The impact of climate change on economic output across industries in Chile

in which climate change is completely controlled, RCP 4.5 being a scenario in which the global temperature rise is likely to fall below 2.0, and RCP 8.5 being considered the worst scenario in which no country implements policies or mitigators for climate change [14]. The quantitative exercise considers the impact of a given global temperature change in cli- mate change, TRCP x t , in year t for each RCP − x path (with x = 2.6, 4.5, 6.0, 8.5) on the GDP growth rate and on the GDP level of each industry i: I growthi;t ¼ ðP12 s¼1 bs;iÞðTRCP x t T2017Þ and I leveli;t ¼ expðPt t0 ¼2017 I growthi;t0 Þ 1. These RCP paths scenarios are obtained from the average path values of the United Nations modeling experts [14]. These path values are widely used in macro-financial stress tests with climate change factors [34]. We then obtain the estimates of the impact on the aggregate GDP by summing up across all industries, I growtht ¼ P12 i¼1 wi;t I growthi;t and I levelt ¼ P12 i¼1 wi;t I leveli;t , with wi,t denoting the weight of each industry i in the total GDP at time t. Notice that the GDP growth rate and level impact costs are measured relative to a world with no climate change and not relative to 2017. We produce 3 estimates of the impact of climate change on each industry i at horizon t: 1) using all the model’s estimated point coefficients for the effect of the temperature on the indus- trial GDP (b ¼ ð^b1;1; ::; ^bs;i; ::; ^b12;12Þ with s = 1,.., 12 denoting month and i = 1, . . ., 12 denot-

ing industry); 2) using only the statistically significant coefficients at a level of 10% or lower (β = (.., βs,i,..) with bs;i ¼ ^bs;i1 j^bs;ij SEð^bs;iÞ � 1:65 � � , with 1(.) being the indicator function); and 3) using only negative coefficients (that is, disregarding positive impacts of climate change) that are statistically significant (β = (.., βs,i,..) with bs;i ¼ ^bs;i1 j^bs;ij SEð^bs;iÞ � 1:65 � � 1ð^bs;i < 0Þ). Table 6 denotes the values of the estimated sum of the coefficients for all months for each industry i (P12 s¼1 bs;i) under each of these separate assumptions, which represents the impact on the GDP growth rate of each industry in a given year for a uniform increase in temperature of 1 C throughout all the months of the year. If one considers the quarterly weather model from Table 4, then Fishing and Mining are the only industries which are negatively impacted by climate change with a statistical significance, although the coefficient for Mining is very small. By considering the monthly fluctuations model from Table 5, then we find a statistically significant impact of an increase of 1 C in temperature that reduces the growth rate in Agricul- ture, Fishing, Manufacture, EGA (Electricity, Gas, and Water), Construction and Commerce of -3%, -12.1%, -2.2%, -3.9%, -0.3% and -0.7%, respectively. Table 6. Total impact on the industry GDP growth rate (in %) of the estimated models for a one degree Celsius temperature increase throughout the year. Sum of the coefficients’ impact Industries (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Quarterly weather fluctuations model (Table 4) All quarters 0.9 -7.0 -1.5 2.6 -0.1 0.7 1.0 0.3 0.7 -0.6 0.3 -0.4 Statistically significant 0.9 -10.8 -0.1 0.0 1.8 0.0 1.0 0.0 0.6 0.0 0.0 0.0 Significant & negative 0.0 -10.8 -0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Monthly weather fluctuations model (Table 5) All months 1.3 -8.8 -1.9 2.5 1.9 1.2 0.4 0.2 1.1 -0.7 0.1 -0.2 Statistically significant -3.0 -12.1 0.5 -2.2 -3.9 -0.3 -0.7 0.0 1.7 0.0 0.2 0.0 Significant & negative -3.0 -12.1 0.0 -2.2 -3.9 -0.3 -0.7 0.0 0.0 0.0 0.0 0.0 Notes: (1) Agriculture and Forestry, (2) Fishing, (3) Mining, (4) Manufacturing, (5) EGA, (6) Construction, (7) Commerce, Restaurants, and Hotels, (8) Transport and Communications, (9) Financial Services, (10) Home Ownership, (11) Personal Services, (12) Public Administration. https://doi.org/10.1371/journal.pone.0266811.t006 PLOS ONE The impact of climate change on economic output across industries in Chile PLOS ONE | https://doi.org/10.1371/journal.pone.0266811 April 28, 2022 12 / 21