Mapping Arctic renewable energy resources, particularly wind, is important to ensure the transition into renewable energy in this environmentally vulnerable region. The statistical characterisation of wind is critical for effectively assessing energy potential and planning wind park sites and is, therefore, an important input for wind power policymaking. In this article, different probability density functions are used to model wind speed for five wind parks in the Norwegian Arctic region. A comparison between wind speed data from numerical weather prediction models and measurements is made, and a probability analysis for the wind speed interval corresponding to the rated power, which is largely absent in the existing literature, is presented. The results of the present study suggest that no single probability function outperforms across all scenarios. However, some differences emerged from the models when applied to different wind parks. The Nakagami and Generalised extreme value distributions were chosen for the numerical weather predicted prediction and the observed wind speed modelling, respectively, due to their superiority and stability compared with other methods. This paper, therefore, provides a novel direction for understanding the numerical weather prediction wind model and shows that its speed statistical features are better captured than those of real wind.

Purpose: This paper explores the contribution of Industry 4.0 technologies—including artificial intelligence (AI), the Internet of Things and blockchain—to sustainability across sectors such as energy, manufacturing, agriculture, urban development and healthcare. It assesses how these technologies enhance resource efficiency, minimize waste and optimize operational performance. Design/methodology/approach: This study combines a review of scholarly literature with sector-specific case studies to investigate how Industry 4.0 technologies support sustainable practices. It identifies practical applications and examines key challenges, including implementation costs, regulatory constraints and concerns over data privacy. The paper concludes with policy recommendations and suggestions for future research. Findings: Industry 4.0 technologies play a significant role in advancing sustainability. In the energy sector, smart grids and blockchain support renewable energy integration and enable decentralized transactions. In manufacturing, these technologies improve production efficiency, reduce waste and lower energy consumption. Precision agriculture enhances input efficiency and increases crop yields. However, challenges such as high implementation costs and resistance to change remain. Overcoming these obstacles requires joint efforts from both the public and private sectors, along with enabling policy frameworks. Research limitations/implications: This review uniquely combines insights across multiple sectors, offering a cohesive view of how Industry 4.0 technologies can drive sustainable development. It also highlights challenges and provides a roadmap for stakeholders to integrate these technologies effectively for both environmental and economic benefits. Practical implications: The findings offer practical guidance for organizations and policymakers in adopting Industry 4.0 technologies that align with sustainability objectives. They also provide strategies to overcome adoption barriers and promote a digital transformation that fosters environmental sustainability and social equity. Originality/value: This review offers an integrated perspective by synthesizing insights from multiple sectors, presenting a comprehensive view of how Industry 4.0 technologies contribute to sustainable development. It also identifies key challenges and outlines a strategic roadmap for stakeholders to effectively adopt these technologies for both environmental and economic gains.