My Comments on the RGGI September 2017 Stakeholder Meeting

When environmental rules and regulations are promulgated there are rules which include comment periods where interested parties can submit their suggestions about the proposals. This post describes the process, the comments submitted to the recent Regional Greenhouse Gas Initiative (RGGI) stakeholder meeting and includes the comments I submitted.

I have extracted my disclaimer from my comments for use here. I have been involved in the RGGI program process since its inception. In the final years before my retirement I analyzed air quality regulations that could affect electric generating company operations and RGGI was one of the regulations reviewed. The opinions expressed in these comments do not reflect the position of any of my previous employers or any other company I have been associated with; these comments are mine alone. I believe the majority of the stakeholder opinions expressed at meetings and in submitted comments are, in my opinion, very naïve relative to the actual burden implementing their preferred alternatives, overly optimistic about the potential value of continued RGGI reductions and ignore the potential for serious consequences if things don’t work out as planned.

Comment Process

Ideally the comment period enables stakeholders to suggest modifications to the rules or schedules that make the final rule better. Of course the definition of better depends on where you are coming from. Pragmatic environmentalists in industry generally comment on topics that they think will affect their ability to comply with the proposed rules or address issues related to compliance obligations, primarily related to reporting. Our comments depend on evidence so we are careful to provide evidence for our assertions.

I have never worked on the other side of the dynamic between regulators and the regulated community so I am not sure how responses to comments are handled. Generally agencies are required to address all comments submitted and if the responses are debatable the regulated community can litigate the rule to address that problem. It is not clear how regulators handle quality vs. quantity of comments. If there is one comment that makes a strong argument on a particular point and many comments that argue the opposite side of that point with weak technical or purely emotional arguments who wins? There is no doubt in mind however, that even in the most professional regulatory agency with the most educated and experienced staff, that ultimately most decisions are made by political appointees who can ignore the strongest technical arguments depending on the political agendas involved.

In this case the Stakeholder meeting presented the plan for the future of RGGI that includes tightening the emissions restrictions, extending the program beyond 2020, and tweaking some of the program elements. As of the date of this writing, October 19, 2017 there were 12 comments listed for the September 25, 2017 meeting in addition to mine. Eight comments from organizations and a combined into one comment total of 2,051 individual comments from members of the Sierra Club commended the states for their proposal to reduce the emissions cap after 2020. The NY Independent System Operator commented that the analysis used to justify the rule had not handled the New York electric system correctly and that should be corrected. The Environmental Energy Alliance of New York (EEANY) pointed out that substantive issues they had raised throughout the program review process had not been addressed so it was prudent to slow down the implementation schedule. The NJ Department of Environmental Protection submitted comments that seemed to be the issues RGGI needs to address before they would consider re-joining the program. Here is a summary of the comments:

  • Ceres
    • Commends the states for their proposal to extend and strengthen the program through 2030.
  • The City of New York
    • Supports the cap emission reduction and formation of ECR but notes that RGGI States must go further if they are to align with the goals of the Paris Climate Agreement.
  • Collaborative for RGGI Progress
    • Voices strong support for the states’ consensus draft program recommendations released August 23, 2017.
  • Dynegy
    • Voices strong support for the states’ consensus draft program recommendations released August 23, 2017.
  • EEANY
    • Reiterates prior comments that were not addressed
    • Recommends delaying implementation of the cap reductions until we see what happens when the effects of earlier revisions become effective
  • Joint Comments (4 Industry Organizations)
    • Overall, supports the proposed changes and the extension of the program through 2030. We believe that the base cap has been set at a reasonable level
    • Specific recommendations for tweaks for improvement
  • Joint Comments (35 Environmental Organizations)
    • Thanks for open, transparent and responsive process
    • Strongly supports reducing the emissions cap
    • Recommendations for further reductions and tweaks to program
  • Joint Comments (16 Environmental Justice Organizations)
    • Supports proposal
    • Recommends stronger caps and more restrictions
  • NJ Department of Environmental Protection
    • Requested specific information relative to NJ participation in RGGI
    • Offered some criticisms of the supporting documentation
  • NYISO
    • Requested corrections to NY electric system modeling for IPM analyses
  • Physicians for Social Responsibility (PSR)
    • As health professionals who recognize the adverse health impacts of climate change and the need to mitigate those impacts now, we support the governors’ decision to strengthen the Regional Greenhouse Gas Initiative (RGGI) by committing to further cut carbon pollution.
  • Sierra Club Members (2,051 Individuals)
    • These comments were form letters signed by Sierra Club members that thanked the States for protecting the climate, reducing pollution, creating jobs, and growing the economy by reducing the cap.

My recommendation is the same as EEANY. Because past reductions are not going to be as easy as in the past it would be prudent to delay further emission reduction requirements.

In my opinion, all RGGI policy decisions have been and were in this case made for political reasons. As such no amount of technical arguments are ever going to win the day. You might ask then why bother submitting comments. Frankly, the only reason I go through the motions because if the problems I have documented actually happen I want to be in a position to say don’t blame the affected sources because RGGI was told that this could happen and the comments were ignored.

My Comments

I am submitting these comments on the September 25, 2017 RGGI Program Review Meeting and the proposed revision to the emissions cap in light of the RGGI Investment Proceeds report that was issued after the meeting. Based on the CO2 reduction numbers claimed in the Proceeds report the revised emissions cap appears to be risky and threatens the credibility of the program. It would be prudent for RGGI to delay implementation of any cap reductions post-2020 to determine the feasibility of meeting additional reductions based on the actual rate of CO2 reductions produced by RGGI and other programs.

CO2 Reductions from RGGI Investments

Before I use results in the Investment of RGGI Proceeds in 2015 report I want to comment on a relevant issue with it. The Executive Summary notes that “the RGGI states have reduced power sector CO2 pollution over 45 percent since 2005”. There is no better example of the pervasive mis-direction in the reporting on the impact of RGGI in this document and the RGGI reports overall than this statement. The casual reader would certainly conclude that the RGGI program itself was responsible if not for the entire reduction at least a sizeable portion of the reduction. However, looking at CO2 reductions in the RGGI states that is not the case. In the first place, the program started in 2009 not 2005. As shown in Table 1, the reduction from the last year before RGGI (2009) was instituted was 31%, much less than the 45% claimed. (Note that my numbers don’t match the RGGI report which I believe is because I relied on the EPA Clean Air Markets Division database with the assumption that summing all the annual CO2 from the all programs that report CO2 was a good enough approximation. If RGGI only summed data from RGGI-affected units it could certainly account for the difference between numbers.)

However, it is even worse. CO2 emissions in 2015 were 41 million tons less than the 2006-2008 baseline so the investments that were projected to avoid the release of 20.5 million tons of CO2 could account for no more than 50% of the observed reduction. The 20.5 million decrease is only a 16.1% reduction from the 2006-2008 baseline. This is consistent with the white paper submitted to RGGI by the Environmental Energy Alliance of New York which showed that RGGI is only responsible for between 24% and 5% of the observed reduction.

Importantly, there is an implication to the RGGI investments “success” with carbon reductions relative to the proposed 30% reduction in the emissions cap. The proposed program revisions released last month for RGGI call for an annual post-2021 cap reduction of 2,275,000 tons per year. In the Proceeds Investment Report, Table 1 Benefits of 2015 RGGI Investments, it lists the annual benefits of 2015 investments and shows an annual CO2 reduction of 298,410 tons. As also shown in the white paper submitted to RGGI by the Environmental Energy Alliance of New York the affected electrical generation units have made most if not all of the cost effective reductions possible from their operations. As a result, future reductions will have to come from sources outside the affected units and RGGI has no track record providing any assurance that its investments will be sufficient to meet the targets proposed. The fact is that RGGI has not provided a roadmap for the 30% reductions that they have proposed so it is not clear how this will work.

I have personal serious doubts where the additional reductions will come from. There is a lot of hopeful reasoning if the presumption is that other state programs will provide the necessary energy changes needed to reduce CO2 emissions from the affected entities. Even though it has been said before, I will say it again: if a compliance entity has no allowances available to cover emissions their only compliance alternative is to stop running. If that happens then RGGI will have a whole lot of explaining to do in order to salvage any credibility as a template for a successful control program.

Potential Impact

RGGI has never quantified the potential impacts of their program on global warming. In order to address that shortcoming I have adapted data for RGGI emissions in Table 2 RGGI 30% Reduction Impact on Global Warming  from the analysis in Analysis of US and State-By-State Carbon Dioxide Emissions and Potential “Savings” In Future Global Temperature and Global Sea Level Rise. The original analysis of U.S. and state by state carbon dioxide 2010 emissions relative to global emissions quantifies the relative numbers and the potential “savings” in future global temperature and global sea level rise from a complete cessation of all CO2 emissions in the RGGI region as well as the proposed 30% reduction.

My analysis shows current growth rate in CO2 emissions from other countries of the world will quickly subsume any reductions in RGGI CO2 emissions. According to data from the U.S. Energy Information Administration (EIA) and based on trends in CO2 emissions growth over the past decade, global growth will completely replace an elimination of all 2010 CO2 emissions from RGGI states in 190 days. The proposed 30% reduction in the RGGI emissions cap will result in an additional reduction of 22.5 million tons but global growth will completely replace the expected reductions in 10 days.

Furthermore, using assumptions based on the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports we can estimate the actual impact to global warming for a change to the cap. The proposed 30% reduction in the RGGI emissions cap will result in an additional reduction of 22.5 million tons which is projected to ultimately impact global temperature rise by a reduction, or a “savings,” of 0.00033°C by the year 2050 and 0.00069°C by the year 2100.

These predicted temperature savings for the 30% RGGI emission reduction have to be put in context to fully appreciate their insignificance. The National Oceanic & Atmospheric Administration’s Requirements and Standards for NWS Climate Observations states that: “The observer will round the entered data to whole units Fahrenheit”. The nearest whole degree Fahrenheit (0.55°C) is over 1600 times greater than the projected change in temperature so the impact will not be observed.

Another way to relate to the savings is to compare those temperatures differences to climatological temperature variation. Table 3 RGGI 30% Emission Reduction Temperature Savings  compares the projected temperature savings to the temperature climatology of Syracuse, NY. I chose Syracuse because I live there but using any location in the RGGI states would show similar numbers. On an annual basis the temperature range for the highest and lowest recorded temperatures in Syracuse was a 129 deg. F which is 214,000 times greater than the temperature difference that would result from the proposed 30% reduction in emissions. On a seasonal basis the ranges between the daily maximum, minimum and average are all listed and the lowest ratio is that the daily minimum temperature range over the year is 77,000 times greater than the temperature difference that would result from the proposed 30% reduction in emissions. There also is a range in temperature every day and the maximum, minimum, and average hourly maximum and minimum difference ranges are listed. The lowest ratio is for the minimum difference between the observed maximum and minimum temperatures and that is over 22,000 times greater than the temperature difference that would result from the proposed 30% reduction in emissions.

Unfortunately those numbers still don’t completely reflect the futility of claiming that the 30% RGGI emission reduction is anything other than a symbolic gesture. A more relatable context would be to consider them in relation to typical changes in temperature with elevation and latitude. Generally, temperature decreases three degrees Fahrenheit for every 1,000 foot increase in elevation above sea level. The temperature difference projected for the 30% reduction in RGGI emission is equivalent to a one inch drop in elevation. The general rule is that temperature changes three degrees Fahrenheit for every 300 mile change in latitude at an elevation of sea level. The temperature difference projected for the 30% reduction in RGGI emission is equivalent to going south 159 feet. Given that those changes are insignificant compared to everyone’s daily experience it is clear that no environmental impacts caused by global warming could possibly be affected with this emission reduction.

Summary

RGGI has been a success inasmuch as it has successfully demonstrated how a cap and auction program can be run, has contributed to the observed CO2 reductions and has provided worthwhile investments in energy efficiency, energy conservation, and ratepayer direct bill assistance. On the other hand, RGGI has no demonstrated success providing the magnitude of CO2 reductions necessary to meet the proposed post-2021 cap reduction of 2,275,000 tons per year. Therefore, it would be prudent for RGGI to delay implementation of any cap reductions after 2020 to determine the actual rate of CO2 reductions produced by other programs. As shown in my analysis of global warming impacts there is no pressing environmental impact rationale to implement reductions as proposed. The success and the credibility of the program itself is endangered by the reckless insistence on a further 30% reduction in emissions at this time.

Investment of RGGI Proceeds in 2015

This is a post on the latest Regional Greenhouse Gas Initiative (RGGI) report: Investment of RGGI Proceeds in 2015. It is another in a series of posts on RGGI that discusses how RGGI has fared so far (see posts labelled RGGI in the menu). Although the press release, RGGI Report: Investments Generating Consumer Benefits, describes the benefits of the program in glowing terms there are some unsettling numbers buried in the report.

I have been involved in the RGGI program process since its inception. Before retirement from a non-regulated generating company, I was actively analyzing air quality regulations that could affect company operations and was responsible for the emissions data used for compliance. After years dealing with RGGI I worry that whether due to boredom or frustration, that there is very little dissent to the program. It may be because, contrary to EPA and State agency rulemakings, RGGI does not respond to critical comments and rebut concerns raised by stakeholders. After years of making comments that disappear into a void, industry does not seem to think there is value to making comments. The opinions expressed in this post do not reflect the position of any of my previous employers or any other company I have been associated with, these comments are mine alone.

Summary of the Report Results

According to the Executive Summary in this report:

Proceeds from the Regional Greenhouse Gas Initiative (RGGI) have powered a major investment in the energy future of the New England and Mid-Atlantic states. This report reviews the benefits of programs funded in 2015 by RGGI investments, which have reduced harmful carbon dioxide (CO2) pollution while spurring local economic growth and job creation. The lifetime effects of these RGGI investments are projected to save 28 million MMBtu of fossil fuel energy and 9 million MWh of electricity, avoiding the release of 5.3 million short tons of carbon pollution.

As a whole, the RGGI states have reduced power sector CO2 pollution over 45 percent since 2005, while the region’s per-capita GDP has continued to grow. RGGI-funded programs also save consumers money and help support businesses. RGGI investments in 2015 are estimated to return $2.31 billion in lifetime energy bill savings to more than 161,000 households and 6,000 businesses which participated in programs funded by RGGI investments, and to 1.5 million households and over 37,000 businesses which received direct bill assistance.

The report describes how the RGGI investments were used 2015, a brief summary of cumulative investments, and then provides specific information for each state including an example of the programs.

Critique

The first step evaluating the claims of the RGGI states is to convert the pretty graphics to numbers. Chart 4 Cumulative RGGI Investments by Category is an example of the graphical data provided. In the table, All-Time RGGI Investments by Category %, I extracted the numbers shown for each analogous State graphic. In the State information sections the amount of money invested, received and diverted to the general fund for each state is listed. In the table, All-Time RGGI Investments by Category $Millions, I listed those numbers then multiplied them by the State category percentages to get total category amounts.

When you extract the numbers a less complimentary picture of the RGGI proceeds investments emerges. For starters, Chart 4 RGGI Investments by Category does not include the embarrassing raids of RGGI funds by New York and New Hampshire. In reality, the correct percentages are shown as the last row in the All-Time RGGI Investments by Category $Millions table. Similarly, the individual state category investments do not include the General Fund raids which would have shown that 10% of the New York “investments” went to political expediency.

There are some financial issues raised when the amounts are available for each state. Note the difference between the RGGI monies received and investments made and the column labelled “investments received %” which is simply investments divided by received. There is a huge range between the states. Vermont is the most efficient turning the money received into investments but six of the nine states all managed to invest at least 80% by the end of 2015. Delaware, Rhode Island and New York did much worse. It is probably no coincidence that the Administrative cost percentages for Delaware, Rhode Island and New York were among the highest four state percentages.

Administrative costs in general and the tithe to RGGI total over $100 million.   Coupled with the general fund raids there is clearly a cautionary tale for carbon tax advocates who suggest that returning those taxes revenues to the public will minimize impacts. In my opinion, whenever there is a large pot of money available there will be politicians abusing their power to the disadvantage of the public.

The cost effectiveness of these investments is not presented in the report. The report notes that the lifetime effects of the 2015 RGGI investments are projected to save 28 million MMBtu of fossil fuel energy and 9 million MWh of electricity, avoiding the release of 5.3 million short tons of carbon pollution. In Chart 2 there is a note that states that the RGGI states invested $410,158,329 in 2015. The lifetime effects cost $15 per MMBtu, $46 per MWh of electricity and $77 per ton of carbon. I am unfamiliar with the benchmark costs per MMBtu and MWh for comparison with the RGGI effectiveness. However, the common justification for carbon reduction costs is the social cost of carbon. At a 3% discount rate, EPA says that the 2015 social cost of carbon was $36. RGGI investments are reducing carbon at twice the rate claimed to value the climate impact of rulemakings so their investments are not cost effective in this regard.

There is one other aspect of this report that needs to be addressed. The Executive Summary notes that “the RGGI states have reduced power sector CO2 pollution over 45 percent since 2005”. There is no better example of mis-direction in the reporting on the impact of RGGI in this document than this statement. The casual reader would certainly conclude that the RGGI program itself was responsible if not for the entire reduction at least a sizeable portion of the reduction. However, looking at CO2 reductions in the RGGI states that is not the case. In the first place, the program started in 2009. As shown in the EPA CAMD Annual CO2 Trend Data table the reduction from the last year before RGGI was instituted to 2015 was 31%, much less than the 45% claimed. (Note that my numbers don’t match the RGGI report which I believe is because I relied on the EPA Clean Air Markets Division database with the assumption that summing all the annual CO2 from the all programs that report CO2 was a good enough approximation. If RGGI only summed data from RGGI-affected units it could certainly account for the difference between numbers.)

However, it is even worse. The 20.5 million decrease is only a 16.1% reduction from the 2006-2008 baseline. This is consistent with my previous post on CO2 reductions due to RGGI which showed that RGGI is only responsible for between 24% and 5% of the observed reduction.

There is one final implication to the RGGI investments “success” with carbon reductions. The proposed program revisions released last month for RGGI call for an annual post-2021 cap reduction of 2,275,000 tons per year. In the Proceeds Investment Report Table 1: Benefits of 2015 RGGI Investments Program the annual benefits of 2015 investments lists an annual reduction of 298,410 tons. As shown in a white paper submitted to RGGI by the Environmental Energy Alliance of New York the affected electrical generation units have made most if not all of the cost effective reductions possible from their operations. As a result, future reductions will have to come from other investments but RGGI has no track record providing any assurance that RGGI investments will be sufficient to meet the targets proposed. For the record if a compliance entity has no allowances available to cover emissions their only compliance alternative is not run. If that happens then RGGI states will have a whole lot of explaining to do.

 

 

Academic RGGI Economic Theory of Allowance Management

This is another in a series of posts on the Regional Greenhouse Gas Initiative (RGGI). The program includes periodic reviews to consider program successes, impacts, and design elements. In the current program review process one issue is the potential to add a new component to the program called the Emissions Containment Reserve. Resources for the Future (RFF) and University of Virginia (UVA) had a webinar on June 14, 2017 on the results of their analyses of that component and RFF followed up with comments submitted to RGGI on July 17, 2017. This post addresses what I believe is a fundamental problem with the academic theory of RGGI allowance management.

I have been involved in the RGGI program process since its inception. Before retirement from a Non-Regulated Generating company, I was actively analyzing air quality regulations that could affect company operations and was responsible for the emissions data used for compliance. The opinions expressed in this post do not reflect the position of any of my previous employers or any other company I have been associated with, these comments are mine alone.

Dr. William Shobe (UVA) and Dr. Dallas Burtraw (RFF) described their analysis of the effect of an Emissions Containment Reserve (ECR) in the June 14, 2017 webinar. RFF used a model to project future allowance supply, demand and cost. UVA did a laboratory experiment at the UVA Economics Laboratory using students as allowance managers.

I believe they both have the same perception of the economic theory of RGGI allowance management. The UVA theory is summarized in slide 19 of the webinar slide presentation. That slide states that it is the long-run supply that counts. “In markets for storable commodities (like allowances, for example), the current price and the plan for accumulation of a stock of the commodity depend on

  • The expected long-run total supply compared to
  • The expected long-run total demand.”

Similarly, in the RFF comments to RGGI it is noted that the allowance banking “propagates and adjusts the value of an allowance over time in light of the opportunity cost of holding the allowance as a financial asset (versus buying one at a later point in time).”

However, I believe that RGGI allowance management is different because the affected sources do not treat allowances as a storable commodity or a financial asset in the usual sense of the term. Instead allowance management is overwhelmingly driven by regulatory requirements for the current compliance period. i.e., do I have enough allowances to cover expected emissions? Financially it is simply another cost of operating and not a potential profit center. The important difference is that the academic economic theory holds that affected sources are looking years down the road but in reality there is no such long-term time horizon for affected sources. Their decision to buy allowances is driven by their expected operations in the period between auctions and at most the entire compliance period and to include a small margin for operational variations and regulatory compliance.

I have worked in New York for a long time and I have been unable to find a single company that will admit to long-run allowance planning. In the first place, allowance purchases cost a significant amount of money. New York electric generating companies are on a tight margin with little extra money available, so the idea that money could be available to purchase allowances for needs more than three years in the future is laughable amongst my sources. It is also important to note that in New York that the non-regulated generating companies have been in a constant state of change since de-regulation began before 2000. Very few facilities are still owned and operated by the same companies that purchased when de-regulation began. As a result of that turmoil there are few incentives to purchase allowances for future needs because the expectation is that facility ownership changes will continue going forward.

In addition, the RGGI cap and auction CO2 allowance program is different than a traditional cap and trade program for SO2 or NOx. In a traditional program, allowances are allocated proportionally to affected sources based on historical operations. When the cap is established the total emissions in the State have to be lowered to that level. On the basis of the cap level, affected sources can determine if it makes sense to install control equipment or purchase allowances to comply with their compliance obligations. As long as somebody can over control and generate surplus allowances to subsidize their control investments then allowances should be available on the market for use if control costs are not cost-effective at a particular affected sources.

In the RGGI cap and auction system, everyone has to buy allowances. Even if there were an option to control CO2 emissions (and there isn’t anything available for existing sources that is cost effective) a source installing controls has to buy allowances so there wouldn’t be an offset to the control costs. As a result of all these factors the affected sources have universally adopted an allowance management strategy with a short time horizon.

So how does this affect the analyses and what could be done?

I am not sure how this affects the modeling and the lab experiment for allowance management. I note, however, that the presentation and the comments both imply decisions should be based on future expectations of allowance costs. I believe that is an outgrowth of their mistaken RGGI allowance management theory. Instead I believe that the ECR should be based on observed allowance price behavior.

I would love to see the UVA allowance tests be repeated using actual industry allowance managers and industry allowance compliance staff. It would be educational for both the academics and industry staff and would confirm or blow up my perception of the differences between academic theory and actual allowance management practice. It would also confirm whether the conclusions based on the academic theory are consistent.

One final note relative to the economic analyses done by RFF and UVA. As far as I can tell they both presume future emission cap reductions (RFF modeled a 3.5% reduction). The presentation and comments both implicitly assume that the primary motivation for the ECR is because there is a potential that future emission reductions will be greater than cap reductions leading to an over-supply of allowances and lower prices than are deemed acceptable.

I believe the more likely scenario is that emissions don’t continue at the historical decrease rate and that an allowance deficit is more likely[1]. Consequently, I think that using the ECR to determine future emission reductions instead of arbitrarily picking a percentage reduction is less risky. The academic presumption that future reductions are “easy” also influences their recommendations relative to the ECR. At the very least they should consider a scenario where reductions less than expected.

[1] My rationale is that historical emissions decreases have been largely driven by fuel switching from a more expensive to cheaper and less emitting fuel. The potential for similar future reductions is largely gone. In an earlier post I showed that the experience so far of RGGI induced emission reductions is pretty low. The upper bound is an econometric model that estimates that emissions would have been 24 percent higher without the program. RGGI estimates that emissions would have been 17% higher than without a program. If you assume that all the savings in fossil fuel use only displaced natural gas use then emissions would have been only 5% higher.

RGGI Costs Relative to NYS Electric Supply Rate Requests

This is another in a series of posts on the Regional Greenhouse Gas Initiative (RGGI). The program includes periodic reviews to consider program successes, impacts, and design elements. In the current program review process one of the big issues is whether to set new lower caps after 2020. Ultimately however ratepayers will have to bear the costs of further reductions. This post compares the proposed costs relative to recent rate requests and approved rate increases by New York State (NYS) electric utilities.

I have been involved in the RGGI program process since its inception. In the final years before my retirement I analyzed air quality regulations that could affect electric generating company operations. The opinions expressed in this post do not reflect the position of any of my previous employers or any other company I have been associated with, these comments are mine alone. I am motivated to write these posts on RGGI because the majority of the stakeholder opinions expressed at meetings and in submitted comments are, in my opinion, overly optimistic about the potential value of continued RGGI reductions and ignore the potential for serious consequences if things don’t work out as planned. I am particularly disappointed that a bunch of government bureaucrats can simply decree additional costs to ratepayers without any substantive scrutiny by the respective State public service organizations or public knowledge.

Ratepayers in New York State have electricity bills made up of delivery and supply charges. The delivery charge is what is paid to transport electricity to the customer over power lines. The supply charge is what is paid for the electricity used. In order for the local electric utility to change the rates paid to transport the electricity delivered they have to go through a rate request process with the New York State Department of Public Service. On the other hand the price paid for electricity used is not directly regulated.

I think it is instructive to compare the indirect allowance costs with recent rate case costs for the delivery charge. I was unable to find a single summary of the most recent requested and granted rate case values for the delivery charge component of ratepayer bills. I searched for numbers and using a combination of news reports and rate case documents came up with an estimate. The NY total for the most recent rate requests for electric delivery for the investor owner utilities and LIPA is $1,282 million (Table 1, NYS Electric Delivery Rate Cases).

The cost of allowances eventually and indirectly works its way back to ratepayers. RGGI is a “Cap and Auction” program that caps electric generating unit emissions and then auctions permits to emit CO2 or allowances. The proceeds from the auctions are supposed to be invested in “strategic energy and consumer programs” but on two occasions New York Governors have raided the RGGI proceeds for other uses. The allowance costs are buried in the supply charge.

The RGGI states use the production cost model Integrated Planning Model (IPM) to analyze the impacts of air quality policies including emissions and allowance costs. This is a massive model that purports to estimate how the entire United States utility sector will react to changes in air quality regulations. In order to do that they have to model not only generator operations, fuel costs and control equipment strategies, but also the transmission system.  I think there are problems with the IPM results that will be addressed in another post but for this analysis I will just accept the numbers the RGGI states are using.

In the current program review analysis there are three draft policy scenarios for possible changes to the RGGI program after 2020. As it stands now there are no further reductions promulgated for the RGGI cap after 2020 but the RGGI states are considering and have evaluated three reduction scenarios: continuing the 2.5% reduction in place up to 2020 until 2030, a 3% reduction and a 3.5% reduction. The IPM model results for the scenarios are compared to a reference case so the results are consistent. In order to cover the full range of outcomes sensitivities are run for each scenario. In the high sensitivity cases assumptions are made for plausible reasons why emissions could be higher than the reference base case and in the low sensitivity the emissions are lower. In addition, sensitivity cases with and without a national program were run. As a result there were a total of nine policy case runs.

On the RGGI website in the 2016 program review documents the model output for each of the policy runs (June 27, 2017 Meeting Materials) and the reference cases (April 20, 2017 Meeting Materials) are listed in spreadsheets. Each spreadsheet lists the model estimates of capacity additions (what electric generating capacity the model and what the states tell the model to include because of regulations); generation (how much the existing and projected units will produce); prices (including firm power prices, energy prices, capacity prices, allowance prices, natural gas prices, and renewable energy credit prices); total CO2 emissions; fuel consumption for different fuel types; and transmission flows into and out of the RGGI power grids. Those results are presented by region and each state. Note that the model output does not include projections for every year. For the 14 years 2017-2030 there is model output for six years.

For this analysis I extracted and consolidated the projected emissions and allowance prices. I estimated annual emissions and prices by interpolating between model projections. The ultimate cost to the ratepayer should be equal to the total revenue at the auctions which equals emissions times the allowance prices. Table 2, IPM 2017 modeling of annual CO2 credit price, CO2 emissions and CO2 allowance auction, lists the results from 2017 to 2030 for all the reference case scenarios and all the policy case scenarios. The Key to the 14 different modeling runs lists the assumptions made for each run. RGGI compares the reference case to the IPM modeling results. The difference between the reference case, no national program and each of the policy scenarios is shown in Table 3. Depending on the changes made to the program, consumer costs for allowance revenues are projected to increase between $134 and $604 million in 2021 to between $254 million and $1,011 million in 2030 for the nine RGGI states. For each scenario the relative share of New York emissions is listed for 2030 along with the cost for just New York. The New York share of those costs ranges from $108 million to $391 million. Table 3 also compares the RGGI allowance costs to total electric delivery costs ($1,282 million).

My point is that completely outside of any DPS review and the glare of ratepayer advocacy scrutiny, government bureaucrats are contemplating additional costs of between 8% and 31% of the most recent rate requests. I think that it would be in the best interests of the State that there was more recognition of this process.

RGGI as the Electric Sector Compliance Tool to Achieve 2030 State Climate Targets

This is another in a series of posts on the Regional Greenhouse Gas Initiative (RGGI). The program includes periodic reviews to consider program successes, impacts, and design elements. In the current program review process one of the big issues is whether the cap on CO2 emissions should continue to decrease after 2020 when the current program ends. Not surprisingly many environmental organization advocate continued reductions based on reductions made to date and cite a report prepared by Synapse Energy Economics entitled “The RGGI Opportunity 2.0, RGGI as the Electric Sector Compliance Tool to Achieve 2030 State Climate Targets” (hereinafter the “Synapse Study”).  In previous posts I have looked at emission reductions and this post looks at the claims made in the Synapse Study.

I have been involved in the RGGI program process since its inception. In the final years before my retirement I analyzed air quality regulations that could affect electric generating company operations. The opinions expressed in this post do not reflect the position of any of my previous employers or any other company I have been associated with, these comments are mine alone. I am motivated to write these posts on RGGI because the majority of the stakeholder opinions expressed at meetings and in submitted comments are, in my opinion, very naïve relative to the actual burden implanting their preferred alternatives, overly optimistic about the potential value of continued RGGI reductions, and ignore the potential for serious consequences if things don’t work out as planned.

The Synapse Study was commissioned by the Sierra Club, Pace Energy and Climate Center and Chesapeake Climate Action Network. The introduction to the Synapse report states that it “…builds upon Synapse’s prior analysis of emission reductions in the electric and transportation sectors by additionally analyzing emission reductions in the building sector to create a robust least-cost buildout of compliance with RGGI states’ 2030 climate goals.” During the stakeholder process associated with the current RGGI program review a number of organizations have endorsed the implementation of a steeply declining RGGI emissions cap between 2020 and 2030, primarily based on the assertions found in the Synapse Study. Specifically, two comments posted to the Program Review record on June 29, 2016 from a total of 23 environmental and health organizations contained the following statement:

“Throughout the program review, numerous groups have requested that the RGGI states model a scenario with capped emissions declining by 5% of the 2020 baseline each year through the modeling horizon, resulting in a 2030 cap level of 39 million tons. This request is based on the dual justification that A) such a trajectory is consistent with RGGI emissions reductions to date, and B) multisector analysis has demonstrated that this is the most cost-effective pathway to achieving the RGGI states’ 2030 economy-wide GHG commitments”.

In my previous posts I have pointed out that the emissions trajectory to date is not reflective of further reductions because emissions from the primary source of past reductions (coal and residual oil generation) are already close to de minimus levels. This post addresses the “multi-sector analysis” prepared by Synapse for future reductions. I focus on the viability of the measures proposed to reduce CO2 emissions and whether all the necessary costs for implementation have been included for the ultimate cost-effectiveness evaluation.

Based on the following analysis of various statements this study does not make a strong case. I did not try to cover every proposed aspect of the multi-sector analysis. Instead I evaluated four components. The Synapse study text is shown in italics; my comments in regular text.

 Half of Emission Reductions Come from the Electric Sector (p. 7, Synapse Study)

Electric-sector efficiency and renewables are responsible for nearly half of the additional required reductions in 2030. Figure 5 presents emission reductions in the electric sector for the baseline and 40 percent emission reduction policy scenarios. In the 40 percent emission reduction scenario, Northeast states’ electric sector emissions are capped at 39 million short tons in 2030 compared to the currently mandated RGGI cap of 78 million short tons in 2020.

The latest EIA data indicates that 2014 electric sector emissions (Table 1) were 86.1 million tons compared to the 78 million ton current cap. Note that coal and residual oil made up 34.6 million tons of the total sector emissions and that natural gas emissions were 51.5 million tons. Since 1990 most of the electric sector emission reductions have been as the result of coal and residual oil reductions primarily due to retirements and changes in operations driven by economics and not necessarily RGGI. Natural gas use went up as it displaced the other two fuels.

These data suggest that future reductions necessary to meet the current 2020 cap of 78 million tons is close enough to be achievable. However, an additional 39 million ton reduction is necessary to meet the proposed 2030 cap. In a previous post I showed the bounds for CO2 reductions that could be attributed to RGGI investments to date. The upper bound is an econometric model that estimates that emissions would have been 24 percent higher (31.9 million tons) without the program. RGGI estimates that emissions would have been 17% higher (22.6 million tons) than without a program. If you assume that all the savings in fossil fuel use earned by RGGI investments only displaced natural gas rather than historical use as the RGGI estimate did, then emissions would have been only 5% higher (4.2 million tons). My point is that future reductions will have to come as the result of RGGI and state programs not fuel economics and depending on how you calculate the impact of RGGI programs to date this could be relatively easy or not. Given that future reductions will be from displacing natural gas I believe it is more difficult that presumed by this study.

Efficiency, Wind, and Solar Drive Down Electric-Sector Emissions (p. 8, Synapse Study)

Under the 40 percent emission reduction scenario new, lower RGGI caps drive deeper, more wide-spread changes in the RGGI states’ electric system. Figure 6 reports the impact of these measures in terms of generation by resource. Coal, oil, and natural gas-fired generation are replaced by efficiency and renewables. Note that electric sector generation is lower in the 40 percent emission reduction scenario than in the RGGI baseline even though substantial generation is needed to power electric vehicles and heat pumps: savings from energy efficiency outweigh additional electricity sold to owners of electric vehicles and heat pumps.

 Renewables supply one-half of the RGGI region’s electric generation in 2030 (p. iv, Synapse Study). Adding 50,000 gigawatt-hours of new wind and solar in the 40 percent emission reduction scenario results in a future where half of all electricity generation comes from renewable resources in 2030, compared to just 30 percent in the baseline RGGI scenario.

The Synapse study neglects a major aspect of the electric system in their assumptions that renewables can replace coal, oil, and natural gas to the extent proposed. The electric power system is very complex and must operate within narrow parameters while balancing loads and resources and supporting synchronism. In countries like Germany, it has only been possible to develop an aggressive level of renewable generation in their power system because Germany is able to rely on neighboring country’s conventional facilities in the grid for load support. Synapse has presumed that renewables in the RGGI system can provide the necessary ancillary support but has not shown that it can provide all the important parameters provided by central power stations. For example:

Conventional rotating machinery such as coal, nuclear, and gas plants as well as hydro generation provide a lot of support to the system. This includes reactive power (vars), inertia, regulation of the system frequency and the capability to ramping up and down as the load varies. Most renewable resources lack these important capabilities and are only intermittently available (e.g., not dispatchable). Unlike conventional generators that rotate at constant speed, wind turbines must rotate at variable speeds so that their rotational energy offers no support to the system.

Some, but not all of the disadvantages of solar and wind energy can be mitigated at extra costs through electronic and mechanical means. When these resources make up only a small percentage of the generation on the system, overall system stability is not adversely impacted in a significant way. Stated another way, when the overall system is robust enough, utilities can allow a small percentage of solar “lean” on the system and still provide a stable source of electricity. As the penetration of solar and wind energy increases the system robustness will degrade and reliability will be compromised without costly improvements. Such additional costs are not generally applied to the evaluation of renewable resources at this time, and it certainly appears that the Synapse study has failed to take any of these types of costs and issues into account.

As noted above, the German grid relies on its neighbors to provide a wide range of support services. It may not be possible for the RGGI electrical systems to support the Synapse study’s presumed high penetration of renewable power and the provision of those services are not incorporated in their costs projections. In order for the Synapse projections to work in a real-world scenario the RGGI grid operators will also end up relying on neighboring power systems to provide this support, thus promoting  “emissions leakage”.

Energy Efficiency Savings Are One-Third of Total Emission Reductions (p. 9, Synapse Study)

 Efficiency measures will continue to lower consumers’ bills. Applying Massachusetts’ expected electric energy efficiency savings in terms of percent of sales—based on their current three-year plan—to all RGGI states lowers electric sales by 11 percent by 2030. These efficiency savings have been determined to be cost effective in Massachusetts.

This presumption does not account for the current state of energy efficiency in other RGGI states. If a state is presently more efficient than Massachusetts then it is inappropriate to assume that the same rate of efficiency savings is possible simply because easier energy efficiency targets have already been implemented.

Wallet Hub analyzed Energy Efficiency RGGI State Rankings using data from the U.S. Census Bureau, the National Climatic Data Center, the U.S. Energy Information Administration and the Federal Highway Administration. Their conclusions are highlighted below:

“To identify the most energy-efficient states, WalletHub analyzed data for 48 states based on two key dimensions, including “home-energy efficiency” and “car-energy efficiency.” We obtained the former by calculating the ratio between the total residential energy consumption and annual degree days. For the latter, we divided the annual vehicle miles driven by gallons of gasoline consumed. Each dimension was weighted proportionally to reflect national consumption patterns.

In order to obtain the final ranking, we attributed a score between 0 and 100 to correspond with the value of each dimension. We then calculated the weighted sum of the scores and used the overall score to rank the states. Together, the points attributed to the two major categories add up to 100 points.

Home-Energy Efficiency – Total Points: 55

Home-Energy Efficiency = Total Residential Energy Consumption per Capita / Degree-Days

Car-Energy Efficiency – Total Points: 45

Car-Energy Efficiency = Annual Vehicle Miles Driven / Gallons of Gasoline Consumed

The Wallet Hub 2015 Energy Efficiency RGGI State Rankings are listed in Table 2. Four states are more efficient than Massachusetts and New York and Vermont are markedly more efficient than Massachusetts. Therefore, the presumption that New York and Vermont will be able to reduce emissions by 11% or the same as the Massachusetts expected electrical energy efficiency savings level is difficult to justify and appears to be unfounded.

1.3 Million Electric Heat Pumps Replace Oil Heaters (p. 11, Synapse Study)

In 2015, over 4 million families in the RGGI region were still heating their homes with oil. By 2030, this number is expected to shrink to 3 million households in the RGGI baseline scenario as households move to more efficient forms of heating. These oil furnaces and boilers would release 20.4 million short tons of CO2 into the atmosphere in 2030.

The 40 percent emission reduction scenario shifts 1.3 million of the remaining 3 million households from oil to air-source heat pumps by 2030 (see Figure 9). Heat pumps are appliances that use electricity to absorb heat energy in cold areas (i.e., outside) and transfer it to indoor areas. Heat pumps have the advantage of being able to work in reverse—not only can they provide heating in winter months, but they take the place of a central air conditioning systems in the summer months. Heat pump technology has existed for decades, and these units are commonplace in Europe and Asia, but high-performing systems that function well in cold-weather climates as in many of the Northeast states have just recently begun to make inroads in the United States. By shifting heating consumption from inefficient, high-emitting oil boilers and furnaces to highly efficient heat pumps, 9 million short tons of CO2 are avoided.

 Despite the Synapse disclaimer heat pumps are at a disadvantage in cold climates like New York and other RGGI states there are physical issues: “An air-source heat pump works well as long as temperatures are above freezing. Below that temperature, less heat is available, and the pump may have to rely on its supplemental heating coil to warm your home. This coil uses electricity to heat and will increase heating costs.”

Traditionally, heat pumps have not enjoyed a wide level of penetration in housing markets because consumers are primarily interested in cost of ongoing operation. Furthermore, there does not appear to be any in-place incentives that would lead to a wide-scale shift from conventional oil- and gas-fired boilers to heat pumps. Absent some regulatory requirement or financial incentive program, the Synapse study assumption that one million conventional furnaces will be replaced with heat pumps over a fifteen year period has no basis in fact and appears to be a highly unlikely scenario.

Ten Million Electric Vehicles Offset 28 Million Short Tons of CO2 (p. 12, Synapse Study)

The 40 percent emission reduction scenario adds 10 million battery electric vehicles in the nine RGGI states by 2030, above what is currently in place and expected in the baseline forecast (see Figure 10).9 The stock of electric vehicles in the RGGI baseline is based on the Energy Information Administration’s 2015 projections and reaches 46,000 vehicles in the RGGI region in 2030. In contrast, Synapse’s 40 percent emission reduction scenario assumes that one-third of the RGGI region’s light-duty vehicles run on electricity by 2030 based on the Federal Highway Administration’s projection of the potential for electric vehicle adoption. These new electric vehicles reduce total RGGI state emissions by 28 million short tons of CO2 in 2030.

The Energy Information Administration’s Annual Energy Outlook 2016 includes tables with projections of future vehicle stocks. The vehicular data are categorized by region not state so it was not possible to reproduce the Synapse Study numbers. Table 40. Light-Duty Vehicle Stock by Technology Type notes that, nationwide, in the reference case there are 340,481 conventional light duty cars in the 100 mile electric vehicle and 200 mile electric vehicle classes in 2016 and in 2030 predicts there will be 3,534,097 vehicles in the reference case and 3,542,276 vehicles in the reference case without the Clean Power plan.

In New York there are about nine million light duty vehicles registered. The Synapse study claims that it is possible to replace one third of the 8.7 million gas powered vehicles with electric vehicles, which equates to 2.9 million electric vehicles in New York by 2030. That would be 82% of the EIA projected total for 2030.  In order to reach the projected total in the Synapse study presumption, over 190,000 per year would have to be sold. At the current time, there are no regulatory structures and only insufficient financial incentives in place to support this massive level of electric vehicle penetration.

Presuming for a moment that it would be possible to effect such a significant change in the driving habits of New Yorkers, the Synapse study has not taken into consideration that there are significant infrastructure requirements for the 2.9 million electric vehicles projected. One of the greatest impediments to the further development of electric vehicle market is that charging stations in public places have not yet been implemented on a widespread basis nor has a satisfactory cost model been developed on how to finance such a massive infrastructure build-out. Clearly, if one in every third car parked on a New York City street is to be an electric vehicle, significant costly changes to the existing electrical systems must come about. The Synapse study has not taken those costs into account in its presumption that an unprecedented change in the vehicle market will lead to the emission reductions proposed.

Summary

I looked at four components of the Synapse study: electric sector, energy efficiency, home heating and electric vehicles. Synapse claims 103 million ton CO2 emission reductions when these recommendations are implemented. In the following I offer alternative estimates of the tons saved based on the aforementioned evaluation.

In the Synapse electric sector analysis (section 2.4, p. 7) half of the emission reductions or 39 million tons come from the electric sector. The big unknown is how much renewable will displace the natural gas usage and it was argued above that as little as 4.3 million tons of reductions occurred because of RGGI itself. Synapse assumes that the state renewable portfolio standards will be implemented but does not explain how. For example, they note that:

For New York, in addition to modeling the existing RPS (approximately 24 percent of retail electric sales by 2015), we modeled an additional 3,000 MW of utility-scale photovoltaic (PV) solar added by 2023 and an additional 1,600 MW of wind added by 2029, in line with the New York State Energy Research and Development Authority’s (NYSERDA) projections for capacity that will come online as a result of the NY-Sun and Large-Scale Renewables programs

The missing piece is how much generation will that presumed additional capacity generate and how much will it displace natural gas generation. It is not enough to assume that it is a straight ratio because the ancillary services provided by traditional power plants would not be included. Synapse did not account for electric system reliability issues in its projected penetration of renewables into the RGGI states power system. For an upper bound estimate of potential reductions I used the 2014 EIA data and assume that petroleum products are already at their de minimus level, coal goes to zero, and that natural gas stays the same assuming that the replacement of coal by natural gas equals the reduction by renewables. Using those assumptions there are only 22.6 million tons of savings.

The energy efficiency Synapse projection (section 2.6, p. 9) accounts for one third of total emission reductions or 27 million tons. I think that Synapse overestimated the amount of emissions savings that are available from energy efficiency programs absent additional policy and/or regulatory structures.  Their analysis presumes that the Massachusetts rate of energy efficiency improvement can be applied to four states that have better efficiency but that is inappropriate. I assumed that the expected energy improvement of 11% in those four states would only be half of that, scaled the reductions as a function of residential and commercial end use and determined that instead of 17 million tons of electric energy efficiency there would only by 12.1 million tons. The remaining 10 million tons of Synapse energy efficiency comes from gas energy. They simply assumed without much justification that it could be improved by 1% per year but I accept their guess.

For home heating (section 2.7, p. 11) Synapse projects that 1.3 million heat pumps will replace of the 3 million home heating furnaces still on oil saving 9 million tons of CO2. Their analysis does not address the physical constraints of heat pumps in freezing weather. Synapse has not provided a plausible structure that will bring about its projected transition to electrically-powered heat pumps. Rather than their high bound assumption that 43% of the furnaces will be converted to heat pumps I think a realistic lower bound for conversion is 10% so the CO2 savings would be 2.1 million tons.

The Synapse electric vehicle scenario (section 2.8, p. 12) projects that there will be 10 million electric cars in RGGI by 2030 and also notes that EIA projects only 46,000 more electric vehicles by 2030. That equates to some kind of incentive program to get RGGI drivers to buy 9,954,000 electric vehicles and even if the incentive is only $1,000 that is over $9 billion dollars. Synapse failed to appreciate the complexities and costs associated with a massive conversion of driving preferences to electric vehicles. In the real world a more realistic estimate would be RGGI incentivizes an order of magnitude more electric vehicles (460,000) than EIA and the CO2 savings would be 1.3 million tons.

Synapse claims 103 million ton CO2 emission reductions when their recommendations for these four components are implemented. However, based on my evaluation I expect only 51.6 million tons of reductions from these four components.

RGGI Containment Reserves

This is another in a series of posts on the Regional Greenhouse Gas Initiative. Previous posts have looked at how the program has been working from the viewpoint of an outsider. This post is a technical discussion of two components of the system currently being discussed in the 2016 Program Design process: the Cost Containment Reserve (CCR) and the proposed Emissions Containment Reserve (ECR).

I have been involved in the RGGI program process since its inception. Before retirement from a Non-Regulated Generating company, I was actively analyzing air quality regulations that could affect company operations and was responsible for the emissions data used for compliance. The opinions expressed in this post do not reflect the position of any of my previous employers or any other company I have been associated with, these comments are mine alone. I am motivated to write these posts on RGGI because the majority of the stakeholder opinions expressed at meetings and in submitted comments are, in my opinion, overly optimistic about the potential value of continued RGGI reductions and ignore the potential for serious consequences if things don’t work out as planned.

Overview of Containment Reserves

The RGGI website has an overview of the cap and auction system that includes a description of the CCR. One of the original stakeholder concerns was cost. In order to put a limit on the cost of allowances the CCR was established to add allowances to the program when the cost exceeds a threshold. The theory is that adding allowances will reduce the allowance cost. The trigger price for adding 5,000,000 million allowances in 2014 or 10,000,000 allowances thereafter was $4 in 2014, $6 in 2015, $8 in 2016, and $10 in 2017, rising by 2.5 percent each year thereafter. The CCR has been triggered twice, in the first quarter of 2014 and the third quarter of 2015, adding 15,000,000 allowances to the system.

One of the primary stakeholder topics of the RGGI 2016 program review is future reductions in the cap. As it stands now the cap declines by 2.5% per year until 2020 and remains at the level. Proponents of future reductions claim that past performance suggests the cap can continue to decline. The ECR was proposed as a possible solution to the cap adjustments. In particular, the ECR decreases the number of allowances auctioned if the price gets too low. The theory is that if the costs are too low that means there is a surplus of allowances and the cap can be lowered accordingly. It seems to me that it could be used as the mechanism to adjust the cap in the future in addition to its price control aspect.

RGGI Allowance Status

The status of the cap and implications on compliance and cost need to be addressed in the context of these containment reserves. Another stakeholder topic in the 2016 Program Design Review is the appropriate size of the allowance bank. The allowance bank is the surplus allowances over and above the compliance requirements. In the first two compliance periods of RGGI the number of authorized allowances far exceeded actual emissions. As a result the bank of allowances was so large that the RGGI states made interim adjustments to subsequent auctions to lower the number of allowances available.

The appropriate size of the bank is controversial. Advocates for more reductions want a smaller bank so that reductions occur sooner. Proponents of higher allowance prices want to reduce the size of the bank because fewer available allowances should drive the price up. However, there are reasons that the bank should not get too small. Emissions are directly proportional to operating times which are strongly related to weather-related demand. Affected sources want to have sufficient banked allowances in their accounts to be able to supply power in periods of increased demand. In addition, companies prefer to have a margin in order to address monitoring problems. Ultimately, if insufficient allowances are available then affected sources will not be able to operate.

In that context consider the RGGI allowance status today. In the first compliance period (2009-2011), 429,381,635 allowances were auctioned, sold or awarded, the CO2 emissions were 382,075,544 tons so the margin between the available allowances and emissions (the allowance bank) at the end of the compliance period was 47,306,091 allowances. The allowance bank at the end of the second compliance period was 120,175,954 allowances. As it stood at the end of 2016 the allowance bank as defined as the difference between the allowances auctioned, sold, or awarded and the RGGI allowances retired was 173,105,751. Note, however, that there is a difference between the 2015-2016 allowances retired and emissions. RGGI retires 50% of the compliance obligation at the end of the year instead of waiting until the end of the compliance period to retire all the emissions. If all the emissions are withdrawn the allowance bank at the end of 2016 is 90,446,582 allowances. For comparison purposes the 2016 total emissions were 80,624,392 tons.

There is an affected source or compliance entity concern with the allowance bank trends. The RGGI allowance database is not as open and transparent as the EPA allowances databases for their programs. EPA provides ownership information on all allowances but RGGI does not. The RGGI market monitoring reports provide the only breakdown between the allowances held by compliance entities and that is only the percentage of the total bank held by compliance entities. At the end of the first compliance period 97% or 45,886,908 of the allowances were owned by entities that needed the allowances to comply with the requirements of the program. At the end of the second compliance period the compliance entity share was down to 81% or 97,342,522 allowances. At the end of 2016 the compliance entity share was only 54% or 93,477,106 allowances. However, remember that RGGI only considers the retired allowances so the compliance entities have to cover 82,659,170 tons of emissions with their share at the end of 2016. Consequently the true compliance entity share of the allowance bank is only 12% or 10,817,936 allowances and that concerns the compliance entities.

In particular, the trend shows that compliance entities will have to go to the non-compliance entities to obtain enough allowances to operate. This problem will be exacerbated if RGGI cuts the cap and/or makes adjustments to the allowance bank. In my former life I was responsible for trading program compliance tracking and had input into corporate compliance strategy. Environmental staff and all corporations I have dealt with consider compliance the highest priority and the only way to insure that is to only generate emissions less than allowances in hand. Theoretically, you could generate and then go out to the market to cover what you emitted but, especially in the case of a constrained market, there is a compliance risk. RGGI is evolving towards the ultimate constrained market and there are two serious potential consequences. Firstly, affected sources could be forced to purchase allowances from an entity that knows they have a compliance obligation and could require them to pay an exorbitant amount for the allowances. This has two downsides: the windfall of money is not anything that will be invested like the RGGI auction proceeds (i.e., there is no societal benefit to those higher priced allowances) and eventually the price will show up on ratepayer bills[1].

Secondly, a company could choose not to run because they don’t have the allowances and that will affect the power system badly, in the worst case it could even affect reliability. Were it up to me because environmental compliance is number one I would advise option 2.

Emission Containment Reserve

I think the ECR is an elegant solution to the issue of whether or not the cap should continue to decline. Ideally, the RGGI cap should create an allowance market where the price is within the acceptable range determined by the RGGI states. The CCR prevents the cost from getting too high. The ECR works on the lower end of the range to keep the price from getting too low. As an alternative to a declining cap the ECR determines the appropriate cap level by withdrawing allowances from the market to the point that the price rises above the low threshold of the target range. As proposed the withdrawn allowances are put into a reserve but it could be set up so that the allowances displace the allowances in the CCR and when there are more than 10,000,000 allowances displaced then allowances from the cap would be withdrawn. I want to emphasize however, that my support of this approach is in lieu of a specified declining cap.

In my opinion future reductions will not be as easy as the past. The majority of the reductions in the RGGI region to date have occurred because of coal unit retirements and cutbacks in the use of residual oil which were driven by the economics of low natural gas fuel prices. However, most of the coal facilities have retired and residual oil emissions are about as low as they can go so future reductions will have to displace more economic natural gas. Therefore, the ECR approach addresses this uncertainty. If further reductions are easily obtained then the ECR will lower the cap. If not then the RGGI program is not in danger of non-compliance meeting an artificial cap.

Unfortunately RGGI seems to be headed towards a mandated declining cap. The last round of proposed policy cases did not even consider the possibility that further reductions may not be available or even slower than expected primarily because all scenarios assume compliance with proposed state programs. While it is appropriate to include the scenario in which it is assumed that the state programs that propose to increase renewable power come to fruition, the fact is that they will necessarily have to increase costs. In my opinion a scenario which only considers economics of conversion also should be included to address the possibility that the political winds could change. In addition, the timing component should be included. Industry has had to deal with licensing delays for years and there is no reason to expect that the infrastructure necessary for the renewable deployments required will not also be affected.

Finally, the issue of the compliance entity share of allowances relative to a declining cap should be addressed. The unintended consequence of further reductions is to exacerbate the cost implications and increased reliability risks. Ultimately, society will pay those costs and I predict that the blame will fall back on the generating companies and not those who recklessly advocate more and more reductions. The existing program has worked as well. The CCR has limited allowance costs from increasing too much and the ECR seems to be a viable alternative to not only limit costs that go too low but also to be the mechanism that reduces the cap based on what is happening to the generating mix.

[1] There are those folks who would applaud increasing costs to the generating companies. To them I say that electricity is an essential driver to the health and welfare of our society. As with all essentials, electricity should be as reliable, secure and cost-effective as possible. It is no less important than food and water. Somehow, someway those increased costs will impact society, most likely impacting those who can least afford it the most.

How Much Has the Regional Greenhouse Gas Initiative Reduced Other Pollutant Emissions?

The Regional Greenhouse Gas Initiative (RGGI) was supposed to be nearing completion of a 2016 Program Review but the election of Donald Trump and the fate of the national Clean Power Plan has delayed that process. This is the third post in a series of posts that will discuss how RGGI has fared so far and how that could affect the program review. As noted previously, I believe that RGGI allowance prices add to the cost of doing business but because the cost of allowances can be added into the bid price it is a nuisance and not a driver of decisions. This post addresses the effect RGGI has had on other pollutants as estimated by Abt Associates: Analysis of the Public Health Impacts of the Regional Greenhouse Gas Initiative, 2009 -2014.

According to the Energy Coordinating Agency this report shows that since 2009, RGGI has significantly reduced air pollution from fossil fuel power plants. The report goes on to estimate improvements to the health of people living in the Northeast as a result of those pollution reductions. According to the study the effort to curb carbon emissions has prevented 300-830 adult deaths, avoided 13,000 – 16,000 respiratory illnesses and staved off 39,000 – 47,000 lost work days for workers.

I have been involved in the RGGI program process since its inception. Before retirement from a Non-Regulated Generating company, I was actively analyzing air quality regulations that could affect company operations and was responsible for the emissions data used for compliance. The opinions expressed in this post do not reflect the position of any of my previous employers or any other company I have been associated with, these comments are mine alone.

Approach

I will briefly describe the three steps that Abt Associates used in their analysis. This post will primarily analyze the first step in their process. I compare their predicted generation and emissions reductions relative to the total reductions observed in the measured data. For the last two steps I offer some general comments.

Abt Associates used a three-step analytic process to estimate the impacts on air quality and public health resulting from implementation of the RGGI program from 2009 to 2014. In each of these steps they used specific modeling tools and datasets to estimate the incremental impacts of RGGI on the following variables: generation (in megawatt-hours (MWh)) by power plants, air pollution emissions, air quality, and public health. The approach is sequential. Results steps one and two were input to the final step that projected health impacts. Abt Associates noted that they “reviewed draft results at a highly disaggregated level and performed quality control before using results as an input to the next analytic step. In many cases, draft results were benchmarked to results from similar analyses and studies as another cross-check.”

The three steps are described as follows:

Step 1: Estimate annual changes in electric generation and emissions of air pollutants at power plants as a result of RGGI implementation from 2009 to 2014 using electricity dispatch modeling and EPA emissions data for EGUs.

Step 2: Estimate annual changes in air quality at the county level associated with changes in SO2 and NOx emissions from power plants, by year. EPA Co-Benefits Risk Assessment (COBRA) screening model

Step 3: Assess public health impacts associated with changes in air quality due to RGGI implementation from 2009 to 2014. EPA Benefits Mapping and Analysis Program (BenMAP).

Generation Reductions

Abt Associates calculated changes in electricity generation due to RGGI first on an absolute basis and then expressed them as a percentage relative to generation that would have occurred in the No RGGI scenario. They note that:

In competitive electricity markets, changes in any number of variables, including fuel prices, weather, and plant and system operational changes, can cause variations in the level of electricity dispatched by a given power plant (or group of plants) from year to year. In this analysis, however, our modeling results account for all of these factors and thereby isolate the incremental effect of RGGI on electricity markets and dispatch. Thus, we interpret RGGI-induced changes in generation to be a result of the combination of (1) RGGI states’ investments in energy efficiency and renewable energy and (2) the effect of CO2 allowance prices on electricity dispatch.

Therefore their Table 4 results in Comparison of Abt Associates Generation Changes Due to RGGI relative to Total Generation Changes are the reductions they assume that would not have occurred were it not for RGGI. Let’s try to compare their estimates with the total generation reductions that occurred.

I used measured data for my estimate of the total load reduction and that methodology has differences relative to their approach. I assume that their annual differences in Table 4 are relative to 2008. The EPA Clean Air Markets Division website provides gross load generation data from all electric generating units that participate in RGGI. Modeling electricity generation in their approach uses net load so there is a difference there.   In my analysis I did not include New Jersey and they did for the first compliance period. I did not go to the effort to manually include only those sources that participate in RGGI for the 2008 data. Instead I calculated the load from all programs that report to EPA in 2008. I believe that is a minor error because the difference in the RGGI only and “All Program” data sets in the years 2009-2014 ranged from 0.50% to 0.71%.

In my approach I calculated the total change in generation from 2008. The Abt Associates approach calculated reductions in annual generation ranging from a low of 2.0 percent in 2011 to a high of 7.0 percent in 2013. The difference in gross load between 2008 and the six years in the first two RGGI compliance periods ranged from a low of 3.5% in 2010 to a high of 18.7% in 2014. If we assume the two approaches are compatible, then we can estimate the fraction of the total reduction resulting from the Abt Associates estimates of RGGI investments and CO2 allowance prices on electricity dispatch. In 2010, this comparison suggests that the fraction of reductions induced by RGGI was over 80% of the total reduction. Even in the lowest year, 2011, the fraction of reductions induced by RGGI was 26.4%.

Disappointingly, the Abt Associates analysis does not provide an estimate of CO2 emission reductions determined by their approach so that we can directly compare alternative approaches provided in previous work. Although generation is not directly comparable to CO2 emissions it is relevant to point out the conclusions from my previous post:

The upper bound in CO2 emissions reductions due to RGGI is an econometric model that estimates that emissions would have been 24 percent higher without the program. RGGI estimates that emissions would have been 17% higher than without a program. If you assume that all the savings in fossil fuel use only displaced natural gas use instead of some other aggregation of fuels then emissions would have been only 5% higher.

These numbers are starkly different and need to be addressed before the Abt Associates analysis can be considered credible. To that end I recommend that Abt Associates provide their CO2 emission reduction estimates.

Emissions Reductions

In the first step Abt Associates estimated annual changes in electric generation and emissions of air pollutants at power plants as a result of RGGI implementation from 2009 to 2014 using electricity dispatch modeling and EPA emissions data for Electric Generating Units (EGUs). Essentially they are estimating the emissions that would have occurred were it not for the RGGI investments and CO2 adder cost to the dispatch cost of the plants. Using the same methodology as for the generation estimate I calculated the total emission reductions. Sulfur dioxide cumulative reductions from a 2008 baseline totaled 1,673,601 tons from all nine RGGI states compared to the ABT Associates estimated impact of RGGI reduction of 109,000 tons so the estimated reduction of RGGI is 6.5% of the total.   Nitrogen oxides cumulative reductions from a 2008 baseline totaled 335,440 tons from all nine RGGI states compared to the ABT Associates estimated impact of RGGI reduction of 46,000 tons so the estimated reduction of RGGI is 13.7% of the total.   While I have reservations that RGGI had any impact other than from the load associated with RGGI investments, I can accept them as upper bound estimates of the SO2 and NOx emission reductions due to RGGI.

Second Step

In the second step Abt Associates used EPA’s Co-Benefits Risk Assessment (COBRA) screening model. According to EPA, COBRA works as follows:

  1. COBRA contains detailed emission estimates of PM2.5, S02, NOX, NH3, and VOCs for the year 2017 as developed by the U.S. EPA. Users create their own scenario by specifying increases or decreases to the baseline emission estimates. Emission changes can be entered at the county, state, or national levels, and outcomes can be modeled nationwide or for smaller geographic areas.
  2. COBRA uses a reduced form air quality model, the Source-Receptor (S–R) Matrix, to estimate the effects of emission changes on ambient PM.
  3. Using an approach to estimating avoided health impacts and monetized benefits that is generally consistent with EPA practice, the model translates the ambient PM changes into human health effects and monetizes them.
  4. Users can view the results in tabular or geographic form.

There are multiple problems with this model for this application. EPA notes that it is “inflexible and simple” such that it is limited to the 2017 timeframe and there is no ability to import a different baseline. Abt Associates applied the model to different periods and used a different baseline. The RGGI sources are point sources and this model does not address their specific factors that convert emissions from these plants to particulate matter as opposed to the generic factors used. Finally, note that all the air quality impacts are a function of particulate matter.

Secondly, BenMAp “estimates the number and economic value of heath impacts resulting from changes in air quality – specifically, ground-level ozone and fine particles but note that the COBRA impacts are solely based on particulate matter. The model presumes that the health impacts of both pollutants include premature death and aggravated asthma. Ambient levels of sulfur dioxide, nitrogen oxides and particulate matter have all been reduced in recent years but at the same time asthma rates have been increasing. This inconsistency needs to be addressed before this approach can be accepted.

There also is a deeper issue associated with EPA epidemiological work with PM and health impacts that is the basis of BenMAP. EPA’s epidemiological work is based on data sets that are not available for independent review, were prepared by organizations that were being paid by EPA (and would lose funding if this were not an issue) and the relationship they claim is not present in other similar data sets. Moreover, EPA has yet to determine the specific effect of PM on humans that triggers the claimed impacts of their epidemiological studies. Given those limitations I do not accept these health effects. I encourage you read Steve Milloy’s book “Scare Pollution: Why and How to Fix the EPA” (2016) Bench Press for the complete story of this travesty.

Finally, we can make an order of magnitude estimate of the health outcomes using their methodology by simply scaling their emission reductions relative to the total emission reductions (Abt Associates Cumulative RGGI Health Benefits and Total Cumulative Health Benefits, 2009-2014). This estimate does not account for even the gross site specific implications of the Abt Associates analysis. However, because the air quality estimated concentrations are proportional to the emissions input the general concept is acceptable. There is one other gross assumption. Because fine particulates in the northeast are primarily related to SO2 and BenMAP calculates health impacts of particulates, I only scaled the SO2 emissions to predict the health impact differences. My point of these numbers is that the predicted impacts are large enough that someone should be able to prove this model works by evaluating the observed health effect data. Until that is done I will remain skeptical of this approach.

Whines

Not to be petty but in order for an analysis to be useful in public policy debate the results have to be reproducible. This analysis went out of its way to make replicating their numbers difficult. Frankly when there are obvious obfuscations you start to wonder if it was deliberate. To prevent that perception providing numbers up front is the safest course. Also note that I did ask the corresponding author for data but did not get a response. The following is a list of specific whiny issues I had with the report.

What was the baseline? I could not find this definition anywhere. I assume that because annual numbers were used that the year before the program started was the baseline. In my previous analyses of RGGI data I use the three-year period 2006 to 2008 as the baseline to compare with the three-year RGGI compliance periods.

SO2 and NOX data in metric tons. I do not understand why the emissions are reported in metric tons. In order to compare their results with any EPA report they have to be converted. Moreover, the input to the COBRA model is in short tons.

No tables with numbers. In order to compare their projections with actual emissions I had to manually scale bars on their figures. I think my estimates are good enough but this was an unnecessary hassle.

No CO2 reductions report. This was serious enough to include in the main body but bears repeating. In order for this study to be credible it needs to be compared to previous work. Without CO2 that is impossible.

Emission reductions in Figures 6 and 8 don’t add up to the emission reductions in Figure 7. After manually estimating the bars in the two figures it was very frustrating to find they didn’t match their totals. Double checking my work it is clear that there is a mistake in the Abt numbers.