In this regard, we do not intend to address these gaps. On the contrary, we want to study the sensitivity of our results to alternative mitigation cost functions. To do this, we keep the initial socio-economic setting of our DICE model unchanged and implement the three reduction functions we have recalibrated with a process model for SSP sensitivity analysis (see above). In addition, we control uncertainty in our calibration process. To do this, we use the variance of the parameters estimate and the estimated optimal value to deduce normal distributions for each parameter and each SSP. Each of these distributions is scanned for 1000 sets of parameters, or 1000 alternative mitigation cost functions. Burke, M. et al. Opportunities for progress in the climate economy. Science 352, 292-293 (2016). This research was funded under the European Union`s Horizon 2020 framework programme (subsidy agreement 641811). Glotter, M. J., Pierrehumbert, R.
T., Elliott, J.W., Matteson, N. J. & Moyer, E. J. A simple presentation of the Co2 cycle for economic and political analysis. Amendment 126, 319-335 (2014). These simulations are based on the baseline effect estimate, as is the case in Figure 2, with a climate sensitivity of 2.9oC. The « und hatched » field indicates the range of values recommended by the IPCC-AR529 report. The black star shows the DICE-201316 calibration. The red line marks the Isoquant 2 C. van Vuuren, D.
P. et al. How do integrated assessment models simulate climate change? Amendment 104, 255-285 (2011). While our study is based on recent BHM estimates, which have a non-linear link between temperature increase and economic growth, we are testing here whether our optimal temperature results at the end of the century could also take into account the DJO estimate results. The climate effect function φ (t) is not synonymous with harmful functions normally used in iAmen. These injury functions generally describe the reduction in GDP, which can be seen as a reduction in labour and capital productivity.