1、EQUITY?RESEARCH|April?,2024|?0:?PM EDTGoldman Sachs does and seeks to do business with companies covered in its research reports.As a result,investors should be aware that the firm may have a conflict of interest that could affect the objectivity of this report.Investors should consider this report

2、as only a single factor in making their investment decision.For Reg AC certification and other important disclosures,see the Disclosure Appendix,or go to employed by non-US affiliates are not registered/qualified as research analysts with FINRA in the U.S.The Goldman Sachs Group,Inc.Brian Singer,CFA

3、+1 212 902-8259 Goldman Sachs&Co.LLCAI/data centers global power surge and the Sustainability impactFor years,power demand from data centers was flat despite a near tripling in the?data workload.Now with the pace of power e?ciencies decelerating and AI demand building,our updated analysis implies?da

4、ta center power demand is poised to grow 160%by the end of the decade,which should?drive a signi?cant acceleration to a level of electricity growth in the US and Europe not seen in a generation.We believe?this should?enhance investment opportunities across the power supply chain bene?ting from Green

5、 Capex,?volume growth and innovation.We continue to see a?myriad of potential bene?ts from AI towards advancing Sustainable goals;our expectations for a?more than doubling of data center carbon dioxide emissions?in 2030 vs.2022?will likely focus Sustainable investors on measuring how AIs bene?ts can

6、 o?set the expected?increase in data center-related emissions.Derek R.Bingham+1 415 249-7435 Goldman Sachs&Co.LLCBrendan Corbett+1 415 249-7440 Goldman Sachs&Co.LLCCarly Davenport +1 212 357-1914 Goldman Sachs&Co.LLCAlberto Gandol?+39 02 8022-0157 alberto.gandol? Goldman Sachs Bank Europe SE-Milan b

7、ranchSee full list of authors inside 495ec6c41ca54c4b8bcddff421724db1NoteNote:The following is a redacted version of the original report published April 28,2024 37 pages.AuthorsBrian Singer,CFA+1 212 902-Goldman Sachs&Co.LLCCarly Davenport+1 212 357-Goldman Sachs&Co.LLCAllen Chang+852 2978-Goldman S

8、achs(Asia)L.L.C.Evan Tylenda,CFA+44 20 7774-Goldman Sachs InternationalNeil Mehta+1 212 357-Goldman Sachs&Co.LLCJoe Ritchie+1 212 357-Goldman Sachs&Co.LLCJerry Revich,CFA+1 212 902-Goldman Sachs&Co.LLCJacqueline Du+86 21 2401-Goldman Sachs(China)SecuritiesCompany LimitedJohn Miller+1 646 446-Goldman

9、 Sachs&Co.LLCDerek R.Bingham+1 415 249-Goldman Sachs&Co.LLCAlberto Gandolfi+39 02 8022-Goldman Sachs Bank Europe SE-Milan branchTimothy Zhao+852 2978-Goldman Sachs(Asia)L.L.C.Emma Jones+61 2 9320-Goldman Sachs Australia Pty LtdBrian Lee,CFA+1 917 343-Goldman Sachs&Co.LLCApoorva Bahadur+91 22 6616-Go

10、ldman Sachs India SPLRyo Harada+81 3 6437-Goldman Sachs Japan Co.,Ltd.Grace Chen+44 20 7774-Goldman Sachs InternationalAti Modak+1 212 902-Goldman Sachs&Co.LLCBrendan Corbett+1 415 249-Goldman Sachs&Co.LLCToshiya Hari+1 646 446-Goldman Sachs&Co.LLCVarsha Venugopal+1 415 393-Goldman Sachs&Co.LLCMadel

11、ine Meyer+44 20 7774-Goldman Sachs InternationalDaniela Costa+44 20 7774-Goldman Sachs InternationalMark Delaney,CFA+1 212 357-Goldman Sachs&Co.LLCChristian Hinderaker,CFA+44 20 7774-Goldman Sachs InternationalXavier Zhang+852 2978-Goldman Sachs(Asia)L.L.C.495ec6c41c1c1 a5a5a54c4b8bcddff42172 d4d4db

12、1AI/data centers impact on power,emissions,Sustainable Investing,stocks:Six key takeawaysQuantifying the AI and broader data center power surgeData center emissions:Potential to double by 2030 AIs Sustainability opportunities:Where to watch and how to measure 28 April 2024 2Goldman SachsGS SUSTAIN:G

13、enerat ional Growt hTable of Cont ent s AI/dat a cent ers impact on power,emissions,Sust ainable Invest ing,st ocks:Six key t akeaways We believe data demand driven in part by AI and in part from deceleration in efficiency gains will catalyze generational growth in global power demand.Our updated an

14、alysis(discussed within)suggests a 160%increase in data center power demand by decade end,with data centers rising to 3%-4%from 1%-2%of overall global power demand(to 8%from 3%in the US).We continue to see a myriad of potential benefits from AI towards advancing Sustainable goals and believe semicon

15、ductors contribution to avoided emissions and decarbonization,infrastructure and clean water goals warrant consideration for thematic investment.We see favorable tailwinds for the Green Capex-exposed stocks.At the same time,our analysis suggests a more than doubling in data center carbon dioxide emi

16、ssions in 2030 vs.2022(the increase representing about 0.6%of energy emissions),which will likely focus Sustainable investors on measuring how AIs benefits can offset the expected increase in data center emissions.Whats inside.In our report we update our outlook for data center power demand/emission

17、s(initially in our Greenablers:The critical role of Semiconductors towards a sustainable future report),discuss implications for opportunities and risks across the supply chain,estimate what value and equivalent innovations would be needed to offset the expected emissions increase,and update our out

18、look for data center avoided emissions.Six key takeaways from our analysis(1)Power demandData center power demand likely to rise 160%by the end of the decade vs.2023 from 1%-2%to 3%-4%of overall global power demand by 2030.If all of the projected data center electricity demand growth was concentrate

19、d into a new country,it would be among the top 10 power consuming countries.Our US Utilities Research team sees US power demand growth accelerating to a 2.7%5-year CAGR by 2030 vs.0%for the past 10 years,with data centers driving 0.9%CAGR increase and representing 8%of US power demand by 2030(vs.3%i

20、n 2022).In Europe,our Utilities Research team sees EU-27 power demand accelerating to a 3.7%5-year CAGR by 2030 from 0%for the past decade.(2)EmissionsData center power demand growth is on track to drive a more than 100%increase(about 215-220 mn tons)in data center carbon dioxide emissions by 2030 v

21、s.2022 per our updated analysis,the increase representing about 0.6%of global energy emissions.This assumes data centers fund renewable PP As for around 30%of total needs in the coming years and that natural gas fills the bulk of power generation on the28 April 2024 3Goldman SachsGS SUSTAIN:Generat

22、ional Growt h495ec6c41ca54c4b8bcddff421724db1margin.(3)Data center efficienciesData centers ex-AI have continued to become less power intensive,but the pace of efficiency gains appears to have considerably slowed.Relative to 2015 baseline,we see avoided carbon dioxide emissions from data centers of

23、1,000-2,000 million tons in 2030 in our updated analysis.Notably,much of the avoided emissions were based on 2015-19 efficiency gains.Demand growth is currently more than offsetting power efficiency,driving higher expected emissions.A re-acceleration of efficiencies to levels greater than our base c

24、ase could mitigate the emissions growth we forecast,and we note corporate optimism that this can be done.(4)Valuing AIs Sustainable benefits&costsWe continue to see potential AI benefits via speeding up drug/vaccine/therapeutic discoveries,improving crop yields,and increasing energy efficiency among

25、 other potential innovations,though with uncertainty on the magnitude and timing.The AI power surge and emissions increase is likely to prompt greater interest among Sustainable investors to quantify AI value added(similar to avoided emissions frameworks)vs.the rising emissions from AI power consump

26、tion.We estimate a present value of about$125-$140 bn social cost from the data center carbon dioxide emissions growth we forecast(AI+non-AI)in 2024-30,which could act as a benchmark for measuring offsetting benefits.(5)Sustainable Investing outlook implicationsAI power demand growth and rising data

27、 center emissions add to broader Sustainable Investing uncertainties in 2024(interest rates,elections,actual vs.needed investment towards sustainable goals)that we believe support investments in companies that benefit simultaneously from investment and underinvestment towards Decarbonization and Ada

28、ptation.Our AI and data center analysis supports owning stocks levered to overlapping themes among decarbonization,Adaptation,Circular Economy,Biodiversity and Affordability/Accessibility.(6)StocksWe see potential beneficiaries among Utilities driven by volume growth(complementing investment towards

29、 Adaptation/grid hardening and fleet transformation towards renewables),renewables providers as we see acceleration in renewable power purchase agreements(PPAs)by technology companies,energy efficiency enablers and infrastructure contractors.28 April 2024 4Goldman SachsGS SUSTAIN:Generat ional Growt

30、 h495ec6c41ca54c4b8bcddff421724db1Exhibit 1:As efficiency gains have decelerated,data demand growth is driving a surge in data center power use,with an AI kicker on the way Dat a cent er elect ricit y consumpt ion,TWh(LHS)and power efficiency gains ex-AI,%(RHS)?Sour ce:Masanet et al.(2020),IEA,Cisco

31、,Gol dman Sachs Gl obal Invest ment Resear ch28 April 2024 5Goldman SachsGS SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8bcddff421724db1Exhibit 2:Generational Growth:Our Utilities Research teams now expect USA and EU-27 electricity consumption accelerating through the end of the decade to levels no

32、t seen in 20+years 5-yr CAGR for US and European elect ricit y demand;forecast s from our US and Europe Ut ilit ies Research t eams?Sour ce:EIA,EMBER,Gol dman Sachs Gl obal Invest ment Resear chQuant ifying t he AI and broader dat a cent er power surge The combination of AI,ex-AI increases in data d

33、emand and a material slowdown in power efficiency gains is making data centers a critical driver of accelerating global and US electricity demand growth.We assume data center power demand excluding cryptocurrency will grow by 160%in 2030 vs.2023 levels(bear/bull range of 80%-240%growth),representing

34、 an increase of about 650 TWh by 2030 in our base case(330/1,000 TWh in our bear/bull cases).Our US Utilities Research team estimates data centers alone will contribute a 0.9%CAGR to overall US power demand,bringing the total expected CAGR to 2.4%through 2030.We see data centers adding a 0.3%CAGR to

35、 overall global power demand.Our base case implies data center power demand moves from 1%-2%of overall global power demand to 3%-4%by 2030.The increase in the US is even greater from 3%to 8%.Our estimates for overall data center power demand are above IEA forecasts(2026),and our outlook for AI to re

36、present about 19%of data center power demand in 2028 is above recent corporate forecast.Exhibit 5:After being flat for 2015-19,we have seen data center power demand accelerate in 2021-23 and expect a 160%increase through the rest of the decade Global dat a cent er elect ricit y consumpt ion,TWh;incl

37、udes AI and excludes crypt ocurrency?Sour ce:Masanet et al.(2020),Cisco,IEA,Gol dman Sachs Gl obal Invest ment Resear ch28 April 2024 6Goldman SachsGS SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8bcddff421724db1Data demand has been growing,but until recently power demand from data centers was flatt

38、ish.Between 2015 and 2019,data center workload demand nearly tripled while data center power demand was relatively flat.There were two main drivers of this:Power consumption efficiency gains(i.e.,pace of lower power intensity)withinncloud and hyperscale data centers.Annual efficiency gains averaged

39、about 15%.Mix shift towards cloud and hyperscale data centers which have materially lowernpower consumption intensity than traditional data centers.By 2020 more than 90%of data center workload demand was at cloud/hyperscale centers.We estimate in 2020 data center power demand was about 240 TWh,based

40、 on data from the IEA,Cisco and academic sources.Exhibit 6:We see 2030 power use from data centers 1.8x-3.4x 2023 levels in our bear/bull case Elect ricit y demand from dat a cent ers in TWh,base case,bear case and bull case?Sour ce:Masanet et.al (2020),Cisco,IEA,Gol dman Sachs Gl obal Invest ment R

41、esear ch28 April 2024 7Goldman SachsGS SUSTAIN:Generat ional Growt hHowever,in the past three years,power consumption from data centers has been on the rise.With much of the mix shift already done,the gains from the shift to cloud/hyperscale have narrowed.Additionally,we have seen efficiency gains a

42、ppear to wane within cloud/hyperscale data centers(reported data to calculate efficiencies is not consistently available annually and as such for some historical years are estimated or implied).The IEA estimates 2022 data center power consumption was about 350 TWh,excluding contribution from cryptoc

43、urrency;all else equal this implies cloud/hyperscale annual efficiency gains decelerated to about 1%-2%in 2020-22.Exhibit 7:Data center workload demand nearly tripled between 2015-2019 but electricity consumption from data centers was flat Dat a cent er workload demand(RHS)in million comput e inst a

44、nces;dat a cent er power demand(LHS)in TWh?Sour ce:Masanet eet al.(2020),Cisco,IEA,Gol dman Sachs Gl obal Invest ment Resear ch28 April 2024 8Goldman SachsGS SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8bcddff421724db1Three key assumptions will help drive data center power demand forecast.Data cons

45、umption outlook both AI and non-AInPower efficiency gainsnPotential infrastructure constraintsnAI to accelerate power demand growth We assume power demand from AI rises about 200 TWh in 2024-30(bear/bull case of 110-330 TWh),with AI representing about 20%of overall data center power demand by 2030 i

46、n our base case.We see a wide range in our bear/bull scenario driven by uncertainty over demand and power efficiency.As demand for AI training grows in the medium term and for inference longer term,we see demand growth well exceeding the efficiency improvements that are leading to meaningful reducti

47、ons in high power AI server power intensity.We note that a ChatGPT search consumes around 6x-10 x the power as a traditional Google search(see Exhibit 9).Exhibit 8:Data center efficiency gains and the shift to cloud/hyperscale have been critical drivers of the moderate increase in data center power

48、demand,but decelerating efficiency gains have helped to drive a pickup in power demand from data centers in recent years Dat a cent er power int ensit y(LHS)in KWh per comput e inst ance;share of cloud/hyperscale dat a cent ers(RHS)as%of workload?Sour ce:Masanet et al.(2020),Cisco,IEA,Gol dman Sachs

49、 Gl obal Invest ment Resear ch28 April 2024 9Goldman SachsGS SUSTAIN:Generat ional Growt hWe have seen new AI innovations increase both computing speed and max power consumption per server but increase computing speed at a much greater pace,representing meaningful reduction in power intensity.As dem

50、and for GPUs grows,there is still notable company-projected intensity reductions.As an example of how innovations have reduced power intensity per server but increased overall power per server:The NVIDIA DGX A100 system is listed to net 5 petaFLOPS and consuming 6.5 kWnmax,or 1.30 kW per pFLOPS.The

51、more recent NVIDIA DGX H100 system is listed at 32 petaFLOPS andnconsuming 10.2 kW max,or 0.32 kW per pFLOPS.The new generation NVIDIA DGX B200 system using the new Blackwell chips isnlisted to net 72 petaFLOPS(training)and consuming 14.3 kW max,or 0.20 kW perpFLOPS.Exhibit 9:ChatGPT queries are 6x-

52、10 x as power intensive as traditional Google searches Power consumpt ion per query/search(Wh)?Sour ce:Googl e,SemiAnal ysisExhibit 10:We have seen new AI innovations increase max power consumption per server but increase computing speed per server by an even greater level,representing meaningful re

53、duction in power intensity Recent evolut ion of NVIDIA server syst em specificat ions are indicat ive of increasing max power per server but wit h lower power int ensit y relat ive t ocomput ing speed(for t raining)Max power required(kW)Compute speed(petaFLOPS)56.5(1.30 kW/pFLOPS)10.2(0.32 kW/pFLOPS

54、)3214.3(0.20 kW/pFLOPS)72NVIDIA DGX B2002x the power15x the compute speedNVIDIA DGX H1001.5x the power7x the compute speedNVIDIA DGX A1001 petaFLOPS=One quadrillion(one thousand trillion)operations per second Sour ce:NVIDIA,Gol dman Sachs Gl obal Invest ment Resear ch28 April 2024 10Goldman SachsGS

55、SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8bcddff421724db1Our global TMT team expects AI server units shipped to grow at a 76%CAGR in 2024-26,representing a 50%CAGR in server revenues;whether demand,budgets or neither is the constraint to growth is critical for energy use forecasting.A key questi

56、on impacting compute demand is whether that demand is pent-up(ie available new servers will be bought regardless of budget),not pent-up and constrained by demand itself,or constrained by customer budgets.In other words,will customers buy equal amounts of the more powerful servers as they would the l

57、ess powerful ones?If in a scenario in which a customer initially desires to buy 10 AI servers,and a new generation is announced with 10 x the compute speed for 5x the price,will the customer:Buy the same number of servers(10),spend more money(5x)and substantially1.increase its compute speed as a res

58、ult of the innovation(10 x)?This would representno constraints(i.e.,unlimited budgets,so demand dictated by available supply).Buy 1/10 the number of servers(1),maintaining the same level of compute power2.as prior to the innovation and spending less money(0.5x)?This would representdemand as the cons

59、traint.Buy 1/5 the number of servers(2),thereby maintaining budget allocated for server3.purchases but get ting a greater compute speed(2x)?This would represent budgetas the constraint.Confidence in the above question is key to whether forecasting methodology should be weighted towards server-based(

60、requiring nuance given varied and dynamic power consumption intensity and compute intensity)vs.demand based(forecasting compute power and power intensity per unit of compute power).New generation AI servers consume more power and provide more compute speed,even as the power intensity has fallen mean

61、ingfully.There could be meaningful upside to our base case if appetite for purchase and utilization of servers is unconstrained.There could be downside to our Exhibit 11:Extent of pent-up demand for AI server supply and voraciousness of technology capex budgets will be critical for pace of AI power

62、consumption Indicat ive scenario analysis of how demand vs.budget const raint s could impact AI comput e speed and power use Assumes power gener at ion,t r ansmission and int er connect ion ar e not a const r aint for indicat ive pur poses Sour ce:Gol dman Sachs Gl obal Invest ment Resear ch28 April

63、 2024 11Goldman SachsGS SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8bcddff421724db1base case if power efficiency is higher than expected or if power/compute speed efficiencies lead to fewer servers purchased than expected.We note corporate optimism that AI innovations can drive significant efficie

64、ncies to ultimately mitigate power demand and emissions growth.We have applied both a server supply driven forecast and a compute speed demand driven forecast,with a heavier weight applied towards the supply-driven methodology.On the server supply-driven side,we apply our global Technology teams for

65、ecastnfor server shipments per year mentioned above,divided among high energy vs.lowenergy servers.We assume server replacement every 5 years.Our TMT team seesa sharp increase in high power servers(greater electricity consumption per server,higher ASP)increasing sharply in the next few years to meet

66、 demand for AI training,while the team sees greater weighting of growth from lower power servers(lowerelectricity consumption per server,lower ASP)towards the back of the decade as AIdemand weighting shifts more towards inference.We assume 5%-8%annual powerefficiency gains per year to reflect expect

67、ed future server innovation relative todemand-based energy intensities where saw higher annual efficiencies early in lifecycle,we assume slower pace in a supply-based approach to reflect timing ofadoption.The result is falling power intensity per computing speed and sharpincreases in overall power d

68、emand due to the growth in new servers with higherweighting within higher power servers towards the newer generation that asmentioned have lower power intensity but higher power consumption per server.On the demand-driven side,we use our China Media,Internet&Telecom teams AIncompute power forecast t

69、o drive global AI compute demand outlook.We assumepower efficiency gains of 8%-15%annually in our base case through the end of thedecade this reflects similar efficiency gains as seen ex-AI in 2015-20.Together,this implies sharp increase in electricity use from AI,even as revenues from servers shipp

70、ed are implied flattish in 2027-30.Our global TMT teams server shipment and ASP forecasts imply flat tish revenues in 2027-30,driven by greater weighting towards lower power we see a greater mix shift towards lower cost lower power servers within the mix,in addition to expectations for ASP declines.

71、However,with the mix shift and ASP reductions offset by increased volumes and with the overall number of servers in operation continuing to grow,we see sharp demand growth from AI for electricity even despite expected efficiencies.Based on the framework and assumptions described above,we see AI powe

72、r demand moving up by around 200 TWh in 2030 vs.2023.28 April 2024 12Goldman SachsGS SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8bcddff421724db1Data center power demand ex-AI We expect continued strong growth in workload unrelated to AI.We assume double digit growth in workload compute instances w

73、ith deceleration from the 20%-25%of compute instances growth seen since 2017 down to 12%-18%annual growth in 2026-30,owing to combination of maturity and expected overlap with demand via AI.A key question on ex-AI demand is whether AI modeling and user demand for AI will partially replace users need

74、 for searches or other workload.We are broadly assuming minimal replacement i.e.,AI is largely incremental to the broader trend of data center workload demand.Power intensity efficiency gains among cloud/hyperscale data centers have decelerated;we assume modest re-acceleration in our base case.We as

75、sume power efficiency gains which as mentioned averaged around 15%annually in 2015-19 but decelerated to around low single digits in 2020-22 will remain relatively low.However,we assume a slight re-acceleration to an average of 3%in our base case in 2024-30,as industry discussions suggest continued

76、efforts towards innovation efficiencies,especially around the power intensity given the prospects for significant power needs ahead.The implications of the efficiency gains assumption leads to a wide range between our bear and bull cases for ex-AI data center demand growth.Based on the above framewo

77、rk and assumptions,we see about 450 TWh of ex-AI power demand growth in 2030 vs 2023 in our base case vs.225/650 TWh in our bear/bull cases.As we have highlighted earlier,we believe the efficiency assumption is critical driver of power demand outlook ex-AI our bear/bull cases assume 7%/0%annual inte

78、nsity reductions on average in 2024-30.Exhibit 12:AI servers in operation expected to grow sharply through 2030 even as revenues from AI server shipments flattens in 2027-30 AI servers in operat ion and implied revenues from our global TMT t eam forecast s Exhibit 13:We see AI power demand growing r

79、apidly even as power use per AI server falls later in the decade due to mix shift and expected efficiencies AI power use,TWh(LHS);max power use per AI server,KW(RHS)?Sour ce:Gol dman Sachs Gl obal Invest ment Resear chSour ce:Gol dman Sachs Gl obal Invest ment Resear ch28 April 2024 13Goldman SachsG

80、S SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8bcddff421724db1Exhibit 14:We expect power efficiency gains at cloud/hyperscale data centers to continue,but remain at a lower pace going forward relative to 2015-20 3-year rolling average%change in cloud/hyperscale KWh per comput e inst ance?Sour ce:Ma

81、sanet et al.(2020),Cisco,IEA,Gol dman Sachs Gl obal Invest ment Resear ch28 April 2024 14Goldman SachsGS SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8bcddff421724db1Dat a cent er emissions:Pot ent ial t o double by 2030 Incremental demand for power from data centers in the absence of PPAs would oth

82、erwise lead to a 150%increase in data center carbon dioxide emissions in 2030 vs.2023 per our analysis.The sharp rise in power demand from both AI and non-AI demand at data centers is likely to lead to a sharp increase in carbon dioxide emissions.Before considering power purchase agreements(PPAs)to

83、underwrite new renewables capacity,we would otherwise expect emissions from data centers to rise 150%by 2030 vs.2023 levels based on increased electricity demand highlighted earlier which we assume is predominantly met with thermal power plants on the margin.Technology companies likely to accelerate

84、 signing of renewables PPAs a positive for renewable generation developers.Technology companies have historically been very active in both pursuing technologies that can drive energy efficiency and in signing power purchase agreements(PPAs)to underwrite development of renewable power generation.Powe

85、r purchase agreements are multi-year contractual agreements to buy electricity which allow generation developers to fund capacity expansions the resulting power generated does not necessarily directly supply the demand for the company signing the PPA.We expect PPAs for renewable energy will only acc

86、elerate given the sharp increase in AI demand.Conversations with technology companies indicate continued confidence in driving down energy intensity but less confidence in meeting absolute emissions forecasts on account of rising demand.Based on discussions with our Clean Technology team,we expect t

87、o see an increase in PPAs signed by technology companies from about 17-18 GW per year in 2020(already a significant increase from the run rate in 2018-20 when power demand at data centers was relatively flat tish)to 20-30 GW per year over the next 5 years.We estimate this level of new PPAs per year

88、could broadly lead to technology companies covering 30%of electricity demand from renewables PPAs in 2028-30 after taking into account capacity factors that are lower relative to gas-fired plants due to intermit tency.28 April 2024 14Goldman SachsGS SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8bcdd

89、ff421724db1This could result in net emissions doubling by 2030,after being flattish in 2015-20.Overall we expect data center emissions to double in 2030 vs.2023 levels,with our bear/bull case implying emissions+50%/+210%.The prospects for the rise in emissions despite power intensity improvements ar

90、e likely to elevate discussions on risks around meeting Net Zero goals and potentially increase the level of investment in Green Capex required to mitigate emissions.We address this further in the forthcoming chapter.We believe this will also push both technology companies and Sustainable investors

91、to quantify benefits of AI,and technology companies are likely to increase R&D towards further innovations that can limit energy footprint.Exhibit 15:We see data center emissions doubling in 2030 vs.2023 levels,net of impact of power purchase agreements(PPAs)from technology companies Carbon dioxide

92、emissions in millions of t ons(LHS);percent of 2022 energy emissions(RHS)?Sour ce:IEA,Gol dman Sachs Gl obal Invest ment Resear ch28 April 2024 15Goldman SachsGS SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8bcddff421724db1Exhibit 16:The emissions increase is despite efficiency gains lowering power

93、intensity for both non-AI and AI drivers of data center power demand Power int ensit y from AI and non-AI dat a cent er demand?Sour ce:Masanet et al.(2020),IEA,Cisco,Gol dman Sachs Gl obal Invest ment Resear chThe nuclear option Multiple factors are coalescing around greater interest in expanding nu

94、clear generation capacity.While there remains budget and timing concerns towards further development of large-scale nuclear plants in the US,willingness to support nuclear generation expansions continues to expand.Even before nuclear being considered as a data center power demand source/solution,sen

95、timent has been shifting more favorably for nuclear expansion as a low-carbon source of baseload generation.This sentiment improvement started with energy reliability issues in 2021 in Europe/US/China,was furthered by energy security concerns driven by Russia-Ukraine/geopolitical events,and culminat

96、ed with the COP 28 communiqu calling for a tripling of nuclear capacity by 2050(although for some countries this remains an area of contention).Corporate investment towards emerging nuclear generation technologies receiving boost from technology companies.As more technology(and energy)companies look

97、 to rein in their carbon footprints(some based on Scope 1/2,others based on Scope 3),we have seen support for non-binding commitments to take power from emerging Small Modular Reactor(SMR)technologies increase.Oklo recently announced agreements with Equinix and Diamondback Energy.Microsoft has publi

98、cly highlighted the need for advanced nuclear technologies and investments to help speed innovation.However,clarity is still needed regarding viability of SMR technologies.The Nuclear Energy Institute in September highlighted that in the US there are 2 advanced reactor licensees approved,3 under rev

99、iew and 11 in pre-application phase.Based on the timing of approvals and lead time for pilots and initial project developments,we believe SMRs are more likely a potential low-carbon force to meet generation needs in 28 April 2024 16Goldman SachsGS SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8bcddff

100、421724db1the 2030s than over the medium term given time lag for technological development,government approvals and construction.We have assumed 5 GW of nuclear PPAs coming online in 2030 to reflect credit for some early success/development the next 1-2 years will be key to guide timeline and magnitu

101、de.Traditional nuclear plants have avery high capacity factor(90%+),meaning that nuclear capacity would have 3x theenergy generation per GW as a solar/wind plant.To fully offset expected data centeremissions growth in 2024-30,we would need to see about 50 GW of nuclear capacityadditions globally(ass

102、umes 95%capacity factor),in addition to already forecastedrenewables PPAs.Avoided emissions:Data center efficiency gains have kept electricity use,emissions from being meaningfully higher Efficiency plays a key role in avoided emissions,historically to an even greater degree than renewables generati

103、on.Global energy related CO2 emissions increased 10%in 2010-2022,despite a 40%expansion in real GDP.Our analysis discussed in our July 2023 report suggests energy efficiency gains across the economy have contributed to 60%of the avoided emissions globally between 2010-2022,with renewables contributi

104、ng the remaining 40%(Exhibit 2 in that report).Despite their impact,sectors that enable energy efficiency ranging from Industrials,Materials to Technology are still broadly under-represented in Sustainable Investing portfolios in part because their benefits are less visible and often unmeasured.We b

105、elieve sectors tied to energy efficiency can benefit from increasing investor support when assessed through the lens of avoided emissions.Data center efficiencies have been key Greenabler of avoided emissions.As we discussed in our November 2021 Greenablers:The critical role of Semiconductors toward

106、s a sustainable future report,the sharp improvement in data center efficiency gains that led power demand to stay flat tish even as data center workload almost tripled in 2015-19 represents a significant driver of emisssions.Using 2015 as a baseline year Exhibit 17:Over the past decade,energy effici

107、ency gains across sectors have played a greater role in driving avoided emissions than renewables deployment At t ribut ion analysis of avoided emissions from renewable energy deployment and energy efficiency gains(2010 baseline)?Sour ce:IEA,Wor l d Bank,UN,Gol dman Sachs Gl obal Invest ment Resear

108、ch28 April 2024 17Goldman SachsGS SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8bcddff421724db1for data center power intensity(2023 for the AI component)and making no adjustments to demand,data centers have avoided more than 750 TWh of electricity demand in 2023,which we believe is likely to continu

109、e to rise going forward.Notably,however,much of the projected increase is driven by the 2015-19 efficiency gains,which appear to have waned in recent years.Nevertheless,more efficient chips/servers are reducing power intensity within AI(which may decelerate somewhat if AI sees a mix shift from train

110、ing to inference over time),and we continue to expect non-AI efficiency gains going forward.Exhibit 18:Data center efficiency gains have led to significant avoided electricity consumption and emissions,much of which was driven by efficiencies before 2022 Dat a cent er power use in our base case and

111、assuming 2015 and 2022(2023 for AI)as baselines for power int ensit y(LHS),TWh;avoided carbon dioxide emissions range assuming 2015 baseline(2023 for AI)(RHS),million t ons?Sour ce:Masanet et al.(2020),IEA,Cisco,Gol dman Sachs Gl obal Invest ment Resear chWe expect commentary from companies to highl

112、ight intensity improvements,but with less visibility regarding absolute emissions targets.As we have discussed,demand growth is leading emissions to rise despite efficiency gains and emissions intensity decreases,which is pushing technology companies to increase PPAs and pursue clean reliable energy

113、 emerging technologies to mitigate the impact on absolute emissions.We believe this could lead to greater discussion among Sustainable Investors regarding avoided emissions approaches,particularly using the at tributional vs.consequential methods(i.e.,whether to make adjustments for demand growth st

114、emming from product innovation).The World Resources Institute argues the consequential approach is more comprehensive and potentially more useful for policy decisions.However,the WRI recommends the“At tributional Approach”as an interim solution if data availability is limited.Our avoided emissions a

115、nalysis reflects an attributional approach.For more details,please see our July 2023 report,How quantifying Avoided Emissions can broaden the decarbonization investment universe.What enabling value does AI/data centers need to demonstrate to offset 28 April 2024 18Goldman SachsGS SUSTAIN:Generat ion

116、al Growt h495ec6c41ca54c4b8bcddff421724db1the rise in emissions?Putting 215-220 mn tons of emissions into context what would represent equivalent offsetting benefits?A 215-220 mn ton increase in carbon dioxide emissions from data centers would represent a 0.6%increase in overall energy emissions rel

117、ative to 2022 levels.Before considering AI/data center enabled innovations that do not directly reduce emissions(i.e.,health improvement,agricultural efficiency as examples)we highlight 4 examples of potential innovations or solutions that would individually equate to reduced carbon dioxide emission

118、s of 215-220 mn tons.More efficient internal combustion engine vehicles or alternative vehicles that lowerngasoline demand globally by 7%.Solutions that accommodate lower global electricity consumption by about 2%.nThe addition of about 50 GW of nuclear capacity assuming 95%capacity factor.nInnovati

119、on towards aviation jet fuel efficiency or towards affordable sustainablenaviation fuel that lowers consumption of traditional jet fuel by 18%.In the absence of direct emissions reductions enabled by data centers/AI,the expected surge in data center demand and the contribution from AI/data centers t

120、o higher emissions will likely lead to a value assessment.Measuring the tradeoff of higher power consumption for enabled benefits may not be as easy as an avoided emissions analysis where avoided emissions are compared to actual emissions.In this case,the tradeoff may need to be value based value en

121、abled vs.cost of increased emissions.In determining the cost of emissions,the$/ton social cost is an area of debate.Notably,the US EPA after a November 2023 update to its framework applies a$190 per ton social cost of carbon dioxide emissions1,and a September 2022 article in Nature2 argues for a$185

122、 per ton cost.Using a$190 per ton social cost of carbon 215-220 million tons of expected data center emissions increase in 2030 vs.2022would suggest a present value range(7%-10%discount rate)of about$125-$140bn for the social cost of the incremental emissions from data centers.1 In its report,the EP

123、A characterizes the social cost of carbon as“the value of all future climate change impacts(both negative and positive),including changes in net agricultural productivity,human health effects,property damage from increased flood risk,changes in the frequency and severity of natural disasters,disrupt

124、ion of energy systems,risk of conflict,environmental migration,and the value of ecosystem services.”2 Rennert,K.,Errickson,F.,Prest,B.C.et al.Comprehensive evidence implies a higher social cost of CO2.28 April 2024 19Goldman SachsGS SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8bcddff421724db1AIs Su

125、st ainabilit y opport unit ies:Where t o wat ch and how t o measure As Artificial Intelligence touches an expanding scope of business activities,we see interest from Sustainable investors in understanding how AI can accelerate progress across a range of sustainability objectives.Our previous report,

126、AIs Sustainable Solutions,frames AI-related opportunities across key sectors,including Human Capital,Healthcare,Education,Agriculture,and Climate(Exhibit 19).We identify SDGs in which we see the greatest potential for AI-impact and propose metrics that may help investors assess the value of AI-benef

127、its relative to the negative value caused by higher emissions and social risks.nHealthcare:Accelerating discovery and care(SDG 3).Massive amounts of healthcare data are produced every day from a range of diverse sources,providing a rich opportunity for the application of AI-based technologies to dri

128、ve innovation across drug discovery&design,clinical trials,healthcare analytics,tools&diagnostics,and personalized care.oMetrics:Value for new drug/vaccine/therapeutic products linked to AI acceleration,value for efficiency gains for swifter drug development timeline to market,value for efficiencynA

129、griculture:Improving yields and reducing waste(SDG 2).AI-enabled applications stand to improve agricultural outcomes all the way from enhancedExhibit 19:We see AI accelerating progress across a range of Sustainable Development Goals(SDGs)AIs SuAIs Suststainabainabilitility y opoppopor rtutunitnities

130、:Wheries:Where e toto w watcatch,h,SDGSDG croscrossosov verer,and hand ho ow w toto m measureeasureHealthcare:Accelerating discovery and care(SDG 3)Metrics:Value for new drug/vaccine/therapeutic products linked to AI acceleration,value for efficiency gains for swifter drug development timeline to ma

131、rket,value for efficiencyAgriculture:Improving yields and reducing waste(SDG 2).Metrics:Value of improved crop yield,value of reduced resource usage(water,fertilizer)Climate Solutions:Optimization and efficiency in power generation and physical assets(SDG 7,9).Metrics:Value of linked power generatio

132、n/utilization efficiency,value/level of reduction in emissions and emissions intensityHuman Capital:The opportunity and need for reskilling and upskilling(SDG 8).Metrics:Economic productivity,value of employees re-skilled/re-purposed for different roles,value of certifications earnedEducation:A step

133、 change in interactivity and personalization(SDG 4).Metrics:Value of linked improvement to student test scores,value of linked enablement of certifications/degrees earnedSour ce:Gol dman Sachs Gl obal Invest ment Resear ch28 April 2024 20Goldman SachsGS SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8

134、bcddff421724db1insights into what to plant and when to improve logistics that reduce time-to-market for perishable goods.Precision agriculture data driven approaches to farming(crops&livestock),is the broadest category where leveraging AI can help to improve yields and reduce waste.However,beyond th

135、e myriad of AI applications that fall under the precision ag umbrella,AI also stands to help improve agricultural supply chain safety,speed and transparency.oMetrics:Value of improved crop yield,value of reduced resource usage(water,fertilizer)nClimate Solutions:Optimization and efficiency in power

136、generation and physical assets(SDG 7,9).AI holds promise for being a contributor in mitigating the impact of climate change in a wide range of applications,including renewable energy optimization(e.g.,weather forecasting,operational scheduling,battery storage optimization);physical asset and power u

137、se optimization(e.g.,dynamic heating/cooling,data center efficiency,manufacturing efficiencies);and climate modeling.oMetrics:Value of linked power generation/utilization efficiency,value/level of reduction in emissions and emissions intensitynHuman Capital:The opportunity and need for reskilling an

138、d upskilling(SDG 8).Increasing adoption of advanced technologies such as AI create a growing need for educational platforms that enable reskilling and upskilling.We also anticipate greater demand for human capital management tools that can help identify and match rapidly shifting worker competencies

139、 with organizational needs(skills matching).oMetrics:Economic productivity,value of employees re-skilled/re-purposed for different roles,value of certifications earnednEducation:A step change in interactivity and personalization(SDG 4).AI is complementing e-learning effectiveness through leaps forwa

140、rd in personalization and interactivity.Software-based e-learning platforms can leverage AI to create cost-effective custom study sequences to fit an individuals needs,track progress,and enable self-directed learning,while AI-powered chatbots are making the future of interactivity a reality.Educatio

141、n is especially relevant in periods of technological acceleration in which workers must upskill,and job seekers must gain new skills.oMetrics:Value of linked improvement to student test scores,value of linked enablement of certifications/degrees earnedKey risks include both how AI models work and ho

142、w theyre likely to be used.The first order of concerns related to AI center on how the models work,including informational accuracy,transparency,potential algorithmic bias,and intellectual property protections.A next order of risk encompasses the weaponization of AI models,and includes privacy,acade

143、mic integrity,disinformation,fraud and cyber at tacks.In each of these cases,AI enables potential threats to be propagated at new levels of speed,scale and customization.Potential impact on jobs is perhaps a less immediate,but overarching concern.Our economists estimate that roughly two-thirds of cu

144、rrent jobs are exposed to some degree of AI automation,and that generative AI could substitute 28 April 2024 21Goldman SachsGS SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8bcddff421724db1up to one-fourth of current work.The highest order of potential impacts includes more extreme tail risks of soci

145、etal destabilization and disaster scenarios,such as the manipulation of elections,political repression,or automated warfare(Exhibit 20).What can be done to mitigate key risks?Creators of AI models will be pressed for transparency and accountability.This would include mechanisms to alert users to AI-

146、generated content and authorship(watermarking),warning labels,and disclosures of how the systems are trained and how they work.Voluntary commitments from creators are already being made including the enablement of independent audits and third-party testing,while corporate users are putting into plac

147、e frameworks for ethical use of AI.Regulations will also play a central role in mitigating AI risks.With the EU AI Act,Europe was the first to pass a comprehensive AI-related legal framework.The AI Act proposes a risk-based framework that classifies AI systems in four tiers of risk and lists prohibi

148、ted technologies,disclosures and obligations of AI systems operators,and penalties for non-compliance.The US,UK,and China are among other nations that have proposed their own AI-focused rules and regulations.Exhibit 20:We frame four principal tiers of risks,ordered by time horizon and potential impa

149、ct,and mitigants for each AI-relat ed risks and mit igant s Sour ce:Gol dman Sachs Gl obal Invest ment Resear ch28 April 2024 22Goldman SachsGS SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8bcddff421724db1AI/dat a cent er power surge support ive of owning st ocks exposed t o overlapping sust ainable

150、 t hemes Emissions growth from AI/data centers risks adding to near-to medium-term uncertainty regarding investment needed towards sustainable goals,which we believe supports owning stocks levered to overlapping Sustainable themes among Decarbonization and Adaptation.We believe the secular theme of

151、an expanding investable universe and an Aspiration-to-Action shift towards whats needed to accomplish Sustainable Development Goals will complement cyclical bot toming of sentiment towards quality renewables laggards.AI and data center emissions growth may contribute to uncertainties while at the sa

152、me time provide near/medium/long-term boosts to renewables generation supply chain via increasing PPAs.We remain secularly bullish on themes like Green Capex,and,in a capital constrained world,efficiency and risk management-driven investment themes like Circular Economy,Adaptation,Biodiversity and A

153、ffordability are likely to increasingly resonate with investors.Furthermore,a shift in investor focus from investment needs to investments that will actually be made is likely to:(a)further focus on return on capital,not just capital employed;and(b)bring the Dawn of Thematic Convergence,with greater

154、 recognition of overlap and interplay among Green Capex sub-themes.This Dawn of Thematic Convergence suggests a focus on stocks levered to multiple Sustainability themes.What are implications if actual investment falls short of whats needed?Higher Exhibit 21:We recommend owning companies that are mi

155、cro beneficiaries of investment across multiple Sustainability themes to mitigate 2024 uncertaitny Secular OutlookBroadening of Sustainable Investment universeDirection of Travel and Opportunity in 2024Cyclical OutlookWell-owned Renewables stocks near bottomDrivers of UncertaintyInterest rate implic

156、ationsOpportunity from UncertaintySimultaneous beneficiaries of investment+underinvestmentGreen Capex,Adaptation,Circular Economy,Biodiversity,Affordability/Accessibility Stocks with thematic overlapSustainable Development Goal investments:Actual vs.neededUSA/EU electionsPush to quantify impact/impr

157、ovementLinking Sustainability with stock performanceGreen Capex growthAI/data center impact on emissionsSour ce:Gol dman Sachs Gl obal Invest ment Resear ch28 April 2024 23Goldman SachsGS SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8bcddff421724db1interest rates among other factors are driving grea

158、ter investor concern over both ability and willingness to fully satisfy needed investments in Green Capex among other Sustainable themes.We believe this will lead to valuation disparities based on which products have cost competitiveness and ability to scale vs.those more challenged/longer term.We a

159、lso believe the risk of underinvestment will lead to greater focus on driving consumption efficiency and on preparation/reaction to temperature rise.We believe this will support four themes in particular Circular Economy,Adaptation(reactive and potentially proactive),Affordability and Biodiversity.T

160、his Dawn of Thematic Convergence will likely push investors towards looking for companies/products levered to multiple themes that could benefit from both investment or consequences of underinvestment.Exhibit 22:We see increasing thematic overlap among Infrastructure,Circular Economy,Biodiversity an

161、d social themes in part driven by investments(and/or underinvestment)towards Decarbonization and Adaptation?Sour ce:Gol dman Sachs Gl obal Invest ment Resear ch28 April 2024 24Goldman SachsGS SUSTAIN:Generat ional Growt h495ec6c41ca54c4b8bcddff421724db1Disclosure Appendix Reg AC We,Brian Singer,CFA,

162、Derek R.Bingham,Brendan Corbet t,Carly Davenport,Alberto Gandolfi,Toshiya Hari,Allen Chang,Timothy Zhao,Varsha Venugopal,Evan Tylenda,CFA,Emma Jones,Madeline Meyer,Neil Mehta,Brian Lee,CFA,Daniela Costa,Joe Ritchie,Apoorva Bahadur,Mark Delaney,CFA,Jerry Revich,CFA,Ryo Harada,Christian Hinderaker,CFA

163、,Jacqueline Du,Grace Chen,Xavier Zhang,John Miller and Ati Modak,hereby certify that all of the views expressed in this report accurately reflect our personal views about the subject company or companies and its or their securities.We also certify that no part of our compensation was,is or will be,d

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166、ns and Multiple are calculated by using normalized ranks for specific metrics for each stock.The normalized ranks for the metrics are then averaged and converted into percentiles for the relevant at tribute.The precise calculation of each metric may vary depending on the fiscal year,industry and reg

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