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As China progresses with its electricity market reforms in pursuit of "carbon peak and carbon neutrality" objectives, the increasing integration of renewable energy sources introduces new risks and uncertainties, necessitating the development of an efficient electricity financial market. This paper outlines the fundamental principles of electricity financial derivatives, assesses their applicability to the Chinese market through an analysis of international experiences from the United States, Nordic countries, and Australia, and highlights critical issues for the construction of a robust market framework. It offers strategic recommendations regarding the structural and developmental aspects of China's electricity financial market and proposes derivative instruments tailored to China's market to improve liquidity and risk management mechanisms, thereby facilitating the renewable energy transition. The study demonstrates that these derivatives are instrumental in mitigating price volatility, managing transmission congestion, and supporting the shift to renewable energy. This provides a pragmatic approach for the reform and advancement of China's electricity financial market, aligning with global strategies and addressing the unique challenges of China's energy transition. [ABSTRACT FROM AUTHOR]

As China's financial markets become increasingly integrated and the carbon market undergoes financialization, the impact of carbon emission price fluctuations on financial markets has emerged as a key area of systemic risk research. This study employs the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model and the optimal Copula function to investigate the dynamic correlation between carbon prices and China's financial markets. Building on this, the Monte Carlo simulation and Copula CoVaR models are used to explore the spillover effects of carbon price volatility on China's financial markets. The findings reveal the following: (1) Carbon price fluctuations generate spillover effects on all financial markets, but the intensity varies across different markets. The foreign exchange market experiences the strongest spillover effect, followed by the bond market, while the stock and money markets are relatively less affected. (2) The optimal Copula functions differ between the carbon market and China's financial markets, indicating heterogeneous characteristics across regional markets. (3) There is a degree of interdependence between the carbon market and various sub-markets in China's financial system. The carbon market has the strongest positive correlation with the commodity market and a relatively high negative correlation with the real estate market. These findings underscore the importance of integrating carbon price volatility into financial risk management frameworks. For policymakers, it highlights the need to consider market stability measures when crafting carbon emission regulations. Market managers can leverage these insights to develop strategies that mitigate risk spillover effects, while investors can use this analysis to inform their portfolio diversification and risk assessment processes. [ABSTRACT FROM AUTHOR]

Blockchain tokens have accumulated tremendous market value but remain highly controversial, given their price volatility and seemingly speculative nature. Ironically, this very characteristic can foster token retention as users wait for occasions of appreciation. In this paper, we conduct an empirical analysis with 58 tokens in two steps: first, an investigation of the drivers of user activity and token price volatility using a new blockchain token classification framework, searching for possible tokenomics links. Our findings suggest that there is an intrinsic relationship between the way tokens are used as a means of exchange and how token usage dynamics influence user engagement oppositely to market stability. Only some features, such as earning potential and voting rights, foster token-holding strategies, while only Ethereum ecosystem membership has positive effects on price volatility. Second, we analyze the direct relationship between price volatility and active users. Results show that, on average, a 10% increase in volatility is related to a decrease in active addresses ranging between 3.96% and 5.88%. The finding is supportive of the hypothesis that token price volatility may be treated as an opportunity to increase token retention. [ABSTRACT FROM AUTHOR]

This study examines the moderating role of profitability in the relationship between leverage and dividend policy in listed manufacturing firms in Nigeria and South Africa. Using a sample of 915 firm-year observations from 2013 to 2022, the analysis employs panel Tobit regression to manage the censored nature of dividend data, with logistic regression applied as a robustness check. The findings reveal a negative association between leverage and dividend payout ratio for Nigerian firms, while this association is less pronounced and statistically insignificant in South Africa, reflecting a more flexible financial environment. Profitability strengthens the leverage–dividend policy relationship in Nigeria, enabling firms to maintain dividends despite high leverage; however, this moderating effect is weaker in South Africa. These results underscore the importance of context-specific financial strategies, recommending that Nigerian policymakers improve access to affordable credit, while South African policymakers focus on sustaining market stability. This study advances the understanding of dividend policy in emerging markets by clarifying how leverage and profitability interact to shape dividend practices. [ABSTRACT FROM AUTHOR]

This study sought to ascertain a machine learning algorithm capable of predicting crises in the African stock market with the highest accuracy. Seven different machine-learning algorithms were employed on historical stock prices of the eight stock markets, three main sentiment indicators, and the exchange rate of the respective countries' currencies against the US dollar, each spanning from 1 May 2007 to 1 April 2023. It was revealed that extreme gradient boosting (XGBoost) emerged as the most effective way of predicting crises. Historical stock prices and exchange rates were found to be the most important features, exerting strong influences on stock market crises. Regarding the sentiment front, investors' perceptions of possible volatility on the S&P 500 (Chicago Board Options Exchange (CBOE) VIX) and the Daily News Sentiment Index were identified as influential predictors. The study advances an understanding of market sentiment and emphasizes the importance of employing advanced computational techniques for risk management and market stability. [ABSTRACT FROM AUTHOR]

This study assesses the application of artificial intelligence (AI) algorithms for optimizing resource allocation, demand-supply matching, and dynamic pricing within circular economy (CE) digital marketplaces. Five AI models—autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), random forest (RF), gradient boosting regressor (GBR), and neural networks (NNs)—were evaluated based on their effectiveness in predicting waste generation, economic growth, and energy prices. The GBR model outperformed the others, achieving a mean absolute error (MAE) of 23.39 and an R 2 of 0.7586 in demand forecasting, demonstrating strong potential for resource flow management. In contrast, the NNs encountered limitations in supply prediction, with an MAE of 121.86 and an R 2 of 0.0151, indicating challenges in adapting to market volatility. Reinforcement learning methods, specifically Q-learning and deep Q-learning (DQL), were applied for price stabilization, resulting in reduced price fluctuations and improved market stability. These findings contribute a conceptual framework for AI-driven CE marketplaces, showcasing the role of AI in enhancing resource efficiency and supporting sustainable urban development. While synthetic data enabled controlled experimentation, this study acknowledges its limitations in capturing full real-world variability, marking a direction for future research to validate findings with real-world data. Moreover, ethical considerations, such as algorithmic fairness and transparency, are critical for responsible AI integration in circular economy contexts. [ABSTRACT FROM AUTHOR]

The rapid increase in distributed energy resources has augmented the significance of virtual power plants (VPPs), which are essential for the aggregation and management of variable renewable energy resources (RERs). The inherent variability and uncertainty of RERs necessitate the implementation of deviation penalties to address the discrepancies between the awarded bids and actual generation, which is crucial in maintaining market stability and ensuring reliable grid operations. Therefore, this study proposes a framework for deviation penalty structures, categorizing penalties based on three factors: the penalty scope, penalty rate, and penalty coefficient. The simulation results show that the penalty scope significantly influences the revenue of VPPs, with over-generation penalty structures typically yielding higher profitability. Conversely, dual-sided penalty structures result in lower total revenues compared to one-sided penalty structures. For instance, when the penalty price coefficient is set to 0.1, the total revenue of a dual-sided penalty structure is approximately 62.26% lower than that of a one-sided penalty structure during the morning period. The results also demonstrate that deviation penalty structures have a direct impact on power deviations and curtailment behavior. Finally, we offer recommendations for the design of an effective penalty structure aimed at assisting policymakers and distributed system operators (DSOs) in structuring market mechanisms, which not only facilitate the integration of RERs but also enhance their economic viability within electricity markets. [ABSTRACT FROM AUTHOR]

Lithium, a crucial raw material for new energy vehicles, is experiencing significant market price fluctuations due to escalating geopolitical conflicts, periodic mismatches in supply and demand, and increased attention to lithium resources from countries around the world. These factors may adversely affect the development of the new energy vehicle industry. This paper adopts the TVP-VAR-DY model, which measures dynamic spillover effects by allowing for variance changes through the estimation of a stochastic Kalman filter, thereby measuring risk spillover among upstream and downstream firms in the lithium industry chain. We selected 16 listed companies and six regional financial markets as the research sample, with the sample period from 4 July 2018, to 30 June 2023. The main conclusions are as follows: Between 2018 and 2020, the overall risk spillover in the lithium industry chain demonstrated a declining trend, though it experienced a sudden surge in 2020 as a result of the COVID-19 pandemic. This increase was followed by a gradual decline as the global economy improved and market stability was restored, leading to a reduction in risk aversion. Regarding the reception of risk spillovers, upstream firms exhibited a generally consistent level of directional risk spillovers, whereas downstream firms experienced more significant fluctuations. Chinese firms exhibited a higher level of received risk spillovers compared to their international counterparts, with less variation in these spillovers. From the perspective of risk spillover effects, significant variations were observed between firms in both the upstream and downstream markets. Chinese firms exhibited a higher level of risk inflow than international firms, with more pronounced changes in risk spillovers. Upstream enterprises should enhance their market competitiveness to mitigate the adverse effects of economic uncertainty. Downstream enterprises can alleviate the rise in raw material costs resulting from market price fluctuations through strategic cooperation. Additionally, the government should increase the market supply of resources, which will contribute to the establishment of a more robust lithium industry chain system. [ABSTRACT FROM AUTHOR]

The collapse of Silicon Valley Bank (SVB) on 11 March 2023, and the subsequent depegging of the USDC stablecoin highlighted vulnerabilities in the interconnected financial ecosystem. While prior research has explored the systemic risks of stablecoins and their reliance on traditional banking, there has been limited focus on how banking sector shocks affect digital asset markets. This study addresses this gap by analyzing the impact of SVB's collapse on the stability of major stablecoins—USDC, DAI, FRAX, and USDD—and their relationships with Bitcoin and Tether. Using daily data from October 2022 to November 2023, we found that the SVB incident triggered a series of depegging events, with varying effects across stablecoins. Our results indicate that USDC, often viewed as one of the safer stablecoins, was particularly vulnerable due to its reliance on SVB reserves. Other stablecoins experienced different impacts based on their collateral structures. These findings challenge the notion of stablecoins as inherently safe assets and underscore the need for improved risk management and regulatory oversight. Additionally, this study illustrates how machine learning models, including gradient boosting and random forests, can enhance our understanding of financial contagion and market stability. [ABSTRACT FROM AUTHOR]

The System Marginal Price (SMP) is the cost of the last unit of electricity supplied to the grid, reflecting the supply–demand equilibrium and serving as a key indicator of market conditions. Accurate SMP forecasting is essential for ensuring market stability and economic efficiency. This study addresses the challenges of SMP prediction in Turkey by proposing a comprehensive forecasting framework that integrates machine learning, deep learning, and statistical models. Advanced feature selection techniques, such as Minimum Redundancy Maximum Relevance (mRMR) and Maximum Likelihood Feature Selector (MLFS), are employed to refine model inputs. The framework incorporates time series methods like Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional LSTM (ConvLSTM) to capture complex temporal patterns, alongside models such as Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Extreme Learning Machine (ELM) for modeling non-linear relationships. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE) across regular weekdays, weekends, and public holidays. XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating exceptional accuracy and robustness. Among all of the models, XGBoost combined with MLFS consistently achieved the lowest MAPE values, demonstrating superior accuracy and robustness. The results highlight the inadequacy of traditional models like ARIMA and SARIMA in capturing non-linear and highly volatile patterns, reinforcing the necessity of using advanced techniques for effective SMP forecasting. Overall, this study presents a novel and comprehensive approach tailored for complex electricity markets, significantly enhancing predictive reliability by incorporating economic indicators and sophisticated feature selection methods. [ABSTRACT FROM AUTHOR]

The globe is transitioning from traditional methods of electricity generation to renewable resources in order to achieve sustainable goals. Solar energy is a promising and abundant renewable resource that shows great potential as a viable alternative to traditional energy. Furthermore, the production of electricity from solar energy is the most cost-effective compared to other kinds of renewable energy. Nevertheless, the execution of solar initiatives in underdeveloped nations is encountering several obstacles. Identifying the most significant obstacles in the execution of solar projects is of utmost importance. This study uses a linear regression model (LRM) and an analytical hierarchical process (AHP) to determine the main barriers to the implementation of renewable energy projects in a developing economy, i.e., Pakistan. By conducting an extensive review of the relevant literature and consulting with experts, the most significant categories of obstacles were determined. A survey based on 429 responses was collected from the participants working at solar projects. Subsequently, the responses were subjected to processing and analysis using the relative importance index (RII), AHP, and linear regression modeling techniques. The linear regression analysis revealed several significant variables that hinder progress, including financial conditions, policies, technological awareness, institutional support, social and environmental awareness, market stability, and other miscellaneous factors. The AHP analysis revealed the key factors that have the greatest impact, which include effective policies, financial stability, technological expertise, institutional support, market stability, various aspects, and social and environmental awareness. The study's conclusions are beneficial for all stakeholders and project managers in enhancing the project management of solar initiatives. It would also facilitate prompt decision-making regarding policy formulation and implementation. [ABSTRACT FROM AUTHOR]

As a colossal developing economy, irrational, and inefficient trades broadly exist in China's stock market and are intensified by the once-in-a-century COVID-19 pandemic. This atypical but prominent event enhances systemic risk and requires a more effective analysis tool that adapts to the investors' sentiment and behavior. Based on the behavioral asset pricing model, this paper verifies the existence of noise traders in China's stock market, measures the intensity of the noise with the NTR indicator, and examines the market noise with IANM. Furthermore, the mechanism of how COVID-19 influences the market noise through investors' behaviors is analyzed with the event study method. The findings show that, based on 92 Chinese companies, the market noise significantly exists, and the noise is associated with psychological biases including over-confidence, herding effects and regret aversion. These biases are affected to varying degrees by COVID-19-related events, leading to notable implications for market stability and investor behavior during crises. Our study provides critical insights for policymakers and investors on managing market risks and understanding behavioral impacts during unprecedented events. [ABSTRACT FROM AUTHOR]

Based on different carbon quota trading mechanisms, the price and emission reduction strategies of oligopoly manufacturers in the low-carbon market and the government carbon quota mechanism are considered. A dynamic game evolution model of the two oligopoly manufacturers with competitive relations is established. The stability of the equilibrium point of the game model, the price adjustment speed of the decision variable, the impact of carbon emission reduction investment, and the government carbon quota on the system are discussed. Through nonlinear dynamics research, it is found that the advantage of the grandfathering method is that it is conducive to maintaining market stability when the government's carbon quota decision changes; the advantage of the benchmarking method is that when manufacturers formulate price adjustment strategies, the benchmarking method carbon quota mechanism has a stronger stability range for the market, the manufacturer's profit price adjustment speed is positively correlated, and the government carbon quota decision and emission reduction investment are also positively correlated. Decision makers need to choose appropriate carbon quota mechanisms and manufacturers' emission reduction strategies according to actual market changes to maintain supply chain stability. [ABSTRACT FROM AUTHOR]

The supply of livestock products depends on many internal and external factors. Omitting any one factor can make it difficult to describe the market patterns. So, forecasting livestock indexes such as prices and supplies is challenging due to the effect of unknown factors. This paper proposes a Stacking Forest Ensemble method (SFE-NET) to forecast pork supply by considering both internal and external factors, thereby contributing to sustainable pork production. We first analyze the internal factors to explore features related to pork supply. External factors such as weather conditions, gas prices, and disease information are also collected from different sources. The combined dataset is from 2016 to 2022. Our SFE-NET method utilizes Random Forest, Gradient Boosting, and XGBoost as members and a neural network as the meta-method. We conducted seven experiments for daily, weekly, and monthly pork supply using different sets of factors, such as internal, internal and external, and selected. The results showed the following findings: (a) The proposed method achieved Coefficient of Determination scores between 84% and 91% in short and long periods, (b) the external factors increased the performance of forecasting methods by about 2% to 12%, and (c) the proposed stacking ensemble method outperformed other comparative methods by 1% to 18%. These improvements in forecasting accuracy can help promote more sustainable pork production by enhancing market stability and resilience. [ABSTRACT FROM AUTHOR]

No form of economic activity is unaffected by climate change, which has emerged as a new risk factor impacting financial market stability and sustainable development. This study examines the impact of extreme weather on the stock returns of A-share listed companies in China. Utilizing a decade-long dataset, we construct monthly proportions of extreme high-temperature days and extreme humid days using a percentile comparison approach. The findings reveal a significant negative impact of extreme weather on stock returns. Specifically, each standard deviation increase in the monthly proportion of extreme high-temperature days and extreme humid days corresponds to a decrease in annualized returns by 0.09% and 0.15%, respectively. The mediation analysis suggests that extreme weather primarily affects stock returns through its influence on investor sentiment, impacting economic decision making, with minimal direct effects on corporate performance. Additionally, the sensitivity of stock returns to extreme weather varies notably among different types of companies. Larger, more profitable, and less risky firms show lower sensitivity to extreme weather. The impact is observed not only in heat-sensitive industries but also in non-heat-sensitive industries and remains significant even after excluding company announcement days. This study offers new insights and relevant recommendations for businesses and policymakers on sustainable development and financial stability. [ABSTRACT FROM AUTHOR]

This research examines the effect of ESG disclosure on the cost of capital for non-financial firms in the UK, indexed by the FTSE All-Share Index, during the period from 2014 to 2018. Using multivariate analysis with ordinary least squares (OLS), fixed effects, robust regression, and Tobit models, this research assesses the effect of ESG reporting, governance, and the cost of capital, including robustness checks using an alternative ESG indicator, the environmental pillar score. Contrary to expectations, ESG reporting is positively associated with the cost of capital. However, corporate governance moderates this relationship, weakening the positive correlation and reversing it to a negative association for firms with strong governance practices, consistent with the hypotheses. This research also finds that firm size, liquidity, profitability, and leverage, positively affect the cost of capital, while board size, independent board composition, audit committee independence, and auditor type do not significantly influence it. Notably, non-executive directors on the audit committee have a significant negative effect on the cost of capital. These findings are valuable for investors, companies, regulators, auditors, policymakers, and the academic and research community. Specifically, for investors, this study provides insights into how ESG disclosures can influence investment risks and returns, highlighting the importance of robust corporate governance. Companies can leverage these insights to enhance their governance practices and optimize their capital costs. Regulators and policymakers can use the findings to develop guidelines that encourage transparent ESG reporting and strong governance frameworks, thereby improving market stability and investor confidence. Auditors can utilize the results to better understand the effect of non-financial reporting on financial metrics, helping to provide more accurate audits and assessments. These findings inform investors, companies, regulators, auditors, and academia, in fostering a more sustainable and transparent financial environment. [ABSTRACT FROM AUTHOR]

The stock market operates on informed decisions based on information gathered from heterogeneous sources, encompassing diverse beliefs, strategies, and knowledge. This study examines the validity of rational bubbles in stock market prices, focusing on eight African stock markets: South Africa, Nigeria, Kenya, Egypt, Morocco, Mauritius, Ghana, and Botswana. Utilizing newly developed econophysics-based unit root tests and the Dynamic Conditional Correlation Multivariate Generalized Autoregressive Conditional Heteroskedasticity (DCC MGARCH) models, the authors analyzed daily data from 1996 to 2022. Our findings indicate that these markets experienced bubbles at various points, often followed by bursts. These bubbles coincided with significant economic changes, suggesting a strong link between stock market behavior and economic growth. For instance, financial crises, political instability, and global economic downturns significantly influenced bubble formation and bursts in these markets. The study reveals that market-specific events, such as regulatory changes and shifts in investor sentiment, also contributed to the occurrence of bubbles. Three key policy options are proposed to address bubbles in the studied markets including, enhancing regulatory frameworks to monitor and mitigate bubble formation, improving financial literacy among investors to promote informed decision-making, and strengthening economic policies to stabilize macroeconomic conditions and reduce vulnerability to external shocks. By implementing these measures, policymakers can enhance market stability and foster sustainable economic growth in African stock markets. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the impact of short-stay accommodation (SSA) on housing dynamics, community welfare, and economic development in the Margaret River regional area. Grounded in an exploratory research paradigm, qualitative methods were used to gather insights from key stakeholders such as property owners, realtors, and council officials. Purposive sampling was utilized to select participants with diverse experiences and perspectives on SSA. Semi-structured interviews were conducted, totaling 21 interview sessions spanning almost 600 min, to capture in-depth and nuanced perspectives on SSA's impact and regulatory landscape. Thematic analysis of the interview data, which was divided into two segments, revealed significant concerns regarding housing affordability, community well-being, and regulatory effectiveness. Additionally, the study uncovered a complex relationship between SSA and population dynamics, highlighting implications for the local housing infrastructure and market stability. The findings underscore the urgency of collaborative policy interventions to address the housing crisis and mitigate the adverse effects of SSA on local communities. Policymakers and stakeholders can utilize these insights to develop holistic strategies that prioritize equity, inclusivity, and shared prosperity in shaping the future of housing in regional areas such as Margaret River. This study contributes original insights by providing a nuanced understanding of the multifaceted implications of SSA on housing dynamics and community welfare in a regional context. By using qualitative methods and engaging key stakeholders, the research offers unique perspectives on the challenges and opportunities associated with the proliferation of SSA, thereby advancing scholarly discourse, and informing evidence-based policymaking in the field of housing studies. [ABSTRACT FROM AUTHOR]

This paper investigates the relationships of the Bloomberg Gender Equality Index and the MSCI World Index in global financial markets. The main objective is to analyze the degree of integration of each index from a fractional perspective for the years 2014–2021. The methodology involves fractional integration to assess the consistency and integration levels of both indices, revealing that they are remarkably consistent with integration orders close to 1 and no evidence of mean-reverting behavior. When examining potential cointegrating relationships between the two indices using the classical two-step method of Engle and Granger, the order of integration of the estimated errors is very close to 1, showing no evidence of cointegration. However, employing the more robust fractional CVAR (FCVAR) approach, the results strongly support the hypothesis of cointegration, indicating evidence of long-term co-movements between the two indices. The findings suggest that investment strategies should incorporate gender diversity criteria, as companies aligning with these benchmarks may enhance co-movements with the Bloomberg Gender Equality Index. Policymakers should promote transparency and initiatives that support gender diversity to improve market stability. [ABSTRACT FROM AUTHOR]

We studied the relationship between sustainable investment indexes and examine whether this relationship varies in bullish, bearish, and stable financial markets. To understand this issue more deeply, we analyzed the connectedness between three indexes—the Sustainable Impact investments, Paris-aligned stocks, and green bonds indexes—using the daily closing prices from 1 June 2017 to 15 April 2024, encompassing 1793 observations. We used a quantile vector autoregressive (QVAR) model to understand the dynamic relationship among the considered indices. The findings indicate that sustainable investments are strongly interconnected in both high and low quantiles, but this connection weakens significantly during periods of market stability. The Sustainable Impact investments and Paris-aligned stocks indexes are net transmitters of impacts to other sustainable alternatives, while the green bonds index is a net receiver. We also observed an increase in interconnectedness across all quantiles during the pandemic, the Russia–Ukraine military conflict, and changes in the European Union and the United States' monetary policies. [ABSTRACT FROM AUTHOR]

This article deals with the liability of the supervisory authority of the financial market. It could be questioned whether the supervisory authority, as the public authority, is liable for the supervisory performance. If the answer is yes, then the question is what kind of liability could be found and if any special conditions (prerequisites) are needed. In general, there could be two lines of public liability found. One is systemic liability for the safe financial market—e.g., financial market stability. The other perspective is individual liability for damages caused by unlawful administrative procedure or maladministration, where unlimited strict liability is granted. This kind of liability might be widely questioned, especially when the central bank is the supervisory authority, like in the Czech Republic, Slovakia, Hungary, and other EU member states. This article aims to evaluate the liability of the supervisory authority in the Czech Republic and Hungary concerning the European level of such liability. [ABSTRACT FROM AUTHOR]

The oil and gas industries are crucial to global economies, influencing geopolitics, driving technological advancements, employing millions, and impacting financial markets. The complexity and the volume of data generated by these industries demonstrate the need for efficient information management, where effective contract audits play a key role in ensuring market stability, transparency, fair revenue distribution, corruption mitigation, and enhancing industry integrity to attract investors. This study employs bibliometric analysis to explore the application of machine learning (ML) in detecting anomalous contracts within the oil and gas industry. This analysis identifies key research and challenges, laying the groundwork for further computational ML advancements. The PRISMA guidelines identify ML's role from 2018 to 2023, including post-COVID-19. Principal component analysis (PCA) evaluates the bibliometric contributions of different countries and institutions. China, Indonesia, Egypt, Saudi Arabia, the University of Antwerp Operations Research Group, and the University of Pittsburgh emerge as significant contributors. These findings underscore ML's pivotal role in fraud detection, risk mitigation, and cost savings, concluding that anomalous contract detection remains open to newer ML techniques and ongoing research. [ABSTRACT FROM AUTHOR]

The performance of technology companies listed on NASDAQ significantly impacts larger economic trends. Investors need specific information to navigate market volatility and make informed decisions in an increasingly complex marketplace. Furthermore, amidst the ongoing digital revolution, legislators and regulatory agencies must comprehend the operational dynamics of technology companies to develop frameworks that support innovation while maintaining market stability. Our study assesses the impact on the overall operational efficiency of NASDAQ-listed firms from 2011 to 2023, resulting from the interdependence of critical variables such as selling, general, and administrative expenses (SGA), cost of goods and services sold (COGS), and investments in research and development (R&D). Johansen's cointegration methodology and pairwise Granger causality tests were employed to unveil long-term relationships, equilibrium adjustments, and causal relationships among the considered variables. The results provide critical insights into the strategic management of operational variables by the listed companies. The economic significance of the results obtained underscores the paramount importance of efficiently managing the cost of goods and services sold to achieve superior operating performance among these leading technology firms. [ABSTRACT FROM AUTHOR]

With the increasingly competitive environment in the steel market and the proposed dual carbon goals, the government will need to consider many factors, such as the realization of energy conservation and emission reduction targets, the production game between enterprises, and the adjustment of production strategies of enterprises. Therefore, this research constructs a repeated dynamic game model including carbon trading policy and other mixed reduction policies, introduces a bounded rationality output adjustment strategy, and studies the response, stability, and complexity of different scenarios in the steel industry. The results are as follows: (1) With the gradual increase in emission reduction targets, the output adjustment policies that enterprises can implement will show an increasing trend under the single carbon trading policy. (2) Under the mixed emission reduction policy, the output adjustment policies that affect enterprises with larger outputs will show an increasing trend when targets continue to increase. (3) Smaller-output enterprises will be restricted and affected by more factors, such as emission reduction targets and larger output enterprises. (4) The influence of carbon trading benchmarks on market stability region is not obvious. In summary, enterprises should comprehensively consider emission reduction policies, output adjustment policies, carbon trading benchmarks, and other factors to ensure that the enterprises and the entire market will not fall into an imbalanced state. [ABSTRACT FROM AUTHOR]

Energy plays a predominant role in the development of society. With advancements in technology and the growth of society (buildings and infrastructures), the demand for energy is rapidly increasing. Developing countries typically rely on the import of fossil fuels and capital investments in infrastructure development to meet their energy needs. The execution of solar PV projects in developing countries is currently not being implemented promisingly. Therefore, the determination of the critical success factors hindering the implementation of solar PV projects is the need of the hour. The aim of this study is to determine the factors that hinder the implementation of solar PV projects through the use of a multiple linear regression model (MLRM) and a rule-based decision support system (RBDSS). Seven categories of factors were identified through a detailed literature review and interviews with energy experts. Four hundred and twenty-nine complete responses were collected in total through a questionnaire, and they were analyzed using relative importance indexing (RII) and MLRM and RBDSS approaches. A comparison was carried out against both methodologies to determine the most critical barriers to the implementation of solar PV projects. The findings regarding the MLRM approach showed that the top seven critical factors were economic conditions, encouraging policies, technological knowledge, organizational support, social awareness, market stability, and miscellaneous aspects. Similarly, the results for the RBDSS approach identified that the top seven critical factors were encouraging policies, economic conditions, organizational support, technological knowledge, market stability, social awareness, and miscellaneous aspects. The application of MLRM and RBDSS will help stakeholders in making timely decisions and corrections during the implementation phase, providing a systematic way to support the performance and execution of solar projects. [ABSTRACT FROM AUTHOR]

The financial markets, shaped by dynamic forces, including macroeconomic trends and technological advancements, are influenced by a multitude of factors impacting the S&P 500 stock index, a pivotal indicator in the US equity markets. This paper highlights the significance of understanding the exogenous variables affecting the index's profitability for academics, portfolio managers, and investment professionals. Amid the global ramifications of the S&P 500, particularly in combating the eroding purchasing power caused by inflation, investing in stock indexes emerges as a means to safeguard wealth. The study employs various statistical techniques, emphasizing a methodical approach to uncover influential variables, and using static regression and autoregressive models for immediate and time-lagged effects. In conclusion, the findings have broad practical implications beyond investment strategy, extending to portfolio construction and risk management. Acknowledging inherent uncertainties in financial market forecasts, future research endeavors should target long-term trends, specific influences, and the impact of exchange rate fluctuations on index evolution. Collaboration across regulatory bodies, academia, and the financial industry is underscored, holding the potential for effective risk monitoring and bolstering overall economic and financial market stability. This research serves as a foundational step towards enhancing market understanding and facilitating more efficient investment decision-making approaches. [ABSTRACT FROM AUTHOR]

Accurate yield estimation before the wheat harvest is very important for precision management, maintaining grain market stability, and ensuring national food security. In this study, to further improve the accuracy of winter wheat yield estimation, machine learning models, including GPR, SVR, and DT, were employed to construct yield estimation models based on the single and multiple growth periods, incorporating the color and multispectral vegetation indexes. The results showed the following: (1) Overall, the performance and accuracy of the yield estimation models based on machine learning were ranked as follows: GPR, SVR, DT. (2) The combination of color indexes and multispectral vegetation indexes effectively improved the yield estimation accuracy of winter wheat compared with the multispectral vegetation indexes and color indexes alone. The accuracy of the yield estimation models based on the multiple growth periods was also higher than that of the single growth period models. The model with multiple growth periods and multiple characteristics had the highest accuracy, with an R2 of 0.83, an RMSE of 297.70 kg/hm2, and an rRMSE of 4.69%. (3) For the single growth period, the accuracy of the yield estimation models based on the color indexes was lower than that of the yield estimation models based on the multispectral vegetation indexes. For the multiple growth periods, the accuracy of the models constructed by the two types of indexes was very close, with R2 of 0.80 and 0.80, RMSE of 330.37 kg/hm2 and 328.95 kg/hm2, and rRMSE of 5.21% and 5.19%, respectively. This indicates that the low-cost RGB camera has good potential for crop yield estimation. Multi-temporal and multi-sensor remote sensing data fusion can further improve the accuracy of winter wheat yield estimation and provide methods and references for winter wheat yield estimation. [ABSTRACT FROM AUTHOR]

This study assesses the dynamic relationship between macroeconomic factors and bank asset quality based on changes in the condition of stock market returns. A dynamic panel two-step system, the Generalized Method of Moments (system GMM) model, is employed using panel data from 18 universal banks spanning the period of 2007 to 2021. The analysis revealed that the real GDP growth rate, the average lending rate, and the real exchange rate represent a set of macroeconomic factors with a marked influence on banks' asset quality, where a unit increase in these variables drive 0.02 percent, 0.98 percent, and 0.27 percent improvement in asset quality, respectively. In addition, a high-inflation rate was found to exert an adverse effect of −0.32 percent on asset quality, as it affects borrowers' financial ability to meet loan repayment obligations. Furthermore, the study verified the existence of a positive relationship between market condition and asset quality, where a rise in the market return drives a 0.07 percent improvement in bank asset quality. This implies that bank performance adapts to changes in market conditions as posited under the Adaptive Market Hypothesis (AMH). Bank managers should consolidate banks' asset bases during conditions of market stability to withstand periodic market fluctuations to boost trading momentum. Policy recommendations are suggested to foster a conducive business environment for bank stability. [ABSTRACT FROM AUTHOR]

With the global economic situation still uncertain and various businesses interconnected within the finance system, financial hazards exhibit characteristics such as rapid propagation and wide scope. Therefore, it is of great significance to analyze evolving changes and patterns of hazard overflow in the finance industry and construct a financial hazard alert system. We adopt the time-varying parameter vector auto-regressive model to examine the degree and evolving characteristics of financial hazard alerts from an industry perspective and construct financial hazard measurement indicators. To effectively prevent financial hazards and consider the non-linear causal relationship between financial hazards and macroeconomic variables, we utilize the long/short-term memory network model, which can capture temporal features, to construct a financial hazard alert system. Furthermore, we explore whether the inclusion of an online sentiment indicator can enhance the accuracy of financial hazard alerts, aiming to provide policy recommendations on strengthening financial market stability and establishing a hazard alert mechanism under macro-prudential supervision. [ABSTRACT FROM AUTHOR]

This study empirically investigated the role played by government policy in the financial industry in promoting sustainable innovation, business performance, and risk management. An original dataset, comprising data from the Taiwan Economic Journal (TEJ), Taiwan Patent Search System, and company annual reports from the period 2015–2019 was used to analyze the effects of government policy on the financial industry in Taiwan. The research results showed that a firm's sustainable commitment is conducive to its business growth and does not increase its risk in the financial industry. The financial industry can report on FinTech news that highlights business growth, while companies with high capital adequacy rates are better equipped to manage the risks associated with innovation commitment. Financial companies are suggested to engage in sustainable innovation and thus improve their profitability. In addition, policymakers should mandate that financial companies increase their capital adequacy ratios, improve their risk-bearing capacity, and maintain financial market stability. [ABSTRACT FROM AUTHOR]

Crude oil has dual attributes of finance and energy. Its price fluctuation significantly impacts global economic development and financial market stability. Therefore, it is necessary to predict crude oil futures prices. In this paper, a hybrid forecast model of EEMD-CNN-ILSTM for crude oil futures price is proposed, which is based on Ensemble Empirical Mode Decomposition (EEMD), Convolutional Neural Network (CNN), and Improved Long Short-Term Memory (ILSTM). ILSTM improves the output gate of Long Short-Term Memory (LSTM) and adds important hidden state information based on the original output. In addition, ILSTM adds the learning of cell state at the previous time in the forget gate and input gate, which makes the model learn more fully from historical data. EEMD decomposes time series data into a residual sequence and multiple Intrinsic Mode Functions (IMF). Then, the IMF components are reconstructed into three sub-sequences of high-frequency, middle-frequency, and low-frequency, which are convenient for CNN to extract the input data's features effectively. The forecast accuracy of ILSTM is improved efficiently by learning historical data. This paper uses the daily crude oil futures data of the Shanghai Energy Exchange in China as the experimental data set. The EEMD-CNN-ILSTM is compared with seven prediction models: Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), LSTM, ILSTM, CNN-LSTM, CNN-ILSTM, and EEMD-CNN-LSTM. The results of the experiment show the model is more effective and accurate. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic negatively impacting the whole service sector, including higher education, has forced universities to quickly innovate and recreate. The sanitary crisis resulted in the greatest disruption to educational systems in human history, as well as a shift in the nature, qualifications, and mix of the workforce. The reopening of higher education institutions is another concern, with numerous new operational procedures in place, new opportunities, and prospective curriculum adjustments based on labour market realities. Due to the development of technology, businesses' requirements for human resource credentials and job types underwent several modifications. This study's goal is to investigate how COVID-19 has influenced curriculum revisions and employability requirements. The research used a mixed methodology, with quantitative analysis of changes in enrolled students by major and a qualitative study including two different sets of surveys based on the innovation and employability theories addressed to five human resource (HR) directors from institutions and organizations in Lebanon, as well as three universities. The objective was to answer the following questions: How can universities adapt to the changing demands of the labour market specifically in times of crisis? Should university curricula place greater emphasis on students' personal growth than on technical and conventional learning? The results lead to re-thinking about what higher education systems and institutions can do to redesign their curricula in accordance with the job market and the expectations of the students in this challenging context, where employment security and job market stability issues are more urgent due to the economic crisis and advanced technologies. According to the findings, the research implications include boosting the implementation of the new curriculum through improved HR practices from the Ministry of Education. This will also encourage innovative performance, which will necessitate realistic, swift technical procedures to be unbeatable, creative, and competitive. This study adds significantly to the literature by suggesting curriculum adjustments for online courses and e-training. [ABSTRACT FROM AUTHOR]

The impact of COVID-19 is global, and uncertain information will affect product quality and worker efficiency in the complex supply chain network, thus bringing risks. Aiming at individual heterogeneity, a partial mapping double-layer hypernetwork model is constructed to study the supply chain risk diffusion under uncertain information. Here, we explore the risk diffusion dynamics, drawing on epidemiology, and establish an SPIR (Susceptible–Potential–Infected–Recovered) model to simulate the risk diffusion process. The node represents the enterprise, and hyperedge represents the cooperation among enterprises. The microscopic Markov chain approach (MMCA) is used to prove the theory. Network dynamic evolution includes two removal strategies: (i) removing aging nodes; (ii) removing key nodes. Using Matlab to simulate the model, we found that it is more conducive to market stability to eliminate outdated enterprises than to control key enterprises during risk diffusion. The risk diffusion scale is related to interlayer mapping. Increasing the upper layer mapping rate to strengthen the efforts of official media to issue authoritative information will reduce the infected enterprise number. Reducing the lower layer mapping rate will reduce the misled enterprise number, thereby weakening the efficiency of risk infection. The model is helpful for understanding the risk diffusion characteristics and the importance of online information, and it has guiding significance for supply chain management. [ABSTRACT FROM AUTHOR]

This study explores the association of novel COVID-19 with the dominant financial assets, global uncertainty, commodity prices, and stock markets of the top ten corona-affected countries. We employ a wavelet coherence technique to unearth this linkage using daily data of COVID-19 deaths and reported cases from 1 January 2020 until 26 February 2021. The study finds a weak coherence between COVID-19 and global uncertainty variables in the short and medium term, while a strong positive correlation has been witnessed in the long run. The COVID-19 cases impact the stock markets in the short and medium term, while no significant impact is reported in the long run. On the other hand, a substantial impact of the COVID-19 outbreak has also been found on the exchange rate. In addition, the real asset market, such as gold, remains more stable during the COVID-19 outbreak. Thus, the study recommends that investors and portfolio managers should add such assets to their investment options to safeguard the excessive risk and downside momentum of the equity market. The study also has implications for regulators who are concerned with the neutrality of the COVID-19 effect and market stability. [ABSTRACT FROM AUTHOR]

This paper considers a dynamic Stackelberg game model for a manufacturer-led supply chain with risk aversion. Cooperative advertising strategy is applied to the marketing decisions of supply chain participants. Based on Stackelberg game and system dynamic theory, the game and complex dynamical behaviors are studied through the use of several methods, such as the stability region of the system, bifurcation diagram, attractor diagram, and the largest Lyapunov exponent diagram. The expected utilities of participants are given and compared by numerical simulation. The results illustrate that a series of variations in adjustment speed of advertising expenditure, participation rate of local advertising expenditure by manufacturer, risk tolerance levels, and the effect coefficient of advertising expenditure may cause a loss of stability to the system and evolve into chaos. Meanwhile, the Nash equilibrium point and the expected utility of the manufacturer and retailer will change greatly. The parameter control method is further applied to control the chaos phenomenon of the system effectively. By means of analyzing the impact of relevant factors on the game model, the manufacturer and retailer can make optimal strategy decisions in the supply chain competition. The findings of this study mainly include the following three aspects. Firstly, for market stability and maximizing revenue, the manufacturer adjusts the participation rate appropriately, avoiding too high or too low values. Secondly, the manufacturer will try to reduce their own risk tolerance level for the economic revenue, and the retailer appropriately adjust the risk tolerance level to adapt to their own development according to their own enterprise strategy. Finally, both the manufacturer and retailer reduce their own effect coefficients of advertising expenditure. Meanwhile, they will attempt to increase their opponent's effect coefficient to gain the most revenue. The research results of this study can provide important reference for the advertising expenditure decision and revenue maximization of participants in the context of risk aversion. [ABSTRACT FROM AUTHOR]

This paper investigates how a short-lived rumor of residential redevelopment triggered herding trading and housing price overreactions in a local housing market in Hangzhou. Through event studies, we find that herding purchasing caused a short-term housing price overreaction. Simultaneously, existing homeowners became reluctant to sell, and the number of new listings for sale decreased temporarily. However, we find no evidence of a decrease in market efficiency. A herding investor who purchased an average home may have suffered a loss of CNY 593,907 after the rumor weakened, equivalent to 8.7 years of income for an average resident in Hangzhou in 2021. This study reveals the importance of government policy communication, and the detrimental impact of ambiguous urban renewal policies on housing market stability and wealth redistribution. [ABSTRACT FROM AUTHOR]

Using the event study methodology, the paper studies the effects of 22 key events in countries' process of entering and exiting the European Union on returns of European Union Allowance (EUA) future prices in the EU Emissions Trading System (EU ETS). The events include 17 entry events concerning the signing of relevant agreements, becoming a candidate or potential candidate country, the process of a negotiation and formally entering the EU, and five exit events including the process of Brexit and the suspension of Iceland. The results show that two entry events involving Albania and Ukraine, respectively, have a significant positive impact, and five entry events have a significant negative impact. Among the exit events, the announcement of the Brexit referendum results causes significant negative market reaction. Most events regarding small carbon emitters entering the EU lead to negative cumulative abnormal returns (CAR) of EUA prices, and a significant negative correlation between the countries' annual average carbon emissions and CAR is found, while the change of national allocation plans does not affect the market reaction notably. In the process of establishing a unified carbon market, regulators should carry out appropriate policy arrangements of emission allowances allocation when new members join, in order to guide market expectations and enhance market stability. [ABSTRACT FROM AUTHOR]

The early and accurate acquisition of crop yields is of great significance for maintaining food market stability and ensuring global food security. Unmanned aerial vehicle (UAV) remote sensing offers the possibility of predicting crop yields with its advantages of flexibility and high resolution. However, most of the existing remote sensing yield estimation studies focused solely on crops but did not fully consider the influence of soil on yield formation. As an integrated system, the status of crop and soil together determines the final yield. Compared to crop-only yield prediction, the approach that additionally considers soil background information will effectively improve the accuracy and reduce bias in the results. In this study, a novel method for segmenting crop and soil spectral images based on different vegetation coverage is first proposed, in which pixels of crop and soil can be accurately identified by determining the discriminant value Q. On the basis of extracting crop and soil waveband's information by individual pixel, an innovative approach, projected non-negative matrix factorization based on good point set and matrix cross fusion (PNMF-MCF), was developed to effectively extract and fuse the yield-related features of crop and soil. The experimental results on winter wheat show that the proposed segmentation method can accurately distinguish crop and soil pixels under complex soil background of four different growth periods. Compared with the single reflectance of crop or soil and the simple combination of crop and soil reflectance, the fused yield features spectral matrix FP obtained with PNMF−MCF achieved the best performance in yield prediction at the flowering, flag leaf and pustulation stages, with R2 higher than 0.7 in these three stages. Especially at the flowering stage, the yield prediction model based on PNMF-MCF had the highest R2 with 0.8516 and the lowest RMSE with 0.0744 kg/m2. Correlation analysis with key biochemical parameters (nitrogen and carbon, pigments and biomass) of yield formation showed that the flowering stage was the most vigorous season for photosynthesis and the most critical stage for yield prediction. This study provides a new perspective and complete framework for high-precision crop yield forecasting using UAV remote sensing technology. [ABSTRACT FROM AUTHOR]

As countries scramble for cleaner energy production and to meet carbon reduction targets, natural gas seems to become an increasingly attractive option with liquified natural gas (LNG) as a popular transportation choice. In this paper, we first conduct a literature review and discuss the various factors affecting the global natural gas market, its recent history, current state, and future. Then we look at the possibility of East Asia becoming an alternative market to Europe for Russian LNG. We also bring in the US both as a political force that employs economic sanctions and as a potential LNG supplier. As a case study, we define a 3-player game between Russia, The United States, and Japan which results in relative market stability. In the case of sanctions against Russia, we conclude that it will lose its foothold in the Japanese market in the long term. Finally, we discuss the potential of LNG trading as the first step for East Asia's energy transition to a low-carbon economy. [ABSTRACT FROM AUTHOR]

Online social networks (OSNs) have steadily become the primary mechanism of product promotion. However, previous studies have paid little concern to the irrational consumer behavior (e.g., overconfidence) and network effects that influence product diffusion in OSNs. We use overconfidence theory, network effects theory, and evolutionary game theory to build a multi-agent simulation model that captures the nonlinear relationship between individual actions to examine the effects of overconfidence and network effects on product diffusion in OSNs. We found that (1) overestimation is profitable for improving the diffusion level of product diffusion in OSNs and maintaining market stability; however, the closer the degree of overprecision is to 1 (i.e., individuals are more rational), the more stable the market will be. We also found that (2) moderate network effect intensity can better promote product diffusion on the social network. When the network effect intensity is small, the non-overconfident scenario has the highest percentage of adoption. The overprecision scenario has the highest percentage of adoption where the network effect intensity is high. Additionally, we found that (3) the scale-free network is more conducive to the diffusion of products in OSNs, while the small-world network is more susceptible to overconfidence and network effect. This research laid the groundwork for investigating dynamic consumer behavior utilizing a multi-agent method, network effects theory, and a psychological theory. [ABSTRACT FROM AUTHOR]

Finance-Informed Neural Networks (FINNs), inspired by Physical Information Neural Networks (PINNs) and computational finance, aim to enhance risk assessment and support regulatory decision-making. Despite their promising potential, existing FINNs face significant challenges related to learning efficiency and overall performance. This paper examines the KAFIN framework, based on the Kolmogorov–Arnold representation, which aims to improve financial modeling and analytical calculations. To evaluate the performance of FINNs, this study uses generated data instead of real market data. This choice enables controlled simulations of various financial scenarios while avoiding the complexities and noise inherent in actual market data. By relying on generated data, we are able to isolate and assess the core capabilities of KAFIN under well-defined theoretical conditions, facilitating a clearer analysis of its performance. This study focuses on European option pricing as a case study, using generated input data that simulate a range of market scenarios, including typical regulatory conditions in financial markets. Testing KAFIN under these controlled conditions allows for a rigorous evaluation of its ability to handle the complexities of pricing options across different market assumptions and regulatory constraints. Empirical results demonstrate that KAFIN significantly outperforms the baseline method, improving pricing accuracy by minimizing residual errors and aligning closely with analytical solutions. The architecture of KAFIN effectively captures the inherent complexities of option pricing, integrating financial principles with advanced computational techniques. Performance indicators reveal that KAFIN achieves lower average total losses and reduces the variability in loss components, highlighting its advantage in modeling and analyzing complex financial dependencies while ensuring compliance with regulatory standards. [ABSTRACT FROM AUTHOR]

The increasing adoption of renewable energy sources and the emergence of distributed generation have significantly transformed the traditional energy landscape, leading to the rise of local energy markets. These markets facilitate decentralized energy trading among different market participants at the community level, fostering greater energy autonomy and sustainability. As local energy markets gain momentum, the application of artificial intelligence techniques, particularly reinforcement learning, has gained substantial interest in optimizing energy trading strategies by interacting with the environment and maximizing the rewards by addressing the decision complexities by learning. This paper comprehensively reviews the different energy trading projects initiated at the global level and machine learning approaches and solution strategies for local energy markets. State-of-the-art reinforcement learning algorithms are classified into model-free and model-based methods. This classification examines various algorithms for energy transactions considering the agent type, learning methods, policy, state space, action space, and action selection for state, action, and reward function outputs. The findings of this work will serve as a valuable resource for researchers, stakeholders, and policymakers to accelerate the adoption of the local energy market for a more efficient, sustainable, and resilient energy future. [ABSTRACT FROM AUTHOR]

In response to the growing environmental concerns and sustainability imperatives in the GCC and the world in general, companies are increasingly being urged to integrate environmental accountability into their core strategies and operations by accounting for environmental performance in their financial statements and the impact on supply chains and sustainable development. The problem of the study is to identify the impact of environmental accounting disclosure challenges on supply chains and sustainable development in companies. By taking the experiments of 20 companies and representatives of the study community interested in the topic and using the heuristic study approach through the questionnaire tool and a set of key variables, including the adoption of disclosure on environmental accounting performance (DEAP), supply chain sustainability performance (SCP), Sustainable Development Index (SD), and the (PLS) program, through statistical analysis, the study reached the most important results. The study revealed a positive relationship between the disclosure of environmental accounting performance, improved supply chains, and sustainable development in the companies represented by the study sample in the GCC, where both financial performance and environmental performance contribute to achieving the Sustainable Development Goals and supply chains. Accordingly, the study recommends many recommendations, the most important of which are: the need for companies to be encouraged to disclose the performance of environmental accounting as a strategic necessity, promote more sustainable supply chain management, improve financial performance, and effectively contribute to achieving the Sustainable Development Goals, thereby enhancing sustainability within companies and across supply chains. The study also recommends the need to continue studies in this aspect, especially in the GCC and East Asian countries, to urge companies to disclose the performance of environmental accounting in their financial statements. [ABSTRACT FROM AUTHOR]

Against the pressing backdrop of global climate change, various environmental issues are becoming increasingly prominent, posing unprecedented challenges to both the global economic system and business operations. Green technology innovation, as a critical response to climate change, is vital not only for the sustainable development of firms, but also for fostering the harmonious coexistence of the economy and environment. However, whether climate change itself affects green technology innovation activities is still a topic that needs to be explored in depth. This paper utilizes data from the China Meteorological Administration (CMA), State Intellectual Property Office of China (SIPO), and CRNDS database to empirically examine the impact of climate change on green technology innovation of Chinese A-share listed firms from 2011 to 2020. The findings indicate the following: (1) Climate change significantly inhibits green technology innovation. (2) Entrepreneurs' green human capital can mitigate the negative impact of climate change on green technology innovation. (3) When faced with higher investor attention or stronger environmental regulation, firms will pay more attention to their green technology innovation when adversely affected by climate change. (4) Resource-based cities show greater resilience against the negative impacts of climate change on green technology innovation compared to non-resource-based cities, suggesting that climate change-induced adaptive behavior may break the resource curse. (5) Mature, non-polluting, high-tech, and non-manufacturing firms are more effective in resisting the inhibitory effects. This research contributes to understanding climate risks and managing them effectively. [ABSTRACT FROM AUTHOR]

To achieve economic resilience and green, low-carbon development are two goals of China's high-quality economic development. This paper uses the entropy weight method and coupling coordination degree model to estimate the coupling coordination level of economic resilience and green, low-carbon development. Kernel density estimation, Moran index, Dagum Gini coefficient, Markov chain, and obstacle degree model are used to explore the spatiotemporal evolution characteristics and obstacle factors. The results are as follows. (1) The coupling coordination degree between China's economic resilience and green, low-carbon development has increased overall. However, the eastern region has the highest, and the central region has the fastest growth. (2) The coupling coordination degree shows positive spatial autocorrelation, with most provinces exhibiting high–high or low–low aggregation characteristics. (3) The contribution of imbalance mainly comes from inter-regional differences, but the contribution of intra-regional differences to imbalance is increasing. (4) The spatio-temporal evolution pattern is generally better, and the probability of the coupling coordination degree maintaining the initial state is the largest. The neighborhood's state affects the transition probability but does not affect that of high-level provinces. (5) Innovation capacity is the main obstacle to improving economic resilience, and per capita water resources are the main obstacle to green and low-carbon development. Finally, this paper puts forward suggestions for creating a good innovation environment, increasing R&D investment, promoting green technology progress, optimizing regional cooperation and resource allocation, and promoting industrial green transformation. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between economic growth and energy consumption—both renewable and non-renewable—in European Union countries during the green transition. Using a panel dataset of 28 EU countries from 1995 to 2021, we employ econometric techniques—including the Westerlund cointegration test and a fixed-effect panel threshold model—to assess long-term equilibrium relationships. The results indicate that while both renewable and non-renewable energy consumption are associated with economic growth, their roles differ. Renewable energy consumption shows a positive but less robust relationship with economic growth. In contrast, non-renewable energy consumption demonstrates a more robust bidirectional causality with economic growth, indicating a more intertwined relationship with economic growth during the study period. Interestingly, in countries with high levels of non-renewable energy consumption—classified as regime 2 in the panel threshold model—increased non-renewable energy consumption is associated with a decrease in economic activity. Our results have significant policy recommendations, indicating that promoting renewable energy sources does not hinder economic growth. Moreover, such promotion has the potential to contribute substantially to economic growth in the future. Therefore, in addition to other crucial benefits, such as increased energy security, the development of renewable energy sources does not threaten the economy. This is particularly relevant as many EU countries, including Poland, Romania, Hungary, Bulgaria, Slovakia, and Lithuania, still have underdeveloped renewable energy sectors. [ABSTRACT FROM AUTHOR]

Brazil's wind power industry (WPI) has thrived since the early 2000s, driven by a successful auction-based expansion plan. However, the recent rise of cost-competitive solar power and policy shifts favoring other energy sources, such as natural gas, have created uncertainty about the future of wind energy in Brazil and reduced the wind sector's legitimacy. Additionally, the cancellation of wind power auctions and support for other energy sources (evidenced by the new regulation for natural gas) has sent mixed signals to the market. These actions have sparked concerns regarding the future trajectory of the WPI. This paper focuses on the long-term effects of this energy policy decision on the so-called legitimacy function of the technological innovation systems (TIS) for the case of WPI in Brazil. The study aims to identify challenges arising from the growing appeal of solar power that may hinder wind energy adoption and to offer policy recommendations to strengthen the wind sector's legitimacy. A system dynamics model is proposed to quantify such impacts in the long run, showing the interactions between the wind power capacity, wind generation costs, and the legitimacy function of the TIS. Results show the importance of policy consistency and institutional support in fostering a stable environment for renewable energy technologies like wind power to flourish. [ABSTRACT FROM AUTHOR]

The genus Vaccinium, which includes approximately 450 species, features economically significant berries such as bilberries (Vaccinium myrtillus) and blueberries (Vaccinium corymbosum). Bilberries flourish in acidic, well-drained soils, typically found in heathlands and coniferous forests, while blueberries benefit from a broader range of soil types and intensive agricultural practices. Sustainable cultivation strategies, including organic fertilization and efficient water management, are vital for optimizing production and addressing the environmental challenges posed by climate change. Both berries are rich in antioxidants and other nutrients, driving consumer interest and market growth despite competition from alternative crops. Additionally, tailored fertilization techniques are crucial for maximizing yield and fruit quality. By implementing circular economy principles, the production of bilberries and blueberries can enhance sustainability and profitability, ensuring their long-term success in agricultural systems. [ABSTRACT FROM AUTHOR]