The climate in climate economics

TL;DR

This paper develops a transparent calibration strategy for climate emulators used in climate economics, demonstrating how calibration choices significantly impact the social cost of carbon and model predictions.

Contribution

It introduces a generic calibration and evaluation framework for climate emulators based on CMIP5 data, improving the accuracy and robustness of climate-economic models.

Findings

Calibrated the DICE-2016 climate component using multi-model mean.

Model uncertainty can alter the social cost of carbon by a factor of four.

Calibrated model shows less sensitivity to discount rates and predicts about one degree less warming.

Abstract

To analyze climate change mitigation strategies, economists rely on simplified climate models - climate emulators. We propose a generic and transparent calibration and evaluation strategy for these climate emulators that is based on Coupled Model Intercomparison Project, Phase 5 (CMIP5). We demonstrate that the appropriate choice of the free model parameters can be of key relevance for the predicted social cost of carbon. We propose to use four different test cases: two tests to separately calibrate and evaluate the carbon cycle and temperature response, a test to quantify the transient climate response, and a final test to evaluate the performance for scenarios close to those arising from economic models. We re-calibrate the climate part of the widely used DICE-2016: the multi-model mean as well as extreme, but still permissible climate sensitivities and carbon cycle responses. We demonstrate that the functional form of the climate emulator of the DICE-2016 model is fit for purpose, despite its simplicity, but its carbon cycle and temperature equations are miscalibrated. We examine the importance of the calibration for the social cost of carbon in the context of a partial equilibrium setting where interest rates are exogenous, as well as the simple general equilibrium setting from DICE-2016. We find that the model uncertainty from different consistent calibrations of the climate system can change the social cost of carbon by a factor of four if one assumes a quadratic damage function. When calibrated to the multi-model mean, our model predicts similar values for the social cost of carbon as the original DICE-2016, but with a strongly reduced sensitivity to the discount rate and about one degree less long-term warming. The social cost of carbon in DICE-2016 is oversensitive to the discount rate, leading to extreme comparative statics responses to changes in preferences.