Inspired by Aguais and Forest (2023), who argue that shocks and volatility (not smooth growth drifts) drive credit losses, this project embeds volatility channels into an endogenous growth framework. It combines a Romer-style innovation engine with stock-flow-consistent macrodynamics (Giraud & Valcke, 2023’s IDEE spirit) and a climate block. Climate change affects the economy by thickening the tails of shocks (heatwaves, floods, energy price spikes), raising credit-factor volatilities, widening risk premia, and endogenously shifting the composition and timing of R&D and capital investment. This approach contrasts with NGFS-aligned assessments that adjust long-run GDP paths smoothly, instead emphasizing volatility as the main transmission mechanism. The project plugs the volatility mapping (temperature anomalies → credit factor vol) into an IDEE-like macro core and tests welfare/policy comparisons versus BCE/IAM baselines. It is promising because if climate-induced volatility depresses innovation when it is most needed, macroprudential and risk-sharing policies (e.g., climate catastrophe bonds, countercyclical green credit buffers) could deliver outsized growth dividends even holding average mitigation constant. The impact is a reprioritization of climate policy toward volatility-smoothing (insurance architecture, liquidity backstops, diversified energy systems) as growth policy, providing regulators a way to connect climate risk stress testing with innovation-led growth outcomes—something current IAMs miss.
References:
If you are inspired by this idea, you can reach out to the authors for collaboration or cite it:
@misc{gpt-5-from-trends-to-2025,
author = {GPT-5},
title = {From Trends to Turbulence: An endogenous growth model where climate-driven volatility, not average trends, drives finance, R&D, and growth},
year = {2025},
url = {https://hypogenic.ai/ideahub/idea/VoZZLiRkJwFw5X3ZaFwM}
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