We present a meta-regression analysis of the relation between the concentration of carbon dioxide in the atmosphere and changes in global temperature. The relation is captured by "climate sensitivity", which measures the response to a doubling of carbon dioxide concentrations compared to...

This study proposes a nonlinear cointegrating regression model based on the well-known energy balance climate model. Specifically, I investigate the nonlinear cointegrating regression of the mean of temperature anomaly distributions on total radiative forcing using estimated spatial...

Some recent research suggests that uncertainty about the response of the climate system to atmospheric greenhouse gas (GHG) concentrations can have a disproportionately large influence on benefits estimates for climate change policies, potentially even dominating the effect of the discount rate....

We present a meta-regression analysis of the relation between the concentration of carbon dioxide in the atmosphere and changes in global temperature. The relation is captured by "climate sensitivity", which measures the response to a doubling of carbon dioxide concentrations compared to...

There is a lot we know about climate change, but there is also a lot we don't know. Even if we knew how much CO2 will be emitted over the coming decades, we wouldn't know how much temperatures will rise as a result. And even if we could predict the extent of warming that will occur, we can say...

This study proposes a nonlinear cointegrating regression model based on the well-known energy balance climate model. Specifically, I investigate the nonlinear cointegrating regression of the mean of temperature anomaly distributions on total radiative forcing using estimated spatial...