Search Results (40)

Artificial intelligence is transforming industrial marketing by reshaping processes, decision-making, and inter-firm relationships. However, research remains fragmented, with limited evidence on how adoption drivers create new capabilities and sustainability outcomes. This study develops and empirically validates an integrated framework that combines technology, organization, environment, user acceptance, resource-based perspectives, dynamic capabilities, and explainability. A convergent mixed-methods design was applied, combining survey data from industrial firms with thematic analysis of practitioner insights. The findings show that technological readiness, organizational commitment, environmental pressures, and user perceptions jointly determine adoption breadth and depth, which in turn foster marketing capabilities linked to measurable improvements. These include shorter quotation cycles, reduced energy consumption, improved forecasting accuracy, and the introduction of carbon-based pricing mechanisms. Qualitative evidence further indicates that explainability and human–machine collaboration are decisive for trust and practical use, while sustainability-oriented investments act as catalysts for long-term transformation. The study provides the first empirical integration of adoption drivers, capability building, and sustainability outcomes in industrial marketing. By demonstrating that artificial intelligence advances competitiveness and sustainability simultaneously, it positions marketing as a strategic lever in the transition toward digitally enabled and environmentally responsible industrial economies. We also provide a simplified mapping of theoretical lenses, detail B2B-specific scale adaptations, and discuss environmental trade-offs of AI use. Given the convenience/snowball design, estimates should be read as upper-bound effects for mixed-maturity populations; robustness checks (stratification and simple reweighting) confirm sign and significance. Full article

Against the backdrop of ever-increasing energy demand and growing awareness of environmental protection, the research and optimization of hydrogen-related multi-energy systems have become a key and hot issue due to their zero-carbon and clean characteristics. In the scheduling of such multi-energy systems, a typical problem is how to describe and deal with the uncertainties of multiple types of energy. Scenario-based methods and robust optimization methods are the two most widely used methods. The first one combines probability to describe uncertainties with typical scenarios, and the second one essentially selects the worst scenario in the uncertainty set to characterize uncertainties. The selection of these scenarios is essentially a trade-off between the economy and robustness of the solution. In this paper, to achieve a better balance between economy and robustness while avoiding the complex min-max structure in robust optimization, we leverage artificial intelligence (AI) technology to generate enough scenarios, from which economic scenarios and feasible scenarios are screened out. While applying a simple single-layer framework of scenario-based methods, it also achieves both economy and robustness. Specifically, first, a Transformer architecture is used to predict uncertainty realizations. Then, a Generative Adversarial Network (GAN) is employed to generate enough uncertainty scenarios satisfying the actual operation. Finally, based on the forecast data, the economic scenarios and feasible scenarios are sequentially screened out from the large number of generated scenarios, and a balance between economy and robustness is maintained. On this basis, a multi-energy collaborative optimization method is proposed for a hydrogen-based multi-energy microgrid with consideration of the coupling relationships between energy sources. The effectiveness of this method has been fully verified through numerical experiments. Data show that on the premise of ensuring scheduling feasibility, the economic cost of the proposed method is 0.67% higher than that of the method considering only economic scenarios. It not only has a certain degree of robustness but also possesses good economic performance. Full article

We propose the Commodity-Specific Sustainability Index (CSSI), a multidimensional system for quantifying Brazil–China commodity trade dependence that integrates environmental, economic, and social sustainability metrics with conventional trade dynamics. Traditional trade metrics often overlook sustainability risks due to their focus on volume or monetary value. The CSSI combines three dimensions of sustainability risk (environmental impact, economic resilience, and social well-being) into a single assessment framework for major commodities, including soybeans and iron ore. The framework uses a dynamic weighting mechanism that adjusts sub-indices depending on policy priorities and stakeholder inputs, and a Transformer-based time series model captures relationships between CSSI trends with bilateral trade flows along with external shocks, enabling the predictive analysis of sustainability-driven trade adjustments. Furthermore, the CSSI replaces conventional trade volumes with sustainability-adjusted counterparts that are then incorporated into standard trade frameworks such as gravity equations. Our analysis of soybeans and iron ore from 2015 to 2022 shows that conventional dependence metrics overestimate trade dependence by 12–19% (95% CI: 10.8–21.2%, p < 0.001) for commodities with a high environmental footprint. The predictive model, built entirely based on publicly accessible data sources, produces a mean absolute error of 5.5% (±0.8%) in forecasting quarterly trade flows, outperforming ARIMA (6.8% ± 0.5%) and LSTM (6.1% ± 0.6%). The CSSI’s novelty is its holistic approach to sustainability–trade connections, providing policy makers and researchers with a tool to assess long-term commodity resilience, beyond traditional economic metrics. Full article

This study analyzes the volatility of Bitcoin using stochastic volatility models fitted to one-minute transaction data for the BTC/USDT pair between 1 April 2023, and 31 March 2024. Bernstein polynomial terms were introduced to accommodate intraday and intraweek seasonality, and flexible return distributions were used to capture distributional characteristics. Seven return distributions—normal, Student-t, skew-t, Laplace, asymmetric Laplace (AL), variance gamma, and skew variance gamma—were considered. We further incorporated explanatory variables derived from the trading volume and price changes to assess the effects of order flow. Our results reveal structural market changes, including a clear regime shift around October 2023, when the asymmetric Laplace distribution became the dominant model. Regression coefficients suggest a weakening of the volume–volatility relationship after September and the presence of non-persistent leverage effects. These findings highlight the need for flexible, distribution-aware modeling in 24/7 digital asset markets, with implications for market monitoring, volatility forecasting, and crypto risk management. Full article

River flow forecasting remains a critical yet challenging task in hydrological science, owing to the inherent trade-offs between physics-based models and data-driven methods. While physics-based models offer interpretability and process-based insights, they often struggle with real-world complexity and adaptability. Conversely, purely data-driven models, though powerful in capturing data patterns, lack physical grounding and often underperform in extreme scenarios. To address this gap, we propose PESTGCN, a Physical-Enhanced Spatio-Temporal Graph Convolutional Network that integrates hydrological domain knowledge with the flexibility of graph-based learning. PESTGCN models the watershed system as a Heterogeneous Information Network (HIN), capturing various physical entities (e.g., gauge stations, rainfall stations, reservoirs) and their diverse interactions (e.g., spatial proximity, rainfall influence, and regulation effects) within a unified graph structure. To better capture the latent semantics, meta-path-based encoding is employed to model higher-order relationships. Furthermore, a hybrid attention mechanism incorporating both local temporal features and global spatial dependencies enables comprehensive sequence learning. Importantly, key variables from the HEC-HMS hydrological model are embedded into the framework to improve physical interpretability and generalization. Experimental results on four real-world benchmark watersheds demonstrate that PESTGCN achieves statistically significant improvements over existing state-of-the-art models, with relative reductions in MAE ranging from 5.3% to 13.6% across different forecast horizons. These results validate the effectiveness of combining physical priors with graph-based temporal modeling. Full article

Indonesia’s dependence on fossil fuel exports, particularly coal and crude oil, presents a dual challenge: sustaining economic growth while addressing rising CO₂ emissions. Despite significant attention to domestic energy consumption, the environmental implications of export activities remain underexplored. This study examines the dynamic relationship between energy exports, crude oil prices, and CO₂ emissions in Indonesia using a Vector Autoregressive (VAR) model with annual data from 2002 to 2022. The analysis incorporates Impulse Response Functions (IRFs) and Forecast Error Variance Decomposition (FEVD) to trace short- and long-term interactions among variables. Findings reveal that coal exports are strongly persistent and positively linked to past emission levels, while oil exports respond negatively to both coal and emission shocks—suggesting internal trade-offs. CO₂ emissions are primarily self-driven yet increasingly influenced by oil export fluctuations over time. Crude oil prices, in contrast, have limited impact on domestic emissions. This study contributes a novel export-based perspective to Indonesia’s emission profile and demonstrates the value of dynamic modeling in policy analysis. Results underscore the importance of integrated strategies that balance trade objectives with climate commitments, offering evidence-based insights for refining Indonesia’s nationally determined contributions (NDCs) and sustainable energy policies. Full article

In the digital age, e-commerce has become a critical part of modern economies, shaping global economic growth and the pursuit of the Sustainable Development Goals (SDGs). This study uses robust statistical methods to explore the complex relationships between traditional trade, e-commerce, and key economic and sustainability indicators. The General Linear Model (GLM), factor analysis, and linear regression reveal that conventional trade remains vital for GDP growth, even though e-commerce clearly influences SDG performance. The study emphasizes the catalytic role of e-commerce in advancing sustainability by showing how treating it as a dependent variable speeds up SDG progress through Brown, Holt, and ARIMA forecasting models. Additionally, cluster analysis uncovers a strong link between higher SDG scores and increased e-commerce activity, with countries scoring better on sustainability often having more companies in the digital economy and earning more online. This research provides a comprehensive understanding of how e-commerce can support global sustainability goals, along with integrated policy recommendations that promote digital transformation and long-term environmental and social resilience. Full article

Paleoclimate records have long documented the existence of multicentury hydroclimate anomalies in the Altiplano of South America. However, the causes and mechanisms of these extended events are still unknown. Here, we present a climate modeling experiment that explores the oceanic drivers and atmospheric mechanisms conducive to long-term precipitation variability in the southern Altiplano (18–25° S; 70–65 W; >3500 masl). We performed a series of 100-year-long idealized simulations using the Weather Research and Forecasting (WRF) model, configured to repeat annually the oceanic and atmospheric forcing leading to the exceptionally humid austral summers of 1983/1984 and 2011/2012. The aim of these cyclical experiments was to evaluate if these specific conditions can sustain a century-long pluvial event in the Altiplano. Unlike the annual forcing, long-term negative precipitation trends are observed in the simulations, suggesting that the drivers of 1983/1984 and 2011/2012 wet summers are unable to generate a century-scale pluvial event. Our results show that an intensification of the anticyclonic circulation along with cold surface air anomalies in the southwestern Atlantic progressively reinforce the lower and upper troposphere features that prevent moisture transport towards the Altiplano. Prolonged drying is also observed under persistent La Niña conditions, which contradicts the well-known relationship between precipitation and ENSO at interannual timescales. Contrasting the hydroclimate responses between the Altiplano and the tropical Andes result from a sustained northward migration of the Atlantic trade winds, providing a useful analog for explaining the divergences in the Holocene records. This experiment suggests that the drivers of century-scale hydroclimate events in the Altiplano were more diverse than previously thought and shows how climate modeling can be used to test paleoclimate hypotheses, emphasizing the necessity of combining proxy data and numerical models to improve our understanding of past climates. Full article

Renewable energy, especially wind power, is required to reduce greenhouse gas emissions and fossil fuel use. Variable wind patterns and weather make wind energy integration into modern grids difficult. Energy trading, resource planning, and grid stability demand accurate forecasting. This study proposes a hybrid deep learning framework that improves forecasting accuracy and interpretability by combining advanced deep learning (DL) architectures, explainable artificial intelligence (XAI), and metaheuristic optimization. The intricate temporal relationships in wind speed data were captured by training Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), LSTM-GRU hybrid, and Bidirectional LSTM-GRU following data preprocessing and normalization. To enhance transparency, Local Interpretable Model-Agnostic Explanations (LIMEs) were applied, revealing key time-step contributions across three urban datasets (Los Angeles, San Francisco, and San Diego). The framework further incorporates the Snake Optimizer Algorithm (SOA) to optimize hyperparameters such as LSTM units, dropout rate, learning rate, and batch size, ensuring improved training efficiency and reduced forecast error. The model predicted 2020–2040 wind speeds using rolling forecasting; the SOA-optimized LSTM model outperformed baseline and hybrid models, achieving low MSE, RMSE, and MAE and high R² scores. This proves its accuracy, stability, and adaptability across climates, supporting wind energy prediction and sustainable energy planning. Full article

This paper explores the impact of trade openness and exchange rate volatility on South Africa’s industrial growth from 1980 to 2024 through a hybrid econometric framework combining Autoregressive Distributed Lag (ARDL) and Structural Vector Autoregression (SVAR) models. It captures both long-term relationships and short-term economic patterns; the analysis reveals that gross domestic product (GDP) is the most significant and consistent driver of industrial value added (IVAD), while trade openness and currency volatility exert limited standalone effects. Structural shocks, notably the 2008 global financial crisis and the COVID-19 pandemic, had significant negative short-term impacts on industrial performance, highlighting systemic vulnerabilities. Robustness tests, including rolling window ARDL and first-difference GDP estimation, confirm the persistence of these relationships. Impulse response functions and forecast error variance decomposition underscore the transient and moderate influence of external shocks compared with the dominant role of internal macroeconomic fundamentals. These findings indicate that liberalisation and exchange rate flexibility must be embedded within a broader developmental strategy underpinned by institutional strength, resilience building, and sustainability principles. This study provides fresh insights supporting policy frameworks that prioritise domestic industrial capacity, macroeconomic stability, and alignment with Sustainable Development Goal 9—inclusive and sustainable industrialisation. Full article

Predicting stock prices is a popular area of study within the realms of data mining and machine learning. Precise forecasting can assist investors in mitigating the risks associated with their investments. Given the unpredictable nature of the stock market, influenced by policy changes, stock data often display high levels of fluctuation and randomness, aligning closely with the prevailing market sentiment. Moreover, diverse datasets related to stocks are rich in historical data that can be leveraged to forecast future trends. However, traditional forecasting models struggle to harness this information effectively, which restricts their predictive capabilities and accuracy. To improve the existing issues, this research introduces a novel stock prediction model based on a deep-learning neural network, named after EL-MTSA, which leverages the multifaceted characteristics of stock data along with ensemble learning optimization. In addition, a new evaluation index via market-wide sentiment analysis is designed to enhance the forecasting performance of the stock prediction model by adeptly identifying the latent relationship between the target stock index and dynamic market sentiment factors. Subsequently, many demonstration experiments were conducted on three practical stock datasets, the CSI 300, SSE 50, and CSI A50 indices, respectively. Experiential results show that the proposed EL-MTSA model has achieved a superior predictive performance, surpassing various comparison models. In addition, the EL-MTSA can analyze the impact of market sentiment and media reports on the stock market, which is more consistent with the real trading situation in the stock market, and indicates good predictive robustness and credibility. Full article

Circulating fluidized bed (CFB) boilers excel in low emissions and high efficiency, with bed temperature serving as a critical indicator of combustion stability, heat transfer efficiency, and pollutant reduction. This study proposes a novel framework for predicting bed temperature in CFB boilers under complex operating conditions. The framework begins by collecting historical operational data from a power plant Distributed Control System (DCS) database. Next, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm is employed to decompose the raw signals into distinct modes. By analyzing the trade-offs of combining modes with different energy levels, data denoising and outlier reconstruction are achieved. Key features are then selected using Normalized Mutual Information (NMI), and the inflection point of NMI values is used to determine the number of variables included. Finally, an iTransformer-based model is developed to capture long-term dependencies in bed temperature dynamics. Results show that the CEEMDAN-NMI–iTransformer framework effectively adapts to diverse datasets and performs better in capturing spatiotemporal relationships and delivering superior single-step prediction accuracy, compared to Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Transformer models. For multi-step predictions, the model achieves accurate forecasts within 6 min and maintains an R² above 0.95 for 24 min predictions, demonstrating robust predictive performance and generalization. Full article

This study aims to investigate how innovation development drives green finance in the Visegrad countries by analyzing the role of R&D investments, high-tech trade, and patent activity in attracting greenfield investments. Using a vector autoregression (VAR) model with data from 2007 to 2022, this study employs forecasting techniques, impulse response functions, and variance decomposition analyses to assess the dynamic relationship between innovation and green financial flows. The findings reveal that R&D expenditures are the strongest driver of green investments, explaining over 93% of the variance in Poland and Hungary. High-tech trade significantly influences investment trends, contributing up to 84% of the variance in the Czech Republic, while patent applications initially boost greenfield investments but show diminishing returns over time. Although innovation-driven investments remain stable overall, the impact of trade and patents varies across countries, reflecting regional differences. This study identifies key challenges, such as commercialization gaps and policy disparities, highlighting the need for targeted financial and innovation policies. To sustain green finance growth, policymakers should expand R&D funding, strengthen trade infrastructure, and enhance intellectual property commercialization. Additionally, financial institutions and investors should play a more active role in developing green investment markets to support long-term economic resilience and sustainability. Full article

This research determines the contribution of Corporate Social Responsibility (CSR) to reducing financial risks and, consequently, to the sustainable development of companies in different sectors of the economy and at different phases of the economic cycle (using Russia as an example). The informational and empirical base comprises data on the dynamics of stock prices of sectoral indices of the Moscow Exchange’s total return “gross” (in Russian rubles): oil and gas, electricity, telecommunications, metals and mining, finance, consumer sector (retail trade), chemicals and petrochemicals, and transportation, as well as the “Responsibility and Openness” index in 2019 (before the crises), in 2020 (COVID-19 crisis), 2022 (sanction crisis), and 2024 (Russia’s economic growth). Economic–mathematical models, compiled through regression analysis, showed that the contribution of CSR to reducing the financial risks of companies is highly differentiated among economic sectors and phases of the economic cycle. The research presents a new sectoral perspective on counter-cyclical management of the financial risks of companies through CSR, enabling a deeper study of the cause-and-effect relationships of such management for the sustainable development of companies from different economic sectors. This is the theoretical significance of this research, its novelty, and its contribution to the literature. The research has practical significance, revealing previously unknown best practices for the sustainable development of companies from different economic sectors of Russia across different phases of the economic cycle. The systematized experience will be useful for forecasting the financial risks of companies during future economic crises in Russia and improving the practice of planning and organizing the financial risk management of Russian companies through CSR. The authors’ conclusions have managerial significance because they will help enhance the flexibility and efficiency of corporate financial risk management by considering the sectoral specifics and cyclical nature of the economy when implementing CSR. Full article

The development of electricity spot markets necessitates more refined and accurate load forecasting capabilities to enable precise dispatch control and the creation of new trading products. Accurate load forecasting relies on high-quality historical load data, with complete load data serving as the cornerstone for both forecasting and transactions in electricity spot markets. However, historical load data at the distribution network or user level often suffers from anomalies and missing values. Data-driven methods have been widely adopted for anomaly detection due to their independence from prior expert knowledge and precise physical models. Nevertheless, single network architectures struggle to adapt to the diverse load characteristics of distribution networks or users, hindering the effective capture of anomaly patterns. This paper proposes a PLS-VAE-BiLSTM-based method for anomaly identification and correction in load data by combining the strengths of Variational Autoencoders (VAE) and Bidirectional Long Short-Term Memory Networks (BiLSTM). This method begins with data preprocessing, including normalization and preliminary missing value imputation based on Partial Least Squares (PLS). Subsequently, a hybrid VAE-BiLSTM model is constructed and trained on a loaded dataset incorporating influencing factors to learn the relationships between different data features. Anomalies are identified and corrected by calculating the deviation between the model’s reconstructed values and the actual values. Finally, validation on both public and private datasets demonstrates that the PLS-VAE-BiLSTM model achieves average performance metrics of 98.44% precision, 94% recall rate, and 96.05% F1 score. Compared with VAE-LSTM, PSO-PFCM, and WTRR models, the proposed method exhibits superior overall anomaly detection performance. Full article