Deconstructing the Rosenfeld Curve

The Wall Street Journal, Forbes, and, most recently, the Sacramento Bee have pieces on Arik Levinson’s new NBER working paper, “California Energy Efficiency: Lessons for the Rest of the World, or Not?”   The paper makes a nice point, but I worry that it is being misinterpreted.

Levinson starts with the well-known “Rosenfeld Curve” (below), named after energy-efficiency pioneer Arthur Rosenfeld.  For the last four decades, residential electricity use per capita in California has been nearly flat, while growing 75 percent in the rest of the United States.

Residential Electricity Use per Capita 1963-2009

Fig 1

While some have attributed the difference to California’s energy-efficiency policies, Levinson argues that California’s temperate climate, changing demographics, and other factors can explain almost 90% of the gap. For example, Levinson shows that part of the explanation for the increase in other U.S. states is that more and more people are living in the Southwest, where air-conditioning is used more intensively.

Levinson’s paper is thoughtfully done and deserves to be widely read, but the results are not terribly surprising. Even energy-efficiency proponents have long understood that at least half of the gap is likely due to non-policy factors (Sudarshan and Sweeney, 2008; Rosenfeld and Poskanzer, 2009).

But the truth is that it is hard to learn much from this type of aggregate data.  The challenge with any empirical analysis is how to construct a counterfactual. What would have happened to California’s electricity use without energy-efficiency policies?  This is a deceptively difficult question because of all the ways, large and small, that California is different from the rest of the United States. These differences accumulate over the 40+ year time horizon, obscuring the causal impact of energy-efficiency policies.

And none of these comparisons capture some of the broader impacts. California has consistently pushed the national agenda on energy-efficiency.  For example, in 1976 California was the first state to introduce appliance energy-efficiency standards. Other states quickly followed, leading eventually to national appliance standards in 1988. These national “spillovers” don’t show up in these analyses because they result in decreased electricity use both in and out of California.

So the “Rosenfeld Curve” does not prove that California’s energy-efficiency policies have worked. But nor does Levinson’s analysis prove that the policies haven’t worked.  With aggregate data it is impossible to answer this question definitively, let alone to say anything about which particular types of policies are most effective, or about how differences in program design impact effectiveness.

To be fair, Levinson understands all this. But people tend to have strong views on energy-efficiency. So when Levinson pokes holes in some of the best known “evidence”, it is tempting to run to the other extreme. Let’s not.  It doesn’t make sense to have such extreme views when the evidence is so incomplete. As it often is, the truth is probably somewhere in the middle.

We need more studies using better data. This is the motivation for a new project, E2e, which brings together researchers from UC Berkeley and MIT.  Our goal is to apply more scientific rigor to the analysis of energy-efficiency to better understand how to best design policy. We are particularly focused on randomized control trials and other experimental approaches. In fact, we are organizing a one-day course on the topic August 26th in Berkeley. Click here for more information.

About Lucas Davis

Lucas Davis is an Associate Professor of Economic Analysis and Policy at the Haas School of Business at the University of California, Berkeley. His research focuses on energy and environmental markets, and in particular, on electricity and natural gas regulation, pricing in competitive and non-competitive markets, and the economic and business impacts of environmental policy.
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16 Responses to Deconstructing the Rosenfeld Curve

  1. Lucas,

    I agree with your characterization of Arik Levinson’s excellent paper, including the fact that he himself is quick to point out this caveat. That said, let’s recognize that most greens and many others in California would be very surprised — to say the least — to learn that there’s evidence that close to 90% of California’s impressive energy-efficiency gains have not been due to California public policies.

    In addition to the work with randomized control methods, I hope that the E2e project can carry out some retrospective analysis of the factors that have driven California’s energy-efficiency relative to the rest of the country. This remains a frequently-discussed issue in California energy and environmental circles, as you know.

    Best,

    Rob

  2. Dan says:

    The Inland Empire and Great Central Valley have summer building conditioning requirements. But much of the housing stock is relatively new and thus with decent insulation. It would be interesting to see if the slight growth in CA is a result of population increase inland.

  3. Jack Ellis, Tahoe City, CA says:

    I think there’s another point to consider, which is the growth in peak demand over that same period. I don’t have the statistics at hand but I do know that load factors continue to decline, which indicates that peak demand is growing faster than total demand, or total energy consumption. There are times when it makes sense to use a bit more energy if doing so leads to lower peak demand, but this seems to get lost in the energy efficiency discussion in California and elsewhere.

    One of the early drivers of energy efficiency was to avoid building power plants. Energy use is flat, but if peak demand continues to grow (on a per-capita bases even after allowing for the loss of a lot of industrial load), then it’s not clear we’ve accomplished what we set out to do.

  4. sapitosetty says:

    LA & SF have a moderate climate, but all the housing construction in recent years has been in Inland Empire, Fresno, etc. You can’t use climate to explain that away. Plus, if climate matters, why did California used to rise so fast? And why do less heavily regulated Mediterranean climate zones see steadily increasing use of heating and cooling in homes?

  5. sapitosetty says:

    LA & SF have a moderate climate, but all the housing construction in recent years has been in Inland Empire, Fresno, etc. You can’t use climate to explain that away. Plus, if climate matters, why did California used to rise so fast? And why do less heavily regulated Mediterranean climate zones see steadily increasing use of heating and cooling in homes?

    I first left that paragraph as a comment before reading the Levinson paper. Now I see that it’s even worse than I thought. The guy does no geographic analysis at all of where construction is really happening. He looks not at all at California’s internal growth trends, looking only at state-to-state movement. The paper reads as if the author has never really been to California, or at least not to the fast-growing regions. Climate is a non-explanation. His media-friendly 90% number is bull.

  6. Kenneth Gillingham says:

    Thank you Lucas. Well-put! More experimental approaches are critical to pull back the layers of what’s really going on here.

  7. Sam Borgeson says:

    I’m excited to see more work being done on efficiency outcomes using randomized control trials (the measurement of short run impacts can surely be improved), but you must already know that they are poorly suited to resolving questions about macroeconomic change. Partially for this reason, California’s efficiency programs focus on short term payback to satisfy narrow requirements of cost effectiveness, with any longer term industry or macroeconomic spillovers seen as additional to programs already justified by short term gains. As a result we pursue our longer term interests in a haphazard and inefficient manner. I will be the first in line to offer a critique of the methods used for calculating savings, but we should also remember that they are the outcome of a profoundly political process and the result of an awkward arrangement that encourages utilities to sell less of their product rather than more. With a billion dollars a year spent according to the logic of those calculations, is anyone actually surprised that there are exaggerated savings claims?

    I am also puzzled by the efficiency proponents vs. detractors dichotomy. I get that it is a nice conflict for our lazy media to cover both sides of, but for anyone who takes climate mitigation seriously (as any serious person should), the alternative to successful efficiency is increasingly expensive and complex deployment of low/zero carbon energy infrastructure, not business as usual. For this reason, renewables, efficiency, and demand response are complementary. Once you accept this, belief in the under performance of programs suggests greater investment in efficiency, not less – unless there is a cheap, abundant, easy to deploy, zero carbon energy source that doesn’t require demand mitigation to scale economically.

    • Richard McCann says:

      I don’t think the issue is about efficiency measures–yes or no–but rather about how effective are government or utility run programs versus decentralized approaches that rely much more on price signals to incent efficiency investments. Investments in renewables is another example–to what degree can we rely on carbon pricing to change the relative costs of conventional and alternative technologies? Or do we have to have mandates to achieve generation targets? So these analyses are not so much about the overall targets but about the relative effectiveness of different means of achieving those targets.

  8. Howard Chong says:

    Levinson’s paper is precisely the approach we want to be taking: using data carefully studied to look at evaluation of standards. Looking at US vs CA with no explanatory variables is not good. That the CEC and other policy-making bodies use this curve as the lede is misleading.

    Levinson is using some “top-down” methods that the CEC and others have also used, and also at the heart of the “Rosenfeld Curve”. Take a look at the RASS appendix to see an example of a gov’t report with a similar approach. (If you have a problem with Levinson, you have a problem with RECS and RASS too.)

    For building science types who want to see something that is more granular (and bottom up), take a look at my 2013 article: Building vintage and electricity use: Old homes use less electricity in hot weather. This paper conditions on one region of California (so it is immune from geographic composition issues). My paper is also more paranoid about functional form than Levinson’s.

    (Published: http://econpapers.repec.org/article/eeeeecrev/v_3a56_3ay_3a2012_3ai_3a5_3ap_3a906-930.htm)

    A freely accessible working paper version is here:

    http://ei.haas.berkeley.edu/pdf/working_papers/WP211.pdf

    • Sam Borgeson says:

      Howard – I have a simple modeling question related to your paper. It seems to be standard practice in econ circles to run regressions on the log of energy demand. However, expatiating both sides implies that the demand is the product of the linear terms in the logged model. This seems implausible to me. What is the justification? Physical interpretation? Origin of this practice?

  9. Azmat says:

    It may simply be the same factors at play here as longevity. More educated and more wealthy people generally seem to ‘live’ longer [ie they may be 'kept alive' by doctor interventions etc at the right times].
    The data are ‘per capita’ so it doesnt have anything to do with population movement; it could mean that the younger-poorer have moved away [happened a lot in the 1970's and 1990's with the economic downturns].
    It could be that the wealthy are able to afford energy efficiency retrofits more easily than the not-so-wealthy even though the former can more easily pay the cost of higher energy usage. It is quite possible that the ‘poor’ are not even aware of how much energy they are consuming, specially if they live in high-density housing, or if ‘someone else’ is paying for the energy.
    It could also be simply the human behavior factor: immediate gratification [more common among the less educated, and a higher 'subjective discount rate' for decision making to compare near-term vs. long-term factors; the poor and less educated usually have a higher discount rate].

    So I am for looking not a energy conservation policies, but at demographics as related to education and wealth. That is where I expect we will find a lot more difference. Of course one should compare California with some other higher education-wealth states, not just Ca vs rest-of-US.

  10. publius says:

    There are two unexamined questions here.
    The first is true versus false efficiency. The former means a greater return of economic value from every Joule expended ; the latter means having to make do with less, which can also reasonably be called poverty.
    The other is about efficiency itself as a primary good. Energy efficiency isn’t free. It has costs, direct and indirect ; investment in efficiency improvements only make sense when those costs are less than the costs (again, direct and indirect) of expanding energy production.
    Nobody seems to want to tackle these basic issues, instead pretending that using less energy is a moral good. It ain’t. The steam engine freed the slaves.

    • stratdouglas says:

      Actually, the steam engine increased the demand for slaves by increasing the demand for cotton to industrial textile mills, leading to the expansion of King Cotton’s empire across the old Southwest. So in fact the steam engine indirectly increased both the number and suffering of slaves. But I agree with your larger point, that reduction of energy usage (as opposed to reduction of pollution) has neutral-to-negative implications for human development.

    • Appliance efficiency standards do not change appliance function, and therefore are, in your terminology, “true” efficiencies. There is a continuum between your “true” and “false” endpoints. Wearing a sweater and lowering the thermostat to 65°F cannot be construed as poverty. Where your dichotomy really gets messy is when we leave the realm of economic return and instead talk about situations where an individual has a choice between actions that are equally desirable, but have vastly different energy use levels. What is the economic return of biking? or skiing? Did you have to fly to go skiing?

      With regards to whether using less energy is a moral good, it is the side effects and not the energy itself that is the issue. You want to put solar on your house? Go ahead. You want to increase the amount of greenhouse gases that you generate – yes, that is a moral issue.

      Bob – using my wife’s account

  11. Pingback: Repost: Deconstructing the Rosenfeld Curve | Economics Outside the Cube

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