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AbstractWho influenced whom during the 2008 crisis? Did academic economists shape central bankers’ attitudes towards fiscal policy, or did central bankers impose their agenda? In an attempt to answer these questions, we analyse established attitudes and yearly topics found in speeches by bankers from six central banks and in working papers published by academic economists. Methodologically, both attitudes and topics are reconstructed through textual analysis. The results show that context matters. [ABSTRACT FROM AUTHOR]

The ability to initiate volitional action is fundamental to human behaviour. Loss of dopaminergic neurons in Parkinson's disease is associated with impaired action initiation, also termed akinesia. Both dopamine and subthalamic deep brain stimulation (DBS) can alleviate akinesia, but the underlying mechanisms are unknown. An important question is whether dopamine and DBS facilitate de novo build-up of neural dynamics for motor execution or accelerate existing cortical movement initiation signals through shared modulatory circuit effects. Answering these questions can provide the foundation for new closed-loop neurotherapies with adaptive DBS, but the objectification of neural processing delays prior to performance of volitional action remains a significant challenge. To overcome this challenge, we studied readiness potentials and trained brain signal decoders on invasive neurophysiology signals in 25 DBS patients (12 female) with Parkinson's disease during performance of self-initiated movements. Combined sensorimotor cortex electrocorticography and subthalamic local field potential recordings were performed OFF therapy (n = 22), ON dopaminergic medication (n = 18) and on subthalamic deep brain stimulation (n = 8). This allowed us to compare their therapeutic effects on neural latencies between the earliest cortical representation of movement intention as decoded by linear discriminant analysis classifiers and onset of muscle activation recorded with electromyography. In the hypodopaminergic OFF state, we observed long latencies between motor intention and motor execution for readiness potentials and machine learning classifications. Both, dopamine and DBS significantly shortened these latencies, hinting towards a shared therapeutic mechanism for alleviation of akinesia. To investigate this further, we analysed directional cortico-subthalamic oscillatory communication with multivariate granger causality. Strikingly, we found that both therapies independently shifted cortico-subthalamic oscillatory information flow from antikinetic beta (13–35 Hz) to prokinetic theta (4–10 Hz) rhythms, which was correlated with latencies in motor execution. Our study reveals a shared brain network modulation pattern of dopamine and DBS that may underlie the acceleration of neural dynamics for augmentation of movement initiation in Parkinson's disease. Instead of producing or increasing preparatory brain signals, both therapies modulate oscillatory communication. These insights provide a link between the pathophysiology of akinesia and its' therapeutic alleviation with oscillatory network changes in other non-motor and motor domains, e.g. related to hyperkinesia or effort and reward perception. In the future, our study may inspire the development of clinical brain computer interfaces based on brain signal decoders to provide temporally precise support for action initiation in patients with brain disorders. [ABSTRACT FROM AUTHOR]

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This paper proposes a panel Granger causality approach for heterogeneous panels with cross-sectional dependence. We define a panel VAR model with unobserved common factors and apply the PANIC procedure to obtain the de-factored data. We then estimate the lag augmented (LA)-VAR model for each cross section and construct the panel statistics based on the meta-analytic approach that combines the p-values of the individual statistics. The Monte Carlo simulations indicate that the combination tests show good size and power properties and appear suitable for the panels where cross sections may have different unit root or co-integration properties. We finally re-investigate Granger causality between export and economic growth in OECD countries. The results shed light on the importance of accounting for cross-sectional dependence within a factor model framework in determining direction of Granger causality for country-specific analysis. The results further reveal that export and economic growth do not cause each other in the majority of the European Union countries. [ABSTRACT FROM AUTHOR]

Across the globe, many transport bodies are advocating for increased cycling due to its health and environmental benefits. Yet, the real and perceived dangers of urban cycling remain obstacles. While serious injuries and fatalities in cycling are infrequent, "near misses"-events where a person on a bike is forced to avoid a potential crash or is unsettled by a close vehicle-are more prevalent. To understand these occurrences, researchers have turned to naturalistic studies, attaching various sensors like video cameras to bikes or cyclists. This sensor data holds the potential to unravel the risks cyclists face. Still, the sheer amount of video data often demands manual processing, limiting the scope of such studies. In this paper, we unveil a cutting-edge computer vision framework tailored for automated near-miss video analysis and for detecting various associated risk factors. Additionally, the framework can understand the statistical significance of various risk factors, providing a comprehensive understanding of the issues faced by cyclists. We shed light on the pronounced effects of factors like glare, vehicle and pedestrian presence, examining their roles in near misses through Granger causality with varied time lags. This framework enables the automated detection of multiple factors and understanding their significant weight, thus enhancing the efficiency and scope of naturalistic cycling studies. As future work, this research opens the possibility of integrating this AI framework into edge sensors through embedded AI, enabling real-time analysis. [ABSTRACT FROM AUTHOR]

Causal multivariate time-series analysis, combined with network theory, provide a powerful tool for studying complex ecological interactions. However, these methods have limitations often underestimated when used in graphical modelling of ecological systems. In this opinion article, I examine the relationship between formal logic methods used to describe causal networks and their inherent statistical and epistemological limitations. I argue that while these methods offer valuable insights, they are restricted by axiomatic assumptions, statistical constraints and the incompleteness of our knowledge. To prove that, I first consider causal networks as formal systems, define causality and formalize their axioms in terms of modal logic and use ecological counterexamples to question the axioms. I also highlight the statistical limitations when using multivariate time-series analysis and Granger causality to develop ecological networks, including the potential for spurious correlations among other data characteristics. Finally, I draw upon Gödel's incompleteness theorems to highlight the inherent limits of fully understanding complex networks as formal systems and conclude that causal ecological networks are subject to initial rules and data characteristics and, as any formal system, will never fully capture the intricate complexities of the systems they represent. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'. [ABSTRACT FROM AUTHOR]

Primary open-angle glaucoma (POAG) is accompanied with gray matter (GM) changes across the brain. However, causal relationships of the GM changes have not been fully understood. Our aim was to investigate the causality of GM progressive changes in POAG using Granger causality (GC) analysis and structural MRI. Structural MRI from 20 healthy controls and 30 POAG patients with elevated intraocular pressure (IOP) were collected. We performed voxel-wise GM volume comparisons between control and POAG groups, and between control and four POAG subgroups (categorized by IOP). Then, we sequenced the structural MRI data of all POAG patients and conducted both voxel-wise and region of interest (ROI)-wise GC analysis to investigate the causality of GM volume changes in POAG brain. Compared to healthy controls, reduced GM volumes across the brain were found, GM volume enlargements in the thalamus, caudate nucleus and cuneus were also observed in POAG brain (false discovery rate (FDR) corrected at q< 0.05). As IOP elevated, the reductions of GM volume were more severe in the cerebellum and frontal lobe. GC analysis revealed that the bilateral cerebellum, visual cortices, and the frontal regions served independently as primary hubs of the directional causal network, and projected causal effects to the parietal and temporal regions of the brain (FDR corrected at q < 0.05). POAG exhibits progressive GM alterations across the brain, with oculomotor regions and visual cortices as independent primary hubs. The current results may deepen our understanding of neuropathology of POAG. [ABSTRACT FROM AUTHOR]

The study aims to investigate the impact of agricultural production on environmental degradation in the case of India, an emerging market economy, based on time series data from 1990 to 2020. Methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) emissions have been used as indicators of degradation. Autoregressive distributive lag bound tests examine the long-run cointegrating relationship among the variables. To investigate the existence of the environmental Kuznets curve (EKC) between agricultural production and CH4, N2O, and CO2, we used a fully modified ordinary least squares (FMOLS) technique, and the robustness of the results of FMOLS were checked by dynamic ordinary least squares estimators. We also used Granger causality to check for unidirectional and bidirectional causalities. Results indicate an inverted U-shaped relationship in the case of both CH4 and N2O emission, thus confirming the EKC hypothesis. The relationship of CO2 emission with agricultural production does not verify the EKC hypothesis, and we find a U-shaped relation in the long-run. Lastly, policy measures have been suggested to mitigate greenhouse gas emissions from agricultural activities that may help attain a more sustainable economy. JEL Classification Q1, Q22, Q23, Q53, C33 [ABSTRACT FROM AUTHOR]

Epilepsy is one of the most common clinical diseases of the nervous system. The occurrence of epilepsy will bring many serious consequences, and some patients with epilepsy will develop drug-resistant epilepsy. Surgery is an effective means to treat this kind of patients, and lesion localization can provide a basis for surgery. The purpose of this study was to explore the functional types and connectivity evolution patterns of relevant regions of the brain during seizures. We used intracranial EEG signals from patients with epilepsy as the research object, and the method used was GRU-GC. The role of the corresponding area of each channel in the seizure process was determined by the introduction of group analysis. The importance of each area was analysed by introducing the betweenness centrality and PageRank centrality. The experimental results show that the classification method based on effective connectivity has high accuracy, and the role of the different regions of the brain could also change during the seizures. The relevant methods in this study have played an important role in preoperative assessment and revealing the functional evolution patterns of various relevant regions of the brain during seizures. [ABSTRACT FROM AUTHOR]

Homeostatic regulation plays a fundamental role in maintenance of multicellular life. At different scales and in different biological systems, this principle allows a better understanding of biological organization. Consequently, a growing interest in studying cause-effect relations between physiological systems has emerged, such as in the fields of cardiovascular and cardiorespiratory regulations. For this, mathematical approaches such as Granger causality (GC) were applied to the field of cardiovascular physiology in the last 20 years, overcoming the limitations of previous approaches and offering new perspectives in understanding cardiac, vascular and respiratory homeostatic interactions. In clinical practice, continuous recording of clinical data of hospitalized patients or by telemetry has opened new applicability for these approaches with potential early diagnostic and prognostic information. In this review, we describe a theoretical background of approaches based on linear GC in time and frequency domains applied to detect couplings between time series of RR intervals, blood pressure and respiration. Interestingly, these tools help in understanding the contribution of homeostatic negative feedback and the anticipatory feedforward mechanisms in homeostatic cardiovascular and cardiorespiratory controls. We also describe experimental and clinical results based on these mathematical tools, consolidating previous experimental and clinical evidence on the coupling in cardiovascular and cardiorespiratory studies. Finally, we propose perspectives allowing to complete the understanding of these interactions between cardiovascular and cardiorespiratory systems, as well as the interplay between brain and cardiac, and vascular and respiratory systems, offering a high integrative view of cardiovascular and cardiorespiratory homeostatic regulation. [ABSTRACT FROM AUTHOR]

AbstractThis special issue is a collection of papers inspired by Dr. Molenaar’s work and innovations – a tribute to his passion for advancing science and his ability to ignite a spark of creativity and innovation in multiple generations of scientists. Following Dr. Molenaar’s creative breadth, the papers address a wide variety of topics – sharing of new methodological developments, ideas, and findings in idiographic science, study of intraindividual variation, behavioral genetics, model inference/identification/selection, and more. [ABSTRACT FROM AUTHOR]

The evolution of temperature anomalies in the North Atlantic (15° N – 70° N 80° W – 8° W) is analyzed. The results are based on the decomposition of the GECCO3 (1948–2018), ORA-S5 (1979–2018) and SODA3.12.2 (1980–2017) oceanic reanalyses data using extended empirical orthogonal functions. The leading pattern during the entire 16–month period (January–April of the next year) in the 0–300 m layer is a tripole, with temperature anomalies of the same sign in the tropical and high latitudes and the opposite sign in the subtropical gyre. The evolution of the leading pattern shows the deepening of temperature anomalies in winter, their preservation with a maximum in the summer seasonal thermocline (~ 65–90 m) and partial weakening in the subsurface layer in summer, and their emergence on the ocean surface in the subsequent autumn–winter season. The temporal evolution of the reemergence of the tripole pattern of the sea surface temperature anomalies (SSTAs), explained by the first extended principal component (EPC1), is in good agreement with the variability of the main atmospheric circulation mode over the North Atlantic on interannual scales – the North Atlantic Oscillation (NAO), especially after 1979. The recurrence of SSTAs is found in all three centers of action of the tripole pattern. In the subpolar and midlatitude centers of action, the reemerging signal maximum appears in the subsurface layer in late summer and early autumn and in the subsequent autumn–winter season ~ 2/3 of this signal occurs at the surface. In the subtropical center of action, the reemerging signal maximum appears in the mixed layer in winter–spring and in the subsequent autumn–winter season ~ 1/2 of this signal occurs at the surface. For the subtropical center of action, a moderate correlation was found between the regional EPC1 and the mixed layer depth and the wind stress modulus over a 16–month period. Recurrence of temperature anomalies in all centers of action of the tripole structure from January 2014 to April 2015 associated with the repeated positive NAO phase is shown. [ABSTRACT FROM AUTHOR]

Climate change poses an unprecedented global challenge, which prompts nations to adopt new strategies to mitigate greenhouse gas emissions. The European Union emissions trading system (EU ETS) is a cornerstone of the EU's efforts towards a cost-effective fight against climate change. This study examines the effectiveness of the EU ETS by analyzing monthly data from December 2008 to December 2021, with the focus on CO2 emission allowance futures prices, renewable energy indices, coal prices, oil prices, and fossil energy indices. The key findings are as follows: The CO2 emission allowance futures prices have averaged EUR 14.83 per ton, ranging from EUR 2.87 to EUR 76.81, which shows a significant upward trend. The renewable energy index also demonstrated strong growth, with a mean 1562.07 and maximum 4571.96. Coal prices have averaged EUR 65.32 per ton, while Brent oil prices averaged EUR 59.85 per barrel. A cointegration analysis revealed a long-run equilibrium relationship between these variables. The Vector Error Correction model (VECM) revealed significant negative responses to long-run equilibrium deviations of the renewable energy index (−0.0155) and oil prices (−0.0236), a significant negative short-run response of CO2 prices to their own lagged values (−0.223), and a significant positive short-run effect of oil prices on the fossil energy index (0.254). These results suggest the EU ETS has created significant linkages between carbon, energy, and financial markets. The study concludes that while the EU ETS has made progress in motivating emissions reductions and promoting renewable energy, the system's efficacy still needs improvement. [ABSTRACT FROM AUTHOR]

Abstracting causal knowledge from process measurements has become an appealing topic for decades, especially for fault root cause analysis (RCA) based on signals recorded by multiple sensors in a complex system. Although many causality detection methods have been developed and applied in different fields, some research communities may have an idiosyncratic implementation of their preferred methods, with limited accessibility to the wider community. Targeting interested experimental researchers and engineers, this paper provides a comprehensive comparison of data-based causality detection methods in root cause diagnosis across two distinct domains. We provide a possible taxonomy of those methods followed by descriptions of the main motivations of those concepts. Of the two cases we investigated, one is a root cause diagnosis of plant-wide oscillations in an industrial process, while the other is the localization of the epileptogenic focus in a human brain network where the connectivity pattern is transient and even more complex. Considering the differences in various causality detection methods, we designed several sets of experiments so that for each case, a total of 11 methods could be appropriately compared under a unified and reasonable evaluation framework. In each case, these methods were implemented separately and in a standard way to infer causal interactions among multiple variables to thus establish the causal network for RCA. From the cross-domain investigation, several findings are presented along with insights into them, including an interpretative pitfall that warrants caution. [ABSTRACT FROM AUTHOR]

The prospect of decoupling economic development from CO2 emissions in Ghana is examined in this paper, with an emphasis on the manufacturing, export, and adoption of renewable energy sectors. The paper investigates the long-term and short-term correlations among CO2 emissions, renewable energy consumption (RNE), population growth (POP), manufacturing value-added (MVA), Economic growth (GDP), and exports (EXP) using an Autoregressive Distributed Lag (ARDL) bounds testing method using time series data from 1990 to 2020. The outcomes show that the variables are long-term cointegrating. Long-term GDP and CO2 emissions show a positive but negligible correlation, whereas exports show a negative and insignificant correlation. In the near term, using renewable energy has a markedly undesirable consequence on releases, yet, over time, the connection is positive and negligible. Because the turning point when economic expansion results in lower emissions is not apparent, the Environmental Kuznets Curve concept is not entirely substantiated. The report emphasizes how specific laws encouraging the use of renewable energy, environmentally friendly export and manufacturing processes, and technical advancements are necessary to help Ghana move toward a low-carbon economy. [ABSTRACT FROM AUTHOR]

In this study, we analyzed the impact of government regulatory institutions on households' natural gas use behavior and suggested that the conventional view of natural gas as a social utility is inappropriate. Pursuing this goal, we applied correlation analysis, regression analysis and the Granger causality test to assess the statistically significant impact of particular factors (environmental temperature, price and tariff on natural gas) on household gas consumption. Our study was based on the data on household gas consumption in 2019–2022. Ultimately, the lowest rate of influence was recorded by the Granger causality test (2.47%), compared to 6.88% in the test for the significance of the correlation coefficient and 9.23% in the t-test for the statistical significance of the regression coefficients. One has to note that the Granger causality test used in our study is considered the most sensitive model for analyzing economic data. Using statistical methods, we concluded that regulatory factors have a negligible impact on the volume of natural gas consumption by households. Our results suggest that the Ukrainian regulatory authorities should be cautious about using non-market mechanisms, such as price caps, in the energy sector. [ABSTRACT FROM AUTHOR]

This article, published in Applied Economics Letters, investigates the impact of weather risks on food prices in the continental US. The study uses a time-varying Granger causality test and a unique extreme weather risk index called the Actuaries Climate Index (ACI). The results indicate that weather disruptions, such as drought and heat, can lead to increases in food prices. The article provides detailed information on the dataset, research methodology, and empirical findings. The findings have implications for traders, investors, and policymakers, emphasizing the importance of diversifying food exports to mitigate the negative effects of weather risks. Additionally, the article includes supplementary materials on data descriptions and descriptive statistics of the variables used in the analysis. [Extracted from the article]

Abstract Background The relationship between alcohol consumption and mental health is complex; drinking may exacerbate anxiety, and in turn, anxiety can lead to excessive drinking. This study explores the relationship between alcohol consumption patterns including wine, beer, and spirits, and anxiety prevalence in selected 13 South American nations. Methods This study utilises secondary data spanning 29 years from 1991 to 2019 obtained from the Our World in Data database. It investigates the causal link between the prevalence of anxiety and alcohol consumption in the selected countries using the Granger causality test. Results Anxiety was found to have a unidirectional effect on wine and beer consumption in Chile, Suriname, Uruguay, and Trinidad and Tobago. Additionally, drinking alcohol consumption appears to impact anxiety levels in Brazil. Argentina demonstrates a bidirectional relationship between anxiety and all three types of alcohol consumption, with similar patterns observed in Brazil (wine and beer), Chile (spirits), and Paraguay (spirits). Conclusion No significant causal relationships for alcohol consumption patterns were found in other nations. The identified Granger causal links follow four distinct directions in this study. These findings provide valuable insights for policymakers, governments, and international investors for informed decision-making regarding regulation and policy tools.

Abstract Across the globe, many transport bodies are advocating for increased cycling due to its health and environmental benefits. Yet, the real and perceived dangers of urban cycling remain obstacles. While serious injuries and fatalities in cycling are infrequent, “near misses”-events where a person on a bike is forced to avoid a potential crash or is unsettled by a close vehicle-are more prevalent. To understand these occurrences, researchers have turned to naturalistic studies, attaching various sensors like video cameras to bikes or cyclists. This sensor data holds the potential to unravel the risks cyclists face. Still, the sheer amount of video data often demands manual processing, limiting the scope of such studies. In this paper, we unveil a cutting-edge computer vision framework tailored for automated near-miss video analysis and for detecting various associated risk factors. Additionally, the framework can understand the statistical significance of various risk factors, providing a comprehensive understanding of the issues faced by cyclists. We shed light on the pronounced effects of factors like glare, vehicle and pedestrian presence, examining their roles in near misses through Granger causality with varied time lags. This framework enables the automated detection of multiple factors and understanding their significant weight, thus enhancing the efficiency and scope of naturalistic cycling studies. As future work, this research opens the possibility of integrating this AI framework into edge sensors through embedded AI, enabling real-time analysis.

This paper examines the difference between pure contagion and financial interconnection by studying the U.S. and some American and Asian markets in the subprime crisis context. These markets are affected by the mortgage crisis, with data available from January 1, 2003 to December 30, 2011. The paper first identifies the turmoil period via the wavelet technique and adopts cointegration and Granger causality approaches by estimating vector autoregressive (VAR) and vector error correction models (VECM) models. Based on daily returns from stock market indices in five American countries (Mexico, Brazil, Canada, Argentina, and the U.S.) and eight Asian ones (Hong Kong, Japan, India, Indonesia, Malaysia, Singapore, Korea, and China), the results show eight cases of pure contagion and 10 cases of financial interconnection. In addition, there were high co-movements in the short term and low co-movements in the long term for financial interconnection cases. These findings have several implications for investors looking to diversify their portfolios internationally and for portfolio managers to expect and limit market risk. The results provide additional guidance to regulators and policymakers.

The prospect of decoupling economic development from CO2 emissions in Ghana is examined in this paper, with an emphasis on the manufacturing, export, and adoption of renewable energy sectors. The paper investigates the long-term and short-term correlations among CO2 emissions, renewable energy consumption (RNE), population growth (POP), manufacturing value-added (MVA), Economic growth (GDP), and exports (EXP) using an Autoregressive Distributed Lag (ARDL) bounds testing method using time series data from 1990 to 2020. The outcomes show that the variables are long-term cointegrating. Long-term GDP and CO2 emissions show a positive but negligible correlation, whereas exports show a negative and insignificant correlation. In the near term, using renewable energy has a markedly undesirable consequence on releases, yet, over time, the connection is positive and negligible. Because the turning point when economic expansion results in lower emissions is not apparent, the Environmental Kuznets Curve concept is not entirely substantiated. The report emphasizes how specific laws encouraging the use of renewable energy, environmentally friendly export and manufacturing processes, and technical advancements are necessary to help Ghana move toward a low-carbon economy.

Abstract Epilepsy is one of the most common clinical diseases of the nervous system. The occurrence of epilepsy will bring many serious consequences, and some patients with epilepsy will develop drug-resistant epilepsy. Surgery is an effective means to treat this kind of patients, and lesion localization can provide a basis for surgery. The purpose of this study was to explore the functional types and connectivity evolution patterns of relevant regions of the brain during seizures. We used intracranial EEG signals from patients with epilepsy as the research object, and the method used was GRU-GC. The role of the corresponding area of each channel in the seizure process was determined by the introduction of group analysis. The importance of each area was analysed by introducing the betweenness centrality and PageRank centrality. The experimental results show that the classification method based on effective connectivity has high accuracy, and the role of the different regions of the brain could also change during the seizures. The relevant methods in this study have played an important role in preoperative assessment and revealing the functional evolution patterns of various relevant regions of the brain during seizures.

ABSTRACTAcademic discussions have frequently examined the interrelation between regional employment growth and firm locations. Two growth patterns emerge: employment growth induced through new firm locations or vice versa, where firms locate in areas experiencing employment supply growth. The specific causal relationship responsible for regional employment growth in Germany remains uncertain. In the German context, however, more research is needed to identify contributors to employment growth, as most existing studies rely on highly aggregated data or focus on specific case studies. This paper aims to approach this subject by using a uniquely matched dataset of firm locations and the individual employment of 480 multi-locational firms in the knowledge economy and comparing it to total employment in Germany. We assume that a change in knowledge-intensive firms’ employment may affect regional employment growth. The study uses longitudinal historical employment data at the functional urban area (FUA) level from 1999 to 2019, aggregated to knowledge-intensive high-tech and advanced producer services (APS) sectors. The analysis employs aggregated and individual Granger causality tests, evaluating the relationship between employment in knowledge-intensive sectors and overall employment change. Results are spatialised using GIS to provide evidence of where the Granger causalities occur at the FUA level in Germany. Findings indicate that, in general, knowledge-intensive employment growth Granger causes total employment growth in a few economically more active FUAs. In contrast, for a greater number of FUAs, total employment Granger causes knowledge-intensive employment.

In this article, we investigate the relationship between the concept of Granger causality and the notion of prediction space. In particular, we obtain three characterizations of the condition of non causality based on the concept of predictor space. [ABSTRACT FROM AUTHOR]

The popularity of econometric analyses that include variables obtained from text mining is growing rapidly. A frequently applied approach is to identify topics from large corpora, which makes it possible to determine trends that reflect the changing relevance of topics over time. We address the question of whether such topic trends are linked to quantitative economic indicators typically used for analysing the objects described by a topic. The analysis is based on academic economic articles from Poland and Germany from 1984 to 2020. There is a specific focus on whether relationships between topic trends and indicators are similar across national economies. The connection between topic trends and indicators is analysed using vector autoregressive models and Granger causality tests.

Abstract In today's highly advanced industrialised and modernised world, China's economy is still growing, and its demand for energy is increasing daily. It is crucial to examine the connection between energy consumption, carbon emissions, and economic growth in order to promote economic growth based on energy conservation and emission reduction. Using Dezhou City in Shandong Province as an example, the study builds a VAR model of carbon emission, energy consumption, and economic growth in Dezhou City based on simplified macroeconomic sub-models, energy sub-models, and environmental sub-models. It then determines the correlation and influence mechanism between the three using tests like ADF unit root and Granger causality. The pertinent elements affecting Dezhou's carbon emissions were then investigated using grey correlation analysis. Finally, based on the study's findings, policy suggestions are made regarding energy use, carbon emissions, and economic expansion. It is necessary not only to restrain high-energy consumption industries and fundamentally optimize the energy consumption structure, but also to find new economic growth points and improve economic growth channels, so as to optimize the industrial structure. In this process, increasing the proportion of the tertiary industry is a key measure. In addition, the government needs to advocate the citizens to adopt a low-carbon lifestyle, and the concept of low-carbon environmental protection will be deeply rooted in the hearts of the people. This study will provide suggestions and theoretical guidance for China's energy consumption and carbon emissions, and help achieve high-quality growth of China and even the world economy.

Yuping Zhu,1 Xunfu Lai,1 Mengting Wang,2 Xin Tang,1 Tianyi Wan,3 Bin Li,1 Xiaoming Liu,1 Jialin Wu,1 Lei He,1 Yulin He1 1Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, People’s Republic of China; 2Department of Radiology, Yichang Central People’s Hospital, Yichang, People’s Republic of China; 3Department of Radiology, Jiangxi Provincial People’s Hospital, Nanchang, People’s Republic of ChinaCorrespondence: Yulin He, Department of Radiology, The First Affiliated Hospital of Nanchang University, 17 Yongwaizheng Street, Nanchang, Jiangxi, 330006, People’s Republic of China, Tel +86 0791 8869 3802, Email ndyfy02171@ncu.edu.cnObjective: To separate the resting-state network of patients with dental pain using independent component analysis (ICA) and analyze abnormal changes in functional connectivity within as well as between the networks.Patients and Methods: Twenty-three patients with dental pain and 30 healthy controls participated in this study. We extracted the resting-state functional network components of both using ICA. Functional connectivity differences within 14 resting-state brain networks were analyzed at the voxel level. Directional interactions between networks were analyzed using Granger causality analysis. Subsequently, functional connectivity values and causal coefficients were assessed for correlations with clinical parameters.Results: Compared to healthy controls, we found enhanced functional connectivity in the left superior temporal gyrus of anterior protrusion network and the right Rolandic operculum of auditory network in patients with dental pain (p< 0.01 and cluster-level p< 0.05, Gaussian random field corrected). In contrast, functional connectivity of the right precuneus in the precuneus network was reduced, and were significantly as well as negatively correlated to those of the Visual Analogue Scale (r=− 4.93, p=0.017), Hamilton Anxiety Scale (r=− 0.46, p=0.027), and Hamilton Depression Scale (r=− 0.563, p< 0.01), using the Spearman correlation analysis. Regarding the causal relationship between resting-state brain networks, we found increased connectivity from the language network to the precuneus in patients with dental pain (p< 0.05, false discovery rate corrected). However, the increase in causal coefficients from the verbal network to the precuneus network was independent of clinical parameters.Conclusion: Patients with toothache exhibited abnormal functional changes in cognitive-emotion-related brain networks, such as the salience, auditory, and precuneus networks, thereby offering a new imaging basis for understanding central neural mechanisms in dental pain patients.Keywords: toothache, independent component analysis, resting-state networks, Granger causality

Background: Neuropsychopharmacological compounds may exert complex brain-wide effects due to an anatomically and genetically broad expression of their molecular targets and indirect effects via interconnected brain circuits. Electrophysiological measurements in multiple brain regions using electroencephalography (EEG) or local field potential (LFP) depth-electrodes may record fingerprints of such pharmacologically-induced changes in local activity and interregional connectivity (pEEG/pLFP). However, in order to reveal such patterns comprehensively and potentially derive mechanisms of therapeutic pharmacological effects, both activity and connectivity have to be estimated for many brain regions. This entails the problem that hundreds of electrophysiological parameters are derived from a typically small number of subjects, making frequentist statistics ill-suited for their analysis. Methods: We here present an optimized interpretable machine-learning (ML) approach which relies on predictive power in individual recording sequences to extract and quantify the robustness of compound-induced neural changes from multi-site recordings using Shapley additive explanations (SHAP) values. To evaluate this approach, we recorded LFPs in mediodorsal thalamus (MD), prefrontal cortex (PFC), dorsal hippocampus (CA1 and CA3), and ventral hippocampus (vHC) of mice after application of amphetamine or of the dopaminergic antagonists clozapine, raclopride, or SCH23390, for which effects on directed neural communication between those brain structures were so far unknown. Results: Our approach identified complex patterns of neurophysiological changes induced by each of these compounds, which were reproducible across time intervals, doses (where tested), and ML algorithms. We found, for example, that the action of clozapine in the analysed cortico-thalamohippocampal network entails a larger share of D1--as opposed to D2-receptor induced effects, and that the D2-antagonist raclopride reconfigures connectivity in the delta-frequency band. Furthermore, the effects of amphetamine and clozapine were surprisingly similar in terms of decreasing thalamic input to PFC and vHC, and vHC activity, whereas an increase of dorsal-hippocampal communication and of thalamic activity distinguished amphetamine from all tested anti-dopaminergic drugs. Conclusion: Our study suggests that communication from the dorsal hippocampus scales proportionally with dopamine receptor activation and demonstrates, more generally, the high complexity of neuropharmacological effects on the circuit level. We envision that the presented approach can aid in the standardization and improved data extraction in pEEG/pLFP-studies. [ABSTRACT FROM AUTHOR]

Changing patterns of risk aversion may follow a non-linear counter-cyclical process. However, the evidence so far has not considered developing cryptocurrency markets. Given some unique features of cryptocurrencies, it is interesting to distinguish how these assets differ from traditional products. This paper investigates the time effects of periodicity on risk aversion for a selection of major cryptocurrencies compared to major financial assets. Significant periodic time-varying patterns are identified when analysing risk aversion. Further, bilateral and bidirectional Granger causalities are identified within cryptocurrencies, as well as between cryptocurrencies and traditional financial assets. Bitcoin is identified as a leading information transmitter of the spillover of risk aversion upon other cryptocurrencies, while estimated risk aversion of traditional financial markets plays a dominant role in the spillover processes upon the cryptocurrency cluster. The latter finding presents further evidence of developing cryptocurrency market maturity. The COVID-19 pandemic is found to have significantly influenced the connectedness of risk aversion among cryptocurrency and traditional financial markets. [ABSTRACT FROM AUTHOR]

By using annual time series data spanning 1995 to 2021, this study examines the key factors that influence foreign direct investment (FDI) inflows to Malaysia. This study employed the conventional determinants of FDI and incorporated an under-studied corruption variable to capture the political impact on FDI inflows to Malaysia. The ARDL bounds test results identified short- and long-run positive relationships between FDI inflows and two tested variables: market size and education. A positive long-run relationship was also found between inflation rates and FDI inflows. By contrast, infrastructure facilities were found to be negatively related to FDI inflows in the long run. More importantly, the results ascertained that higher corruption levels hamper FDI inflows to Malaysia in the long term. Moreover, the Granger causality test revealed that market size, inflation rate, and infrastructure facilities are critical causal factors that explain the fluctuations in FDI inflows to Malaysia. In light of the results obtained, some policy recommendations are highlighted to help enhance the attractiveness of FDI, thereby stimulating economic growth in Malaysia. [ABSTRACT FROM AUTHOR]

Nonrenewable energy sources have been shown to be a cause of conflict and terrorism, highlighting the global conflict aspect, but little is known about the causal relationship between the energy system and terrorism in Turkey. This study aims to fill this gap by examining the causal links among renewable energy consumption, fossil fuels, terrorist attacks, education, trade opening, and geopolitical risks in Turkey from 1980 to 2016. Using the autoregressive distributed lag (ARDL) approach and Granger causality tests, the study analyzes the short and long‐term relationships between the variables. Additionally, robustness tests are conducted using a powerful multiresolution ARDL approach to ensure the stability of the statistical findings. The results reveal the existence of long‐term relationships between all the variables, particularly among terrorism, renewable energy, and education. In the short term, a one‐way relationship exists between terrorism and education to renewable energies and from trade openness to terrorism. The study demonstrates that nonrenewable energy increases terrorism in the long term, whereas renewable energy and trade openness reduce terrorism, highlighting the potential impact of global conflicts on Turkey's sustainable development. Therefore, renewable energy is a powerful tool to fight against terrorism, and Turkey has encouraged its use and deployment of diplomatic efforts to resolve political and military conflicts, particularly in the Middle East. This study provides insights into the complex relationship among sustainable energy consumption, terrorism, education, and trade opening, contributing to the understanding of the geopolitical risks and economics in Turkey. It has implications for policymakers in the region, highlighting the importance of renewable energy and trade openness as tools for conflict resolution and sustainable development in the face of global conflicts. [ABSTRACT FROM AUTHOR]

We study Granger causality testing for high-dimensional time series using regularized regressions. To perform proper inference, we rely on heteroskedasticity and autocorrelation consistent (HAC) estimation of the asymptotic variance and develop the inferential theory in the high-dimensional setting. To recognize the time-series data structures, we focus on the sparse-group LASSO (sg-LASSO) estimator, which includes the LASSO and the group LASSO as special cases. We establish the debiased central limit theorem for low-dimensional groups of regression coefficients and study the HAC estimator of the long-run variance based on the sg-LASSO residuals. This leads to valid time-series inference for individual regression coefficients as well as groups, including Granger causality tests. The treatment relies on a new Fuk–Nagaev inequality for a class of τ -mixing processes with heavier than Gaussian tails, which is of independent interest. In an empirical application, we study the Granger causal relationship between the VIX and financial news. [ABSTRACT FROM AUTHOR]

In recent years, there has been intense debate in the literature as to whether the Atlantic Multidecadal Oscillation (AMO) is a genuine representation of natural climate variability or is substantially driven by external factors. Here, we perform an analysis of the influence of external (natural and anthropogenic) forcings on the AMO behaviour by means of a linear Granger causality analysis and by a nonlinear extension of this method. Our results show that natural forcings do not have any causal role on AMO in both linear and nonlinear analyses. Instead, a certain influence of anthropogenic forcing is found in a linear framework. [ABSTRACT FROM AUTHOR]

This paper focuses on the sovereign crisis of the Euro debt crisis era, and we address the existence of the relationship of CDS and bond markets sovereign credit risk pricing for selected core and periphery EMU countries, during and after the 2009 EMU crisis. We study this relationship in conjunction to geopolitical risk as a measure of macroeconomic uncertainty. We use daily observations for several bond maturities and CDS premium with reference to the core (France and Germany) versus periphery EMU countries (Portugal, Italy, Ireland Spain, and Greece) for the period 2009 to 2014. To measure global geopolitical risk, we employ the Caldara and Iacoviello (2022) global geopolitics index (GPR). Using alternative econometric approaches, we find adequate evidence of volatility spillovers between the geopolitical risk index and sovereign risk markets mainly during the crisis period (2009–2012) and weaker during the easing of the eurozone debt crisis period (2012–2014). Moreover, based on Granger causality the estimation of the short‐ term dynamics reveals a significant linkage during the post‐crisis period rather than during crisis. During the crisis period, we found significant dynamic responses between GPR and bond yields. [ABSTRACT FROM AUTHOR]

In investigating global patterns of biodiversity through deep time, many large-scale drivers of diversification have been proposed, both biotic and abiotic. However, few robust conclusions about these hypothesized effectors or their roles have been drawn. Here, we use a linear stochastic differential equation (SDE) framework to test for the presence of underlying drivers of diversification patterns before examining specific hypothesized drivers. Using a global dataset of observations of skeletonized marine fossils, we infer origination, extinction and sampling rates (collectively called fossil time series) throughout the Phanerozoic using a capture–mark–recapture approach. Using linear SDEs, we then compare models including and excluding hidden (i.e. unmeasured) drivers of these fossil time series. We find evidence of large-scale underlying drivers of marine Phanerozoic diversification rates and present quantitative characterizations of these. We then test whether changing global temperature, sea-level, marine sediment area or continental fragmentation could act as drivers of the fossil time series. We show that it is unlikely any of these four abiotic factors are the hidden drivers we identified, though there is evidence for correlative links between sediment area and origination/extinction rates. Our characterization of the hidden drivers of Phanerozoic diversification and sampling will aid in the search for their ultimate identities. [ABSTRACT FROM AUTHOR]

In today's highly advanced industrialised and modernised world, China's economy is still growing, and its demand for energy is increasing daily. It is crucial to examine the connection between energy consumption, carbon emissions, and economic growth in order to promote economic growth based on energy conservation and emission reduction. Using Dezhou City in Shandong Province as an example, the study builds a VAR model of carbon emission, energy consumption, and economic growth in Dezhou City based on simplified macroeconomic sub-models, energy sub-models, and environmental sub-models. It then determines the correlation and influence mechanism between the three using tests like ADF unit root and Granger causality. The pertinent elements affecting Dezhou's carbon emissions were then investigated using grey correlation analysis. Finally, based on the study's findings, policy suggestions are made regarding energy use, carbon emissions, and economic expansion. It is necessary not only to restrain high-energy consumption industries and fundamentally optimize the energy consumption structure, but also to find new economic growth points and improve economic growth channels, so as to optimize the industrial structure. In this process, increasing the proportion of the tertiary industry is a key measure. In addition, the government needs to advocate the citizens to adopt a low-carbon lifestyle, and the concept of low-carbon environmental protection will be deeply rooted in the hearts of the people. This study will provide suggestions and theoretical guidance for China's energy consumption and carbon emissions, and help achieve high-quality growth of China and even the world economy. [ABSTRACT FROM AUTHOR]

The unprecedented growth in the export of services and its accompanying economic incentives has invited discussions on service export determinants. Against this background, the present article is an attempt to test whether the contemporary theories on causal links between exports, financial development and growth hold for services export. For realizing the objective, we employed the panel cointegration technique for Organization for Economic Co-operation and Development (OECD) countries over a period of 23 years (2000–22). Findings of both first and second generation cointegration test indicate a long run equilibrium between services export, financial development and growth. However, there are differences in the short and long association, as outcomes of panel auto autoregressive distributed lag (ARDL) approach show financial development to be significant determinant of services export only over long run. Additionally, granger causality test suggests a feedback mechanism prevalent between services export-financial development, services export-growth and financial development-growth. The empirical evidence offered in the study elucidates the significance of continuously developing the financial markets and institutions for achieving sustained growth in export of services. The bi-directional relationship between the variables also presents an opportunity to strengthen economic growth and financial development process through policies aimed at expanding services export. [ABSTRACT FROM AUTHOR]

Taking a sample of 24 OECD countries, using a cointegrated panel, empirical evidence is provided at group and individual level of the positive effect of spending on research, development and innovation on economic growth during the 2000-2019 period. Assuming that patents act as a proxy for innovation and using the ordinary least squares dynamic estimator, the existence of a long-term equilibrium relationship is corroborated by which, and in per capita terms, an increase of 1.0% in the stock of patents generates an increase in GDP of 0.52%. Similarly, an increase in R&D spending of 1.0% translates into GDP growth of 1.27%. Additionally, by implementing a Granger causality test for a panel, a positive and significant relationship is found between R&D spending and the stock of patents, patent stock and economic growth, and R&D spending and economic growth. [ABSTRACT FROM AUTHOR]

Background: Motor dysfunctions in children with cerebral palsy (CP) are caused by nonprogressive brain damage. Understanding the functional characteristics of the brain is important for rehabilitation. Purpose: This paper aimed to study the brain networks of children with CP during bilateral lower limb movement using functional near‐infrared spectroscopy (fNIRS) and to explore effective fNIRS indices for reflecting functional brain activity. Methods: Using fNIRS, cerebral oxygenation signals in the bilateral prefrontal cortex (LPFC/RPFC) and motor cortex (LMC/RMC) were recorded from fifteen children with spastic CP and seventeen children with typical development (CTDs) in the resting state and during bilateral lower limb movement. Functional connectivity matrices based on phase‐locking values (PLVs) were calculated using Hilbert transformation, and binary networks were constructed at different sparsity levels. Network metrics such as the clustering coefficient, global efficiency, local efficiency, and transitivity were calculated. Furthermore, the time‐varying curves of network metrics during movement were obtained by dividing the time window and using sparse inverse covariance matrices. Finally, conditional Granger causality (GC) was used to explore the causal relationships between different brain regions. Results: Compared to CTDs, the connectivity between RMC‐RPFC (p = 0.017) and RMC‐LMC (p = 0.002) in the brain network was decreased in children with CP, and the clustering coefficient (p = 0.003), global efficiency (p = 0.034), local efficiency (p = 0.015), and transitivity (p = 0.009) were significantly lower. The standard deviation of the changes in global efficiency of children with CP during motion was also greater than that of CTDs. Using GC, it was found that there was a significant increase in causal strength from the RMC to the RPFC (p = 0.04) and from the RMC to the LMC (p = 0.042) in children with CP during motion. Additionally, there were significant negative correlations between the PLV of LMC‐RMC (p = 0.002) and the Gross Motor Function Classification System (GMFCS) and between the GMFCS and the clustering coefficient (p = 0.01). Conclusions: During rehabilitation training of the lower limbs, there were significant differences in brain network indices between children with CP and CTDs. The indicators proposed in this paper are effective at evaluating motor function and the real‐time impact of rehabilitation training on the brain network and have great potential for application in guiding clinical motor function assessment and planning rehabilitation strategies. [ABSTRACT FROM AUTHOR]

• Innovation has been quantified with patents and significant innovations. • Innovation occurred in waves as Schumpeter indicated. • Major innovations were followed by incremental advances. • Process innovation predominated at first, followed by product innovation. • Indigenous innovation favored the absorption of foreign technology. Although Spain was not a leading country in technology, it generated numerous innovations, which must be analyzed in order to understand its particular development trajectory. This paper makes a contribution in this line by quantifying the innovation activity of the country's business elite between c. 1870 and 1970. In addition, we have sought to verify whether some of the well-known debated dynamics of innovation over time are fulfilled in this case. First with a descriptive analysis and then through the VAR technique, it has been found that innovation occurred in waves, as Schumpeter pointed out despite Kuznets; major innovations were followed by incremental advances; process innovations predominated in the early stages of modern development and product innovations gained preponderance later; and that indigenous innovation favored the importation of technology. A structural break test has identified sharp fluctuations in innovation, apparently related to institutional factors and relevant changes in the economy. [ABSTRACT FROM AUTHOR]

With the lack of finance, many developing nations are struggling for investment in several areas to generate an opportunity for sustainable livelihood, and Foreign Direct Investment (FDI) emerged as an engine of economic growth, especially in developing nations. It helps to strengthen the economy by mainly promoting capital inflow and technical improvement into the recipient economies. However, some literatures found economic growth, responsible for the inflow of FDI. Therefore, this paper attempts to examine the causal relationship between FDI and Economic Growth in India. By running the Granger causality test in VECM settings, this study found, GDP causes inflow of FDI in India. [ABSTRACT FROM AUTHOR]

The current research explores the dynamic causality relationship between a country's economic growth and its environmental, social, and governance performance in a global sample of 118 countries. Using annual data from 1999 to 2015, this study makes use of Granger causality test for panel data models developed by Dumitrescu and Hurlin (Economic Modelling 29(4), 1450–1460, 2012) and Han et al. (Econometric Reviews, 36, (1–3), 225–240, 2017)'s lag length selection criteria techniques to identify the causal direction across the full sample, low and lower middle-income countries (LMLI) and upper middle (UMI) and high-income (HI) countries. Our empirical findings show the presence of a bidirectional causality between environmental and social performance and economic growth, whereas unidirectional causality is from governance to growth for all countries. Unlike the clear overall pattern of the full sample results, the empirical evidence for different income groups of countries is mixed. The policy implications are provided. [ABSTRACT FROM AUTHOR]

Introduction: Improving living standards in developing countries and rapid population growth have significant effects on the economy and environment. Population growth leads to an increase in demand for agricultural products, which increases environmental pollution, reduces the productivity of natural resources, and has a negative effect on economic growth. Therefore, the aim of this research is to investigate the short-term and long-term relationship between agricultural production, economic growth, and environmental pollution in Iran. Material and Methods: In this research, time series data for the period of 1991-2020 were collected from the database of the World Bank and Food and Agriculture Organization (FAO), and the Autoregressive Distributed Lag (ARDL) models were used. First, the augmented Dickey-Fuller (ADF) and Phillips-Perron tests were performed to test the stationarity. Then, according to the values of Akaike, Schwarz, and Bayesian information criterion, the optimal number of lags was selected. ARDL bounds test was used to test the presence of the long-run relationship between the variables, and then short and long-run relationships and error correction models (ECM) were estimated. Finally, the causality between pairwise variables was investigated by using the Granger causality test. Results and Discussion: The results of short-term relationships show that a one percent increase in economic growth, rural population, gross capital formation, and agricultural production increases CO2 emissions by 0.307%, decreases by 2.937%, and increases by 0.087%, and 0.065%, respectively. The effect of foreign direct investment on CO2 emissions in the short term was not significant. However, a one percent increase in the lag of foreign direct investment will increase CO2 emissions by 0.01%. The long-term results show that a one percent increase in economic growth, rural population, agricultural products, foreign direct investment, and gross capital formation will increase CO2 emissions by 0.662%, decrease by 3.807%, and increase by 0.141%, by 0.024% and 0.188%, respectively. The results of the Granger causality test show the bidirectional causality relationship between economic growth, agricultural production, foreign direct investment, and CO2 emissions, as well as foreign direct investment and agricultural production. Also, there is causality in only one direction between gross capital formation and CO2 emissions, agricultural production and economic growth, foreign direct investment and economic growth, agricultural production and rural population, rural population and foreign direct investment, and rural population and CO2 emissions. In addition, there is a long-term positive and significant relationship between CO2 emissions and economic growth, gross capital formation, agricultural production, and foreign direct investment. The long-run result demonstrated by the FMOLS and DOLS methods was the same as the finding of the ARDL approach. Conclusion: In Iran, enhancing agricultural mechanization, promoting renewable energy, enforcing environmental regulations, adopting green technologies, investing in R&D, and attracting foreign investments are crucial to reduce CO2 emissions and pollution. Fossil fuel-dependent industries, capital formation, and rural populations also impact environmental pollution, emphasizing the need for employment opportunities in rural areas to mitigate these effects [ABSTRACT FROM AUTHOR]

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