This paper investigates strategies to address climate change by promoting sustainable energy technologies and consumption practices. It examines renewable energy sources such as solar, wind, and geothermal and their potential to reduce greenhouse gas emissions. The study also explores energy optimization techniques, focusing on genetic algorithms (GAs) and smart energy systems like smart grids and microgrids, which enhance energy efficiency and sustainability. The research highlights the role of the circular economy in fostering sustainable consumption through recycling and waste management. Furthermore, the paper explores the economic trade-offs between energy consumption and environmental harm, focusing on the impact of both renewable and fossil fuel energies. A dual methodological approach is employed: first, an endogenous growth model excluding environmental factors, followed by a modified version incorporating environmental considerations. Using a continuous genetic algorithm and data from 2000 to 2025, the study forecasts the optimal trajectory for renewable energy’s share in global energy consumption under two scenarios. The findings suggest that by 2025, renewable energy could represent 82.4% of the total energy consumption under environmental constraints, up from the current share of 45%. This growth is hindered by challenges like droughts, which impact hydropower production. The study concludes that achieving a sustainable energy transition requires comprehensive policies integrating renewable energy expansion, energy efficiency, and environmental protection. These findings provide important insights into optimizing energy pathways for economic growth and environmental sustainability. They also serve as a foundation for future research and policy recommendations, aiming to ensure a low emission future by balancing the need for energy consumption with the preservation of the environment.

Environmental, social, and governance (ESG) factors are crucial in evaluating a company’s value. High ESG scores reflect ethical practices, social responsibility, and effective governance. This paper examines the impact of ESG criteria on firm value within the airline industry, focusing on their influence on operational efficiency, risk reduction, and financial performance. Using panel data analysis, the study evaluates ESG scores from 32 airline companies over the period of 2018–2023, with an explanatory power of 36.5%. The research explores how integrating environmental, social, and governance factors into strategic management can foster sustainable competitive advantage. It focuses on utilizing internal resources, meeting the needs of various interested parties, and balancing financial, social, and environmental performance. The findings indicate that while ESG practices enhance firm value through improved efficiency and risk management, they do not always lead to higher short-term firm value. Moreover, the study underscores the significance of governance in the airline industry, where robust governance structures can mitigate risks but may also increase costs. This research contributes to the literature by providing empirical evidence of the link between ESG performance and firm value in the airline industry, emphasizing the importance of integrating ESG principles into strategic management for long-term sustainability and financial success.

This study presents a novel approach to managing the cost–time–quality trade-off in modern agriculture by integrating fuzzy logic with a genetic algorithm. Agriculture faces significant challenges due to climate variability, economic constraints, and the increasing demand for sustainable practices. These challenges are compounded by uncertainties and risks inherent in agricultural processes, such as fluctuating yields, unpredictable costs, and inconsistent quality. The proposed model uses a fuzzy multi-objective optimization framework to address these uncertainties, incorporating expert opinions through the alpha-cut technique. By adjusting the level of uncertainty (represented by alpha values ranging from 0 to 1), the model can shift from pessimistic to optimistic scenarios, enabling strategic decision making. The genetic algorithm improves computational efficiency, making the model scalable for large agricultural projects. A case study was conducted to optimize resource allocation for rice cultivation in Asia, barley in Europe, wheat globally, and corn in the Americas, using data from 2003 to 2025. Key datasets, including the USDA Feed Grains Database and the Global Yield Gap Atlas, provided comprehensive insights into costs, yields, and quality across regions. The results demonstrate that the model effectively balances competing objectives while accounting for risks and opportunities. Under high uncertainty (α = 0\alpha = 0α = 0), the model focuses on risk mitigation, reflecting the impact of adverse climate conditions and market volatility. On the other hand, under more stable conditions and lower market volatility conditions (α = 1\alpha = 1α = 1), the solutions prioritize efficiency and sustainability. The genetic algorithm’s rapid convergence ensures that complex problems can be solved in minutes. This research highlights the potential of combining fuzzy logic and genetic algorithms to transform modern agriculture. By addressing uncertainties and optimizing key parameters, this approach paves the way for sustainable, resilient, and productive agricultural systems, contributing to global food security.

This study presents a modeling framework for multi-objective optimization in agricultural finance, emphasizing profitability, risk management, and sustainability. The proposed Advanced Financial Framework for Temporal Synergistic Optimization (AFFTSO) does not introduce a new algorithm; rather, it structures existing optimization workflows to explicitly integrate temporal dynamics, evolving objectives, feedback loops, and sustainability-oriented considerations. AFFTSO is designed to support long-term planning under fluctuating economic and environmental conditions. To demonstrate its applicability, AFFTSO is applied to a 25-year Turkish agricultural dataset (2000–2025), encompassing production, financial, market, and climate indicators. Two widely used evolutionary algorithms—Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO)—are benchmarked within this framework, optimizing profit, financial risk, and resource-use efficiency simultaneously. Results show that NSGA-II consistently outperforms MOPSO, yielding a 12.4% increase in cumulative net profit, a 20.3% reduction in financial risk, and a 15.7% improvement in resource-use efficiency. These outcomes confirm that embedding temporal structures, adaptive objectives, and sustainability considerations into multi-objective optimization models enhances the robustness and resilience of financial planning. Overall, AFFTSO offers a practical approach for guiding resource allocation, investment planning, and risk-aware decision-making in agriculture. By bridging computational optimization with sustainability-oriented financial strategies, this framework supports the development of resilient agricultural systems that align economic performance with environmental and social objectives.

Climate variability, environmental uncertainty, and carbon-market dynamics increasingly challenge agricultural investment and resource allocation decisions worldwide. This study proposes an integrated hybrid decision-support framework combining Long Short-Term Memory (LSTM) deep learning, Interval Type-2 Fuzzy Logic Systems, and Genetic Algorithms to support climate-resilient agricultural investment analysis under uncertainty. The framework integrates predictive modeling, uncertainty representation, and multi-objective optimization within a unified computational architecture. The empirical analysis was conducted using agricultural, climate, and carbon-market datasets covering Europe, Asia, and Africa over the 2010–2025 period. Agricultural productivity indicators, commodity price variables, climate-risk parameters, and carbon-market data were integrated into the modeling process. LSTM models were employed to analyze temporal agricultural and climate-related dynamics, while Interval Type-2 fuzzy systems were used to represent ambiguity associated with environmental and market uncertainty. Genetic Algorithms were subsequently applied to optimize investment allocation under conflicting objectives related to profitability, sustainability, and risk. The findings suggest that the proposed hybrid framework may improve adaptive investment evaluation and optimization performance under uncertain climate conditions relative to standalone computational approaches within the scope of the analyzed datasets. The results further highlight the importance of integrating predictive analytics, uncertainty modeling, and sustainability-oriented optimization within agricultural decision-support systems. However, the framework should be interpreted as a climate-resilient decision-support architecture rather than a universally deterministic forecasting mechanism. Overall, the study contributes to the emerging literature on agricultural sustainability and climate-resilient investment by presenting a transparent and uncertainty-aware computational framework under evolving environmental and carbon-market conditions.

This study investigates the effect of foreign exchanges differences on the cash flow statement, the applied part of this study depends on the actual real data of one of the banks in Syria related to 2013. There have been significant fluctuations in exchange rates as a result of the Syrian crisis, these fluctuations have affected all financial transactions, especially banks transactions. The study aims to investigate that effect on the cash flow statement practically to provide more transparency and fairness with related to the statement of cash flow. This investigation has been done depending on the financial statements of an Islamic bank located in Syria for the year 2013, during the investigation it has been noticed that some accounts in foreign currency have been fixed balances with no changes during the year such as deposits, but because of the fluctuations in foreign exchange rates those accounts have shown a change in the balance in local currency, which was reflected on the financial statements, especially cash flows statement. The main conclusion of this study is that changes in foreign exchange rates that affect the value of fixed balances should be considered separate from actual changes and should not be included in the preparation of financial statements. Instead, they should be shown as separate items, specifically in the cash flow statement. Additionally, the study recommends that these effects should be disclosed and explained in the financial report to provide more transparency, clarity, and fairness for readers.