Global demand for animal-derived foods, particularly beef, continues to rise, intensifying greenhouse gas (GHG) emissions and accelerating climate change. Robust carbon footprint assessments at system and product levels are therefore critical, especially in organic and extensive livestock systems that simultaneously contribute to biodiversity conservation. In this study, we quantified GHG emissions from an organic, extensive Brachycheros cattle farm and its associated beef products using a Tier 2 life cycle assessment (LCA) and a cradle-to-gate boundary. Total farm-level emissions were estimated at 325,821 kg CO₂-eq, with enteric methane representing the predominant source. Beef emissions’ intensity reached 98.29 kg CO₂-eq per kilogram of produced meat, reflecting the lower carcass yields of locally adapted breeds under extensive systems. Product-level emissions were calculated at 20.19 kg CO₂-eq and 24.80 kg CO₂-eq when applying literature-based and study-specific beef intensity values, respectively. Overall, the findings underscored the strong influence of production system characteristics and indigenous breed performance on carbon footprints, highlighting the importance of farm-specific GHG assessments in traditional livestock systems.

In this research, I analyzed the relationship between the renewable energy transition, financial development (FD), and sustainability in Saudi Arabia over a period covering the years 1990 through 2024. My primary purpose of this research was to determine the impact that financial, environmental, and institutional factors have on the transition to renewable energy under the auspices of Saudi Vision 2030. To achieve this, I utilized time-series data over a span of 30 years and employed advanced econometric methods, including the Autoregressive Distributed Lag (ARDL) Bounds Testing Technique, Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS). All three techniques revealed statistically significant relationships between the dependent and independent variables in the short-run and long-run. Therefore, the results were replicated several times in the empirical section of the study. The findings support the existence of a long-term stable correlation between independent and dependent variables, particularly the positive influence of financial services development, human capital accumulation, improvements in energy efficiency (EE), increased government spending on renewable energy, greater trade openness, and more foreign direct investment (FDI) on the shift to renewable energy. However, carbon dioxide (CO₂) emissions and oil rents have a negative impact on the shift to renewable energy due to the reliance on fossil fuels. Moreover, the evidence also demonstrates that the renewable energy transition is a key source of structural diversification of sustainable development within Saudi Arabia’s economy. As recommendations for practice, I suggest to strengthen green financing instruments, expand renewable energy investments, enhance institutional quality, and continue to develop human capital to accelerate the renewable energy transition.

As cities grow and intelligent urban areas expand, effectively managing escalating waste volumes, from their generation and sorting to their final disposal, becomes increasingly essential. This investigation introduces a sophisticated deep-learning (DL) approach for categorizing solid waste into multiple waste categories, including glass, metal, paper, plastic, cardboard, and trash. Our model addresses issues overlooked in previous studies, including class imbalance and sensitivity to hyperparameters in classification tasks. It uses wide, dilated convolutional layers that adeptly identify and combine key features for precise classification. Addressing class imbalance, we implement a reinforcement learning (RL) approach, where the agent evaluates each sample individually and classifies it. For every accurate classification, the agent earns rewards, whereas inaccuracies lead to penalties, with greater penalties/rewards applied to the less prevalent class. This system enables the agent to develop an optimal strategy guided by specific reward functions and a well-defined learning environment. To enhance hyperparameter optimization, the proposed approach improves the differential evolution (DE) algorithm using a clustering-guided mutation strategy based on k-means clustering. This strategy clusters the candidate hyperparameter population using k-means, selects the cluster with the lowest average objective value, and uses the best candidate within that cluster to guide the mutation process. A unique method revitalizes the candidate solutions throughout the population, advancing the hyperparameter adjustment process. Exhaustive evaluations on the TrashNet and Trash datasets demonstrate the efficacy of our model, achieving a high classification accuracy of 89.908% on TrashNet and 87.438% on Trash. These findings highlight the system’s capacity to properly handle particular difficulties in solid waste classification, especially data imbalance and hyperparameter sensitivity.

Synthetic dyes contained in industrial wastewater have become major sources of pollution for the environment and human health owing to their persistence and difficulty in degradation through traditional wastewater treatment processes. In this study, ozonation was studied as a method for the oxidation of methylene blue (MB) dye in water as an advanced oxidation process (AOP). This research is a combination of process optimization, kinetic modeling, energy requirement assessment, and ecotoxicological evaluation. To determine the optimal conditions (effect of O₃ dose, pH, and contact time) for MB removal, mineralization (chemical oxygen demand-COD; total organic carbon-TOC), residual ozone level, and toxicity toward Daphnia magna and Vibrio fischeri, a Taguchi L9 (3³) experimental design was utilized. Optimal conditions for MB removal achieved 98%, COD removal 88%, TOC removal 82%, a minimal level of residual ozone (0.32 mg L⁻¹), and low ecotoxicological risk. Kinetic analysis confirmed the pseudo-first-order reaction rate with k = 0.112 min⁻¹ (R² = 0.987). The energy cost of ozonation (electrical energy per order (EEO)) was 7.06 kWh m⁻³ order⁻¹, which made it energy-efficient among AOPs. According to the results obtained, the key parameters for optimizing the direct ozone oxidation and OH-radical oxidation pathways included the contact time and O₃ dose under neutral pH values.

Green economy has been attracting attention around the world. The "dual carbon" policy has been proposed to emphasize the pursuit of high-quality economy in China, and improving energy efficiency is considered an effective way to achieve this strategic target. Moreover, the Yangtze River Economic Belt acts as the principal carrier for China's economic development; therefore, analyzing the energy efficiency is crucial. Three phases constituted the analysis in this paper: a) Using the LDA topic model to conduct the evaluation system of energy efficiency based on the "dual carbon" policy texts; b) using the SBM-Undesirable model to measure the energy efficiency of 11 provinces and cities in the Yangtze River Economic Belt between 2011 and 2020; and c) using the fsQCA method to further analyze the configurational impact of influential factors on the energy efficiency of the Yangtze River Economic Belt. Through systematic research, the evolving trends within the Yangtze River Economic Belt could be examined from two perspectives. Chronologically, energy efficiency within the region exhibited fluctuating development patterns. From a geographical perspective, overall energy efficiency exhibited a tiered distribution pattern of "downstream > midstream > upstream", indicating that the midstream and upstream sectors possessed greater potential for improvement compared to the downstream sector. Therefore, four configuration paths that affect energy efficiency were finally identified toward achieving the "dual carbon" target.

Air pollution forecasting is a critical component of air quality management, especially in areas experiencing persistent particulate matter issues. However, in practice, the performance of forecasting models depends on local climatic conditions and human activities in each area, resulting in varying forecasting patterns based on spatial contexts. This research aimed to compare the performance between time-series methodologies and machine learning in forecasting hourly PM2.5 and PM10 concentrations, focusing on analysis at air quality monitoring stations in Udon Thani province, Thailand, a medium- sized urban area influenced by both local emissions and regional meteorological patterns. This study used hourly datasets collected from January 1, 2023, to November 30, 2024, comprising a total of 16,800 records obtained from the pollution control department’s monitoring station at Nong Phra Chak Park. Since missing values are frequently encountered in real- world data collection, a linear interpolation method was employed to handle these gaps and enhance the efficiency of the proposed methods. The tested models included three time-series methods- seasonal autoregressive integrated moving average (SARIMA), seasonal naïve, and Holt–Winters exponential smoothing (ETS)-and four machine learning methods: random forest (RF), support vector regression (SVR), artificial neural networks (ANN), and extreme gradient boosting (XGBoost). The study utilized 80% of the data for training and 20% for testing, with performance evaluated through MAE, RMSE, R², MASE, and sMAPE indicators. The findings reveal that while time-series models can effectively reflect basic seasonal structures, they possess limited capability in capturing short-term fluctuations and sudden pollution surges, with MAE ranging from 8.096 to 14.106. In contrast, machine learning models consistently demonstrated lower forecasting errors, particularly the support vector regression methodology, which provided the most accurate and stable performance for both PM2.5 (MAE = 2.4899) and PM10 (MAE = 3.7340). This demonstrates its efficiency in modeling nonlinear relationships and short-term dynamics under the environmental conditions of Udon Thani province. The experimental results suggest that forecasting models aligned with local characteristics can yield reliable predictions, which will benefit environmental agencies and policymakers in developing air quality surveillance systems and implementing mitigation measures that effectively reflect regional specificities.

Horizontal well post-pressure monitoring is important in testing wellbore integrity, reservoir response, and stability after interventions involving pressure. However, existing pressure-based evaluation methods lack systematic integration to capture transient behavior and translate pressure dynamics into actionable well condition assessments. The quantitative description of post-pressure behavior is aided by smoothed pressure trends, rolling variance, and derived stability indices, whereas the statistical approach to detect abnormal pressure deviations is based on the Z-score. This study optimized horizontal well monitoring, supporting sustainable energy by enhancing production efficiency and minimizing risks. A key finding of this study is the development of a clear rule-based decision framework that transforms pressure-derived metrics into operationally meaningful well condition states (stable, warning, and risk), shifting into a quantitative analysis and field-level decision-making. The proposed framework was confirmed with real post-pressure information from a horizontal oil well (Well No. 807). The proposed framework contributes to sustainable energy and environmental solutions by enabling reliable post-pressure well monitoring, improving operational efficiency, reducing intervention risks, and supporting optimized resource utilization in horizontal well systems. The results indicate a clear transition from initial transient pressure behavior to a stable equilibrium state, with approximately 65% in the stable category, 25% in the warning category, and 10% in the risk category. In addition, the role of visual imaging logging is considered at a conceptual level due to the absence of real imaging data, intended to support future interpretation of pressure anomalies.

Long-term air quality patterns in the Northern Great Plains remain insufficiently characterized, particularly for gaseous pollutants that are sensitive to seasonal atmospheric processes and regional emission changes. This study provides a multidecadal analysis of nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) concentrations from 1995 to 2025 at two monitoring stations in Fargo and Bismarck, North Dakota, using seasonally averaged (summer and winter) observations. Historical trends show substantial declines in both pollutants over the 30-year period, with winter concentrations consistently exceeding summer values. To evaluate future air quality trajectories, four machine-learning models (i.e., random forest, extreme gradient boosting, support vector machine, and a feedforward neural network) were trained using engineered lag features and standardized predictors. Model validation with independent 2020–2024 data demonstrated that the neural network achieved the highest predictive accuracy across all scenarios, with coefficients of determination exceeding 0.96 and minimal forecast error. Using a recursive forecasting framework, pollutant levels were projected from 2025 to 2050. The forecasts indicate continued improvement in some cases, stabilization in others, and slight upward tendencies for specific pollutant-season combinations, particularly winter NO₂ in Bismarck and summer SO₂ in both cities. These results highlight that while historical emission reductions have been highly effective, future progress may not be uniform across pollutants or seasons. The findings underscore the importance of sustained monitoring and proactive regulatory planning to preserve North Dakota’s historically clean air.