February 27, 2021 (OPINION): “How economic theories influence energy policy“, Austin American Statesman
“Simply put, more of us need to think about the broader relationship between energy and economic theory” Read more …
Texas House of Representatives Joint Hearing
Joint Hearing: State Affairs and Energy Resources
February 25, 2021
Summary and notes by Carey W King (http://careyking.com/)
|Chair:||Rep. Chris Paddie|
|Vice Chair:||Rep. Ana Hernandez|
|Members:||Rep. Joe Deshotel|
|Rep. Sam Harless|
|Rep. Donna Howard|
|Rep. Todd Hunter|
|Rep. Phil King|
|Rep. Eddie Lucio III|
|Rep. Will Metcalf|
|Rep. Richard Peña Raymond|
|Rep. Matt Shaheen|
|Rep. Shelby Slawson|
|Rep. John T. Smithee|
|Chair:||Rep. Craig Goldman|
|Vice Chair:||Rep. Abel Herrero|
|Members:||Rep. Rafael Anchia|
|Rep. Tom Craddick|
|Rep. Drew Darby|
|Rep. Jake Ellzey|
|Rep. Charlie Geren|
|Rep. Tracy O. King|
|Rep. Ben Leman|
|Rep. Oscar Longoria|
|Rep. Ron Reynolds|
Mr. Morgan stated: “I was a big proponent of this [ERCOT energy only] market, and my faith has been shaken.” We’re risking this economic engine of this country [implied as Texas].
Main problems affecting the blackout:
We produced 35% of total ERCOT generation during February 15-17, and we generally have 18% market share.
Coal Power: They mine coal for Oak Grove and they had issues of their local coal delivery (via local rail). Also, they don’t normally cover their coal piles that got wet or froze up. So the coal became gummy and blocked the process of pulverizing coal to input into boilers.
Natural Gas: Their natural gas fleet was 90% available at NG plants, but they could not get the gas at the pressures that were needed to operate.
Nuclear: They came within 3 minutes of having the Comanche Peak (nuclear power plant) automatically trip off due to low grid frequency.
Fossil fuels vs. renewables: Mr. Morgan brought up how zero marginal costs of renewables stresses the market structure. We’re going to need to investigate a change in market structure. This is a policy level change in decision to figure out how to deal with this “We are at a crossroads”. We need to continue to figure out how to evolve the competitive markets. “There is a policy angle here”. “There is a big debate in this country on the future of fossil fuels”. We need to have revenues for companies to invest in capacity.
The lack of an enforced weatherization standard is not the entire problem that occurred on Feb. 14 and 15, 2021.
When asked why his company does not have reserve gas on site, or if more NG on site would have prevented the ERCOT blackouts the week of Feb. 14 (e.g., with 3 or 5 days of storage)? Mr. Morgan: The system has been so reliable (99.99%) that it doesn’t make economic sense to store more NG. We do have some offsite storage and deliver it through price. More storage would have helped, but hard to say how much. Some of us have dual fuel capability, and we did lean on that a bit (and was limited by diesel trucks coming in).
None of their customers will be exposed to high energy bills.
They have 8 GW of generation capacity and it performed at 80% of its capacity. They produced almost 2X the amount of electricity as they produced in normal winter times at the beginning of the month.
There were 3 main reasons for lower generation from NRG fleet:
There are many factors that impacted their power plant performance, and they are:
Gutierrez said there is now the chance for the Legislature to define “reliability” and “resilience” for the electric grid. The state agencies can find the best way to achieve these goals. “Give industry the guideposts”. There is no standard for weatherization. There is the opportunity to have that conversation.
When asked why his company does not have reserve gas on site, or if more NG on site would have prevented the ERCOT blackouts the week of Feb. 14 (e.g., with 3 or 5 days of storage)? Gutierrez: We need to think about the quantity of gas needed to store many days of NG on site. You’d have to store NG as LNG because the quantity is so large. This is cost prohibitive.
It is important that we look at extreme weather in a different light, given what we have seen. I believe climate change is real, and as a company we [NRG] are targeting to reduce carbon emissions 50% by 2035 and net zero by 2050 in accordance with United Nations targets to 1.5 degree C rise.
He stated it seems ambiguous as to whether ERCOT is a governmental organization, and this is in decision at Texas Supreme Court. He wondered if there is any reason we can’t make ERCOT a government agency under PUC or other agency?
To each person testified, Representative Hunter asked pointed questions about who (persons or entities) were at fault. He asked questions such as: Who’s at fault? I don’t want to hear about “systems”? I want people to know who screwed up? I want details.
Answers to these questions were generally that many parties are responsible: 1) natural gas suppliers, 2) power generators, 3) Texas Public Utility Commission, 4) ERCOT, and 4) transmission and distribution utilities
Representative Hunter emphasized the need for better communication by all parties (power generators, utilities, ERCOT, Public Utility Commission, Governor’s office).
This “feast or famine” 9000 $/MWh creates volatility that doesn’t really incentivize financing.
He emphasized the need to revisit how to incentivize investment in ERCOT outside of the 9000 $/MWh offer cap of the Operating Reserve Demand Curve.
Rep. Anchia expressed that the current ERCOT market design seems to have the perverse outcome that we can’t incentivize new baseload power to be installed unless we have extreme weather that juices up costs.
Rep. Anchia: Does the market still have perverse outcomes and increases volatility that we need to address?
Mr. Morgan responded to Rep. Anchia that much of their equipment (they buy?) is designed to operate at 10 deg. (F?), and they weatherized $10 million was supplemented to try to get us closer to 0 degree capability, and these were more short term changes
Mr. Morgan responded to Rep. Anchia stating that the ERCOT market has have increased volatility relative to years ago due to the 9000 $/MWh offer cap and steepness of the Operating Reserve Demand Curve.
Rep. Anchia stated we need to create a market that avoids too much volatility and scares investors. A recent Wall Street Journal survey suggests that the ERCOT wholesale matket design has caused Texas customers to pay 10s of billions (?) more than other areas.
Asked for ideas in 1 week: Last session I introduced HB 34 on emergency communication system. It didn’t get a hearing to pass. Hopefully now it will. He asked those giving testimony to tell the legislature what can be done to make this better, and report back in 1 week.
On subsidies: During testimony of Mr. Clark and Mr. Hemmeline, Representative Raymond stated that we have to try to move away from politics as last week’s events are so tragic. He realized that people sometimes don’t like discussing tax credits and subsidies etc. But he said we should talk about all the tax credits that oil and gas gets (state and federal), let’s put it all on the table.
I’m interested in the market issues. We sort of know how things work but not all of the details. What about a lower price cap with a capacity market? Do you know what you would really want? What structure meets the needs of reliability, affordability, and lower risk (even it not financial risk, but risk to livelihoods)? How do we restructure? Since NG prices are not capped, this sounds like it doesn’t work.
Morgan: We could consider a capacity market. This does keep some old capacity on the system that only runs 1 week or so per year. But that might be nice in the situation such as last week. There are things we can do on the “all energy” market. We can increase the amount of reserves that are required and then drop the price cap. Let’s remember, ORDC is a construct, but the pure marginal cost system wasn’t working either. We could add a specific product for fuel security. We can’t ignore the idea of re-regulation, but there is a reason we left that idea and I think it has served us well, so I’m not suggesting we go back. I like idea of some reduction in price cap with an increase in reserves, but I’m not advocating yet.
Hill: We had about 2000 MW going offline at about the same time.
We started losing gas supply broadly by late morning or early afternoon Feb. 15th. These were back on Wednesday (Feb. 16th). We had some firm capacity and producers cut us off since they couldn’t get the gas into pipelines. 40% of their deliveries were cut from suppliers on Wed. Feb. 16. There were times when 40% were offline due to loss of gas supply.
Freezing at out power plants: We started in 2011 on best practices with ERCOT and TRE, and we do this every year. We still lost 2 of 12 plants due to freezing conditions.
Hemmeline: Solar output generated more than ERCOT planned (starting Feb. 15?) given expected generation. Several solar sites were set offline, many (200+) inverters had to be manually reset, and one solar photovoltaic plant was shut down due to icing on transmission lines.
Jeffrey Clark: We represent, wind, solar, batteries, investors, and even NG customers. This issue has shaken my confidence in a reliable system, and we have to fix this going forward. We also had a failure of transmission. Freezing fog is what affected some wind turbines. We’re looking at ways to shed ice from wind blades, limit ice accumulation, and other methods to keep wind turbine blades from icing.
Jeffrey Clark (on renewable industry obligations in ERCOT market): It is important that most renewables are funded via Power Purchase Agreements. There is a perception that wind and solar can just show up and produce whenever they want to, and don’t feel repercussions. These generators have been affected by the grid blackout. When do we change any ERCOT rules? What are the mechanisms? You can be assured that wind and solar members of my organization did not benefit from the high prices and events we experienced.
Jeffrey Clark (on wind turbine performance): Mechanically, we’re aware of weather issues, but we are assessing if this is a “new normal” and how do we make the power available when needed. Freezing fog in San Angelo/Sweetwater area had a particularly rough impact. We’re looking into that. Shaking ice off of blades is possible but tricky (e.g., if you shake off ice above the nacelle onto the nacelle). We’re talking with OEMs on the options for de-icing on wind turbine blades.
Cold weather by itself is not an issue in itself. There are heating elements to keep lubricants functioning properly. The typical [wind turbine] machine can go to -15 deg. C (some to -30 deg C). So it is not the cold itself that is the problem.
Question: Is snow generally a problem? Clark: Every region had trouble with power generation, even in SPP and West Virginia. It is the icing of the wind turbine blades that seems to be the major problem here.
Clark was asked: If we are going to have a discussion of how we get more capacity, are we going to have to consider how subsidies for wind and solar affect generation investment? Clark: I think we need to look to all subsidies. Coal has been subsidized since before we were a country.
Nye: We did plan to do rolling outages, and we did this at first late Feb. 14 and early Feb. 15. At 2:02 am Feb 15 we experienced 59.302 Hz and this was a critical event. By 4:43 we know multiple gen was offline. By 8:03 state was shedding 16 GW. Why was 2:02 am important? The importance of under frequency relays are required to have an automatic response to drop load faster than people can respond. They are the last measure to prevent total grid blackout. They are set to trip at 59.3 Hz. There were within 0.002 Hz of getting to this limit. We did have some under frequency relays trip, we don’t know why some did or didn’t. The situation was so fast we were worried about customers being in dark for say 1 month if ERCOT went completely dark.
What about inability to do rolling blackouts? Nye: We’ve got to communicate better. We should have contacted people to let them know that rolling blackouts of 15-30 minutes were not going to happen anymore.
We are required to have 25% of our load on feeders that have special devices called under frequency relays. To get to 25% of load, this is 40% of their feeders that have these relays, and these circuits stay online 100% of the time to act in emergencies and these feeders STAY ON ALL THE TIME because we need that automatic control capability. The distribution of these is to GEOGRAPHICALLY DIVERSE to buffer the system (he can show the maps of these) and they are generally heavier when you have more load. There are more in urban areas since there is more load there. They are distributed based on where the load is NOT rich vs. poor areas. He gives a campfire analogy to say these relays are like
2nd bucket of critical circuits make up 8-9% of his feeders that serve hospitals, critical care facilities, airports, 911 call centers. Now this leaves only 51% of feeders that are left as even possible to rotate or turn off.
My Nye knew that equity would be a question. He called COO Debbie Dennis and Chief Counsel to make sure they were not cutting off people in an inequitable manner. We (Oncor) were as equitable as we could be.
Total premises out of power in Oncor regions:
Other metrics they used, # underfrequency relays, # loadshed feeders, % customers interrupted and all of these are pretty equal metrics. I see no institutional equity.
Speaker Craddick: Are O&G producing wells not on critical infrastructure with you, Oncor, but they are with Centerpoint? Thus you shed load associated with O&G production, so do you know how that impacted the rest of the grid?
Nye: On gas supply issue, after 2011, there was a push that critical gas facilities were on a critical list to provide gas. There is a process to be designated as such, and to your point (Craddick), I don’t know how power plants get gas or who is delivering. The NG producers have to tell me (Oncor) if they are delivering gas to generators, otherwise I don’t know. We had an identified 35 specific facilities that were delivering gas and we never cut those off from electricity. During the event, PUC Chairperson Walker would call with names of folks indicating desire to turn on the power so they could deliver gas, and we did this, and by the end of the event we added 168 more locations to the critical list and we never again turned them off. We need better coordination of NG producers and generators with T&D.
Question from Representative Hunter: Here comes March, another storm is coming, what do you think you can do better?
Nye: Communication for sure. We received more inbound telephone calls in 2 days than we did in all of 2020. Our phone system (with AT&T) crashed and our customers could not get through to us. Make sure we have adequate technology for people to contact us. Make sure we are talking to legislature better. Better communication with O&G suppliers to not cut them off. Work on water districts and know much they can be rotated. Biggest issue with water utilities is they don’t like to be surprised (so tell them the plan). We had about 1.33 M out on their system at one time, but on Wed. when they were allowed to turn on more, all but ~140K were able to come online and this was mostly due to ice storm damage around Waco/Temple/Killeen area. SO this tells me Oncor was generally ready to come online when they could (T&D was generally working). This last ~ 100K customers was actually pretty tough to turn back on. We can use data analytics to more quickly know what parts of our system are broken. We can encourage conservation (as mentioned by Mercado as Centerpoint has done). For our industrial customers, who are mostly at transmission voltage, and they are at backbone of system and we didn’t want to turn on transmission level lines. Over 50% of industrial customers voluntarily came down, so we can use that idea going forward.
Rep. Herndandez: [To Mercado]: We had a visit how you determine who to turn off. There are “buckets” of types of loads.
Mercado: We look at the circuits in thinking of load shed, we don’t look at neighborhoods. The load shed is pre-programmed. Heavier demand for load shed where there are larger loads. We got 59 orders for load shed from ERCOT, and each order changed the buckets of load shed changed. We don’t have the infrastructure in our telecommunications to do more coordinated load shed using smart meters? We have to measure and trigger load shed in milliseconds. The only way we can actually measure load is via SCADA system. We need to figure out a way to get the meter speed of communication to our feeders faster.
Question from Representative Hunter: Here comes March, another storm is coming, what do you think you can do better?
Mercado: Not so much to add. Communications is key. I realized how important it was to be an expert communicating with the public in real time. The problem with my message on Day 1 (with NPR, Houston stations, etc. that I then learned to correct) was I could not tell them “how long” the grid will be down. I only know what was happening in our company, so I was limited in knowledge, and we need more information to make people comfortable. We need a better software approach for better load shedding. We planned for losing 2-3 units, but now we have a situation when 50% of generation can go out, and we need a better solution. We need to be more transparent and dynamic in our load shed while still protecting critical loads and underfrequency relay feeders.
Mercado: Due to turning circuits on and off many times, we had to replace 1000 pad-mounted transformers after this event (due to their cycling broke them)
Magness: Texas suffered. Our goals is to prevent this type of event again. We are like air traffic control for the ERCOT grid (90% of customers, 75% of land mass). We can only stay at 59.4 Hz for 9 minutes before generators automatically trip offline from low frequency relays. Figuring out how to actually do rotating blackouts is critical.
Mr. Magness was repeatedly criticized for ERCOT not having a more effective and direct communication that the grid situation was so severe that rolling blackouts were not going to be able to be in place and that some regions of the grid were going to be in a long-term power outage while some regions were going to stay on.
[note from this note taker: Apologies for not as many notes on Bill Magness testimony]
Walker: By statute ERCOT is accountable to the PUC. PUC can apply penalties per PURA or ERCOT protocols. Can impost administrative penalties, and they max at $25K/day/violation. Largest to date is $2M. She noted that the FERC 2011 report had 26 recommendations but none to PUC [[Note: but PUC would have been involved in overseeing and agreeing to any new mandates]]. Quanta Energy Services ensured that 2011 legislation calling for a report on preparedness, that a report was filed, and it had 9 recommendations for PUC and PUC implemented all of them.
I don’t think PUC has explicit authority to require winterization of generation because we’ve not been given clear authority. The 2011 TX bill did have this requirement but it was removed before passed.
Each year ERCOT is to file a report on weatherization plans, and it filed that report in January 2021 stating compliance of the power plants.
Regarding the PUC order on Feb. 15: They heard that how their models we rerunning, in load shed that you will go to 9000 $/MWh cap. But ERCOT was getting reserves and bringing on customers, and so the reserve was sitting there and sending the signal that not all generation was needed. So we told them to move to 9000 $/MWh. On Feb. 21 we had another emergency meeting, we urged all REPs to delay their invoicing to customers, and not disconnect them, and give the option for a long-term payment plan. This only applies to IOUs, not municipal utilities or co-ops.
Here are some things for PUC and community to work on:
On Wed. Feb. 10, we knew a storm was coming in, but not 100% sure how bad. I had a generator call me (Feb. 10 or 11) and said 3 of their units we going to be curtailed and needed help getting NG. The RRC quickly passed an order to allow electric generation to have high priority getting gas. In the moment, she was discussing coordination in the NG supply chain (well heads, processing plants) and worked with Oncor to get these facilities power turned on. Typically their call center is not open on the holidays or weekends, so she did keep it open on the weekend. We “worked” with the industrial customers telling them they need to have them come offline. That Thursday and Friday she was asking generators and TDUs checking to see that PUC could do for them. What did you know and what did you know … really I started actions on Wednesday (before the storm?)
Representative Lucio criticized the Chairwoman Walker and the Public Utility Commission as largely lacking any communication to the public on the severity of the electricity crises and what they can do to prepare even though he believes the PUC (not ERCOT) had the responsibility for this communication.
[note from this note taker: Apologies for not as many notes on DeAnn Walker testimony]
MarketWatch, February 17, 2021
In the face of ERCOT’s rolling blackouts during the 2021 Valentine’s Day winter storm Uri, resist blaming one power source because that detracts from making the entire electricity and energy system reliable. Read more …
Listen to Carey discuss his book on the podcast the Energy Transition Show, of which host Chris Nelder states: “Heady stuff about the macroeconomics of energy transition!” (and he gives it a Geek Rating of 7.
December 6, 2020
In this blog I use my HARMONEY (“Human And Resources with MONEY”) economic growth model (also see this free early version) to demonstrate the dynamics of the Jevons Paradox: that an increase in end-use efficiency leads to an increase in total resource extraction, rate of resource depletion, and final level of depletion.
Here I summarize some important features and assumptions of the HARMONEY model to give context for why it exhibits the behavior summarized below:
Here I’ll focus on changing the parameter that affects how many resources are required for consumption to operate capital to produce a unit of its output. Equation (1) describes the quantity of natural resource consumption required during the operation of capital where K is the amount of physical capital, CU is the capacity utilization (a number between 0 and 1 indicating the fraction of the time the capital operates), and η is like an efficiency term, but its units are different. The symbol η represents “resources consumed per unit of capital” and its units are [resources/(time·capital)].
natural resource consumption to operate capital = η·K·CU (1)
Equation (1) holds for both goods sector operation (e.g., fuel to operate machines that make more machines) and extraction sector operation.
Demonstrating Jevons Paradox (or the backfire effect)
I will show a series of outputs from the HARMONEY model. There are two simulation results shown on each figure. The solid line represents the simulation where each η remains at a constant value of 0.16 throughout the simulation. The dashed line represents the simulation where each η decreases, starting at year = 50, from its maximum value of 0.16 to a minimum value of 0.0533 as a function of how fast new capital is created (e.g., rate of investment). Importantly, a decreasing η represents the SAME effect as increasing thermodynamic efficiency of an electric motor, steam turbine, combustion engine, etc. Figure 1 shows the constant η for the first simulation and the decreasing η for the second simulation.
|Figure 1. The amount of resources consumption, or η, to operate a unit of capital in (left) the extraction sector and (right) the goods sector. Solid line = constant η (constant efficiency). Dashed line = decreasing η (increasing efficiency).|
Figure 2 shows the amount of resources that resides in the environment, yet to be extracted. In the constant efficiency scenario, the resource can get extracted to a level of near 65 units of resources remaining (65% of its maximum possible level) at its most depleted state. In the increasing efficiency case, the resource is depleted to near half of its maximum level at about 50 units of resources remaining at its most depleted state.
|Figure 2. The amount of natural resources available, or remaining, in the environment for the economy and population to consume decreases when machines become more efficient. Solid line = constant η (constant efficiency). Dashed line = decreasing η (increasing efficiency).|
Figure 3 shows the resources extraction rate that increases for the dashed line increasing efficiency scenario. (NOTE: By the definition of the resource as a forest, the maximum resource extraction rate occurs when the resource is depleted to half of its maximum level. I have chosen the parameters to not extract (much) past the 50% level for the purposes of this blog as this most accurately represents our primary use of fossil fuels.)
|Figure 3. The rate of resource extraction increases when machines become more efficient. Solid line = constant η (constant efficiency). Dashed line = decreasing η (increasing efficiency).|
Figure 4 shows a higher human population for the scenario in which there is increased machine efficiency. If a model assumes a constant or exogenous population growth, then it probably cannot exhibit the backfire effect (or Jevons Paradox). The reason that population increases with higher efficiency is that after resources are consumed to (i) operate machines and (ii) make more machines, the higher efficiency allows for more resources to be left for human consumption such that death rates remain low for a longer period of time that in turn allows population to increase for a longer period of time. Eventually, population levels off even in the increased efficiency scenario, but in a world also with more machines (more capital).
|Figure 4. The human population increases when machines become more efficient. Solid line = constant η (constant efficiency). Dashed line = decreasing η (increasing efficiency).|
Figure 5 shows the amount of total capital that is higher for the scenario in which there is increased machine efficiency. The reason that the capital stock increases with higher efficiency is that after resources are consumed to operate machines, the higher efficiency allows for more resources to be left for the creation of more machines.
|Figure 5. The total capital in the economy (both for the extraction and goods sectors) increases when capital operates with higher resources efficiency. Solid line = constant η (constant efficiency). Dashed line = decreasing η (increasing efficiency).|
With increased capital, population, and resources extraction (the of the major “factor inputs” to economic production) as efficiency increases, there is also increased net output of the economy as shown in Figure 6.
|Figure 6. The net output, or GDP, of the model economy is about two times larger after the increase in capital resources consumption efficiency. Solid line = constant η (constant efficiency). Dashed line = decreasing η (increasing efficiency).|
Clearly increased efficiency allows for (i) higher resource depletion, (ii) higher extraction rate of resources, (iii) an increased population, and (iv) more capital accumulation. For the latter two stocks, population and capital, higher operational resource efficiency enables more resources to be allocated to the accumulation of both more people and capital that do more work. This is consistent with the idea that more useful work, which is all energy inputs (technically exergy) times their full conversion efficiencies, goes hand in hand with more GDP. Thus, by making choices to increase efficiency in the real economy, so far (globally to date) this has translated to more useful work, resource extraction, and net output (or GDP).
Figure 7 shows the real price of natural resources and goods (or machines). With increasing efficiency (or decreasing η in the model), the price for a unit of natural resources increases relative to a constant efficiency world, and this is precisely the trend we’ve experienced for world oil prices that increased after the 1970s, when oil efficiency efforts started. To date, oil prices have yet to return to the low prices experienced for the 90 years previous to 1974. The price of goods (or machines) decreases with the increasing efficiency in their operation.
|Figure 7. (a) The price of natural resources increases as η, the amount of resources to operate a unit of capital, decreases. (b) The price of goods (or machines) decreases as η, the amount of resources to operate a unit of capital, decreases. Solid line = constant η (constant efficiency). Dashed line = decreasing η (increasing efficiency).|
For the net energy geeks out there, Figure 8 shows the “net external power ratio”, or NEPR, which is the same concept that many people refer to as EROI = “energy return on (energy) invested”. (See my previous publication for reasons why I prefer to use NEPR as a more specific term.) Equation (2) expresses the idea behind the mathematics.
NEPR = (resource flows available for use outside of the extraction sector) / (resource inputs to operate extraction capital + resource inputs to create new extraction capital) (2)
When machines consume less resources for their operation, then there is period in which NEPR increases before reaching a maximum from which it again declines. This increase in tandem with increasing efficiency represents an increased net resource flow available as “net output,” which in the economic sense is that output available for consumption and investment.
|Figure 8. The net external power ratio (NEPR), often referred to as EROI (= energy return on (energy) invested), increases in response to an increase in capital operating efficiency since a higher fraction of total resource flows can temporarily go to net output. Solid line = constant η (constant efficiency). Dashed line = decreasing η (increasing efficiency).|
Figure 9 shows the wage share, or percentage of GDP that is paid to wages, increases during the time that efficiency is increasing from about year = 50 to year = 110. An increasing consumption of resources and GDP can thus translate to a higher (or at least a share decreasing at a slower rate) distribution to wages.
|Figure 9. The share of GDP that is paid to wages (or workers). Solid line = constant η (constant efficiency). Dashed line = decreasing η (increasing efficiency).|
Figure 10 shows that the debt ratio is temporarily lower while efficiency increases. Not shown (due to stopping the simulation) is that the debt ratio in the efficiency case also starts to decrease because resource constraints eventually inhibit investment below profits such that companies pay back debt. See the paper for more details. To understand the current economic situation in the U.S. and most OECD countries, it is critical to understand the context of high levels of private debt of companies and consumers (debt of consumers is not part of the HARMONEY 1.0 model). Increasing efficiency can increase output faster than debt (hence lowering debt ratio), but only for a while. Ultimately, the second law of thermodynamics limits the efficiency of energy conversations.
|Figure 10. The debt ratio (debt/GDP) of the economy increases more slowly while efficiency increases (e.g., decreasing η). Solid line = constant η (constant efficiency). Dashed line = decreasing η (increasing efficiency).|
The HARMONEY model shows many trends that are indicative of the real world economy. Thus far, globally, we keep making end-use devices more efficient, and we keep consuming energy at higher rates. When η is decreased, or efficiency is increased, in the HARMONEY model, resource extraction increases, population increases, capital increases, and net output increases. Increasing efficiency is a tactic to increase consumption and output, not a tactic to reduce overall resource consumption. The Jevons Paradox is only a paradox to those who are not thinking about the dynamics of the economy and how it responds to changes in efficiency over time.
How do economic analyses account for the roles and impacts of both the cost and quantity of natural resource consumption?
This question has been debated perhaps as long as there has been the profession of economics. Before the use of fossil fuels, early “classical” economists knew that most products of interest, such as food and building materials, came from the land as it harnesses the energy from the sun. Thus, land as a natural resource was front-and-center to economic thinking.
With industrialization and the use of fossil fuels (that provide energy independent of current sunlight) economic analyses became less focused on the role of natural resources as an input into economic production such that in the 1900s most mainstream (i.e., Neoclassical) growth models do not directly account for energy and natural resources. Many researchers, including myself, think we must explicitly consider the use of natural resources if we are to understand economic growth and the distribution of the stocks (e.g., debt) and flows (e.g., wages, profits) of money within the economy.
I have recently published a paper on my economic growth model that consistently and simultaneously accounts for both the use of natural resources, such as energy, and debt.
The paper sheds new light on some of the most important contemporary economic trends in the United States and other economies of the OECD. In particular, the model provides the foundation to directly link changes in the rate of energy consumption to increases in wage inequality and debt that began during the 1970s.
This publication is in a 2020 volume of the journal Ecological Economics as “An Integrated Biophysical and Economic Modeling Framework for Long-Term Sustainability Analysis: the HARMONEY Model”. The name of the model, “HARMONEY,” is an acronym for “Human And Resources with MONEY.”
Figures 1 and 2 show comparisons of model results to U.S. data. For these comparisons the qualitative similarities in the general sequence of long-trends and structural change are important, not the relation of magnitudes of variables or specific model times to specific years in the U.S. data.
Figure 1 shows the wage share and per capita energy consumption of the U.S. The wage share is the percentage of GDP allocated to hourly or salaried workers. Notice how both the wage share and per capita energy consumption have a different trend before versus after the early 1970s. Before 1973, wage share remained constant at about 50% of GDP, and energy consumption per person increased at 3%/yr. After 1973, wage share declined at about 1.5-2% per decade as energy consumption per person declined slightly or remained relatively constant.
|Figure 1. (a) In the same way as the U.S. data, the wage share (left axis) from the HARMONEY model shows the same simultaneous turning point in long-term trend, from a constant value to a declining value, when per capita resource consumption reaches its peak. (b) Data for the U.S. wage share (left axis) and per capita energy consumption (right axis) both change their long-term trends in the 1970s.|
The model results show practically the exact same trends as in the U.S. data. When initially formulating the model, I had no immediate goal to mimic this type of relationship. I did want a model that had several important elements, but I didn’t anticipate my first results would so clearly relate to real world data. In the HARMONEY model, the wage share emerges because of how its systems-oriented structure relates the elements to one another, as described further below.
The HARMONEY model also provides insight into debt accumulation. Figure 2 shows private U.S. debt in terms of the debt ratio (debt divided by GDP) for corporations and financial institutions. These two categories are equivalent to the concept of debt included in the model. It was the accumulation U.S. private debt (and household debt in mortgages) and associated interest payments that triggered the 2008 Financial Crisis. The crisis was not triggered by government debt.
The new insight from this research is that it shows how increasing debt ratios can arise from a slowdown in resource consumption rates. In essence a debt crisis cannot be analyzed independently of the longer-term context of natural resource consumption.
|Figure 2. Both the (a) U.S. data and (b) HARMONEY model show a slow rise in private debt ratio before a more rapid increase. The transition occurs soon after the peak in per capita energy consumption for the U.S. and peak in resource extraction per person for the model. U.S. data are from U.S. Federal Reserve Bank z.1 Financial Accounts of the United States, Table L.208 (Debt, listed as liabilities by sector). Model results are from the scenario labeled as “Renewable-High(b)” in the paper.|
Note how private debt ratio increases much more rapidly after the 1970s than before, and the increase in financial sector debt drives the overall trend for the U.S. This same breakpoint occurs in the HARMONEY model and for the same reasons. In both the U.S. data and the model, when per capita resource consumption was rapid, the debt ratio increased but at a much slower rate than after per capita consumption stagnated. Note that “mainstream” neoclassical economic theory does not account for the concept of debt, and it assumes the quantity of money has no fundamental role in long-term trends. Steve Keen’s research provided a simple way to include debt into economic growth modeling. In his 2011 book Debunking Economics, Keen states the problem clearly:
“This [lack of consideration of debt], along with the unnecessary insistence on equilibrium modeling, is the key weakness in neoclassical economics: if you omit so crucial a variable as debt from your analysis of a market economy, there is precious little else you will get right.” –– Steve Keen (2011)
This lack of consideration of debt is the fundamental reason why mainstream economists could not foresee or anticipate the 2008 Financial Crisis. Their theory tells them not to model debt, the direct cause of the crisis itself!
For those that want more details that explain these model results, then you can keep reading. Also, at the bottom of this blog I provide links to videos where I describe the model structure and results.
The wage share decline is driven by two quantities: the accounting for depreciation for an increasing quantity of capital and the interest payments on a rising debt ratio. The pattern occurs if you assume, as observed in the U.S. data, that companies keep investing more money than their profits. Since the 1920s, U.S. corporations typically invest 1.5 to 2.5 times more each year than they make in profits. Thus, in this face of constant or slower increase in total energy consumption, the economy accumulates capital that either operates less or requires less energy to operate (e.g., efficient equipment, computers).
Think about the patterns in Figures 1 and 2 the following way. We can assume four major distributions from GDP (or ‘value added’) in national economic accounting: government (as taxes), private profits including interest (or rent) payments to capital owners, depreciation (on capital), and wages (to workers).
In a capitalist system based on maintaining private sector profits, if both the debt ratio and the amount of capital per person increase, then increasing shares of GDP go to two categories: depreciation and interest payments. To minimize interest payments at high debt, you must lower the interest rate, and that is why central bank interest rates have remained at historic lows, sometimes even negative, since 2008. Assuming a constant share of GDP to government taxes, when there is a restriction in the growth rate of GDP and energy consumption, the prioritization of profits, taxation, and depreciation means that the workers’ share is the only portion available to take the hit.
A short trip down memory lane provides the context for why I’ve performed this research.
In 1972, the book The Limits to Growth brought an idea mainstream discussion: physical growth on a finite plant cannot continue. There were both detractors and proponents of the conceptual and mathematical models used in the book. When the authors updated the modeling in their 1992 Beyond the Limits, William Nordhaus (Nobel Laureate awarded in 2018) again critiqued the approach as he’d done in 1973 in his paper Lethal model 2: The limits to growth revisited. Whether any “limits to growth” exist is contested in the economic literature, but there is little doubt in the ecological literature. Many, including Ugo Bardi in his The Limits to Growth Revisited, state that the critiques of Nordhaus were ignorant of the mathematical and computational methods used in The Limits to Growth models. However in a commentary within Nordhaus’ 1992 critique, Martin Weitzman effectively summarized the differences in worldviews between an ecological approach to economics and the mainstream view:
“There may be a some value in trying to understand a little better why the advocates of the limits-to-growth view see things so differently and what, if anything, might narrow the differences.
I think that there are two major differences in empirical world views between mainstream economists and anti-growth conservationists. The average ecologist sees everywhere that carrying capacity is a genuine limit to growth. Every empirical study, formal or informal, confirms this truth. And every meaningful theoretical model has this structure built in. Whether it is algae, anchovies, or arctic foxes, a limit to growth always appears. To be sure, carrying capacity is a long-term concept. There may be temporary population upswings or even population explosions, but they always swing down or crash in the end because of finite limits represented by carrying capacity. And Homo sapiens is just another species-one that actually is genetically much closer to its closest sister species, chimpanzees, than most animals are to their closest sister species.
Needless to say, the average contemporary economist does not readily see any long-term carrying capacity constraints for human beings. The historical record is full of past hurdles to growth that were overcome by substitution and technological progress. The numbers on contemporary growth, and the evidence before one’s eyes, do not seem to be sending signals that we are running out of substitution possibilities or out of inventions that enhance productivity.” — Martin Weitzman (1992)
Per Weitzman, I have been interested in “narrowing the differences” between economic and ecological worldviews by coherently including them in the same framework. It was with that goal in mind that I created the model summarized in this article. The model is based on a similar concept as that in The Limits to Growth in that it has an allocation of resources and capital between the “resource extraction” and “other” parts of the economy. But to better communicate with economists it also includes economic factors such as debt and wages. Without this type of combination we can’t understand if and how energy and resource consumption play a role in the trends of debt ratios and wage inequality that now dominate contemporary social, economic, and political discussion.
It is easier to propagate the meme of your model if you give it a memorable name, so I called my model HARMONEY for “Human And Resources with MONEY”. The HARMONEY model is a combination of two other existing models. The first is a simple model of an agrarian society that harvests a forest-like resource to feed itself. The second is a model of a simple economy with fluctuating business cycles, tracking capital, wages, and employment, while also considering the real world tendency of businesses to invest more than their profits by borrowing money from a bank. This borrowing is what “creates money” as debt within the model, just like commercial banks create money when they provide a loan to a business.
From the standpoint of natural resource use, HARMONEY has three key features that are consistent with real-world physical activities and that drive the patterns in Figures 1 and 2. First, natural resources are required to operate capital. This is the same as saying you need fuel to run your car, and a factory needs electricity to operate manufacturing machinery and computers. Second, natural resources are required to make new capital. This is the same as saying that all of the objects around you now (coffee mugs, computers, buildings, etc.) are made of natural resources. Third, natural resources are required to sustain human livelihood. This is the same as saying that, at a very basic level we need food to survive, and at a higher level more resource consumption leads to more longevity. Thus, whatever the flow of natural resources, those resources must be allocated between the three aforementioned uses.
These three features for modeling the use of natural resources, combined with the concept of private debt as loans from banks, give us tremendous insight into contemporary economic discussions.
For further learning you can access the article directly and watch videos of me presenting the model background and results in videos (video 2018, video 2019) via my website: http://careyking.com/publications/ and http://careyking.com/presentations/.
In his 2014 book Superintelligence: Paths, Dangers, and Strategies, Nick Bostrom discussed issues related to whether we could prevent a superintelligent artificial intelligence (AI) computer system from posing an existential risk to humanity. In 2014 he also presented for Talks at Google. In that presentation, an audience member (at 49 min 35 sec) posed the idea that a superintelligent computer could become a utility monster. The utility monster is an idea of philosopher Robert Nozik, and it relates to the philosophical concept of utilitarianism.
In utilitarianism, only the maximum happiness, or utility, of the group is what matters. The distribution of utility within the group does not matter. Consider the idea of marginal utility which is how much utility comes from consuming the next increment of resources. Because the superintelligent AI system might be much smarter than all of humanity, it could have a higher marginal utility than that of humans. The machine could conclude that total utility was maximized by its consuming one-hundred percent of natural resources because in doing so, it could maximize overall utility simply by maximizing its own utility.
Bostrom then discussed the paper clip maximizer as a classic AI thought experiment. What if the superintelligent AI system only tries to maximize the number of paper clips (the paper clip is an arbitrary placeholder)? The AI system would likely determine that keeping humans alive is detrimental to the goal of maximizing the number of paper clips in the world. Humans need resources to survive, and these resources could be used to make more paper clips. It is not that the AI machine dislikes or specifically tries to harm humanity. It is just that the superintelligent AI system is indifferent to our existence.
Now think about “the economy” and the metric of gross domestic product (GDP) which is usually used as a metric of the size, or throughput, of the economy. GDP is roughly treated as utility in economics. GDP is now a substitute for paper clips. Could we tell the difference between a world that is run by a superintelligent GDP maximizer and the world that we live in right now? That is to say if certain politicians, business owners and executives, and economists are pushing for rules that maximize GDP with , then is that “the economy” simply a mechanism to maximize GDP without regard for how money is distributed?
Philip Mirowski points out that one of Friedrich Hayek’s ideas was that the economy was smarter than any one person or group of persons. Government officials, for example, can’t know enough to make good economic decisions. Mirowski discusses Hayek’s idea in his book The Road from Mont Pelerin which explores the history of the “neoliberal thought collective”. Mirowski points out that Hayek saw the economy as the ultimate information processor. Thus, markets are able to aggregate data in the most effective way to produce the “correct” signal, say the price, to direct people on what to make and what to buy.
Need better decisions? Make another market! There is little to no need for people to think.
In an extreme world with markets for everything, each of us becomes an automaton responding to price signals to maximize collective utility, or GDP, that might have very little to do with our personal well-being.
How could we know if we have allowed the economy to simply become a GDP maximizing utility monster? Perhaps GDP would keep going up, but if it didn’t, perhaps we’d start adding activities to GDP that have existed for centuries, but had previously not been counted due to illegality or other reasons. Prostitution and legalizing previously drugs are examples. Check on that one.
Perhaps if all we wanted to do was increase GDP, we’d cut corporate taxes to spur investment in capital versus spending on education, which is for people. Perhaps human life expectancy would go down, and drug sales would be up (the utility monster is indifferent to people). Perhaps we’d see increases in wealth or income inequality. Perhaps people would contract with “transportation network companies” to drive around, wait for algorithmic signals on where to drive to pick up a person or thing, and then deliver that person or thing as directed.
Most macroeconomic analyses are based upon the concept of maximizing utility, which is usually interpreted as the value of what “we” consume over all time into the future. Many interesting (troubling to many) trends are occurring in the U.S. regarding health, distribution of income, and the ability of people to separate concepts of fact and truth. Thus, we should consider whether the superintelligent AI future some fear might already in action, but at perhaps a slower and more subtle pace than some pontificate might happen after “the singularity” when AI becomes more capable than humans.
The recent populist political movements in the U.S. and other countries could in fact be a rejection of the “algorithm of GDP maximization” associated with our current economic system.
In a new multidisciplinary study, researchers find the answer to our electric future is blowing in the wind — and burning natural gas. A Q&A in two parts.
Read the article https://medium.com/texas-mccombs/our-electric-future-b0c46bdb3aba
The following is an Energy Institute commentary piece coauthored with Dr. Josh Rhodes also of the University of Texas at Austin Energy Institute, January 2018.
Our economic system operates within intellectual, social, and physical constraints. Each of these constraints can feedback to affect the others. To produce more goods and services we have to 1) know how to produce them, 2) make them desirable, acceptable and affordable, and 3) have the required natural resources. The finite size of the Earth increasingly affects socioeconomic outcomes across the globe, including within the developed economies.
Ecologists, anthropologists, and systems scientists have anticipated this since the 1970s. However, the physical constraints on societal and economic organization and equality are largely unappreciated and misunderstood.