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This paper explores how conflicting institutional logics shape the behaviors of macro- and micro-level actors in their use of a calculative practice. Thereby, this paper explains how quantification can undermine the intended purpose of a governance system based on a single number.

The study draws upon the literature on calculative practices and institutional logics to present the case of how a single number—specifically the conversion factor for Atlantic Cod, established by macro-level actors for the purposes of governance within the Norwegian fishing industry—is interpreted and used by micro-level actors in the industry. The study is based on documents, field observations and interviews with fishers, landing facilities, and control authorities.

The use of the conversion factor, while intended to protect fish stock and govern industry actions, does not always align with the institutional logics of micro-level actors. Especially during the winter season, these actors may seek to serve their interests, leading to potential system gaming. The reliance on a single number that overlooks seasonal nuances can motivate unintended behaviors, undermining the governance system’s intentions.

Integrating the literature on calculative practices with an institutional logics perspective, this study offers novel insights into the challenges of using quantification for the governance of complex industries. In particular, the paper reveals that when the logics of macro- and micro-level actors conflict in a single-number governance system, unintended outcomes arise due to a domination of the macro-level logics.

The main purpose of this study resides essentially in the development of a new tool to quantify the biomass in the bioreactor operating under steady state conditions.

Modeling is the most relevant tool for understanding the functioning of some complex processes such as biological wastewater treatment. A steady state model equation of activated sludge model 1 (ASM1) was developed, especially for autotrophic biomass (XBA) and for oxygen uptake rate (OUR). Furthermore, a respirometric measurement, under steady state and endogenous conditions, was used as a new tool for quantifying the viable biomass concentration in the bioreactor.

The developed steady state equations simplified the sensitivity analysis and allowed the autotrophic biomass (XBA) quantification. Indeed, the XBA concentration was approximately 212 mg COD/L and 454 mgCOD/L for SRT, equal to 20 and 40 d, respectively. Under the steady state condition, monitoring of endogenous OUR permitted biomass quantification in the bioreactor. Comparing XBA obtained by the steady state equation and respirometric tool indicated a percentage deviation of about 3 to 13%. Modeling bioreactor using GPS-X showed an excellent agreement between simulation and experimental measurements concerning the XBA evolution.

These results confirmed the importance of respirometric measurements as a simple and available tool for quantifying biomass.

Artificial intelligence (AI) models are demonstrating day by day that they can find long-term solutions to improve wastewater treatment efficiency. Artificial neural networks (ANNs) are one of the most important of these models, and they are increasingly being used to forecast water resource variables. The goal of this study was to create an ANN model to estimate the removal efficiency of biological oxygen demand (BOD), total nitrogen (TN), total phosphorus (TP) and total suspended solids (TSS) at the effluent of various primary and secondary treatment methods in a wastewater treatment plant (WWTP).

The MATLAB App Designer model was used to generate the data set. Various combinations of wastewater quality data, such as temperature(T), TN, TP and hydraulic retention time (HRT) are used as inputs into the ANN to assess the degree of effect of each of these variables on BOD, TN, TP and TSS removal efficiency. Two of the models reflect two different types of primary treatment, while the other nine models represent different types of subsequent treatment. The ANN model’s findings are compared to the MATLAB App Designer model. For evaluating model performance, mean square error (MSE) and coefficient of determination statistics (R2) are utilized as comparative metrics.

For both training and testing, the R values for the ANN models were greater than 0.99. Based on the comparisons, it was discovered that the ANN model can be used to estimate the removal efficiency of BOD, TN, TP and TSS in WWTP and that the ANN model produces very similar and satisfying results to the APPDESIGNER model. The R-value (Correlation coefficient) of 0.9909 and the MSE of 5.962 indicate that the model is accurate. Because of the many benefits of the ANN models used in this study, it has a lot of potential as a general modeling tool for a range of other complicated process systems that are difficult to solve using conventional modeling techniques.

The objective of this study was to develop an ANN model that could be used to estimate the removal efficiency of pollutants such as BOD, TN, TP and TSS at the effluent of various primary and secondary treatment methods in a WWTP. In the future, the ANN could be used to design a new WWTP and forecast the removal efficiency of pollutants.

This paper outlines the development of the principle of materiality in the European accounting framework, from the Modernization Directive (2003/51/EC) to the NFI Directive (2014/95/EU) and on to the proposals for a Corporate Sustainability Reporting (CSR) Directive (2021/0104 (COD)). The authors highlight how the requirements for corporate reporting in terms of sustainability matters have changed, underlining the main issues that require further attention by practitioners, researchers and legislators.

This paper is based upon a historic analysis of European Union (EU) regulations in the field of non-financial and sustainability reporting and how these have changed over time. A conceptual comparison of different reporting concepts is presented, and changes in their relevance to the EU accounting framework are discussed as part of the historic analysis. Implications from corporate practice are derived from previous empirical findings from the EU Commission's consultations.

The proposed change from non-financial to sustainability reporting within the EU affects more than simply the terminology used. It implies that a different understanding is needed of both the purposes of company reporting on sustainability matters and the aims of carrying out such reporting. This change was driven by the need and desire to appropriately interpret the principle of materiality set forth in the NFI Directive. However, the recent redefinition in the shift within the EU Commission's proposals presents considerable challenges–and costs–in practice.

Future research on the conceptualization and operationalization of ecological and social materiality, as well as on the use of this information by different stakeholder groups, is necessary in order to (a) help companies that are applying the reporting requirements in practice, (b) support the increased harmonization of the reports published by these companies and (c) fully assess the costs and benefits associated with the increase in reporting requirements for these companies.

Companies have to establish relevant reporting processes, systems and formats to fulfil the increasing number of reporting requirements.

This paper outlines the historic development of the principle of materiality regarding mandatory non-financial or sustainability reporting within the EU. It outlines a shift in rationales and political priorities as well as in implications for European companies that need to fulfil the reporting requirements. In consequence, it describes appropriate interpretations of this principle of materiality under current and upcoming legislation, enabling users to apply this principle more effectively.

Sex and gender are regarded as critical structural determinants of mental health and mental illness. Mental illness is a complex phenomenon, and risky behaviour and substance use commonly occur simultaneously or subsequent to one another. A gendered vulnerability in biological, environmental, and behavioural risk factors has been registered in the development and escalation of mental illness. Studies have found that women who use drugs experience greater physical and mental health repercussions than men. Women who use drugs present higher rates of depression and anxiety, suicidal tendencies, isolation and general psychological distress. This chapter addresses the most common mental illnesses associated with women who use drugs: depression, anxiety, trauma-related disorders, and eating disorders.