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Redirect Innovation to foster Positive tipping Points and prevent Lock-ins in the clean Energy transition.

Project description

How green technologies accelerate energy transition

The climate is approaching critical tipping points, yet national commitments remain insufficient. Positive tipping points (rapid shifts to clean energy technologies through targeted interventions) offer a vital pathway to a green economy. However, current climate and economic models often misjudge costs, feasibility, and decision-making complexities. The ERC-funded RIPPLE project aims to address these gaps by exploring how green technology innovation and adoption can reduce costs and hasten the transition from fossil fuels. By integrating advanced economic strategies into a climate-energy-economic model, the project will analyse non-linear interactions among technologies while evaluating material and financial viability. It will also examine risks of lock-ins caused by material shortages, funding gaps, and international dependencies.

Objective

The climate is approaching dangerous points of no return, yet national climate commitments are lagging. One, and possibly the only, way for a timely transition to a green economy is through positive tipping points, i.e. rapid, large and irreversible shifts towards clean energy technologies due to strategic interventions and momentum buildup above critical thresholds. The current generation of coupled climate-energy-economic models, which guide climate mitigation strategies, is highly inadequate to represent and leverage these shifts properly. Current models overestimate technological costs, underestimate feasibility concerns, and oversimplify the decision-making process.

The RIPPLE project aims to challenge these limitations and advance the empirical and modelling foundations of positive tipping points. First, it explores how innovation and diffusion of key green technologies can drive costs down and enable a swift transition away from fossil fuels. Novel economic approaches are integrated into a climate-energy-economic model to comprehensively map the space of alternative non-linear transitions of key technologies and their interdependencies. Second, the project investigates material and financial feasibility concerns. It augments a climate-energy-economic model to study lock-ins due to scarcity of critical materials, insufficient funds and fragile international dependencies. Third, the project elaborates on new ways of identifying early signals of positive tipping and generating policies that respond to those signals. Interpretable machine learning is used to turn noisy techno-economic data into actionable green energy tipping interventions, while exploratory modelling highlights the trade-offs between costs, speed, feasibility, fairness and robustness to uncertainties. The ultimate outcome is the first quantitative integrated framework to understand how to redirect innovation for the green economy to tip before the climate does.

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) ERC-2024-STG

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Host institution

TECHNISCHE UNIVERSITEIT DELFT
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 496 994,00
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 496 994,00

Beneficiaries (1)

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