<table><caption>Table A5: Standard and land-adjusted Ricardian estimates for the urban sector</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable:<br>log(building land price)</th><th colspan="4">Dependent variable:<br>log(building land price × urban area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Spring Temperature</td><td>2.91 ***<br>(0.65)</td><td>2.50 ***<br>(0.67)</td><td>3.65 ***<br>(0.63)</td><td>3.23 ***<br>(0.66)</td><td>4.14 ***<br>(0.82)</td><td>3.75 ***<br>(0.84)</td><td>3.78 ***<br>(0.67)</td><td>2.60 ***<br>(0.68)</td></tr><tr><td>Spring Temperature Squared</td><td>-0.12 ***<br>(0.03)</td><td>-0.12 ***<br>(0.04)</td><td>-0.12 ***<br>(0.03)</td><td>-0.11 ***<br>(0.03)</td><td>-0.16 ***<br>(0.04)</td><td>-0.16 ***<br>(0.05)</td><td>-0.11 ***<br>(0.03)</td><td>-0.09 ***<br>(0.03)</td></tr><tr><td>Summer Temperature</td><td>-0.69<br>(0.73)</td><td>-0.83<br>(0.70)</td><td>-1.89 ***<br>(0.60)</td><td>-1.99 ***<br>(0.58)</td><td>-2.16 ***<br>(0.91)</td><td>-2.26 ***<br>(0.88)</td><td>-2.16 ***<br>(0.66)</td><td>-1.68 **<br>(0.66)</td></tr><tr><td>Summer Temperature Squared</td><td>0.00<br>(0.02)</td><td>0.01<br>(0.02)</td><td>0.03 *<br>(0.02)</td><td>0.03 *<br>(0.02)</td><td>0.04<br>(0.03)</td><td>0.04<br>(0.03)</td><td>0.04 *<br>(0.02)</td><td>0.03 *<br>(0.02)</td></tr><tr><td>Autumn Temperature</td><td>-1.04<br>(1.37)</td><td>0.24<br>(1.42)</td><td>0.03<br>(1.15)</td><td>1.01<br>(1.12)</td><td>0.66<br>(1.81)</td><td>1.79<br>(1.86)</td><td>1.34<br>(1.30)</td><td>2.39<br>(1.24)</td></tr><tr><td>Autumn Temperature Squared</td><td>0.08<br>(0.06)</td><td>0.04<br>(0.06)</td><td>0.02<br>(0.05)</td><td>-0.01<br>(0.05)</td><td>0.00<br>(0.08)</td><td>-0.04<br>(0.08)</td><td>-0.06<br>(0.05)</td><td>-0.09<br>(0.05)</td></tr><tr><td>Winter Temperature</td><td>-1.01 ***<br>(0.33)</td><td>-1.33 ***<br>(0.35)</td><td>-1.22 ***<br>(0.26)</td><td>-1.42 ***<br>(0.28)</td><td>-1.57 ***<br>(0.42)</td><td>-1.83 ***<br>(0.46)</td><td>-1.51 ***<br>(0.30)</td><td>-1.54 ***<br>(0.32)</td></tr><tr><td>Winter Temperature Squared</td><td>0.06 **<br>(0.03)</td><td>0.08 **<br>(0.03)</td><td>0.06 **<br>(0.03)</td><td>0.07 **<br>(0.03)</td><td>0.15 ***<br>(0.04)</td><td>0.17 ***<br>(0.04)</td><td>0.13 ***<br>(0.03)</td><td>0.15 ***<br>(0.03)</td></tr><tr><td>Spring Precipitation</td><td>-0.65 **<br>(0.31)</td><td>-0.72 **<br>(0.31)</td><td>-0.54 **<br>(0.27)</td><td>-0.68 **<br>(0.28)</td><td>-1.28 ***<br>(0.40)</td><td>-1.37 ***<br>(0.41)</td><td>-0.52 ***<br>(0.31)</td><td>-0.58 ***<br>(0.32)</td></tr><tr><td>Spring Precipitation Squared</td><td>0.03<br>(0.02)</td><td>0.03<br>(0.02)</td><td>0.02<br>(0.02)</td><td>0.02<br>(0.02)</td><td>0.07 ***<br>(0.02)</td><td>0.07 ***<br>(0.02)</td><td>0.02<br>(0.02)</td><td>0.02<br>(0.02)</td></tr><tr><td>Summer Precipitation</td><td>-0.43 **<br>(0.22)</td><td>-0.43 **<br>(0.22)</td><td>-0.38 **<br>(0.17)</td><td>-0.36 **<br>(0.17)</td><td>-0.19<br>(0.27)</td><td>-0.16<br>(0.28)</td><td>-0.22<br>(0.20)</td><td>-0.23<br>(0.20)</td></tr><tr><td>Summer Precipitation Squared</td><td>0.03 ***<br>(0.01)</td><td>0.03 ***<br>(0.01)</td><td>0.02 **<br>(0.01)</td><td>0.02 **<br>(0.01)</td><td>0.02 **<br>(0.01)</td><td>0.02 *<br>(0.01)</td><td>0.01<br>(0.01)</td><td>0.01<br>(0.01)</td></tr><tr><td>Autumn Precipitation</td><td>0.70 ***<br>(0.16)</td><td>0.43 **<br>(0.17)</td><td>-0.23<br>(0.19)</td><td>-0.34 *<br>(0.19)</td><td>1.00 ***<br>(0.23)</td><td>0.78 ***<br>(0.24)</td><td>-0.23<br>(0.22)</td><td>-0.30<br>(0.21)</td></tr><tr><td>Autumn Precipitation Squared</td><td>-0.02 *<br>(0.01)</td><td>-0.01<br>(0.01)</td><td>0.02 *<br>(0.01)</td><td>0.02 *<br>(0.01)</td><td>-0.03 ***<br>(0.01)</td><td>-0.02 **<br>(0.01)</td><td>0.02 *<br>(0.01)</td><td>0.02 *<br>(0.01)</td></tr><tr><td>Winter Precipitation</td><td>-0.48 **<br>(0.19)</td><td>-0.34 *<br>(0.18)</td><td>0.18<br>(0.13)</td><td>0.21<br>(0.13)</td><td>-0.38<br>(0.23)</td><td>-0.28<br>(0.22)</td><td>0.23<br>(0.15)</td><td>0.23<br>(0.14)</td></tr><tr><td>Winter Precipitation Squared</td><td>0.01<br>(0.01)</td><td>0.01<br>(0.01)</td><td>-0.01<br>(0.01)</td><td>0.00<br>(0.01)</td><td>-0.01<br>(0.02)</td><td>-0.01<br>(0.02)</td><td>-0.02 **<br>(0.01)</td><td>-0.02 **<br>(0.01)</td></tr><tr><td>Number of observations</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td>Controls</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.47</td><td>0.54</td><td>0.68</td><td>0.73</td><td>0.52</td><td>0.56</td><td>0.77</td><td>0.81</td></tr></tbody></table>

NOTE. The figures report the estimates seasonal climate variables on building land price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of urban area by building land price in columns (5) to (8), in line with the land-adjusted Ricardian approach. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets.
A10 Marginal climate impacts on agriculture

Table A6 shows the marginal impacts of seasonal climate variables on farmland value for various standard and land-adjusted Ricardian estimations recomputed from Table A3. For each analysis type, the model specifications differ by the inclusion of year fixed effects and controls.

<table><caption>Table A6: Marginal climate impacts on agriculture with the standard and land-adjusted Ricardian approaches</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable: log(farmland price)</th><th colspan="4">Dependent variable: log(farmland price × agricultural area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.32 **<br>(0.15)</td><td>0.08<br>(0.16)</td><td>0.70 ***<br>(0.14)</td><td>0.80 ***<br>(0.21)</td><td>0.40 **<br>(0.18)</td><td>0.17<br>(0.26)</td><td>0.50 ***<br>(0.16)</td><td>-0.37<br>(0.26)</td></tr><tr><td>Summer</td><td>-0.34 ***<br>(0.12)</td><td>-0.26 *<br>(0.14)</td><td>-0.49 ***<br>(0.13)</td><td>-0.62 ***<br>(0.14)</td><td>-0.38 **<br>(0.17)</td><td>-0.27<br>(0.22)</td><td>-0.37 **<br>(0.16)</td><td>0.05<br>(0.26)</td></tr><tr><td>Autumn</td><td>0.29<br>(0.24)</td><td>0.51<br>(0.37)</td><td>0.30 *<br>(0.16)</td><td>0.58 ***<br>(0.21)</td><td>0.02<br>(0.34)</td><td>0.17<br>(0.45)</td><td>-0.12<br>(0.34)</td><td>-0.02<br>(0.48)</td></tr><tr><td>Winter</td><td>-0.19<br>(0.17)</td><td>-0.27<br>(0.27)</td><td>-0.35 **<br>(0.15)</td><td>-0.54 ***<br>(0.17)</td><td>0.10<br>(0.36)</td><td>0.08<br>(0.44)</td><td>0.10<br>(0.30)</td><td>0.26<br>(0.41)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.08<br>(0.07)</td><td>-0.13<br>(0.08)</td><td>-0.18 ***<br>(0.06)</td><td>-0.28 ***<br>(0.05)</td><td>-0.11 **<br>(0.04)</td><td>-0.17 ***<br>(0.04)</td><td>0.02<br>(0.07)</td><td>0.08<br>(0.09)</td></tr><tr><td>Summer</td><td>0.04<br>(0.07)</td><td>0.06<br>(0.08)</td><td>0.01<br>(0.06)</td><td>0.05<br>(0.04)</td><td>0.12 **<br>(0.06)</td><td>0.16 ***<br>(0.06)</td><td>0.10 **<br>(0.05)</td><td>0.12 ***<br>(0.03)</td></tr><tr><td>Autumn</td><td>0.14 ***<br>(0.04)</td><td>0.09 *<br>(0.05)</td><td>0.03<br>(0.05)</td><td>0.01<br>(0.04)</td><td>0.04<br>(0.03)</td><td>0.01<br>(0.03)</td><td>-0.03<br>(0.06)</td><td>-0.05<br>(0.04)</td></tr><tr><td>Winter</td><td>-0.17 ***<br>(0.05)</td><td>-0.11 *<br>(0.06)</td><td>-0.01<br>(0.04)</td><td>0.04<br>(0.05)</td><td>-0.18 *<br>(0.10)</td><td>-0.12<br>(0.10)</td><td>-0.17 *<br>(0.09)</td><td>-0.18 *<br>(0.10)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.46</td><td>0.53</td><td>0.57</td><td>0.63</td><td>0.41</td><td>0.47</td><td>0.47</td><td>0.54</td></tr></tbody></table>

<footnote>NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on farmland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of farmland area by farmland price in columns (5) to (8), in line with the land-adjusted Ricardian approach. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.</footnote>
A11 Marginal climate impacts on forestry

Table A7 shows the marginal impacts of seasonal climate variables on forest value for various standard and land-adjusted Ricardian estimations recomputed from Table A4. For each analysis type, the model specifications differ by the inclusion of year fixed effects and controls.

<table><caption>Table A7: Marginal climate impacts on forestry with the standard and land-adjusted Ricardian approaches</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable: log(forestland price)</th><th colspan="4">Dependent variable: log(forestland price × forest area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.36 ***<br>(0.11)</td><td>0.15<br>(0.12)</td><td>0.38 **<br>(0.16)</td><td>-0.27<br>(0.38)</td><td>0.11<br>(0.16)</td><td>-0.13<br>(0.24)</td><td>0.22 *<br>(0.14)</td><td>-0.26<br>(0.36)</td></tr><tr><td>Summer</td><td>-0.47 ***<br>(0.09)</td><td>-0.37 **<br>(0.15)</td><td>-0.42 ***<br>(0.09)</td><td>-0.10<br>(0.22)</td><td>0.20<br>(0.14)</td><td>0.49 **<br>(0.20)</td><td>0.27<br>(0.19)</td><td>0.73 **<br>(0.36)</td></tr><tr><td>Autumn</td><td>0.51 **<br>(0.24)</td><td>0.64 **<br>(0.31)</td><td>0.47 **<br>(0.22)</td><td>0.56 **<br>(0.28)</td><td>-0.72<br>(0.51)</td><td>-1.12 *<br>(0.66)</td><td>-0.76<br>(0.48)</td><td>-1.32 **<br>(0.66)</td></tr><tr><td>Winter</td><td>-0.41 ***<br>(0.14)</td><td>-0.44 **<br>(0.21)</td><td>-0.41 ***<br>(0.13)</td><td>-0.28<br>(0.21)</td><td>0.51<br>(0.34)</td><td>0.86 **<br>(0.44)</td><td>0.47<br>(0.37)</td><td>1.02 **<br>(0.52)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.41 ***<br>(0.08)</td><td>-0.46 ***<br>(0.07)</td><td>-0.40 ***<br>(0.10)</td><td>-0.37 ***<br>(0.08)</td><td>-0.28 ***<br>(0.09)</td><td>-0.33 ***<br>(0.08)</td><td>-0.29 ***<br>(0.08)</td><td>-0.28 ***<br>(0.10)</td></tr><tr><td>Summer</td><td>0.14 *<br>(0.08)</td><td>0.17 **<br>(0.07)</td><td>0.13<br>(0.08)</td><td>0.15 **<br>(0.07)</td><td>0.17 *<br>(0.09)</td><td>0.23 **<br>(0.10)</td><td>0.16 *<br>(0.09)</td><td>0.22 ***<br>(0.08)</td></tr><tr><td>Autumn</td><td>0.12 **<br>(0.06)</td><td>0.09 *<br>(0.05)</td><td>0.09<br>(0.06)</td><td>0.07<br>(0.05)</td><td>0.25 ***<br>(0.09)</td><td>0.24 **<br>(0.11)</td><td>0.16 **<br>(0.07)</td><td>0.19 **<br>(0.07)</td></tr><tr><td>Winter</td><td>0.01<br>(0.03)</td><td>0.06 *<br>(0.04)</td><td>0.07<br>(0.04)</td><td>0.07<br>(0.05)</td><td>-0.11<br>(0.07)</td><td>-0.09<br>(0.09)</td><td>0.05<br>(0.08)</td><td>0.01<br>(0.09)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.45</td><td>0.50</td><td>0.49</td><td>0.54</td><td>0.17</td><td>0.21</td><td>0.34</td><td>0.38</td></tr></tbody></table>

<footnote>NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on forestland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of forestland area by forestland price in columns (5) to (8), in line with the land-adjusted Ricardian approach. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.</footnote>
## A12 Marginal climate impacts on urban sector

Table A8 shows the marginal impacts of seasonal climate variables on urban value for various standard and land-adjusted Ricardian estimations recomputed from Table A5. For each analysis type, the model specifications differ by the inclusion of year fixed effects and controls.

<table><caption>Table A8: Marginal climate impacts on urban sector with the standard and land-adjusted Ricardian approaches</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable:<br>log(building land price)</th><th colspan="4">Dependent variable:<br>log(building land price × urban area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.63 *<br>(0.37)</td><td>0.15<br>(0.42)</td><td>1.38 ***<br>(0.27)</td><td>1.14 ***<br>(0.39)</td><td>1.06 ***<br>(0.37)</td><td>0.65<br>(0.42)</td><td>1.72 ***<br>(0.27)</td><td>0.98 ***<br>(0.32)</td></tr><tr><td>Summer</td><td>-0.62 ***<br>(0.22)</td><td>-0.45 *<br>(0.27)</td><td>-0.79 ***<br>(0.18)</td><td>-0.76 ***<br>(0.18)</td><td>-0.87 ***<br>(0.23)</td><td>-0.73 ***<br>(0.26)</td><td>-0.88 ***<br>(0.21)</td><td>-0.54 ***<br>(0.16)</td></tr><tr><td>Autumn</td><td>0.69<br>(0.56)</td><td>1.11<br>(0.75)</td><td>0.46<br>(0.33)</td><td>0.81<br>(0.53)</td><td>0.65<br>(0.80)</td><td>1.02<br>(1.05)</td><td>0.14<br>(0.53)</td><td>0.35<br>(0.74)</td></tr><tr><td>Winter</td><td>-0.59 *<br>(0.32)</td><td>-0.75 *<br>(0.45)</td><td>-0.78 ***<br>(0.21)</td><td>-0.93 ***<br>(0.30)</td><td>-0.44<br>(0.46)</td><td>-0.57<br>(0.63)</td><td>-0.52<br>(0.38)</td><td>-0.44<br>(0.46)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.21 **<br>(0.08)</td><td>-0.27 **<br>(0.13)</td><td>-0.29 ***<br>(0.09)</td><td>-0.38 ***<br>(0.06)</td><td>-0.36 ***<br>(0.12)</td><td>-0.44 ***<br>(0.16)</td><td>-0.26 ***<br>(0.09)</td><td>-0.28 ***<br>(0.09)</td></tr><tr><td>Summer</td><td>-0.10<br>(0.10)</td><td>-0.08<br>(0.10)</td><td>-0.18 **<br>(0.07)</td><td>-0.13 **<br>(0.06)</td><td>0.02<br>(0.13)</td><td>0.06<br>(0.14)</td><td>-0.11<br>(0.09)</td><td>-0.06<br>(0.10)</td></tr><tr><td>Autumn</td><td>0.32 ***<br>(0.03)</td><td>0.22 ***<br>(0.06)</td><td>0.03<br>(0.06)</td><td>0.00<br>(0.05)</td><td>0.45 ***<br>(0.06)</td><td>0.37 ***<br>(0.10)</td><td>0.05<br>(0.07)</td><td>0.02<br>(0.07)</td></tr><tr><td>Winter</td><td>-0.30 ***<br>(0.08)</td><td>-0.18 *<br>(0.10)</td><td>0.09 **<br>(0.05)</td><td>0.14 ***<br>(0.05)</td><td>-0.47 ***<br>(0.13)</td><td>-0.37 ***<br>(0.16)</td><td>-0.01<br>(0.08)</td><td>0.01<br>(0.07)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.47</td><td>0.54</td><td>0.68</td><td>0.73</td><td>0.52</td><td>0.56</td><td>0.77</td><td>0.81</td></tr></tbody></table>

<footnote>NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on forestland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of urban area by building land price in columns (5) to (8), in line with the land-adjusted Ricardian approach. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.</footnote>
A13 Standard vs. land-adjusted Ricardian estimates for alternative uses

Table A9 shows the land-adjusted Ricardian estimates obtained for the whole land sector, including or not controls and year fixed effects.

<table><caption>Table A9: Land-adjusted Ricardian estimates for the whole land sector</caption><thead><tr><th></th><th colspan="4">Dependent variable:</th></tr><tr><th></th><th colspan="4">log(∑<sup>3</sup><sub>i=1</sub> land price of type i × area of type i)</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Spring Temperature</td><td>3.61 ***<br>(0.71)</td><td>3.28 ***<br>(0.73)</td><td>3.28 ***<br>(0.60)</td><td>2.14 ***<br>(0.61)</td></tr><tr><td>Spring Temperature Squared</td><td>-0.14 ***<br>(0.04)</td><td>-0.14 ***<br>(0.04)</td><td>-0.10 ***<br>(0.03)</td><td>-0.07 **<br>(0.03)</td></tr><tr><td>Summer Temperature</td><td>-1.98 **<br>(0.81)</td><td>-2.06 ***<br>(0.78)</td><td>-1.83 ***<br>(0.58)</td><td>-1.30 **<br>(0.59)</td></tr><tr><td>Summer Temperature Squared</td><td>0.04 **<br>(0.02)</td><td>0.04 **<br>(0.02)</td><td>0.03 *<br>(0.02)</td><td>0.03 *<br>(0.02)</td></tr><tr><td>Autumn Temperature</td><td>0.15<br>(1.59)</td><td>1.05<br>(1.64)</td><td>0.55<br>(1.16)</td><td>1.39<br>(1.10)</td></tr><tr><td>Autumn Temperature Squared</td><td>0.01<br>(0.07)</td><td>-0.01<br>(0.07)</td><td>-0.02<br>(0.05)</td><td>-0.06<br>(0.04)</td></tr><tr><td>Winter Temperature</td><td>-1.17 ***<br>(0.38)</td><td>-1.37 ***<br>(0.41)</td><td>-1.08 ***<br>(0.27)</td><td>-1.04 ***<br>(0.29)</td></tr><tr><td>Winter Temperature Squared</td><td>0.12 ***<br>(0.04)</td><td>0.13 ***<br>(0.04)</td><td>0.10 ***<br>(0.02)</td><td>0.11 ***<br>(0.02)</td></tr><tr><td>Spring Precipitation</td><td>-1.16 ***<br>(0.35)</td><td>-1.25 ***<br>(0.36)</td><td>-0.45 *<br>(0.28)</td><td>-0.49 *<br>(0.29)</td></tr><tr><td>Spring Precipitation Squared</td><td>0.06 ***<br>(0.02)</td><td>0.06 ***<br>(0.02)</td><td>0.02<br>(0.02)</td><td>0.02<br>(0.02)</td></tr><tr><td>Summer Precipitation</td><td>-0.10<br>(0.23)</td><td>-0.06<br>(0.24)</td><td>-0.12<br>(0.18)</td><td>-0.13<br>(0.17)</td></tr><tr><td>Summer Precipitation Squared</td><td>0.01<br>(0.01)</td><td>0.01<br>(0.01)</td><td>0<br>(0.01)</td><td>0.01<br>(0.01)</td></tr><tr><td>Autumn Precipitation</td><td>0.90 ***<br>(0.20)</td><td>0.71 ***<br>(0.21)</td><td>-0.15<br>(0.19)</td><td>-0.20<br>(0.18)</td></tr><tr><td>Autumn Precipitation Squared</td><td>-0.03 ***<br>(0.01)</td><td>-0.02 **<br>(0.01)</td><td>0.01<br>(0.01)</td><td>0.01<br>(0.01)</td></tr><tr><td>Winter Precipitation</td><td>-0.28<br>(0.20)</td><td>-0.18<br>(0.19)</td><td>0.21 *<br>(0.13)</td><td>0.21 *<br>(0.12)</td></tr><tr><td>Winter Precipitation Squared</td><td>-0.01<br>(0.01)</td><td>-0.01<br>(0.01)</td><td>-0.02 *<br>(0.01)</td><td>-0.02 *<br>(0.01)</td></tr><tr><td>Number of observations</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td>Controls</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.52</td><td>0.56</td><td>0.77</td><td>0.81</td></tr></tbody></table>

NOTE. The figures report the seasonal climate estimates on the sum of the product of sector-specific area by sector-specific price in columns (1) to (4), in line with the land-adjusted Ricardian approach. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
A14 Marginal climate impacts on the whole land sector

Table A10 shows the marginal impacts of seasonal climate variables on the whole land use sector for various land-adjusted Ricardian estimations recomputed from Table A9. The different specifications differ by the inclusion of year fixed effects and controls.

<table><caption>Table A10: Marginal climate impacts for the whole land sector with the land-adjusted Ricardian approach</caption><thead><tr><th></th><th colspan="4">Dependent variable:</th></tr><tr><th></th><th colspan="4">log(∑<sup>3</sup><sub>i=1</sub> land price of type <i>i</i> × area of type <i>i</i>)</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.92 ***<br>(0.32)</td><td>0.55 *<br>(0.35)</td><td>1.47 ***<br>(0.23)</td><td>0.75 **<br>(0.30)</td></tr><tr><td>Summer</td><td>-0.74 ***<br>(0.19)</td><td>-0.59 ***<br>(0.22)</td><td>-0.72 ***<br>(0.17)</td><td>-0.37 **<br>(0.16)</td></tr><tr><td>Autumn</td><td>0.47<br>(0.69)</td><td>0.75<br>(0.92)</td><td>0.01<br>(0.45)</td><td>0.12<br>(0.63)</td></tr><tr><td>Winter</td><td>-0.30<br>(0.40)</td><td>-0.38<br>(0.56)</td><td>-0.36<br>(0.32)</td><td>-0.22<br>(0.40)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.33 ***<br>(0.11)</td><td>-0.40 **<br>(0.15)</td><td>-0.23 ***<br>(0.08)</td><td>-0.24 **<br>(0.09)</td></tr><tr><td>Summer</td><td>0.05<br>(0.11)</td><td>0.09<br>(0.12)</td><td>-0.06<br>(0.07)</td><td>-0.02<br>(0.08)</td></tr><tr><td>Autumn</td><td>0.39 ***<br>(0.05)</td><td>0.32 ***<br>(0.08)</td><td>0.05<br>(0.07)</td><td>0.03<br>(0.06)</td></tr><tr><td>Winter</td><td>-0.41 ***<br>(0.12)</td><td>-0.32 **<br>(0.15)</td><td>-0.02<br>(0.08)</td><td>-0.01<br>(0.08)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.52</td><td>0.56</td><td>0.77</td><td>0.81</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on the sum of the product of sector-specific area by sector-specific price in columns (1) to (4), in line with the land-adjusted Ricardian approach. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
## A15 Sensitivity analysis: quadratic instead of log-quadratic specifications

Our preferred Ricardian estimates in Section 5 could be biased if the functional form was misspecified. In particular, while most Ricardian studies have used log-quadratic functional form (Massetti and Mendelsohn, 2011), the initial application of the Ricardian approach by Mendelsohn et al. (1994) rather relied on strict quadratic models. We thus estimate the standard and land-adjusted Ricardian models using quadratic functional forms and compare them with those obtained previously with the log-quadratic functional forms. Tables A11, A12 and A13 display the recomputed marginal climate impacts using the standard and land-adjusted Ricardian approaches with such quadratic specification. The climate estimates are in line with those reported in Section 5.1. However, compared to our preferred estimations in Section 5.1, the quality of adjustment reduces for all the uses and models with the quadrati thec models. The main difference is that the Ricardian estimates for urban sector are less precisely estimated with the quadratic specification.

Table A14 shows the recomputed climate impacts using the land-adjusted Ricardian approach for the whole sector, with the quadratic specification. The obtained estimates for the whole land sector are close to those identified in our preferred analysis in Section 5.2. As for the other uses, the quality of adjustment reduces, advocating for the use of the log-quadratic Ricardian models.

<table><caption>Table A11: Marginal climate impacts on agriculture with the standard and land-adjusted Ricardian approaches, with quadratic specifications</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable: farmland price</th><th colspan="4">Dependent variable: farmland price × farmland area</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.28 **<br>(0.12)</td><td>0.02<br>(0.17)</td><td>0.73 ***<br>(0.20)</td><td>0.81 ***<br>(0.30)</td><td>0.32 **<br>(0.13)</td><td>0.07<br>(0.20)</td><td>0.45 ***<br>(0.15)</td><td>-0.36 *<br>(0.21)</td></tr><tr><td>Summer</td><td>-0.31 **<br>(0.13)</td><td>-0.15<br>(0.14)</td><td>-0.50 **<br>(0.19)</td><td>-0.54 **<br>(0.23)</td><td>-0.46 ***<br>(0.12)</td><td>-0.38 **<br>(0.16)</td><td>-0.52 ***<br>(0.11)</td><td>-0.19<br>(0.13)</td></tr><tr><td>Autumn</td><td>0.27<br>(0.28)</td><td>0.30<br>(0.43)</td><td>0.30<br>(0.23)</td><td>0.34<br>(0.33)</td><td>0.47 ***<br>(0.15)</td><td>0.74 ***<br>(0.17)</td><td>0.43 ***<br>(0.19)</td><td>0.71 ***<br>(0.20)</td></tr><tr><td>Winter</td><td>-0.20<br>(0.20)</td><td>-0.14<br>(0.29)</td><td>-0.37 *<br>(0.21)</td><td>-0.40<br>(0.27)</td><td>-0.23<br>(0.17)</td><td>-0.34<br>(0.20)</td><td>-0.30 *<br>(0.15)</td><td>-0.28 ***<br>(0.05)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.09<br>(0.07)</td><td>-0.13 **<br>(0.07)</td><td>-0.22 ***<br>(0.06)</td><td>-0.31 ***<br>(0.05)</td><td>-0.17 ***<br>(0.06)</td><td>-0.22 ***<br>(0.05)</td><td>-0.09<br>(0.08)</td><td>-0.04<br>(0.07)</td></tr><tr><td>Summer</td><td>-0.02<br>(0.09)</td><td>0.01<br>(0.09)</td><td>-0.06<br>(0.08)</td><td>-0.01<br>(0.06)</td><td>0.12 **<br>(0.06)</td><td>0.15 ***<br>(0.04)</td><td>0.11 **<br>(0.05)</td><td>0.11 ***<br>(0.03)</td></tr><tr><td>Autumn</td><td>0.19 **<br>(0.07)</td><td>0.15 **<br>(0.07)</td><td>0.07<br>(0.08)</td><td>0.06<br>(0.08)</td><td>0.03 **<br>(0.01)</td><td>-0.02<br>(0.01)</td><td>-0.04<br>(0.05)</td><td>-0.07<br>(0.04)</td></tr><tr><td>Winter</td><td>-0.18 ***<br>(0.05)</td><td>-0.13 *<br>(0.07)</td><td>0.02<br>(0.06)</td><td>0.05<br>(0.06)</td><td>-0.09 *<br>(0.05)</td><td>-0.03<br>(0.06)</td><td>-0.07<br>(0.06)</td><td>-0.07<br>(0.06)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.44</td><td>0.47</td><td>0.53</td><td>0.54</td><td>0.44</td><td>0.52</td><td>0.48</td><td>0.58</td></tr></tbody></table>

<footnote>NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on farmland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of farmland area by farmland price in columns (5) to (8), in line with the land-adjusted Ricardian approach. The computations use the estimates obtained with the quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.</footnote>
<table><caption>Table A12: Marginal climate impacts on forestry with the standard and land-adjusted Ricardian approaches, with quadratic specifications</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable: forestland price</th><th colspan="4">Dependent variable: forestland price × forest area</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.36<br>(0.23)</td><td>0.13<br>(0.27)</td><td>0.38<br>(0.27)</td><td>-0.38<br>(0.56)</td><td>0.29<br>(0.24)</td><td>0.09<br>(0.30)</td><td>0.43<br>(0.27)</td><td>0.29<br>(0.54)</td></tr><tr><td>Summer</td><td>-0.63 **<br>(0.23)</td><td>-0.54 **<br>(0.27)</td><td>-0.55 **<br>(0.20)</td><td>-0.19<br>(0.34)</td><td>-0.01<br>(0.28)</td><td>0.25<br>(0.31)</td><td>0.06<br>(0.23)</td><td>0.34<br>(0.40)</td></tr><tr><td>Autumn</td><td>0.87 **<br>(0.31)</td><td>1.08 **<br>(0.42)</td><td>0.76 **<br>(0.31)</td><td>0.89 **<br>(0.41)</td><td>-0.56<br>(0.46)</td><td>-0.94<br>(0.60)</td><td>-0.62 *<br>(0.36)</td><td>-1.17 **<br>(0.56)</td></tr><tr><td>Winter</td><td>-0.60 ***<br>(0.2)</td><td>-0.67 ***<br>(0.29)</td><td>-0.54 ***<br>(0.19)</td><td>-0.42<br>(0.30)</td><td>0.32<br>(0.30)</td><td>0.66<br>(0.39)</td><td>0.31<br>(0.27)</td><td>0.76 *<br>(0.42)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.55 **<br>(0.20)</td><td>-0.60 ***<br>(0.19)</td><td>-0.56 ***<br>(0.23)</td><td>-0.52 ***<br>(0.19)</td><td>-0.22**<br>(0.10)</td><td>-0.28 ***<br>(0.09)</td><td>-0.30 ***<br>(0.1)</td><td>-0.35 ***<br>(0.12)</td></tr><tr><td>Summer</td><td>0.17<br>(0.18)</td><td>0.20<br>(0.17)</td><td>0.16<br>(0.18)</td><td>0.17<br>(0.15)</td><td>0.02<br>(0.16)</td><td>0.08<br>(0.15)</td><td>0.00<br>(0.15)</td><td>0.08<br>(0.12)</td></tr><tr><td>Autumn</td><td>0.22 **<br>(0.09)</td><td>0.18 **<br>(0.08)</td><td>0.18 *<br>(0.09)</td><td>0.16 *<br>(0.08)</td><td>0.31 **<br>(0.13)</td><td>0.31 **<br>(0.14)</td><td>0.22 **<br>(0.10)</td><td>0.25 **<br>(0.11)</td></tr><tr><td>Winter</td><td>-0.01<br>(0.05)</td><td>0.05<br>(0.06)</td><td>0.08<br>(0.06)</td><td>0.08<br>(0.06)</td><td>-0.14 *<br>(0.08)</td><td>-0.12<br>(0.1)</td><td>0.06<br>(0.08)</td><td>0.05<br>(0.09)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.42</td><td>0.45</td><td>0.45</td><td>0.49</td><td>0.19</td><td>0.21</td><td>0.33</td><td>0.35</td></tr></tbody></table>

<footnote>NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on forestland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of forestland area by forestland price in columns (5) to (8), in line with the land-adjusted Ricardian approach. The computations use the estimates obtained with the quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.</footnote>
<table><caption>Table A13: Marginal climate impacts on urban sector with the standard and land-adjusted Ricardian approaches, with quadratic specifications</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable: building land price</th><th colspan="4">Dependent variable: building land price × urban area</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.57<br>(0.53)</td><td>0.13<br>(0.48)</td><td>1.44 ***<br>(0.39)</td><td>1.59 ***<br>(0.61)</td><td>1.08 **<br>(0.39)</td><td>0.77 *<br>(0.43)</td><td>1.69 ***<br>(0.35)</td><td>0.86<br>(0.60)</td></tr><tr><td>Summer</td><td>-0.50<br>(0.39)</td><td>-0.26<br>(0.27)</td><td>-0.70 **<br>(0.25)</td><td>-0.74 **<br>(0.32)</td><td>-0.88 ***<br>(0.17)</td><td>-0.74 ***<br>(0.18)</td><td>-0.78 ***<br>(0.19)</td><td>-0.29<br>(0.30)</td></tr><tr><td>Autumn</td><td>0.40<br>(0.63)</td><td>0.53<br>(0.82)</td><td>0.10<br>(0.25)</td><td>0.10<br>(0.48)</td><td>0.72<br>(0.71)</td><td>0.90<br>(0.96)</td><td>0.02<br>(0.17)</td><td>-0.12<br>(0.28)</td></tr><tr><td>Winter</td><td>-0.35<br>(0.35)</td><td>-0.31<br>(0.48)</td><td>-0.52 **<br>(0.20)</td><td>-0.52<br>(0.36)</td><td>-0.53<br>(0.33)</td><td>-0.57<br>(0.51)</td><td>-0.50 ***<br>(0.13)</td><td>-0.16<br>(0.26)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.20<br>(0.15)</td><td>-0.26<br>(0.17)</td><td>-0.34 ***<br>(0.12)</td><td>-0.48 ***<br>(0.09)</td><td>-0.32 ***<br>(0.11)</td><td>-0.36 **<br>(0.15)</td><td>-0.19 **<br>(0.07)</td><td>-0.14 *<br>(0.08)</td></tr><tr><td>Summer</td><td>-0.09<br>(0.12)</td><td>-0.06<br>(0.13)</td><td>-0.17 *<br>(0.09)</td><td>-0.09<br>(0.04)</td><td>-0.01<br>(0.07)</td><td>0.01<br>(0.08)</td><td>-0.16 ***<br>(0.01)</td><td>-0.13 ***<br>(0.01)</td></tr><tr><td>Autumn</td><td>0.39 ***<br>(0.10)</td><td>0.31 ***<br>(0.07)</td><td>0.08<br>(0.09)</td><td>0.08<br>(0.09)</td><td>0.49 ***<br>(0.07)</td><td>0.43 ***<br>(0.10)</td><td>0.06<br>(0.07)</td><td>0.06<br>(0.07)</td></tr><tr><td>Winter</td><td>-0.33 **<br>(0.15)</td><td>-0.24 *<br>(0.13)</td><td>0.10<br>(0.08)</td><td>0.15 *<br>(0.09)</td><td>-0.42 ***<br>(0.12)</td><td>-0.34 **<br>(0.15)</td><td>0.08<br>(0.05)</td><td>0.06<br>(0.06)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.43</td><td>0.47</td><td>0.66</td><td>0.68</td><td>0.44</td><td>0.45</td><td>0.75</td><td>0.77</td></tr></tbody></table>

<footnote>NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on forestland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of urban area by building land price in columns (5) to (8), in line with the land-adjusted Ricardian approach. The computations use the estimates obtained with the quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.</footnote>
<table><caption>Table A14: Marginal climate impacts for the whole land sector with the land-adjusted Ricardian approach, with quadratic specification</caption><thead><tr><th></th><th colspan="4" style="text-align:center;">Dependent variable:</th></tr><tr><th></th><th colspan="4" style="text-align:center;">∑<sup>3</sup><sub>i=1</sub> land price of type i × area of type i</th></tr><tr><th></th><th style="text-align:center;">(1)</th><th style="text-align:center;">(2)</th><th style="text-align:center;">(3)</th><th style="text-align:center;">(4)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.98 **<br>(0.35)</td><td>0.68 **<br>(0.38)</td><td>1.53 ***<br>(0.32)</td><td>0.73<br>(0.53)</td></tr><tr><td>Summer</td><td>-0.81 ***<br>(0.16)</td><td>-0.67 ***<br>(0.17)</td><td>-0.72 ***<br>(0.18)</td><td>-0.26<br>(0.27)</td></tr><tr><td>Autumn</td><td>0.65<br>(0.63)</td><td>0.81<br>(0.85)</td><td>0.03<br>(0.16)</td><td>-0.09<br>(0.25)</td></tr><tr><td>Winter</td><td>-0.47<br>(0.30)</td><td>-0.50<br>(0.45)</td><td>-0.45<br>(0.13)</td><td>-0.14<br>(0.22)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.30 ***<br>(0.09)</td><td>-0.35 **<br>(0.14)</td><td>-0.19 **<br>(0.07)</td><td>-0.14 *<br>(0.08)</td></tr><tr><td>Summer</td><td>0.00<br>(0.06)</td><td>0.03<br>(0.07)</td><td>-0.13 ***<br>(0.01)</td><td>-0.10 ***<br>(0.01)</td></tr><tr><td>Autumn</td><td>0.44 ***<br>(0.07)</td><td>0.39 ***<br>(0.10)</td><td>0.06<br>(0.07)</td><td>0.05<br>(0.06)</td></tr><tr><td>Winter</td><td>-0.38 ***<br>(0.11)</td><td>-0.31 **<br>(0.13)</td><td>0.07<br>(0.05)</td><td>0.05<br>(0.06)</td></tr><tr><td><strong>Number of observations</strong></td><td colspan="4">344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td colspan="4">0.44</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on the sum of the product of sector-specific area by sector-specific price in columns (1) to (4), in line with the land-adjusted Ricardian approach. sector-specific land prices are measured by the corresponding median land price in the department (instead of the average land price in our preferred analyses). The computations use the estimates obtained with the quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
## A16 Sensitivity analysis: annual instead of seasonal climate conditions

Our preferred analyses in Section 5 use seasonal climate conditions in terms of temperature and precipitation as the preferred set of climate variables for our Ricardian estimations. However, other Ricardian analyses sometimes rely on alternative set of climate variables. If we mentioned the distinction between harmful and beneficial degree days within the growing season as a common practice of the Ricardian approach when applied to agriculture (e.g. Ortiz-Bobea, 2020), the use of annual climate conditions as also been sometimes considered as a suitable practice for the Ricardian approach (e.g., Bozzola et al., 2018). This is what we do in this Appendix, replacing *seasonal* climate conditions by *annual* climate conditions – in terms of temperature and precipitation – as our set of climate variables for the Ricardian estimations. In line with the Ricardian approach, we however conserved the measurements of such climate conditions as long-term averages over 30 years. Tables A15, A16 and A17 display the recomputed marginal climate impacts using the standard and land-adjusted Ricardian approaches when using annual climate conditions. The results reported here remain in line with those reported in Section 5.1, with significant differences between the standard and land-adjusted Ricardian approach. The main difference with Section 5.1 is that the quality of adjustment of our Ricardian models shrinks for all the uses compared to the use of seasonal climate conditions.

Table A18 shows the climate impacts using the land-adjusted Ricardian approach for the whole sector, when using annual climate conditions instead of the seasonal ones. The results for the whole land sector remain in line with those in Section 5.2.

<table><caption>Table A15: Marginal climate impacts on agriculture with the standard and land-adjusted Ricardian approaches, with annual climate conditions</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable:<br>log(farmland price)</th><th colspan="4">Dependent variable:<br>log(farmland price × farmland area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Annual Temperature (°C)</td><td>0.04<br>(0.05)</td><td>0.03<br>(0.06)</td><td>0.11<br>(0.08)</td><td>0.07<br>(0.08)</td><td>0.05<br>(0.03)</td><td>0.03<br>(0.03)</td><td>-0.03<br>(0.05)</td><td>-0.08<br>(0.06)</td></tr><tr><td>Annual Precipitation (cm/month)</td><td>-0.03<br>(0.05)</td><td>-0.04<br>(0.05)</td><td>0.00<br>(0.04)</td><td>-0.01<br>(0.04)</td><td>-0.14 **<br>(0.06)</td><td>-0.15 **<br>(0.06)</td><td>-0.05<br>(0.04)</td><td>-0.07<br>(0.05)</td></tr><tr><td>Number of observations</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td>Controls</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.08</td><td>0.20</td><td>0.32</td><td>0.42</td><td>0.13</td><td>0.21</td><td>0.35</td><td>0.44</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on farmland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of farmland area by farmland price in columns (5) to (8), in line with the land-adjusted Ricardian approach. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
<table><caption>Table A16: Marginal climate impacts on forestry with the standard and land-adjusted Ricardian approaches, with annual climate conditions</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable:<br>log(forestland price)</th><th colspan="4">Dependent variable:<br>log(forestland price × forest area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Annual Temperature (C)</td><td>-0.04<br>(0.04)</td><td>-0.06<br>(0.05)</td><td>-0.07<br>(0.05)</td><td>-0.12 **<br>(0.05)</td><td>-0.01<br>(0.03)</td><td>-0.03<br>(0.04)</td><td>0.12<br>(0.10)</td><td>0.08<br>(0.10)</td></tr><tr><td>Annual Precipitation (cm/month)</td><td>-0.15 ***<br>(0.05)</td><td>-0.16 ***<br>(0.04)</td><td>-0.06 *<br>(0.04)</td><td>-0.08 **<br>(0.03)</td><td>0.00<br>(0.03)</td><td>-0.01<br>(0.03)</td><td>0.04<br>(0.03)</td><td>0.03<br>(0.03)</td></tr><tr><td>Number of observations</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td>Controls</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.10</td><td>0.20</td><td>0.31</td><td>0.42</td><td>0.03</td><td>0.09</td><td>0.21</td><td>0.25</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on forestland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of forestland area by forestland price in columns (5) to (8), in line with the land-adjusted Ricardian approach. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.

<table><caption>Table A17: Marginal climate impacts on urban sector with the standard and land-adjusted Ricardian approaches, with annual climate conditions</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable:<br>log(building land price)</th><th colspan="4">Dependent variable:<br>log(building land price × urban area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td>Annual Temperature (°C)</td><td>0.04<br>(0.07)</td><td>0.00<br>(0.08)</td><td>0.14<br>(0.09)</td><td>0.05<br>(0.10)</td><td>0.17 **<br>(0.07)</td><td>0.13 *<br>(0.08)</td><td>0.18 **<br>(0.07)</td><td>0.07<br>(0.07)</td></tr><tr><td>Annual Precipitation (cm/month)</td><td>-0.09<br>(0.10)</td><td>-0.12<br>(0.09)</td><td>-0.05<br>(0.07)</td><td>-0.07<br>(0.07)</td><td>-0.14<br>(0.13)</td><td>-0.18<br>(0.13)</td><td>-0.01<br>(0.08)</td><td>-0.05<br>(0.09)</td></tr><tr><td>Number of observations</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td>Controls</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.08</td><td>0.24</td><td>0.45</td><td>0.60</td><td>0.10</td><td>0.23</td><td>0.60</td><td>0.73</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on forestland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of urban area by building land price in columns (5) to (8), in line with the land-adjusted Ricardian approach. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
<table><caption>Table A18: Marginal climate impacts for the whole land sector with the land-adjusted Ricardian approach, with annual climate conditions</caption><thead><tr><th></th><th colspan="4">Dependent variable:</th></tr><tr><th></th><th colspan="4">log(∑<sup>3</sup><sub>i=1</sub> land price of type i × area of type i)</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>Annual Temperature (°C)</td><td>0.14 **<br>(0.06)</td><td>0.11 *<br>(0.06)</td><td>0.16 **<br>(0.06)</td><td>0.06<br>(0.06)</td></tr><tr><td>Annual Precipitation (cm/month)</td><td>-0.13<br>(0.11)</td><td>-0.15<br>(0.11)</td><td>-0.01<br>(0.07)</td><td>-0.04<br>(0.07)</td></tr><tr><td>Number of observations</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td>Controls</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effects</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.10</td><td>0.24</td><td>0.60</td><td>0.75</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on the sum of the product of sector-specific area by sector-specific price in columns (1) to (4), in line with the land-adjusted Ricardian approach. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
## A17 Sensitivity analysis: median price instead of average price

Our preferred analyses in Section 5 use *average* departmental land price as proxy for land rents. However, because the distribution of land price is usually not Gaussian, one can question the suitability of such an indicator, and would prefer to use alternative statistical metrics. This is what we do in this Appendix, replacing *average* departmental land price by *median* departmental land price as proxy for land rents. Tables A19, A20 and A21 display the recomputed marginal climate impacts using the standard and land-adjusted Ricardian approaches when using such median prices. All of the reported estimates are in line with those reported in Section 5.1. The main difference is that the Ricardian estimates for forestry are less precisely estimated with the median departmental prices instead of the average ones, reflecting the low number of transactions for forestland in France in many departments. By comparison, most of the precisely estimated coefficients for agriculture and urban in Section 5.1 remain precisely estimated in Tables A19 and A21.

Table A22 shows such climate impacts using the land-adjusted Ricardian approach for the whole sector, when land rents are approximated by *median* departmental land prices. The obtained estimates for the whole land sector are identical to those identified in our preferred analysis in Section 5.2.

<table><caption>Table A19: Marginal climate impacts on agriculture with the standard and land-adjusted Ricardian approaches, with *median* departmental farmland prices as proxy for farmland rents</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable: log(farmland price)</th><th colspan="4">Dependent variable: log(farmland price × farmland area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.02<br>(0.17)</td><td>-0.09<br>(0.22)</td><td>0.33 **<br>(0.16)</td><td>0.88 ***<br>(0.29)</td><td>0.10<br>(0.19)</td><td>0.01<br>(0.27)</td><td>0.13<br>(0.14)</td><td>-0.30<br>(0.25)</td></tr><tr><td>Summer</td><td>-0.19<br>(0.17)</td><td>-0.15<br>(0.21)</td><td>-0.32 **<br>(0.15)</td><td>-0.63 ***<br>(0.18)</td><td>-0.23<br>(0.18)</td><td>-0.17<br>(0.25)</td><td>-0.20<br>(0.14)</td><td>0.04<br>(0.21)</td></tr><tr><td>Autumn</td><td>0.34<br>(0.26)</td><td>0.43<br>(0.38)</td><td>0.35 **<br>(0.17)</td><td>0.48 **<br>(0.24)</td><td>0.08<br>(0.31)</td><td>0.09<br>(0.45)</td><td>-0.07<br>(0.35)</td><td>-0.12<br>(0.45)</td></tr><tr><td>Winter</td><td>-0.15<br>(0.21)</td><td>-0.19<br>(0.29)</td><td>-0.25 *<br>(0.16)</td><td>-0.47 **<br>(0.19)</td><td>0.14<br>(0.31)</td><td>0.16<br>(0.42)</td><td>0.19<br>(0.30)</td><td>0.34<br>(0.37)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.10<br>(0.09)</td><td>-0.12<br>(0.09)</td><td>-0.22 **<br>(0.08)</td><td>-0.34 ***<br>(0.06)</td><td>-0.13 **<br>(0.06)</td><td>-0.16 ***<br>(0.05)</td><td>-0.02<br>(0.08)</td><td>0.01<br>(0.07)</td></tr><tr><td>Summer</td><td>0.02<br>(0.10)</td><td>0.03<br>(0.10)</td><td>0.01<br>(0.08)</td><td>0.04<br>(0.07)</td><td>0.10 **<br>(0.05)</td><td>0.12 **<br>(0.06)</td><td>0.10 *<br>(0.06)</td><td>0.11 ***<br>(0.02)</td></tr><tr><td>Autumn</td><td>0.08<br>(0.05)</td><td>0.06<br>(0.06)</td><td>0.01<br>(0.06)</td><td>0.01<br>(0.05)</td><td>-0.01<br>(0.02)</td><td>-0.02<br>(0.02)</td><td>-0.05<br>(0.07)</td><td>-0.05<br>(0.06)</td></tr><tr><td>Winter</td><td>-0.09<br>(0.06)</td><td>-0.06<br>(0.07)</td><td>0.04<br>(0.06)</td><td>0.09<br>(0.06)</td><td>-0.09<br>(0.09)</td><td>-0.08<br>(0.11)</td><td>-0.12<br>(0.10)</td><td>-0.13<br>(0.11)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.40</td><td>0.41</td><td>0.47</td><td>0.50</td><td>0.41</td><td>0.42</td><td>0.46</td><td>0.47</td></tr></tbody></table>

<footnote>NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on farmland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of farmland area by farmland price in columns (5) to (8), in line with the land-adjusted Ricardian approach. Farmland prices are measured by median farmland price in the department (instead of average farm-land price in our preferred analysis). The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.</footnote>
<table><caption>Table A20: Marginal climate impacts on forestry with the standard and land-adjusted Ricardian approaches, with median departmental forestland prices as proxy for forestland rents</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable:<br>log(forestland price)</th><th colspan="4">Dependent variable:<br>log(forestland price × forest area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.19<br>(0.17)</td><td>0.14<br>(0.20)</td><td>0.16<br>(0.19)</td><td>0.14<br>(0.32)</td><td>-0.07<br>(0.26)</td><td>-0.14<br>(0.34)</td><td>0<br>(0.19)</td><td>0.15<br>(0.32)</td></tr><tr><td>Summer</td><td>-0.53 ***<br>(0.10)</td><td>-0.57 ***<br>(0.12)</td><td>-0.46 ***<br>(0.16)</td><td>-0.51 ***<br>(0.16)</td><td>0.14<br>(0.17)</td><td>0.28 *<br>(0.18)</td><td>0.24<br>(0.21)</td><td>0.31<br>(0.29)</td></tr><tr><td>Autumn</td><td>0.86 ***<br>(0.25)</td><td>1.09 ***<br>(0.35)</td><td>0.80 ***<br>(0.21)</td><td>1.03 ***<br>(0.25)</td><td>-0.37<br>(0.54)</td><td>-0.67<br>(0.76)</td><td>-0.44<br>(0.42)</td><td>-0.85<br>(0.67)</td></tr><tr><td>Winter</td><td>-0.56 ***<br>(0.16)</td><td>-0.70 ***<br>(0.23)</td><td>-0.50 ***<br>(0.16)</td><td>-0.65 ***<br>(0.15)</td><td>0.36<br>(0.40)</td><td>0.61<br>(0.52)</td><td>0.38<br>(0.35)</td><td>0.65<br>(0.52)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.41 ***<br>(0.11)</td><td>-0.44 ***<br>(0.12)</td><td>-0.43 ***<br>(0.13)</td><td>-0.48 ***<br>(0.13)</td><td>-0.28 **<br>(0.13)</td><td>-0.31 ***<br>(0.13)</td><td>-0.32 ***<br>(0.15)</td><td>-0.39 ***<br>(0.15)</td></tr><tr><td>Summer</td><td>0.09<br>(0.11)</td><td>0.10<br>(0.11)</td><td>0.08<br>(0.1)</td><td>0.09<br>(0.1)</td><td>0.12<br>(0.13)</td><td>0.16<br>(0.14)</td><td>0.11<br>(0.12)</td><td>0.17<br>(0.12)</td></tr><tr><td>Autumn</td><td>0.13 ***<br>(0.05)</td><td>0.12 **<br>(0.06)</td><td>0.11 ***<br>(0.04)</td><td>0.10 **<br>(0.04)</td><td>0.26 **<br>(0.10)</td><td>0.27 **<br>(0.13)</td><td>0.19 **<br>(0.08)</td><td>0.21 **<br>(0.08)</td></tr><tr><td>Winter</td><td>-0.02<br>(0.05)</td><td>0.01<br>(0.05)</td><td>0.05<br>(0.05)</td><td>0.07<br>(0.05)</td><td>-0.14<br>(0.10)</td><td>-0.14<br>(0.12)</td><td>0.02<br>(0.11)</td><td>0.01<br>(0.12)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.53</td><td>0.54</td><td>0.59</td><td>0.60</td><td>0.26</td><td>0.27</td><td>0.44</td><td>0.45</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on forestland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of forestland area by forestland price in columns (5) to (8), in line with the land-adjusted Ricardian approach. Forestland prices are measured by median forestland price in the department (instead of average forestland price in our preferred analysis). The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
<table><caption>Table A21: Marginal climate impacts on urban sector with the standard and land-adjusted Ricardian approaches, with median departmental building land prices as proxy for urban rents</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable:<br>log(building land price)</th><th colspan="4">Dependent variable:<br>log(building land price × urban area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.63<br>(0.40)</td><td>0.02<br>(0.45)</td><td>1.48 ***<br>(0.30)</td><td>0.94 *<br>(0.51)</td><td>1.06<br>(0.4)</td><td>0.53<br>(0.45)</td><td>1.81<br>(0.31)</td><td>0.78<br>(0.36)</td></tr><tr><td>Summer</td><td>-0.65 **<br>(0.26)</td><td>-0.40<br>(0.30)</td><td>-0.87 ***<br>(0.19)</td><td>-0.66 **<br>(0.31)</td><td>-0.91 ***<br>(0.25)</td><td>-0.68 **<br>(0.29)</td><td>-0.95 ***<br>(0.22)</td><td>-0.45 ***<br>(0.18)</td></tr><tr><td>Autumn</td><td>0.83<br>(0.63)</td><td>1.21<br>(0.82)</td><td>0.62 *<br>(0.37)</td><td>0.91<br>(0.63)</td><td>0.78<br>(0.87)</td><td>1.13<br>(1.12)</td><td>0.30<br>(0.58)</td><td>0.44<br>(0.79)</td></tr><tr><td>Winter</td><td>-0.69 *<br>(0.38)</td><td>-0.79<br>(0.50)</td><td>-0.91 ***<br>(0.22)</td><td>-0.93 ***<br>(0.36)</td><td>-0.53<br>(0.51)</td><td>-0.62<br>(0.67)</td><td>-0.65<br>(0.41)</td><td>-0.44<br>(0.48)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.27 ***<br>(0.07)</td><td>-0.34 ***<br>(0.13)</td><td>-0.40 ***<br>(0.09)</td><td>-0.45 ***<br>(0.15)</td><td>-0.42 ***<br>(0.10)</td><td>-0.51 ***<br>(0.17)</td><td>-0.37 ***<br>(0.08)</td><td>-0.35 ***<br>(0.08)</td></tr><tr><td>Summer</td><td>-0.11<br>(0.10)</td><td>-0.09<br>(0.10)</td><td>-0.18 **<br>(0.07)</td><td>-0.13 **<br>(0.07)</td><td>0.01<br>(0.12)</td><td>0.05<br>(0.13)</td><td>-0.11<br>(0.08)</td><td>-0.07<br>(0.10)</td></tr><tr><td>Autumn</td><td>0.34 ***<br>(0.01)</td><td>0.22 ***<br>(0.06)</td><td>0.05<br>(0.06)</td><td>0.02<br>(0.08)</td><td>0.46 ***<br>(0.05)</td><td>0.36 ***<br>(0.1)</td><td>0.06<br>(0.08)</td><td>0.04<br>(0.07)</td></tr><tr><td>Winter</td><td>-0.29 ***<br>(0.08)</td><td>-0.15<br>(0.11)</td><td>0.12 **<br>(0.06)</td><td>0.15 **<br>(0.07)</td><td>-0.46 ***<br>(0.14)</td><td>-0.34 **<br>(0.17)</td><td>0.01<br>(0.09)</td><td>0.02<br>(0.09)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.47</td><td>0.57</td><td>0.68</td><td>0.73</td><td>0.55</td><td>0.59</td><td>0.77</td><td>0.81</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on forestland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of urban area by building land price in columns (5) to (8), in line with the land-adjusted Ricardian approach. Building land prices are measured by the median building land price in the department (instead of the average building land price in our preferred analysis). The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
<table><caption>Table A22: Marginal climate impacts for the whole land sector with the land-adjusted Ricardian approach, with median departmental land prices as proxy for land rents</caption><thead><tr><th></th><th colspan="4">Dependent variable:</th></tr><tr><th></th><th colspan="4">log(∑<sup>3</sup><sub>i=1</sub> land price of type <i>i</i> × area of type <i>i</i>)</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.93 ***<br>(0.36)</td><td>0.46<br>(0.41)</td><td>1.59 ***<br>(0.28)</td><td>0.65 *<br>(0.36)</td></tr><tr><td>Summer</td><td>-0.80 ***<br>(0.23)</td><td>-0.60 **<br>(0.27)</td><td>-0.83 ***<br>(0.19)</td><td>-0.35 **<br>(0.18)</td></tr><tr><td>Autumn</td><td>0.69<br>(0.80)</td><td>0.98<br>(1.05)</td><td>0.23<br>(0.53)</td><td>0.32<br>(0.73)</td></tr><tr><td>Winter</td><td>-0.44<br>(0.47)</td><td>-0.51<br>(0.64)</td><td>-0.53<br>(0.37)</td><td>-0.31<br>(0.45)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.40 ***<br>(0.1)</td><td>-0.47 ***<br>(0.15)</td><td>-0.34 ***<br>(0.08)</td><td>-0.32 ***<br>(0.08)</td></tr><tr><td>Summer</td><td>0.03<br>(0.10)</td><td>0.06<br>(0.11)</td><td>-0.08<br>(0.07)</td><td>-0.04<br>(0.08)</td></tr><tr><td>Autumn</td><td>0.42 ***<br>(0.05)</td><td>0.34 ***<br>(0.09)</td><td>0.06<br>(0.07)</td><td>0.04<br>(0.06)</td></tr><tr><td>Winter</td><td>-0.41 ***<br>(0.13)</td><td>-0.30 **<br>(0.16)</td><td>0.01<br>(0.09)</td><td>0.02<br>(0.09)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.55</td><td>0.59</td><td>0.77</td><td>0.81</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on the sum of the product of sector-specific area by sector-specific price in columns (1) to (4), in line with the land-adjusted Ricardian approach. sector-specific land prices are measured by the corresponding median land price in the department (instead of the average land price in our preferred analyses). The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
A18 Sensitivity analysis: weights by sector-specific land area

Our preferred analyses in Section 5 use OLS to estimate our standard and land-adjusted Ricardian approaches. However, the Ricardian literature has often weighted the observations to reflect the agricultural specialization of the considered regions (e.g., Deschênes and Greenstone, 2007). This is what we do in this Appendix, weighting all the observations by their corresponding area. Specifically, we weight the observations in the standard and land-adjusted Ricardian approaches applied to agriculture (resp. forest and urban) by their farmland (resp. forestland and urban land) area. While this correction particularly makes sense for the standard Ricardian approach, we also examine its impacts on the land-adjusted Ricardian approach.

Tables A23, A24 and A25 display the recomputed marginal climate impacts using the standard and land-adjusted Ricardian approaches with such weighting procedure. A set of standard Ricardian estimates that correspond to those reported for the land-adjusted Ricardian approach would imply that a weighting procedure scheme empirically recover the land-adjusted approach, and would thus rightly assess the climate change costs for agriculture. Compared to the previous sensitivity analyses, the reported estimates are less in line with those reported in Section 5.1, at least for agriculture. Indeed, the amplitude and precision of most of the standard Ricardian estimates for agriculture are affected by the weighting procedure. They however remain significantly different from the land-adjusted Ricardian estimates in Section 5.1, or even the weighted ones in Table A23 – see in particular when both controls and year fixed effects are introduced. The weighting procedure sometimes implemented in the Ricardian approach is thus not sufficient to recover an appropriate assessment of the climate value for agriculture (or for any other use). Forestry and urban are less affected by the weighting procedure, the Ricardian estimates remaining close to those identified in Section 5.1.

Table A26 shows such climate impacts using the land-adjusted Ricardian approach for the whole sector, when observations are weighted by the whole departmental area. The obtained estimates for the whole land sector are identical to those identified in our preferred analysis in Section 5.2.
<table><caption>Table A23: Marginal climate impacts on agriculture with the standard and land-adjusted Ricardian approaches, with farmland area weights</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable: log(farmland price)</th><th colspan="4">Dependent variable: log(farmland price × farmland area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.35 **<br>(0.16)</td><td>0.06<br>(0.17)</td><td>0.68 ***<br>(0.13)</td><td>0.62 ***<br>(0.18)</td><td>0.37 **<br>(0.16)</td><td>0.10<br>(0.21)</td><td>0.54 ***<br>(0.14)</td><td>-0.21 ***<br>(0.24)</td></tr><tr><td>Summer</td><td>-0.33 **<br>(0.12)</td><td>-0.25 *<br>(0.16)</td><td>-0.47 ***<br>(0.12)</td><td>-0.54 ***<br>(0.12)</td><td>-0.40 ***<br>(0.11)</td><td>-0.33 **<br>(0.16)</td><td>-0.49 ***<br>(0.09)</td><td>-0.23 *<br>(0.14)</td></tr><tr><td>Autumn</td><td>0.24<br>(0.26)</td><td>0.52<br>(0.39)</td><td>0.23<br>(0.24)</td><td>0.60 *<br>(0.29)</td><td>0.32 **<br>(0.13)</td><td>0.63 ***<br>(0.16)</td><td>0.29<br>(0.24)</td><td>0.69 ***<br>(0.25)</td></tr><tr><td>Winter</td><td>-0.17<br>(0.20)</td><td>-0.28<br>(0.30)</td><td>-0.28 *<br>(0.18)</td><td>-0.49 **<br>(0.22)</td><td>-0.18<br>(0.15)</td><td>-0.31<br>(0.20)</td><td>-0.26<br>(0.18)</td><td>-0.34<br>(0.21)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.08<br>(0.06)</td><td>-0.13 *<br>(0.07)</td><td>-0.16 **<br>(0.06)</td><td>-0.24 ***<br>(0.07)</td><td>-0.11 ***<br>(0.03)</td><td>-0.17 ***<br>(0.03)</td><td>-0.05<br>(0.07)</td><td>0.00<br>(0.07)</td></tr><tr><td>Summer</td><td>0.08<br>(0.06)</td><td>0.10<br>(0.06)</td><td>0.06<br>(0.05)</td><td>0.10<br>(0.04)</td><td>0.10 **<br>(0.04)</td><td>0.12 ***<br>(0.02)</td><td>0.09 ***<br>(0.03)</td><td>0.09 **<br>(0.04)</td></tr><tr><td>Autumn</td><td>0.10 ***<br>(0.03)</td><td>0.04<br>(0.04)</td><td>0.01<br>(0.04)</td><td>-0.02<br>(0.04)</td><td>0.00<br>(0.02)</td><td>-0.06 *<br>(0.04)</td><td>-0.07<br>(0.06)</td><td>-0.11 **<br>(0.04)</td></tr><tr><td>Winter</td><td>-0.15 ***<br>(0.05)</td><td>-0.08<br>(0.06)</td><td>-0.02<br>(0.05)</td><td>0.03<br>(0.05)</td><td>-0.10 **<br>(0.05)</td><td>-0.03<br>(0.06)</td><td>-0.07<br>(0.08)</td><td>-0.06<br>(0.06)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.42</td><td>0.54</td><td>0.50</td><td>0.60</td><td>0.42</td><td>0.52</td><td>0.47</td><td>0.57</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on farmland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of farmland area by farmland price in columns (5) to (8), in line with the land-adjusted Ricardian approach. Observations are weighted by the farmland area. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
<table><caption>Table A24: Marginal climate impacts on forestry with the standard and land-adjusted Ricardian approaches, with forestland area weights</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable: log(forestland price)</th><th colspan="4">Dependent variable: log(forestland price × forest area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.40 ***<br>(0.11)</td><td>0.23 *<br>(0.15)</td><td>0.40 ***<br>(0.15)</td><td>-0.21<br>(0.39)</td><td>0.18<br>(0.12)</td><td>0.04<br>(0.18)</td><td>0.28 **<br>(0.12)</td><td>0.16<br>(0.40)</td></tr><tr><td>Summer</td><td>-0.45 ***<br>(0.12)</td><td>-0.37 *<br>(0.20)</td><td>-0.41 ***<br>(0.08)</td><td>-0.10<br>(0.26)</td><td>0.09<br>(0.15)</td><td>0.29<br>(0.21)</td><td>0.12<br>(0.10)</td><td>0.30<br>(0.31)</td></tr><tr><td>Autumn</td><td>0.33<br>(0.25)</td><td>0.41<br>(0.36)</td><td>0.33<br>(0.24)</td><td>0.39<br>(0.33)</td><td>-0.39<br>(0.39)</td><td>-0.71<br>(0.52)</td><td>-0.30<br>(0.26)</td><td>-0.61<br>(0.47)</td></tr><tr><td>Winter</td><td>-0.31 **<br>(0.15)</td><td>-0.29<br>(0.26)</td><td>-0.35 ***<br>(0.14)</td><td>-0.20<br>(0.25)</td><td>0.24<br>(0.25)</td><td>0.53 *<br>(0.32)</td><td>0.07<br>(0.17)</td><td>0.34<br>(0.39)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.35 ***<br>(0.06)</td><td>-0.41 ***<br>(0.06)</td><td>-0.33 ***<br>(0.09)</td><td>-0.32 ***<br>(0.06)</td><td>-0.28 ***<br>(0.10)</td><td>-0.33 ***<br>(0.09)</td><td>-0.30 ***<br>(0.07)</td><td>-0.33 ***<br>(0.07)</td></tr><tr><td>Summer</td><td>0.09<br>(0.08)</td><td>0.13 *<br>(0.08)</td><td>0.07<br>(0.08)</td><td>0.11 *<br>(0.07)</td><td>0.12<br>(0.09)</td><td>0.18 **<br>(0.09)</td><td>0.10<br>(0.07)</td><td>0.15 **<br>(0.08)</td></tr><tr><td>Autumn</td><td>0.12 **<br>(0.06)</td><td>0.10 *<br>(0.06)</td><td>0.06<br>(0.06)</td><td>0.06<br>(0.05)</td><td>0.25 ***<br>(0.09)</td><td>0.25 **<br>(0.10)</td><td>0.12 **<br>(0.06)</td><td>0.13 **<br>(0.06)</td></tr><tr><td>Winter</td><td>0.00<br>(0.03)</td><td>0.04<br>(0.03)</td><td>0.06<br>(0.05)</td><td>0.06<br>(0.05)</td><td>-0.05<br>(0.05)</td><td>-0.04<br>(0.07)</td><td>0.12 **<br>(0.05)</td><td>0.12 **<br>(0.06)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.38</td><td>0.41</td><td>0.43</td><td>0.48</td><td>0.19</td><td>0.21</td><td>0.43</td><td>0.45</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on forestland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of forestland area by forestland price in columns (5) to (8), in line with the land-adjusted Ricardian approach. Observations are weighted by the farmland area. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
<table><caption>Table A25: Marginal climate impacts on urban sector with the standard and land-adjusted Ricardian approaches, with urban area weights</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable:<br>log(building land price)</th><th colspan="4">Dependent variable:<br>log(building land price × urban area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.85 **<br>(0.32)</td><td>0.37<br>(0.38)</td><td>1.55 ***<br>(0.27)</td><td>1.16 ***<br>(0.42)</td><td>1.33 ***<br>(0.36)</td><td>0.92 **<br>(0.42)</td><td>1.91 ***<br>(0.28)</td><td>1.04 ***<br>(0.37)</td></tr><tr><td>Summer</td><td>-0.75 ***<br>(0.17)</td><td>-0.54 **<br>(0.21)</td><td>-0.89 ***<br>(0.21)</td><td>-0.75 ***<br>(0.22)</td><td>-1.11 ***<br>(0.13)</td><td>-0.97 ***<br>(0.17)</td><td>-1.09 ***<br>(0.22)</td><td>-0.69 ***<br>(0.19)</td></tr><tr><td>Autumn</td><td>0.64<br>(0.58)</td><td>0.92<br>(0.74)</td><td>0.44<br>(0.39)</td><td>0.69<br>(0.55)</td><td>0.89<br>(0.73)</td><td>1.23<br>(0.94)</td><td>0.45<br>(0.46)</td><td>0.63<br>(0.61)</td></tr><tr><td>Winter</td><td>-0.64 *<br>(0.34)</td><td>-0.69<br>(0.46)</td><td>-0.79 ***<br>(0.25)</td><td>-0.84 ***<br>(0.34)</td><td>-0.73 *<br>(0.41)</td><td>-0.85<br>(0.56)</td><td>-0.80 **<br>(0.33)</td><td>-0.68 *<br>(0.38)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.14 **<br>(0.06)</td><td>-0.19 *<br>(0.11)</td><td>-0.27 ***<br>(0.08)</td><td>-0.33 ***<br>(0.08)</td><td>-0.26 ***<br>(0.07)</td><td>-0.32 **<br>(0.13)</td><td>-0.27 ***<br>(0.09)</td><td>-0.24 **<br>(0.10)</td></tr><tr><td>Summer</td><td>-0.14<br>(0.10)</td><td>-0.12<br>(0.09)</td><td>-0.20 **<br>(0.08)</td><td>-0.15 *<br>(0.09)</td><td>-0.05<br>(0.12)</td><td>-0.03<br>(0.12)</td><td>-0.17<br>(0.10)</td><td>-0.14<br>(0.11)</td></tr><tr><td>Autumn</td><td>0.32 ***<br>(0.05)</td><td>0.24 ***<br>(0.07)</td><td>0.07<br>(0.06)</td><td>0.04<br>(0.05)</td><td>0.42 ***<br>(0.08)</td><td>0.35 ***<br>(0.11)</td><td>0.07<br>(0.07)</td><td>0.05<br>(0.06)</td></tr><tr><td>Winter</td><td>-0.36 ***<br>(0.09)</td><td>-0.26 **<br>(0.11)</td><td>0.04<br>(0.06)</td><td>0.08<br>(0.06)</td><td>-0.50 ***<br>(0.12)</td><td>-0.41 ***<br>(0.14)</td><td>-0.01<br>(0.07)</td><td>0.00<br>(0.06)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.49</td><td>0.56</td><td>0.68</td><td>0.73</td><td>0.53</td><td>0.57</td><td>0.78</td><td>0.81</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on forestland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of urban area by building land price in columns (5) to (8), in line with the land-adjusted Ricardian approach. Observations are weighted by the urban area. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
<table><caption>Table A26: Marginal climate impacts for the whole land sector with the land-adjusted Ricardian approach, with departmental area weights</caption><thead><tr><th></th><th colspan="4">Dependent variable:</th></tr><tr><th></th><th colspan="4">log(∑<sup>3</sup><sub>i=1</sub> land price of type i × area of type i)</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.96 ***<br>(0.32)</td><td>0.60 *<br>(0.35)</td><td>1.50 ***<br>(0.27)</td><td>0.73 **<br>(0.38)</td></tr><tr><td>Summer</td><td>-0.81 ***<br>(0.15)</td><td>-0.67 ***<br>(0.17)</td><td>-0.82 ***<br>(0.18)</td><td>-0.48 ***<br>(0.15)</td></tr><tr><td>Autumn</td><td>0.67<br>(0.55)</td><td>0.96<br>(0.74)</td><td>0.27<br>(0.33)</td><td>0.48<br>(0.47)</td></tr><tr><td>Winter</td><td>-0.49 *<br>(0.28)</td><td>-0.58<br>(0.41)</td><td>-0.58 **<br>(0.23)</td><td>-0.49 **<br>(0.24)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.30 ***<br>(0.09)</td><td>-0.38 ***<br>(0.13)</td><td>-0.24 **<br>(0.09)</td><td>-0.23 **<br>(0.09)</td></tr><tr><td>Summer</td><td>0.02<br>(0.09)</td><td>0.06<br>(0.10)</td><td>-0.08<br>(0.08)</td><td>-0.04<br>(0.08)</td></tr><tr><td>Autumn</td><td>0.35 ***<br>(0.06)</td><td>0.29 ***<br>(0.09)</td><td>0.03<br>(0.07)</td><td>0.01<br>(0.05)</td></tr><tr><td>Winter</td><td>-0.36 ***<br>(0.10)</td><td>-0.27 **<br>(0.12)</td><td>0.02<br>(0.06)</td><td>0.03<br>(0.05)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.52</td><td>0.57</td><td>0.77</td><td>0.81</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on the sum of the product of sector-specific area by sector-specific price in columns (1) to (4), in line with the land-adjusted Ricardian approach. Observations are weighted by the departmental area. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
A19 Sensitivity analysis: weights by total departmental area or its inverse

While we show in Section A18 that our preferred estimates were robust to sector-specific area weights, they remain obtain in our land-adjusted approach under the implicit assumption that the total departmental area is the same across the whole France. While this is true that the size of such administrative division is much less varying that the US counties (or states), the size of the departments differ across France. While small, our summary statistics indeed indicate a positive coefficient of variation for the total departmental area (specifically equal to 0.21), with the difference between the area of the smallest and largest department amounting to about two thirds (the difference between Q1 and Q3 is limited to 21%). Moreover, there is no reason to assume that these differential sizes depend on climate change given that they have been created at the time of the French revolution, and have not evolved since 1922 in mainland France (with the integration of a new department). These element indicates that the biases associated to the difference of department sizes in our approaches are likely limited. To formally test whether our land-adjusted Ricardian estimates – and secondly our standard Ricardian estimates – are sensitive to the differential departmental areas, we proceed to two alternative weighting procedure schemes. In the first one, we weight all the observations by the total departmental area. In the second, we instead weight the observations by the *inverse* of the total departmental area. That is, while the first procedure gives more weights to the largest departments, the second gives more weights to the smallest departments.

Tables A27, A28 and A29 display the recomputed marginal climate impacts using the standard and land-adjusted Ricardian approaches with total departmental weights. These tables indicate that our estimates are robust to this weighting procedure. Estimates in Table A26 leads to similar conclusion for the whole land sector.

Tables A31, A32 and A33 display the recomputed marginal climate impacts using the standard and land-adjusted Ricardian approaches with the weighting procedure by the *inverse of the total departmental area*. As shown in these tables, our standard Ricardian estimates are robust to such an alternative weighting procedure. However, it results in a different set of estimates than our preferred ones for the land-adjusted Ricardian approach, at least for the temperature impacts on agriculture (see Section 5.1). Nevertheless, these land-adjusted Ricardian estimates are significantly different to the standard ones with this weighting procedure, confirming the presence of bias in the standard Ricardian approach. Moreover, Table A26 shows that, compared to Section 5.2, the land-adjusted Ricardian estimates for the whole sector are robust to the weights by the inverse of the total departmental area.
<table><caption>Table A27: Marginal climate impacts on agriculture with the standard and land-adjusted Ricardian approaches, with total departmental area weights</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable: log(farmland price)</th><th colspan="4">Dependent variable: log(farmland price × farmland area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.31 **<br>(0.14)</td><td>0.07<br>(0.16)</td><td>0.66 ***<br>(0.13)</td><td>0.71 ***<br>(0.17)</td><td>0.40 **<br>(0.15)</td><td>0.16<br>(0.21)</td><td>0.54 ***<br>(0.14)</td><td>-0.19<br>(0.24)</td></tr><tr><td>Summer</td><td>-0.33 ***<br>(0.11)</td><td>-0.24 *<br>(0.15)</td><td>-0.48 ***<br>(0.12)</td><td>-0.58 ***<br>(0.13)</td><td>-0.44 ***<br>(0.12)</td><td>-0.38 **<br>(0.15)</td><td>-0.50 ***<br>(0.10)</td><td>-0.22<br>(0.15)</td></tr><tr><td>Autumn</td><td>0.27<br>(0.25)</td><td>0.47<br>(0.40)</td><td>0.29<br>(0.18)</td><td>0.59 **<br>(0.25)</td><td>0.36 ***<br>(0.14)</td><td>0.63 ***<br>(0.16)</td><td>0.29<br>(0.24)</td><td>0.60<br>(0.48)</td></tr><tr><td>Winter</td><td>-0.16<br>(0.19)</td><td>-0.22<br>(0.29)</td><td>-0.31 **<br>(0.15)</td><td>-0.51 **<br>(0.20)</td><td>-0.22<br>(0.17)</td><td>-0.33<br>(0.21)</td><td>-0.27<br>(0.18)</td><td>-0.28<br>(0.41)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.08<br>(0.07)</td><td>-0.13 *<br>(0.07)</td><td>-0.17 **<br>(0.06)</td><td>-0.27 ***<br>(0.05)</td><td>-0.11 **<br>(0.04)</td><td>-0.17 **<br>(0.04)</td><td>-0.03<br>(0.07)</td><td>0.01<br>(0.07)</td></tr><tr><td>Summer</td><td>0.05<br>(0.07)</td><td>0.08<br>(0.07)</td><td>0.03<br>(0.06)</td><td>0.07<br>(0.04)</td><td>0.09 **<br>(0.04)</td><td>0.13 ***<br>(0.02)</td><td>0.07 **<br>(0.03)</td><td>0.08 **<br>(0.03)</td></tr><tr><td>Autumn</td><td>0.13 ***<br>(0.04)</td><td>0.09 **<br>(0.05)</td><td>0.03<br>(0.04)</td><td>0.01<br>(0.04)</td><td>0.00<br>(0.01)</td><td>-0.04<br>(0.03)</td><td>-0.06<br>(0.06)</td><td>-0.09 **<br>(0.03)</td></tr><tr><td>Winter</td><td>-0.17 ***<br>(0.05)</td><td>-0.11 *<br>(0.06)</td><td>-0.02<br>(0.05)</td><td>0.03<br>(0.05)</td><td>-0.10<br>(0.06)</td><td>-0.04<br>(0.07)</td><td>-0.08<br>(0.08)</td><td>-0.07<br>(0.07)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.46</td><td>0.54</td><td>0.55</td><td>0.62</td><td>0.42</td><td>0.50</td><td>0.46</td><td>0.56</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on farmland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of farmland area by farmland price in columns (5) to (8), in line with the land-adjusted Ricardian approach. Observations are weighted by the total departmental area. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
<table><caption>Table A28: Marginal climate impacts on forestry with the standard and land-adjusted Ricardian approaches, with total departmental area weights</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable:<br>log(forestland price)</th><th colspan="4">Dependent variable:<br>log(forestland price × forest area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.34 ***<br>(0.10)</td><td>0.11<br>(0.10)</td><td>0.36 **<br>(0.14)</td><td>-0.43<br>(0.35)</td><td>0.21<br>(0.22)</td><td>0.01<br>(0.26)</td><td>0.30 **<br>(0.13)</td><td>-0.01<br>(0.35)</td></tr><tr><td>Summer</td><td>-0.44 ***<br>(0.07)</td><td>-0.33 **<br>(0.14)</td><td>-0.41 ***<br>(0.08)</td><td>-0.03<br>(0.21)</td><td>0.13<br>(0.13)</td><td>0.35 **<br>(0.17)</td><td>0.12<br>(0.11)</td><td>0.41 *<br>(0.27)</td></tr><tr><td>Autumn</td><td>0.47<br>(0.24)</td><td>0.59 *<br>(0.32)</td><td>0.47 **<br>(0.23)</td><td>0.56 **<br>(0.28)</td><td>-0.50<br>(0.46)</td><td>-0.80<br>(0.62)</td><td>-0.34<br>(0.31)</td><td>-0.65<br>(0.47)</td></tr><tr><td>Winter</td><td>-0.36 **<br>(0.14)</td><td>-0.36 **<br>(0.22)</td><td>-0.38 **<br>(0.14)</td><td>-0.22<br>(0.20)</td><td>0.26<br>(0.29)</td><td>0.55<br>(0.39)</td><td>0.09<br>(0.20)</td><td>0.41<br>(0.36)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.41 ***<br>(0.08)</td><td>-0.46 ***<br>(0.07)</td><td>-0.41 ***<br>(0.10)</td><td>-0.36 ***<br>(0.08)</td><td>-0.26 **<br>(0.10)</td><td>-0.31 ***<br>(0.10)</td><td>-0.33 ***<br>(0.07)</td><td>-0.34 ***<br>(0.09)</td></tr><tr><td>Summer</td><td>0.16 **<br>(0.07)</td><td>0.20 **<br>(0.07)</td><td>0.16 **<br>(0.08)</td><td>0.17 **<br>(0.06)</td><td>0.17 **<br>(0.09)</td><td>0.23 **<br>(0.09)</td><td>0.15 **<br>(0.07)</td><td>0.20 **<br>(0.06)</td></tr><tr><td>Autumn</td><td>0.12 **<br>(0.05)</td><td>0.08 *<br>(0.05)</td><td>0.08<br>(0.06)</td><td>0.07<br>(0.05)</td><td>0.22 **<br>(0.10)</td><td>0.22 *<br>(0.12)</td><td>0.12 **<br>(0.06)</td><td>0.14 **<br>(0.06)</td></tr><tr><td>Winter</td><td>0.00<br>(0.03)</td><td>0.05<br>(0.03)</td><td>0.06<br>(0.04)</td><td>0.05<br>(0.05)</td><td>-0.06<br>(0.07)</td><td>-0.04<br>(0.09)</td><td>0.11 **<br>(0.05)</td><td>0.10 *<br>(0.06)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.47</td><td>0.52</td><td>0.50</td><td>0.56</td><td>0.21</td><td>0.23</td><td>0.46</td><td>0.48</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on forestland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of forestland area by forestland price in columns (5) to (8), in line with the land-adjusted Ricardian approach. Observations are weighted by the total departmental area. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
<table><caption>Table A29: Marginal climate impacts on urban sector with the standard and land-adjusted Ricardian approaches, with total departmental area weights</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable:<br>log(building land price)</th><th colspan="4">Dependent variable:<br>log(building land price × urban area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.66 **<br>(0.33)</td><td>0.19<br>(0.38)</td><td>1.36 ***<br>(0.29)</td><td>0.93 **<br>(0.42)</td><td>1.11 ***<br>(0.37)</td><td>0.70 *<br>(0.42)</td><td>1.73 ***<br>(0.32)</td><td>0.91 **<br>(0.41)</td></tr><tr><td>Summer</td><td>-0.65 ***<br>(0.20)</td><td>-0.45 *<br>(0.25)</td><td>-0.79 ***<br>(0.20)</td><td>-0.64 ***<br>(0.19)</td><td>-0.94 ***<br>(0.18)</td><td>-0.80 ***<br>(0.21)</td><td>-0.96 ***<br>(0.23)</td><td>-0.61 ***<br>(0.15)</td></tr><tr><td>Autumn</td><td>0.69<br>(0.51)</td><td>1.00<br>(0.69)</td><td>0.42<br>(0.35)</td><td>0.71<br>(0.55)</td><td>0.82<br>(0.66)</td><td>1.19<br>(0.88)</td><td>0.37<br>(0.42)</td><td>0.64<br>(0.59)</td></tr><tr><td>Winter</td><td>-0.59 **<br>(0.29)</td><td>-0.66<br>(0.42)</td><td>-0.73 ***<br>(0.22)</td><td>-0.79 **<br>(0.30)</td><td>-0.61 *<br>(0.35)</td><td>-0.73<br>(0.49)</td><td>-0.72 **<br>(0.31)</td><td>-0.66 **<br>(0.31)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.19 **<br>(0.07)</td><td>-0.26 **<br>(0.11)</td><td>-0.26 **<br>(0.09)</td><td>-0.32 ***<br>(0.06)</td><td>-0.33 ***<br>(0.10)</td><td>-0.42 **<br>(0.15)</td><td>-0.27 **<br>(0.10)</td><td>-0.26 **<br>(0.09)</td></tr><tr><td>Summer</td><td>-0.11<br>(0.09)</td><td>-0.08<br>(0.08)</td><td>-0.17 **<br>(0.07)</td><td>-0.12<br>(0.08)</td><td>0.00<br>(0.12)</td><td>0.04<br>(0.12)</td><td>-0.12<br>(0.10)</td><td>-0.08<br>(0.11)</td></tr><tr><td>Autumn</td><td>0.31 ***<br>(0.04)</td><td>0.22 ***<br>(0.06)</td><td>0.04<br>(0.06)</td><td>0.01<br>(0.05)</td><td>0.41 ***<br>(0.07)</td><td>0.34 ***<br>(0.10)</td><td>0.03<br>(0.07)</td><td>0.01<br>(0.06)</td></tr><tr><td>Winter</td><td>-0.30 ***<br>(0.08)</td><td>-0.19 **<br>(0.09)</td><td>0.06<br>(0.05)</td><td>0.1<br>(0.04)</td><td>-0.43 ***<br>(0.11)</td><td>-0.32 **<br>(0.14)</td><td>0.01<br>(0.06)</td><td>0.03<br>(0.04)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.47</td><td>0.54</td><td>0.66</td><td>0.71</td><td>0.52</td><td>0.56</td><td>0.77</td><td>0.80</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on forestland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of urban area by building land price in columns (5) to (8), in line with the land-adjusted Ricardian approach. Observations are weighted by the total departmental area. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
<table><caption>Table A30: Marginal climate impacts for the whole land sector with the land-adjusted Ricardian approach, with departmental area weights</caption><thead><tr><th></th><th colspan="4">Dependent variable:</th></tr><tr><th></th><th colspan="4">log(∑<sup>3</sup><sub>i=1</sub> land price of type i × area of type i)</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.96 ***<br>(0.32)</td><td>0.60 *<br>(0.35)</td><td>1.50 ***<br>(0.27)</td><td>0.73 **<br>(0.38)</td></tr><tr><td>Summer</td><td>-0.81 ***<br>(0.15)</td><td>-0.67 ***<br>(0.17)</td><td>-0.82 ***<br>(0.18)</td><td>-0.48 ***<br>(0.15)</td></tr><tr><td>Autumn</td><td>0.67<br>(0.55)</td><td>0.96<br>(0.74)</td><td>0.27<br>(0.33)</td><td>0.48<br>(0.47)</td></tr><tr><td>Winter</td><td>-0.49 *<br>(0.28)</td><td>-0.58<br>(0.41)</td><td>-0.58 **<br>(0.23)</td><td>-0.49 **<br>(0.24)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.30 ***<br>(0.09)</td><td>-0.38 ***<br>(0.13)</td><td>-0.24 **<br>(0.09)</td><td>-0.23 **<br>(0.09)</td></tr><tr><td>Summer</td><td>0.02<br>(0.09)</td><td>0.06<br>(0.10)</td><td>-0.08<br>(0.08)</td><td>-0.04<br>(0.08)</td></tr><tr><td>Autumn</td><td>0.35 ***<br>(0.06)</td><td>0.29 ***<br>(0.09)</td><td>0.03<br>(0.07)</td><td>0.01<br>(0.05)</td></tr><tr><td>Winter</td><td>-0.36 ***<br>(0.10)</td><td>-0.27 **<br>(0.12)</td><td>0.02<br>(0.06)</td><td>0.03<br>(0.05)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.52</td><td>0.57</td><td>0.77</td><td>0.81</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on the sum of the product of sector-specific area by sector-specific price in columns (1) to (4), in line with the land-adjusted Ricardian approach. Observations are weighted by the departmental area. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
<table><caption>Table A31: Marginal climate impacts on agriculture with the standard and land-adjusted Ricardian approaches, with inverse of total departmental area weights</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable: log(farmland price)</th><th colspan="4">Dependent variable: log(farmland price × farmland area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.35 **<br>(0.18)</td><td>0.08<br>(0.17)</td><td>0.77 ***<br>(0.18)</td><td>0.86 ***<br>(0.21)</td><td>0.38<br>(0.26)</td><td>0.10<br>(0.39)</td><td>0.47 *<br>(0.19)</td><td>-0.77 **<br>(0.32)</td></tr><tr><td>Summer</td><td>-0.34 **<br>(0.13)</td><td>-0.28 **<br>(0.13)</td><td>-0.50 ***<br>(0.14)</td><td>-0.61 ***<br>(0.10)</td><td>-0.08<br>(0.30)</td><td>0.16<br>(0.37)</td><td>-0.03<br>(0.24)</td><td>0.68 *<br>(0.37)</td></tr><tr><td>Autumn</td><td>0.28<br>(0.27)</td><td>0.61 *<br>(0.35)</td><td>0.24 **<br>(0.11)</td><td>0.48 **<br>(0.16)</td><td>-1.13<br>(0.75)</td><td>-1.31 *<br>(0.80)</td><td>-1.22 **<br>(0.56)</td><td>-1.43 **<br>(0.75)</td></tr><tr><td>Winter</td><td>-0.23<br>(0.18)</td><td>-0.39<br>(0.24)</td><td>-0.35 ***<br>(0.13)</td><td>-0.52 ***<br>(0.13)</td><td>1.16 *<br>(0.60)</td><td>1.37 **<br>(0.65)</td><td>0.99 **<br>(0.43)</td><td>1.47 ***<br>(0.55)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.10<br>(0.07)</td><td>-0.13<br>(0.09)</td><td>-0.19 ***<br>(0.05)</td><td>-0.28 ***<br>(0.05)</td><td>-0.13<br>(0.09)</td><td>-0.20 **<br>(0.10)</td><td>0.13<br>(0.10)</td><td>0.23 **<br>(0.09)</td></tr><tr><td>Summer</td><td>0.04<br>(0.08)</td><td>0.05<br>(0.09)</td><td>0.00<br>(0.06)</td><td>0.04<br>(0.05)</td><td>0.25 ***<br>(0.11)</td><td>0.31 ***<br>(0.12)</td><td>0.20 **<br>(0.08)</td><td>0.22 ***<br>(0.06)</td></tr><tr><td>Autumn</td><td>0.14 ***<br>(0.03)</td><td>0.08 **<br>(0.04)</td><td>0.02<br>(0.05)</td><td>0.00<br>(0.05)</td><td>0.16 **<br>(0.06)</td><td>0.14 **<br>(0.06)</td><td>0.01<br>(0.07)</td><td>0.01<br>(0.06)</td></tr><tr><td>Winter</td><td>-0.15 ***<br>(0.04)</td><td>-0.08 *<br>(0.05)</td><td>0.02<br>(0.04)</td><td>0.06<br>(0.05)</td><td>-0.42 ***<br>(0.13)</td><td>-0.38 ***<br>(0.13)</td><td>-0.35 ***<br>(0.10)</td><td>-0.38 ***<br>(0.10)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.45</td><td>0.51</td><td>0.59</td><td>0.63</td><td>0.64</td><td>0.66</td><td>0.72</td><td>0.76</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on farmland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of farmland area by farmland price in columns (5) to (8), in line with the land-adjusted Ricardian approach. Observations are weighted by the inverse of total departmental area. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
<table><caption>Table A32: Marginal climate impacts on forestry with the standard and land-adjusted Ricardian approaches, with inverse of total departmental area weights</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable:<br>log(forestland price)</th><th colspan="4">Dependent variable:<br>log(forestland price × forest area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.41 ***<br>(0.13)</td><td>0.17<br>(0.13)</td><td>0.48 **<br>(0.22)</td><td>-0.15<br>(0.39)</td><td>-0.02<br>(0.15)</td><td>-0.34<br>(0.27)</td><td>0.13<br>(0.11)</td><td>-0.75 **<br>(0.35)</td></tr><tr><td>Summer</td><td>-0.46 ***<br>(0.13)</td><td>-0.37 **<br>(0.18)</td><td>-0.39 **<br>(0.09)</td><td>-0.11<br>(0.21)</td><td>0.49<br>(0.31)</td><td>0.90 **<br>(0.39)</td><td>0.67 **<br>(0.32)</td><td>1.45 ***<br>(0.45)</td></tr><tr><td>Autumn</td><td>0.42<br>(0.35)</td><td>0.64 **<br>(0.35)</td><td>0.31<br>(0.25)</td><td>0.45 *<br>(0.28)</td><td>-1.72 **<br>(0.91)</td><td>-2.33 **<br>(1.00)</td><td>-1.95 **<br>(0.83)</td><td>-2.88 ***<br>(0.91)</td></tr><tr><td>Winter</td><td>-0.39 *<br>(0.22)</td><td>-0.48 **<br>(0.24)</td><td>-0.35 **<br>(0.13)</td><td>-0.27<br>(0.17)</td><td>1.46 ***<br>(0.66)</td><td>1.97 ***<br>(0.73)</td><td>1.48 ***<br>(0.58)</td><td>2.37 ***<br>(0.67)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.43 ***<br>(0.08)</td><td>-0.47 ***<br>(0.06)</td><td>-0.43 ***<br>(0.10)</td><td>-0.40 ***<br>(0.07)</td><td>-0.33 ***<br>(0.11)</td><td>-0.38 ***<br>(0.11)</td><td>-0.21 **<br>(0.10)</td><td>-0.15<br>(0.10)</td></tr><tr><td>Summer</td><td>0.13<br>(0.10)</td><td>0.15 *<br>(0.08)</td><td>0.12<br>(0.09)</td><td>0.13 **<br>(0.07)</td><td>0.27 *<br>(0.14)</td><td>0.34 **<br>(0.14)</td><td>0.25 **<br>(0.12)</td><td>0.31 ***<br>(0.11)</td></tr><tr><td>Autumn</td><td>0.14 **<br>(0.07)</td><td>0.09<br>(0.06)</td><td>0.09<br>(0.07)</td><td>0.07<br>(0.06)</td><td>0.36 ***<br>(0.11)</td><td>0.34 ***<br>(0.11)</td><td>0.21 **<br>(0.10)</td><td>0.26 **<br>(0.10)</td></tr><tr><td>Winter</td><td>0.03<br>(0.04)</td><td>0.09 **<br>(0.04)</td><td>0.11 **<br>(0.05)</td><td>0.12 **<br>(0.05)</td><td>-0.31 ***<br>(0.11)</td><td>-0.28 **<br>(0.11)</td><td>-0.14 *<br>(0.09)</td><td>-0.19 *<br>(0.10)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.43</td><td>0.47</td><td>0.48</td><td>0.52</td><td>0.34</td><td>0.38</td><td>0.43</td><td>0.48</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on forestland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of forestland area by forestland price in columns (5) to (8), in line with the land-adjusted Ricardian approach. Observations are weighted by the inverse of total departmental area. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
<table><caption>Table A33: Marginal climate impacts on urban sector with the standard and land-adjusted Ricardian approaches, with inverse of total departmental area weights</caption><thead><tr><th rowspan="2"></th><th colspan="4">Dependent variable:<br>log(building land price)</th><th colspan="4">Dependent variable:<br>log(building land price × urban area)</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.65<br>(0.43)</td><td>0.11<br>(0.47)</td><td>1.47 ***<br>(0.28)</td><td>1.33 ***<br>(0.45)</td><td>1.03 **<br>(0.40)</td><td>0.55<br>(0.42)</td><td>1.72 ***<br>(0.25)</td><td>0.95 ***<br>(0.28)</td></tr><tr><td>Summer</td><td>-0.63 **<br>(0.24)</td><td>-0.50 *<br>(0.29)</td><td>-0.84 ***<br>(0.18)</td><td>-0.89 ***<br>(0.22)</td><td>-0.71 **<br>(0.29)</td><td>-0.53<br>(0.35)</td><td>-0.71 ***<br>(0.20)</td><td>-0.33 *<br>(0.18)</td></tr><tr><td>Autumn</td><td>0.81<br>(0.64)</td><td>1.45 *<br>(0.81)</td><td>0.58<br>(0.33)</td><td>1.01 *<br>(0.54)</td><td>0.12<br>(1.06)</td><td>0.50<br>(1.26)</td><td>-0.37<br>(0.65)</td><td>-0.27<br>(0.83)</td></tr><tr><td>Winter</td><td>-0.75 **<br>(0.35)</td><td>-1.06 **<br>(0.48)</td><td>-0.94 ***<br>(0.21)</td><td>-1.17 ***<br>(0.31)</td><td>0.03<br>(0.62)</td><td>-0.09<br>(0.77)</td><td>-0.12<br>(0.44)</td><td>0.04<br>(0.53)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.24 **<br>(0.11)</td><td>-0.30 *<br>(0.16)</td><td>-0.33 ***<br>(0.10)</td><td>-0.44 ***<br>(0.15)</td><td>-0.41 **<br>(0.16)</td><td>-0.50 **<br>(0.20)</td><td>-0.22 **<br>(0.07)</td><td>-0.25 **<br>(0.08)</td></tr><tr><td>Summer</td><td>-0.10<br>(0.12)</td><td>-0.09<br>(0.13)</td><td>-0.20 **<br>(0.07)</td><td>-0.14 *<br>(0.08)</td><td>0.10<br>(0.15)</td><td>0.15<br>(0.17)</td><td>-0.07<br>(0.08)</td><td>-0.02<br>(0.10)</td></tr><tr><td>Autumn</td><td>0.32 ***<br>(0.03)</td><td>0.20 **<br>(0.06)</td><td>0.01<br>(0.06)</td><td>-0.02<br>(0.09)</td><td>0.52 ***<br>(0.05)</td><td>0.43 ***<br>(0.08)</td><td>0.07<br>(0.08)</td><td>0.05<br>(0.09)</td></tr><tr><td>Winter</td><td>-0.26 ***<br>(0.08)</td><td>-0.12<br>(0.10)</td><td>0.15 **<br>(0.07)</td><td>0.21 **<br>(0.08)</td><td>-0.58 ***<br>(0.15)</td><td>-0.47 ***<br>(0.16)</td><td>-0.08<br>(0.09)</td><td>-0.06<br>(0.09)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.45</td><td>0.54</td><td>0.71</td><td>0.76</td><td>0.56</td><td>0.60</td><td>0.80</td><td>0.83</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on forestland price in columns (1) to (4), in the spirit of the standard Ricardian approach, and on the multiplication of urban area by building land price in columns (5) to (8), in line with the land-adjusted Ricardian approach. Observations are weighted by the inverse of total departmental area. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
<table><caption>Table A34: Marginal climate impacts for the whole land sector with the land-adjusted Ricardian approach, with inverse of total departmental area weights</caption><thead><tr><th></th><th colspan="4" style="text-align: center;">Dependent variable:</th></tr><tr><th></th><th colspan="4" style="text-align: center;">log(∑<sup>3</sup><sub>i=1</sub> land price of type <i>i</i> × area of type <i>i</i>)</th></tr><tr><th></th><th style="text-align: center;">(1)</th><th style="text-align: center;">(2)</th><th style="text-align: center;">(3)</th><th style="text-align: center;">(4)</th></tr></thead><tbody><tr><td><strong>Temperature (°C)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>0.88 ***= (0.34)</td><td>0.45 (0.36)</td><td>1.46 *** (0.21)</td><td>0.65 *** (0.27)</td></tr><tr><td>Summer</td><td>-0.55 ** (0.27)</td><td>-0.35 (0.33)</td><td>-0.52 *** (0.18)</td><td>-0.07 (0.20)</td></tr><tr><td>Autumn</td><td>-0.14 (0.99)</td><td>0.08 (1.16)</td><td>-0.60 (0.61)</td><td>-0.64 (0.75)</td></tr><tr><td>Winter</td><td>0.26 (0.59)</td><td>0.23 (0.72)</td><td>0.14 (0.40)</td><td>0.40 (0.48)</td></tr><tr><td><strong>Precipitation (cm/month)</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Spring</td><td>-0.38 *** (0.14)</td><td>-0.46 *** (0.18)</td><td>-0.18 ** (0.07)</td><td>-0.19 ** (0.08)</td></tr><tr><td>Summer</td><td>0.14 (0.13)</td><td>0.19 (0.15)</td><td>-0.02 (0.06)</td><td>0.04 (0.08)</td></tr><tr><td>Autumn</td><td>0.47 *** (0.05)</td><td>0.40 *** (0.07)</td><td>0.07 (0.07)</td><td>0.06 (0.08)</td></tr><tr><td>Winter</td><td>-0.54 *** (0.14)</td><td>-0.44 *** (0.15)</td><td>-0.11 (0.09)</td><td>-0.10 (0.09)</td></tr><tr><td><strong>Number of observations</strong></td><td>344</td><td>344</td><td>344</td><td>344</td></tr><tr><td><strong>Controls</strong></td><td>No</td><td>No</td><td>Yes</td><td>Yes</td></tr><tr><td><strong>Year fixed effects</strong></td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td><strong>Adjusted R<sup>2</sup></strong></td><td>0.58</td><td>0.61</td><td>0.80</td><td>0.83</td></tr></tbody></table>

NOTE. The figures report the recomputed marginal impacts of seasonal climate variables on the sum of the product of sector-specific area by sector-specific price in columns (1) to (4), in line with the land-adjusted Ricardian approach. Observations are weighted by inverse of total departmental area. The computations use the estimates obtained with the log-quadratic models and the average variable values in our sample. Climate variables are computed using 30-year averages. Controls include population, average altitude and soil conditions. Standard errors adjusted for spatial dependence and computed using the delta method are displayed in brackets. *, ** and *** indicate a p-value lower than 0.1, 0.05 and 0.01 respectively.
A20 Simulating climate impacts for forestry and urban with the standard and
land-adjusted Ricardian approaches

Figures A10 and A11 display the results of the simulations of the climate impacts on forestry and urban under low (RCP 2.6) and medium (RCP4.5) emission pathways, using the preferred standard and land-adjusted Ricardian estimates.

<figure><img src="image_21.png" /></figure>

Figure A10: Departmental projections of forestland value change by the end of the century under RCP2.6 and RCP 4.5 climate change scenario with the standard and land-adjusted Ricardian approaches. NOTE. Figures display estimated changes of value of forestry in percentage if departments are exposed to hypothetical changes in temperature and precipitation, according to our preferred estimates and to the information provided by the ALADIN climate model for the RCP 2.6 and 4.5 emission pathways scenario between 2070 and 2099.
<figure><img src="image_22.png" /></figure>

Figure A11: Departmental projections of urban value change by the end of the century under RCP2.6 and RCP 4.5 climate change scenario with the standard and land-adjusted Ricardian approaches. NOTE. Figures display estimated changes of value of building lands in percent if departments are exposed to hypothetical changes in temperature and precipitation, according to our preferred estimates and to the information provided by the ALADIN climate model for the RCP 2.6 and 4.5 emission pathways scenario between between 2070 and 2099.