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Andersen, Brisson, Pörtner, and Verner
Regressions for the Impact of Climate Change on Life Expectancy (or Child Mortality) The level of health under a situation of no climate change can be written as: healthi ,NCC = βˆ1 ⋅ tempi ,NCC + βˆ2 ⋅ tempi2,NCC + βˆ 3 ⋅ raini ,NCC k
+ βˆ 4 ⋅ raini2,NCC +
∑ αˆ X j
j ,i
+ εˆi
j =1
where the index i refers to municipality i; temp and rain are the temperature and rainfall variables; the bˆ s are the estimated coefficients on the temperature and rainfall variables; the Xj s are the remaining j explanatory variables including the constant term; the αˆ j s are the coefficient to these variables; and εˆ i are the estimated error terms for each municipality. Equivalently, the level of health under the assumption of climate change can be written as follows: 2 ˆ ˆ ˆ health i ,CC = β1 ⋅ tempi ,CC + β 2 ⋅ tempi ,CC k
+ βˆ 3⋅ raini ,CC + βˆ 4 ⋅ raini2,CC +
∑ αˆ X j
j ,i
j =1
where the only differences are the temperature and rainfall variables. The control variables are held constant, so as to isolate the effects of climate change and variability. The difference in life expectancy that can be directly attributed to climate change and variability can be found as the difference between the two scenarios: ˆ ˆ ˆ Δ CC health i = healthi ,CC − healthi , NCC = βˆ1 ⋅ ( tempi ,CC − tempi ,NCC ) + βˆ 2 ⋅ ( tempi2,CC − tempi2,NCC ) + βˆ 3⋅ ( raini ,CC − raini ,NCC ) + βˆ 4 ⋅ ( raini2,CC − raini2,NCC )
Regressions for the Impact of Climate Change on Poverty and Inequality The regression for analyzing the short-run implications of climate change takes the following form: ln yi = α + β 1⋅ tempi + β 2 ⋅ tempi2 + β 3 ⋅ raini + β 4⋅ raini2 + β 5 ⋅ edui + β6 ⋅ urbi + β7 ⋅ urbi2 + ε i