Catalyzing Innovation Through the India–UK FTA: Integrating Trade, Technology, and Policy for Global Transformation
Abstract: The India–UK Free Trade Agreement (FTA) marks a pivotal milestone in international economic diplomacy, serving not only as a trade pact but as a strategic platform for innovation across domains. This paper investigates how the FTA functions as a catalyst for integrated advancements in trade, emerging technologies, and policy frameworks. Using a mixed-methods approach—combining bilateral trade data, policy analysis, and co-publication metrics—we reveal the emergence of a robust India–UK innovation corridor. Key findings indicate a projected 60% increase in joint research output and a significant acceleration of start-up collaborations in sectors such as clean energy, artificial intelligence, and biotechnology. Furthermore, our analysis highlights policy convergence in areas of data governance, intellectual property rights, and digital trade as critical enablers of this transformation. By interpreting trends in funding flows, joint institutional engagements, and regulatory reforms, we argue that the FTA is not merely facilitating commerce but is reconfiguring the innovation ecosystem between two major global players. The study provides actionable insights for policymakers and stakeholders to institutionalize this momentum through deeper regulatory harmonization, targeted R&D funding, and cross-sectoral knowledge exchange platforms. This paper contributes a new interdisciplinary lens to understand FTAs not just as economic instruments but as frameworks for global innovation leadership. Keywords: India–UK FTA, innovation policy, trade diplomacy, technology collaboration, global governance, digital economy, R&D efficiency
Authors
- Murali Krishna Pasupuleti (ORCID: https://orcid.org/0009-0007-1023-7035)
Publication Details
- Journal
- International Journal of Academic and Industrial Research Innovations(IJAIRI)
- Published
- 2025-05-10
- DOI
- https://doi.org/10.62311/nesx/rp05111
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00