Search Results (89)

Identification of causal factors in traffic crashes has always been a significant challenge in road safety studies. Traditional crash prediction models are limited in elucidating the underlying causal mechanisms in road crashes. This research explores the application of three graphic models, namely, the Gaussian graphical model (GGM), causal Bayesian network (CBN) and graphic extreme gradient boosting (XGBoost), through a case study using highway–railroad-grade crossing (HRGC) inventory and collision data from Canada. The three modelling approaches have generally yielded consistent findings on various risk factors such as crossing control type, track angle, and exposure, showing their potential for identifying causal relationships through the interpretation of causal graphs. With the ability to make better causal inferences from crash data, the effectiveness of safety countermeasures could be more accurately and reliably estimated. Full article

Rapid urbanization and escalating disaster risks necessitate resilient urban governance systems. Smart city initiatives that leverage digital technologies—such as the internet of things (IoT), big data analytics, and artificial intelligence (AI)—demonstrate transformative potential in enhancing emergency management capabilities. However, empirical evidence regarding their causal impact and underlying mechanisms remains limited, particularly in developing economies. Drawing on panel data from 275 Chinese prefecture-level cities over the period 2006–2021 and using China’s smart city pilot policy as a quasi-natural experiment, this study applies a multi-period difference-in-differences (DID) approach to rigorously assess the effects of smart city construction on emergency management capabilities. Results reveal that smart city construction produced a statistically significant improvement in emergency management capabilities, which remained robust after conducting multiple sensitivity checks and controlling for potential confounding policies. The benefits exhibit notable heterogeneity: emergency management capability improvements are most pronounced in central China and in cities at the extremes of population size—megacities (>10 million residents) and small cities (<1 million residents)—while effects remain marginal in medium-sized and eastern cities. Crucially, mechanism analysis reveals that digital technology application fully mediates 86.7% of the total effect, whereas factor allocation efficiency exerts only a direct, non-mediating influence. These findings suggest that smart cities primarily enhance emergency management capabilities through digital enablers, with effectiveness contingent upon regional infrastructure development and urban scale. Policy priorities should therefore emphasize investments in digital infrastructure, interagency data integration, and targeted capacity-building strategies tailored to central and western regions as well as smaller cities. Full article

Climatic events are expected to significantly impact global agricultural production, with China being particularly vulnerable. Research in China emphasizes the urgent need for sustainable agricultural practices that address climate change, implement effective management strategies to mitigate the impacts of climatic events, and ensure food security. Therefore, this study examines the impact of climatic events on agricultural production optimization in Northeast China. To complete this objective, this study uses Method-of-Moments Quantile Regression (MM-QR) and data from 2003 to 2020. The main findings reveal that climatic factors, such as the Standardized Precipitation Index (SPI) and High-Temperature Days (HTDs), have a more pronounced effect on agricultural outcomes at higher production levels, particularly for larger producers. In addition, machinery power (TPAM) enhances productivity. Its role is more focused on risk mitigation than on expanding production. Insurance payouts (AIPE) increase grain production capacity at higher quantiles, while fertilizer use (FEU) has diminishing returns on capacity but encourages planting. Granger causality tests further demonstrate that management factors—such as machinery, irrigation, and insurance—play a more significant role in shaping agricultural outcomes than extreme climatic events. To improve agricultural sustainability in the context of climate change, policy recommendations include promoting climate-resilient crops, investing in smart irrigation systems, expanding affordable agricultural insurance, and encouraging sustainable fertilizer use through incentives and training. These strategies can help mitigate climate risks, enhance productivity, and reduce the environmental impact of agricultural activities. Full article

This study examines the dynamic relationship between carbon markets and clean energy stocks using a quantile Granger-causality framework, capturing nonlinear dependencies across different market conditions. Unlike conventional mean-based approaches, this method identifies asymmetric causal linkages, particularly during periods of extreme market movements. Empirical results reveal a bidirectional Granger-causal relationship between carbon price returns and clean energy stock returns, predominantly at the lower quantile $\tau =0.25$ and upper quantile $\tau =0.75$ of the conditional distribution. This indicates that carbon price fluctuations significantly impact clean energy investments during bullish (>0.50 quantiles) and bearish (<0.50 quantiles) trends, while the effect is weaker during stable periods (0.50 quantile). Additionally, findings suggest that the impact of carbon pricing varies across regions. While the signs of the Granger-causality running from carbon markets to clean energy stocks are less than 0 in global, European, and U.S. markets, China’s policy-driven sustainability initiatives mitigate these risks, enhancing investment stability. These findings underscore the importance of region-specific carbon policies in supporting clean energy growth. Policymakers should consider stabilization mechanisms in volatile markets and strategic carbon pricing to optimize investment incentives. Future research should explore the role of green financial innovations in enhancing carbon market efficiency and reducing investment uncertainty in clean energy transitions. Full article

Financial assets often exhibit explosive price surges followed by abrupt collapses, alongside persistent volatility clustering. Motivated by these features, we introduce a mixed causal–noncausal invertible–noninvertible autoregressive moving average generalized autoregressive conditional heteroskedasticity (MARMA–GARCH) model. Unlike standard ARMA processes, our model admits roots inside the unit disk, capturing bubble-like episodes and speculative feedback, while the GARCH component explains time-varying volatility. We propose two estimation approaches: (i) Whittle-based frequency-domain methods, which are asymptotically equivalent to Gaussian likelihood under stationarity and finite variance, and (ii) time-domain maximum likelihood, which proves to be more robust to heavy tails and skewness—common in financial returns. To identify causal vs. noncausal structures, we develop a higher-order diagnostics procedure using spectral densities and residual-based tests. Simulation results reveal that overlooking noncausality biases GARCH parameters, downplaying short-run volatility reactions to news ($\alpha$) while overstating volatility persistence ($\beta$). Our empirical application to Bitcoin and Ethereum enhances these insights: we find significant noncausal dynamics in the mean, paired with pronounced GARCH effects in the variance. Imposing a purely causal ARMA specification leads to systematically misspecified volatility estimates, potentially underestimating market risks. Our results emphasize the importance of relaxing the usual causality and invertibility assumption for assets prone to extreme price movements, ultimately improving risk metrics and expanding our understanding of financial market dynamics. Full article

Mountain resilience is the ability of mountain regions to endure, adapt to, and recover from environmental, climatic, and anthropogenic stressors. Due to their steep topography, extreme weather conditions, and unique biodiversity, these areas are particularly vulnerable to climate change, natural hazards, and human activities. This paper examines how nature-based solutions (NbSs) can strengthen slope stability and geotechnical resilience, with a specific focus on Slovenia’s sub-Alpine regions as a case study representative of the Alps and similar mountain landscapes worldwide. The proposed Climate-Adaptive Resilience Evaluation (CARE) concept integrates geomechanical analysis with geotechnical planning, addressing the impacts of climate change through a systematic causal chain that connects climate hazards, their effects, and resulting consequences. Key factors such as water infiltration, soil permeability, and groundwater dynamics are identified as critical elements in designing timely and effective NbSs. In scenarios where natural solutions alone may be insufficient, hybrid solutions (HbSs) that combine nature-based and conventional engineering methods are highlighted as essential for managing unstable slopes and restoring collapsed geostructures. The paper provides practical examples, including slope stability analyses and reforestation initiatives, to illustrate how to use the CARE concept and how NbSs can mitigate geotechnical risks and promote sustainability. By aligning these approaches with regulatory frameworks and climate adaptation objectives, it underscores the potential for integrating NbSs and HbSs into comprehensive, long-term geotechnical strategies for enhancing mountain resilience. Full article

Background/Objectives: Investigating the importance and potential causal effects of serum lipid biomarkers in the management of hypertension is vital, as these factors positively impact the prevention and control of cardiovascular disease (CVD). Methods: We surveyed 3373 urban residents using longitudinal data from the CHARLS database, collected between 2015 and 2020. Pearson correlation methods were employed to explore the relationships among the numerical variables. A logistic regression model was utilized to identify the risk factors for hypertension. The dose–effect relationship between serum lipids and BP was assessed using restricted cubic splines (RCS). Additionally, piecewise structural equation modeling (PiecewiseSEM) was conducted to further elucidate the direct and indirect pathways involving individual body indices, serum lipids, and PA on BP responses at different levels of physical activity (PA). Results: The four serum lipids showed significant differences between hypertensive and non-hypertensive residents (p < 0.05). All lipids, except for HDL cholesterol, demonstrated extremely significant positive correlations with both systolic blood pressure (SBP) and diastolic blood pressure (DBP) (p < 0.001). All serum lipid variables were significantly associated with the incidence of hypertension. Specifically, triglycerides (bl_tg), HDL (bl_hdl), and low-density lipoprotein LDL cholesterol were identified as significant risk factors, with odds ratios (ORs) of 1.56 (95% CI: 1.33–1.85, p < 0.001), 1.16 (95% CI: 1.02–1.33, p < 0.05), and 1.62 (95% CI: 1.23–2.15, p < 0.001), respectively. Conversely, cholesterol (bl_cho) was a protective factor for hypertension, with an OR of 0.60 (95% CI: 0.42–0.82, p < 0.01). PA showed weak relationships with blood pressure (BP); however, PA levels had significant effects, particularly at low PA levels. The four serum lipids had the most mediating effect on BP, especially under low PA level conditions, while PA exhibited a partly weak mediating effect on BP, particularly under high PA level conditions. Conclusions: Serum lipids have significant nonlinear relationships with BP and PA levels exert different influences on BP. The significant mediating effects of serum lipids and the weak mediating effects of PA on individual body indices related to SBP and DBP demonstrate significant differences across varying levels of PA, highlighting the importance of low PA levels in hypertension management. This study could provide valuable recommendations and guidance in these areas. Full article

The Intergovernmental Panel on Climate Change (IPCC) provides regular scientific assessments on climate change, its implications, and potential future risks based on estimated energy matrixes and policy pathways. The aim of this publication is to assess the risks climate change poses to biodiversity using projected IPCC climate scenarios for the period 2081–2100, combined with key species-sensitivity indicators and variables as a response to climate change projections. In doing so, we address how climate-change-driven pressures may affect biodiversity. Additionally, a novel causal relationship between extreme ambient temperature exposure levels and the corresponding effects on individual species, noted in this paper as the Upper Thermal-Tolerance Limit and Species Sensitivity Distribution (UTTL-SSD), provides a compelling explanation of how global warming affects biodiversity. Our study indicates that North American and Oceanian sites with humid continental and subtropical climates, respectively, are poised to realize temperature shifts that have been identified as potential key tipping-point triggers. Heat stress may significantly affect approximately 60–90% of mammals, 50% of birds, and 50% of amphibians in North American and Oceanian sites for durations ranging from 5 to 84 days per year from 2080. In the humid temperate oceanic climate of European sites, the climate conditions remain relatively stable; however, moderate cumulative effects on biodiversity have been identified, and additional biodiversity-assemblage threat profiles exist to represent these. Both the integration of IPCC-IUCN profiles and the UTTL-SSD response relationship for the species communities considered have resulted in the identification of the projected threats that climate pressures may impose under the considered IPCC scenarios, which would result in biodiversity degradation. The UTTL-SSD responses developed can be used to highlight potential breakdowns among trophic levels in food web structures, highlighting an additional critical element when addressing biodiversity and ecosystem concerns. Full article

This paper examines the influence of emotions on political polarization, looking at online propagation of conspiracy thinking by extreme right movements in Southern Europe. Integrating insights from psychology, political science, media studies, and system theory, we propose the ‘polarization loop’, a causal mechanism explaining the cyclical relationship between extreme messages, emotional engagement, media amplification, and societal polarization. We illustrate the utility of the polarization loop observing the use of the Great Replacement Theory by extreme right movements in Italy, Portugal, and Spain. We suggest possible options to mitigate the negative effects of online polarization in democracy, including public oversight of algorithmic decission-making, involving social science and humanities in algorithmic design, and strengthening resilience of citizenship to prevent emotional overflow. We encourage interdisciplinary research where historical analysis can guide computational methods such as Natural Language Processing (NLP), using Large Language Models fine-tunned consistently with political science research. Provided the intimate nature of emotions, the focus of connected research should remain on structural patterns rather than individual behavior, making it explicit that results derived from this research cannot be applied as the base for decisions, automated or not, that may affect individuals. Full article

Based on the properties of Rossby waves at the tropopause resonantly forced by solar declination in harmonic modes, which was the subject of a first article, case studies of heatwaves and extreme precipitation events are presented. They clearly demonstrate that extreme events only form under specific patterns of the amplitude of the speed of modulated airflows of Rossby waves at the tropopause, in particular period ranges. This remains true even if extreme events appear as compound events where chaos and timing are crucial. Extreme events are favored when modulated cold and warm airflows result in a dual cyclone-anticyclone system, i.e., the association of two joint vortices of opposite signs. They reverse over a period of the dominant harmonic mode in spatial and temporal coherence with the modulated airflow speed pattern. This key role could result from a transfer of humid/dry air between the two vortices during the inversion of the dual system. Finally, focusing on the two period ranges 17.1–34.2 and 8.56–17.1 days corresponding to 1/16- and 1/32-year period harmonic modes, projections of the amplitude of wind speed at 250 mb, geopotential height at 500 mb, ground air temperature, and precipitation rate are performed by extrapolating their amplitude observed from January 1979 to March 2024. Projected amplitudes are regionalized on a global scale for warmest and coldest half-years, referring to extratropical latitudes. Causal relationships are established between the projected amplitudes of modulated airflow speed and those of ground air temperature and precipitation rate, whether they increase or decrease. The increase in the amplitude of modulated airflow speed of polar vortices induces their latitudinal extension. This produces a tightening of Rossby waves embedded in the polar and subtropical jet streams. In the context of climate change, this has the effect of increasing the efficiency of the resonant forcing of Rossby waves from the solar declination, the optimum of which is located at mid-latitudes. Hence the increased or decreased vulnerability to heatwaves or extreme precipitation events of some regions. Europe and western Asia are particularly affected, which is due to increased activity of the Arctic polar vortex between longitudes 20° W and 40° E. This is likely a consequence of melting ice and changing albedo, which appears to amplify the amplitude of variation in the period range 17.1–34.2 days of poleward circulation at the tropopause of the Arctic polar cell. Full article

Volatility reflects the degree of variation in a time series, and a measurement of the stock performance in the energy sector can help one understand the pattern of fluctuations within this industry, as well as the factors that influence it. One of these factors could be the COVID-19 pandemic, which led to extreme volatility within the stock market in several economic sectors. It is essential to understand this regime of volatility so that robust financial strategies can be adopted to handle it. This study used stock data from the Yahoo! Finance API and data from the energy-price database from the US Energy Information Administration to conduct a comparative analysis of the volatility in the energy sector in Brazil and in the United States, as well as of the energy prices in California. The volatility in these time series were modeled using GARCH. The stock volatility regimes, both before and after COVID-19, were identified with a Markov switching model; the spillover index between the energy markets in the USA and in Brazil was evaluated with the Diebold–Yilmaz index; and the causality between the energy stock price and the energy prices was measured with the Granger causality test. The findings of this study show that (i) the volatility regime introduced by COVID-19 is still prevalent in Brazil and in the USA, (ii) the changes in the energy market in the US affect the Brazilian market significantly more than the reverse, and (iii) there is a causality relationship between the energy stock markets and the energy prices in California. These results may assist in the achievement of effective regulation and economic planning, while also supporting better market interventions. Also, acknowledging the persistent COVID-19-induced volatility can help with developing strategies for future crisis resilience. Full article

In this study, we used a recently developed causal methodology, called Probabilistic Easy Variational Causal Effect (PEACE), to distinguish gliosarcoma (GSM) from glioblastoma (GBM). Our approach uses a causal metric which combines Probabilistic Easy Variational Causal Effect (PEACE) with the XGBoost, or eXtreme Gradient Boosting, algorithm. Unlike prior research, which often relied on statistical models to reduce dataset dimensions before causal analysis, our approach uses the complete dataset with PEACE and the XGBoost algorithm. PEACE provides a comprehensive measurement of direct causal effects, applicable to both continuous and discrete variables. Our method provides both positive and negative versions of PEACE together with their averages to calculate the positive and negative causal effects of the radiomic features on the variable representing the type of tumor (GSM or GBM). In our model, PEACE and its variations are equipped with a degree $d$ which varies from 0 to 1 and it reflects the importance of the rarity and frequency of the events. By using PEACE with XGBoost, we achieved a detailed and nuanced understanding of the causal relationships within the dataset features, facilitating accurate differentiation between GSM and GBM. To assess the XGBoost model, we used cross-validation and obtained a mean accuracy of 83% and an average model MSE of 0.130. This performance is notable given the high number of columns and low number of rows (code on GitHub). Full article

Cassava witches’ broom disease (CWBD) is one of the most devastating diseases of cassava (Manihot esculenta Crantz), and it threatens global production of the crop. In 2017, a phytoplasma, Candidatus Phytoplasma luffae (Ca. P. luffae), was reported in the Philippines, and it has been considered as the causal agent, despite unknown etiology and transmission of CWBD. In this study, the nationwide occurrence of CWBD was assessed, and detection of CWBD’s pathogen was attempted using polymerase chain reaction (PCR) and next-generation sequencing (NGS) techniques. The results showed that CWBD has spread and become severe, exhibiting symptoms such as small leaf proliferation, shortened internodes, and vascular necrosis. PCR analysis revealed a low phytoplasma detection rate, possibly due to low titer, uneven distribution, or absence in the CWBD-symptomatic cassava. In addition, NGS techniques confirm the PCR results, revealing the absence or extremely low phytoplasma read counts, but a surprisingly high abundance of fastidious and xylem-limited fungus, Ceratobasidium sp. in CWBD-symptomatic plants. These findings cast doubt over the involvement of phytoplasma in CWBD and instead highlight the potential association of Ceratobasidium sp., strongly supporting the recent findings in mainland Southeast Asia. Further investigations are needed to verify the etiology of CWBD and identify infection mechanisms of Ceratobasidium sp. to develop effective diagnostic and control methods for disease management. Full article

For perennial plants, newly emerged organs are fresh hot spots for environmental microbes to occupy and assemble to form mature microbial communities. In the microbial community, some commensal fungi can play important roles in microbial succession, thus significantly improving host plant growth and disease resistance. However, their participating patterns in microbial assembly and succession remain largely unknown. In this study, we profiled the fungal community and found a similar fungal succession pattern of spring-emerged leaves from March to October in two pomelo orchards. Specifically, the fungal species, tracked on the old leaves, dominated the spring leaves after emergence and then decreased in relative abundance. This reduction in priority effects on the spring leaves was then followed by an increase in the number of observed species, Shannon and phylogenetic diversity indices, and the pathogen-associated fungal groups. In addition, we found that the temporal fungal succession on the spring leaves highly correlated with the disease occurrence in the orchards and with the temperature and precipitation variation from spring to summer. Of the pathogen-associated fungal groups, an increase in the relative abundance of Mycosphaerellaceae, hosting the causal agent of citrus greasy spot, correlated with the occurrence of the disease, while the relative abundance of Diaporthaceae, hosting the causal agent of melanose, was extremely low during the fungal succession. These results confirm that the two kinds of pathogen-associated fungal groups share different lifestyles on citrus, and also suggest that the study of temporal fungal succession in microbial communities can add to our understanding of the epidemiology of potential plant pathogens. Full article

East Asia is a region that is highly vulnerable to drought disasters during the spring season, as this period is critical for planting, germinating, and growing staple crops such as wheat, maize, and rice. The climate in East Asia is significantly influenced by three large-scale climate variations: the Pacific Decadal Oscillation (PDO), the El Niño–Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD) in the Pacific and Indian Oceans. In this study, the spring meteorological drought was quantified using the standardized precipitation evapotranspiration index (SPEI) for March, April, and May. Initially, coupled climate networks were established for two climate variables: sea surface temperature (SST) and SPEI. The directed links from SST to SPEI were determined based on the Granger causality test. These coupled climate networks revealed the associations between climate variations and meteorological droughts, indicating that semi-arid areas are more sensitive to these climate variations. In the spring, PDO and ENSO do not cause extreme wetness or dryness in East Asia, whereas IOD does. The remote impacts of these climate variations on SPEI can be partially explained by atmospheric circulations, where the combined effects of air temperatures, winds, and air pressure fields determine the wet/dry conditions in East Asia. Full article