This project focuses on discount rates used in accounting for decommissioning costs, clean-up costs, and other related environmental liabilities, as per IAS 37.

Black box accounting: Discounting and disclosure practices of decommissioning liabilities

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This project focuses on discount rates used in accounting for decommissioning costs, clean-up costs, and other related environmental liabilities, as per IAS 37 - Provisions, Contingent Liabilities and Contingent Assets (IAS 37), with the purpose of: 

1. Determining the level of diversity in practice related to the choice of discount rate and investigating country and firm-level factors that might explain this diversity
2. Identifying corporate disclosure and transparency practices that help stakeholders understand the ‘black box’ of decommissioning and other environmental liabilities
3. Clarifying the nature of decommissioning and other environmental liabilities and pointing out the major implications of our findings for standard-setters, policy-makers, preparers and auditors.

Decommissioning commitments are among the largest liabilities for firms in pollution-prone industries, and when these firms fail, the public often bears the cost of the environmental implications of their actions. Decommissioning commitments require estimating the future cash outflows associated with decommissioning an asset and choosing a discount rate for calculating the present value of these future cash outflows. These commitments also entail 
cleaning up and restoring the site on which the asset is located. These decommissioning liabilities (DLs) do not disappear if the polluting firm goes into insolvency but remain associated with the asset and impair any future cash flows of creditors or future owners.

Large DLs may leave creditors or potential buyers with no desire to hold the firm’s residual assets as, unlike financial liabilities, DLs are physical liabilities and any monetary amount attached to them is only a proxy of these physical liabilities (i.e. provisions). No matter how big or small the firm, DLs are implicitly owned by society, which will ultimately sustain the decommissioning costs or, worse, suffer the potential negative environmental impacts (see footnote).

Further, we argue that DLs constitute a dilemma from a conceptual framework (CF) point of view. The CF prescribes that firms disclose information (e.g. discount rates) to help investment and lending/credit decisions, yet investors and lenders/creditors are not claimants and are unlikely to bear the ultimate burden of DLs that have not been addressed. 

Further, the CF defines materiality as the threshold at which the disclosure influences these stakeholders’ decisions, yet those who are more likely to bear the cost of unaddressed DLs neither will, nor can, make the kind of decisions to which the CF refers. Accounting choices and policies on DLs, as well as disclosure practices, are surely important to investors and analysts who are interested in assessing the value or solvency of a firm. However, given 
the important societal implications inherent to the valuation and reporting of DLs, how to account for them in the best interests of the public, rather than those of corporations or shareholders, is an issue that standard-setters and regulators should tackle sooner rather than later. 

The current study uses a large international sample to determine whether global diversity exists in the choice of discount rate and the disclosure of the rate used, as well as other disclosures around DLs. Going beyond the strict technical debates under IAS 37 on whether a firm’s “own credit risk” (i.e. the spread between a risk-free rate and the market rate of debt based on the firm’s credit worthiness) should be included in the discount rate (see Schneider et al., 2017), the project contributes to an informed discussion about which 
choice is appropriate theoretically.

Footnote: Despite the fundamental difference between DLs and financial liabilities, we do note that there are some implicit public guarantees for financial liabilities in the “too big to fail” cases.