. [ABSTRACT FROM AUTHOR]

This paper analyzes the dynamic impact of economic, social, and governance factors on PM2.5 concentrations in 89 countries from 2006 to 2019. Using the GMM-PVAR approach and Impulse-Response Functions, we examine how shocks to specific variables affect PM2.5 concentrations over a 10-year period. Our findings reveal that the influence of these factors on PM2.5 levels varies over time. For example, a shock in urbanization has no effect on PM2.5 concentrations in the first year, but in the second year, pollution increases significantly. In the third period, PM2.5 levels decrease, but they rise again in the fourth period, albeit not significantly. By the fifth period, pollution decreases until a new equilibrium is reached in the sixth period. Additionally, a shock in financial development, government effectiveness, industrialization, trade openness, or GDP has no effect on PM2.5 concentrations in the initial period. However, during the second period, air pollution decreases, followed by an increase in the third period and a decrease again in the fourth period. These dynamic patterns highlight the need for environmental policies that consider the evaluation time horizon. Our analysis is supplemented by the Granger causality test, guiding specific policy recommendations based on our findings. [Display omitted] • The highest concentrations of PM2.5 are observed in Tajikistan, Cameroon, Niger, and Egypt. • The lowest PM2.5 concentrations are found in Norway, Sweden, and Finland. • Reducing air pollution has a significant impact on urbanization levels in countries. • Changes in government effectiveness significantly affect PM2.5 concentrations. • GDP influences PM2.5 concentrations with feedback effects. [ABSTRACT FROM AUTHOR]

Since the Japanese government announced the launch of the Fukushima nuclear contaminated water discharge plan, it has aroused widespread concern and controversy in the international community. As a maritime neighbor of Japan, China has been affected by this incident, and Chinese public sentiment and aquatic product prices have fluctuated to a certain extent.In order to explore the internal relationship between the two, this study based on the microblog (Chinese Twitter) text data obtained by web crawler, and used LDA model to mine the public discussion topics at different stages of the event, perceived the public emotional changes, formed the public emotional time series, and constructed Granger causality test model to explore its relationship with aquatic product price volatility. Through empirical analysis, this paper has the following important findings: First, the impact of the change of negative public sentiment on the price fluctuation of aquatic products is more obvious than that of the overall public sentiment; second, the negative public sentiment is not only the Granger cause of the price fluctuation of seawater products, but also the Granger cause of the price fluctuation of freshwater products.To a certain extent, this study enriches the research on the influencing factors of aquatic product price volatility, especially exploring the relationship between public sentiment on social platforms and aquatic product price volatility under the influence of uncertain events. The research results provide theoretical reference for guiding network public opinion, eliminating public negative emotions, ensuring the stable operation of market prices and guaranteeing people's normal life; At the same time, the study of social media data also provides us with a unique perspective, which enables us to grasp the emotions of the social public more accurately, and thus provides important value for our academic research. [ABSTRACT FROM AUTHOR]

Energy poverty is a major issue that deepens economic challenges, especially in the five fragile states of Turkey, Brazil, South Africa, India, and Indonesia. The objective of this study is to examine the impact of financial inclusion on energy poverty, as removing financial barriers to energy access and utilisation is critical for sustainable development. The study hypothesizes that financial inclusion plays a decisive role in alleviating energy poverty. The study analyses the second generation panel methods such as AMG, Driscool-Kraay, and FMOLS using annual data from 2000 to 2021 and examines the causal relationships between variables using Dumitrescu-Hurlin panel causality test. The findings reveal that financial inclusion, low carbon technology trade, energy efficiency, economic growth, and human capital play a central role in alleviating energy poverty in these economies. On the other hand, a unidirectional causal relationship was found between financial inclusion and low carbon technology trade, human capital, energy efficiency, and energy poverty. In contrast, a bidirectional causal relationship was observed between economic growth and energy poverty. These findings provide important insights for policymakers and stakeholders; understanding the complex dynamics between financial inclusion and energy poverty can help design targeted strategies to increase energy access and promote inclusive economic development in fragile economies. [Display omitted] • This study investigates the impact of Financial Inclusion on Energy Poverty in the Fragile Five Economies. • This research examine the effect of low carbon technology trade using second generation panel of AMG, Driscool-Kraay and FMOLS. • The findings reveal that financial inclusion, low carbon technology trade and energy efficiency play a central role in alleviating energy poverty in these economies. • A unidirectional causal relationship was found between financial inclusion and low carbon technology trade and energy efficiency and energy povert [ABSTRACT FROM AUTHOR]

Within this study, we analyse green cryptocurrencies versus sustainable investments dynamics by calculating a multifractal multiscale analysis (MMA) with Hurst surfaces paired with powerlaw distributional coherence tests for each series. Next, we determine multifractal cross-correlations by applying a maximal overlap discrete wavelet transform (MODWT) based trend-filtered variation of a multifractal detrended cross-correlation analysis (MF-DCCA). Finally, to determine the strength and directionality of potential causations, we determine the results of a nonlinear Granger causality test. The results for the MMA state q -dependent unstable multifractality for each series. The coherence tests indicate that the series follow powerlaws or exponentially nested powerlaws and yield fat-tails in some cases. Moreover, we find strong scaling and multifractal cross-correlations between the cryptocurrencies and the sustainability series. Finally, the nonlinear Granger causality tests across four lags indicate a complex interplay between some of the selected cryptocurrencies and various indices. These results suggest the existence of potential predictive powers of these cryptocurrencies on the market indices. [ABSTRACT FROM AUTHOR]

Climate policy is essential for fostering sustainable development and addressing climate-related issues, while climate policy uncertainty (CPU) introduces complex challenges in providing a consistent framework for action. In this study, we delve into the spillover dynamics among China's domestic CPU, foreign CPU, physical assets, and financial assets across various market scenarios, through a quantile-based connectedness method and evidence from China. The findings reveal: (1) The spillover dynamics are asymmetric across quantiles and time, and the COVID-19 pandemic influences the total connectedness. (2) The complexity of spillover patterns in physical assets is likely contingent upon market conditions, policy sources, asset types, and industrial-chain positions, while the spillovers of financial assets are quantile-dependent and time-varying, with the agricultural equity market showing relative resilience to the pandemic. (3) There is no significant Granger causality in quantiles between domestic CPU and foreign CPU under the entire sample period, and the rankings regarding the spillovers from CPUs to assets vary across market conditions. Implications for policymakers, investors, and firms are provided. • The spillovers of climate policy uncertainties (CPUs) and assets are asymmetric. • The pandemic influences the quantile-dependent total connectedness. • The spillover dynamics rely on market conditions and asset types. • Granger causality between China's domestic and foreign CPU is absent. • The agricultural sector shows relative resilience to the pandemic shock. [ABSTRACT FROM AUTHOR]

This study investigates the lead-lag effect between crude oil futures and petroleum products from the view of price predictability. The findings of the Granger causality test provide evidence for the leading role of crude oil futures. Further empirical results show that crude oil futures demonstrate strong predictive power for petroleum products both in- and out-of-sample, and this predictability is a short-term phenomenon. During economic recessions, petroleum product prices exhibit stronger predictability, with out-of-sample R 2 reaching a maximum of 39.161 %. Additionally, the outstanding forecasting performance remains robust across a range of settings. Finally, two potential economic sources underlying the lead-lag relationship between crude oil and petroleum product markets are provided: the differential response speed to common energy market information and the perspective of raw material supply. • Explore the lead-lag effect between crude oil futures and petroleum products. • Short-term price predictability of petroleum products has been found both in- and out-of-sample. • Provide two potential economic sources underlying the superior predictability. • Further extension and robustness analyses confirm the outstanding forecasting performance. [ABSTRACT FROM AUTHOR]

Ensuring the reliable operation of diesel/Heavy fuel oil (HFO) engines in Thermoelectric Power Plants (TPPs) requires constant monitoring and control of operational parameters. Time series forecasting methods can predict physical system characteristics based on historical data, which can help regulate engine operational parameters. Despite the widespread use of these techniques in coal and natural gas powered TPPs, further examination of their application in large diesel/HFO engines in Brazilian TPPs operating under dispatch conditions is needed. This work investigated the fuel consumption generalization capacity of linear, nonlinear, and hybrid prediction models, both considering univariate and multivariate approaches, related to a TPP engine-driven generator in Pernambuco, Brazil. For multivariate model development, exogenous variables were selected based on change of causality overtime analysis, which sought not only to infer the relationship between signals but also to identify the influence regime of each causality during operations. The feature selection step initially identified a performance improvement when eight additional features were used. However, each feature causality throughout the operations indicated that only four signals helped significantly predict consumption in different ways. The exogenous variables introduction to ARIMA and NAR models resulted in significant improvements only to the nonlinear approach, NARX, which could recognize operational disturbances more quickly when compared to other analyzed models. In addition, the application of additive and multivariate hybrid models provided the ability to detect more complex variations and, at the same time, maintain model stability during full-load operation, benefiting from linear and nonlinear characteristics capturing ability related to the combined model. • Engines' FOC was analyzed based on univariate and multivariate prediction models. • Linear, nonlinear, and hybrid prediction models were used. • Exogenous variables were selected based on change of causality overtime analysis. • Exogenous variable's introduction to NAR resulted in significant improvements. • Hybrid prediction models detected complex variations and maintained model stability. [ABSTRACT FROM AUTHOR]

Renewable energy is considered to be a useful tool to mitigate global carbon dioxide emissions and its benefits are linked to globalization. To investigate the heterogeneous effects of renewable energy and globalization at different levels of carbon dioxide (CO 2), the present study utilized the Quantile Autoregressive Lagged (QARDL) model. In addition, data for Mexico from 1990Q1-2018Q4 is used with the framework of the Environmental Kuznets Curve (EKC). The findings show that renewable energy mitigates CO 2 emissions regardless of the quantile level. However, globalization is found to raise the ratio of CO 2 emissions while interacting with only the higher quantiles of CO 2 emissions. Additionally, the Spectral Causal analysis reveals that the GDP and GDP squared granger cause the CO 2 emissions only in the long run. Also, renewable energy and globalization significantly granger cause CO 2 emissions in the short, medium, and long-run, suggesting that any policy shock in any independent series will lead to cause carbon emissions. Besides, the findings confirm the existence of the EKC hypothesis for Mexico. Some useful policy implications are suggested based on the short and long-run parameters. • Examines relationship between renewable energy, economic growth, and globalization with CO 2 emissions. • QARDL method is applied over the spanning from 1990Q1-2018Q4. • EKC is verified from middle to higher Quantile. • Globalization increase environmental pollution. • Renewable energy significantly reduces the level of environmental degradation. [ABSTRACT FROM AUTHOR]

Sustainable development has become a new engine to promote economic growth, in which scientific and technological progress, green finance and the efficient development of a green economy have become the main driving forces of economic and societal development. Hence, the continuous improvement of innovation ability through scientific research innovation, as well as the rapid development of the technology market, is an important force for the sustainable development of China. Therefore, by first analysing the related data from 2001 to 2020 of China's technology market, the time series value of the comprehensive evaluation is obtained. Then, based on the unit root test, cointegration test and Granger causality, this study explores the relationship between the technology market turnover and scientific research innovation. The results show a second-order cointegration between the technology market turnover and scientific research innovation. Scientific research innovation can promote the development of the technology market from the perspective of long-term equilibrium and reverse correction in the short term. Mutual Granger causality and a first-order significant lag are observed between technology market development and scientific research innovation. It is necessary to further improve the management mechanism of the technology market. The government should promote the continuous innovative development of the technology market to improve the efficiency of resource allocation and enhance the connection between the technology market and innovation subjects. [ABSTRACT FROM AUTHOR]

This paper analyzes the relationship between interest rates, inflation expectations and inflation rates in Australia using the approach suggested by Toda and Yamamoto (1995) to test for Granger causality. The empirical evidence reported here suggests that uni-directional Granger causality is running from medium- and long-term government bond yields to short-term inflation expectations as measured by a survey among consumers. Moreover, there is bidirectional Granger causality among short-term interest rates and short-term inflation expectations among consumers. Additionally, interest rates and the sentiment data measuring inflation expectations can help to predict inflation rates. These findings are supportive for the Fisher effect and also seem to indicate that the bond market is quite efficient predicting inflation rates. Furthermore, some problems of more traditional tests of the Fisher hypothesis are discussed. In this context the role of financial deregulation in Australia is examined. Thus, the Lucas critique seems to be of some relevance testing the Fisher effect (see Lucas, 1976). [ABSTRACT FROM AUTHOR]

This study uses the economies of Brazil, Russia, India, China and South Africa (BRICS) to examine how economic growth, agriculture, renewable energy, information and communication technology (ICT) and human capital affect carbon emission during the period of 1990–2019. Several econometric techniques such as Pedroni Cointegration test, Mean Group techniques and Pairwise granger causality test are employed. The result from Augmented Mean Group suggests the existence of agriculture induced environmental Kuznets curve hypothesis for BRICS economies. The roles of both the renewable energy and ICT use are negative and significant on the carbon emission. Furthermore, the moderation effect of renewable energy with agriculture shows that it can moderate the agriculture's positive contribution towards the climate change while the moderation effect of ICT and human capital with agriculture do not yield any significant outcomes. The pairwise granger causality result further establishes bidirectional causality between CO 2 emission and renewable energy, ICT and CO 2 , agriculture and GDP, ICT and GDP, renewable energy and agriculture as well as between renewable energy and ICT. Finally, the study provides policy implications and insights for the BRICS governments and policymakers in their efforts to tackle the climate change through the use of renewable energy. [ABSTRACT FROM AUTHOR]

In the contemporary era, environmental recovery is the primary objective of emerging and industrialized economies, where scholars and policy-makers propose adopting environmentally friendly energy resources such as renewables. Still, economies are paying less attention towards renewable energy consumption. In this sense, the current study aims to investigate the factors influencing renewable energy consumption in the emerging seven economies throughout 1990–2021. Specifically, this study explores the influence of renewable energy (electricity) output and energy efficiency on renewable energy consumption. Economic growth, carbon emissions, and technological innovation are control variables. Using various panel data approaches, the results verified slope coefficient heterogeneity, cross-section dependence, and the existence of the long-run cointegration relationship. Following the asymmetric distribution of data, this study uses novel method of moments quantile regression. The estimated results asserted that renewable energy output, energy efficiency, economic growth, and carbon emissions are the significant factors of renewable energy consumption. However, technological innovation substantially reduces renewable energy consumption. The robustness of the results is validated by bootstrap quantile regression, while the [1] panel Granger causality test confirms the two-way causal nexus between renewable energy consumption and the listed variables. Further, this study suggests increased investment in renewable energy output, the energy efficiency sector, and environmentally related technological innovation to encourage renewable energy consumption. • Renewable energy consumption key drivers are analyzed for emerging economies. • Data employed from 1990 to 2021 using new panel techniques. • Renewable energy output and energy efficiency encourage renewable energy. • Economic growth and carbon emissions also statistically affect renewable energy. • Policy makers should encourage growth with energy efficiency and renewable energy. [ABSTRACT FROM AUTHOR]

Sleep disturbance is associated with autonomic dysregulation, but the temporal directionality of this relationship remains uncertain. The objective of this study was to evaluate the temporal relationships between objectively measured sleep disturbance and daytime or nighttime autonomic dysregulation in a co-twin control study. A total of 68 members (34 pairs) of the Vietnam Era Twin Registry were studied. Twins underwent 7-day in-home actigraphy to derive objective measures of sleep disturbance. Autonomic function indexed by heart rate variability (HRV) was obtained using 7-day ECG monitoring with a wearable patch. Multivariable vector autoregressive models with Granger causality tests were used to examine the temporal directionality of the association between daytime and nighttime HRV and sleep metrics, within twin pairs, using 7-day collected ECG data. Twins were all male, mostly white (96%), with mean (SD) age of 69 (2) years. Higher daytime HRV across multiple domains was bidirectionally associated with longer total sleep time and lower wake after sleep onset; these temporal dynamics were extended to a window of 48 h. In contrast, there was no association between nighttime HRV and sleep measures in subsequent nights, or between sleep measures from previous nights and subsequent nighttime HRV. Daytime, but not nighttime, autonomic function indexed by HRV has bidirectional associations with several sleep dimensions. Dysfunctions in autonomic regulation during wakefulness can lead to subsequent shorter sleep duration and worse sleep continuity, and vice versa, and their influence on each other may extend beyond 24 h. • Autonomic function and sleep are closely inter-related. • Daytime rather than nighttime autonomic function has bidirectional associations with sleep quality measures. • The temporal dynamics of the association may extend beyond a 24-h period. [ABSTRACT FROM AUTHOR]

• We test for Granger causality in mean and quantiles between wage share and capacity utilization in developed countries. • We employ confidence intervals generated by bootstrap and the Wald test for joint significance. • Capacity utilization positively Granger causes the wage share in eight countries at the center, lower and upper tails. • The positive Granger causal effect of capacity utilization is strong, heterogeneous, and larger for extreme quantiles. • The wage share negatively Granger causes capacity utilization in all quantiles in Spain and above the median in Italy. This paper tests for Granger causality in quantiles between the wage share and capacity utilization in twelve developed countries using annual data ranging from 1960 to 2019. Instead of focusing only on the conditional mean, we test for Granger causality in the entire conditional distribution of the variables of interest. This method detects Granger causal dynamic relations in both the mean and the whole conditional distribution using bootstrap confidence intervals and the Wald test. Our results indicate that capacity utilization positively affects the wage share in eight out of the twelve sample countries. This Granger causal effect is strong and heterogeneous across quantiles, being larger for more extreme quantiles. Capacity utilization positively Granger causes the wage share in all conditional quantiles in the U.S., Sweden, and Italy. Meanwhile, the wage share negatively Granger causes capacity utilization in all conditional quantiles in Spain and in some quantiles above the median in Italy. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the dynamic asymmetric impact of equity market uncertainty on the energy market. For that reason, we first apply the asymmetric Granger causality test to capture both positive and negative EMV and oil shocks. We find the existence of the asymmetric effect, displaying as negative EMV shocks are the primary driving factors of positive oil shocks, while positive EMV shocks can affect negative oil shocks. Further, on the basis of three rolling-window procedures, we extend the asymmetric causality test to detect the structural breakpoints and time-varying characteristics of the causal relationships running from EMV shocks to oil shocks. Results show that some extreme events may lead to a structural breakpoint in their relationships. In particular, the negative effects of EMV shocks on oil shocks seem to be more lasting than the positive ones. Our findings may provide a new perspective on asset allocation, energy portfolio construction, and risk management. • This paper investigates the asymmetric impact of EMV shocks on crude oil shocks. • We extend the asymmetric causality test based on three rolling-window procedures. • The results show a significant asymmetric impact of EMV on the crude oil market. • We find the structural breakpoints and time-varying characters of their causality. [ABSTRACT FROM AUTHOR]

To achieve environmental sustainability, it is important to change the energy pattern from dirty fossil fuels to renewable-clean energy sources. For this transition, countries should use their financial resources effectively and efficiently. Against this background, this study aims to examine the impact of financial development on renewable energy consumption in the United States over the period 1980–2019, considering urbanization, economic structure, and economic growth as control variables. The study contributes to the existing literature by testing the effect of six sub-indicators, namely efficiency, depth, and accessibility of financial markets and institutions on renewable energy consumption. This is done using the novel Fourier quantile causality test with wavelet transforms. The empirical results show that financial development encourages renewable energy consumption at high quantiles in the medium- and long-run. Moreover, depth and access to financial markets are the two most important factors that promote renewable energy consumption. Based on the overall findings, the study suggests that the United States government should adopt policies that improve the depth and access to financial markets rather than financial institutions to support green growth. [Display omitted] • Is there any impact of financial sector on renewable energy consumption in the USA? • A Fourier and wavelet based quantile causality analysis is used. • Financial development stimulates renewable energy consumption at higher quantiles. • Improving financial depth and access is crucial for renewable energy consumption. [ABSTRACT FROM AUTHOR]

Environmental pollution is a principal concern for all countries. In particular, the issue is alarming for Australia as the country has been suffering from various environmental disasters for some recent years. Therefore, examination the roles of some variables that are related to environment are vital. To this end, this work endeavors to ascertain the impacts of industrialization, non-renewable and renewable energy use, and financial development on environmental pollution in Australia. Using the data period of 1990–2020, a series of econometric techniques such as the Augmented Dickey-Fuller test, Phillips-Perron test, Autoregressive Distributive Lag (ARDL) bounds test and the pairwise Granger causality test are applied. The findings reveal that industrialization and non-renewable energy use increase while the square of industrialization, renewable energy use, and financial development reduces CO 2 emissions in Australia. The validation of the environmental Kuznets' curve (EKC) hypothesis is also found. Bidirectional and unidirectional causal association of the studied variables with CO 2 emissions are attained. All the outcomes are theoretically and empirically relevant and have important policy implications. [ABSTRACT FROM AUTHOR]

The rapid growth in economic activities, feeble resource utilizations, and financial assistance for energy extensive megaprojects alarm significant environmental challenges in emerging economies. Therefore, this research delves into investigating the effects of the stock market, financial development, renewable energy, economic complexity, natural resources, and globalization on environmental sustainability in emerging economies. The pooled mean group (PMG), mean group (MG), and dynamic fixed effect (DFE) estimation approaches are adopted to examine the long-run relationship, which accounts for cross-sectional dependence and slope heterogeneity among panels. The Dumitrescu-Hurlin panel Granger causality test was adopted to analyze the causal relationships among the modeled variables. The study's findings demonstrate that renewable energy, the stock market, economic complexity, and globalization have a negative impact on the ecological footprint. It demonstrates that renewable energy, the stock market, economic complexity, and globalization increase environmental sustainability in emerging economies by reducing ecological footprints. However, financial development and natural resources are positively related to ecological footprints, indicating that financial development and natural resource consumption boost ecological footprints, and their unsustainable use causes ecological problems. Additionally, the causal relationship shows that the stock market causes ecological footprints. Bidirectional causal links between renewable energy, natural resources, financial development, and globalization have been detected. Governments and policymakers need to establish policies to boost the sustainable use of both financial development and natural resources to enhance environmental quality in selected emerging economies. [ABSTRACT FROM AUTHOR]

The unclear causal links of carbon emissions among global cities challenge policy development. This study develops two causal discovery algorithms to aid in this understanding. The first, scalable causal discovery, excels in unraveling complex causal relationships within extensive non-Euclidean networks encompassing thousands of nodes. The second, knowledge-injection causal discovery, merges expert expertise with artificial intelligence's data mining capabilities, employing a human-computer interaction approach for precise causal analysis. The proposed algorithms outperform leading causal discovery methods in the Granger causality test and causal structural consistency. This study investigates the emission causal networks across global cities and key international organizations, including the Organization for Economic Cooperation and Development, the Commonwealth, G20, the Belt and Road Initiative, and the Asia-Pacific Economic Cooperation. The analysis encompasses networks, countries, cities, and emission sources, providing insights for developing collaborative urban emission reduction policies. It underscores the tightly interconnected nature of the worldwide emission network, where the effects are rapidly disseminated. Furthermore, sub-networks reveal consistency and variability in their causal patterns, with core cities exerting significant influence over various dynamics. It is essential to leverage the unique structural characteristics inherent in each sub-network to enhance the effectiveness of coordinated emission reduction initiatives. [Display omitted] • Global city carbon emissions form an interconnected network with systemic impacts. • Tailored climate policies are essential for specific urban networks globally. • Even countries with low carbon emissions are crucial in unified reduction efforts. • Climate policy must focus on the urban industry-transport-power causal link. • Prioritizing cities with the greatest network impact is key in global governance. [ABSTRACT FROM AUTHOR]

This study examines the impact of cryptocurrency uncertainty indices on green bonds, currency, and commodity markets by using weekly data from January 1, 2014, to December 30, 2022. The study analyzes such relationships employing the time-varying robust Granger-Causality test coupled with the TVP-VAR-DY approach. The empirical findings unfold the heterogeneous effects of uncertainty indices toward diverse financial instruments pronounced during financial or economic turbulence. The DY approach indicates that total connectedness among financial assets varies significantly over time. The green bond market is the net receiver, while ishares Global Clean Energy ETF (ICLN) and VanEck Low Carbon Energy ETF (SMOG) indices transmit the shocks for the whole period. The findings suggest that holdings in the green bond market after the health crisis offer greater hedging opportunities to investors. The results have significant ramifications for financing, hedging, and policymaking. • We examine the impact of cryptocurrency uncertainty indices on green bonds, currency, and commodity markets. • Time-varying robust Granger-Causality test and TVP-VAR-DY techniques are employed. • The effect is more pronounced during financial or economic turbulence. • The Green Bond market is the net receiver, while ETF (ICLN) and ETF (SMOG) indices transmit the shocks for the whole period. [ABSTRACT FROM AUTHOR]

Prior studies ignored the dimensions of green innovations, green energies, and resource utilization on ecological load capacity factors (LCF). Besides, environmental modellers, particularly in Brazil, Russia, India, China, and South Africa (BRICS), failed to consider the perspective of LCF. Remarkably, LCF explains the peculiarities of the natural environment's demand and supply sides. Panel series of the relevant predictors of LCF during 1980–2021 were analysed for empirical inferences. The method of moments quantile regression (mmqr), fully generalized least squares, the Prais-Winsten panel regression, augmented mean group, and cross-sectional autoregressive distributed lags undergirded the empirical analysis. Applying the panel-frequency domain Granger causality test enriches the novel perspective of the study. Cointegration was established based on the Westerlund and Banerjee & Carrion-i-Silvestre procedures. Notably, green technologies produced unimpressive positive effects on LCF, whereas green transition produced significant appealing effects on LCF at location and across the quantile distributions. This further highlights the poor status of green technologies within the BRICS. Among the control variables, urbanization contributed more significantly to lowering LCF than resource utilization and economic expansions. The findings underscored the essence of green innovations and green transitions to improved LCF in BRICS. [ABSTRACT FROM AUTHOR]

This study principally concentrates on evaluating the effects of Chinese commodity and stock market on the green market. The Maximum Overlap Discrete Wavelet Transform (MODWT) technique is utilized for decomposing time series into diverse frequency components. Following this, quantile-on-quantile regression (QQ) is implemented to meticulously demonstrates the dependency structure among the variables under diverse market conditions. Results demonstrate that energy has no short-term impact, while non-ferrous metals, palm oil, sugar, and stock impact the green market at high and low quantiles. Booming gold and declining stock negatively affect the green market. In the medium term, the influence of stocks and most commodities diminishes. Bullish coal and lead prices have a positive impact, while bearish zinc, corn, and palm oil prices have a negative impact. Crude oil does not significantly affect the green market. In the long term, both stock and commodities have a positive impact on the green market, particularly at extreme quantiles. When both markets decline, they negatively affect the green market. Finally, Granger causality test reveals a bi-directional linear and non-linear causality between commodity and stock markets and the green market across all time scales, enabling investors to predict green market returns based on commodity and stock returns. [ABSTRACT FROM AUTHOR]

• We use language models to construct sentiment indices for the shipping market. • Similar to the stock market, shipping market sentiment also has a reversal phenomenon. • Shipping market sentiment is an important factor for pricing other than fundamentals. • Shipping sentiment indices eliminate the reverse causal relationship with market prices. • Language models significantly outperform the lexicon-based approaches for sentiment analysis. This paper applies language models to the shipping market for the first time and studies the impact of changes in shipping market sentiment on freight rates. First, based on language models and Clarksons' commentary reports, this paper proposes the sentiment indices for the entire shipping market and the sub-markets for bulk ships, tankers, and container ships. Second, empirical results indicate that, apart from the container shipping market sentiment index, all other shipping sentiment indices including the total shipping market sentiment index, the dry bulk shipping market sentiment index and the tanker shipping market sentiment index serve as positive predictive indicators for shipping freight rate indices. Third, this paper investigates the interaction between the shipping sentiment index and market prices through a vector autoregressive model and the Granger causality test. We find that the total shipping market sentiment index is the Granger cause of the Baltic Dry Index and the Baltic Dirty Tanker Index. The dry bulk shipping market sentiment index and the container shipping market sentiment index are the Granger causes of the Baltic Dry Index and the China Containerized Freight Index, respectively. Last, this paper compares the shipping sentiment index constructed by market-specific language models and lexicon-based sentiment analysis. It is evident that language models significantly outperform the lexicon-based approaches for sentiment analysis and are expected to be useful for analyzing textual sentiment in the field of asset pricing research. [ABSTRACT FROM AUTHOR]

In this study, we explore the interconnections among Chinese energy stocks, renewable energy stocks, and commodity markets by employing time-frequency connectedness and causality-in-quantiles methodologies. This research contributes to the existing body of literature by scrutinizing the time-varying causal relationships between Chinese energy/renewable energy stocks and commodity markets. Utilizing data spanning from January 2011 to April 2023, we discover that while the energy stock market typically exhibits susceptibility to external influences over the long term, it plays a contributing role in the short term. Notably, during the COVID-19 period, there were more pronounced long-term repercussions across the markets. Furthermore, the influence of specific markets has undergone changes; for instance, petrochemical stocks exert a significant impact on commodity markets, whereas renewable energy stocks exhibit minimal influence. Insights derived from quantile causality analysis underscore the predictive utility of stock markets in forecasting the movements of commodity markets across a broad quantile of commodities. • Study explores interconnections among Chinese energy and commodity markets. • Time-varying relationships between energy stocks and commodities found. • COVID-19 amplifies long-term effects on energy and commodity markets. • Renewable energy stocks show minimal influence on commodity markets. [ABSTRACT FROM AUTHOR]

Drought changes can profoundly influence vegetation growth while vegetation, in turn, may impact drought by modulating water and energy exchanges between the land and atmosphere. Previous studies have primarily focused on the unidirectional effects of drought on vegetation dynamics. However, the impacts of vegetation on drought and their bidirectional dependencies have remained underexplored. In this study, we employ the Leaf Area Index (LAI) and two drought indices (the Standardized Precipitation Index, SPI, and, the Standardized Precipitation Evapotranspiration Index, SPEI) to investigate the interaction between vegetation greenness and drought globally over the period 2001 to 2020. The results demonstrate a statistically significant and consistent trend between LAI and SPI (SPEI) in 66.9% (54.01%) of global vegetated lands. SPI commonly displays a 2-month delayed response to LAI, whereas SPEI shows no evident lag, implying a more rapid response of SPEI to LAI compared to SPI. A substantial proportion of global vegetated areas, around 66% for LAI-SPI and 52% for LAI-SPEI, exhibit a significant positive relationship. The Granger causality test reveals a bidirectional causal relationship between vegetation greenness and drought across approximately 20% of global vegetated areas. Vegetation greenness is identified as the Granger cause of SPI (SPEI) in >60% (30%) of global vegetated lands, providing statistical observational evidence indicating that vegetation can influence drought in many regions within terrestrial ecosystems. Interestingly, the ratio of areas where LAI serves as the Granger cause of SPI is more extensive than the corresponding ratio where SPI is identified as the Granger cause of LAI, a pattern opposite to that observed with SPEI. Overall, our investigation into the interactions between vegetation greenness and drought holds significance for enhancing our understanding of land-atmosphere interplays in the context of climate change and contributes to advancing sustainable development for ecosystem services. [Display omitted] • A significant coherent trend between LAI and SPI (SPEI) is observed in most areas. • Most areas exhibit an evident positive relationship for both LAI-SPI and LAI-SPEI. • SPI shows a common 2-month delayed response to LAI while SPEI shows no evident lag. • Around 20% of global areas exhibit bidirectional causality between LAI and drought. • Provides observational evidence that vegetation can affect surface water conditions in many regions. [ABSTRACT FROM AUTHOR]

With the development of industrialization, greenhouse gas emissions caused by human activities, especially carbon dioxide emissions, continue to increase. The BRICS countries (Brazil, Russia, India, China, and South Africa) account for a significant share of global carbon emissions. This study focuses on the BRICS countries as the subject of inquiry, employing carbon emission-related data spanning the temporal range from 2000 to 2020. This study systematically assesses the interrelationships among variables using the Pearson correlation analysis and Granger causality test. Variables exhibiting pronounced correlation and causality, as discerned from the analytical outcomes, are subsequently identified and selected for further examination. Use the GCAM (Global Change Assessment Model) to simulate the Rcp-ref scenario (baseline scenario) and the Rcp2.6 scenario (assuming that the temperature will not rise by 2 °C), and compare the optimal paths of key variables in the two scenarios, and draw the following conclusions. (1) By the end of this century, the total carbon emissions of the BRICS countries under Rcp2.6 scenario will be 6139MTC less than those under Rcp-ref scenario. (2) Compared with the Rcp-ref scenario, in the Rcp2.6 scenario, the proportion of fossil fuels such as oil, natural gas, and coal in the main energy consumption significantly reduces, while the proportion of renewable energy sources such as wind, solar, and biomass increases. It is worth noting that the proportion of nuclear is significantly rising. (3) The demand for electricity increases significantly in the Rcp2.6 scenario. This study reveals the specific directions for improving the energy structure of BRICS countries to achieve the global average temperature rise of no more than 2 °C. It provides suggestions for the future policies of these countries' governments. In addition, BRICS countries should focus on observing changes in some factors, such as the increase in nuclear energy consumption and the increase in electricity demand. The reasons behind the changes in these factors are the direction of future research. [ABSTRACT FROM AUTHOR]

When making decisions, investors who want to buy specific companies often pay attention to the situation on the entire market or in a given industry. Therefore, the starting points are the historical and current quotations of companies and indexes published by Warsaw Stock Exchange. The capital market is a system in which there are various types of connections, in particular between companies or companies and indexes. The main goal of the paper is the examining of the dependence between quotations of the companies from the food sector and the main market indicators. The indexes taken into account are WIG, WIG20, mWIG40, sWIG80 and WIG-food. In order to answer the question whether there are dependencies between the food industry (represented by the WIG-food index) and other market segments, i.e. the segment of small, medium and large companies and the entire market, research was conducted using the Granger causality test. Various lags were considered. Previously, the autocorrelation of return rates and their stationarity were examined. The data concerned the period 2008-2019, for which the monthly rates of return were determined. The rates are calculated in standard not-logarithmic way. The period of time taken into account is after crisis time but the market situation was unstable. There were periods of downward and uprise market. The paper shows that the WSE broad market indices have an impact on the WIG-food index, but the opposite is not true. The use of Granger's causality model confirmed earlier conclusions from the analysis of the coefficients of effectiveness and linear regression models, which were the subject of earlier works by the authors and others. It occurred that one period lags had the most influence on the correlations (also two, three and six lags were examined). That is why Granger causality test were performed for these cases. The final results show that the dependences between main indexes of Warsaw Stock Exchange and WIG-food are rather weak. [ABSTRACT FROM AUTHOR]

To investigate the contribution of green credit (GC) to improving air quality in China, the bootstrap sub-sample rolling-window Granger causality test is employed. This test considers the structural changes of parameters that affect the accuracy of the conclusion and analyzes the dynamic relationship between GC and air quality. The sub-sample empirical results show that GC's infancy and mature stages have diverse impacts on the environment. In the early stage of GC, due to a low proportion of GC in overall credit and lack of the incentive to provide GC for banking institutions, the environmental effect of GC has not been demonstrated. With the continuous improvement of the GC system, the positive effects of GC on air quality begin to emerge later, which is supported by GC and air quality interaction theory. This discovery is beneficial for policymakers to find flaws in the current GC system and grasp the direction of a well-designed GC policy to construct a modern economic system with sustainable development Moreover, this paper provides another perspective that air quality has a feedback effect on GC policy. This evidence that air quality is a leading indicator helps enterprises predict banking institutions' credit preference changes and adjust financing strategies in advance. It is significant to build a virtuous cycle between GC and the environment and promote sustainable economic development. [ABSTRACT FROM AUTHOR]

The increasing level of carbon emissions is one of the most serious concerns in human history facing in today's world. Different countries adopt different policies and approaches to mitigate climate change severity. The current study evaluates the effect of carbon emissions, renewable energy sources, ICT, governance, and GDP in Morocco employing a time-series dataset over the period of 1985–2020. In this paper, we utilized the dynamic ARDL simulations model to explore the association among carbon emissions, renewable energy sources, ICT, governance, and GDP. The ARDL bounds test reveals a long-run connection among the variables. The findings suggest that renewable energy sources (i.e. solar, wind, hydroelectric), ICT, and effective governance are the key indicators to reduce carbon emissions. In addition, we utilized the Granger causality test to probe a causal connection between the study variables. The outcomes have long-run implications for carbon emissions degradation and Morocco's policy towards the fight against climate change. [ABSTRACT FROM AUTHOR]

In this paper, we examine a potential causal link between vehicle emission standards and local automotive industry performance, using a 42-country panel data set covering a 19-year period between 2000 and 2018. Our panel logit analysis shows that having a sizeable local automotive industry decreases the chance of adopting stringent vehicle emission standards, while the stock of automotive FDI increases the chance. This finding suggests that the primary motivations for vehicle emission standards may not be purely environmental. Our follow-up Granger causality test demonstrates that such non-environmental motivations have in fact translated into an actual industry-promotion outcome. Stricter emission standards in OECD member countries tend to promote local automotive output and international vehicle export with a 3-year time lag, while this causal link is limited to up-market exports for non-OECD panels. Automotive FDI hosts with large domestic markets and/or local-brand automakers also present a similar causal link, but in this case significant long-term industry-promotion effects appear only after a short-term negative shock. These results suggest that a regulation-induced short-term efficiency loss may be partly offset by a long-term productivity gain through advance/timely local adoption of cleaner technologies. • Vehicle emission standards involve non-environmental motivations, as well. • Local production volume and FDI stock affect the stringency of the standards. • Regulation-induced efficiency loss can be partly offset by long-term productivity gain. • Stricter standards tend to have pro-export effects with a 3-year lag. • The causal link depends on domestic market size and local-brand automakers. [ABSTRACT FROM AUTHOR]

• Employing panel VAR model to explore the relationship between trade shocks and productivity. • Accounting for the heterogeneity related to the level of openness and export structure. • Higher dependence on primary commodity export is associated with lower levels of productivity. • Countries with higher level of openness are likely to experience trade-led productivity gains. This paper investigates the relationship between trade openness and labor productivity by accounting for the level of openness and the structure of exports in 61 developing countries over 1999–2018. Using panel vector autoregressive and Granger causality tests, we provide new evidence on the trade-productivity literature. We show that the trade-led productivity hypothesis does not hold for all countries, and only countries with higher levels of openness display essential productivity gains. We provide evidence for both export-led productivity and productivity-driven export hypotheses in our analysis. It is also evidenced that productivity gains can make domestic market production more efficient by reducing imports and improving the structure of exports. Following the prediction of the Dutch Disease phenomenon, we demonstrate that dependence on primary commodity exports is associated with lower labor productivity. Finally, we find that commodity-based economies should improve the overall labor productivity of their economies to accelerate economic diversification. [ABSTRACT FROM AUTHOR]

This research attempts to examine the relationship between renewable and non-renewable electricity consumption, carbon dioxide (CO 2) emissions and economic policy uncertainty for the United States. For empirical analysis, the study employs monthly data for the period of 1985 M1 to 2020 M12 and used Bootstrap Rolling approach. The empirical findings revealed that, for full sample Granger causality test, there is a unidirectional causality running from electricity consumption to economic policy uncertainty and bidirectional causality between CO 2 emissions and economic policy uncertainty. The estimated parameters are statistically unstable for three models. Overall, the conclusion of study supports the sustainable development goals (SDGs: 7, 10, 13) of the United States. The study argues that policymakers and political leadership of US should be aware of climate change consequences in order to fulfill carbon neutrality target and should develop economic policies accordingly in-line with energy security, clean and greener energy for all and sustainable cleaner production objectives. Further, the uncertainty issues should be considered while designing the environmental regulations. [ABSTRACT FROM AUTHOR]