From Brown to Green: Climate Transition and Macroprudential Policy Coordination
Bibliographic record
Abstract
We develop a dynamic, stochastic general equilibrium (DSGE) model for the euro area that accounts for climate change-related risk considerations. The model features polluting (“brown”) firms and non-polluting (“green”) firms and a climate module with endogenous emissions modeled as a byproduct externality. In the model, exogenous shocks propagate throughout the economy and affect macroeconomic variables through the impact of interest rate spreads. We assess the business cycle and policy implications of transition risk stemming from changes in the carbon tax, and the implications of the micro- and macroprudential tools that account for climate considerations. Our results suggest that a higher carbon tax on brown firms dampens economic activity and volatility, shifting lending from the brown to the green sector and reducing emissions. However, it entails welfare costs. From a policy-making perspective, we find that when the financial regulator integrates climate objectives into its policy toolkit, it can minimize the trade-off between macroeconomic volatility and welfare by fully coordinating its micro- and macroprudential policy tools.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".