Climate Change Around the World

Transcription

1 Climate Change Around the World Per Krusell Institute for International Economic Studies, NBER, CEPR Anthony A. Smith, Jr. Yale University, NBER The Macro and Micro Economics of Climate Change Laboratory for Aggregate Economics and Finance University of California, Santa Barbara May 22 and 23, 217

2 The project Construct global model of economy-climate interactions featuring a high degree of geographic resolution (1 1 regions). Use the model as a laboratory to quantify the distributional effects of climate change and climate policy. If a set of regions imposes a carbon tax (or a quantity restriction on emissions), how does the path of global emissions respond? Which regions gain and which lose, and by how much? Related to small new literature on spatial equilibrium models of climate change: Desmet and Rossi-Hansberg; Krusell and Hassler; Brock, Engström, Grass, and Xepapadeas; Brock, Cai, and Xepapadeas.

3 The data Unit of analysis: 1 1 cells containing land. The model contains 19, regions (or cell-countries). Matsuura and Willmott: gridded (.5.5 ) monthly terrestrial temperature data for Nordhaus s G-Econ database: gross domestic product (GDP) and population for all such cells in 199.

4 Average temperature ( )

5 Global average land temperature (by year) Year

6 Log of GDP in

7 Natural-science background I: the climate Energy balance (inflow from the Sun equals outflow from the Earth) determines the Earth s temperature. Forcing, F, from CO 2 in the atmosphere (relative to pre-industrial) is: F = η ln(s/ S) ln(2), where S = 84GtC and S = 6GtC are current and pre-industrial stocks. Equilibrium temperature, T (relative to pre-industrial), is: T = κf = λ ln(s/ S) ln(2), where κ depends on various feedback effects. λ 3 ± 1.5 is climate sensitivity.

8 Natural-science background II: the carbon cycle Carbon cycle: how emissions of CO 2 enter/exit atmosphere. Key: emissions spread globally very quickly ( global externality ). Depreciation structure of atmospheric CO 2 : smooth, but very slow; some stays forever in atmosphere nonlinear but linear approximation okay. Emissions: 1GtC/year; S t 4.5GtC/year. Estimated remaining carbon: oil + gas = 3GtC, coal much bigger (> 3,GtC?). So coal is key! To summarize: emissions carbon in atmosphere forcing temperature. Bad if higher T causes damages : the mother of all externalities (Stern).

9 Integrated assessment models Pioneered by Nordhaus (DICE, RICE). Quantitative theory, computational. Key components: climate system (as above) carbon cycle (as above) economic model of emissions AND damages Economic model: needs to be dynamic, forward-looking, possibly allowing stochastics (temperature variations, disasters). Here: climate system more elaborate (regional variation) economic model and damages new the one-region version of the model is close to the representative-agent DSGE climate-economy model in Golosov, Hassler, Krusell, and Tsyvinski (214)

10 Overview for remainder of talk 1. our climate modeling 2. our damage specification 3. economic model 4. calibration, computation 5. results 6. conclusions, future

11 Our climate modeling How will region l s climate respond to global warming? Answer given by complex global and regional climate models. But not feasible (yet) to combine these with economic model. Therefore, use pattern scaling (aka statistical downscaling ): statistical description of temperature in a given region as a function of a single state variable average global temperature. Capture sensitivity of temperature in region l to global temperature T in a coefficient (linear structure; standard). With help of climate scientists, use runs of (highly) complex climate models into the future to estimate sensitivities.

12 Sensitivity to changes in global temperature

13 Our damage specification What are the damages in region l as a result of global warming? Damage measurements: overall the weakest part of quantitative climate-economy models, especially for regional damages. Two common approaches: bottom up (Nordhaus, IPCC) and top down (Dell et al). Our approach: formulate a damage function D of local temperature that is: common across all regions; like Nordhaus s, a drag on total factor productivity (TFP); consistent with Nordhaus s worldwide damage function when aggregated across all regions. Desmet and Rossi-Hansberg (214) also use a common U-shape in a spatial application.

14 Nordhaus s damage function (percentage of GDP) Percentage Temperature

15 Damage function: productivity vs. temperature 1.75 Fraction of optimum Temperature (degrees centigrade)

16 Share of world GDP vs. temperature Share of GDP Temperature (degrees centigrade)

17 Share of world population vs. temperature Share of population Temperature (degrees centigrade)

18 Share of world GDP vs. productivity (as a fraction of optimum) Share of GDP Productivity

19 Share of world population vs. productivity (as a fraction of optimum) Share of population Productivity

20 Damage coefficient x 1 (at temperature in )

21 The economic model Forward-looking consumers and firms in each region determine their consumption, saving, and energy use. No migration. Neoclassical production technologies, different TFPs both exogenously and due to climate. Energy as an input: coal, produced locally, at constant marginal cost (no profits). Coal slowly, exogenously replaced by (same-cost) green energy. Market structure: two cases. Autarky (regions only linked via emission externality). Unrestricted borrowing/lending (world interest rate clears market). Summary: like Aiyagari (1994) and our previous work, though no shocks in this version. Adaptation: consumption smoothing and, in case with international markets, capital mobility ( leakage ).

22 Regional problem In a recursive equilibrium, region l solves v t (ω, A, k, S; l) = max k,b [U(c) + β v t+1(ω, A, k, S ; l)], s.t. c = ω k q t ( k, S)b ω = max e [F (k, (1 D(T l (S )))A, e ) pe )] + (1 δ)k + b A = (1 + g)a k = H t ( k, S) S = Φ t ( k, S ). Can be interpreted as a decentralized equilibrium. Set up to deal with shocks, aggregate and/or local.

23 Calibration Annual time step, log utility, discount factor β =.985. Production function in region l: CES in kl α((1 D l)a l L) 1 α and energy e l, with: share parameter θ; elasticity = (1 ρ) 1 (set ρ = for now); α =.36; Al grows at rate g = 1%. Capital depreciates at rate δ = 6%. Initial distribution of region-specific capital, k l, and level of productivity, A l, chosen to: (1) match regional GDP per capita in 199 and; (2) equalize MPKs across regions. Price of coal and θ chosen to match: (1) total carbon emissions in 199; and (2) energy share of 6% along a balanced growth path. Green energy replaces coal slowly (logistic).

24 Fraction of carbon emissions abated 1.75 Fraction Year

25 Carbon cycle The total stock of atmospheric carbon, S t, is the sum of a permanent stock, S 1t, and a (slowly) depreciating stock, S 2t : S t = S 1t + S 2t. S 1t =.25E t + S 1,t 1, where E t is total carbon emissions. S 2t =.36(1.25)E t +.998S 2,t 1. Half-life of a freshly-emitted unit of carbon is 3 years; half-life of the depreciating stock (given no new emissions) is 3 years.

26 Computation Richard Feynman: Imagine how much harder physics would be if electrons had feelings! Transition + heterogeneity = nontrivial fixed-point problem: guess on a temperature path, solve backwards for decisions, run globe forwards to confirm guessed path. Use mostly well-known methods but heterogeneity vast: exogenous TFP wealth/capital l captures entire path of future regional TFP endogenous to climate (this feature NOT one-dimensional); we don t actually solve 19,235 DP problems but so much heterogeneity that we need to solve 7 DPs and then nonlinearly interpolate decision rules between 7 types. Fortran 9 + OpenMP with 2 cores: less than five minutes.

27 Experiments Laissez-faire. Main policy experiment: all regions impose common path for carbon taxes, financed locally (no interregional transfers). Throughout: focus on relative effects, not aggregates.

28 Main findings Climate change affects regions very differently. Stakes big at regional level. Though a tax on carbon would affect welfare positively in some average sense, there is a large disparity of views across regions (56% of regions gain, while 44% lose). Findings are very close for two extreme market structures (autarky and international capital markets).

29 behavior of aggregates over time

30 Global emissions of atmospheric carbon (in gigatons) (no taxes; free capital movement) Gigatons of carbon Year

31 Optimal carbon tax (dollars per ton of carbon) Dollars Year

32 Global emissions of atmospheric carbon (in gigatons) (taxes vs. no taxes; free capital movement) Gigatons of carbon Year

33 Gigatons of atmospheric carbon (no taxes; free capital movement) Gigatons of carbon Year

34 Gigatons of atmospheric carbon (taxes vs. no taxes; free capital movement) Gigatons of carbon Year

35 Temperature (degrees centrigrade above pre indudstrial) (no taxes; free capital movement) Degrees centigrade Year

36 Temperature (degrees centrigrade above pre indudstrial) (taxes vs. no taxes; free capital movement) Degrees centigrade Year

37 World GDP (trillions of dollars; detrended) (no taxes; free capital movement) Trillions of dollars Year

38 World GDP (trillions of dollars; detrended) (taxes vs. no taxes; free capital movement) Trillions of dollars Year

39 World consumption (trillions of dollars; detrended) (no taxes; free capital movement) 21.7 Trillions of dollars Year

40 World consumption (trillions of dollars; detrended) (taxes vs. no taxes; free capital movement) 21.7 Trillions of dollars Year

41 movie: temperature, laissez-faire animation:

42 Temperature in

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63 movie: change in temperature, laissez-faire animation:

64 movie: 1 minus damage coefficient, laissez-faire animation:

65 Damage coefficient (x 1) in

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86 movie: percentage change in gdp, laissez-faire animation:

87 Percentage change in GDP: 2 vs

88 Percentage change in GDP: 21 vs

89 Percentage change in GDP: 22 vs

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108 movie: distribution of percentage changes in GDP animation:

109 Distribution of percentage change in GDP: 2 vs Fraction Percentage change in GDP

110 Distribution of percentage change in GDP: 21 vs Fraction Percentage change in GDP

111 Distribution of percentage change in GDP: 22 vs Fraction Percentage change in GDP

112 Distribution of percentage change in GDP: 23 vs Fraction Percentage change in GDP

113 Distribution of percentage change in GDP: 24 vs Fraction Percentage change in GDP

114 Distribution of percentage change in GDP: 25 vs Fraction Percentage change in GDP

115 Distribution of percentage change in GDP: 26 vs Fraction Percentage change in GDP

116 Distribution of percentage change in GDP: 27 vs Fraction Percentage change in GDP

117 Distribution of percentage change in GDP: 28 vs Fraction Percentage change in GDP

118 Distribution of percentage change in GDP: 29 vs Fraction Percentage change in GDP

119 Distribution of percentage change in GDP: 21 vs Fraction Percentage change in GDP

120 Distribution of percentage change in GDP: 211 vs Fraction Percentage change in GDP

121 Distribution of percentage change in GDP: 212 vs Fraction Percentage change in GDP

122 Distribution of percentage change in GDP: 213 vs Fraction Percentage change in GDP

123 Distribution of percentage change in GDP: 214 vs Fraction Percentage change in GDP

124 Distribution of percentage change in GDP: 215 vs Fraction Percentage change in GDP

125 Distribution of percentage change in GDP: 216 vs Fraction Percentage change in GDP

126 Distribution of percentage change in GDP: 217 vs Fraction Percentage change in GDP

127 Distribution of percentage change in GDP: 218 vs Fraction Percentage change in GDP

128 Distribution of percentage change in GDP: 219 vs Fraction Percentage change in GDP

129 Distribution of percentage change in GDP: 22 vs Fraction Percentage change in GDP

130 movie: level change in gdp, laissez-faire

131 Change in GDP (in millions of $): 2 vs

132 Change in GDP (in millions of $): 21 vs

133 Change in GDP (in millions of $): 22 vs

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152 movie: percentage change in gdp, taxes

153 Percentage change in GDP: 2 vs

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174 pictures: winners and losers from tax

175 Welfare gains from taxation (with free capital movement)

176 Welfare gains from taxation (in autarky)

177 Welfare gains from taxation (with free movement) (as a percentage of consumption) Fraction Percentage of consumption

178 Welfare gains from taxation (in autarky) (as a percentage of consumption) Fraction Percentage of consumption

179 Difference in gains from taxation (autarky vs. free movement) (as a percentage of consumption) Fraction Percentage of consumption

180 Welfare changes from tax: summary measures One region = one vote: 56% gain. One person = one vote: 84% gain. One dollar = one vote: 68% gain. Average gain across all regions: 2.11% (of consumption). Average gain weighted by regional GDP:.6%. Average gain weighted by regional population: 1.74%. World consumption path: gain of.37%.

181 Welfare changes from tax in U.S. and China only One region = one vote: 56% gain (vs. 56%). One person = one vote: 83% gain (vs. 84%). One dollar = one vote: 69% gain (vs. 68%). Average gain across all regions:.55% (vs. 2.11%). Average gain weighted by GDP:.16% (vs..6%). Average gain weighted by population:.44% (vs. 1.74%). World consumption path: gain of.1% (vs..37%). 27% of regions in U.S. gain (vs. 41%). 27% of regions in China gain (vs. 36%). 6% of regions in rest of world gain (vs. 58%).

182 Percentage of population in favor of carbon tax (by country)

183 Welfare gain from carbon tax (as a percentage of consumption)

184 picture: welfare gains from free capital movement (laissez-faire, then taxes)

185 Welfare gains from free capital movement (without taxes) (as a percentage of consumption) Fraction Percentage of consumption

186 Welfare gains from free capital movement (with taxes) (as a percentage of consumption) Fraction Percentage of consumption

187 movie: distribution of mpks animation:

188 Distribution of marginal product of capital in 1999 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

189 Distribution of marginal product of capital in 29 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

190 Distribution of marginal product of capital in 219 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

191 Distribution of marginal product of capital in 229 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

192 Distribution of marginal product of capital in 239 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

193 Distribution of marginal product of capital in 249 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

194 Distribution of marginal product of capital in 259 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

195 Distribution of marginal product of capital in 269 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

196 Distribution of marginal product of capital in 279 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

197 Distribution of marginal product of capital in 289 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

198 Distribution of marginal product of capital in 299 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

199 Distribution of marginal product of capital in 219 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

200 Distribution of marginal product of capital in 2119 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

201 Distribution of marginal product of capital in 2129 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

202 Distribution of marginal product of capital in 2139 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

203 Distribution of marginal product of capital in 2149 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

204 Distribution of marginal product of capital in 2159 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

205 Distribution of marginal product of capital in 2169 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

206 Distribution of marginal product of capital in 2179 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

207 Distribution of marginal product of capital in 2189 (triangle = unweighted; circle = weighted by GDP) Marginal product of capital

208 Conclusions Take-away: Results from our model: climate change is about relative effects much more than about average effects! In particular, large disagreements about taxes (so large transfer payments needed to compensate those losing from carbon tax). Methodological insight: we thought the market structure (because it admits more or less adaptation) would be important for the results, but it isn t.

209 Some caveats On one hand, damages too local and symmetric: no common aggregate damages. There are potentially such effects: world technology development (level or growth) can be impacted; biodiversity, ocean acidification,... ; spillovers through trade, migration, tourism,... On other hand, maybe not enough regional heterogeneity yet (rural vs. urban, manufacturing vs. agriculture,... ).

210 Near-future follow-up Within present model/paper: More on heterogeneous taxes. How does climate change influence migration pressure at borders? Easy to compute. (PICTURE!) Sea-level rise and coastal damages (straightforward to incorporate). Applications: Temperature shocks; can be problematic at higher T s because of extreme weather events (developed new computational tools to handle aggregate uncertainty + transition). Rising volatility as globe warms. Agricultural sector and food supplies (includes adding precipitation)....

211 Log of lifetime wealth (per effective unit of labor)