In the context of emerging economies, central banks are increasingly facing recurrent climate and energy shocks. How can we extend the traditional Taylor Rule to incorporate climate-related variables (e.g., agricultural productivity, CO₂ emissions, and green investment)?
I would be interested in discussing both theoretical (DSGE-based) and empirical (Bayesian estimation, panel methods, machine learning) approaches. Are there ongoing projects or recent works that could lead to collaborations in this direction?
1. Abdelhamid, M., Hicham, O., Hicham, E. O., & Houda, L. (2025). Carbon Taxes and Inflationary Pressures: A DSGE Exploration of Economic Responses and Macroeconomic Challenges. Computational Economics, 1-24.
2. El Ouazzani, H., Ouakil, H., & Moustabchir, A. (2024). Monetary Policy and Economic Stability: a DSGE Approach to Trend Inflation in Morocco. Statistika: Statistics & Economy Journal, 104(4).
The extension of the Taylor rule to climate variables represents a natural evolution of monetary policy in the face of new systemic challenges. However, its successful implementation requires careful resolution of multiple methodological and public policy questions.
Emerging economies, due to their structural vulnerability to climate shocks, offer particularly relevant contexts for developing and testing these frameworks. Robust empirical evidence will be crucial to inform both the theoretical design and practical implementation of these innovations in monetary policy.
A possible extension could be:
r_t = r + π_t + α(π_t - π) + β(y_t - y*) + γ₁(E_t - E*) + γ₂(A_t) + γ₃(I_green_t) + δS_climate_t
Where climate variables capture:
E_t: Deviation of CO₂ emissions from the target
A_t: Agricultural productivity shock
I_green_t: Green investment/GDP ratio
S_climate_t: Composite climate stress index.
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