If you work or track data in the energy sector, you know that the U.S. Environmental Protection Agency (EPA) released its finalized Clean Power Plan (CPP) early last month. The EPA anticipates that the CPP (assuming it survives legal challenges by several states) will cut carbon pollution from the power sector by 32 percent from 2005 levels by 2030, boosting the percentage of nationwide renewable generation to at least 21 percent in the process, with some states’ proportions being significantly higher.
The CPP comes at a time when other developments also portend an increase in renewable electricity: Lawrence Berkeley National Laboratory’s most recent “Tracking the Sun” report found the price of solar power has declined by an average of 13 percent to 18 percent per year since 2009, and in July, 13 businesses helped the White House launch the American Business Act on Climate Pledge, promising support for emissions reductions and a new international climate treaty. In August, President Obama announced a number of measures to further spur renewables, including expanded loan guarantees and lowered barriers to property-assessed clean energy (PACE) financing.
These developments seem to be setting up another big boost to the renewable energy market, but they are going to both require and create huge amounts of data: data needed by the states to ensure CPP compliance, and market data on the clean energy deployed to meet the CPP’s goals. How such data is tracked is important to Clean Edge; tracking and indexing clean-energy data is one of the company’s key business and market intelligence activities. Data tracking will become increasingly important in the coming years, but unfortunately, for many critical clean-energy sectors, data tracking and transparency is lacking.
Let’s take a couple of examples. If you were tasked with increasing your state’s renewable electricity to comply with the CPP, wouldn’t you want to know how much you have to start with? That seems logical, but it is apparently more difficult than it sounds. There are plenty of estimates out there that tally up the amount of installed solar in the U.S., for instance, but they tend to differ, particularly where distributed generation is concerned.
Greentech Media and the Solar Energy Industries Association (SEIA) put out one estimate together every quarter; SEIA releases its yearly numbers; and the U.S. Energy Information Administration (EIA) does the same, though EIA data tends to lag by nearly a year, and its EIA-860 dataset doesn’t include distributed generation smaller than 1 MW.
Then there’s the curious case of Nevada, where Vivint Solar recently pulled out of the state entirely, partly because local utility NV Energy had miscalculated the amount of solar on its system, leading the state to approach its net metering cap months before it had anticipated. It’s not clear how this happened, but better data certainly seems like it could have helped in this instance.
Energy efficiency is another good example. The EPA considers efficiency to be a big component of compliance action plans that states must submit, even if it’s no longer one of the CPP “building blocks.” Tracking of energy efficiency savings in the CPP is complicated. If a state aims to reduce its total emissions of CO2, it doesn’t have to track anything; the results will simply show up in emissions reductions. But if pursuing a CO2-per-MWh target, the state needs to set up a measurement and verification (M&V) system, which can be a tricky proposition.
Adding to the complexity: any efficiency measure installed after 2012 (long before the compliance period begins) and still producing benefits in the 2022-2030 period “counts” from a compliance perspective. The EPA has issued draft guidance on how to set up a M&V system, but ultimately it’s up to each state to put in place its own scheme. They’re going to need accurate, up-to-date data in order to do that.
So why does accurate data availability matter? Well, simply put: you can’t know where you’re going if you don’t know where you are or where you’ve been. Those responsible for creating and implementing CPP state action plans – such as public utility commissions and state energy offices – need this data to do their jobs. Getting it wrong could result in missed compliance targets.
And it isn’t just renewables and efficiency data that is lacking. Energy storage is beginning to take off in a big way. As with solar, the several storage tracking databases differ in their estimates, and some of them are not publicly available.
“Green” jobs is another key area where national data is sorely lacking. The last Bureau of Labor Statistics data on green jobs was from 2011, but while some trade groups continue to put out estimates in their sectors, the BLS hasn’t tracked jobs at a national level since. (Clean Edge has written about this extensively in the past, but green jobs tracking was one of the first casualties of “sequester” cuts in early 2013.) The lack of accurate jobs data is a problem, since many people are touting the job-producing benefits of the CPP.
How will we know how many clean-energy jobs the CPP produces if we don’t know how many we have now, how many get added, and what it does, if anything, to jobs displaced in other industries?
Accurate, transparent data is important to companies deploying clean energy as well. If the CPP – along with other predominant trends – leads to a continued boom in clean energy, that growth is going to produce plenty of market data. As Vivint found out in Nevada, bad data can lead to bad investment decisions. Lastly, accurate, accessible data is not only critical to companies and governments, but to investors, non-government organizations, and any others seeking to make sense of the evolving clean-tech markets.
There is, to be sure, plenty of good data available, but unfortunately, there are also spots where it is severely lacking. As CPP compliance moves to center stage in the coming years, with rapidly increasing deployment of renewables and energy efficiency, the deficiencies will become more glaring.
Now is the opportune time to fill the gaps. After all; you can’t manage what you can’t measure.
Lead image: 3D Digital display. Credit: Shutterstock.