Search Results (538)

In recent years, the green financial market has been exhibiting heightened volatility daily, largely due to policy changes and economic shifts. To explore the broader potential of predictive modeling in the context of short-term volatility time series, this study analyzes how fine-tuning hyperparameters in predictive models is essential for improving short-term forecasts of market volatility, particularly within the rapidly evolving domain of green financial markets. While traditional econometric models have long been employed to model market volatility, their application to green markets remains limited, especially when contrasted with the emerging potential of machine-learning and deep-learning approaches for capturing complex dynamics in this context. This study evaluates the performance of several data-driven forecasting models starting with machine-learning models: regression tree (RT) and support vector regression (SVR), and with deep-learning ones: long short-term memory (LSTM), convolutional neural network (CNN), and gated recurrent unit (GRU) applied to over a decade of daily estimated volatility data coming from three distinct green markets. Predictive accuracy is compared both with and without hyperparameter optimization methods. In addition, this study introduces the quantile loss metric to better capture the skewness and heavy tails inherent in these financial series, alongside two widely used evaluation metrics. This comparative analysis yields significant numerical and graphical insights, enhancing the understanding of short-term volatility predictability in green markets and advancing a relatively underexplored research domain. The study demonstrates that deep-learning predictors outperform machine-learning ones, and that including a hyperparameter tuning algorithm shows consistent improvements across all deep-learning models and for all volatility time series. Full article

The global construction industry faces persistent challenges of low productivity and labor shortages, positioning Off-Site Construction (OSC) as a critical solution. However, standardized industrialization indices for objectively evaluating OSC adoption remain underdeveloped, particularly in emerging markets. This study aims to identify a benchmark policy model and derive design principles for future indices. Specifically, this study focuses on ‘policy-driven markets’ where strong government intervention is essential for initial ecosystem formation, excluding mature market-driven economies where the ecosystem is already established (e.g., USA, Sweden, Japan). To identify an optimal benchmark, a comparative assessment was conducted on five institutional frameworks across four countries (UK, Malaysia, Singapore, and China). Notably, within China, Hong Kong SAR was analyzed as a distinct regulatory jurisdiction separate from Mainland China due to its unique construction governance system. This assessment was based on five key policy dimensions: Legal Mandate, Scope, Indicator Composition, Enforcement Mechanism, and Sustainability. The analysis identified Singapore’s ‘Buildability Score’ as the most comprehensive model in terms of systemic completeness and practical efficacy. A virtual project simulation demonstrated that the scoring system functions as a powerful regulatory mechanism, effectively driving the adoption of standardized, dry-process, and modularized high-productivity methods from the earliest design stages. While Singapore’s system serves as an effective policy tool for OSC proliferation, it exhibits clear limitations regarding reduced architectural design flexibility and insufficient sustainability integration. Consequently, future industrialization indices must evolve to balance productivity with architectural design diversity and integrate sustainability criteria while reflecting specific regional construction ecosystems. Full article

In the grand context of the global convergence of the “dual-carbon” strategy and the digital economy, the underlying mechanisms by which corporate fintech impacts ESG performance remain a “black box” waiting to be explored. To this end, this study reveals the path by which corporate fintech unlocks ESG performance by constructing a theoretical framework that integrates resources, technology and governance. Based on data from Chinese A-share listed companies from 2011 to 2023, we found that corporate fintech can significantly improve ESG performance. Its core mechanism is to optimize resource allocation by alleviating financing constraints, promote green innovation-driven technological upgrades, and reduce agency costs to improve internal governance. Heterogeneity analysis further reveals that this effect is particularly prominent in companies with financial difficulties or high proportions of independent directors, and areas with weak institutional environments, highlighting the catalytic role of corporate fintech in specific situations. This study not only provides micro-mechanism evidence for digital technology to empower the sustainable development of enterprises but also offers important policy implications for emerging markets to leverage fintech to make up for institutional shortcomings and promote green transformation. Full article

Digitalization is increasingly central to economic growth strategies, yet robust macro-level evidence on the role of SME-led e-commerce remains limited. Drawing on the Resource-Based View, this study examines how SME digitalization, internet finance, and platform-based activities influence regional economic growth in China, and how these effects depend on digital infrastructure readiness (DIR). We construct an annual panel of 30 provincial-level regions in China over 2015–2024 and estimate dynamic relationships using two-step system GMM to address endogeneity and growth persistence. The results show that SME digitalization, supply-chain efficiency, mobile payment penetration, tech-driven employment growth, platform-economy contribution, and DIR all exert statistically significant positive effects on GDP growth. Quantitatively, a 10-percentage-point increase in SME digitalization is associated with approximately 0.3-percentage-point higher regional GDP growth, while a 10-point increase in DIR corresponds to about 0.4-percentage-point higher growth. Moderation analyses reveal that DIR significantly amplifies the growth effects of e-commerce expansion, mobile payments, and digital marketing, whereas its moderating role is weaker or insignificant for cross-border payments and supply-chain efficiency. These findings reconceptualize digitalization as a coordinated bundle of complementary resources and position DIR as a critical enabling capability for translating SME digital transformation into macroeconomic growth. The study offers policy-relevant evidence for targeting infrastructure investment and digital-economy strategies in emerging platform economies. Full article

The Meetings, Incentives, Conventions, and Exhibitions (MICE) sector has become a strategic driver of regional economic development, yet secondary cities often lack the structural, governance, and experiential capacities required for competitive MICE positioning. This study proposes and empirically validates a triangulated analytical framework that integrates structural readiness, stakeholder governance capacity, and tourist perceptions to capture systemic misalignments in emerging MICE destinations, going beyond conventional applied readiness assessments. This study evaluates the preparedness of Nakhon Si Thammarat, Thailand, to develop as a sustainable MICE destination using a triangulated mixed-methods design comprising (1) a city readiness assessment based on TCEB’s eight criteria, (2) a survey of 400 tourists and MICE visitors, and (3) in-depth interviews with 20 key stakeholders. The weighted assessment indicated a moderate overall readiness score (3.48/5), with strengths in environmental management, safety, supporting activities, and accommodation. However, MICE venue capacity and city image remained notably weak. Tourists consistently perceived high readiness across most areas, whereas stakeholders highlighted major systemic issues, including fragmented governance, inconsistent MICE service quality, limited capacity for large events, and inadequate transportation integration. Triangulating these viewpoints reveals three analytically distinct preparation gaps—structural, policy implementation, and experience expectations—demonstrating a fundamental misalignment between experiential appeal and institutional capabilities. This study conceptualizes preparedness as a relational outcome impacted by infrastructure, governance procedures, and market perceptions, adding to the MICE destination and governance literature. The methodology can be used to examine comparable misalignments in other emerging or secondary MICE destinations. The findings guide governance-driven MICE city development plans for sustainability and competitiveness. Full article

This study examines how investment efficiency and risk jointly shape sustainable grain supply-chain upgrading. Using firm-level panel data for 25 listed grain supply-chain firms in China from 2015 to 2023, this study examines efficiency–risk structures and their heterogeneity across upstream, midstream, and downstream segments. A three-stage data envelopment analysis (DEA) is applied to measure investment efficiency while controlling for environmental heterogeneity and statistical noise, and a multidimensional investment risk index is constructed using principal component analysis (PCA), with an emphasis on sustainability metrics. The results reveal a clear supply-chain gradient: downstream firms exhibit the highest mean third-stage investment efficiency (crete = 0.633) and scale efficiency (scale = 0.634), midstream firms are intermediate (crete = 0.308; scale = 0.326), and upstream firms remain lowest (crete = 0.129; scale = 0.138). This ordering is also visible year by year, while risk profiles indicate higher exposure upstream and pronounced volatility midstream. Efficiency decomposition shows that upstream inefficiency is mainly driven by scale inefficiency rather than insufficient pure technical efficiency. Overall, efficiency–risk mismatch—manifested as persistent low scale efficiency and elevated risk exposure in upstream, volatility in midstream, and stability in downstream—constitutes a key micro-level barrier to long-term and resilient upgrading. The study thus offers policy-relevant insights for segment-specific interventions that align with sustainable agricultural development: facilitating land consolidation and integrated risk management for upstream scale inefficiency, promoting supply-chain finance and digital integration for midstream risk volatility, and leveraging downstream stability to drive coordinated upgrading and sustainable value creation through market-based incentives. Full article

This study critically examines how Artificial Intelligence for Social Responsibility (AI for SR) is enacted within Thai education, using this Global South context to expose the universal dynamics of educational marketization. Drawing on Freire’s critical pedagogy and Habermas’s theory of lifeworld, the research employs a qualitative design grounded in critical phenomenology. Analysis of interviews, observations, and policy documents reveals that AI for SR is driven less by ethical participation than by policy compliance, funding agendas, and portfolio-driven competition. This dynamic transform responsibility from a moral practice into symbolic capital. Students become producers of symbolic output, and educators act as image managers for institutional displays. The study concludes by proposing a critical pedagogical framework that reclaims AI for SR as a public good, emphasizing dialog and social justice to resist this commodification. Full article

Against the backdrop of global energy transition and sustainable development, advancing the new energy industry has become a critical pathway for optimizing energy structures and achieving the dual carbon goals. However, while China’s new energy sector has experienced rapid growth, it has also exposed a series of challenges, including insufficient innovation momentum, irrational resource allocation, and low conversion rates of R&D outcomes. To delve into the root causes and propose improvement pathways, this study selected 76 listed new energy enterprises from 2021 to 2023 as samples. It comprehensively employed the DEA-BCC model, Malmquist productivity index, and Tobit regression model to conduct empirical analysis across three dimensions: static, dynamic, and influencing factors. The findings revealed: firstly, during the study period, overall static efficiency remained low, with only about 32.90% of enterprises operating efficiently. Efficiency decomposition indicated that low and unstable pure technical efficiency constrained overall efficiency gains. In contrast, while scale efficiency was relatively high, its growth was sluggish, and some enterprises exhibited significant scale irrelevance. Secondly, dynamic total factor productivity exhibited fluctuating growth primarily driven by technological progress. However, declining technical efficiency—particularly the deterioration of scale efficiency—indicated that while the new energy industry advanced technologically and expanded in scale, its management capabilities had not kept pace. This mismatch among the three factors trapped the industry in a “high investment, low efficiency” dilemma. Thirdly, regression analysis of influencing factors indicated that corporate governance and market competitiveness were pivotal to innovation efficiency: the proportion of independent directors and revenue growth rate exerted significant positive impacts, while equity concentration showed a significant negative effect. Firm size had a weaker influence, and government support did not demonstrate a significant positive impact. Accordingly, this paper proposes pathways to enhance innovation efficiency in new energy enterprises, including optimizing corporate governance structures, formulating differentiated subsidy policies, and improving the innovation ecosystem. The findings of this study not only provide empirical references for the innovative development of the new energy industry but also offer theoretical support for relevant policy formulation. Full article

The rapid expansion of artificial intelligence (AI) and digitalization in contemporary education has intensified global debates on sustainable education, frequently framed around efficiency, personalization, and technological innovation. At the same time, these developments have accelerated processes of technologization and commodification, raising concerns about the erosion of educational values and human-centered purposes. This tension calls for a critical reassessment of what sustainability should mean in AI-mediated educational contexts. The objective of this study is to examine under what conditions AI contributes to sustainable education as a value-based and human-centered project, and under what conditions it undermines it. Methodologically, the article adopts a qualitative, value-critical analysis of contemporary scholarly literature and policy-oriented debates, employing the distinction between sustainable education, sustainability in education, and education for sustainable development as a heuristic entry point within a broader theoretical dialogue. The analysis demonstrates that AI does not exert a uniform or inherently progressive influence on education. While AI can enhance access, personalization, and instructional support in ethically grounded and well-governed contexts, it may also intensify educational inequalities, reinforce the commodification of knowledge, weaken academic integrity, and marginalize the formative and human dimensions of education under market-driven and weakly regulated conditions. These dynamics are particularly visible in culturally and religiously grounded educational contexts, where AI reshapes epistemic authority and educational meaning. The study concludes that achieving sustainable education in the digital age depends not on AI adoption per se, but on subordinating AI and digitalization to coherent normative, ethical, and governance frameworks that prioritize educational purpose, social justice, and human dignity. Full article

This study presents a novel methodology that integrates Transformer-based textual analysis from professional networking platforms with traditional demographic variables within a survival analysis framework to predict turnover. Using a dataset comprising 4087 work events from Maimai (a leading professional networking platform in China) spanning 2020 to 2022, our approach combines sentiment analysis and deep learning semantic representations to enhance predictive accuracy and interpretability for HR decision-making. Methodologically, we adopt a hybrid feature-extraction strategy combining theory-driven methods (sentiment analysis and TF-IDF) with a data-driven Transformer-based technique. Survival analysis is then applied to model time-dependent turnover risks, and we compare multiple models to identify the most predictive feature sets. Results demonstrate that integrating textual and demographic features improves prediction performance, specifically increasing the C-index by 3.38% and the cumulative/dynamic AUC by 3.43%. The Transformer-based method outperformed traditional approaches in capturing nuanced employee sentiments. Survival analysis further boosts model adaptability by incorporating temporal dynamics and also provides interpretable risk factors for turnover, supporting data-driven HR strategy formulation. This research advances turnover prediction methodology by combining text analysis with survival modeling, offering small and medium-sized enterprises a practical, data-informed approach to workforce planning. The findings contribute to broader labor market insights and can inform both organizational talent retention strategies and related policy-making. Full article

Australia’s National Housing Accord aims to deliver 1.2 million new dwellings between mid-2024 and mid-2029, representing 240,000 annual completions—a 37% increase above the 2024 baseline of 175,000. This study employs a comprehensive system dynamics model with 79 equations (10 stocks, 69 auxiliary variables) to analyze whether this target is structurally achievable, given construction industry capacity constraints. The model integrates builder population dynamics, workforce capacity, construction cost inflation, material supply constraints, and financial market conditions across a ten-year simulation horizon (2024.5–2035). Three policy scenarios test the effectiveness of interventions, including capacity expansion (±10–15%), cost inflation management (±15–20%), planning reforms (+5–15% efficiency), and workforce development programs (+1000–4000 annual graduates). Model validation against Australian Bureau of Statistics data from 2015 to 2024 demonstrates strong empirical foundations. Results show that structural capacity constraints—driven by three simultaneous bottlenecks in material supply, workforce availability, and financing—create a supply ceiling of around 180,000–195,000 annual completions. Even under optimistic policy assumptions, the model projects cumulative completions of 880,000–920,000 dwellings over the Accord period, falling 23–27% short of the 1.2 million target. Critical findings include the following: (1) builder insolvencies exceeding entry rates by 15–25% annually under stress conditions, (2) capacity decline trends of 0.6–0.8% per year due to productivity losses, infrastructure bottlenecks, and regulatory burden, (3) system efficiency degradation from 100% to 96% over the projection period, and (4) non-linear capacity utilization, showing saturation above 82% baseline levels. The analysis reveals that demand-side policies cannot overcome supply-side structural limits, suggesting that policymakers must either substantially reduce targets or implement transformative capacity-building interventions beyond current policy contemplation. Full article

The urgency of research into innovation and digital marketing is driven by growing competition within the tourism industry, which demands greater destination visibility (DV) and tourist engagement (TE). At the same time, Areia Branca Beach, a prominent destination in Timor-Leste, has not been managed optimally to support sustainable tourism. Furthermore, the utilisation of governance innovation and digital marketing—particularly the integration of content marketing (CM), immersive technology (IT), and digital data analytics (DDA)—remains limited and has yet to be substantiated by robust empirical evidence at the scale of a developing destination. This study aims to investigate the role of DDA in the causality between CM and IT in influencing DV and TE. A quantitative approach was employed, using moderated regression analysis (MRA) to test the empirical relationships between the variables. Primary data were collected through face-to-face field surveys of tourists who had visited Areia Branca Beach, located northeast of Dili, Timor-Leste, on at least two occasions. The study adopted simple random sampling (SRS) with a finite population correction (FPC). A total of 364 tourists were selected to assess their perceptions using a structured questionnaire. The study reveals four main findings. First, CM significantly affects DDA and DV. Second, IT influences DDA, but not TE. Third, DDA significantly affects both DV and TE. Fourth, DDA moderates the effect of CM on DV and the effect of IT on TE. The findings underscore that the collaborative governance concept, through governance and marketing innovations, is not yet optimal for shaping sustainable tourism. Finally, future academic and practical policy implications require more in-depth exploration to emphasise the enhancement of resource management capacity genuinely needed in the subjects studied, beyond governance and digital marketing innovations within the sustainable tourism framework. Full article

In the context of Poland’s commitments under the European Union’s climate policy, including the European Green Deal and the Fit for 55 package, as well as the decision to ban imports of hard coal from Russia and Belarus, ensuring the stability of the domestic market for energy commodities is becoming a key challenge. The response to these needs is the Coal Platform concept developed by the KOMAG Institute of Mining Technology (KOMAG), which aims to integrate data on hard coal resources, production, and demand. The most important problem is not the just-in-time (JIT) strategy itself, but the lack of accurate, up-to-date data and the high technological and organizational inertia on the production side. The JIT strategy assumes an ability to predict future demand well in advance, which requires advanced analytical tools. Therefore, the Coal Platform project analyses the use of artificial intelligence algorithms to forecast demand and adjust production to actual market needs. The developed mathematical model (2024–2030) takes into account 12 variables, and the tested forecasting methods (including ARX and FLNN) exhibit high accuracy, which together make it possible to reduce overproduction, imports, and CO₂ emissions, supporting the country’s responsible energy transition. This article describes approaches to issues related to the development of the Coal Platform and, above all, describes the concept, preliminary architecture, and data model. As an additional element, a mathematical model and preliminary results of research on forecasting methods in the context of historical data on hard coal production and consumption are presented. The core innovation lies in integrating the just-in-time (JIT) philosophy with AI-driven forecasting and scenario-based planning within a cloud-ready Coal Platform architecture, enabling dynamic resource management and compliance with decarbonization targets. Full article

The integration of biomethane into the natural gas infrastructure is a critical element of energy-sector decarbonization, yet optimal infrastructure development scenarios remain insufficiently compared using unified decision frameworks. This study evaluates three biomethane market integration scenarios—direct connection to the gas system, biomethane injection points (compressed biomethane transported by trucks to the gas system), and off-grid delivery using the multi-criteria decision-making method TOPSIS. Environmental, economic, and technical dimensions are jointly assessed. Results indicate that direct connection to the system provides the most balanced overall performance, achieving the highest integrated score (Ci = 0.70), driven by superior environmental and technical characteristics. Biomethane injection points demonstrate strong economic advantages (Ci = 0.49), particularly where capital investments need to be reduced or there is limited access to the gas system, but show weaker environmental and technical performance. Off-grid solutions perform poorly in integrated assessment (Ci = 0.00), reflecting limited scalability and high logistical complexity, although niche applications may remain viable under specific conditions. Sensitivity analysis confirms the robustness of these rankings across a wide range of weighting assumptions, strengthening the reliability of the findings for policy and infrastructure planning. This study provides one of the first integrated multi-criteria assessments explicitly incorporating virtual pipeline logistics, offering a transferable decision-support framework for sustainable biomethane development in diverse regional contexts. Full article

This study investigates regional heterogeneity and spatial interdependence in digital skills mismatch across Indonesia by constructing a Digital Skills Supply–Demand Ratio (DSSDR) from the Indonesia Digital Society Index (IMDI). In line with SDG 10 (Reduced Inequalities) and SDG 4 (Quality Education), the study aims to provide policy-relevant evidence to support a more inclusive and balanced digital transformation. Using district-level data and spatial econometric models (OLS, SAR, and the SDM), the analysis evaluates both local determinants and cross-regional spillover effects. Model comparison identifies the Spatial Durbin Model as the best specification, revealing strong spatial dependence in digital skills imbalance. The results show that most local socioeconomic and digital readiness indicators do not have significant direct effects on DSSDR, while school internet coverage exhibits a consistently negative association, indicating that digital demand expands faster than local supply. In contrast, spatial spillovers are decisive: a higher share of ICT study programs in neighboring regions improves local DSSDR through knowledge and human-capital diffusion, whereas higher GRDP per capita in adjacent regions exacerbates local mismatch, consistent with a talent-attraction mechanism. These findings demonstrate that digital skills mismatch is a spatially interconnected phenomenon driven more by interregional dynamics than by local conditions alone, implying that policy responses should move beyond isolated district-level interventions toward coordinated regional strategies integrating education systems, labor markets, and digital ecosystem development. The study contributes a spatially explicit, supply–demand-based framework for diagnosing regional digital inequality and supporting more equitable and sustainable digital development in Indonesia. Full article