PG&E spends $275 million a year on energy efficiency investments that reduce demand by 100 MW. It also spends $65 million a year on demand response to reduce peak loads by 400 MW. If we assume that energy efficiency investments are effective an average of 12 years the incremental cost of those investments is $66 per MWH (6.6 cents per kWh). For demand response the incremental cost, which should match the market value, is $163 per kilowatt-year (or $13.60 per kW-month). Both of these values are reasonable investments for long-term resources.
Yet, PG&E argues in the PCIA exit fee proceeding and its annual ERRA generation cost proceeding that the appropriate market valuation for its resources are the short-term fire sale values that it realizes in the daily markets. According to PG&E, customers do not realize any additional value from holding these resources beyond what those resources can be bought and sold for the CAISO markets and in bilateral short-term deals.
So we are left with the obvious question: Why is PG&E continuing to invest in energy efficiency and demand response if the utility states that it can meet all of its needs in the short-term markets? This hypocrisy is probably best explained by PG&E manipulating the regulatory process. PG&E’s proposed “market valuation” sets the exit fee for community choice aggregation (CCA) at a high level. Instead, that market valuation should reflect how much CCAs have saved bundled customers in avoided procurement, and what PG&E pays for adding new resources.
Even though I have conducted regional economic impact studies, I’m always a bit skeptical when a project is touted as a huge payoff for taxpayer investment. Amazon’s HQ2 is a case in point. New York is claiming a $24 billion net return over 25 years from the $3.6 billion in tax breaks, based on impact analysis done with the REMI economic model. I would be interested in a retrospective analysis on the impact of Amazon’s HQ1 in Seattle. The campus is fairly self contained and it should be fairly straightforward to track the growth of Amazon employment in Seattle since the last 1990s. Clearly, there would be uncertainty about how to attribute regional economic activity to Amazon activity, but we could see bounds on various factors such as jobs and tax revenues. We could then see a comparison against the estimates for New York City.
Harvard is being sued by a group of Asian-American students for discriminating against them through affirmative action admission standards. The Trump Administration is joining on their side. I have been troubled by the reliance on standardized tests and inflated GPAs as appropriate metrics for college admission. I propose two solutions to both of these problems:
- Colleges and universities should create mission statements about what the university wants to accomplish through educating students. These statements should go well beyond just saying “a well-rounded individual who is prepared for a career.” It should state that it wants a set of alumni that positively impact their communities in a number of dimensions (some not easily quantified) in a manner that improves social well being in a manner chosen by the university’s policy makers. This will require developing a consensus among those policy makers (e.g., the Board of Regents), but it will prompt a larger debate about the purpose of a college education that is badly needed right now.
- The colleges and universities should then conduct at least two statistical/econometric studies to determine the mix of traits that it wants in its student body to accomplish its objectives:
- The first one would establish what preparation will lead to the most likely academic success in that college. This would completely eliminate the need for standardized tests. The key is that most colleges have an extensive set of data on the GPAs of their students, the high schools that they went to and their GPAs in high school (along with other academic data). The study (a regression) would regress the college GPA on the high school GPA weighted by the high school (this adjusts for differences between high schools) plus a specific set of other academic / extracurricular data. This would be socio-economic blind. But most importantly, these results would NOT be the singular admission standard. It would be just a measure of likely academic success that could help identify how to deploy resources for those students once they enroll.
- The second would evaluate how alumni impact their communities. This would rely on data about alumni on occupations, income, community activities, rate of return to certain communities based on socio-economic status, and community well-being. Alumni characteristics would include incoming socio-economics traits, study major, college GPA, extracurricular activities, and other factors thought to be important. This study (again, likely econometric) would measure the relative impact on community well-being (or other chosen objectives) of admitting students with certain characteristics and likelihood of certain majors and academic performance. For example, it might show admitting a student from Watts with a lower predicted GPA has a bigger community impact than admitting another from Beverly Hills with a higher GPA.
- The desirable traits found in the second study would be used with the results of the first study to set admission policies in a fairly transparent way. A third study might assess what mix of student body traits is most likely to achieve the mission statement objectives. That could further help make the admission process more transparent.
This study published in the American Journal of Agricultural Economics seems to have a surprising finding, at least to academic economists, that farmers with riskier water supplies rely less on irrigation! What? If you’re uncertain about whether you will get water every year, you are less likely to count on that water to irrigate your crops? Who possibly would think that way?
Finally, a real world example of how benefit-cost analysis should be used in practice. Alberta takes the revenues that represent a portion of the society wide benefits and distributes those to the losers from the policy change. Economists have almost always ignored the problem of how to compensate losers in changes in social policy, and of course those who keep losing increasingly oppose any more policies. Instead of dreaming up ways to invest carbon market revenues in whiz bang solutions, we first need to focus on who’s being left behind so they are not resentful, and become a key political impediment to doing the right thing.
Severin Borenstein’s post raises an important issue that economists have ignore for too long. I posted the following comment there:
We gave politicians the tool of benefit-cost analysis which they have used to justify their policy objectives, but we completely failed to drive home the requirement that those parties who are on the losing end need to be compensated as well. I looked in my edition of Ned Gramlich’s book on Benefit-Cost Analysis (who taught my course), and the word “compensation” is not even in the index! Working on environmental regulations, I regularly see agency staff derive large positive ratios for the “general public” and then completely dismiss the concerns of particular groups that will be carrying all the burdens of delivering those benefits. If we’re going to teach benefit-cost analysis, we need to emphasize the “cost” side as much as the “benefit” that politicians love to extol.
Source: Creative Pie Slicing To Address Climate Policy Opposition |
The recent jobs report may be indicating that any additional stimulus such as tax cuts or infrastructure investment will be ineffectual, or even counterproductive.