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.

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 offers a comprehensive analysis of the performance and systemic dynamics of green finance investments in environmentally sustainable technologies from 2000 to 2025, complemented by scenario-based projections extending to 2050. Empirical results indicate a consistent increase in portfolio returns—from 5.2% in 2000 to 11.8% in 2025—accompanied by a significant reduction in annualized volatility, declining from 8.1% to 3.0%. Concurrently, the portfolio’s sustainability score improved from 0.45 to a full alignment score of 1.00, reflecting a strategic shift towards high-impact green assets. Building on these observed trends, this study introduces the Eco-Financial Resonance Theory (EFRT), an original conceptual framework that interprets sustainable transitions as emergent phenomena arising from resonant interactions among four interdependent domains: financial flows, technological innovation, policy and regulation, and environmental outcomes. Scenario analyses highlight the pivotal roles of policy ambition and innovation pathways in shaping long-term risk-return profiles, with optimistic forecasts projecting returns exceeding 40% by 2050, alongside markedly reduced risks. Regional analysis reveals persistent disparities, underscoring the necessity for context-specific strategies to enhance systemic coherence. Beyond its theoretical contributions, EFRT offers actionable insights for investors and policymakers aiming to align profitability with ecological sustainability. Collectively, these findings position green finance not merely as a niche or ancillary activity but as a transformative mechanism for enabling scalable and resilient sustainability transitions amid accelerating global environmental challenges.

The paper investigates the incorporation of emotional AI into Shark Algorithms to enhance trading performance through affective computing. By integrating sentiment analysis, these advanced algorithms are equipped with emotional intelligence to improve the accuracy of price predictions, devise sentiment-driven trading strategies, and optimize risk management techniques. The research highlights that embedding sentiment scores into conventional trading models provides a more nuanced understanding of market behavior, leading to enhanced decision-making processes.Key findings demonstrate that sentiment-enhanced price prediction models outperform traditional methods in capturing market trends, offering a significant edge in forecasting accuracy. The study further reveals the profitability of trading strategies driven by sentiment analysis, as they capitalize on emotional market responses, which are often overlooked by purely quantitative approaches. Additionally, the research emphasizes the effectiveness of sentiment-based position sizing as a tool for risk mitigation, allowing traders to adjust their exposure based on the emotional state of the market, thereby reducing potential losses during periods of heightened volatility.However, the integration of emotional AI into trading algorithms is not without challenges. The paper identifies issues related to the quality and reliability of sentiment data, which can vary widely depending on the source and the methodology used for analysis. Ethical considerations also arise, particularly concerning the potential for market manipulation and the broader impact of emotionally driven trading on financial markets. Technical complexities, such as the integration of real-time sentiment analysis with high-frequency trading systems, present additional hurdles.The study concludes by proposing future research directions, including the development of more sophisticated sentiment analysis techniques that can better capture the nuances of market sentiment. It also suggests advancements in real-time data processing to enhance the responsiveness of trading algorithms. Improved risk management strategies are recommended to address the specific risks associated with sentiment-driven trading. Furthermore, the paper advocates for the establishment of ethical frameworks to guide the use of emotional AI in financial markets, along with long-term studies to assess its impact on market efficiency and stability. The integration of emotional AI into trading algorithms represents a transformative step in financial technology, with the potential to influence market dynamics and trading practices significantly.

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.

This article addresses the growing computational demands of modern trading algorithms and the pressing need for sustainable practices by introducing "Green Shark Algorithms," a new class of trading systems engineered to emphasize energy efficiency, sustainability, and ethical investment principles. In response to the escalating energy consumption and environmental impact of traditional trading operations, this research proposes a comprehensive approach that merges technological optimization with responsible investing. The study specifically focuses on the Istanbul Stock Exchange (BIST), analyzing how these innovative algorithms can reduce energy usage, lower carbon footprints, and maintain robust financial performance.The development of Green Shark Algorithms involves optimizing algorithmic design and enhancing the efficiency of data center operations. By refining computational processes and leveraging energy-efficient technologies, these algorithms aim to significantly reduce the power demands of trading activities without compromising the speed or accuracy critical to financial markets. The research underscores the potential of these advancements to contribute to the broader sustainability goals of the financial industry by decreasing the environmental impact associated with high-frequency trading.In addition to technical innovations, the integration of Environmental, Social, and Governance (ESG) criteria into trading strategies is a central theme of this study. The inclusion of ESG factors within algorithmic decision-making frameworks ensures that trading activities align with ethical and sustainable investment practices. By prioritizing investments in companies with strong ESG performance, Green Shark Algorithms not only promote financial returns but also foster a more responsible approach to market participation, encouraging corporate behaviors that contribute positively to society and the environment.The findings suggest that the implementation of Green Shark Algorithms on the BIST can lead to a dual benefit: reduced energy consumption and enhanced sustainability in trading operations, coupled with financial performance that meets or exceeds traditional benchmarks. However, the research also highlights challenges, such as the need for reliable ESG data and the complexities of balancing energy efficiency with algorithmic precision.Future research directions include the exploration of advanced energy-efficient technologies, the refinement of ESG metrics for more accurate assessments, and the developmentof regulatory frameworks to support the adoption of sustainable trading practices. The Green Shark Algorithms represent a pivotal step toward reconciling the demands of modern trading with the imperatives of environmental stewardship and ethical investment, potentially reshaping the future of financial markets.