Somehow I forgot to mention our latest release:
The “Confirmed Proposals” emissions above translate into temperature rise of 3.9C (7F) in 2100. More details on the CI blog. The widget still stands where we left it in Copenhagen:
Author: Tom
The lure of border carbon adjustments
Are border carbon adjustments (BCAs) the wave of the future? Consider these two figures:
The first shows the scale of carbon embodied in trade. The second, even if it overstates true intentions, demonstrates the threat of carbon outsourcing. Both are compelling arguments for border adjustments (i.e. tariffs) on GHG emissions.
I think things could easily go this route: it’s essentially a noncooperative route to a harmonized global carbon price. Unlike global emissions trading, it’s not driven by any principle of fair allocation of property rights in the atmosphere; instead it serves the more vulgar notion that everyone (or at least every nation) keeps their own money.
Consider the pros and cons:
Advocates of BCAs claim that the measures are intended to address three factors. First, competitiveness concerns where some industries in developed countries consider that a BCA will protect their global competitiveness vis-a-vis industries in countries that do not apply the same requirements. The second argument for BCAs is ‘carbon leakage’ – the notion that emissions might move to countries where rules are less stringent. A third argument, of the highest political relevance, has to do with ‘leveraging’ the participation of developing countries in binding mitigation schemes or to adopt comparable measures to offset emissions by their own industries.
from a developing country perspective, at least three arguments run counter to that idea: 1) that the use of BCAs is a prima facie violation of the spirit and letter of multilateral trade principles and norms that require equal treatment among equal goods; 2) that BCAs are a disguised form of protectionism; and 3) that BCAs undermine in practice the principle of common but differentiated responsibilities.
In other words: the advocates are a strong domestic constituency with material arguments in places where BCAs might arise. The opponents are somewhere else and don’t get to vote, and armed with legalistic principles more than fear and greed.
Feedbackwards
In the 80s, my mom had an Audi 5000. It’s value was destroyed by allegations of sudden, uncontrollable acceleration. No plausible physical mechanism was ever identified.
Today, Toyota’s suffering from the same fate. A more likely explanation? Operator error. Stepping on the gas instead of the brake transforms the normal negative feedback loop controlling velocity into a runaway positive feedback:
… A driver would step on the wrong pedal, panic when the car did not perform as expected, continue to mistake the accelerator for the brake, and press down on the accelerator even harder.
This had disastrous consequences in a 1992 Washington Square Park incident that killed five and a 2003 Santa Monica Farmers’ Market incident that killed ten …
Given time, the driver can model the situation, figure out what’s wrong, and correct. But, as my sister can attest, when you’re six feet in front of the garage with the 350 V8 Buick at full throttle, there isn’t a lot of time.
Read more at the Washington Examiner
Idle wind in China?
Via ClimateProgress:
China finds itself awash in wind turbine factories
China’s massive investment in wind turbines, fueled by its government’s renewable energy goals, has caused the value of the turbines to tumble more than 30 percent from 2004 levels, the vice president of Shanghai Electric Group Corp. said yesterday.
There are now “too many plants,” Lu Yachen said, noting that China is idling as much as 40 percent of its turbine factories.
The surge in turbine investments came in response to China’s goal to increase its power production capacity from wind fivefold in 2020.
The problem is that there are power grid constraints, said Dave Dai, an analyst with CLSA Asia-Pacific Markets, noting that construction is slowed because of that obstacle. Currently, only part of China’s power grid is able to accept delivery of electricity produced by renewable energy. “The issues with the grid aren’t expected to ease in the near term,” he said. Still, they “should improve with the development of smart-grid investment over time.”
The constraints may leave as much as 4 gigawatts of windpower generation capacity lying idle, Sunil Gupta, managing director for Asia and head of clean energy at Morgan Stanley, concluded in November.
China has the third-largest windpower market by generating capacity, Shanghai Electric’s Yachen said.
It’s tempting to say that the grid capacity is a typical coordination failure of centrally planned economies. Maybe so, but there are certainly similar failures in market economies – Montana gas producers are currently pipeline-constrained, and the rush to gas in California in the deregulation/Enron days was hardly a model of coordination. (Then again, electric power is hardly a free market.)
The real problem, of course, is that coal gets a free ride in China – as in most of the world – so that the incentives to solve the transmission problem for wind just aren’t there.
Fuzzy VISION
Like spreadsheets, open-loop models are popular but flawed tools. An open loop model is essentially a scenario-specification tool. It translates user input into outcomes, without any intervening dynamics. These are common in public discourse. An example turned up in the very first link when I googled “regional growth forecast”:
The growth forecast is completed in two stages. During the first stage SANDAG staff produces a forecast for the entire San Diego region, called the regionwide forecast. This regionwide forecast does not include any land use constraints, but simply projects growth based on existing demographic and economic trends such as fertility rates, mortality rates, domestic migration, international migration, and economic prosperity.
In other words, there’s unidirectional causality from inputs to outputs, ignoring the possible effects of the outputs (like prosperity) on the inputs (like migration). Sometimes such scenarios are useful as a starting point for thinking about a problem. However, with no estimate of the likelihood of realization of such a scenario, no understanding of the feedback that would determine the outcome, and no guidance about policy levers that could be used to shape the future, such forecasts won’t get you very far (but they might get you pretty deep – in trouble).
The key question for any policy, is “how do you get there from here?” Models can help answer such questions. In California, one key part of the low-carbon fuel standard (LCFS) analysis was VISION-CA. I wondered what was in it, so I took it apart to see. The short answer is that it’s an open-loop model that demonstrates a physically-feasible path to compliance, but leaves the user wondering what combination of vehicle and fuel prices and other incentives would actually get consumers and producers to take that path.
First, it’s laudable that the model is publicly available for critique, and includes macros that permit replication of key results. That puts it ahead of most analyses right away. Unfortunately, it’s a spreadsheet, which makes it tough to know what’s going on inside.
I translated some of the model core to Vensim for clarity. Here’s the structure:
Bringing the structure into the light reveals that it’s basically a causal tree – from vehicle sales, fuel efficiency, fuel shares, and fuel intensity to emissions. There is one pair of minor feedback loops, concerning the aging of the fleet and vehicle losses. So, this is a vehicle accounting tool that can tell you the consequences of a particular pattern of new vehicle and fuel sales. That’s already a lot of useful information. In particular, it enforces some reality on scenarios, because it imposes the fleet turnover constraint, which imposes a delay in implementation from the time it takes for the vehicle capital stock to adjust. No overnight miracles allowed.
What it doesn’t tell you is whether a particular measure, like an LCFS, can achieve the desired fleet and fuel trajectory with plausible prices and other conditions. It also can’t help you to decide whether an LCFS, emissions tax, or performance mandate is the better policy. That’s because there’s no consumer choice linking vehicle and fuel cost and performance, consumer knowledge, supplier portfolios, and technology to fuel and vehicle sales. Since equilibrium analysis suggests that there could be problems for the LCFS, and disequilibrium generally makes things harder rather than easier, those omissions are problematic.
The Trouble with Spreadsheets
As a prelude to my next look at alternative fuels models, some thoughts on spreadsheets.
Everyone loves to hate spreadsheets, and it’s especially easy to hate Excel 2007 for rearranging the interface: a productivity-killer with no discernible benefit. At the same time, everyone uses them. Magne Myrtveit wonders, Why is the spreadsheet so popular when it is so bad?
Spreadsheets are convenient modeling tools, particularly where substantial data is involved, because numerical inputs and outputs are immediately visible and relationships can be created flexibly. However, flexibility and visibility quickly become problematic when more complex models are involved, because:
- Structure is invisible and equations, using row-column addresses rather than variable names, are sometimes incomprehensible.
- Dynamics are difficult to represent; only Euler integration is practical, and propagating dynamic equations over rows and columns is tedious and error-prone.
- Without matrix subscripting, array operations are hard to identify, because they are implemented through the geography of a worksheet.
- Arrays with more than two or three dimensions are difficult to work with (row, column, sheet, then what?).
- Data and model are mixed, so that it is easy to inadvertently modify a parameter and save changes, and then later be unable to easily recover the differences between versions. It’s also easy to break the chain of causality by accidentally replacing an equation with a number.
- Implementation of scenario and sensitivity analysis requires proliferation of spreadsheets or cumbersome macros and add-in tools.
- Execution is slow for large models.
- Adherence to good modeling practices like dimensional consistency is impossible to formally verify
For some of the reasons above, auditing the equations of even a modestly complex spreadsheet is an arduous task. That means spreadsheets hardly ever get audited, which contributes to many of them being lousy. (An add-in tool called Exposé can get you out of that pickle to some extent.)
There are, of course, some benefits: spreadsheets are ubiquitous and many people know how to use them. They have pretty formatting and support a wide variety of data input and output. They support many analysis tools, especially with add-ins.
For my own purposes, I generally restrict spreadsheets to data pre- and post-processing. I do almost everything else in Vensim or a programming language. Even seemingly trivial models are better in Vensim, mainly because it’s easier to avoid unit errors, and more fun to do sensitivity analysis with Synthesim.
LCFS in Equilibrium II
My last post introduced some observations from simulation of an equilibrium fuel portfolio standard model:
- knife-edge behavior of market volume of alternative fuels as you approach compliance limits (discussed last year): as the required portfolio performance approaches the performance of the best component options, demand for those approaches 100% of volume rapidly.
- differences in the competitive landscape for technology providers, when compared to alternatives like a carbon tax.
- differences in behavior under uncertainty.
- perverse behavior when the elasticity of substitution among fuels is low
Here are some of the details. First, the model:
Notice that this is not a normal SD model – there are loops but no stocks. That’s because this is a system of simultaneous equations solved in equilibrium. The Vensim FIND ZERO function is used to find a vector of prices (one for each fuel, plus the shadow price of emissions intensity) that matches supply and demand, subject to the intensity constraint.
How to critique a model (and build a model that withstands critique)
Long ago, in the MIT SD PhD seminar, a group of us replicated and critiqued a number of classic models. Some of those formed the basis for my model library. Around that time, Liz Keating wrote a nice summary of “How to Critique a Model.” That used to be on my web site in the mid-90s, but I lost track of it. I haven’t seen an adequate alternative, so I recently tracked down a copy. Here it is: SD Model Critique (thanks, Liz). I highly recommend a look, especially with the SD conference paper submission deadline looming.
A Tale of Three Models – LCFS in Equilibrium
This is the first of several posts on models of the transition to alternative fuel vehicles. The first looks at a static equilibrium model of the California Low Carbon Fuel Standard (LCFS). Another will look at another model of the LCFS, called VISION-CA, which generates fuel carbon intensity scenarios. Finally, I’ll discuss Jeroen Struben’s thesis, which is a full dynamic model that closes crucial loops among vehicle fleets, consumer behavior, fueling infrastructure, and manufacturers’ learning. At some point I will try to put the pieces together into a general reflection on alt fuel policy.
Those who know me might be surprised to see me heaping praise on a static model, but I’m about to do so. Not every problem is dynamic, and sometimes a comparative statics exercise yields a lot of insight.
In a no-longer-so-new paper, Holland, Hughes, and Knittel work out the implications of the LCFS and some variants. In a nutshell, a low carbon fuel standard is one of a class of standards that requires providers of a fuel (or managers of some kind of portfolio) to meet some criteria on average – X grams of carbon per MJ of fuel energy, or Y% renewable content, for example. If trading is allowed (fun, no?), then the constraint effectively applies to the market portfolio as a whole, rather than to individual providers, which should be more efficient. The constraint in effect requires the providers to set up an internal tax and subsidy system – taxing products that don’t meet the standard, and subsidizing those that do. The LCFS sounds good on paper, but when you do the math, some problems emerge:
We show this decreases high-carbon fuel production but increases low-carbon fuel production, possibly increasing net carbon emissions. The LCFS cannot be efficient, and the best LCFS may be nonbinding. We simulate a national LCFS on gasoline and ethanol. For a broad parameter range, emissions decrease; energy prices increase; abatement costs are large ($80-$760 billion annually); and average abatement costs are large ($307-$2,272 per CO tonne). A cost effective policy has much lower average abatement costs ($60-$868).
Continue reading “A Tale of Three Models – LCFS in Equilibrium”
Dumb and Dumber
Not to be outdone by Utah, South Dakota has passed its own climate resolution.
They raise the ante – where Utah cherry-picked twelve years of data, South Dakotans are happy with only 8. Even better, their pattern matching heuristic violates bathtub dynamics:
WHEREAS, the earth has been cooling for the last eight years despite small increases in anthropogenic carbon dioxide
They have taken the skeptic claim, that there’s little warming in the tropical troposphere, and bumped it up a notch:
WHEREAS, there is no evidence of atmospheric warming in the troposphere where the majority of warming would be taking place
Nope, no trend here:
