Wind energy is considered as one of the most prominent sources of energy for sustainable development. This technology is of interest owing to its capability to produce clean, eco-friendly, and cost-effective energy for small-scale users and rural areas where grid power availability is insufficient. Wind power generation has developed rapidly in the past decade and is expected to play a vital role in the economic development of countries. Therefore, studying dominant economic factors is crucial to properly approach public and private financing for this emerging technology, as industrial growth and energy demands may outpace further economic studies earlier than expected. In this study, a strategy-focused method for performing economic analysis on wind energy based on financial net present value, levelized cost of energy, internal rate of return, and investment recovery period is presented. Numerical and simulation results depict the most optimal and economical system from a 3 and a 10 kW wind energy conversion system (WECS). Moreover, the aforementioned criteria are used to determine which WECS range is the most suitable investment with the shortest payback period. Finally, an economically viable and profitable wind energy system is recommended.

Achieving affordable and clean energy targets is essential for sustainable development and high-quality economic growth, in alignment with the United Nations Agenda 2030. Previous studies have examined the effects of clean and non-clean energy on economic growth, yet limited attention has been given to assessing the role of Sustainable Development Goal 7 (SDG—7) and its interaction with economic growth. This study employs pre- and post-estimation tests, selecting the Autoregressive Distributed Lag (ARDL) and Machine Learning (ML) approaches to evaluate the interconnectedness among SDG—7 indicators and economic growth using annual time series data from 1990 to 2023. The findings reveal that the ARDL model offers significant understanding into both short- and long-term relationships between the underlined variables. Notably, energy indicators, including access to electricity, energy intensity, installed renewable energy capacity, and international financial inflows, were found to substantially contribute to long-term economic growth. Additionally, the Granger causality test identified both bidirectional and unidirectional causal relationships, with most variables exhibiting unidirectional causality. To validate the ARDL results, this study applies ML techniques, including Random Forests (RF) and Generalized Additive Models (GAMs), as robustness checks. Using four ML evaluation metrics (Mean Squared Error, Root Mean Squared Error, R 2 , and Out-of-Bag Error), the model demonstrated 93.4 % accuracy. These novel findings for small, open economies highlight critical policy implications, emphasizing the strategic prioritization of SDG—7 to foster environmental sustainability and sustainable economic growth.

This study investigates the optimization of urban parcel delivery by integrating logistics vehicles and onboard drones within a static road network. A centralized delivery hub is responsible for coordinating both modes of transport to minimize total vehicle operation costs and customer waiting times. A simulation-based framework is developed to accurately model the delivery process. An enhanced Ant Colony Optimization (ACO) algorithm is proposed, incorporating a multi-objective formulation to improve route planning efficiency. Additionally, a scheduling algorithm is designed to synchronize the operations of multiple delivery bikes and drones, ensuring coordinated execution. The proposed integrated approach yields substantial improvements in both cost and service efficiency. Simulation results demonstrate a 16% reduction in vehicle operation costs and an 8% decrease in average customer waiting times relative to benchmark methods, indicating the practical applicability of the approach in urban logistics scenarios.