A second and different methodology is based on the Mendelsohn and Schlesinger (1999) reduced form Agricultural Response Functions in the formulation proposed by Cline (2007), where the variation ( _DY_ ) in output per hectare is expressed as a function of temperature _T_ , precipitation _P_ and CO2 concentration _K_ : We need to link changes in yield to variations in average temperature only. To this purpose, we rely on temperature and precipitation data from the USGS Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Climate Change Viewer (GCCV), averaging results from many Global Circulation Models<sup>1</sup> . We collected information on baseline levels and variation in average annual temperature and annual precipitation, by country, comparing the period 1980-2004 (central year 1992) with the period 2050-2074 (central year 2062) under the RCP 8.5 scenario. We also assume that from 1992 to 2062 (70 years) the concentration of CO2 rises (from a baseline level of 365 ppm) at an annual rate of 2.11 ppm.

We use the variation in temperature as an indicator, expressing how much the climate has changed. By dividing the country-specific variation in precipitation with the one of temperature we get a precipitation to temperature coefficient _p_ . In the same way, we get a CO2 concentration to temperature coefficient _k_ , so that we can write: Finally, we need to transform _DY_ to percentage changes _DY/Y_ , which can be done by dividing _DY_ by the output per hectare _Y_ , in millions of dollars. Cline (2007) uses estimated values for the year 2003 which, unfortunately, vary widely (for example, from 29 in Australia to 8707 in the Republic of Korea), ultimately producing unrealistically volatile percentage changes for agricultural productivity.

Here we follow a different strategy, which is based on the “calibration” of the output per hectare _Y._ The latter is chosen so that the percentage change for +3°C is “in line” with a simple mathematical average of estimated variations in the yield of the three crops maize, wheat and rice, for the same temperature change. “In line” means in the range +/-1%, but conditional on a minimum level for _Y_ of 500 and a maximum level of 10,000.

After calibrating the output per hectare, the percentage variation of agricultural output for 1, 2, 3, 4 and 5°C increases in temperature can be computed for each of the 140 GTAP9 countries and regions. The results are shown in Table A4 of the Appendix.

The variation in temperature refers to the average annual temperature specific to each country or region, which may differ from the variation in the global average temperature. On the basis of actual global and regional temperature variations, we estimated for each region a correction factor, which can be used to get an approximated regional variation in temperature through multiplication from the global change. These correction factors are displayed in Table A5 of the Appendix. When only information on the change in global temperature is available, one could therefore estimate the corresponding change in regional temperature using the correction factors.

A quick inspection of the table reveals that variations in regional temperature are typically wider at a higher latitude and whenever the region has limited or no access to the sea or ocean.

1 <u>http://regclim.coas.oregonstate.edu/visualization/gccv/cmip5-global-climate-change-viewer/</u> 8