Artificial intelligence and information management in the energy transition of India: lessons from the global IT heart

Abstract

Purpose India is a fast-growing economy, that has a majority share in the global information technology industry (IT). Rapid urbanisation and modernisation in India have strained its energy sector, which is being reformed to cope. Despite being the global IT heart and having above average research output in the field of artificial intelligence (AI), India has not yet managed to leverage its benefits to the full. This study aims to address the role of AI and information management (IM) in India’s energy transition to highlight the challenges and barriers to its development and use in the energy sector. Design/methodology/approach The study, through analysis of proposed strategies, current policies, available literature and reports, discusses the role of AI and IM in the energy transition in India, highlighting the current situation and challenges. Findings The results show dispersed research and development incentives for IT in the Indian energy sector; however, the needed holistic top-down approach is lacking, calling for due attention in this matter. Adaptive and swift actions from policymakers towards AI and IM are warranted in India. Practical implications The ongoing transition of the Indian energy sector with the integration of smart technologies would result in increased access to big data. Extracting the maximum benefits from this would require a comprehensive AI and IM policy. Social implications The revolution in AI and robotics must be carried out in line with sustainable development goals, to support climate action and to consider privacy issues – both areas in India must be strengthened. Originality/value The paper offers an original discussion on certain applicable solutions regarding the energy transition of AI coming from the Global South; they are based on lessons learned from the Indian case studies presented in this study.

The environmental challenges of AI in EU law: lessons learned from the Artificial Intelligence Act (AIA) with its drawbacks

“…20, that is, a new set of obligations that the European Commission has inserted in the proposal, following the advice of its own legal Group of Experts on liability and emerging technologies (HLEG, 2019). Disclosed data should be used by regulators to better allocate risks triggered by AI (Chawla et al ., 2022), which includes the environmental impact and sustainability of the technology, determining on this basis the parameters of the integration principle for each EU policy and legal initiative. This approach should likely strengthen the coordination between the different components of the Green Deal: circular economy, environmental law, fundamental rights and the wise regulation of emerging technologies.…”

Artificial intelligence and robotics on the frontlines of the pandemic response: the regulatory models for technology adoption and the development of resilient organisations in smart cities

“…The history of the development of such areas as telecommunications, energy, or aviation shows that the regulatory action is becoming the preferred approach (Sokołowski 2020a ; Dempsey and Gesell 2013 ; Kolasa-Sokołowska 2022 ; Dempsey 1990 ). This tendency also applies to AI (see Scherer 2015 ; Clarke 2019 ), where the race to AI has also spurred a race towards AI regulation (Smuha 2021 ; see Chawla et al 2022 ).…”

AI-coherent data-driven forecasting model for a combined cycle power plant

“…Integrating AI-based tools, techniques and algorithms, particularly machine learning algorithms, is crucial for contemporary energy policies to meet techno-economic and environmental sustainability constraints. A comprehensive and systematic examination of energy policy development approaches outlines the key characteristics and applications of commonly used approaches, adapted and selected from a comprehensive and competitive reference for sustainability assessment [34]. This information will be valuable for researchers regarding well-understanding energy systems in the context of technical and institutional constraints for proper decision-making on optimization parameters aligned with the objectives [35].…”