Search Results (135)

This study examines the long- and short-run effects of sustainable development, economic growth, energy consumption, urbanization, investment and trade openness on Carbon Dioxide Emissions (CO₂) in the GCC countries utilizing the PMG-ARDL approach by including the data spanning from 2000 to 2022. In the short -run, the sustainable development index demonstrates a positive and substantial impact while it exhibits adverse long-run impact on CO₂ emission. The study also indicates a U-shaped correlation between economic growth and emissions, contrasting with the conventional Environmental Kuznets Curve (EKC) where economic growth at lower income levels often leads to a reduction in emissions; however, income increases beyond around USD 29,942 per capita correlate with higher emissions. Besides, energy use is identified as the primary factor influencing emissions, reflecting global patterns that indicate greater energy usage, particularly from fossil fuels directly boosts emissions. Moreover, the urbanization intensifies this problem, resulting in higher energy demand and greater emissions. Additionally, the study finds that gross capital formation and investments in infrastructure contribute to emissions in the short run, though these effects diminish over time. Our results are robust as it similar to the outcomes obtained from dynamic panel-data System GMM. The GCC policymakers must utilize the sustainable development framework to legally mandate national planning towards low-carbon paths while balancing for short-term transition costs with significant long-run emission reductions. This necessitates the implementation of market-oriented carbon pricing to address the post-threshold U-shaped emissions rebound, the systematic elimination of fossil fuel subsidies to promote renewable energy adoption, and the enforcement of sustainable development regulations to mitigate urbanization pressures. Full article

Against the coordinated advancement of building a maritime power, high-quality development of marine tourism and ecological civilization construction, realizing positive interaction between marine resource conservation and tourism industrial development has emerged as a pivotal issue for high-quality growth in coastal regions. Taking 11 coastal provincial-level administrative regions in China spanning 2008 to 2024 as the research sample, this paper first establishes an evaluation indicator system covering marine resources and the tourism industry. It further adopts an integrated empirical framework encompassing the coupling coordination degree model, spatial Markov chain model, obstacle degree model, fixed-effect model and geographically and temporally weighted regression (GTWR) model to systematically unpack the spatiotemporal differentiation characteristics, internal restrictive obstacle factors and external driving determinants of the two-system coupling coordination. On this basis, a marine resource compensation mechanism for tourist destinations is formulated. Empirical results demonstrate four core findings: (1) In terms of temporal evolution, the overall coupling coordination level keeps rising and goes through three phases: initial development, rapid improvement and post-shock recovery. After a short-term decline triggered by the pandemic, the index rebounds markedly after 2023, showing that the two systems can recover and stabilize. (2) In terms of spatial layout, a persistent stratified spatial pattern featuring “higher coordination in southern coast versus lower coordination in northern coast with three-tier hierarchical differentiation” is identified; high-level neighboring regions exert prominent positive spatial spillover effects, whereas low-level adjacent areas are prone to fall into development lock-in traps. (3) For internal constraint obstacles, the marine resource subsystem is persistently restricted by resource exploitation limits and coastal spatial scarcity, while the dominant bottleneck of the tourism industrial subsystem shifts from insufficient market scale to inadequate human capital supply. (4) Regarding external driving forces, the proportion of tertiary industry and the digital infrastructure constitute core driving contributors, whereas marketization progress and opening-up degree act as primary restrictive factors, with pronounced spatial heterogeneity existing across all driving indicators. Finally, in line with the quasi-public-good attribute and ecological externality of marine resources, this study constructs a differentiated and synergistic marine resource compensation mechanism from three dimensions: stakeholder identification, compensation implementation pathways and institutional guarantee systems. The proposed framework provides theoretical references and practical policy options to facilitate high-level coupling and coordinated development between marine resource preservation and the coastal tourism industry. The marginal contribution of this research lies in integrating coupling coordination measurement, obstacle factor diagnosis, driving mechanism identification and compensation mechanism design into an integrated analytical framework, which delivers theoretical foundations and operable policy solutions for coastal marine resource protection, tourism industrial upgrading and differentiated compensation system construction. Full article

The global financial system is constantly evolving through technological integration. This has led to the inception and rise in the cryptocurrency market, opening new avenues of comparative studies on market behavior. Therefore, the current study aimed to identify nuances in stock and cryptocurrency behavior. Based on the socionomic theory of finance, the study is a pioneer in considering the interplay of economic, market, and social media sentiments while providing a comparative view of cryptocurrencies and stocks. The study utilizes data of economic news sentiments, cryptocurrency fear and greed index, CNN fear and greed index, and Twitter sentiments against the movement of S&P Cryptocurrencies and S&P 500 stock index return spanning from 2018 to 2023. The study applied a vector autoregressive-based spillover model to assess the theorized linkage and applied robustness measures, including linear regression and the Granger causality test, for validation. The findings unveil distinct weak and moderate associations of sentiments across cryptocurrencies and stocks, respectively. The former is primarily driven by market sentiments while shaping economic news and social media sentiments. Meanwhile, the findings for stock return movements are found to be significantly associated with economic and market sentiments. This led to the inference that the cryptocurrency environment is an isolated system driven by internal sentiments, while stock markets are more economically integrated, and in both cases, social media sentiments are found to be the receiver of market spillover, weakly influencing economic news. The study is pioneering in its exploration of the interlinkage between selected macroeconomic sentiments; additionally, the comparative findings further add to the existing debate on influence of sentiment across financial markets. The varying realities identified in the findings hold significant practical implications for portfolio optimization, risk assessment and policy making. Full article

This paper presents a comprehensive spatiotemporal decomposition of equity returns for nine top-weighted constituents of the Dow Jones Industrial Average (DJIA) over a twenty-year period spanning January 2004 through December 2023, encompassing 5033 trading days and multiple market regimes, including the Global Financial Crisis (2008–2009), the COVID-19 crash and recovery (2020), and the Federal Reserve tightening cycle (2022–2023). Daily price movements are systematically partitioned into two orthogonal sessions: the open-to-close (OTC, or daytime) session, capturing within-session price discovery, and the close-to-open (CTO, or overnight) session, capturing the accumulated information arrival and liquidity dynamics between market closes and subsequent opens. Within this bipartite return framework, we construct and rigorously evaluate 24 distinct trading strategies, spanning directional (long/short), neutral (cash), momentum (inertia), and contrarian (reversal) approaches, applied independently to each session or in combinatorial cross-session configurations. Each strategy is evaluated under three transaction cost regimes (0, 1, and 2 basis points per trade) using an initial investment of $100, and assessed using annualized return, annualised volatility, Sharpe ratio, Sortino ratio, and maximum drawdown. The study universe—comprising UnitedHealth Group (UNH), Goldman Sachs (GS), Microsoft (MSFT), Home Depot (HD), Caterpillar (CAT), Amgen (AMGN), McDonald’s (MCD), Salesforce (CRM), and Honeywell (HON)—captures cross-sector heterogeneity across Healthcare, Financials, Technology, Consumer Discretionary, Industrials, Biotech, and Consumer Staples. The universe is selected from the top-weighted DJIA constituents as of early 2026; the paper is, therefore, best read as a focused, in-depth case study of index-representative large-cap names rather than a general cross-sectional statement about all U.S. equities. The principal findings are threefold. First, the overnight session consistently delivers superior risk-adjusted performance: seven of nine stocks record higher Sharpe ratios during the overnight period versus the daytime period, with the mean overnight Sharpe ratio (0.662) substantially exceeding the mean daytime Sharpe ratio (0.357), a statistically and economically significant overnight premium. Second, the hybrid Strategy #18—Long Overnight coupled with Daytime Reversal—emerges as the dominant cross-asset configuration, generating portfolio values as high as $8464 from a $100 initial investment (AMGN; Sharpe: 0.991) over the 20-year horizon. Third, Trajectory Change Analysis reveals (i) Lévy-stable tails with a mean stability index $\overline{\alpha }=1.667$ across all constituents, substantially below the Gaussian benchmark of $\alpha =2.0$; (ii) Hurst exponents clustering below 0.5 ($\overline{H}=0.417$), confirming dominant mean-reverting dynamics; and (iii) positive rolling CAPM alpha in 51–79% of rolling windows, indicating persistent risk-adjusted outperformance above the S&P 500 benchmark. These findings provide a rigorous empirical foundation for session-aware algorithmic trading system design and challenge the prevailing assumption of temporal homogeneity in equity return processes. Full article

This study compares the performance of protective put and covered call strategies and analyzes their return determinants. The analysis uses SET50 Index Options contracts with trading volume, covering maturities from January 2021 to December 2025. The empirical model investigates three groups of explanatory variables: market expectation variables (implied volatility and basis), option market condition variables (open interest and trading volume), and option-specific characteristics (time to maturity and moneyness). The model also incorporates fixed effects for different maturity years (with 2025 as the base year) and quarterly maturity dummies. Standard errors are clustered by monthly expiration groups, and statistical significance is further validated using the wild cluster bootstrap method to improve the reliability of p-values. Overall, the findings indicate that the covered call strategy outperforms the protective put strategy over the sample period, except in 2025. Option strategy performance is primarily driven by market expectation variables rather than contract-specific characteristics. Implied volatility and the basis are the most important determinants of returns for both protective put and covered call strategies, while option market condition variables are relevant mainly for covered call strategies. These results highlight the importance of market conditions in shaping hedging strategy outcomes in the Thai options market. Full article

Public data is a foundational resource and new production factor in the digital economy era. Scientific assessment of its economic effects on urban–rural integration is of great significance for promoting the revitalization of rural areas and achieving common prosperity. This study employs a double machine learning (DML) model for causal inference, using the establishment of “data trading platforms” as an exogenous policy shock to measure public data openness (Opendata). The level of urban–rural integration (Uri) is assessed through a comprehensive index system encompassing economic, population, social, spatial, and ecological dimensions, with weights assigned using the CRITIC method. Based on panel data from 259 prefecture-level cities in China (2012–2024), the analysis is conducted using machine learning algorithms such as Lasso regression, supplemented by a series of robustness and endogeneity tests. Research has found that public data openness can significantly promote urban–rural integration, and this conclusion still holds true after a series of robustness tests. Mechanism analysis indicates that public data openness promotes urban–rural integration by facilitating the flow of factors between urban and rural areas and enhancing technological innovation. Heterogeneity analysis shows that the enhancing effect of public data openness on urban–rural integration is more significant in eastern cities and non-resource-based cities. Based on these conclusions, it is recommended to further accelerate the cultivation of a standardized and unified data element market and enrich the “digital soil”, solve the problems of unsmooth flows of factors and resource constraints, strengthen data empowerment in urban–rural information sharing, and promote common prosperity in urban and rural areas. Full article

Transportation plays a fundamental role in tourism development, serving as the critical link between tourism demand and supply. China’s domestic demand-oriented strategy has positioned tourism as an important driver of economic recovery during the post-COVID-19 transition period, highlighting the urgent need to strengthen tourism system resilience. Tourism economic resilience is measured via the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, transportation accessibility is quantified using a composite index, and a Geographically Weighted Random Forest (GWRF) model is applied across 73 prefecture-level cities in the Yellow River Basin to map spatial patterns and examine the association between transportation accessibility and tourism economic resilience. The results reveal: (1) pronounced spatial disparities in both tourism resilience and accessibility, displaying a clear “core–periphery” pattern; (2) strong spatial coupling between high resilience and high accessibility in the east, and low–low clusters in the west (e.g., Qinghai, Gansu, Sichuan); and (3) a relatively strong association between transportation accessibility and tourism resilience, particularly in supporting recovery, adaptability, and renewal capacity. Other influential factors include tourist density, openness to external markets, and industrial structure. This study provides a quantitative foundation for understanding the spatially heterogeneous associations of transport infrastructure with tourism system resilience and offers both theoretical insights and practical guidance for formulating regionally differentiated, transport-led policy strategies to foster sustainable tourism development in river-basin economies. Full article

This study investigates the forecasting of the XBANK banking index traded on Borsa Istanbul by integrating financial and textual data within a deep learning framework. Unlike the majority of existing studies that focus on stable market environments, this paper explicitly examines a period of heightened political uncertainty, namely the cancellation and re-run of the 2019 Istanbul local elections. This setting provides a unique opportunity to analyze how political events and news-driven information flows influence financial market dynamics. The empirical analysis is based on a comprehensive dataset that includes daily price indicators (opening, closing, high, and low values), technical indicators, selected macroeconomic variables, and Turkish-language news headlines. Textual data are processed using topic modeling techniques to extract latent information embedded in financial news, allowing for the incorporation of qualitative signals into the forecasting framework. From a methodological perspective, this study employs a feedforward deep neural network model designed to capture nonlinear relationships across heterogeneous and contemporaneous features. Feature selection is conducted using the Boruta algorithm, while hyperparameters are optimized via grid search. The model structure reflects a deliberate design choice aimed at capturing short-term, news-driven shocks and cross-feature interactions, which are particularly relevant during periods of political uncertainty. The results indicate that incorporating textual information significantly improves forecasting performance and that news topics related to political decisions, central bank policies, and geopolitical developments have a measurable impact on the XBANK index. Furthermore, the findings suggest that the political uncertainty surrounding the 2019 local elections led to increased market sensitivity and volatility, highlighting the role of information shocks in emerging financial markets. Overall, this study contributes to the literature by combining financial and textual data in an emerging market context, utilizing Turkish-language news sources, and providing empirical evidence on the impact of political uncertainty on the BIST bank index. Full article

With the gradual implementation of the RCEP agreement, China’s service sector market has opened up further. According to statistics from the OECD database, in 2023, China’s service imports from other RCEP member states accounted for approximately 33% of its total service imports. The growing volume of service trade underscores the importance of trade with other RCEP member states in helping China achieve its goals of enhancing the quality of its service sector and establishing a sustainable and healthy development model. Based on the virtual water trade theory and using input–output tables for each year provided by China’s National Bureau of Statistics, this paper calculates the virtual water imports and exports associated with China’s service trade with other RCEP member states from 2007 to 2020. Based on these results, the paper analyzes the spatiotemporal patterns of service trade between China and other RCEP member states. By constructing a water resource carrying capacity evaluation system, this study analyzes whether China’s service trade with other RCEP member states aligns with virtual water theory. The results indicate that China has consistently been a net importer of virtual water in its service trade with other RCEP member states. Net imports rose from approximately 56 million cubic meters in 2007 to approximately 380 million cubic meters in 2020. From a spatiotemporal perspective, China’s virtual water trade in services with other RCEP member states has been evolving toward a diversified and balanced import pattern. In terms of the water carrying capacity index, although China ranks in the middle range, an index of water resource carrying capacity based on the entropy weighting method indicates that China is in a state of mild overload. It still imports virtual water from regions with lower water carrying capacity. This paper provides a reference for analyzing China’s virtual water trade in services under the RCEP framework. Full article

This paper examines the relationship between corruption and foreign direct investment (FDI) inflows in European Union member states using a dynamic panel framework. Using an unbalanced EU panel from 2002 to 2022 and an Arellano–Bond difference-GMM specification, we model inward FDI inflows per capita as a function of institutional integrity (measured by Transparency International’s Corruption Perceptions Index), market size, development level, and trade integration. The results show a robust positive association between improvements in perceived integrity (higher CPI scores) and increases in inward FDI inflows per capita, conditional on macroeconomic controls and dynamic adjustment. Market size and trade variables have the expected signs, while GDP per capita is the empirically sensitive margin, consistent with the idea that higher development can indicate greater purchasing power but also higher costs and saturation effects in advanced economies. Robustness checks using the inverse hyperbolic sine transformation—suited to heavy tails, zeros, and negative net flows—confirm that the governance association is not an artifact of scaling. The findings highlight the importance of institutional quality and market openness as correlates of FDI attractiveness within the EU. Full article

Massive Open Online Courses (MOOCs) have emerged as a popular choice for learners as accessible and flexible education across the globe. Micro -are short skill-focused certifications offered within MOOCs to online learners. The interplay between multiple stakeholders, including universities, MOOCs providers, policy makers and industrial leaders, plays a decisive role in MOOC adoption. This study employed Educational Data Mining techniques to extract patterns in learner behavior, course design, institutional collaboration, etc., from the determinants of adoption and completion of the micro-credentials within MOOCs. The determinants were extracted from major online MOOCs databases, whereas additional parameters not captured in these databases were collected through an online survey from learners, industry professionals, and higher education institutions. A data mining-based framework is proposed to support stakeholders in planning effective course offerings, guiding learners in selecting suitable courses and helping MOOCs providers to align course credentials with market demands. Classification and predictive analysis revealed that course-related attributes, such as course certification type, course organization, course rating, course difficulty level, and whether the course was free or paid, play decisive roles in determining MOOC adoption. The decision tree classifier, based on the information gain and Gini index, ranked these attributes by order of preference with high accuracy, whereas regression analysis predicted multiple independent variables yielding good performance, as reflected in the confusion matrix. Full article

Digitalisation is reshaping commercial systems in Europe, yet the joint evolution of national digital capabilities, e-commerce and macroeconomic performance remains imperfectly understood. This article develops a parsimonious Digital Maturity Index for the EU-27 over 2015–2023 and examines its association with the share of enterprise turnover generated through e-commerce using a systems-oriented econometric design. Two-way fixed-effects and dynamic panel models show that e-commerce turnover is strongly persistent within countries and systematically higher in more trade-open economies and in labour markets with slightly higher unemployment, after controlling for income and unobserved heterogeneity. The marginal effect of digital maturity on e-commerce intensity is small and statistically fragile, suggesting that digital capabilities act more as a slow-moving state variable than as a direct short-run driver of online sales. The marginal within-country effect of digital maturity on e-commerce intensity is small and statistically fragile once unobserved heterogeneity is controlled for, whereas trade openness and labour-market conditions remain robust correlates. The PVAR results suggest a stable system with strong persistence in e-commerce and digital maturity, limited spillovers to growth and a pronounced temporary contraction in output during the COVID-19 shock. Full article

Deep reinforcement learning (DRL) plays a pivotal role in decision-making within financial markets. However, DRL models are highly reliant on raw market data and often overlook the impact of future trends on model performance. To address these challenges, we propose a novel framework named Cluster Embedding-Proximal Policy Optimization (CE-PPO) for trading decision-making in financial markets. Specifically, the framework groups feature channels with intrinsic similarities and enhances the original model by leveraging clustering information instead of features from individual channels. Meanwhile, zero-shot prediction for unseen samples is achieved by assigning them to appropriate clusters. Future Open, High, Low, Close, and Volume (OHLCV) data predicted from observed values are integrated with actually observed OHLCV data, forming the state space inherent to reinforcement learning. Experiments conducted on five real-world financial datasets demonstrate that the time series model integrated with Cluster Embedding (CE) achieves significant improvements in predictive performance: in short-term prediction, the Mean Absolute Error (MAE) is reduced by an average of 20.09% and the Mean Squared Error (MSE) by 30.12%; for zero-shot prediction, the MAE and MSE decrease by an average of 21.56% and 31.71%, respectively. Through data augmentation using real and predicted data, the framework substantially enhances trading performance, achieving a cumulative return rate of 137.94% on the S&P 500 Index. Beyond its empirical contributions, this study also highlights the conceptual relevance of symmetry in the domain of algorithmic trading. The constructed deep reinforcement learning framework is capable of capturing the inherent balanced relationships and nonlinear interaction characteristics embedded in financial market behaviors. Full article

This exploratory study addresses the problem of limited research on quality assessments of newly emerging multi-use market formats that function as social hubs and their management as privately managed public spaces. Using Āgenskalns Market, a revitalised multi-use market hall in Riga, as a case study, the authors apply an assessment framework based on Yuri Impens’ study on covered food halls, incorporating quality criteria from Vikas Mehta’s Public Space Index and the UN-Habitat’s Site-Specific assessment methodology. Leclercq et al.’s works on privatisation of public spaces are integrated in the analysis of “publicness”. This framework evaluates user and observer perceptions across four dimensions: environmental quality and comfort, accessibility and amenities, social experience, and market offer. Data comprised an online survey of 318 respondents and 21 structured observations conducted during summer in 2024 and 2025. The preliminary results suggest users perceive the market as a well-maintained, aesthetically pleasing, accessible space, while identifying room for improvement regarding restroom facilities, indoor thermal regulation, noise mitigation, outdoor weather protection and parking arrangements. As for meaningful use and promoting sociability, findings highlight that flexible seating areas that allow high degrees of temporary personalisation and appropriation, alongside tailored programming and diverse activities beyond retail and dining, play an important role in attracting and retaining diverse audiences. While pricing concerns were noted for specific product groups, exclusionary effects appear to be counterbalanced by openness and inclusivity of cultural programmes and free events. The findings contribute to broader urban scholarship discussions calling for new typologies that better capture the changing character of public space use. This research suggests that private-public partnerships involving multiple stakeholders can enhance “publicness” by promoting inclusivity and social life through accessible infrastructure, diverse activities and free events, as well as enabling opportunities for temporary appropriation by users. Full article

In this paper, we propose a novel sparse clustering method designed for high-dimensional mixed-type data, integrating Azzalini’s score-based encoding for ordinal variables. Our approach aims to retain the inherent nature of each variable type—continuous, ordinal, and nominal—while enhancing clustering quality and interpretability. To this end, we extend classical distance metrics and adapt the Davies–Bouldin Index (DBI) to better reflect the structure of mixed data. We also introduce a weighted formulation that accounts for the distinct contributions of variable types in the clustering process. Empirical results on simulated and real-world datasets demonstrate that our method consistently achieves better separation and coherence of clusters compared to traditional techniques, while effectively identifying the most informative variables. This work opens promising directions for clustering in complex, high-dimensional settings such as marketing analytics and customer segmentation. Full article