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GD-2019-04: Joint Metrics and BCA Framework Committee Meeting 1325 G Street, N.W., Suite 800
Washington, D.C. 20005
1
GD-2019-04-M, IN THE MATTER OF THE IMPLEMENTATION OF THE 2019 CLEAN ENERGY DC OMNIBUS ACT COMPLIANCE REQUIREMENTS
FIRST JOINT METRICS AND BCA FRAMEWORK COMMITTEE
MEETING MINUTES Meeting Commencement Following establishment and several meetings of the Clean Energy Act Implementation Working Group (“CEAI WG”) in Commission Docket GD2019-04-M, the WG was divided into three committees, the Metrics Committee, the BCA Framework Committee, and the Reporting Requirements Committee. The joint Metrics and BCA Framework Committee (“Committee”) meeting was held virtually on December 1, 2020, from 10am to approximately 12:30pm. Attendees Sign-in Sheet (see Attachment No. 1) Issues Discussed As per Agenda (see Attachment No. 2)
Synopsis of Issues Discussed
Staff states that this is a joint committee meeting of committees 1 and 2 for the working group.
PJM Presentation
PJM starts the presentation with answers to questions about the PJM Emissions Report. 1) Average and Marginal Emissions Rates calculations are explained. 2) Emissions factors are majority unit-specific or are non-emitting resources. PJM will follow up on Staff’s question when the annual Emissions Report is released.
GD-2019-04: Joint Metrics and BCA Framework Committee Meeting 1325 G Street, N.W., Suite 800
Washington, D.C. 20005
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PJM discusses its Carbon Pricing study, started about 2 years ago, to look at potential impact of a carbon price and on potential “leakage” mitigation. PJM is not setting out to propose a carbon price, that is left up to the States. All mechanisms are compatible with Regional Greenhouse Gas Initiative (RGGI). PJM takes a wholesale market view, instead of looking at State specific approaches. It has scenarios for both one-way and two-way transfers. Conclusion is carbon prices across a footprint will reduce emissions. Variables looked at are a) different carbon prices taken from RGGI program, and b) different border adjustment options. Most likely scenario to play out is for VA and PA to be included in the carbon price sub-region. In scenario with no border adjustments, an increasing carbon price reduces generation production in the sub-region as it is more likely to be deployed in the rest of the RTO where there is no carbon price. Biggest drop in sub-region is in coal generation. One-way adjustment does not impact generation shift in scenarios. But once two-way adjustments are deployed, there is an increase in generation emissions in the sub-region, as the impact of carbon price is somewhat mitigated. Increased carbon price results in a net decrease in generation across RTO, despite shifts in generation. Allowing two-way adjustments actually has a net increase in generation emissions across RTO, driven by net exports increasing. Locational Marginal Prices (LMPs) across the RTO will increase with an increasing carbon price. LMPs will decrease in two-way adjustment case. To determine the actual generation leakage under two-way adjustments, one would have to look at the entire Eastern Interconnection area, which was not researched in this study. One-way adjustments had minimal impacts in all scenarios. Washington Gas asks why PJM did not use $100 per ton as one of the scenarios, and whether there is a DC specific emissions number available. PJM states no stakeholder requested this information. In the California example, it is not tracking power flows, but dispatch decisions, which is a least-cost solution. Cleaner resources are generally assigned first, as they tend to be cheaper because of high carbon costs in CAISO. PJM calculated emissions by location. But the carbon pricing region was set as the entire PJM footprint, not specific by State. Washington Gas asks whether a third region could be modeled and which generation source is being imported in each region to look at emissions rates. PJM states the model becomes exponentially complicated as more regions are added. Staff asks about combining Illinois with RGGI states, and have Illinois and DC been combined in any scenario given the other RGGI states were clustered together? PJM did not look into a carbon price case as DC had almost entirely clean energy generation and would not be impacted much by carbon pricing. Each scenario has specific results for DC. Staff asked PJM to send us the link for more information. Staff asked about considerations for States with and without clean energy goals – RPS and clean energy GHG emission reduction goals. PJM states that it has not done this at this point. Staff asks what impacts arise from FERC policy statement. Does the policy statement affect PJM’s continuation of the carbon pricing task force? PJM stated that states would need to voice their desire to continue at this point not so much based on stakeholders. TCG asked about production shifts within the RTO, and if PJM considered a scenario with a change in generation mix. PJM mentioned it looked at a few scenarios with carbon prices more
GD-2019-04: Joint Metrics and BCA Framework Committee Meeting 1325 G Street, N.W., Suite 800
Washington, D.C. 20005
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aligned with projections, in addition to the scenarios using lower prices from RGGI, and found that the lower carbon prices in the $6-$14 range had practically no impact on generation mix, but at $50/ton and higher, the model predicted a change in generation mix. DCCA asked about the definition of leakage. PJM stated it is the shift in emissions between regions. Carbon prices are meant to reduce that leakage which is more of a transfer than actual reductions. Sierra Club asked how a renewable portfolio standard would feature in PJM’s assessment of what the marginal emissions rate for DC is. In particular, if an increase in electricity demand in DC leads to additional buildout of renewable generation capacity over time then that capacity would likely displace infra-marginal units. This could result in a situation where increased load results in no actual additional emissions, but the concept of marginal emissions employed by PJM would not capture that. PJM acknowledged the problems of a marginal emissions rate when new resources are built to meet State RPS requirements. Even if regional emissions decreased, the one-for-one replacement would have no impact on marginal emissions rate.
Sierra Club Presentation
Sierra Club noted that using a social cost of carbon in a benefit-cost analysis is a useful step forward, but needs to be integrated in an appropriate overall framework. Possible risks are that a utility does not make proposals that align its business model with the climate targets or that it proposes a plan which is a least cost approach that modestly lowers CO2 emissions, but does not reach far enough to reach carbon neutrality. Relying on only a social cost of carbon is insufficient. The presentation then moved to discussing the social cost of carbon and noted that it is difficult to establish a correct price, given the large uncertainty in the factors determining the social cost of carbon and the wide range of available estimates. It is also unclear whether any chosen price would be consistent with the value supporting the climate commitments. An alternative process works backward from the District’s climate commitments. One needs to find the mix of resources that reach the District’s climate targets at least cost and identify the actors to best provide the resource mix. This approach would provide an abatement cost of carbon, after the required least-cost resources have been established. This approach would focus on different metrics and processes. Washington Gas asked about the alternative method, would there not be a legal requirement for a BCA? Is there a suggested cost of abatement? Sierra Club stated we need to look at which technologies will reach the District goals at least cost. BCA can’t be done in a vacuum, as results will change as a specific cost of carbon is set. More time and effort is needed to figure out a cost of abatement, including bringing in experts. DOEE commented that California uses a “planning cost of carbon” that is imposed to meet targets, and inviting CPUC staff to speak with the Working Group would be helpful. Staff mentioned the California Order used in the Integrated Resource Plan (IRP) uses a rising emissions cost, for example, the 2020 GHG planning prices for use in IRP is $16.94 per metric ton and 2030 $150, an increasing function with time. On the other hand, the demand side cost effectiveness analysis using $80.31 for 2020 as the adder. So CA is using different values for
GD-2019-04: Joint Metrics and BCA Framework Committee Meeting 1325 G Street, N.W., Suite 800
Washington, D.C. 20005
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different purposes. Staff agreed inviting CPUC staff to present is a good idea. Staff mentioned new Formal Case 1167 order and will circulate this order to the group which looks at any utility filings of implementing climate change proposals. Staff is also gathering information about New York, California, Rhode Island etc. and will share the results with the group. OPC asked about how the analytical tool would be used in practice for approving a filing that does not meet BCA benchmark.
DCSEU Presentation
NMR Group is the contractor, and Demand Side Analytics is the subcontractor for the DCSEU program. Presentation looks at Benefit-Cost Modeling, the DCSEU process and Societal costs and benefits. BC Modeling pays back capital costs over time from energy savings. Requires net present value and discount rate to calculate. Four standard tests: Participant Cost Test, Ratepayer Impact Measure Test, Utility Cost Test, Total Resource Cost Test. Societal Cost Test is a variant of TRC. DCSEU SCT is an annual benchmark with annual true-up of assumptions, and a periodic review of avoided costs to reflect market conditions and best practices. Incentives are neither a cost nor benefit. Incremental measure cost is the largest cost component. It depends on whether it is a retrofit, replacement, or other upgrade. In response to Staff’s question, DCSEU presentation covers electricity and gas Energy Efficiency and some renewable energy programs. SCT is viewed as a marginal cost to the total system. Electricity capacity benefits are calculated on summer demand savings. No time or seasonal differences for natural gas. Benefits primarily come from carbon reductions and electric energy. Avoided cost of energy methodology updated in 2018 for the Pepco region. The presentation continued on all of the categories of benefits savings. Staff asked a question about when the updated data will be used for avoided energy etc., NMR indicates that update will be made by next cycle which starts October 2021. Policy assumption of $100 per short ton abatement cost is taken from New England Avoided Cost of Energy Supply studies. If carbon price is assumed to be zero then B/C for DC SEU is around one. The $100 carbon price contributes to the B/C ratio of approximately two (2) for the entire DCSEU portfolio. Put another way, nearly all the net benefit is emission related, especially CO2 related. Washington Gas asked why a New England study using cost of abatement rather than the EPA Social Cost of Carbon (SCC) was chosen. Specifically, Washington Gas asked whether it was it more detailed, or methodologically superior? DCSEU said it did not have an opinion on SCC. NMR presented numerous estimates of SCC and showed DCSEU $100/ton was among the highest cost of carbon used. Washington Gas noted that $100 per short ton SCC indicates that DC would be willing to pay up $700 million a year to reduce emissions assuming emissions were 7 million tons per year (i.e. 7 million times $100/ton). NMR responded that the assessment was correct and pretty rich. Washington Gas asked whether there was any attempt to determine whether that was a reasonable level – e.g. comparing willingness to pay compared to the total DC budget of approximately $11 billion; the response was no. Washington Gas responded to a question
GD-2019-04: Joint Metrics and BCA Framework Committee Meeting 1325 G Street, N.W., Suite 800
Washington, D.C. 20005
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about the cost of compliance as likely below the willingness to pay by stipulating and emphasizing that willingness to pay is a demand measure, and actual costs are determined by other factors as well.1 Washington Gas asked NMR to confirm that in one of their recent reports, NMR indicated that the costs of accommodating peak demand growth could be as high as $300-400 per kW, and asked about what happened when NMR stated in the same report that given the uncertainty they asked for someone “to reach out” to Pepco for more information. Specifically, Washington Gas asked if there are studies of the impacts of large increases in peak electricity demand on the Pepco distribution system – e.g. 50% to 100%.2 DCSEU confirmed that their report made those statements. DCSEU clarifies that the $300-400 per kW value is looking at specific substations, and that the system average is much lower Pepco corrected that the costs aren’t going to be needed at every feeder and substation for electrification. DOEE noted that the DC goals are for 2032 and 2050, and that the 7 million tons of savings will be approached incrementally, i.e. 2021 incremental cost will be substantially less than the total of $700 million. Sierra Club noted that the emissions reductions can partially be met by much more cost-effective methods, such as energy efficient light bulbs etc. The $100 amount per ton is a maximum cost. This represents a maximum willingness to pay, and hence is the maximum economic cost. Staff asked about the B/C ratio being approximately equal to one (1). DCSEU clarifies that is true if the cost of carbon is $0. The presentation compares carbon price methodologies in different jurisdictions, with particular note of California IRP adjusting to existing policy goals. Natural Gas avoided CO2 amount is straightforward (such as 117 lbs per MMBTU from EIA). Electricity needs emissions rate assumptions. DCSEU uses the four periods (summer on-peak, summer off-peak, winter on-peak, winter off-peak), with higher rate on-peak than off-peak. SCT will require avoided costs for 30 years. DCSEU assumes PJM generation mix becomes cleaner over time. Washington Gas asked about dropping value to zero (0) given DC RPS requirements. DCSEU noted that they are assuming most marginal generation will be natural gas. Sierra Club asked about the scenario with projected lower renewable energy but there being curtailed renewable energy, but there is no curtailed energy in DCSEU model until around 2050. DCSEU said it focuses on SCT, which is a bit simplified of an approach. More approaches and assumptions can be added for DC specific considerations. Washington Gas mentioned modeling current programs. Presentation continued with other benefit streams including adders that are real benefits but are challenging to quantify. The adders aren’t multiplying, they are capped before carbon. DCSEU addresses earlier questions.
1 Washington Gas would have liked to add given the potential for confusion and the methodological importance of this issue: the actual cost is also partly determined by supply of emission reductions as well the market structure – i.e. whether there is a single market clearing price for reductions or average costs or some combination. 2 Washington Gas has repeatedly recommended additional study of this issue and is not singling PEPCO out on this issue; Washington Gas is not aware of any detailed study by any utility of large increases in peak demand as a result of ambitious long term decarbonization plans.
GD-2019-04: Joint Metrics and BCA Framework Committee Meeting 1325 G Street, N.W., Suite 800
Washington, D.C. 20005
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Staff asked about other benefit streams, does the RPS compliance cost reduction update annually? DCSEU updates it annually based on SREC value. Risk adder is for increased reliability and reduced outages factor. Non-energy adder is items such as health and safety. Washington Gas asked about other impacts on RPS compliance cost, such as regular REC price, not just SREC. DCSEU needs to follow up about how the amount of 11 cents on page 19 was derived. Washington Gas asked about electrification and the DCSEU process for determining parameters generally. DCSEU stated for one completed retrofit pilot project, it was not cost effective based on some initial analysis for retrofitting the existing building. DCSEU process starts with its own assumption models and annual input updates. SEU initiates any review process.
Meeting Action Items • PJM will share links for the results of their study and the timeframe for release of annual
emissions report • Staff will share FC 1167 Order • Staff will provide snapshot of other States’ BCA framework • DCSEU will answer about factors impacting the benefit value on RPS Compliance Costs
(page 19) • Staff will discuss with DOEE about inviting CA staff to discuss CA carbon pricing, B/C
analysis and other GHG related issues
Next Steps (Revised) • Draft Minutes Circulated to Participants: December 3, 2020 • Comments from Participants to PSC Staff: December 7, 2020 • Minutes Filed with Commission: December 9, 2020
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GD2019-04-M
CLEAN ENERGY ACT DC IMPLEMENTATION WG
JOINT METRICS-BCA COMMITTEE MEETING 12-1-20
ATTENDANCE
AOBA: Keith Townsend - [email protected]
Kevin Carey - [email protected]
DCG/DOEE: Alexandra Fisher - [email protected]
Edward Yim - [email protected]
Hussain Karim - [email protected]
Alex Lopez - [email protected]
DCSEU: Patti Boyd – [email protected]
Ted Trabue – [email protected]
Lancelot Loncke - [email protected]
Jesse Smith – [email protected]
Tom Mauldin - [email protected]
EDF: Mary Gade - [email protected]
GRID2.0: Larry Martin - [email protected]
Larisa Dobriansky - [email protected]
Eugene Imhoff - [email protected]
OPC: Sarah Kogel-Smucker - [email protected]
Dr. Elizabeth Stanton - [email protected]
Attachment 1
2
PACE: Jessica Laird - [email protected]
PEPCO: Andrea Harper - [email protected]
Tom Shetty - [email protected]
Ethan Holmes:(PHI) - <[email protected]>
Megan Partridge - [email protected]
Pearl Donohoo-Vallett - [email protected]
Dennis Jamouneau - [email protected]
Joe Cohen - [email protected]
Sierra Club: Susan Miller - [email protected]
Matthias Paustian - [email protected]
Peter Quinn-Jacobs - [email protected]
SOLAR UNITED NEIGHBORS OF DC (DC SUN):
Chinyere A. Osuala - [email protected]
WGL: Melissa Adams [email protected]
Cathy Thurston-Seignious - [email protected]
Judah Rose [email protected]
John Friedman - [email protected]
COMMISSION STAFF: Rick Herskovitz – [email protected]
Merancia Noelsaint – [email protected]
Grace Hu – [email protected]
Roger Fujihara – [email protected]
Matthew Mercogliano - [email protected]
Jason Cross – [email protected]
3
John Howley – [email protected].
Dugan Marieb – [email protected]
Isabella Cotrupi - [email protected]
Patrice Hunter - [email protected]
The Clark Group (TCG): Holly Reuter - [email protected]
Smita Chandra Thomas - [email protected]
PJM: Lori Murphy Lee - [email protected]
Anthony Giacomoni – [email protected]
GUEST: Nina Dodge - [email protected]
Clean Energy Act Implementation (CEAI) Working Group
1325 G Street, N.W., 8th Floor Washington, D.C. 20005
Joint Committee Meeting - Subjects #1 and #2, Metrics and BCA Topic: Discussion of Social Cost of Carbon and BCA Components
Virtual Meeting – December 1, 2020 10 am – 12 pm (2 hours)
AGENDA
I. IntroductionA. Participant introductions
• In alphabetical order of organizations scheduled to be presentB. Re-state Objectives and Goals for Working Group and Meeting
• Review of project objectivesII. Matthias Paustian, Sierra Club (10 minutes)III. PJM Presentation (40 minutes)IV. DCSEU Presentation (40 minutes)V. Outstanding QuestionsVI. Next Steps
A. Action Items for organizations/individualsB. Objectives for next meetingC. Confirm schedule for next meetingD. Meeting minutes
• Draft distributed by December 3, 2020
• Comments due by December 5, 2020
• File minutes with Commission by December 7, 2020
ADJOURNMENT
Attachment 2
PJM©2020www.pjm.com | Public
PJM Carbon Pricing Study Update
Anthony Giacomoni, Ph.D.Senior Market StrategistDC CommissionDecember 1, 2020
Attachment 3
PJM©20202www.pjm.com | Public
Methodology for Calculating Average Emissions Rates
• Average Emissions Rate Calculation:– Generation for each PJM generator is received monthly from the
PJM Market Settlement Reporting System.
– The energy output of each generator is multiplied by an emission factor, and a weighted-average emission rate is calculated for all PJM generation for the month.
– The PJM System Average annual value is a weighted average accounting for higher loads during the summer and winter months.
PJM©20203www.pjm.com | Public
Methodology for Calculating Marginal Emissions Rates
• Marginal Emissions Rate Calculation:– For any five-minute interval during the operating day, there is one
marginal unit on the system, plus an additional marginal unit for each transmission constraint that is being experienced.
– The mathematical average of the emissions rates for all marginal units in each five-minute interval forms a marginal emissions rate for that interval.
– These five-minute emissions rates are averaged to form the monthly marginal emissions rates.
PJM©20204www.pjm.com | Public
Vast Majority of Emission Factors are Unit-Specific
• Approximately 97 percent of all PJM generation either was a non-emitting resource or was assigned a unit-specific emission rate calculated using EPA CEMS data.
• A small percentage of generation was assigned an emission factor based on EPA eGRID data.
• Only a tiny percentage of PJM generation was assigned a fuel-type default emission factor.– 2015 – 2019 CO2, SO2 and NOX Emission Rates – Page 1
• https://www.pjm.com/~/media/library/reports-notices/special-reports/2019/2019-emissions-report.ashx
PJM©20205www.pjm.com | Public
Example Emission FactorsCO2 (lbs/MWh) Nox (lbs/MWh) SO2 (lbs/MWh)
Baseload Coal 2107.63 1.3079 3.9420
Baseload Coal 2078.61 1.2849 3.1849
Baseload Coal 2140.08 0.8047 0.6622
Baseload Coal 2136.10 1.2132 0.9225
Baseload Coal 1982.26 2.6908 1.1396
Baseload Coal 2038.66 2.9134 1.3861
Baseload Gas CC 833.86 0.0285 0.0042
Baseload Gas CC 858.61 0.0443 0.0043
Baseload Gas CC 798.08 0.0411 0.0040
Baseload Gas CC 756.08 0.0354 0.0038
Baseload Gas CC 792.59 0.0402 0.0040
Baseload Gas CC 834.57 0.0421 0.0042
Gas CT 1394.17 1.9804 0.0073
Gas CT 1339.07 1.8817 0.0070
Gas CT 1238.47 0.9477 0.0062
Gas CT 1228.96 0.9322 0.0062
Gas CT 1426.39 2.4955 0.0074
Gas CT 1446.67 2.2880 0.0075
PJM©20206www.pjm.com | Public
Objectives of Analysis
PJM is studying the potential impacts of a carbon price and potential leakage mitigation mechanisms in order to inform stakeholders and policy makers.
• PJM is not proposing to establish a carbon price.
• PJM is conducting this study to inform carbon pricing discussions in the Carbon Pricing Senior Task Force (CPSTF) stakeholder process.
• Policy makers in the PJM region are ultimately responsible for environmental policy and any associated revenue generated through its application.
• All mechanisms studied can coexist with the Regional Greenhouse Gas Initiative (RGGI).
PJM©20207www.pjm.com | Public
Approaches to Leakage Mitigation
The study does not account for state-specific approaches to leakage mitigation:
• In study: Border price adjustment constraints within the wholesale electricity market
– One-way transfers (into the carbon-price sub-region)– Two-way transfers (into and out of the carbon-price sub-region)
• Not in study: State-specific approaches– Programs that reduce electricity demand – Load-based greenhouse gas compliance obligations– Allowance allocation– Support for increasing low/zero-emitting in-state generation
PJM©20208www.pjm.com | Public
Carbon Price Analysis Summary
• Carbon prices work. They can be used effectively to reduce emissions across the footprint while maintaining a least-cost, reliable dispatch.
• Energy price and emission impacts depend on the implementation (sub-region definition, generation emissions intensities, carbon price-level, etc.).
• The year 2023 was simulated in the study.
Study Variables:
-Carbon Price Level
-Border Price Adjustment Approaches
-Sub-Region Definition
PJM©20209www.pjm.com | Public
RGGI Program helps to determine low and high carbon price values
• Applied in counterfactual case to quantify the impact of adding a carbon price on generation, emissions, and energy prices$0 /short ton of CO2
• Low-end reference• Trigger price for the RGGI Emissions Containment Reserve
(ECR) in 2023$6.87 /short ton of CO2
• High-end reference• Trigger price for the RGGI Cost Containment Reserve (CCR)
in 2023$14.88 /short ton of CO2
PJM©202010www.pjm.com | Public
Study Variables: Border Adjustment ApproachesNo Border Adjustment
Non-Carbon-
Pricing Region
Carbon-Pricing
RegionDetermine baseline for economic and
environmental leakage between regions
OfferOfferOfferOffer with CO2 Price
PJM©202011www.pjm.com | Public
Study Variables: Border Adjustment ApproachesOne-Way Border Adjustment
Account for impactsof carbon price on transfers into the
carbon-pricing region
Carbon-Pricing
Region
Offer OfferOffer with CO2 Price
Non-Carbon-
Pricing Region
OfferOffer with CO2 Price
PJM©202012www.pjm.com | Public
Study Variables: Border Adjustment ApproachesTwo-Way Border Adjustment
Account for impacts of carbon price on transfers into carbon-pricing region
and transfers from the carbon-pricing region
Non-Carbon-
Pricing Region
Carbon-Pricing
Region
Offer OfferOffer with CO2 Price Offer with
CO2 PriceOffer Offer
PJM©202013www.pjm.com | Public
Rest of RTO
39%
1%16%
17%
16%
10%
1%
Carbon-Price Sub-Region: DE, MD,
NJ, PA, VA
15%
7%
43%
9%
15%
3%8%
PA and VA Are Included in the Carbon-Price Sub-Region
Percent of
Nameplate
Capacity:
108,894 MW
Results depend on the generation mix, and emissions intensities, of each sub-region.
Percent of
Nameplate
Capacity:
93,866 MW
PJM©202014www.pjm.com | Public
Impacts of Potential Border Adjustments for Leakage Mitigation
Case RGGI Price/Short Ton Border Adjustment
Case 0-0W $0/short ton None
Case 1-0W $6.87/short ton None
Case 1-1W $6.87/short ton One-Way
Case 1-2W $6.87/short ton Two-Way
Case 2-0W $14.88/short ton None
Case 2-1W $14.88/short ton One-Way
Case 2-2W $14.88/short ton Two-Way
The year 2023 was simulated for the following cases for the carbon-price sub-region that includes DE, MD, NJ, PA and VA:
PJM©202015www.pjm.com | Public
0
100,000
200,000
300,000
400,000
500,000
2023 Generation Production by Sub-Region: No Border Adjustment
Carbon-Price Sub-RegionDE, MD, NJ, PA, VA
Rest of RTO
Case 0-0W
$0/ton
Case 1-0W
$6.87/ton
Case 2-0W
$14.88/ton
Case 0-0W
$0/ton
Case 1-0W
$6.87/ton
Case 2-0W
$14.88/ton
* There may also be shifts in generation within
the carbon-price sub-region, as the carbon
price is only applied to RGGI generators.
Total Generation
Production (GWh) As the carbon price
increases, generation:
• Decreases in the carbon-pricesub-region*
• Increases in the rest of the RTO
PJM©202016www.pjm.com | Public
0
100,000
200,000
300,000
400,000
500,000
2023 Generation Production by Sub-Region: $14.88/ton CO2
Carbon-Price Sub-Region(DE, MD, NJ, PA, VA)
Rest of RTO
Case 2-0W Case 2-1W Case 2-2W Case 2-0W Case 2-1W Case 2-2W
With the addition of a border adjustment, generation:• Increases in the carbon-price
sub-region
• Decreases in the rest of the RTO
This shift is larger with a two-way
border adjustment, compared to a
one-way border adjustment.
Generation
Production (GWh)
PJM©202017www.pjm.com | Public
2023 Total CO2 Emissions: No Border Adjustment
278266 259
129103
85
149162
174
0
50
100
150
200
250
300
Case 1-0W Case 6-0W Case 7-0WCase 0-0W
$0/ton
Case 1-0W
$6.87/ton
Case 2-0W
$14.88/ton
Generation shift from increasing carbon price results in CO2:• Decrease in carbon-price sub-region• Increase in rest of RTO (no carbon
price)• Net decrease across the RTO
Note:
• Emissions are for PJM only and do not account
for changes in external regions
• Shifts in RTO generation and external
interchange between cases are driving
changes in emissions
DE, MD, NJ, PA, VA
Million Tons of CO2
PJM©202018www.pjm.com | Public
Impact of Border Adjustment on CO2 Emissions: With Border Adjustments
Generation shift from one-way border adjustment results in a small emissions shift
between sub-regions, and a small net decrease across Net-RTO.
Two-way border adjustment results in an emissions increase in the carbon-price
sub-region, a decrease in the rest of the RTO and a net increase across Net-RTO.
266 266 277259 259
275
103 103129
85 85
128
162 162148
174 174147
0
50
100
150
200
250
300
Case 6-0W Case 6-1W Case 6-2W Case 7-0W Case 7-1W Case 7-2WCase 1-0W Case 1-1W$6.87/ton
Case 1-2W Case 2-0W Case 2-1W$14.88/ton
Case 2-2W
Million Tons of CO2
Note:
• Emissions are for PJM only and do not
account for changes in external regions
• Shifts in RTO generation and external
interchange between cases are driving
changes in emissions
DE, MD, NJ, PA, VA
PJM©202019www.pjm.com | Public
30.7832.34
35.57
29.08 29.8731.73
29.6830.97
33.63
0
5
10
15
20
25
30
35
40
Case 1-0W Case 6-0W Case 7-0W
2023 PJM Average Yearly LMPs* by Sub-Region & Carbon Price:No Border Adjustment
*Average yearly LMPs are time-
weighted averages of load-
weighted hourly LMPs.Case 0-0W
$0/ton
Case 1-0W
$6.87/ton
Case 2-0W
$14.88/ton
On average, LMPs increase in both sub-regions with an increasing carbon price.
Carbon-Price Sub-Region:
DE, MD, NJ, PA, VA
Net-RTO
Rest of RTO
LMP ($/MWh)
PJM©202020www.pjm.com | Public
32.34 32.13 31.11
35.57 35.46
31.70
29.87 29.68 29.3531.73 31.60
29.85
30.97 30.77 29.98
33.63 33.51
30.54
0
5
10
15
20
25
30
35
40
Case 6-0W Case 6-1W Case 6-2W Case 7-0W Case 7-1W Case 7-2W
2023 PJM Average Yearly LMPs* by Sub-Region:With Border Adjustments
With the addition of a border adjustment, LMPs tend to decrease for both regions.
Case 1-1W
$6.87/ton
Case 2-1W
$14.88/ton
Case 1-0W Case 1-2W Case 2-0W Case 2-2W
Carbon-Price Sub-Region:
DE, MD, NJ, PA, VA
Net-RTO
Rest of RTO
LMP ($/MWh)
*Average yearly LMPs are time-weighted averages of load-weighted hourly LMPs.
PJM©202021www.pjm.com | Public
Results Summary
Carbon prices work effectively without border adjustments to reduce emissions. They are likely more effective at higher carbon price levels and when applied more broadly.
Results depend on the generation mix, and emissions intensities, of each sub-region. Two-Way Border Adjustment
(Compared to the No Border Adjustment Case)
– Generation & Emissions
• Increase in carbon-price sub-region
• Decrease in rest of RTO
• Increase in Net-RTO due to increase in net exports from PJM to external
regions (note: this is not true for all carbon-price sub-regions)
– Energy Prices
On average, as the carbon price increases, a two-way border adjustment
results in greater price decreases
One-Way
Border
Adjustment
Almost no
measureable
impacts
PJM©202022www.pjm.com | Public
PJM Carbon Pricing Study Scenario Summary
Carbon Price RegionCase
NumbersCarbon Prices*
CPSTF
Materials
DE, MD, NJ 1, 2, 3
• $0/short ton (counterfactual)• $6.87/short ton (2023 RGGI ECR trigger price)• $14.88/short ton (2023 RGGI CCR trigger price)
1.14.2020
DE, MD, NJ, VA 1, 4, 5 2.25.2020
DE, MD, NJ, VA, PA 1, 6, 7 2.25.2020
DE, MD, NJ, PA 1, 8, 9 5.19.2020
DE, MD, NJ, VA, PA, IL 1, 16, 17 8.21.2020
Analysis of RGGI Carbon Price in sub-regions of PJM & border adjustment constraints for leakage mitigation
Analysis of increasing carbon price points
* Applied to offers of resources that meet the RGGI program’s “CO2 Budget Source”
definition
Carbon Price Region Case Numbers Carbon Prices*CPSTF
Materials
DE, MD, NJ, VA, PA 1, 10, 11• $0/short ton (counterfactual)• $25/ton• $50/ton
5.19.2020
RTO-wide 1, 12, 13, 14, 15
• $0/short ton (counterfactual)• $6.87/short ton (2023 RGGI ECR trigger price)• $14.88/short ton (2023 RGGI CCR trigger price)• $25/ton• $50/ton
5.19.2020
NMR Group, Inc.
District of Columbia Public Service CommissionDecember 1, 2020
Societal Cost Test for DC SEU Programs
Overview of Key Inputs and Assumptions
Attachment 4
NMR Group, Inc. 1
Roles and Responsibilities
Oversight and Contracting
Program Delivery
Program Evaluation
NMR Group, Inc.
• Prime EM&V contractor to DOEE
since FY2017
• Responsible for gross and net
impacts, process evaluations, and
overall project success
• Principal-in-charge Tom Mauldin
2
• Subcontractor to NMR
• Responsible for benefit-cost
modeling and gross impact
evaluation support
• Principal-in-charge Jesse Smith
NMR Group Demand Side Analytics
NMR Group, Inc.
• Overview of Benefit-Cost Modeling
• The DC SEU Process and Common Assumptions
• SCT Costs
• SCT Benefits
• Discussion
3
Agenda
NMR Group, Inc.
• Conservation programming generally
requires upfront investment in equipment
and services
• Benefits accrue over the life of the installed
measures (20+ years)
– Requires a net present value calculation
– The discount rate is the assumed time
preference of money
• 𝐵𝐶 𝑅𝑎𝑡𝑖𝑜 = ൗ𝑃𝑉 𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠𝑃𝑉 𝐶𝑜𝑠𝑡𝑠
• 𝑃𝑉 𝑜𝑓 𝑁𝑒𝑡 𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠 = 𝑃𝑉 𝐵𝑒𝑛𝑒𝑓𝑖𝑡𝑠 − 𝑃𝑉 𝐶𝑜𝑠𝑡𝑠
4
Overview of Benefit Cost (BC) Modeling
PCT• Participant Cost Test
• Perspective of program participant
RIM
• Ratepayer Impact Measure Test
• Perspective of the non-participating customer
UCT
• Also known as the Program Administrator Test
• Perspective of the utility
TRC
• Total Resource Cost Test
• Perspective of the utility and participants
• Societal Cost Test (SCT) is a variant
NMR Group, Inc.
• Presented annually by the NMR team in the Performance Benchmarks report
for at least three scenarios1) Modified Replica
2) Gross Verified Savings
3) Net Verified Savings
• Annual true-up of certain core assumptions– Future inflation: Based on past ten years of consumer price index data published by the U.S. Labor
Department. Typically ~ 1.5%
– Real Discount Rate: Ten-year treasury rate posted in the Wall Street Journal on the first business
day of October plus 2%. Typically 3-5%
– Effective Useful Life: How many years of benefits does a widget produce?
• Periodic review of avoided costs to reflect market conditions or best practices
– NMR reviews DCSEU screening assumptions at the beginning of each Fiscal Year to avoid
the need for an “Updated Avoided Costs” scenario
5
The DC SEU Societal Cost Test
NMR Group, Inc.
• Incentives are not a cost or benefit in the SCT
• Incremental measure cost is the largest component
– Cost basis varies by measure vintage (retrofit, replace on burnout etc.)
– Known for some project types, assumed for others
– Only the portion offset by incentives is included in the program budget. The rest is borne by
participants
6
Societal Cost Test – Cost Elements
NMR Group, Inc.
• Marginal cost to the system – Always wholesale, never retail
• Inclusive of losses– Electric Energy: 1.046
– Electric Demand: 1.077
– Natural Gas: 1.053
• Capacity benefits are calculated from
summer demand savings– 2pm-6pm non-holiday weekdays June-August
– PJM definition of summer peak
7
Societal Cost Test - Benefits
SCT Benefits
Avoided Energy Costs (kWh, MMBtu)
Avoided Generation Capacity Costs (kW)
Avoided T&D Capacity Costs (kW)
Avoided Water Cost
Reduced Risk/Increased Reliability
Reduced Operation and Maintenance (O&M) Cost
Benefits from reducing environmental externalities, including air and
water pollution, GHG emissions, and cooling water use.
Non-energy Benefits (NEBs), including comfort, noise reduction,
aesthetics, health and safety, ease of selling/leasing home or building,
improved occupant productivity, reduced work absences due to illness,
ability to stay in home/avoided moves, and macroeconomic benefits.
NMR Group, Inc. 8
Breakdown of Benefits – FY2019 DC SEU Verified Gross
NPV Benefits = $218 Million
NMR Group, Inc.
• Four costing periods– Summer On-Peak, Summer Off-Peak, Winter On-Peak, Winter Off-Peak
– Tied to PJM definition of On-Peak (7am to 11pm on weekdays)
• Methodological change in 2018 imposed by NMR– Loads and LMPs for PEPCO zone from January 2015 to May 2018 were used to calculate load-
weighted marginal price by costing period (in $2017)
– Escalation based on EIA Annual Energy Outlook forecasts for the Mid-Atlantic region.
– DCSEU applies inflation each fiscal year
– Refresh recommended for next cycle
• Alternative techniques
9
Avoided Cost of Electric Energy
$0.0485
$0.0423
$0.0472
$0.0319
$0.00
$0.01
$0.02
$0.03
$0.04
$0.05
$0.06
Winter On-Peak Winter Off-Peak Summer On-Peak Summer Off-Peak
$ p
er
kW
h
Avoided Cost per kWh (2020)
NMR Group, Inc.
• Single avoided cost year-round
• Methodological change in 2018
imposed by NMR
– Anchor projections in the EIA Annual
Energy Outlook for the Mid-Atlantic
Region
– Use industrial sector costs to
approximate the marginal cost
– DCSEU applies inflation each fiscal year
– Refresh recommended for next cycle
• $6.10 per MMBTU in 2020
• $7.15 per MMBTU in 2050 ($2020)
10
• Minor consideration in the District
• EIA projections for the industrial sector
(Mid-Atlantic Region)
Natural Gas Delivered Fuels
Year$/MMBtu
Propane Fuel Oil Kerosene
2020 $13.10 $20.89 $16.76
• Based on PJM's base residual auction results for the delivery
year (PEPCO zone)
• PJM delivery years don't line up with DC SEU fiscal years or
calendar years so some shifting is needed.
• Auctions only happen 3 years out so some projection is needed
for long-run values.
• 15-year average is the basis for escalation ($59.83 in $2020)
11
Avoided Cost of Generation Capacity
NMR Group, Inc.
• $31.75 per kW-year
• Transmission rate based on PEPCO
Docket No. ER09-1159 Informational
Filing of 2019 Formula Rate Annual
Update to FERC
12
• $64.02 per kW-year
• Deduced from the 2017 filing of
PEPCO's Application for Authority to
Increase Existing Retail Rates and
Charges for Electric Distribution Service
and Supporting Testimony and Exhibits
• Formal Case No. 1150 and the
subsequent DC Public Commission
Order No. 19433.
Transmission Capacity Distribution Capacity
Flagged in 2018 for update by NMR and developed in collaboration with DC SEU.
Current levels are reasonable and regionally consistent.
Increased coordination with PEPCO on these assumptions would be beneficial.
• High efficiency equipment may require less ongoing
maintenance
• LED lighting lasts longer than baseline technologies– Cost of avoided future replacements (equipment and labor)
– Assumptions are documented in the DC SEU Technical Reference Manual
13
Operation and Maintenance Benefits
• $100 per short ton
• $110.23 per metric ton– Policy assumption pre-dates the NMR
team
– Rooted in the New England Avoided Cost of Energy Supply Studies
– Has not increased with inflation (decreasing in real dollars)
• Marginal abatement cost method
– AESC also presents a $68 per ton value based on the carbon abatement cost of offshore wind in ISO-NE
14
Electric and Fuel Externalities
NMR Group, Inc. 15
Carbon Price Comparison – December 2019
NMR Group, Inc.
• Natural gas is straightforward – 117 pounds per MMBTU (EIA)
– The District could consider extraction and transport in addition to
combustion
• Electricity requires an assumption about the emissions rate of the
marginal generating unit
– Pounds of CO2 per MWh
– Increasing natural gas in PJM
16
The Other Side of the Avoided CO2 Valuation Coin
NMR Group, Inc.
• For DC SEU we develop marginal emission
rates for each of the four energy costing
periods using PJM’s Emission Reports
• Emission rate is higher on-peak than off-
peak. Slightly higher in the summer.
• At 1,200 pounds per MWh (0.6 short tons)
the value is $60 per MWh or $0.06 per kWh– Emissions value is greater than the energy itself
• Notice the trend in marginal emissions rates
17
The Other Side of the Avoided CO2 Valuation Coin
NMR Group, Inc.
• The SCT calculations require avoided costs for a 30-year horizon
• Presumably the generation mix in the PJM footprint will become cleaner over that time
horizon
– But the marginal generating unit is likely to be mostly natural gas
– Unlikely to see renewables on the margin until penetration gets very high
18
New for FY2020 – Assumed Decline in Emission Rates
• Assume by 2050 the marginal unit has a heat
rate of an efficient gas plant by today’s
standards
– Off-Peak: Combined Cycle 6,200 BTU/kWh
– On-Peak: Combustion Turbine at 8,550 BTU/kWh
• Fit a linear trend from today’s emission rate
to the 2050 assumption
NMR Group, Inc.
• Adders are applied individually to base benefits (no compounding)
19
Other Benefit Streams
Screening Assumption Value Source
Water Avoided Cost $0.005 per gallon Approved_fy_2018_operating_and_capital_budgets_final.pdf
2017 Engineering Feasibility Report WATER.pdf
Reduced RenewablePortfolio Standard Compliance Costs
$0.11 per kWh Applies to solar only. Tied to SREC price
Risk Adder 5% Specified in the DCSEU contract no. DOEE-2016-C-0002.
Non-Energy Benefits Adder 5% Specified in the DCSEU contract no. DOEE-2016-C-0002.
Low-income Adder for Solar Measures 15%
Applies to low-income solar only. Modeled on regulatory order: State of Vermont Public
Service Board “Order Re Cost-Effectiveness Screening Of Heating And Process-Fuel
Efficiency Measures And Modifications To State Cost-effectiveness Screening Tool,”
2/7/2012.
NMR Group, Inc.
1) Appropriateness of $100 per short ton value for avoided CO2?
2) Increased peak demand from electrification
• Shift from summer to winter peaking is likely to go D->T->G. This creates a host of
planning issues (all coincidence factors are for summer)
• Why does DCSEU use T&D values well below $300-$400/kW-year? System-wide vs. a
location where an upgrade can be avoided or deferred
3) DCSEU recognizes electrification is not cost-effective. What other approach
is being considered?
• The current SCT method likely requires updates to handle electrification. NMR has not
been asked to consider modifications (in DC)
4) What is the process DCSEU uses for decision-making?
20
Washington Gas Discussion Topics
Thank
You!
Tom Mauldin
617-284-6230 x2034
NMR Group, Inc.
Jesse Smith
770-401-9018
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Articleshttps://doi.org/10.1038/s41558-020-0880-3
1SIPA Center on Global Energy Policy, Columbia University, New York, NY, USA. 2Environmental Science and Policy, Smith College, Northampton, MA, USA. 3Joint Global Change Research Institute, University of Maryland, College Park, MD, USA. ✉e-mail: [email protected]
Economists overwhelmingly support pricing CO2 emissions1. How much to charge for each ton of emissions is perhaps the most important element of a carbon pricing policy, yet little
consensus exists among economists about the appropriate level for CO2 prices2.
To find optimal CO2 prices, economists have long focused on a metric called the social cost of carbon (SCC), an estimate of the marginal damages of an additional ton of CO2 emissions. However, the SCC cannot be credibly estimated with sufficient precision to provide practical assistance to policymakers setting CO2 prices. The SCC approach is also disconnected from real-world policy discus-sions that position CO2 prices as one element of a strategy to avoid the risks of exceeding thresholds of global warming.
In the face of these constraints, this paper introduces an alter-native approach. It starts with policymakers selecting a net-zero CO2 emissions target informed by the best available science and economics. Then, near-term to net zero (NT2NZ) CO2 prices are combined with a broader policy strategy to achieve an emissions pathway consistent with the net-zero target in the near term, when the projections of energy–economic models are most useful.
Helping policymakers set CO2 pricesIn textbooks, optimal CO2 prices are identified with perfect preci-sion. Net benefits to society are largest if the government taxes an activity that creates a negative externality (such as CO2 emissions) at the rate that equalizes the marginal social benefits and marginal social costs of emissions reductions3.
Economists have long recognized that, in the real world, approaches for developing optimal policies can be constrained by various uncertainties and measurement difficulties4,5, includ-ing imprecision, ambiguity, intractability and indeterminacy6. Therefore, in addition to maximizing net benefits, analysts should strive to identify CO2 prices using approaches with the following attributes:
• Credible precision. The approach produces a range of CO2 prices that provides policymakers with valuable information to
incorporate into policy design decisions. For example, if adding complexity to a model injects disproportionate uncertainty, it can be more informative to use a simpler framework7.
• Transparency. The approach enables policymakers to under-stand the causes of relatively high or low estimates within the range of CO2 prices identified.
• Consistency with policy objectives. The approach produces CO2 prices that fit the objectives of the policy. The most com-mon rationale for a CO2 price is to reduce emissions as part of a broader global response to the risks of climate change.
Policymakers setting CO2 prices are also concerned with addi-tional factors that are outside our scope, such as public health benefits from reducing air pollution, energy security, competitive-ness, the expected actions of other jurisdictions and (perhaps most importantly) political viability.
Challenges with setting CO2 prices using SCC estimatesThe SCC, commonly used as the optimal CO2 price that maximizes net benefits to society8, is estimated with projections of the follow-ing parameters: global emissions over the next few centuries, the effects of emissions on temperatures and other climate impacts, and the impacts of climate change on the economy and human welfare, using economic methods that aggregate centuries of impacts into a single value representing the net benefits of emissions reductions9.
Unfortunately, the degree of uncertainty in SCC estimates spans virtually any conceivable stringency level for a CO2 pricing policy. Meta-analyses find recent SCC estimates that range from under US$0 per ton of CO2 to over US$2,000 per ton (excluding outliers still leaves a range of hundreds of dollars)8,10.
SCC estimates will continue to improve9,11,12, but methodological advancements are unlikely to narrow the range of SCC estimates much. After all, large uncertainties come from parameters that are inherently uncertain, such as the appropriate discount rates9, risk aversion levels13, issues around inequality14 and attempts to assign monetary values to non-economic climate damages15. In addition, methodological improvements often involve incorporating new
A near-term to net zero alternative to the social cost of carbon for setting carbon pricesNoah Kaufman 1 ✉, Alexander R. Barron 2, Wojciech Krawczyk3, Peter Marsters1 and Haewon McJeon3
The social cost of carbon (SCC) is commonly described and used as the optimal CO2 price. However, the wide range of SCC esti-mates provides limited practical assistance to policymakers setting specific CO2 prices. Here we describe an alternate near-term to net zero (NT2NZ) approach, estimating CO2 prices needed in the near term for consistency with a net-zero CO2 emissions target. This approach dovetails with the emissions-target-focused approach that frames climate policy discussions around the world, avoids uncertainties in estimates of climate damages and long-term decarbonization costs, offers transparency about sensitivities and enables the consideration of CO2 prices alongside a portfolio of policies. We estimate illustrative NT2NZ CO2 prices for the United States; for a 2050 net-zero CO2 emission target, prices are US$34 to US$64 per metric ton in 2025 and US$77 to US$124 in 2030. These results are most influenced by assumptions about complementary policies and oil prices.
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uncertain elements that were omitted from previous estimates, which can widen the range of SCC estimates16,17.
The advantage of the SCC approach is that it attempts to per-fectly maximize net benefits to society; however, the SCC approach provides limited practical value to policymakers setting specific CO2 prices due to its difficulty satisfying the desired attributes identified in the previous section. First, the range of SCC estimates is too wide to credibly support the use of any single CO2 price. (In contrast, the US government developed SCC estimates for use in the separate context of benefit–cost analysis, where a wide range can be incorporated9.) Second, the differences in SCC estimates hinge partly on assumptions that are not usually transparent to policy-makers (such as the value placed on future generations by discount rates). Third, the SCC approach is disconnected from constraining global warming beyond specific levels—the goal of most policy-makers setting CO2 prices. For example, William Nordhaus’s 2018 Nobel Prize Lecture shows an optimal pathway of 4 °C of warming by the mid-2100s as the implication of his SCC estimates18, an out-come far outside the bounds of the Paris Agreement’s goals (which other analyses have found would pass a cost–benefit test19). Helping policymakers set CO2 prices in practice therefore necessitates an approach that balances benefits and costs only imperfectly, such as the NT2NZ approach described in the next section.
The classic alternate approach in the face of this sort of uncertainty is a cap-and-trade programme, which involves mandating emissions targets. We assume that policymakers have other reasons for select-ing a price instrument, such as the interactions with complementary policies, a desire for certainty in business planning, concerns about market manipulation or political economy considerations20.
the Nt2NZ approachNT2NZ CO2 prices are designed to accommodate uncertainties and measurement difficulties and to align with real-world policy objec-tives. They are estimated using the following four steps:
Step 1: select a net-zero CO2 emissions date. While the interna-tional climate change negotiations have focused primarily on tem-perature targets, policymakers are increasingly shifting to net-zero emissions targets for both substantive and political reasons21–24. A global net-zero CO2 emissions target has a science-based rationale: surface temperatures will continue to increase until the sources and sequestration of CO2 are equal, at which point temperatures will roughly stabilize25,26 (reductions in non-CO2 GHGs and land-use change emissions are required for full stabilization24, but we focus on CO2 emissions, which are the bulk of what would be covered by a CO2 price). Views differ on what threshold of global warm-ing should not be exceeded, but failing to achieve net-zero CO2 emissions implies ever-increasing temperatures, which will even-tually reach unacceptably high levels regardless of one’s threshold. Net-zero targets also naturally scale to any jurisdiction, because achieving global net zero requires, on average, all jurisdictions to achieve net zero.
Policymakers must balance a range of factors to set net-zero targets, including the risks of even-higher temperature changes and the additional costs of decarbonizing faster. International cli-mate agreements recognize that countries have the responsibility to decarbonize at different paces27, which means that jurisdictions will set different net-zero target dates. Like estimating an SCC, set-ting a net-zero target involves judgements about concern for future generations, willingness to tolerate risks and aversion to inequality (among other factors). Under the NT2NZ approach, these trade-offs are made by the governing officials selecting the target.
Step 2: select an emissions pathway to the net-zero target. An infi-nite number of pathways are conceivable between current emissions levels and a future net-zero target. Some frameworks emphasize the
benefits of reducing near-term disruption and enabling innovation to bring down technology costs, which argue for a slower initial rate of reductions28. Other frameworks emphasize the importance of near-term deployments in reducing costs and avoiding technology lock-in29–31, as well as benefits from reducing cumulative emissions and respecting intergenerational equity.
Policymakers, weighing these considerations as well as techni-cal and political constraints, may choose a straight-line pathway to net zero for simplicity and transparency, or a different trajectory that fits the circumstances of the jurisdiction (such as a developing country with a peak-and-decline pathway)32.
Step 3: estimate CO2 prices consistent with the emissions pathway in the near term. Energy–economic models can be used to estimate the CO2 prices required to reduce emissions on a desired pathway under a given set of assumptions about future technologies, prices and behaviour33. Unlike the SCC approach, energy–economic mod-els enable analysts to combine CO2 prices with other policy mea-sures to overcome multiple market barriers to emissions reductions.
While net-zero emissions is the long-term goal, the NT2NZ approach focuses on the near term (the next decade, for example). Models that simulate economic and energy systems are built using historical data on production, consumption and market dynamics, which may be a reasonable assumption in the near term. After all, most energy technologies and consumer behaviours evolve relatively slowly. But such models become less useful as the time horizon of the exercise lengthens34. Changes in technologies, preferences and policies will inevitably impact energy systems in unexpected ways in a rapidly decarbonizing country—just as advancements in solar energy technologies and the shale revolution in the United States were almost entirely unforeseen decades ago35.
Focusing on the near term means that CO2 price estimates should not be unduly influenced by assumptions about the highly uncertain long-term evolution of technologies and behaviour. Analysts often use models with ‘foresight’, which means that decisions are contin-gent on assumed future changes to the energy system. For example, if the costs of breakthrough low-carbon technologies are assumed to remain prohibitively high, models with foresight may suggest that the cost-effective pathway involves higher near-term CO2 prices, so that additional emissions reductions can be achieved in sectors where stock turnover is slow. In contrast, if the costs of breakthrough technologies are assumed to fall precipitously, the same model may suggest a cost-effective pathway that involves relatively less near-term mitigation (see Supplementary Information, Appendix 2).
Step 4: periodically update Steps 1–3. Knowledge about climate science and the costs of mitigation technologies will continue to change rapidly, especially given the substantial social and economic shifts associated with decarbonization. This calls for an adaptive management strategy36 whereby the analysis described above is repeated periodically. Using emissions outcomes and other relevant metrics (such as deployment rates of low-carbon infrastructure and progress in hard-to-decarbonize sectors30), both the CO2 price pathway and the broader climate policy strategy can be revised and extended, capturing the most up-to-date information.
Adaptive management can enable jurisdictions to stay close to the desired emissions pathway without making policy details con-tingent on assumptions about highly uncertain long-term variables. Various mechanisms for periodically updating policies have been proposed in recent years37, including CO2 prices that are contingent on emissions outcomes (that is, if emissions exceed target levels, price increases accelerate in future years).
illustrative Nt2NZ CO2 prices for the united StatesWe demonstrate the approach described above to produce illustra-tive NT2NZ CO2 prices for the United States. For this analysis we
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use the 50-state version of the Global Change Assessment Model (GCAM-USA), an integrated assessment model of energy–econ-omy–environment systems, and data available as of 2019 (Methods). Real-world policy design should be informed by multiple analytic tools using the most up-to-date available information32.
We begin with three straight-line annual emissions pathways from current (2020) levels to net-zero CO2 emissions targets in 2060, 2050 and 2040 (Fig. 1a) to reflect a range of emissions path-ways discussed in recent years by US policymakers. In the absence of a consensus favouring larger or smaller near-term emissions reductions38,39, an illustrative straight-line emissions pathway for the United States may be appealing due to its simplicity and transpar-ency. These pathways correspond to 2030 CO2 emissions of 35%, 42% and 57% below 2005 levels, respectively. Consistent with both economic theory and policy practice, we assume that the CO2 price is surrounded by complementary policies that address separate market failures40: efficiency policies, air pollution regulations and early-stage support for the deployment of low-carbon technologies (such as electric vehicles).
For our benchmark scenario, we find NT2NZ CO2 prices in 2025 of US$32, US$52 and US$93 per metric ton (in 2018 dollars) for consistency with net-zero targets in 2060, 2050 and 2040, respec-tively. The NT2NZ CO2 prices in 2030 are roughly two times larger (Fig. 1b), reflecting a much higher annual growth rate than typical CO2 price estimates based on the SCC or rising at the rate of interest (see Supplementary Information, Appendix 2).
For each emissions pathway, we show a range of NT2NZ CO2 prices from sensitivity scenarios that intend to capture uncertainty in influential model inputs (Methods). Figure 2 shows that com-pared with our benchmark scenario (with a 2050 target), more stringent and successful complementary policies (that is, air quality
regulations that lead to higher coal retirements, more aggressive energy efficiency measures and more aggressive early-stage deploy-ment support for certain low-carbon technologies) lowered the CO2 prices by US$10–US$20 per ton. The prices rise by about the same amount with less aggressive complementary policies. Changing the future oil price trajectory from a pathway to either US$45 or US$176 per barrel in 2030 leads to a swing in CO2 prices of US$40 per ton in 2030. Figure 2 also displays the impacts on NT2NZ CO2 prices from changing inputs related to natural gas prices, innovation and economic growth. Appendix 1 in the Supplementary Information provides additional results.
For a 2050 target, the range of NT2NZ CO2 prices is largely con-sistent with the range of CO2 prices in legislation proposed to the US Congress in 2019 (Fig. 1). However, the prices are on the lower end of the range of CO2 prices that global energy–economic models have identified as consistent with constraining average global tempera-ture increases to 1.5 and 2 degrees Celsius41 for at least three reasons. First, NT2NZ CO2 prices will differ by jurisdiction, and the United States has a large amount of coal-fired electricity generation that can be replaced at a relatively low cost. Second, while many stud-ies assume that the CO2 price is implemented without other poli-cies, we assume that multiple policies are implemented to address multiple market barriers to emissions reductions. Third, the actors within GCAM do not have foresight, so their energy consumption is based on their myopic vision of current market conditions and not based on long-term projections of technological progress (see Supplementary Information, Appendix 2 and Extended Data Fig. 1).
Discussion and policy implicationsEconomists have long referred to the SCC as the optimal CO2 price. The use of the SCC as a CO2 price has become commonplace
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2,000
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Benchmark
Fig. 1 | uS CO2 emissions pathways to net zero and associated Nt2NZ CO2 prices. a, Historical emissions (black) and CO2 emissions pathways consistent with a straight-line path to net-zero CO2 in the target year. b, Illustrative ranges of CO2 prices in 2025 and 2030 needed to reduce US net CO2 emissions on each of the three emissions pathways (NT2NZ CO2 prices; bars). The black dots reflect benchmark scenario NT2NZ CO2 prices. The whiskers represent the ranges of CO2 prices in proposals to the US Congress in 2019.
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in recent years, including in federal carbon tax proposals and state-level subsidies for clean electricity generation in the United States (see Supplementary Information, Appendix 3). Using the NT2NZ approach instead offers several advantages.
First, CO2 prices can be estimated with more precision. All of the uncertainties in the CO2 prices estimated using the NT2NZ approach—such as near-term clean energy innovation and fossil fuel prices—are also uncertainties using the SCC approach. But the NT2NZ approach avoids much larger uncertainties, including assigning monetary values to climate change damages. The NT2NZ approach focuses on the most important and (relatively) better understood aspects of the problem.
Second, while any approach can be transparent, the NT2NZ approach focuses on changes to the energy system in the near future, which should enable policymakers to better understand the ratio-nale for selecting CO2 prices of varying levels. An NT2NZ analysis enables external stakeholders to assess whether a jurisdiction’s poli-cies are consistent with its targets and how the outcomes are influ-enced by key assumptions about technology costs and energy prices (Fig. 2). While the full range of our estimates is ~US$40 per ton for any given target, policymakers are not likely to regard all sensitivi-ties as equally likely and can choose within that range accordingly. By comparison, the complexity of modelling the SCC can make it more difficult to communicate why estimates differ (often by hun-dreds of dollars per ton under different plausible assumptions about discounting, adaptation and so on).
Third, the NT2NZ approach is more consistent with the objec-tives of most policymakers implementing CO2 prices. While econo-mists have traditionally recommended a harmonized global CO2 price to maximize efficiency, national annual emissions targets have long been the lingua franca of international negotiations on cli-mate change. The illustrative example in the previous section shows how US policymakers could use the NT2NZ approach to help design a federal carbon price as an element of its 2030 Nationally Determined Contribution to the Paris Agreement. Other countries, groups of countries or subnational jurisdictions could do the same. The NT2NZ approach also enables the CO2 price to be one piece of a broader policy strategy to address multiple market failures around climate, which better aligns with economic theory compared with a CO2-price-alone approach40. It also better aligns with the real-world
practice of combining CO2 prices with a range of other (often sectoral)42 policies, recognizing that preparation for deep decar-bonization31 requires adaptive management using an ecosystem of policies. For example, passenger vehicle decarbonization might include goals and policy measures to encourage electric vehicles, charging infrastructure, fuel economy and modal shifts away from single-occupancy vehicle travel43.
Pairing a long-term emissions target with a set of iterative near-term policies is not novel. The United Kingdom, for example, has adopted a national target of net-zero GHG emissions by 2050 and sets five-year carbon budgets to act as stepping-stones44. Indeed, the Paris Agreement encourages this framework by calling on nations to produce long-term development strategies for low GHG emissions and near-term Nationally Determined Contributions that are updated every five years26.
The NT2NZ approach does not attempt to set CO2 prices by perfectly balancing costs and benefits, so it does not satisfy the definition of an ‘optimal CO2 price’ found in economics textbooks. Instead, it enables policymakers to consider both qualitative and quantitative information about climate science and economics when selecting a net-zero target. Models can then estimate the CO2 prices for cost-effective reductions in the near term, when their pro-jections are most useful, with the understanding that policy details can be updated as uncertainties are resolved.
We describe our results as illustrative because no single model should inform policy setting. The NT2NZ approach should be implemented across a suite of models to further characterize uncer-tainties, identify results that are robust across methods34 and explore how prices are best combined with other policies. Future work should also examine the roles of the domestic land-use sink and non-CO2 GHG emissions.
Online contentAny methods, additional references, Nature Research report-ing summaries, source data, extended data, supplementary infor-mation, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41558-020-0880-3.
Received: 5 March 2020; Accepted: 17 July 2020; Published: xx xx xxxx
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High GDP
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Low complementary
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Low innovation
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Low natural gas prices
High oil prices
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Fig. 2 | uS Nt2NZ CO2 prices for a net zero by 2050 pathway: comparison of sensitivity scenarios to the benchmark scenario. The light and dark bars reflect the differences between the NT2NZ CO2 prices for a given sensitivity scenario compared with the benchmark scenario in 2025 and 2030, respectively. The complementary scenarios reflect proxies for policies that surround the CO2 price and address non-price-related market barriers (see Methods for a description of the sensitivity scenarios).
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MethodsOverview of the model. To numerically estimate CO2 prices using the NT2NZ approach, we use GCAM-USA. GCAM is an economy-wide global integrated assessment model representing the energy and land sectors linked with a climate model; GCAM is used to explore the interactions of emissions-reducing investments and activities across the US and global economies. We begin with the same version of the model and the standard assumption set used for the US Mid-century Strategy (MCS)45. The technical assumptions used in the US MCS are documented in the MCS technical appendix46. We have also made updates and changes reflecting the developments in the energy markets and policies as of late 2019, when this analysis was conducted (before the COVID-19 crisis). These are described in detail below. More detail on GCAM can be found in the Supplementary Information.
Key updated assumptions. Most of the assumptions follow the benchmark scenario described in the MCS technical appendix. We updated key assumptions to align the model with the changing market dynamics in the past several years, using the sources in Supplementary Table 3. Given the uncertain and influential nature of these assumptions, our sensitivity scenarios are designed by selecting low, benchmark and high assumptions for each.
Innovation and low-carbon energy technologies. To reflect the rapidly falling costs of renewable energy, we have updated the renewable energy costs to those from the National Renewable Energy Laboratory’s (NREL) Annual Technology Baseline47. To reflect uncertainties in the evolution of future energy markets as well as concerted research, development and deployment policies to further reduce low-carbon energy costs, we conduct a sensitivity analysis using the high, medium and low estimates of solar, wind and nuclear costs. The detailed cost assumptions are summarized in Supplementary Table 4.
Oil and gas prices. To reflect future uncertainty in oil and gas prices, we have updated the oil and gas price trajectories consistent with the Energy Information Administration’s (EIA) oil and gas price trajectories from Annual Energy Outlook 2019 (ref. 48). Each five-year price point in GCAM is based on a three-year running average of prices to avoid short-term fluctuations. We conduct a sensitivity analysis using the high, medium and low estimates of the oil and gas price trajectories. The detailed assumptions are summarized in Supplementary Table 5. We note that oil prices have fallen considerably since our analysis was performed, and we probably will not know how projections for 2025 or 2030 will be influenced until after the immediate COVID-19 crisis subsides.
Population and GDP. We conducted a sensitivity analysis using high, medium and low estimates of population and GDP. Medium population projections are based on US Census Bureau National Population Projections Tables49. The high population growth scenario assumes 0.1% per year additional growth, and the low population growth scenario assumes 0.1% per year less growth. The GDP variations are based on Annual Energy Outlook 2019, which provides high, medium and low variants of GDP growth projections. The COVID-19 pandemic will considerably depress GDP growth in the near term and has the potential to do so through 2025—future analysis with updated projections from the EIA (or another source) should take this effect into account. The detailed population and GDP data used are tabulated in Supplementary Table 6.
Complementary policies. We developed three complementary policy scenarios (detailed below), both to reflect policies needed to overcome market barriers left unaddressed by CO2 prices and to reflect the uncertain future of sectoral policies that influence CO2 emissions.
Coal retirements. Coal-fired power plants release not only CO2 emissions but also various other air pollutants, such as particulate matter and ozone. Policymakers have long recognized the need for regulations to protect constituents from the harmful impacts of these pollutants. Coal-fired power plants have been rapidly retiring in the United States due to changing market dynamics and concerted efforts to reduce emissions of CO2 and other air pollutants. To capture the potential impact of future environmental regulations on coal generation, we developed high, medium and low coal-retirement scenarios. These coal-retirement pathways assume no CO2 emission mitigation policy; they thus serve as a starting point for our CO2 price scenarios. The low trajectory tracks the EIA Annual Energy Outlook 2018 coal-retirement reference case projections48. The medium (benchmark) trajectory roughly tracks the US Environmental Protection Agency Integrated Planning Model May 2019 reference case50 (which also assumes no new policy). The high trajectory tracks the Integrated Planning Model reference case to 2021 and then assumes a trajectory that is consistent with the Enhanced Engagement scenario in the America’s Pledge Report51 (this post-2021 rate is consistent with a capacity retirement rate between the medium and average retirement rates since 2012). The impact of these coal-retirement trajectories on generation is shown in Table M5. In addition to scheduled retirements, GCAM allows the power plants to prematurely retire when they are no longer profitable in the market.
Sufficiently high CO2 prices can therefore force coal power plants out of the market (Supplementary Fig. 3).
Energy demand. A CO2 price alone is often insufficient to encourage consumers to take advantage of all cost-effective opportunities to reduce energy usage due to market barriers including informational failures and consumer short-sightedness. To reflect uncertainty in the future growth of energy demand, including policies to reduce the rate of energy demand growth, we developed high, medium and low scenarios for future demand growth in key energy sectors using assumptions from the Smart Growth scenario of the MCS report45. Specifically, our low and medium energy demand scenarios assume less vehicle travel and greater efficiency in the buildings sector than does the Benchmark scenario. The detailed energy demand differences are shown in Supplementary Table 7.
Early-stage deployment support for low-carbon technologies. In addition to the lack of a CO2 price, numerous market barriers stand in the way of a large-scale shift to products that do not directly burn fossil fuels (such as electric vehicles and electric heat pumps). To reflect the highly uncertain future of electrification, including policies designed to support electrification, we develop high, medium and low sensitivity scenarios for electrification in buildings and transportation, drawn from the NREL Electrification Futures Study52. The electrification rates are shown in Supplementary Table 7.
Land-sector sink assumptions. We develop three net-zero emissions targets (2040, 2050 and 2060) (Fig. 1). In our analysis, net CO2 emissions are constrained to linearly decrease to zero in the specified target years. In 2030, this formulation corresponds to an emissions range of 3,400 MtCO2 to 2,200 MtCO2 (35–57% below the 2005 emission level). For simplicity, we assume the CO2 emissions from land-use change are constant at current levels: 714 MtCO2 (ref. 53). The US MCS has considered multiple possible pathways for CO2 emissions from land-use change, but given the large fluctuations in year-by-year estimates for land-use change emissions, we did not find other estimates to be of additional value that warrants the additional complexity. A decrease in the land sink over time would require additional emissions reductions from fossil fuel combustion beyond what we model here.
Reporting Summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Data availabilityAll data generated or analysed during this study are from publicly available sources and are either included in this article (and its Supplementary Information files) or available from the corresponding author on reasonable request. Source data are provided with this paper.
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2016); https://unfccc.int/files/focus/long-term_strategies/application/pdf/mid_century_strategy_report-final_red.pdf
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47. Annual Technology Baseline (ATB) from the National Renewable Energy Laboratory (NREL) (NREL, 2019); https://atb.nrel.gov/
48. Annual Energy Outlook 2018 (EIA, 2018); https://www.eia.gov/outlooks/aeo/ 49. 2017 National Population Projections Datasets: Projections for the United
States: 2017 to 2060 (US Census Bureau, 2017); https://www.census.gov/data/datasets/2017/demo/popproj/2017-popproj.html
50. Incremental Documentation for IPM Platform v6 May 2019 Reference Case (US EPA, 2019); https://www.epa.gov/airmarkets/incremental-documentation-ipm-platform-v6-may-2019-reference-case
51. Fulfilling America’s Pledge: How States, Cities, and Business Are Leading the United States to a Low-Carbon Future (America’s Pledge Initiative on Climate, 2018); https://www.americaspledgeonclimate.com/fulfilling-americas-pledge/
52. Jadun, P. et al. Electrification Futures Study: End-Use Electric Technology Cost and Performance Projections through 2050 (NREL, 2017); https://www.nrel.gov/docs/fy18osti/70485.pdf
53. Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2017 (US EPA, 2019).
AcknowledgementsWe thank K. Gillingham, G. Heal, A. Fawcett, J. Larsen, S. Sayre, J. Goffman and P. Kelleher for comments on an early version of this manuscript. This work was made possible by support from the Center on Global Energy Policy at the School of International and Public Affairs of Columbia University.
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Author contributionsN.K., A.R.B., H.M. and P.M. contributed to the design of the research. W.K. and H.M. conducted the modelling. All authors contributed to the analysis of the results. N.K., A.R.B., H.M. and P.M. wrote the manuscript.
Competing interestsThe authors declare no competing interests.
Additional informationExtended data is available for this paper at https://doi.org/10.1038/s41558-020-0880-3.
Supplementary information is available for this paper at https://doi.org/10.1038/s41558-020-0880-3.
Correspondence and requests for materials should be addressed to N.K.
Peer review information Nature Climate Change thanks Emily Wimberger and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Reprints and permissions information is available at www.nature.com/reprints.
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Extended Data Fig. 1 | Si Figure 4. Nt2NZ versus CO2 Prices Rising at the interest Rate. a, Linear emissions reduction pathways to net zero targets (dark lines) vs emissions reduction pathways when the price is constrained to grow at the interest rate (as a rough proxy for perfect foresight). b, CO2 prices for the linear pathways (closed symbols) and interest rate-constrained pathways (open symbols) in the year 2025 and 2030 for net zero targets in 2040 (squares), 2050 (circles), and 2060 (triangles), respectively.
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