The quality of financial information in the extractive industries: a value relevance analysis

The extractive industry is characterized by high levels of risk and uncertainty. These attributes create challenges when applying traditional accounting concepts (such as the revenue recognition and matching concepts) to the preparation of financial statements in the industry. The International Accounting Standards Board (2010) states that the objective of general purpose financial statements is to provide useful financial information to assist the capital allocation decisions of existing and potential providers of capital. The usefulness of information is defined as being relevant and faithfully represented so as to best aid in the investment decisions of capital providers. Value relevance research utilizes adaptations of the Ohlson (1995) to assess the attribute of value relevance which is one part of the attributes resulting in useful information. This study firstly examines the value relevance of the financial information disclosed in the financial reports of extractive firms. The findings reveal that the value relevance of information disclosed in the financial reports depends on the circumstances of the firm including sector, size and profitability. Traditional accounting concepts such as the matching concept can be ineffective when applied to small firms who are primarily engaged in nonproduction activities that involve significant levels of uncertainty such as exploration activities or the development of sites. Standard setting bodies such as the International Accounting Standards Board and the Financial Accounting Standards Board have addressed the financial reporting challenges in the extractive industry by allowing a significant amount of accounting flexibility in industryspecific accounting standards, particularly in relation to the accounting treatment of exploration and evaluation expenditure. Therefore, secondly this study examines whether the choice of exploration accounting policy has an effect on the value relevance of information disclosed in the financial reports. The findings show that, in general, the Successful Efforts method produces value relevant information in the financial reports of profitable extractive firms. However, specifically in the oil & gas sector, the Full Cost method produces value relevant asset disclosures if the firm is lossmaking. This indicates that investors in production and non-production orientated firms have different information needs and these needs cannot be simultaneously fulfilled by a single accounting policy. In the mining sector, a preference by large profitable mining companies towards a more conservative policy than either the Full Cost or Successful Efforts methods does not result in more value relevant information being disclosed in the financial reports. This finding supports the fact that the qualitative characteristic of prudence is a form of bias which has a downward effect on asset values. The third aspect of this study is an examination of the effect of corporate governance on the value relevance of disclosures made in the financial reports of extractive firms. The findings show that the key factor influencing the value relevance of financial information is the ability of the directors to select accounting policies which reflect the economic substance of the particular circumstances facing the firms in an effective way. Corporate governance is found to have an effect on value relevance, particularly in the oil & gas sector. However, there is no significant difference between the exploration accounting policy choices made by directors of firms with good systems of corporate governance and those with weak systems of corporate governance.

Evaluating the spatial transferability and temporal repeatability of remote-sensing-based lake water quality retrieval algorithms at the European scale: a meta-analysis approach

Many studies have shown the considerable potential for the application of remote-sensing-based methods for deriving estimates of lake water quality. However, the reliable application of these methods across time and space is complicated by the diversity of lake types, sensor configuration, and the multitude of different algorithms proposed. This study tested one operational and 46 empirical algorithms sourced from the peer-reviewed literature that have individually shown potential for estimating lake water quality properties in the form of chlorophyll-a (algal biomass) and Secchi disc depth (SDD) (water transparency) in independent studies. Nearly half (19) of the algorithms were unsuitable for use with the remote-sensing data available for this study. The remaining 28 were assessed using the Terra/Aqua satellite archive to identify the best performing algorithms in terms of accuracy and transferability within the period 2001–2004 in four test lakes, namely Vänern, Vättern, Geneva, and Balaton. These lakes represent the broad continuum of large European lake types, varying in terms of eco-region (latitude/longitude and altitude), morphology, mixing regime, and trophic status. All algorithms were tested for each lake separately and combined to assess the degree of their applicability in ecologically different sites. None of the algorithms assessed in this study exhibited promise when all four lakes were combined into a single data set and most algorithms performed poorly even for specific lake types. A chlorophyll-a retrieval algorithm originally developed for eutrophic lakes showed the most promising results (R2 = 0.59) in oligotrophic lakes. Two SDD retrieval algorithms, one originally developed for turbid lakes and the other for lakes with various characteristics, exhibited promising results in relatively less turbid lakes (R2 = 0.62 and 0.76, respectively). The results presented here highlight the complexity associated with remotely sensed lake water quality estimates and the high degree of uncertainty due to various limitations, including the lake water optical properties and the choice of methods.