By Jamie Fine, PhD and Ruiwen Lee
Jamie Fine is EDF’s Senior Energy economist, and a graduate of UC Berkeley’s Energy Resources Group; Ruiwen Lee is an economics fellow and graduate of Princeton University’s Woodrow Wilson School of Public and International Affairs.
California’s energy and climate change policies have saved the state over one hundred billion dollars and dramatically reduced levels of environmental pollution since the early 1970’s. Yet these policies have been in the crosshairs of industry for decades, despite their demonstrated success. It’s not surprising that the latest study sponsored by the state’s main manufacturing lobbying group, the California Manufacturers and Technology Association (“CMTA”), ignores the achievements of these landmark policies while attempting to downplay the benefits of new laws that protect human health and the environment.
EDF’s team of economists looked behind the curtain of CMTA’s most recent tirade against clean air laws and found cherry-picked assumptions, secret modeling calculations, and confusion over basic economic principles. Accordingly, while CMTA’s new report maintains that it modeled the impacts of California energy and climate policies on the state’s economy, the results more closely resemble CMTA members’ manufactured products than actual economic analysis.
Cherry-picked data – CMTA’s report is based on the impact of seven different policies currently underway in California: The 1) Low Carbon Fuel Standard (“LCFS”), 2) Pavley II car standards, 3) SB 375, 4) the Renewable Portfolio Standard (“RPS”), 5) Combined Heat and Power (“CHP”) standards, 6) new efficiency measures, and 7) the Global Warming Solutions Act of 2006 (“AB32”).
Though these policies have been carefully designed and the economic impacts studied, modeled, re-studied, re-modeled, and peer reviewed, the report’s analysis assumes significantly lower benefits and higher costs than nearly every other peer-reviewed analysis that currently exists. With these new assumptions, each described in a mere sentence or two, the goal seems to have been to engineer a flimsy model that puts AB32 into a bad light.
Even more subtle assumptions are not immediately obvious at first glance. For example, in the electricity model, the report uses a simplistic “average change in electricity demand”, based on the ten-year historical decline in electricity use per unit of state output, to project future electricity demand. However, this model does not consider the reduction in electricity demand from the energy efficiency measures that have only recently been implemented, or yet to be imagined by innovators – meaning the modeling output undercounts savings from day one.
In addition, this model is posing extremely pessimistic assumptions about new transportation fuels and the California LCFS. These same assumptions are at the heart of a recent oil industry report, and fail to take into account policy-inspired innovation that will lead to newer transportation fuels in adequate supply. As previously discussed here, here and here, these pessimistic assumptions aren’t grounded in the reality of market incentives for innovation, making the CMTA report eerily similar to other oil industry “sky-is-falling” reports – this one just has a different cover.
Finally, like other faulty anti-AB 32 analyses that have been debunked, this newest CMTA piece fails to take into account the full range of benefits that can be achieved by implementation of California’s regulations. For example, the analysis only calculates the costs to carbon-intensive commodities, ignoring the positive demand impacts on clean products and services.
Secret modeling calculations – The output presented in this new paper is based on an internally designed model that claims to weave together “24 interacting models that measure the combined impacts of AB 32.” However, nothing more is presented than a few spreadsheets of model output and some graphs. This amounts to a clear admission that the model simulated the operations of an entire economy and then offered the output as fact, without any discussion of range or uncertainty. Simply put, CMTA’s new report says, “trust me” even though the findings are vastly different from prior, peer-reviewed scholars.
Confusion over basic economics – The principles that underpin cost analysis, though complex, are fundamental to the accuracy of the overall output of economic models. In this report, CMTA has apparently gotten some of those basic principles backwards, meaning that the modeling results are likely to be fundamentally flawed.
For example, in the description of their direct cost estimation model for electricity, the report wrongly concludes that, “the cost of electricity is decreased by efficiency measures, which drives up demand.” In fact, it is the other way around – efficiency measures shift the demand curve down (given any electricity price, consumers use less electricity than before efficiency measures are implemented), so the cost of electricity is decreased, even as the quantity of electricity consumed is further reduced.
Model inconsistencies – In another area of the report, where it separately modeled electricity and natural gas price and demand impacts, it appears there is double counting and inconsistent assumptions due to confusion over the linkages between commodities markets. For example, even as the report predicts electricity generation from natural gas will decrease by 40% from 2012 to 2020 (Appendix D-6), overall natural gas demand will increase by 6% over the same period (Appendix E-1). A simple question underpins the potential error of these inputs: when so much electricity is currently made using natural gas, how can the state reduce its use in the power sector by 40% while using so much more in the aggregate?
In yet another example, the report purportedly uses a simple summation of impacts (Appendix C-2) from modeling outputs to calculate policy “costs”. However, since at least some of the commodity markets that have been analyzed are substitutes for one another, a simple summation method will almost certainly result in double counting. The report doesn’t make clear which commodity markets were modeled based on direct cost estimations, and which were separately modeled based on aggregated data – a wholly separate problem from the one described above.
The bottom line is, this report is deeply flawed in its analysis and its presumptions. If your summer reading list includes fiction, then by all means take the time to read it. But if you are looking for a well-documented and peer-reviewed examination of California’s anti-pollution laws, this report doesn’t make the grade.