Search Results (216)

This study classifies corporate financial performance in countries in the Middle East and North Africa (MENA) region, addressing the critical need for accurate and early identification of high-, moderate-, and low-performance companies. The selection of the MENA region was driven by its significant economic growth, diverse market structures, and increasing attractiveness for foreign investment, which makes accurate financial performance assessment important. Despite the growing interest in AI applications for corporate financial performance, a research gap still persists. Existing studies focus primarily on bankruptcy and financial distress prediction in developed countries, with rather limited studies on multi-class financial performance classification in the MENA region. This study addresses a significant gap in the corporate financial performance evaluation literature, which is the lack of a robust, comparative evaluation of advanced DL techniques against conventional ML methods for multi-class corporate financial performance prediction using high-dimensional data. This study employs a design science research (DSR) approach by developing an evaluation analytics artifact that integrates structured preprocessing, dimensionality reduction, and comparative ML and DL modeling, following the relevance, design, and rigor cycles. By employing a design science research (DSR) methodology, the research used a dataset from the Compustat database, comprising 7971 firm-year observations from 2013 to 2024. A rigorous dimensionality reduction process, including pairwise correlation filtering, resulted in a final set of 15 key classification features. The study compared three machine learning techniques—random forests (RFs), support vector machines (SVMs), and eXtreme Gradient Boosting (XGBoost), against one deep learning technique, deep neural networks (DNNs), for classifying the corporate financial performance of MENA-region companies. The models were trained to classify corporations into three performance classes (low, moderate, and high), using the earnings per share (EPS) as the target variable. The empirical findings indicate that all four machine learning algorithms achieved meaningful predictive performance in classifying EPS-based corporate performance. Among the benchmark models, the support vector machine (SVM) and random forest (RF) classifiers produced stable and competitive results, indicating strong generalization capabilities across firms and periods. XGBoost consistently outperformed the traditional machine learning models, delivering the highest overall classification accuracy and superior discriminatory power, highlighting its effectiveness in capturing nonlinear relationships and complex feature interactions. Similarly, the deep neural network further improved classification performance relative to the benchmark models and exhibited comparable results to XGBoost, especially in modeling high-dimensional data. This superior performance can substantially enhance earnings performance classification through early performance deterioration and improvement identification, allowing more proactive strategic and operational decisions. Full article

As a pivotal instrument for fostering sustainable development and climate goals, low-carbon city pilot policies (LCCPs) motivate firms to increase environmental investments, thereby harmonizing economic growth with emission reduction. This study employs a difference-in-differences (DID) design to empirically investigate the effects and underlying mechanisms of LCCPs on firms’ environmental investment in China. The results demonstrate that LCCPs lead to a significant increase in corporate environmental investment of approximately 36.5% (with a core coefficient of 0.365, significant at the 1% level) when compared to non-pilot cities. This impact primarily occurs through five channels: technology transformation, environmental regulation compliance, financial support, talent attraction, and policy alignment. Heterogeneity tests further reveal that the effect is stronger for enterprises in the eastern and western regions, non-entrepreneurial boards and non-financial entities, larger firms, and those facing financing constraints and operating in low-industry competitive environments. This study offers evidence for the importance of LCCPs in driving corporate environmental investments, providing valuable policy implications for enhancing regulatory frameworks and fostering green innovation to support carbon neutrality and sustainable economic transitions. Full article

This study is dedicated to analysing Ukraine’s transition to utilising renewable energy sources within the broader context of European integration, the decarbonization process, and the challenges significantly intensified by the full-scale Russia-Ukraine war in 2022. The objective of this study is to assess the effectiveness of managing Ukraine’s energy transition compared with selected European Union countries and to identify governance-related determinants of transition performance. The energy transition process is viewed as a cornerstone for ensuring national resilience, food security, and strategic post-war recovery planning. Despite significant growth rates in installed capacity, stimulated primarily by the implementation of green tariffs and foreign investments, Ukraine faces a range of systemic barriers. These include regulatory uncertainty, war-related infrastructure damage, and institutional fragility. To comprehensively assess managerial effectiveness, a comparative approach is employed, integrating data from the Energy Transition Index, the Worldwide Governance Indicators, and the Bertelsmann Transformation Index for the period 2015–2023. Within the scope of this research, a comparative analysis is conducted of Ukraine with Poland, Romania, and Slovakia, countries that share a post-socialist legacy and experience in European integration. The obtained results demonstrate that, although Ukraine exhibits a relatively high growth index for renewable energy development, at 54.56%, it significantly lags behind its regional partners in the parameters of quality of state governance, policy implementation consistency, and strategic coordination. It is concluded that managerial effectiveness, defined as the complex interplay between institutional capacity, policy stability, and implementation efficiency, is a decisive factor for the success of the energy transition. The research recommendations encompass enhancing regulatory transparency, strengthening strategic planning, and intensifying the attraction of international investments. Full article

Public spaces in peri-urban towns are becoming key focal points of urban regeneration in China due to their geographic advantages, resource endowments, and diverse populations. Substantial investments have been made to improve residents’ living environments and well-being. As over-commercialized urban centers increasingly face congestion and homogenization, the distinctive landscapes and authentic everyday life of peri-urban towns are attracting growing attention from tourists. Understanding both residents’ and visitors’ perceptions of these public spaces is therefore essential for successful regeneration. This study examines Nanquan Street, which lies ina peri-urban heritage town in Wuxi, Jiangnan region, China. Drawing on user-generated content from major Chinese social media platforms (Xiaohongshu and Dianping) and field observations guided by the AEIOU framework, a three-stage grounded theory approach was employed to identify the key factors influencing user satisfaction. The analysis identified twelve sub-dimensions grouped into three overarching categories: foundational preconditions, social developmental factors, and spatial-operational factors, which collectively shape sustained satisfaction in Peri-urban heritage towns. By translating the satisfaction model into sustainable design strategies, this study proposes a set of renewal pathways applicable not only to Nanquan Street but also to similar peri-urban towns facing comparable challenges. Emphasizing multi-user experience, low-intervention strategies, and contextual adaptability, this research contributes to theoretical understandings of sustainable renewal in peri-urban towns. It provides actionable guidance for balancing everyday life, cultural heritage, and sustainable tourism development. Full article

Enhancing corporate green innovation has become a critical question in the context of sustainable development. Prior studies have predominantly examined the macro-level effects of regional integration while largely overlooking its micro-level impacts on enterprises. This study aims to examine the institutional effect of regional integration on corporate green innovation. Taking the Yangtze River Delta integration as a quasi-natural experiment, we utilize panel data from A-share listed companies between 2003 and 2022 and apply a multi-period difference-in-differences method. The empirical results reveal that regional integration significantly enhances corporate green innovation, with a more pronounced effect for non-state-owned firms, large firms, and those located in non-corridor cities. Mechanism analyses further reveal that regional integration promotes corporate green innovation by alleviating financing constraints and attracting foreign direct investment. By identifying regional integration as a critical driver of corporate green innovation, this study broadens the research perspective on corporate green innovation and provides policy implications for promoting sustainability through coordinated regional development strategies. Full article

Background: Agricultural production plays a vital role in the global economy by integrating different sectors and promoting capital circulation across industries. In this context, the dairy sector emerges as a promising avenue for investment. This study aims to assess the economic feasibility of establishing a dairy plant for the production of parmesan and mozzarella cheeses in Lavras, MG, considering both deterministic and probabilistic scenarios. Methods: The analysis was conducted in three stages: data collection, deterministic economic feasibility analysis using traditional financial indicators (NPV, IRR, profitability rate, and payback), and a probabilistic assessment using the Monte Carlo simulation with 100,000 iterations to incorporate uncertainty into the model. Results: The deterministic results indicated a positive Net Present Value (NPV), Internal Rate of Return (IRR) exceeding the Minimum Attractiveness Rate (MAR), and a profitability rate above 1.5, validating the investment’s viability. The probabilistic analysis reinforced these findings, with over 80% of simulated scenarios resulting in a positive NPV and over 77% showing IRR above the MAR. Key variables influencing profitability included market share, Class AB cheese consumer percentage, parmesan markup, operational costs, and per capita cheese consumption. Conclusions: The study confirms the economic feasibility of implementing the proposed dairy plant. The integration of Monte Carlo Simulation enhanced the robustness of the analysis by accounting for uncertainty, providing valuable insights for strategic decision-making. The project presents strong potential for regional development, job creation, and income generation. Full article

The digital economy plays a transformative role in enhancing energy efficiency and promoting sustainable development globally. As a key manifestation of digital governance, e-government has emerged as a vital instrument for accelerating the digital transformation of public administration and modernizing governance systems. This study examines the impact of digital governance on urban energy efficiency by analyzing China’s E-Government Pilot City (EPC) policy as a quasi-natural experiment. Using a Difference-in-Differences (DID) approach and balanced panel data from 282 prefecture-level cities (2006–2020), we find that the EPC policy significantly improves total factor energy efficiency (TFEE) by an average of 2.60%. Mechanism analyses reveal that digital governance enhances energy efficiency through industrial structure upgrading, green technology innovation, and foreign direct investment attraction. Heterogeneity analyses indicate that the policy’s benefits are more pronounced in larger, non-resource-based, and non-old industrial base cities, as well as in regions with stronger institutional environments and advanced digital infrastructure. However, spatial spillover effects suggest that while the EPC policy boosts local energy efficiency, it may inadvertently reduce efficiency in neighboring areas due to competitive dynamics and industrial relocation. These findings underscore the importance of tailored and coordinated policy designs to maximize the energy efficiency benefits of digital governance. Full article

This study examines the role of private investment in promoting multidimensional poverty reduction in a sustainable manner in Vietnam by analyzing both spatial and temporal spillover effects. Provincial panel data for 2010–2024 are employed. To assess the spatial spillover effects, three econometric models are applied: SAR, SEM, and SDM. Diagnostic tests suggest that the SDM model is the most appropriate for the research data. Results based on the contiguity and inverse distance weight matrices show that private investment not only reduces poverty in recipient provinces but also generates benefits for neighboring areas, highlighting the need for coordinated planning of industrial zones and regional economic hubs. To analyze this relationship over both the short-term and long-term horizons, the study employs PMG and CCEP estimators, while the DCCEP model verifies robustness in a dynamic framework. The findings consistently confirm that private investment contributes to multidimensional poverty reduction. An additional result from the DCCEP model indicates that literacy and urbanization rate have significant positive effects on poverty reduction, while these relationships are not detected in other models. This finding carries important implications for building an enabling investment environment to attract and effectively utilize private capital to implement multidimensional poverty reduction strategies towards sustainability and aligned with sustainable development objectives. Full article

This study examines how tourism development in Tunisia responds to environmental degradation, renewable energy consumption, technological innovation, and foreign direct investment. Using annual data for 1990–2023, we apply the Autoregressive Distributed Lag (ARDL) bounds approach to identify long-run equilibria and short-run dynamics and validate the results with Fully Modified Ordinary Least Squares (FMOLS). The bounds tests confirm stable long-run relationships among tourism development and its structural determinants—environmental degradation, renewable energy, technological innovation, and foreign direct investment. The empirical results show that environmental degradation depresses tourism development in the long run, whereas renewable energy and technological innovation promote it. Foreign direct investment provides the strongest positive contribution. Complimentary Granger causality tests confirm unidirectional causality from environmental degradation, renewable energy, and technological innovation to tourism development, and bidirectional causality between tourism and foreign direct investment, validating the robustness and direction of influences among variables. Short-run effects appear weaker and occasionally mixed; however, the negative and highly significant error-correction term indicates convergence toward equilibrium. The FMOLS estimates closely match the ARDL results, providing further confidence in the results. Accordingly, policymakers should bolster environmental management, increase renewable energy as part of tourism infrastructure, advance digital and eco-innovation, and attract FDI in cleaner technologies and higher standards of services. This study fills conceptual and regional evidence gaps by integrating environmental, technological, and financial dimensions within a unified framework. It offers practical guidance consistent with the Sustainable Development Goals; specifically, Goals 7 (clean energy), 8 (sustainable growth and jobs), and 13 (climate action). Full article

Sustainable utilization of energy depends on the establishment of an advanced energy system. As the world’s largest consumer and importer of energy, China’s progress in this field has attracted considerable attention. This study seeks to address the limitations of most existing research, which largely remains at a qualitative level, by expanding perspectives and methodologies. Utilizing think-tank research approaches and indicator system evaluation methods, it quantitatively evaluates the development level of new energy systems across thirty provincial-level administrative regions in China from 2011 to 2023. Machine learning methods were applied to empirically analyze the driving mechanisms of “new” factors through the construction of a random forest model. The results reveal that: (1) China’s new energy system exhibited an overall positive development trend, albeit at a relatively slow pace and with notable spatial disparities. The development levels of the three core objectives followed a gradient pattern, showing marked improvements after the implementation of China’s supply-side structural reform policies. (2) Innovation funding and high-level labor input served as the dominant driving forces for development, while factors such as the scale of the technology market, the proportion of the tertiary sector, and environmental regulation investment played supplementary roles, with regional variations observed. Full article

With the rise of the digital economy, energy management has become increasingly intelligent and data-driven. Environmental, Social, and Governance (ESG) considerations have emerged as a key driver of corporate competitiveness, while microgrid scheduling serves as an essential pathway for enterprises to achieve carbon reduction, attract green investment, and meet low-carbon development goals. However, traditional microgrid economic dispatch algorithms often suffer from low optimization efficiency, limited scalability, and poor flexibility. To address these challenges, this paper proposes a multi-strategy improved sand cat swarm optimization (MISCSO) algorithm for the economic scheduling of microgrids. First, a distribution-optimized initialization method based on adaptive diversity guidance is developed to enhance the quality of the initial population. This approach improves algorithmic performance by generating individuals in high-potential regions to ensure solution quality while maintaining population diversity through the inclusion of individuals from low-potential regions. Subsequently, an elite-centered global random movement strategy is introduced to balance elite guidance and global exploration, thereby improving both convergence speed and optimization accuracy. In addition, an adaptive elastic boundary mapping mechanism is proposed to effectively handle boundary violations, striking a balance between boundary constraints and global search capability. To evaluate the effectiveness of MISCSO, it is compared with 11 state-of-the-art algorithms using the IEEE CEC2017 benchmark set, and statistical analyses are conducted to assess performance differences. Experimental results demonstrate that MISCSO achieves superior optimization accuracy, convergence performance, and robustness. Finally, the applicability of MISCSO is verified through its implementation in microgrid economic scheduling, where it achieves outstanding optimization results. Full article

The digital economy (DE) has become an essential driver of sustainable growth under China’s “Dual Carbon” goals of carbon peaking and neutrality. However, limited evidence exists on the DE’s city-level effects on green and low-carbon transition. This study investigates the impact and mechanisms through which digital economy transition (DET) influences urban low-carbon development, utilizing panel data from 283 Chinese cities between 2011 and 2018. A comprehensive digital economy development (DED) index is constructed to measure regional digitalization levels. The findings reveal the following: (1) DET significantly improves CEE, and a one-standard-deviation increase in DED raises CEE by approximately 3.7%. (2) The effect of DET on CEE exhibits regional and resource-based heterogeneity, with western regions and resource-dependent cities benefiting more substantially. (3) The mechanisms through which DET improves CEE include stimulating the technological innovation level, attracting foreign direct investment (FDI), and promoting the financial development level. These insights provide valuable theoretical and practical implications for policymakers seeking to harness the digital economy to achieve sustainable urban development and carbon neutrality. Full article

Iran is a uniquely compelling case due to its ancient and diverse culinary heritage, coupled with a strategic national mandate to significantly boost tourism, making the development of this high-impact sector a crucial policy imperative. The present study adopts a scenario planning approach to first identify the key factors influencing food tourism in rural areas of Iran, then explores plausible future scenarios for rural tourism development, and finally ranks strategic alternatives for enhancing food tourism in these regions. Methodologically, the research combines a goal-oriented, descriptive-analytical approach with future study techniques. Data for the initial phase were collected through a literature review, field studies (surveys, interviews), and expert surveys, and subsequently analyzed using MICMAC and ScenarioWizard software tools. Strategic alternatives were then evaluated using Picture Fuzzy Sets (PFSs) and the COPRAS method based on six critical factors. The findings reveal that six primary factors—promotional activities, pricing, food quality, infrastructure, government support, and investment—play pivotal roles in advancing food tourism in rural Iran. Based on these six primary factors, the study constructs three future scenarios: optimistic, stagnant, and crisis-driven scenarios. In the third phase of the analysis, employing Picture Fuzzy COPRAS and Picture Fuzzy Analytic Hierarchy Process (PF-AHP), the results indicate that “food festivals and promotional campaigns” carry the greatest weight and are deemed the most influential in attracting tourists, whereas “investment” ranks the lowest. Following normalization and application of weights, COPRAS analysis identifies “improving the quality of tourism infrastructure” as the most effective strategy, receiving the highest score (464.0620). A sensitivity analysis further confirms that the overall ranking of the strategies remains stable despite changes in the criteria weights, with only minor shifts observed among mid-ranked alternatives. These results offer policymakers a practical decision-making tool to allocate limited resources efficiently and focus on high-impact strategies that support the sustainable development of food tourism in Iran’s rural areas. Full article

The strategic goal of the transition to a low-carbon economy in Russia requires the active integration of forest-climatic projects into regional sustainable development strategies, especially for areas with high agricultural pressure such as the central forest-steppe of the European part of the Russian Federation. The region contains over 18 million hectares of forest land, which is approximately 2.1% of the area of Russian forests, and intensive agricultural development increases the need for innovative approaches to restoring forest ecosystems. The work uses indicators of the state forest register, data on 18 reforestation projects and 22 afforestation projects, and the results of forecasting the dynamics of greenhouse gas absorption until 2030. It is estimated that by 2030, the sequestration potential of the forests of the central forest-steppe can be increased by 28–30%, which will neutralize up to 12% of emissions from industrial enterprises in the region. In the paper, to unify the assessment, it is proposed to use the carbon intensity factor of investment costs, which, in a number of implemented projects, ranged from 1.2 to 2.7 RUB/1 kg CO₂ eq., reflecting the cost of achieving one ton of absorbed CO₂ equivalent. At ratios above 1, the economic value of the carbon units created exceeds investment costs by at least 20%. Environmental–economic modeling showed that with an increase in the forest cover of the region by 1% (180 thousand hectares), the annual absorption of CO₂ increases by approximately 0.9–1.1 million tons, and the increase in potential income from the sale of carbon units could amount to 1.6–2.2 billion RUB per year at the current price of 1.8–2 RUB/kg CO₂-eq. The use of an integral criterion of environmental and economic efficiency helps increase the transparency and investment-attractiveness of forest-climatic projects, as well as the effective integration of natural and climatic solutions into long-term strategies for the sustainable development of the Central Forest-Steppe of Russia. Full article

As global economies accelerate their energy transitions, the photovoltaic sector faces critical challenges linked to material supply, security, and sustainability. Solar-grade silicon, enabling over 90 percent of photovoltaic technologies, has become a strategic commodity underpinning the expansion of renewable energy infrastructures. This short communication examines the evolving role of solar-grade silicon within the global energy transition, moving beyond its traditional classification as a technical material to frame it as a commodity of geopolitical and economic significance. We analyze recent price trends, regional production asymmetries, and trade dependencies, identifying key vulnerabilities in current supply chains. Although alternative photovoltaic materials such as perovskites and organics attract research interest, their commercial immaturity reinforces the centrality of silicon. The novelty of this contribution lies in treating solar-grade silicon through a commodity lens, integrating techno-economic metrics with policy and investment considerations. We highlight opportunities for reinforcing supply resilience through domestic production, circular economy strategies such as silicon recovery and reuse, and diversification of technological pathways. Our findings advocate for the inclusion of solar-grade silicon in strategic resource planning and industrial policy frameworks. Recognizing its unique position at the intersection of energy, technology, and trade is essential to achieving secure, scalable, and sustainable photovoltaic deployment worldwide. Full article