Search Results (1,172)

With increasing industrial sophistication and complexity, workplaces are increasingly prone to occupational accidents, causing negative impacts on workers and employers, including economic losses and decreased productivity. South Korea occupational safety and health has implemented new policies addressing potential risks to overcome stagnation in industrial accident reduction and predict site accidents from past cases. Cases are human-classified according to rules, including occurrence type or original causal materials. However, human errors, subjective judgments, synonyms, and terms incorrectly used by classifiers reduce original data quality and impede developments or applications of policies, technologies, and methods preventing accidents based on past accidents. This study proposes three artificial intelligence models to objectively classify the occurrence type of accident cases. Models are developed based on a natural language processing model (KoBERT), which considers Korean language characteristics. Each model is tested by sequentially performing sentence preprocessing, keyword replacement, and morphological analysis. The proposed Model 3 exhibits 93.1% accuracy, which was the highest among tested models. Up to three classification categories for occurrence type are allowed to assist objective classification. The accident case-based occurrence type classification model is effective for industrial accident prevention, aiding in strategy development and reducing social costs. Full article

Standardized suicide mortality rates per 100,000 (SMRs) in Japan consistently decreased from 2009 to 2019 but increased from 2020. The causes of these temporal SMR fluctuations remain to be clarified. Therefore, this study was conducted to identify the causalities underlying the recently transformed fluctuations of suicide mortality in Japan. Monthly suicide numbers disaggregated by sex and social standing, and political uncertainty indices, such as economic policy uncertainty (EPU) and government management instability (AENROP), were obtained from Japanese government databases. Interrupted time-series analysis was performed to analyze temporal fluctuations of SMRs disaggregated by sex/social standing associated with the three General Principles of Suicide Prevention Policy (GPSPP) periods and the COVID-19 pandemic. Panel data and vector autoregressive analyses were conducted to investigate causalities from political uncertainties to SMRs. During the first and second GPSPPs (2009–2017), all SMRs disaggregated by sex and social standing decreased, whereas those of unemployed females did not change. During the third GPSPP (2017–2022), decreasing trends in all SMRs were attenuated compared to previous periods. All female SMRs, except unemployed females, showed sharp increases synchronized with the pandemic outbreak. No male SMRs showed sharply increasing at the pandemic outbreak. SMRs of unemployed males/females drastically increased in the later periods of the pandemic, while SMRs of employed and multiple-person/single-person household males did not increase during the pandemic. SMR of unemployed males was positively related to AENROP but not EPU. Other male SMRs were positively related to EPU/AENROP. On the contrary, not all female SMRs were related to EPU/AENROP. Increasing AENROP generally contributed to increasing male SMRs throughout the observation period; however, susceptibility to AENROP and/or political information might have unexpectedly contributed to suppressing the sharply increasing male SMRs induced by large-scale social shocks (the COVID-19 pandemic outbreak) in Japan. Full article

Coastal cities have the natural resource endowment and location advantages to develop new energy. However, heterogeneity in the economic development of China’s coastal cities has led to differences in the outcomes of environmental regulatory policies and related programs. To elucidate the difference, this paper obtained 5074 clean development mechanism (CDM) projects, which serves as a key instrument of the Kyoto Protocol designed to assist developing countries in achieving sustainable development through project-based emissions reductions and conducted a causal identification through quasi-experiment. And DID as well as DDD models are applied to identify the CDM effects on cities’ economic development. Main findings are: (1) Through the DID regression, this paper finds that the development of CDM projects have promoted the development of the city’s economy and lead to the upgrading of cities’ industries. (2) The promoting effects in economic development and employment are more prominent in coastal cities with high levels of economic development. (3) CDM can better facilitate economic development and employment in less developed coastal areas when implemented in conjunction with economic promoting policies. By applying quasi-experimental methods, including DID and DDD models, the research introduces a novel approach to assess the causal effects of CDM projects on city economies, offering fresh insights into sustainable development policies. Full article

The contemporary process of economic development necessitates a heightened focus on matters of sustainability, with a particular emphasis on sustainable energy policy. This is of paramount importance for the protection of the natural environment and the achievement of long-term economic growth. In the context of countries such as the Czech Republic and Poland, which have historically relied on high-carbon energy sources, the transition to a more sustainable energy system represents a significant challenge. The objective of this paper is to undertake a comparative analysis of the trends in energy sustainability in the Czech Republic and Poland from 2017 to 2021, with a particular focus on key performance indicators. The analysis, based on data from the OECD database, revealed notable discrepancies in the rate of change between the two countries, with Poland exhibiting a more pronounced surge in the proportion of renewable energy sources (RES). A Student’s t-test confirmed the existence of statistically significant differences in key indicators between the Czech Republic and Poland, thereby underscoring the diverse challenges that both countries encounter in their pursuit of sustainable energy development. The Granger causality test was employed to ascertain whether variables exhibit temporal relationships that may suggest potential correlations. However, it is important to note that this test does not prove direct causality, but rather indicates that the variables are related at a specific point in time. Interpretation of the results must be undertaken with caution, as the test does not account for the full complexity of relationships between variables, including external factors and structural changes in the economy. Meanwhile, the LMDI decomposition analysis identified the principal drivers of alterations in CO₂ emissions. The findings indicate that, despite advancements in sustainable energy development, Poland and the Czech Republic are confronted with distinctive challenges that necessitate the implementation of tailored policy responses. It is therefore recommended that further investment in renewable energy and the modernisation of energy infrastructure be made in order to achieve long-term sustainability goals. Full article

This paper proposes causal economic machine learning (CEML) as a research agenda that utilizes causal machine learning (CML), built on causal economics (CE) decision theory. Causal economics is better suited for use in machine learning optimization than expected utility theory (EUT) and behavioral economics (BE) based on its central feature of causal coupling (CC), which models decisions as requiring upfront costs, some certain and some uncertain, in anticipation of future uncertain benefits that are linked by causation. This multi-period causal process, incorporating certainty and uncertainty, replaces the single-period lottery outcomes augmented with intertemporal discounting used in EUT and BE, providing a more realistic framework for AI machine learning modeling and real-world application. It is mathematically demonstrated that EUT and BE are constrained versions of CE. With the growing interest in natural experiments in statistics and causal machine learning (CML) across many fields, such as healthcare, economics, and business, there is a large potential opportunity to run AI models on CE foundations and compare results to models based on traditional decision-making models that focus only on rationality, bounded to various degrees. To be most effective, machine learning must mirror human reasoning as closely as possible, an alignment established through CEML, which represents an evolution to truly “human AI”. This paper maps out how the non-linear optimization required for the CEML structural response functions can be accomplished through Sequential Least Squares Programming (SLSQP) and applied to data sets through the S-Learner CML meta-algorithm. Upon this foundation, the next phase of research is to apply CEML to appropriate data sets in various areas of practice where causality and accurate modeling of human behavior are vital, such as precision healthcare, economic policy, and marketing. Full article

Wheat (Triticum aestivum L.) is one of the most agriculturally and economically important crops in the world. Wheat fungal diseases are becoming more severe and frequent due to global climate change, threatening wheat yields and security. While fungal diseases such as fusarium head blight, stripe rust, and powdery mildew have been extensively studied, the newly emerged fungal pathogens in wheat are still under-researched. In May 2023, black mold symptoms were observed on wheat spikes in Xinxiang City, Henan Province, China. However, the causal agent of this disease was not known. We employed a combination of morphological examination and molecular techniques to identify the pathogen. The internal transcribed spacer (ITS) region, translation elongation factor 1-alpha (tef1), and actin (act) genes of the fungus were partially sequenced (accession no. OR186209, PQ271633 and PQ271632) and showed 99.59–100% identity with the previously reported Cladosporium cladosporioides, which affects wheat, pokeweed, and black-eyed pea. The pathogenicity of this fungus was confirmed by fulfilling Koch’s postulates. Through a rigorous screening process, we found Simplicillium aogashimaense, Trichothecium roseum, and Bacillus velezensis as effective biocontrol agents, with B. velezensis demonstrating the most potent antagonistic activity against the Cladosporium mold. This discovery showed the potential of B. velezensis as a biocontrol agent for wheat disease management. The findings underscore the importance of the present study in advancing the control of this disease. Full article

The global environment has recently been facing sustainability threats owing to industrial and economic expansions. Accordingly, this study empirically examines the impact of carbon emissions and the directional causality between carbon emissions and environmental quality, financial development, and economic growth. We used data from 65 economies from 2010 to 2021, applying fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) approaches. Generally, the findings from the analysis revealed that the estimated coefficients of carbon emissions were negative and significant across the model, except for greenhouse gas emissions, which produced an insignificant result in developed economies. This result proves that an increase in carbon emissions and other forms of pollution are detrimental to environmental quality, economic growth, and financial development. Further results revealed that fossil fuels are positively and significantly related to the economic growth and financial development of selected countries. Empirical outcomes indicate that ineffective control of environmental pollution and carbon emissions is a major challenge to the economic growth trajectories of the selected countries, especially in emerging economies. The results from directional relationships revealed that bi-directional causality exists between CO₂ and GDP; between total greenhouse gas emissions and economic growth, with no directional relationship of CO₂ emissions to financial development and vice versa; and economic growth to CO₂ emissions from gaseous fuel consumption and vice versa. Generally, this outcome indicates that improved environmental quality control can accelerate economic growth and financial development worldwide. This study provides insights to governments, policymakers, international organizations, researchers, and many other stakeholders. This study suggests that stricter fiscal and monetary policies, laws, and regulations, such as environmental taxes and carbon emission taxes, with strong implementation strategies, especially in emerging economies, are strongly recommended worldwide. Further recommendations suggest the development of technologically innovative policies that can counter all the impacts of devastating human activities on the environment, and these are encouraged. A greater consumption of renewable energy and the use of other innovative machines that are environmentally friendly and can help control various forms of pollution and carbon emissions have been encouraged globally. Full article

Drug-resistant epilepsy is frequent, persistent, and brings a heavy economic burden to patients and their families. Traditional epilepsy detection methods ignore the causal relationship of seizures and focus on a single time or spatial dimension, and the effect varies greatly in different patients. Therefore, it is necessary to research accurate automatic detection technology of epilepsy in different patients. We propose a causal-spatio-temporal graph attention network (CSTGAT), which uses transfer entropy (TE) to construct a causal graph between multiple channels, combining graph attention network (GAT) and bi-directional long short-term memory (BiLSTM) to capture temporal dynamic correlation and spatial topological structure information. The accuracy, specificity, and sensitivity of the SWEZ dataset were 97.24%, 97.92%, and 98.11%. The accuracy of the private dataset reached 98.55%. The effectiveness of each module was proven through ablation experiments and the impact of different network construction methods was compared. The experimental results indicate that the causal relationship network constructed by TE could accurately capture the information flow of epileptic seizures, and GAT and BiLSTM could capture spatiotemporal dynamic correlations. This model accurately captures causal relationships and spatiotemporal correlations on two datasets, and it overcomes the variability of epileptic seizures in different patients, which may contribute to clinical surgical planning. Full article

One of the worst natural, economic, and social catastrophes caused by human activity is the Aral Sea crisis in Central Asia. The Aral Sea’s desiccation, which has an impact on the region’s overall sustainable development, human welfare, security, and survival, is what led to the problem. This study assesses the effects of economic expansion, population ageing, life expectancy, internet usage, and greenhouse gas emissions on the fertility rate in the countries that made up the Aral Sea basin between 1990 and 2021. Several econometric techniques were used in this study, including Pooled OLS (Ordinary Least Squares) with the Driscoll–Kraay estimating method, FMOLS (Fully Modified Ordinary Least Square), and DOLS (Dynamic Ordinary Least Square). Additionally, we used the Hurlin and Dumitrescu non-cause tests to verify the causal links between the variables. The empirical findings verify that a decrease in the fertility rate among women in the nations surrounding the Aral Sea occurs when the population of a certain age (women aged 15–64 as a percentage of the total population) grows and life expectancy rises. Greenhouse gas emissions (GHGs) also have an adverse effect on reproductive rates. Conversely, the region’s fertility rate may rise as a result of increased internet usage and economic growth. Furthermore, this study indicates that certain variables—aside from greenhouse gas emissions (GHGs)—have a causal relationship with the fertility rate. Full article

The environmental degradation in the Middle East and North Africa (MENA) region leads to significant challenges regarding economic sustainability and the attainment of sustainable development goals (SDGs). The extensive use of fossil fuels in the region, as well as rapid urbanization and economic growth, has led to significant carbon emissions, together with unprecedented ecological footprints compromising environmental sustainability. The study aims to elucidate the influence exerted by technological innovation, trade openness, and natural resources on environmental sustainability in Turkey and Egypt for the period 1990–2022. In assessing the empirical relations, the study employed the Fourier function incorporate estimation techniques, that is, Fourier ADF for unit root test, Fourier ARDL, and Fourier NARDL for long-run and short-run elasticities of technological innovation (TI), trade openness (TO,) and natural resources rent (NRR) on load capacity factor (LCF) and inverted LCF (ILCF); finally, the directional causality evaluate through Fourier TY causality test. The results revealed that both Turkey and Egypt have severe environmental problems due to their high carbon emissions and ecological footprints. Technological change and international trade separately negatively affect environmental sustainability; however, these negative impacts have mixed character. On the one hand, technology can improve efficiency and reduce ecological footprints by obviating the use of high-impact processes or allowing cleaner production systems. In the same vein, trade openness helps transfer green technologies more quickly, but it can also lead to unsustainable resource extraction and pollution. The findings of the paper propose that in order to move forward, Turkey and Egypt need strategic policy shifts to ensure environmental sustainability, including transitioning towards renewable energy from fossil fuels while bolstering their capacity for energy efficiency. Policymakers must balance economic development with environmental conservation to reduce the harmful effects of climate degradation and help safeguard continued economic survival in the face of increasing climatic instability. This research helps to inform policy and investment decisions about how the SDGs can be achieved and how they are relevant for sustainable development in the MENA region. Full article

The construction industry is heavily influenced by the volatility of material prices, which can significantly impact project costs and budgeting accuracy. Traditional econometric methods have been challenged by their inability to capture the frequent fluctuations in construction material prices. This paper reviews the application of data-driven techniques, particularly machine learning, in forecasting construction material prices. The models are categorized into causal modeling and time-series analysis, and characteristics, adaptability, and insights derived from large datasets are discussed. Causal models, such as multiple linear regression (MLR), artificial neural networks (ANN), and the least square support vector machine (LSSVM), generally utilize economic indicators to predict prices. The commonly used economic indicators include but are not limited to the consumer price index (CPI), producer price index (PPI), and gross domestic product (GDP). On the other hand, time-series models rely on historical price data to identify patterns for future forecasting, and their main advantage is demanding minimal data inputs for model calibration. Other techniques are also explored, such as Monte Carlo simulation, for both price forecasting and uncertainty quantification. The paper recommends hybrid models, which combine various forecasting techniques and deep learning-advanced time-series analysis and have the potential to offer more accurate and reliable price predictions with appropriate modeling processes, enabling better decision-making and cost management in construction projects. Full article

Botswana has had fair and stable elections since its independence in 1966. It has a relatively high standard of living, a relatively well-functioning welfare state, and relatively low levels of government corruption. Voter participation is among the highest in the world, topping 80 percent in the past elections. Access to education and healthcare is free to all citizens. How can we best explain Botswana’s exceptionalism in the political, economic, and social realms, and what policy lessons does the case of Botswana contain? This article shows that it is Botswana’s millennial tradition of direct village democracy, kgotla, that best explains its exceptional performance. I visited Botswana in May of 2023 to evaluate the importance of participatory democracy in Botswana and assess its explanatory power. When comparing local participation to other, potentially relevant causal factors, I find that local political participation provides the most robust explanation for good governance in Botswana. In Botswana, citizens are able to hold their elected officials accountable, learn how politics works by acquiring the necessary technical knowledge (techne) through participating in regular, monthly public assemblies, and, as a result, make better-informed political decisions. Full article

Background: Diseases of veterinary importance, such as bovine Anaplasmosis, cause significant economic losses. Due to this, the study of various proteins of the causal agent Anaplasma marginale has focused on surface proteins. However, a vaccine for this disease is not yet available. To this end, in this work, moonlighting proteins (MLPs) are presented as an alternative approach for the design of immunogens against A. marginale. Methods: The proteins of the strain MEX-15-099-01 were analyzed, and its MLPs were identified. Subsequently, four virulence-associated MLP genes were selected and identified using PCR. The proteins were analyzed using a structural homology approach and the collection of B-cell epitopes was predicted for each MLP. Finally, a pair of AmEno peptides were synthesized and the antigenic potential was tested using an iELISA. Results: Our bioinformatics analysis revealed the potential of AmEno, AmGroEl, AmEF-Tu, and AmDnaK proteins as promising candidates for designing immunogens. The PCR allowed the gene sequence identification in the genome of the strain MEX-15-099-01. Notably, AmEno-derived synthetic peptides showed antigenicity in an ELISA. Conclusions: Our study has shed light on the potential use of MLPs for immunogen design, demonstrating the antigenic potential of AmEno. Full article

While there is a vast body of literature on environmental sustainability, the disaggregated impact of major non-renewable energy (NRE) consumption on the environmental sustainability of the United States (U.S.) is understudied, particularly in terms of using a load capacity factor (LCF) perspective. In this study, the above research gap is addressed using a dynamic autoregressive distributed lag (DYNARDL) model to analyze the heterogeneous impact of NRE consumption on the environmental sustainability of the U.S. from 1961 to 2022. Given the U.S.’s heavy reliance on energy consumption from NRE sources, this analysis provides an in-depth examination of the long-term effects of this energy consumption on the environment. Based on the analysis of the DYNARDL model, it is found that an increase of one unit of coal, natural gas, and petroleum energy consumption reduces environmental sustainability by 0.007, 0.006, and 0.008 units in the short-run and 0.006, 0.004, and 0.005 units in the long-run, respectively. However, one unit of nuclear energy consumption increases environmental sustainability by 0.007 units in the long-run. The kernel-based regularized system (KRLS) result reveals that coal and petroleum energy consumption have a significantly negative causal link with environmental sustainability, while nuclear energy consumption demonstrates a significant positive causal relationship. The research suggests the expansion of the use of nuclear energy by gradually reducing the utilization of coal and petroleum-based forms of energy, then natural gas, to improve environmental sustainability in the U.S., while considering the social and economic implications of efforts aimed at shifting away from the use of fossil fuels. Full article

The implementation of rural entrepreneurship emerges as a pivotal pathway for fostering rural economic growth. However, unsustainable entrepreneurial endeavors have posed notable ecological threats and environmental degradation. Drawing upon the triple bottom line framework, this research devised a comprehensive evaluation system for sustainable rural entrepreneurship, spanning four dimensions: entrepreneurs, economic, social, and environmental aspects. Employing the fuzzy Decision-Making Trial and Evaluation Laboratory (DANP) approach, we delineated the intricate causal relationships among influencing factors and identified key determinants along with their respective weights. Our findings underscore the prominence of economic dimensions in fostering sustainable rural entrepreneurship. Specifically, entrepreneurial motivation, type of entrepreneurship, financial backing, economic value, favorable policy frameworks, and a conducive business environment emerged as pivotal indicators. Notably, the type of entrepreneurship, financial support, economic value, and favorable policies emerged as propelling factors driving sustainable rural entrepreneurial progress. Conversely, entrepreneurial motivation and the business environment manifested as dependent factors within this causal nexus. This study offers valuable managerial implications for entrepreneurial teams and pertinent government agencies, enabling decision-makers to formulate evidence-based strategies aimed at realizing sustainable rural entrepreneurship. Full article