Downloaded by guest on October 1, 2021 ohScovronick policy Noah mitigation change climate on and growth population of Impact www.pnas.org/cgi/doi/10.1073/pnas.1618308114 conditions), optimality certain interchangeably. terms (under these SCC use the its we to (or equal emissions is dioxide price carbon climate of for ton equivalent) consequences additional the an expressed by of cost, change caused economic dollars, the is present-day which in (SCC), dioxide carbon of which people of the stream of size. future most in a but uncertain by is now, experienced people be by of will costs largely benefits the borne as be nexus, issues will environment other mitigation and population from climate human problem the realized climate at (fully the impacts climate- differentiates the to damages) and vulnerable (emissions) environmen- pressure the be it between tal lag will and time (1–3), extensive people The target impacts. future related climate more given means a therefore also achieve and to emissions more mitigation entails more population larger a equal, T emissions population valuing to approach ethical merely the population. on than depends wellbeing—rather growth savings—again population in cost lowering improvements whether overall that fertil- entails lower show may spending we that than Finally, policies larger ity. development are human in savings for emerge These shortfalls AU future. under distant savings more savings TU; the near-term under large occur that annually) finding ($billions reduc- growth, plausible population by in implied we tions costs Additionally, mitigation commonly avoided discounting. to the time estimate comparable regarding is decisions the TU between debated under SCC the scenarios in difference population The 85% two AU. of under SCC than 5% the vs. rather in TU increase (UN)-high under an Nations larger entails exam- United scenario a population the for that UN-low assuming people: fact 2025, more the in hurts to ple, change responds climate it means because population TU, under the effect larger The increases decreases. is temperature population peak optimal average as but increases, discounted approaches, SCC period’s both time each Under popu- only (SCC) wellbeing. ignores sums dioxide which and carbon (AU), size of lation cost average of social and in models, standard total considers is dis- which and a (TU), wellbeing utilitarianism population: total valuing of to version approaches counted two explore we Climate-Economy model, Integrated show Dynamic the mit- We Using determines decisions. importantly impacts. moreigation valued climate-related is means population to future and how vulnerable that climate emissions be for more will matters entails 2016) people 4, and growth November review uncertain higher for (received is policy: 2017 growth 26, September population approved and Future MA, Cambridge, University, Harvard Clark, C. William by Edited and 08544; NJ Princeton, University, A-2361 Princeton Austria Environment, 78712; Laxenburg, the TX and Austin, Energy Austin, for at Center Texas of University Economics, of 10964; NY Palisades, University, Columbia 138527; Singapore College, a Spears Dean odo isnSho,PictnUiest,Pictn J08544; NJ Princeton, University, Princeton School, Wilson Woodrow u ae on ag ieaueta siae h oilcost social the estimates that literature large a joins paper Our atftr,i e eemnn fciaeplc:Alelse All policy: climate dis- of and determinant near-term key the a in is population, future, human tant the of size he | ∗ lmt change climate eas neooi hoyteotmlcarbon optimal the theory economic in Because . g,h,1 n ainWagner Fabian and , a,1,2 akB Budolfson B. Mark , d etrfrHmnVle,PictnUiest,Pictn J08544; NJ Princeton, University, Princeton Values, Human for Center | oilcs fcarbon of cost social f eateto ehncladArsaeEgneig rneo nvriy rneo,N 08544; NJ Princeton, University, Princeton Engineering, Aerospace and Mechanical of Department a,i,j b,1 | oilwelfare social rni Dennig Francis , h cnmc n lnigUi,Ida ttsia nttt,Dli ni,110016; India, Delhi, Institute, Statistical Indian Unit, Planning and Economics | b eateto hlspy nvriyo emn,Brigo,V 05405; VT Burlington, Vermont, of University , of Department c acFleurbaey Marc , 1073/pnas.1618308114/-/DCSupplemental eto 2 section hsatcecnan uprigifrainoln at online information supporting contains article This under 2 distributed is 1 article BY-NC-ND). (CC 4.0 access License NonCommercial-NoDerivatives open This Submission. Direct PNAS a is article D.S. This interest. and of M.B.B., conflict N.S., no declare N.S., and authors data; The research; analyzed performed F.W. F.W. and paper. and D.S., the R.H.S., D.S., wrote M.F., A.S., M.F., F.D., F.D., M.B.B., M.B.B., N.S., modeling; computer leading a population how explore (DICE2013), to model, 2013 climate–economy cost–benefit model Climate-Economy grated climate for population reduces overall. also desirable that and is how policy SCC purposes the because a determine importance whether will critical society establish by of valued (point is is change aspect population climate This of context (12). the above) in wellbeing of valuation a entetpco ag ieaue(,2 –1 ( 5–11) 2, (1, literature large a of topic the been has popula- future time. gas of through and wellbeing tions consistent greenhouse the a valuing be for with must approach There transparent links objective: social corresponding A peri- (ii) time emissions. their short relatively (4)—and over have even a ods rates—which estimate for growth to account difficult population proved future explicitly population: plausible must to of Analyses respect range pressure: with prerequisites Emissions two (i) has effort gation ∗ the led M.B.B. research; the designed D.S. and M.B.B., N.S., contributions: Author owo orsodnesol eadesd mi:[email protected]. Email: addressed. be should correspondence work. whom this To to equally contributed D.S. and M.B.B., N.S., r nraigyecmasdi h em“oilcs fcarbon.” of which quality, cost air “social externalities term to other the related address in those encompassed not as increasingly do such are We fuels, gas. carbon-based burning greenhouse with a associated as action its through climate eew r eern xlsvl oteipc fcro ixd msin nthe on emissions dioxide carbon of impact the to exclusively referring are we Here ohv mle ouaini epnet lmt change chooses. climate society goals to two response these of in which population on better depends smaller ultimately larger a is a it have than whether to rather that show smaller also a inter- We given dangerous population. avoid climate to the calcu- be with and would ference have it cheaper might much society how that late goals of different each given two pathway these reduction emissions to calculate (optimal) We rather best . or people’s the happy of are level who average the people be increase of should number on goal the ultimate increase importantly our to depends whether about answer to questions The relevant ethical policy. is growth change population climate future how investigate We Significance u ae drse hs usin yuigDnmcInte- Dynamic using by questions these addresses paper Our the and population between link the for true not is same The h ikbtenpplto n msin (point emissions and population between link The miti- optimal and SCC the estimating for framework Any j nentoa nttt o ple ytm nlss(IIASA), Analysis Systems Applied for Institute International ). a,d e nentoa eerhIsiuefrCiaeadSociety, and Climate for Institute Research International se Siebert Asher , . e oetH Socolow H. Robert , www.pnas.org/lookup/suppl/doi:10. raieCmosAttribution- Commons Creative NSEryEdition Early PNAS IAppendix , SI i g Andlinger Department c Yale–NUS i f , above) | f6 of 1 ii

ECONOMIC SUSTAINABILITY SCIENCES SCIENCE growth affects the SCC, optimal peak temperature, and mentation which Dasgupta (19) calls “dynamic average utilitar- mitigation costs under alternative approaches to valuing ianism” and which divides the discounted sum of total wellbeing. across time by the discounted sum of total population across The structure of this paper is as follows. In the Introduc- time. TU and AU each have theoretical advantages and disad- tion we introduce two population-sensitive approaches for valu- vantages which have been explored in the population ethics liter- ing human wellbeing (these approaches are known as “social ature (12, 19, 22), alongside other social objectives (23). AU and objectives”) and explain the treatment of population in cost– TU are formally introduced as SWFs in SI Appendix, section 1. benefit climate–economy models (CEMs). In Main Results: Pop- In leading CEMs, population size is an exogenous variable ulation Growth and the SCC, we use a range of recent pop- calibrated to a population projection at the time of the model ulation projections, together with the DICE model, to show parameterization (15, 18). Although it is clear that updated pop- that has a large effect on climate policy and ulation projections are one source of variation in results esti- the SCC. This is true, although quantitatively different, using mated by different model versions (15), systematic testing of either of the two most common approaches for comparing social alternative projections has not been standard in major assess- wellbeing across of different size, known as total ments (although see refs. 1 and 24 and more recently ref. 25); utilitarianism and average utilitarianism (described below). We even less research has focused on analyzing the importance then extend our analysis by quantifying what these differences of assumptions about how population is valued by society and imply in terms of mitigation expenditures, observing that smaller- what that means for climate policy. In stark contrast, there has population futures imply lower mitigation costs under either eth- been comprehensive treatment of the ethical parameters that ical approach, but on different timescales and with different con- determine the discount rate referred to above—the inequality clusions about the overall desirability of different population aversion parameter and the rate of time preference—with the pathways. choice of these parameters becoming one of the most prominent debates in climate economics (16, 26–29). The lack of attention Population and Wellbeing in CEMs to the social valuation of population growth is particularly sur- CEMs are the main tool for estimating the SCC and correspond- prising because population size, unlike the discounting parame- ing optimal mitigation trajectories. It is important to differenti- ters, is modifiable by policy intervention. ate CEMs from other types of “integrated assessment models.” Here CEMs refer specifically to cost–benefit models that eval- Main Results: Population Growth and the Social Cost of uate the impact on wellbeing of and associated Carbon Dioxide policy decisions and are the basis of estimates of the SCC. They In this section, we explore the consequences for the mitigation are distinct from other types of integrated assessment models policy of assuming different potential future populations and in that they quantify the economic damage from increased tem- social objectives (TU vs. AU). We take our population pro- peratures, alongside descriptions of the economy and emission jections from the 2015 revision of the United Nations (UN)- processes (13). Three widely used cost–benefit-type models are medium, -low, and -high population scenarios (30) as well as a DICE, Climate Framework for Uncertainty, Negotiation, and more extreme (“Ultralow”) case based on Basten et al. (31) (Fig. Distribution (FUND), and Policy Analysis of the Greenhouse 1A). Although we retain the names of the UN projections, we Effect (PAGE) (14). have modified them by extending them beyond 2100, when they The key tradeoff in these models is between mitigation and end (Materials and Methods). climate damages, where mitigation expenditures disproportion- Analyses were conducted using a variant of the 2013 version ately subtract from the wellbeing of people now, while damages of the DICE model (15), with results summarized as differences disproportionately subtract from the wellbeing of people in the in optimal global harmonized carbon prices (Fig. 1B) and future more distant future. The tradeoff is made in a way that maxi- temperature rise (Fig. 1C) over time. We depart from the official mizes the objective of the model, called a “social welfare func- DICE2013 model only by fixing the savings rate at 25.8%, which tion” (SWF). All leading optimization CEMs share a total utili- is consistent with other CEMs; this change has little impact on tarian (TU) SWF, according to which the (social) objective is to results, as shown in SI Appendix, Fig. S1 (ref. 32, Materials and maximize the sum of (discounted) wellbeing across time (15–18). Methods, and SI Appendix, section 2). Globally aggregated CEMs such as DICE calculate the wellbe- Each of these results is an optimal mitigation policy path under ing of the average individual for each point in time as the aver- DICE, meaning that mitigation effort maximizes intertemporal age consumption transformed into wellbeing by a utility function discounted wellbeing, according to the chosen social objective. which embodies “inequality aversion.” The TU objective is the Below, we also discuss results for the same population scenar- sum of the total wellbeing at each time point (average wellbe- ios but constrained by temperature targets. Unless otherwise ing multiplied by the population size), with each time point dis- noted, all model runs assume a 1.5% annual rate of time pref- counted to a present at a chosen rate of “time preference.” erence and a 1.45 inequality aversion parameter (representing Although not currently applied in CEMs, other non-TU social the diminishing marginal utility of consumption), the values cho- objectives are common in the population literature and could sen for DICE2013 by William Nordhaus, the architect of the be used instead (19). As a central example, an average utilitar- model. In SI Appendix a wider range of population projections ian (AU) social objective focuses on per capita wellbeing and are tested, based on the shared socioeconomic pathways (SSPs) ignores population size, unless population size influences aver- and the UN’s probabilistic scenarios (SI Appendix, Fig. S2), as age wellbeing. The average utilitarian concept has had multiple well as sensitivity to the assumption of a near-zero (0.1%) rate of implementations in the literature. The AU SWF that this paper time preference (SI Appendix, Fig. S3). SI Appendix also contains uses provides a clear contrast with TU and integrates straight- results based on a regionalized version of DICE, demonstrating forwardly with the discrete-time structure of DICE and other that our general findings are unaffected when region-specific dif- CEMs: It calculates levels of wellbeing from average consump- ferences in economic and climate variables, as well as population tion in each time period and then maximizes the sum of those estimates, are explicitly represented (SI Appendix, Fig. S4). multiplied by the discount factor, but not by the population. However, other implementations exist: Asheim (20), studying the Carbon Price. The solid lines in Fig. 1B present results with the value of population in the genuine savings criterion, uses a sim- standard TU social objective; dashed lines represent AU. The ilar AU as we do, while Arrow et al. (21) instead use an imple- results demonstrate that future population has a large effect on

2 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1618308114 Scovronick et al. Downloaded by guest on October 1, 2021 Downloaded by guest on October 1, 2021 rwhaeaaoos(e.3 .16and 136 p. population 3, and (ref. preference analogous time time are future of growth of roles wellbeing weighting average to The on added periods: put discounting weight linearly time exponential the be pure determine of would to rate growth the from population subtracted of Under or growth. rate population the exponential TU, constant of case thetical consumption economic objective. social in the in role role population’s runs path’s another model with population diagnostic (emissions) of one use mix the by that isolated two growth be These growth: can population population mechanisms future future of of effects emission effect the from weighting population the in of results Further 6 passes. section time like Appendix, more more emission as become in counterparts paths disparity TU price pop- their AU-optimal increasing in result, an a difference cause As increasing pressure. to the begins as the size diverge however, ulation run, do long aver- paths the the population In price is matters. because AU it that wellbeing population objective; of social future level the age the influence not of do size assumptions the to sitive popula- future wellbeing. of period role of weighting the the its to in in sensitive even growth is other, tion TU each because from diverge term, contrast, quickly since near In paths size. slowly, price population diverge by TU curves periods the time AU weight the not does that AU particular in pol- Note climate distinct optimal icy. the influences elucidate population lines) which (solid by mechanisms results TU the with compared S1). Table and the S5 Africa Fig. Appendix, sub-Saharan popula- (SI with future contributor countries, the greatest developing in of differences paths by projec- tion driven population principally alternative are across SCC tions the in that differences scenario—shows the population given SSP-inspired alternative an optimal an of as the never comparison well of scenario—as is UN-low comparison the disaggregated abatement with regionally UN-medium full A and objective. TU still, the lower scenario UN- Ultralow the far the under in stay prices later Carbon fos- century scenario. all UN-high a vs. with half low competitive price” than more are “backstop fuels)—occurs technologies assumed sil carbon an zero declines (representing which subsequently line at and 1B single peaks Fig. a path along in price carbon point each decarbonization)—the where Relatedly, (100% objective. TU abatement the the with full given compared and scenario UN-high 2025 population the in assuming UN-low higher 2050, 85% in over higher prices 120% carbon with (C policy, prices. optimal Optimal (B) scenario. Ultralow the and 2100) beyond extrapolations (with scenarios rise. ture population -low and -medium, 1. Fig. cvoike al. et Scovronick ABC ohl xli h ehns fti eut osdrahypo- a consider result, this of mechanism the explain help To insen- are paths price AU the term, near the in words, other In 1B Fig. in lines dashed The UN-high, (A) objectives. social AU and TU the under temperature and mitigation optimal on influences corresponding and scenarios Population eaaeteefcso h piie results optimized the on effects the separate rsn eut ne Uand AU under results present eto 1 ). section Appendix, SI SI ouainadcmaio ihteltrtr,see literature, 3 the section with increasing comparison with on falls and temperature detail population peak more optimal (For again TU that TU, effect. result under weighting the stronger population be is the to effect of This because rise sooner. temperature occur mitigation Fur- and full peak lower of population. causing population date future decarbonization), the of accelerates the (100% which increasing effect SCC, AU, the weighting and increases because TU additional AU, both under than an ther, TU is under more reduce there will temperature growth peak population that optimal prediction— display—is unambiguous results theoretically our AU the which under But temperature TU. peak under optimal and decrease or increase it. cally from suffer as people registers more damages when climate cost of social level greater a given true especially a is because This TU, 1C). under (Fig. optima higher-population the in Temperature. Peak Optimal has growth population of rate effect. weighting the about no AU, debated Under frequently preference. assumptions time the quantita- is to policy importance comparable carbon the tively optimal TU, for under pol- assumptions Therefore, population and 28). of economics values 26, climate of the (16, the rate literature bounds in icy A 1.5% common 1.5%. to preference to time 0.1% 0.1% for from from ranging preference preference time changing time pure to of similar is rate trajectories the population in these show between TU we S6 growth, percent- Fig. population 1.4 Appendix, a in SI between to difference comparable constant 2200 is point to scenarios age growth -low and population UN-high in the difference the Because petplce htrdc ouainsz ol edt large 9–11). 7, to 5, 2, lead (1, costs could mitigation climate size avoided through population benefits reduce that devel- policies human opment whether explored has literature prior important An TU Under AU Population and Smaller from Savings Cost Mitigation section. next the trajectory, in lower-population cost consider a mitigation we on which potential world less on the much bearing putting is direct from it savings a sen- AU has under social finding is but This the policy growth, so. population climate of future optimal choice to near-term sitive the TU, upon Under depends objective: policy near- mitigation influence trajectory term population future assumed the in ences nraigteftr ouaingot aecudtheoreti- could rate growth population future the Increasing n oiyipiaino hs eut sta hte differ- whether that is results these of implication policy One .) httedfeec notmlplc under policy optimal in difference the that tiigy eprtrsices less increase temperatures Strikingly, lblaeaetempera- average Global ) NSEryEdition Early PNAS IAppendix, SI | f6 of 3

ECONOMIC SUSTAINABILITY SCIENCES SCIENCE Accordingly, in this section we explore potential cost savings, that some feasible, noncoercive policies (e.g., education and in quantitative terms, of achieving the UN-low vs. UN-medium programs) to promote human development in scenario. These two population scenarios were chosen because the developing world—and thereby reduce fertility—can result our goal is to compare mitigation costs under the central popula- in avoided near-term climate mitigation costs more than large tion projection against the costs under a lower-population alter- enough to pay for the programs. native that may be roughly achievable given additional invest- In contrast, because population growth has a smaller effect on ments in human development. the near-term SCC under AU, policy to reduce future popula- To compute mitigation cost savings, we depart from the pre- tion growth entails almost no near-term mitigation cost savings. vious section, which presented optimal mitigation trajectories In fact, in the 2 ◦C case, the “savings” are slightly negative in without any constraint on the level of temperature increase, by the first few periods: Although the theoretical prediction is clear here estimating the mitigation costs (and savings) needed to that reducing population growth would reduce overall mitigation achieve targets of 2 ◦C and 3 ◦C under both population scenar- costs over the full future time horizon studied, there is no nec- ios. This allows us to compare costs when the level of temper- essary theoretical prediction about this numerical result in these ature rise is held constant, including at the 2 ◦C target that is few periods where mitigation costs are very low under either pop- often considered necessary to prevent dangerous climate change. ulation path. Reduced population growth would eventually offer (Note that a target of 2 ◦C and 3 ◦C requires greater than optimal mitigation cost savings under both TU and AU as reduced pop- mitigation effort in DICE—see Fig. 1C for optimal peak temper- ulation eventually causes reduced emissions pressure (Fig. 2). atures.) Further results in SI Appendix, Table S4 investigate the This result suggests a possible rethinking of the reason why effect of population growth on mitigation costs when mitigation reducing future population growth would reduce climate mitiga- effort is suboptimally low (i.e., results in higher peak tempera- tion costs. The standard argument is that a reduced population tures). would influence mitigation policy because a smaller population With the standard TU social objective, smaller population would have less emissions pressure. However, our AU and TU entails lower near-term carbon prices and therefore results in results have identical populations and associated emissions pres- near-term mitigation cost savings (Fig. 2 and SI Appendix, Tables sure. The large near-term difference in mitigation cost savings S2 and S3). Regional disaggregation for the 2 ◦C target shows with TU compared with AU therefore suggests another reason: that it is the wealthier regions—those most able to finance sus- For any given level of climate change, increasing the future pop- tainable development—that would save the most, in per capita ulation increases the social valuation of climate damages under terms (SI Appendix, Fig. S7). With AU, cost savings occur only in TU but not under AU. the more distant future. Results with additional population sce- narios, robustness checks, and an explanation of the cost savings Does Reducing Population Improve Overall Wellbeing? calculation are available in SI Appendix, section 2 and Tables S2 We have seen that mitigation cost savings arise, albeit on dif- and S3). ferent timescales, under both TU and AU. Does this imply that One debate within the climate literature is whether interven- both TU and AU recommend policies to reduce population size? tions that reduce population growth rates would be effective cli- Not necessarily, because mitigation cost savings are only one way mate policy, because a smaller future population will have lower population size influences wellbeing. Because AU and TU value emissions, all else equal. Our results have an implication for that population differently, the full valuation of a population path is debate: Under TU, a smaller future population implies near- a further question. term mitigation cost savings in the tens of billions of dollars Table 1 shows that the answer again depends on whether annually. As a result, we show in Fig. 2 and associated results AU or TU is chosen: In the context of our CEM, the sign of and discussion in SI Appendix, section 4 and Tables S2 and S3 recommended population policy depends on the treatment of

Fig. 2. Mitigation cost savings from moving from the UN-medium to the UN-low population path, under TU and AU social objectives, given 2 ◦C and 3 ◦C temperature targets. Cost savings stop when full abatement is reached in both population scenarios.

4 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1618308114 Scovronick et al. Downloaded by guest on October 1, 2021 Downloaded by guest on October 1, 2021 TU a aebe hw oiflec rehuegsemissions, globally gas offset largely greenhouse may 7). two influence 6, particu- the (2, to that in suggest shown pop- studies structure although of been age pressure valua- have and emissions the lar the on than However, principally rather ulation. focuses population pro- of paper com- into tion our population trans- technology from because away that and abstract position economy We stock, emissions. the capital and of duction size, subsumes model population DICE reduced-form lates Instead, a composition. into its these to related variables fully more discussed are there These objective, approach. social in the our in to population limitations of role are the of SCC the to in function objective investigation TU Further the CEMs. framework. for other AU SCC substituting the size consider within could population of placed investigation be an to eco- enables of and to approaches wellbeing integration alternative social systematic alongside variables a climatic for and nomic allows DICE respects. eral val- population social the different wellbeing. in of population of uation of role these impact the on Moreover, the dependent impacts projections policy. with immediate interact, mitigation often assumptions climate and projections large recommended alternative have on literature—and population future climate of the of wellbeing—common valuing outside to approaches alternative that show and advantages with 344), approach. p. each 10, of disadvantages pro- (ref. introduces issues” sizes theoretical com- population found general, different regard “in across in children, welfare having of discussed paring of quality have externalities and Wexler climate quantity and the the to O’Neill value depend- As to policy, lives. chooses future people’s climate society of of how number determinant the on result, ing important but a an As term is born. near yet people unknown not the an those including in future, of the number cost in people a benefit primarily incurs will mitigation change Climate Discussion in reduction the outweigh for size. to population population enough reduced large of are benefits wellbeing ben- other average mitigation DICE, climate in and entailed reductions efits only emissions but the growth, beyond population recom- if, lower could encourage objective that social a policies TU with mend A CEMs TU. all assume However, mitigation— objective climate objective. social of social form AU-like a an as requires reduced a be endorse popula- should literature—that growth and tion the reconstruct in to recommendation CEM policy common standard a For recognized: a wellbe- to in moving loss regards worse. TU much no as contrast, is population In smaller there people. foregone and from increased ing is because improvement, wellbeing slight a average UN- as from population moving UN-low regards to AU medium objective. social the in population % UN-low, % UN-medium, % UN-high, AU UN- at objective the Social TU, of and valuation AU of by percentage path paths a medium population as of policies, valuation climate Social optimal 1. Table cvoike al. et Scovronick is,DC osdr nytesz ftepplto,btnot but population, the of size the only considers DICE First, importance the highlight to is analysis our of goal the Because sev- in analyses previous extended has approach modeling Our to framework modeling DICE the used have results Our previously not literature climate the in tension a reveals This u ehglgttrehere. three highlight we but Appendix, SI 192.3 99.5 100 100 100.6 40.4 ecie ndti lehr 1,3) ute eal fteDC oe and in from model DICE presented the (currently downloaded are of details assumptions online as Further its 35). available (15, CEM elsewhere detail freely DICE2013 in described is the which of at website, version Nordhaus’s Excel William the use We Methods and Materials climate the within discussed widely yet climate community. not for change to is important it therefore population is although smaller choice policy, This a mitigation costs. and these recommend larger avoid damages would a climate of AU resulting lives climate expenditures, additional the to the over response value population policy would and TU population magnitude Where the appropriate change: both the determine of also can sign and influ- SCC choice This the policy. ences mitigation climate for consequences ful offers that policy fer- of fertility. on benefits reduce policy nonclimate to opportunities important climate considered any or not or change has tility climate model of our effects of broadly, context endogenous impor- More the the CEM. within on standard values focuses and a which projections study, population our they of of policy, tance 34). scope climate (33, for the important beyond change be are climate may of effects impacts such Although adverse to resilience affect disaggregation. to robust cli- are results and our that fertility gest robustness between Our (2). regions links in across checks uniform the not are that impacts mate indicates work vious edmntaeta u eut r usatvl dnia and identical substantively essentially are is results to our that realistic that savings In demonstrate more for decisions. we it policy appetite pre- climate S1 , find fixed and we Fig. we change a defensible, climate because has to are approach, insensitive frame- society approaches second DICE/RICE that both the a assume use Although in and PAGE; 32). fer and ref. opti- policy FUND see climate are to in work, respond examples is so not (leading (RICE)), example do Economy mization savings leading Climate that (a assumes Integrated poli- another savings DICE/Regional and and of planned damages versions their official climate adjust eco- the the to optimally that in forward and savings assumes look cies of endogenously approach treatments approach, agents One alternative this nomic two literature: explain are To modeling there 39). climate–economy that (32, objec- note function discounted first utility and we logarithmic time-separable optimal a a the an with with as specify savers interpreted tive We private be exception: of can one which rate with 25.8%, savings 3.2) of of rate sensitivity savings climate val- exogenous parameter a default as (including such unchanged ues, is model DICE2013 the ments, and continues 2100 projection between Ultralow zero The to thereafter. 2100. linearly constant past rate tapers through remains growth 2100 estimates population and in the 2195 population ending that of timestep assume the we range 2100 in a beyond 38). provide project Popula- 37, To sources World (30, 2100. project UN’s these Pathway tra- the thus of Socioeconomic of population and Shared Both revision the of prices 2015 and variety carbon Prospects the tion a on optimal primarily specifying compute based we exogenously jectories, study, by this paths In mitigation out- DICE. uniform of in percentage globally able a a sur- of through global loss occur the the (mitigation) price. as carbon with reductions incurred through Emission increase are economy put. which and production future 36), temperature Cobb–Douglas the face (24, a affect damages as via emissions well climate-related unmitigated, output as influ- If influencing output, Population function. by to exogenous. is emissions emissions intensity of Economic prod- ences carbon ratio domestic linked. policies; gross time-varying control are of a emission function that and a (output) component are uct which geophysical emissions, a produces activity and economic an spreadsheet in model result. optimizable every interactive, for the form and data all present also h hieo nA s Usca betv a meaning- has objective social TU vs. AU an of choice The also may growth population affecting policies some Third, pre- paths, population exogenous study we although Second, te hntecagst ouainta r seta oorexperi- our to essential are that population to changes the than Other vari- exogenous an is population the of size the CEMs, leading all Like includes that model optimization (single-region) global a is DICE Briefly, n a been has and aida.econ.yale.edu/∼nordhaus/homepage/documents/) sn einlzdvrino IE sug- DICE, of version regionalized a using Appendix, SI In . 2 section Appendix , SI NSEryEdition Early PNAS aae S1 Dataset IAppendix, SI | f6 of 5 we

ECONOMIC SUSTAINABILITY SCIENCES SCIENCE quantitatively very similar if we instead endogenize optimal savings as Please see SI Appendix, sections 1 and 2 for additional specifics of Materi- in DICE. als and Methods, which includes details on TU and AU, information on how In a small subset of our modeling runs, we also make an additional cost savings were calculated, and a description of the regionalized model- change to DICE to investigate our research questions. To generate one result ing that underlies the results presented in SI Appendix, Fig. S7 and Table S1 reported in the main text, we change the rate of time preference to the (40–47). value that would make the optimal mitigation trajectory of the UN-low population scenario provide the closest fit to that of the UN-high scenario ACKNOWLEDGMENTS. We thank Ciara Burnham and Bert Kerstetter for (SI Appendix, section 2 and Fig. S6). To generate results for the runs maxi- support and also Princeton University’s Climate Futures Initiative, Woodrow Wilson School, Center for Human Values, Institute for International and mizing AU, we altered the social objective accordingly (SI Appendix, Eq. S2). Regional Studies, Andlinger Center for Energy and the Environment, and For Fig. 2, we maximize the social objective (AU or TU) subject to the con- the Princeton Environment Institute. We are grateful for comments from ◦ straint that temperature must never rise above the target in the 2 C and David Anthoff, Robert Kopp, Aurelie´ Mejean,´ and Stephane´ Zuber. We also 3 ◦C cases. thank two anonymous reviewers for their many helpful suggestions.

1. Gaffin SR, O’Neill BC (1997) Population and global warming with and without CO2 American Association for the Advancement of Science (Philadelphia), Vol 15. Avail- targets. Popul Environ 18:389–413. able at www.econ.yale.edu/∼nordhaus/homepage/pop0298.htm. Accessed October 2. O’Neill BC, et al. (2010) Global demographic trends and future carbon emissions. Proc 16, 2017. Natl Acad Sci USA 107:17521–17526. 25. Gillingham K, et al. (2015) Modeling Uncertainty in Climate Change: A Multi-Model 3. O’Neill BC, MacKellar FL, Lutz W (2005) Population and Climate Change (Cambridge Comparison (Cowles, New Haven, CT), Discussion Paper 2022. Univ Press, Cambridge, UK). 26. Nordhaus WD (2007) A review of the ‘Stern review on the economics of climate 4. Lam D (2011) How the world survived : Lessons from 50 years of change’. J Econ Lit 45:686–702. extraordinary demographic history. Demography 48:1231–1262. 27. Dasgupta P (2007) The Stern review’s economics of climate change. Natl Inst Econ Rev 5. Casey G, Galor O (2017) Is faster economic growth compatible with reductions 199:4–7. in carbon emissions? The role of diminished population growth. Environ Res Lett 28. Weitzman ML (2007) A review of the Stern review on the economics of climate 12:014003. change. J Econ Lit 45:703–724. 6. Liddle B (2011) Consumption-driven environmental impact and age structure 29. Adler M, et al. (2017) Priority for the worse-off and the social cost of carbon. Nat Clim change in OECD countries: A cointegration-STIRPAT analysis. Demogr Res 24:749– Change 7:443–449. 770. 30. Population Division (2015) Prospects: 2015 Revision (United Nations 7. O’Neill BC, et al. (2012) Demographic change and carbon dioxide emissions. Lancet Department of Economic and Social Affairs New York). 380:157–164. 31. Basten S, Lutz W, Scherbov S (2013) Very long range global population sce- 8. Spears D (2015) Smaller human population in 2100 could importantly reduce the risk narios to 2300 and the implications of sustained low fertility. Demogr Res 28: of climate catastrophe. Proc Natl Acad Sci USA 112:E2270. 1145–1166. 9. O’Neill BC (2000) Cairo and climate change: A win-win opportunity. Glob Environ 32. Dennig F, Budolfson MB, Fleurbaey M, Siebert A, Socolow RH (2015) Inequality, Change 10:93–96. climate impacts on the future poor, and carbon prices. Proc Natl Acad Sci USA 10. O’Neill BC, Wexler L (2000) The greenhouse externality to childbearing: A sensitivity 112:15827–15832. analysis. Clim Change 47:283–324. 33. Lutz W, Muttarak R, Striessnig E (2014) Universal education is key to enhanced climate 11. Wheeler D, Hammer D (2010) The economics of population policy for carbon adaptation. Science 346:1061–1062. emissions reduction in developing countries (Center for Global Development, 34. Lutz W, Striessnig E (2015) Demographic aspects of climate change mitigation and Washington, DC), Available at https://www.cgdev.org/files/1424557 file Wheeler adaptation. Popul Stud 69:S69–S76. Hammer Economics Pop Policy.pdf. Accessed October 16, 2017. 35. Nordhaus WD, Boyer J (2000) Warming the World: Economic Models of Global Warm- 12. Broome J (2012) Climate Matters: Ethics in a Warming World (Norton, ing (MIT Press, Cambridge, MA). New York). 36. Tol RS (2009) The economic effects of climate change. J Econ Perspect 23: 13. Weyant J (2017) Some contributions of integrated assessment models of global cli- 29–51. mate change. Rev Environ Econ Policy 11:115–137. 37. Lutz W, Butz WP, Samir KC, eds (2014) World Population and Human Capital in the 14. Interagency Working Group on Social Cost of Carbon (2013) Technical Update of the Twenty-First Century (Oxford Univ Press, Oxford). Social Cost of Carbon for Regulatory Impact Analysis Under Executive Order 12866 38. Gerland P, et al. (2014) World population stabilization unlikely this century. Science (US Government, Washington, DC). 346:234–237. 15. Nordhaus W, Sztorc P (2013) DICE 2013R: Introduction and User’s Man- 39. Golosov M, Hassler J, Krusell P, Tsyvinski A (2014) Optimal taxes on fossil fuel in gen- ual. Available at aida.econ.yale.edu/∼nordhaus/homepage/documents/DICE Manual eral equilibrium. Econometrica 82:41–88. 103113r2 000.pdf. Accessed October 11, 2017. 40. Connelly MJ (2008) Fatal Misconception (Harvard Univ Press, Cambridge, MA). 16. Stern N (2006) Stern Review: The Economics of Climate Change (Her Majesty’s Trea- 41. Nordhaus WD (2008) A Question of Balance: Weighing the Options on Global Warm- sury, London), Vol 30. ing Policies (Yale Univ Press, New Haven, CT). 17. Hope C (2011) The social cost of CO2 from the PAGE09 model (Cambridge Judge 42. Abel GJ, Barakat B, Samir K, Lutz W (2016) Meeting the Business School, Cambridge, UK), Discussion Paper 2011–39. goals leads to lower world population growth. Proc Natl Acad Sci USA 113:14294– 18. Anthoff D (2009) Equity and climate change: Applications of FUND. PhD thesis (Max 14299. Planck Institute, Hamburg, Germany). 43. Singh S, Darroch JE, Ashford LS (2014) Adding It Up: The Costs and Benefits of Invest- 19. Dasgupta P (2001) Human Well-Being and the Natural Environment (Oxford Univ ing in Sexual and Reproductive Health. Available at https://www.guttmacher.org/ Press, Oxford). sites/default/files/report pdf/addingitup2014.pdf. Accessed October 11, 2017. 20. Asheim GB (2004) Green national accounting with a changing population. Econ The- 44. Education for All (2015) Global Monitoring Report: Pricing the Right to Education ory 23:601–619. (UNESCO, Paris). 21. Arrow KJ, Dasgupta P, Maler¨ KG (2003) The genuine savings criterion and the value 45. Cleland J, Conde-Agudelo A, Peterson H, Ross J, Tsui A (2012) Contraception and of population. Econ Theory 21:217–225. health. Lancet 380:149–156. 22. Spears D (2017) Making people happy or making happy people? Questionnaire- 46. Bradley S, Croft TN, Fishel JD, Westoff CF (2012) Revising Unmet Need for Family experimental studies of population ethics and policy. Soc Choice Welfare 49:145– Planning (DHS Analytical Studies, Calverton, MD), Report 25. 169. 47. Bongaarts J, Cleland J, Townsend JW, Bertrand JT, Gupta MD (2012) Family Planning 23. Arrhenius G Population Ethics (Oxford Univ Press, New York), in press. Programs for the 21st Century: Rationale and Design (Population Council) Available at 24. Nordhaus W, Boyer J (1998) What are the external costs of more rapid population https://www.popcouncil.org/uploads/pdfs/2012 FPfor21stCentury.pdf. Accessed Octo- growth? Theoretical issues and empirical estimates. 150th Anniversary Meeting of the ber 11, 2017.

6 of 6 | www.pnas.org/cgi/doi/10.1073/pnas.1618308114 Scovronick et al. Downloaded by guest on October 1, 2021