Leveraging AI for streamlined decision-making on granting EU funding, catalysing green tech innovation & promoting energetic sovereignty.

Leveraging AI for streamlined decision-making on granting EU funding, catalysing green tech innovation & promoting energetic sovereignty.

Research Question:

In the rapidly evolving global landscape, innovation is pivotal for success, particularly in the realm of sustainable technologies. The Paris Agreement's guidelines and the European Union's (EU) Green Deal creates a space to improve policy concerning green pioneering (Laura Cavalli, et al., 2023) . Amidst geopolitical shifts, such as the decoupling between China and the USA, Europe's drive for sustainability gains further prominence (Felbermayr, Mahlkow, and Sandkamp, 2023). However, the process of obtaining funding for such innovative projects is often complex and time-consuming indicates Wojcieszek (2023). This research aims to address this challenge by proposing an AI-powered framework to streamline the EU funding process and foster green tech innovation, ultimately contributing to the EU's sustainability goals (European Commission, 2015). As innovation happens at crossroads; strengthened bonds between EU and ENP partner countries will allow for technological pioneering and increased energetic sovereignity of a region (Dannreuther, 2018)

Research Goals:

Enhancing Operational Efficiency and Decision-Making Process: Develop intuitive AI-powered forms that simplify application submissions, bridge information gaps, and offer strategic suggestions. Utilize supervised machine learning models to streamline decision-making while maintaining security and reliability.

Promoting Systematic Collaboration for Sustainable Innovation: Cultivate an environment of collaboration where challenges are openly discussed, and innovators craft solutions that resonate with genuine needs. Through data-driven methodologies, pinpoint ideas poised to drive eco-conscious solutions with both sustainability and financial viability.

Enabling Inclusivity and International Partnerships: Extend the project's scope to European Neighborhood Policy (ENP) partner countries, fostering scientific, business, and socio-political bonds. Establish a solid foundation for collaboration between EU and ENP countries.

Timeframe & Plan:

Phase 1: Preliminary Research (Months 1-3)

Conduct literature review on AI's role in EU funding and green tech innovation.

Plan and design workshops to gather insights from stakeholders.

Host workshops focusing on foundational concepts, innovation challenges, and collaboration opportunities.

Conduct structured interviews with key stakeholders: innovators, society members, public institution representatives, and corporate partners.

Phase 2: Corporate Collaboration and Model Development (Months 4-6)

Engage corporate partners in discussions to gather insights and refine the ML model based on corporate feedback and stakeholder insights.

Integrate corporate insights with findings from Phase 1 to enhance understanding.

Validate the research direction and model's utility with stakeholders.

Phase 3: Validation and Synthesis (Months 7-9)


Implement the refined ML model to address research questions.

Collaborate with stakeholders to validate model effectiveness in real-world scenarios.

Synthesize insights from all phases to address research questions.

Prepare a comprehensive research report with actionable recommendations.

Post-Research Activities:

Present research findings to stakeholders, policy-makers, academia, and industry events.

Disseminate knowledge through publications, presentations, and partnerships.

Share the ML model designed into chatbot widget for easy implementation to institutions disposing EU funds as a tool for enhancing EU funding applications.

References:

Laura Cavalli, Mia Alibegovic, Edward Cruickshank, Luca Farnia & Ilenia G. Romani (2023) The impact of EU Structural Funds on the national sustainable development strategy: a methodological application, Regional Studies, Regional Science, 10:1, 52-69, DOI: 10.1080/21681376.2022.2160655

Felbermayr, G., Mahlkow, H. & Sandkamp, A. Cutting through the value chain: the long-run effects of decoupling the East from the West. Empirica 50, 75–108 (2023). https://doi.org/10.1007/s10663-022-09561-w

Expert from Huge Thing Accelerator Maryla Wojcieszek in a unstructured conversation held on 7 of August, 2023: “We need automated process that will improve overtime. We need a process approach. Sometimes we are waiting up to 6 months for a country support and in case of European funds it can take up to 1 year, that is very long”

European Commission, (2015) Sustainable Development Goals. Available at: https://international-partnerships.ec.europa.eu/policies/sustainable-development-goals_en (Accessed: 20 August 2023)

Dannreuther, Roland. (2018). Energy security in Central and Eastern Europe. 10.4324/9781315651774-2.