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Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

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]

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.

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]

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]

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]

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]

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]

Copyright of Journal of Economics & Administrative Sciences / Afyon Kocatepe Üniversitesi Iktisadi ve Idari Bilimler Fakültesi Dergisi is the property of Afyon Kocatepe University, Faculty of Business Administration and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Economic theory argues that foreign direct investment (FDI) and external debt are expected to enhance economic growth in any given economy. Consequently, this study (i) investigated the relationship between foreign direct investment, external debt servicing, and economic growth in Nigeria; (ii) investigated how foreign direct investment and external debt impact Nigeria's economic growth; and (iii) analyzed the direction of causality among the three macroeconomic variables. Descriptive statistics, time series autoregressive distributive lag, and robust Granger causality tests were adopted as the estimating techniques. The results showed that from 2011 to 2022, Nigeria's FDI continued to decline, Nigeria's external debt servicing continued to grow on an upward trajectory, and the growth of the GDP has been meandering. ARDL analysis results confirmed that the lag of FDI and current exchange rate exert positive effects on current economic growth in Nigeria, with a 1% increase in FDI, current external debt, and current exchange rate increasing growth by 1.49%, 1.58%, and 0.02%, respectively. Results from the Granger causality showed that FDI and external debt do Granger cause GDP in Nigeria. Policymakers should focus on prudent debt management practices and strive to reduce domestic debt levels. [ABSTRACT FROM AUTHOR]

The building sector has implemented code requirements to promote the adoption of all-electric heating systems and electric vehicle (EV)-ready in new construction, aligning with electrification goals. However, existing studies on adoption and equity indicators lack a comprehensive spatiotemporal perspective, impeding the evaluation of trends over time and across communities. To fill these gaps, this study investigates spatiotemporal inequalities and the link between EV charger and heat pump adoption, integrating community characteristics in Seattle using proposed inequality indices and multilevel vector autoregressive models. Key findings include a recent decrease in Gini inequalities for EV charger adoption and a consistent reduction in Gini inequalities for heat pump adoption over time. EV charger adoption demonstrates a stronger association with spatial inequality across tracts. Higher adoption variations over time for both technologies are observed in similar communities. Additionally, communities with higher EV charger adoption exhibit higher heat pump adoption, with only heat pump adoption Granger causing EV charger adoption within a community. Economic factors predict adoption patterns, linking higher income and income inequality to increased EV charger adoption but lower heat pump adoption. The findings suggest tailored strategies in building codes for promoting equitable clean energy technology implementation, considering technology-specific characteristics and spatial dimensions. Recognizing the causal links between technology adoptions is crucial for collaborating with grid operators, particularly in establishing building standards that prevent grid congestion. Aligning codes with clean energy intricacies promotes sustainable construction and urban environment. Striving for electrification in the building sector, the study explores clean energy technology adoption in Seattle. Positive trends emerge, with recent reductions in inequalities for EV chargers and consistent decreases for heat pumps over time. Notably, areas with higher EV charger adoption also experience increased heat pump adoption, with heat pump adoption leading to EV charger adoption. EV charger adoption demonstrates stronger spatial clustering, with higher adoption variations over time observed in similar communities for both technologies. Economic factors, like income, influence these patterns in the opposite direction to the technologies. To ensure a sustainable and fair transition, the research recommends tailored building code strategies, considering each technology's unique aspects and community spatial layouts. Recognizing the interconnections between these technologies is crucial for collaboration with grid operators and implementing building standards that prevent congestion. Aligning codes with clean energy intricacies fosters sustainability in construction and urban development, contributing to a greener future. [ABSTRACT FROM AUTHOR]

This study examines the dynamics between treasury and market capitalization in two Decentralized Autonomous Organization (DAO) projects: OlympusDAO and KlimaDAO. This research examines the relationship between market capitalization and treasuries in these projects using vector autoregression (VAR), Granger causality, and Vector Error Correction models (VECM), incorporating an exogenous variable to account for the comovement of decentralized finance assets. Additionally, a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is employed to assess the impact of carbon offset tokens on KlimaDAO's market capitalization returns' conditional variance. The findings suggest a connection between market capitalization and treasuries in the analyzed projects, underscoring the importance of the treasury and carbon offset tokens in impacting a DAO's market capitalization and variance. Additionally, the results suggest significant implications for predictive modeling, highlighting the distinct behaviors observed in OlympusDAO and KlimaDAO. Investors and policymakers can leverage these results to refine investment strategies and adjust treasury allocation strategies to align with market trends. Furthermore, this study addresses the importance of responsible investing, advocating for including sustainable investment assets alongside a foundational framework for informed investment decisions and future studies in the field, offering novel insights into decentralized finance dynamics and tokenized assets' role within the crypto-asset ecosystem. [ABSTRACT FROM AUTHOR]

The effect of the female labour force participation rate on urbanization, which is the question of reverse causality, was not investigated in the earlier literature despite the widespread belief that urbanization leads to modernization and social transformation. The paper used World Bank data from 217 countries from 1991 to 2022 to address this issue. The Random Effect (RE) Two-Stage least squares (2SLS) regression analysis suggests that urbanization has a detrimental effect on the ratio of female to male labour force participation. On the other hand, the proportion of women to men participating in the labour force positively influences urbanization. The GDP growth rate and the proportion of female employers favourably influence the participation rate of women in the labour force. However, the estimated results do not support the idea that long-term economic growth and the percentage of women in the labour force follow a U-shaped pattern. The results do not support a U-shaped association between the female labour force participation rate and urbanization. However, a causal and long-term stable association exists between female labour force participation rate and urbanization. Finally, we suggest several policies that will benefit women's labour force participation rate during the process of economic growth. [ABSTRACT FROM AUTHOR]

This paper uses the bootstrapped Granger Causality Testing approach to investigate the relationship between electricity consumption, renewable energy production, and the current account of the six OECD countries. One of the main advantages of using this approach is that it captures the cross-section dependence in our sample and applies the Seemingly Unrelated Regression (SUR) to examine the causality relationship between the variables. The empirical findings show the presence of cross-section dependence in our sample as the six Organisation for Economic Co-operation and Development (OECD) countries share resources, capabilities, and key competencies. Notably, a unidirectional causality exists running from electric power consumption to the current account of the USA. The current account balance causes electric power consumption in the case of France and Switzerland. The tri-variate causality relationship between electricity consumption, renewable energy production, and current account balance could not be established in the case of Germany, Finland, or the UK. [ABSTRACT FROM AUTHOR]

Testing for Granger causality relies on estimating the capacity of dynamics in one time series to forecast dynamics in another. The canonical test for such temporal predictive causality is based on fitting multivariate time series models and is cast in the classical null hypothesis testing framework. In this framework, we are limited to rejecting the null hypothesis or failing to reject the null - we can never validly accept the null hypothesis of no Granger causality. This is poorly suited for many common purposes, including evidence integration, feature selection, and other cases where it is useful to express evidence against, rather than for, the existence of an association. Here we derive and implement the Bayes factor for Granger causality in a multilevel modeling framework. This Bayes factor summarizes information in the data in terms of a continuously scaled evidence ratio between the presence of Granger causality and its absence. We also introduce this procedure for the multilevel generalization of Granger causality testing. This facilitates inference when information is scarce or noisy or if we are interested primarily in population-level trends. We illustrate our approach with an application on exploring causal relationships in affect using a daily life study.

Purpose: This study evaluates the nexus between public expenditure and economic growth in South Africa. The study uses time-series data to evaluate the nexus which span through 1986-2022 which is obtained from WDI, 2022. Method: Essentially, the study employed cointegration and VECM test. The cointegration test signifies that all the variables are cointegrated at long-run. The VECM shows a direct connection amid government expenditure and economic growth. Result: The study found a unidirectional causal association between public investment (HE, ML, ED, and INFR) and economic development, as well as a substantial long-run relationship between the variables. Thus, the study's empirical findings indicate that public spending is a major factor in economic expansion. Practical Implication for Economic Growth and Development: The study recommends the expansion of spending should be avoided for economic growth as it causes the economy to accumulate enormous amounts of debt. The direct influence of government expenditure on inflation makes it inappropriate to utilize it as a tool for stabilizing policy.

The current energy crisis has shown all states that energy from renewable sources can be a determining factor in the states’ sustainable development. Several papers have studied the relationship between renewable energy consumption and economic development, finding various situations, but there is no consensus. Thus, this study aims to first investigate the causal relationship between economic growth and total and sectoral renewable energy consumption (European Union and each Member State, for 2004–2020) by testing various linear and non-linear regressions to choose the fit model. Second, the investigation extends to analysing the impact of renewable energy consumption by sector on economic development. A hybrid approach is used, namely structural equation modelling and artificial neural networks. The study findings indicate the effect and the meaning (directly or inversely) exerted by the three sectoral components on economic growth, with different intensities from one country to another. There is a significant influence on the consumption of renewable energy in the heating and cooling sectors and transport on gross domestic product at the European Union level and for most member states. Based on the obtained results, a series of theoretical, practical, and political implications are provided.

In considering today's energy challenges, the link between the usage of renewable and non-renewable energy sources and economic growth has gained substantial policy attention. This research examines the complex relationship between these three variables to understand how non-renewable energy consumption and renewable energy consumption interact and what that means for economic growth. This study uses the Granger causality approach to explore the relationships between non-renewable energy consumption, renewable energy consumption, and economic development. It draws on a comprehensive dataset from the Word Bank database, including 152 nations from 1990 to 2019. The analysis is further disaggregated by four subgroups of countries; least developed, developed, transitional economies and developing countries. The result of this study provides valuable empirical evidence of uni-directional causality running from renewable energy consumption to economic growth and non-renewable energy consumption to economic growth in transitional economies. Furthermore, policymakers should focus on both variables when making decisions because the results show that energy consumption and economic growth are interconnected. Implementing global energy efficiency standards, reducing fossil fuel usage, and adopting regulatory measures are all viable policies for limiting adverse effects on the environment while encouraging economic development.

The human visual system can effortlessly group small components into entities to form an object, but the role of the hemispheres in this processing is still unknown. Understanding the hemispherical processing of perceptual grouping is crucial for unraveling the complexities of visual perception. We have attempted to examine the processing of perceptual grouping in both hemispheres of the human brain. The neural data was collected for 15 healthy subjects while they viewed displays featuring either ‘structure’ (line segments composed of dots) or ‘non-structure’ (random dots). ERPs were recorded and assessed in both frontal and occipital regions of the left and right hemispheres for structure and non-structure stimuli. Our results revealed higher activation for structure compared to non-structure in both brain hemispheres, with notably amplified activity observed in the right hemisphere. Moreover, a decrease in task-related alpha power and an increase in PLI functional connectivity were observed during the perceptual grouping of structures. A novel finding that the Granger causality exhibits a higher value for perceptual grouping when information flows from the right to the left hemisphere, in contrast to communication from left to right, is obtained. Thus, the right hemisphere demonstrated distinct dominance in activation amplitude, task-related alpha power, functional connectivity, and directional functional connectivity related to perceptual grouping. Furthermore, our findings suggest that perceptual grouping involves communication between the frontal and occipital brain regions. By elucidating the hemispherical mechanisms underlying perceptual grouping, this research not only advances our understanding of basic cognitive processes but also offers practical implications for fields such as neurorehabilitation and artificial intelligence.

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.

Previous neuroimaging studies have reported dual-task interference (DTi) and deterioration of task performance in a cognitive-motor dual task (DT) compared to that in a single task (ST). Greater frontoparietal activity is a neural signature of DTi; nonetheless, the underlying mechanism of cortical network in DTi still remains unclear. This study aimed to investigate the regional brain activity and neural network changes during DTi induced by highly demanding cognitive-motor DT. Thirty-four right-handed healthy young adults performed the spiral-drawing task. They underwent a paced auditory serial addition test (PASAT) simultaneously or independently while their cortical activity was measured using functional near-infrared spectroscopy. Motor performance was determined using the balanced integration score (BIS), a balanced index of drawing speed and precision. The cognitive task of the PASAT was administered with two difficulty levels defined by 1 s (PASAT-1 s) and 2 s (PASAT-2 s) intervals, allowing for the serial addition of numbers. Cognitive performance was determined using the percentage of correct responses. These motor and cognitive performances were significantly reduced during DT, which combined a drawing and a cognitive task at either difficulty level, compared to those in the corresponding ST conditions. The DT conditions were also characterized by significantly increased activity in the right dorsolateral prefrontal cortex (DLPFC) compared to that in the ST conditions. Multivariate Granger causality (GC) analysis of cortical activity in the selected frontoparietal regions of interest further revealed selective top-down causal connectivity from the right DLPFC to the right inferior parietal cortex during DTs. Furthermore, changes in the frontoparietal GC connectivity strength between the PASAT-2 s DT and ST conditions significantly correlated negatively with changes in the percentage of correct responses. Therefore, DTi can occur even in cognitively proficient young adults, and the right DLPFC and frontoparietal network being crucial neural mechanisms underlying DTi. These findings provide new insights into DTi and its underlying neural mechanisms and have implications for the clinical utility of cognitive-motor DTs applied to clinical populations with cognitive decline, such as those with psychiatric and brain disorders.

Purpose – The objective of this paper is to investigate the direction of the causal relationship between dividend policy (DP) and earnings management (EM). Design/methodology/approach – This research utilizes the panel data analysis to investigate the causal relationship between EM and DP. It provides empirical insights based on a sample of 280 French nonfinancial companies listed on the CAC All-Tradable index during the period of 2008–2015. The study initiates with a Granger causality examination on the unbalanced panel data and employs a dynamic panel approach with the generalized method of moments (GMM). It further estimates the empirical models simultaneously using the three-stage least squares (3SLS) method and the iterative triple least squares (iterative 3SLS) method. Findings – The estimation of our various empirical models confirms the presence of a bidirectional causal relationship between DP and EM. Practical implications – Our study highlights the prevalence of EM in the French context, particularly within DP. It underscores the need for regulatory bodies, the Ministry of Finance, external auditors and stock exchange organizers to prioritize governance mechanisms for improving the quality of financial information disclosed by companies. Originality/value – This research is, to the best of our knowledge, the first is to extensively investigate the reciprocal causal relationship between DP and EM in France. Previous studies have not placed a significant emphasis on exploring this bidirectional link between these two variables.

Purpose – This paper aims to deepen the understanding of the relationship between global economic uncertainty and price volatility, specifically focusing on commodity, industrial materials and energy price indices as proxies for global inflation, analyzing data from 1997 to 2020. Design/methodology/approach – The dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model is used to study the dynamic relationship between variables over a while. Findings – The results demonstrated a positive relationship between commodity prices and the global economic policy uncertainty (GEPU). Except for 1999–2000 and 2006–2008, the results of the energy price index model were very similar to those of the commodity price index. A predominant positive relationship is observed focusing on the connection between GEPU and the industrial material price index. The results of the pairwise Granger causality reveal a unidirectional relationship between the GEPU – the Global Commodity Price Index – and the GEPU – the Global Industrial Material Price Index. However, there is bidirectional causality between the GEPU – the Global Energy Price Index. In sum, changes in price indices can be driven by GEPU as a political factor indicating unfavorable economic conditions. Originality/value – This paper provides a deeper understanding of the role of global uncertainty in the global inflation process. It fills the gap in the literature by empirically investigating the dynamic movements of global uncertainty and the three most important groups of prices.

The purpose of this study was to investigate the extent of price transmission along the agri-food supply chain in selected regions in Ghana. The extent of transmission was used to assess the efficiency and effectiveness of the agricultural marketing systems in the area. Efficient marketing systems are expected to reduce the issue of regional price fluctuations, encourage fair income distribution throughout the supply chain, and improve food security. The preliminary time series stationarity/unit root and seasonality tests were performed using the Augmented Dickey-Fuller test (ADF)/Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) and Canova-Hansen seasonal stability tests respectively. Following the detection of statistically significant threshold effects by the Supremum Likelihood Ratio (Sup-LR) tests, the extent of price transmission was investigated using two-region threshold vector error correction models. The initial tests found that the monthly wholesale, retail, and producer price series for maize, cowpea, and yam used in the study were first difference stationary rather than seasonally integrated. The findings revealed that price signals in the agri-food markets were not fully transmitted across the supply chain. Thus, the extent of price transmission was generally low, creating unexploited arbitrage opportunities along the chain. The study identified high transfer costs (proxied by the threshold parameter) and actor-specific market power (as revealed by the Granger causality test) as the primary obstacles to effective market functioning in the area. Minimizing transfer costs through infrastructural development such as improvements in road networks linking farming communities and markets, access to price information, and regular stakeholder engagements could help improve the functioning of the agri-food markets.

AbstractThe study critically assessed the impact of foreign aid on the Nigerian economy with a specific interest in official development assistance from 1980 to 2019. It employed the ARDL bounds testing approach to cointegration and finds a long-run relationship among the variables employed. Furthermore, the estimated results suggest that official development assistance as a form of foreign aid and credit extensions does not contribute to the progress of the Nigerian economy, it rather retards it. Also, the study concludes both the short and long run that the labor force contributes to economic progress in Nigeria, whereas gross capital formation just like foreign aid retards growth. The Granger causality test reveals no sign of either unidirectional or bidirectional causal relationship between official development assistance and economic growth in Nigeria. The study recommends that adequate support through credit extensions to SMEs should be fostered to strengthen domestic capital formation. The originality of this work lies in its rigorous analysis of the long-term impact of official development assistance on the Nigerian economy, employing the ARDL bounds testing approach. The findings challenge conventional wisdom and offer valuable insights into the dynamics of foreign aid and economic growth in Nigeria. However, this study has certain limitation. The study temporal scope spans from 1980 to 2019 due to limited data.

AbstractEnvironmental sustainability is a pivotal facet of sustainable development, captivating the attention of development researchers. Within this context, energy consumption emerges as a pivotal determinant influencing environmental sustainability variations among countries. This study delves into the linkages between renewable energy consumption and environmental sustainability within 30 Sub-Saharan African countries, utilising panel data from 2000 to 2020. It contributes to the expanding literature on this subject by considering the impacts of institutional and political factors while addressing challenges related to cross-sectional dependence, heterogeneity, and serial correlation through robust estimation. To this end, the Augmented Mean Group Model was used in the empirical estimation. The study reveals a noteworthy 67.32% mean score for renewable energy consumption in the total final energy consumption across the sampled countries, a positive deviation from the global average of 11.2%. Empirical results signify a positive and statistically significant long-term relationship between renewable energy consumption and environmental sustainability. Nevertheless, the inclusion of a policy dummy variable indicates a significant increase in greenhouse gas emissions post the Millennium Development Goals period. Granger non-causality test results reveal a bidirectional causality between renewable energy consumption and environmental sustainability. Thus, subsidies and tax exemptions for renewable energy production and consumption, as well as supporting sustainable development goals with appropriate environmental investment, are among the policy options that Sub-Saharan African countries and policymakers could pursue to achieve environmental sustainability and sustainable development goals.

The paper aims to probe the tourism-economic growth nexus in the case of India. The paper incorporates a more structural view of sector-specific macroeconomic variables like central government expenditure on tourism (CGET), investment in the tourism industry (IOT), foreign tourist arrivals, and foreign tourist visits as explanatory parameters. Johansen's cointegration and error correction model results support the long-run relationship among the variables. All the independent variables are unidirectional causal on GDP except investment in tourism, which shows long-run bidirectional causality. Thus, the long-run unidirectional tourism-led growth hypothesis is supported. The empirical implications support government and private sector-based resource allocation towards tourism expansion, thereby escalating the country's economic growth.

Changes are projected for the boreal biome with complex and variable effects on forest vegetation including drought‐induced tree mortality and forest loss. With soil and atmospheric conditions governing drought intensity, specific drivers of trees water stress can be difficult to disentangle across temporal scales. We used wavelet analysis and causality detection to identify potential environmental controls (evapotranspiration, soil moisture, rainfall, vapor pressure deficit, air temperature and photosynthetically active radiation) on daily tree water deficit and on longer periods of tree dehydration in black spruce and tamarack. Daily tree water deficit was controlled by photosynthetically active radiation, vapor pressure deficit, and air temperature, causing greater stand evapotranspiration. Prolonged periods of tree water deficit (multi‐day) were regulated by photosynthetically active radiation and soil moisture. We provide empirical evidence that continued warming and drying will cause short‐term increases in black spruce and tamarack transpiration, but greater drought stress with reduced soil water availability. Plain Language Summary: This research explores how climate change could impact the water stress experienced by black spruce and tamarack trees in the western boreal forest of Canada. We focused on a key measure called "tree water deficit" to understand if the trees were under stress due to insufficient water. We examined how tree water deficit relates to environmental factors such as temperature, sunlight, and soil moisture. The findings revealed that, on a daily basis, factors like sunlight and temperature cause trees to release more water into the air. However, over longer periods (days to weeks), the amount of water in the soil becomes crucial, suggesting that trees might face water stress during dry spells. So, while trees could grow more on hotter, sunnier days, they could also experience water stress and reduced growth if the soil becomes too dry for an extended period. This study helps us grasp how various factors interact to influence tree water stress in the boreal forest, providing insights important for managing these ecosystems in a changing climate. Key Points: A novel approach to determine environmental controls of tree water deficit across time scales with wavelet analysis and Granger causalitySoil moisture emerges as a significant control of tree water deficit in boreal trees at longer scales (multi‐days)Daily productivity gains with warming will be mitigated by decreased soil water availability in longer periods of tree water deficit [ABSTRACT FROM AUTHOR]

The causal connectivity of a network is often inferred to understand network function. It is arguably acknowledged that the inferred causal connectivity relies on the causality measure one applies, and it may differ from the network's underlying structural connectivity. However, the interpretation of causal connectivity remains to be fully clarified, in particular, how causal connectivity depends on causality measures and how causal connectivity relates to structural connectivity. Here, we focus on nonlinear networks with pulse signals as measured output, e.g., neural networks with spike output, and address the above issues based on four commonly utilized causality measures, i.e., time-delayed correlation coefficient, time-delayed mutual information, Granger causality, and transfer entropy. We theoretically show how these causality measures are related to one another when applied to pulse signals. Taking a simulated Hodgkin-Huxley network and a real mouse brain network as two illustrative examples, we further verify the quantitative relations among the four causality measures and demonstrate that the causal connectivity inferred by any of the four well coincides with the underlying network structural connectivity, therefore illustrating a direct link between the causal and structural connectivity.We stress that the structural connectivity of pulse-output networks can be reconstructed pairwise without conditioning on the global information of all other nodes in a network, thus circumventing the curse of dimensionality. Our framework provides a practical and effective approach for pulseoutput network reconstruction. [ABSTRACT FROM AUTHOR]