The aim of this study is to evaluate the environmental impact of solar energy by analyzing its emissions, resource consumption, and waste generation throughout its life cycle. As one of the most widely adopted energy sources, solar power offers substantial benefits in reducing greenhouse gas emissions; however, its broader environmental footprint requires careful examination. The production, operation, and disposal of solar panels contribute to pollution, water consumption, and hazardous waste accumulation, with an estimated 250,000 tons of solar waste reported in 2016 alone. Furthermore, solar power generation requires significant water resources, averaging 650 gallons per megawatt-hour of electricity. A key focus of this study is the emissions associated with solar technology, particularly during panel manufacturing and operation. Using HOMER Pro software, this research quantifies the emissions from Trina Solar photovoltaic (PV) panels (345 Wp), revealing an annual output of 49,259 kg of carbon dioxide, 214 kg of sulfur dioxide, and 104 kg of nitrogen dioxide. This Study obtained using HOMER Pro primarily account for operational emissions and do not include full lifecycle impacts such as raw material extraction, transportation, and disposal. These findings highlight the trade-offs between solar energy’s environmental advantages and its indirect ecological costs. While solar systems contribute to energy security and long-term economic savings, their environmental implications must be factored into energy planning and sustainability strategies. This study underscores the importance of developing greener manufacturing processes, improving recycling strategies, and optimizing solar farm operations to reduce emissions and resource depletion. By providing a comprehensive assessment of solar energy’s environmental impact, this research contributes valuable insights for policymakers, researchers, and industry leaders seeking to balance the benefits of solar power with sustainable environmental management.

Accurate forecasting of waste is essential for effective management and allocation of resources. As urban populations grow, the demand for municipal waste systems increases, creating the need for reliable forecasting methods to support planning and decision making. This study compares statistical models Error Trend Seasonality (ETS) and Auto Regressive Integrated Moving Average (ARIMA) with advanced machine learning approaches, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks. Five waste categories were analyzed: dead animal, building, commercial, domestic, and liquid waste. Historical datasets were used for model training and validation, with accuracy assessed through mean absolute error and root mean squared error. Results indicate that ARIMA generally outperforms ETS in forecasting building, commercial, and domestic waste streams, especially in capturing long-term domestic waste patterns. Both statistical models, however, show limitations in predicting liquid waste due to its irregular and highly variable nature, where even baseline models sometimes perform competitively. In contrast, machine learning methods consistently achieve the lowest forecasting errors across all categories. Their capacity to capture nonlinear relationships and adapt to complex datasets highlights their reliability for real-world waste management. The findings underline the importance of selecting forecasting techniques tailored to the characteristics of each waste type rather than applying a uniform method. By improving forecasting accuracy, municipalities and policymakers can design more effective waste management strategies that align with Sustainable Development Goal 11 on sustainable cities and communities, Sustainable Development Goal 12 on responsible consumption and production, and Sustainable Development Goal 13 on climate action.