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Interested Party Testimony Before the Ohio House Public Utilities Committee on House Bill 114

Mar 21, 2017

Chairman Seitz, Vice Chair Carfagna, Ranking Member Ashford, and members of the Committee, thank you for the opportunity to testify today regarding reforms to Ohio’s Renewables Portfolio Standard—or “RPS.”  My name is Greg R. Lawson. I am the Senior Policy Analyst at The Buckeye Institute for Public Policy Solutions, a free market think tank here in Columbus that advocates for low-tax, low-regulation policies for Ohio.

In a recent report, The Buckeye Institute’s Economic Research Center used a dynamic macroeconomic model to study the potential effects of Ohio’s RPS program under four different scenarios. Using historical data, we calculated the percent increase in electricity prices caused by the cost of RPS compliance. Under the RPS, electricity providers purchase renewable energy credits—or RECs—which add expenses above and beyond the cost of buying and distributing wholesale electricity. Providers pass that additional cost on to consumers. Thus, RPS functions very much like a tax on electricity by increasing the product’s price without providing the consumer with any additional benefit or value. Our economic model applied past and projected price increases caused by RPS to estimate the effect of this “tax” on state GDP and employment growth. The analysis revealed that RPS reduces Ohio’s GDP and curbs job growth across the state.

If, for example, the mandates resume to 12.5% and the price of renewable energy credits increases to historical highs, we expect employment to fall 2.9% and the state’s GDP to decline by 2.8%. Such reductions will mean 134,000 fewer jobs in Ohio. Even if REC prices remain constant at historical lows as the mandates resume to 12.5%, Ohio will employ 34,200 fewer people and produce nearly $4 billion less output by the final year of compliance.[1] Such ominous projections strongly support repealing the RPS mandate.

By using a simple methodology, our model’s results do not rely on elaborate assumptions. We merely illustrate the economic impact of the RPS mandate under both high- and low-cost scenarios informed by historical data from the Public Utilities Commission of Ohio. Whether compliance costs are high or low in the future, however, we predict that RPS ultimately will reduce GDP and employment growth. Our report estimates the RPS program’s economic impact under four scenarios, which are all measured against a baseline estimate that assumes no RPS costs at all.  These scenarios are explained more fully in the Appendix attached to my remarks.

Our conclusion that RPS mandates raise electricity prices and reduce job growth—particularly in energy-intensive industries such as manufacturing—should not be controversial. In fact, Governor Kasich summarized our view rather neatly when he rhetorically asked last year: “[Do] [y]ou want to bring more jobs back…in things like manufacturing?”  And then answered: “[Then] [y]ou better have the cheapest energy you can have in the world. Do you know how much these alternative energies cost? A lot more than our traditional energy sources.”[2]

Advocates of the RPS mandates contend that the program’s economic costs and losses are offset by increasing investments and job growth in the renewable energy sector. Our model accounts for such “green job” growth. By using Ohio’s historical RPS, electricity, and employment data, our model picks up “green job” growth and changes to “non-green” sectors attributable to the mandate. We find that “green job” growth was more than offset by losses in other sectors.

This should not be surprising for several reasons.  First, considering Ohio electricity providers can purchase RECs from out-of-state resources. Second, Ohio-based renewable energy companies can sell goods and services to other states and thus maintain employees in Ohio regardless of Ohio policy. Third, the RPS subsidy from REC purchases is relatively small compared to numerous federal tax credits and subsidies. And finally, there are simply far more “other sector” jobs than “green jobs.”

To be sure, some prior studies claim to have found economic benefits from RPS programs. Our model and analysis, however, better reflects the likely economic effects of the policy because it is strictly tailored to the renewable mandate and does not conflate RPS costs with reduced bills from energy-efficiency mandates. Moreover, our fully documented and transparent model is dynamic, and does not rely on a static input-output analysis

Dynamic economic models are better suited than static input-output models for assessing the potential economic impacts of policies like RPS. Input-output models fail to account correctly for behavioral changes such as the effects that a price increase has on electricity demand and total output—especially in energy-intensive industries. In other words, static input-output models incorrectly assume that “green jobs” will be created without taking resources away from other, “non-green” sectors of the economy. In theory, however, the increase in electricity prices caused by the RPS should force job losses and reductions in hiring growth in other sectors that do not receive the benefits of the mandate—and our findings confirm that theory. Thus, unlike other studies, our analysis accounts for economic realities rather than assuming or wishing them away.

One of those realities is that the RPS raises electricity prices for businesses, costing them money that they might have otherwise spent producing goods and creating jobs. Our model research demonstrates that RPS mandates will cost more future jobs and GDP than they will create through renewable energy subsidies. As such, we must face the cold economic fact that continuing Ohio’s “march up Mandate Mountain” will cost thousands of future jobs and billions of dollars. To escape that end, Ohio must eliminate the RPS mandate and retreat from that fateful march. 

Thank you for your time and attention. I appreciate the opportunity to testify today and I welcome any questions you may have.


[1] REC prices likely will rise for three reasons. First, demand for RECs will grow as (1) annual compliance targets increase in states with existing RPS laws, (2) many states (e.g., New York and California) seek to increase existing or implement new RPS targets, and (3) companies (e.g., Amazon and Facebook) seek to “offset” more of their fossil fuel- and nuclear-generated electricity with renewables. Second, the demand for RECs will likely outpace the supply of renewable energy, causing REC prices to rise. Building new renewable generation sources greatly depends on federal tax credits and subsidies—and the most significant of those are scheduled to sunset within the next three to seven years (i.e., 2020 for wind, and 2024 for solar). With the current Trump Administration in charge for at least four years, new federal support and regulations favoring renewable generation investments appear unlikely. Finally, by regulation, Ohio electricity providers may only purchase RECs produced by renewable energy generators located in Ohio or her neighboring states. Ohio’s REC supply is further constrained because her bordering states also rank well below-average in renewable energy potential and therefore are not strong candidates for future renewable energy investments.

[2] Emily Atkin, “Kasich Bashes Clean Energy and Climate Action At Ohio Town Hall,” ThinkProgress, March 14, 2016, https://thinkprogress.org/kasich-bashes-clean-energy-and-climate-action-at-ohio-town-hall-2bee27d343ef#.3ee2168o1.

Appendix

We estimate the RPS program’s future economic impact under four scenarios. Scenario I assumes that the RPS remains suspended at 2014-2016 levels indefinitely and REC prices stay constant at 2014 levels. Scenario II assumes the RPS is suspended indefinitely at 2014-2016 levels and REC prices gradually rise from 2014 levels to their historical maximum in 2026. Scenario III assumes that the RPS mandates increase to 12.5% in 2026 and REC prices stay constant at 2014 levels. Scenario IV assumes that the RPS mandates increase to 12.5% in 2026 and REC prices gradually increase from 2014 levels to their historical maximum in 2026.

These four scenarios are measured against a baseline estimate without RPS costs. That baseline provides a counterfactual that predicts what the Ohio economy would have looked like without an RPS in place, and what the economy would likely become if the RPS is repealed entirely.

Table 1 shows our model’s estimate for all Ohio employers:

 

Table 1: Effects of RPS on the Ohio Economy

 

Baseline Levels

Effect of RPS (Deviations from No RPS Baseline)

 

No RPS

Scenario I

Scenario II

Scenario III

Scenario IV

Year

GDP

Empl.

GDP

Empl.

GDP

Empl.

GDP

Empl.

GDP

Empl.

2011

440,925

4,403,600

-1,183

-12,200

-1,183

-12,200

-1,183

-12,200

-1,183

-12,200

2012

449,850

4,497,000

-820

-8,600

-820

-8,600

-820

-8,600

-820

-8,600

2013

453,837

4,573,000

-1,033

-10,900

-1,033

-10,900

-1,033

-10,900

-1,033

-10,900

2014

465,828

4,646,800

-680

-6,800

-680

-6,800

-680

-6,800

-680

-6,800

2015

473,206

4,646,800

-643

-6,700

-720

-7,200

-643

-6,700

-720

-7,200

2016

480,701

4,646,800

-653

-6,300

-810

-8,300

-653

-6,300

-810

-8,300

2017

488,315

4,646,800

-836

-8,400

-1,204

-11,900

-1,168

-11,900

-1,659

-16,500

2018

496,050

4,646,800

-836

-8,300

-1,335

-12,900

-1,510

-14,900

-2,360

-23,400

2019

503,907

4,646,800

-849

-8,300

-1,470

-14,000

-1,826

-17,600

-3,233

-31,100

2020

511,888

4,646,800

-812

-7,700

-1,623

-15,500

-2,138

-20,400

-4,225

-40,400

2021

519,996

4,646,800

-824

-7,700

-1,819

-17,200

-2,460

-22,800

-5,456

-50,800

2022

528,232

4,646,800

-785

-7,200

-2,020

-18,600

-2,791

-25,400

-6,859

-63,100

2023

536,599

4,646,800

-797

-7,200

-2,241

-20,300

-3,092

-28,000

-8,533

-77,300

2024

545,098

4,646,800

-795

-7,100

-2,469

-22,300

-3,374

-30,100

-10,466

-93,800

2025

553,732

4,646,800

-808

-6,800

-2,759

-24,400

-3,678

-32,100

-12,805

-112,400

2026

562,503

4,646,800

-806

-6,800

-3,099

-27,000

-3,991

-34,200

-15,485

-134,100

Note:

Total GDP of industrial sectors in millions of 2009$

 

Employment in units of full-time equivalent jobs, rounded to the nearest hundred.