Paul L. Caron

Friday, December 2, 2022

Blue J Predicts: The Economic Substance Doctrine And Excise Tax Credits Related To Alcohol-Fuel Mixtures

Benjamin Alarie (Osler Chair in Business Law, University of Toronto; CEO, Blue J Legal) & Christopher Yan (Senior Legal Research Associate, Blue J Legal), Chemoil: Economic Substance, Tax Credits, and Unprofitable Ventures, 177 Tax Notes Fed. 719 (Oct. 31, 2022):

Tax Notes Federal (2022)In this article, Alarie and Yan analyze the economic substance arguments in Chemoil, an ongoing refund suit involving otherwise unprofitable sales of an alcohol-fuel mixture for which the taxpayer was denied excise tax credits under section 6426.

In this month’s Blue J Predicts column, we examine the parties’ economic substance arguments in the ongoing litigation in Chemoil. The case involves tax refunds denied by the IRS for excise tax credits related to alcohol-fuel mixtures. The topic discussed in Chemoil bears a close relationship to last month’s installment of Blue J Predicts, in which we correctly predicted that the D.C. Circuit in Cross Refined Coal would rule for the taxpayer, holding that a business venture that was guaranteed to be unprofitable pretax (and became profitable only after tax credits) could still be considered a bona fide partnership for federal income tax purposes. Similarly, the Chemoil dispute provides an opportunity to explore the relationship between the economic substance doctrine and unprofitable transactions that are rendered economically viable by tax credits.

Chemoil raises a particularly interesting point of contention. Transactions that generate specific excise tax credits (which are designed to encourage otherwise unprofitable activities) may appear to run afoul of the economic substance doctrine, which considers whether transactions have independent economic significance setting aside the tax benefits derived from the transactions. Thus, there is a tension between the congressional intent to provide an incentive for otherwise unprofitable activities and the congressional intent behind the economic substance doctrine, which, by its nature, denies tax benefits that arise out of transactions with no economic effects apart from their federal tax implications. Moreover, if a taxpayer cannot avoid application of the economic substance doctrine to the relevant transactions, the dispute raises the further question of whether transactions whose profitability is derived solely from excise tax credits should still be considered as having economic substance because of the nature of how these tax credits operate to encourage otherwise economically unviable yet ostensibly congressionally endorsed activities.

Unlike an appeals decision, parties before a court of first instance do not have the benefit of presumed findings of fact or conclusions of law. Instead, they must contend with uncertainties regarding sufficiency of the evidence and different characterizations of material facts. This makes understanding the legal significance of each factor more important and allows tax practitioners to focus their arguments on characterizations likely to matter most to courts.

Chemoil Corp.’s responding submissions were not available at the time of this writing. Based solely on the characterization of facts in the government’s motion for summary judgment, Blue J’s economic substance model predicts with 83 percent confidence that a court would find that the transactions in question would not have economic substance. Accordingly, the machine-learning analysis in this installment focuses on exploring the potential positions that Chemoil could adopt in response to this economic substance analysis. In doing so, we investigate the likelihood of various outcomes depending on whether the district court adopts the government’s characterization of facts, what we expect to be the taxpayer’s characterization of facts, or some combination in between. ...

Fundamentally, because this is a contest between conflicting congressional intentions, it is difficult to predict how the case will be resolved. If the government’s characterization of the facts prevails, our machine-learning model points to a likely government win. On the other hand, if Chemoil is able to successfully counter the government’s characterizations, the case could quite reasonably go the other way. Despite the difficulty of predicting the outcome in this case, one thing we can say with confidence in light of our analysis is that insights from machine learning can be an important contributor to tax litigation strategy.

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