Jikku Susan Kurian

This chapter examines the evolving concept of corporate digital sovereignty within the context of global geopolitical dynamics and technological transformation. It highlights how organizations increasingly navigate complex regulatory environments, data localization policies, and geopolitical tensions while maintaining operational efficiency and strategic autonomy. The discussion integrates key dimensions such as digital infrastructure control, data governance, AI sovereignty, and cybersecurity frameworks to explain how firms adapt to fragmented digital ecosystems. Furthermore, the chapter explores the role of sovereign cloud strategies, digital supply chain resilience, and cross-border governance in shaping corporate competitiveness. By linking geopolitical forces with corporate digital capabilities, the study provides a comprehensive framework for understanding how businesses achieve sustainable advantage in a multipolar digital economy.

India and other developing economies are receiving more attention in the context of climate change due to their rapid rates of economic expansion and large populations. In terms of absolute emissions, India surpassed China and the U.S. in 2018 to become the third-largest emitter. Solving this wicked problem calls for climate action across the stakeholder spectrum involving governments, business communities, and citizens. While extant literature has focused significantly on the role of governments and individual perceptions, the business sector needs to be more represented. In this study, we consider business news media as a platform that reflects the industry engagement in climate change and as a source of information on climate change for business decision-makers. Hence, understanding the topic and themes in the nexus of climate and business is important to evaluate the business sector’s stance towards climate change and how it has evolved. This work explores business news related to climate change using natural language techniques. We first experiment with three topic-modeling techniques, such as LDA, NMF, and BERTopic, on the business news and two more benchmark news datasets. Our test data is derived from digital news archives of ’The Economic Times – India’s leading business news daily. We evaluate the performance based on quantitative metrics commonly used for topic models. We choose the algorithm that provides the highest precision for climate-specific information represented by the test dataset. We then apply the algorithm with the best performance, as evaluated by the experiment, to a large corpus of Indian climate news from E.T. spanning from 2008 - 2021. We present how different themes, including industry engagement, evolved over the last two decades. The results suggest that climate cooperation has the highest contribution in the corpus, with other themes on resource management, energy and business gaining traction in recent years.