Exploring Discrepancies in Energy Performance Certificates: Analyzing Energy Efficiency Premiums for Buildings Based on Theoretical Energy Requirements Versus Actual Energy Consumption

The building sector is lagging its needed decarbonization pathway. This paper examines EPC policy impacts on building economics in the Rhein-Main Region in Germany. Energy efficiency premiums for rents and sales prices and the effects of the EPC type are investigated using data from 01/2015 - 06/2023 (N = 212 167 rent sample; N = 159 573 sales sample) and hedonic price models. Energy efficiency premiums are present and range up to 7.0%, 4.6% and 6.9% for cold and warm rents and sales prices, respectively, when comparing an A+ to a D rated building. Consumption certificates reflect warm rents better but have a limited sales price impact. Results are rent efficiency premiums of up to 7.1% (A+), no rent discounts for energy inefficiency and a general sales price discount of about 3%. Requirement certificates are viewed as objective, yet less consumption-indicative, especially in the sales market. Rent efficiency premiums of up to 8.8% (A+) and no rent discounts for energy inefficiency are estimated for a building with a requirement certificate. Sales price efficiency premiums of up to 7.4% (A+) and sales price inefficiency discounts of up to -10.2% (H) exist. Overall, current German EPC policy does not address imperfect information, and it is recommended to revise its implementation. Keywords: energy efficiency; energy performance certificate; EPC; hedonic price model; real estate investments; real estate valuationThe building sector is lagging its needed decarbonization pathway. This paper examines EPC policy impacts on building economics in the Rhein-Main Region in Germany. Energy efficiency premiums for rents and sales prices and the effects of the EPC type are investigated using data from 01/2015 - 06/2023 (N = 212 167 rent sample; N = 159 573 sales sample) and hedonic price models. Energy efficiency premiums are present and range up to 7.0%, 4.6% and 6.9% for cold and warm rents and sales prices, respectively, when comparing an A+ to a D rated building. Consumption certificates reflect warm rents better but have a limited sales price impact. Results are rent efficiency premiums of up to 7.1% (A+), no rent discounts for energy inefficiency and a general sales price discount of about 3%. Requirement certificates are viewed as objective, yet less consumption-indicative, especially in the sales market. Rent efficiency premiums of up to 8.8% (A+) and no rent discounts for energy inefficiency are estimated for a building with a requirement certificate. Sales price efficiency premiums of up to 7.4% (A+) and sales price inefficiency discounts of up to -10.2% (H) exist. Overall, current German EPC policy does not address imperfect information, and it is recommended to revise its implementation. Keywords: energy efficiency; energy performance certificate; EPC; hedonic price model; real estate investments; real estate valuatio

The Role of Hierarchical Differentiation for the Effectiveness of Soccer Teams

The impact of hierarchical differentiation on team effectiveness is heavily discussed in scientific research with strong arguments lined up on both the pro and the contra sides. To contribute to this debate, I investigated the relationship between a specific facet of hierarchical differentiation, pay dispersion, and team effectiveness. I collected data from five seasons of Premier League and conducted a regression analysis to study the effect of pay dispersion on team performance, cooperation and aggressivity. The empirical results show that pay dispersion is positively and directly associated with aggressivity, whilst its relation with team performance and cooperation is moderated through the financial might of teams. The significant interaction effect for team performance means that pay dispersion has a significant negative effect for high financial might teams, and a weak positive effect for low financial might teams. For cooperation the interaction shows a significant positive effect for the low financial might teams and a weak negative effect for the high financial might teams. Thus, I conclude that pay dispersion indeed affects team effectiveness, however the economic power standing behind the teams needs to be considered. Keywords: hierarchical differentiation; pay dispersion; Premier League; sports data; team performanceThe impact of hierarchical differentiation on team effectiveness is heavily discussed in scientific research with strong arguments lined up on both the pro and the contra sides. To contribute to this debate, I investigated the relationship between a specific facet of hierarchical differentiation, pay dispersion, and team effectiveness. I collected data from five seasons of Premier League and conducted a regression analysis to study the effect of pay dispersion on team performance, cooperation and aggressivity. The empirical results show that pay dispersion is positively and directly associated with aggressivity, whilst its relation with team performance and cooperation is moderated through the financial might of teams. The significant interaction effect for team performance means that pay dispersion has a significant negative effect for high financial might teams, and a weak positive effect for low financial might teams. For cooperation the interaction shows a significant positive effect for the low financial might teams and a weak negative effect for the high financial might teams. Thus, I conclude that pay dispersion indeed affects team effectiveness, however the economic power standing behind the teams needs to be considered. Keywords: hierarchical differentiation; pay dispersion; Premier League; sports data; team performanc

Impact of Audit Assurance on the Quality of Sustainability Reporting

The subject of sustainability reporting is becoming increasingly important. In consequence of the implementation of the Corporate Sustainability Reporting Directive, a substantial number of companies will be required to have their sustainability reports audited beginning from financial year 2024. This paper examines the influence of external assurance on the quality of those sustainability reports. Therefore, the reports of all DAX and MDAX companies for financial year 2022 are examined using a novel textual analysis approach, to determine the individual report quality. The results demonstrate that there is no statistically significant relationship between assurance level and the quality of sustainability reports. Conversely, it was found that companies that are acting sustainable disclose a higher quantity of information and are more likely to demand voluntary assurance of their reports. These findings offer insights into the implications of assurance on sustainability reporting. Furthermore, the detailed overview of traditional and state-of-the-art textual analysis methods offers researchers a valuable resource for identifying the most appropriate methods to address their individual research questions. Keywords: audit assurance; CSRD; natural language processing; sustainability reporting; textual analysisThe subject of sustainability reporting is becoming increasingly important. In consequence of the implementation of the Corporate Sustainability Reporting Directive, a substantial number of companies will be required to have their sustainability reports audited beginning from financial year 2024. This paper examines the influence of external assurance on the quality of those sustainability reports. Therefore, the reports of all DAX and MDAX companies for financial year 2022 are examined using a novel textual analysis approach, to determine the individual report quality. The results demonstrate that there is no statistically significant relationship between assurance level and the quality of sustainability reports. Conversely, it was found that companies that are acting sustainable disclose a higher quantity of information and are more likely to demand voluntary assurance of their reports. These findings offer insights into the implications of assurance on sustainability reporting. Furthermore, the detailed overview of traditional and state-of-the-art textual analysis methods offers researchers a valuable resource for identifying the most appropriate methods to address their individual research questions. Keywords: audit assurance; CSRD; natural language processing; sustainability reporting; textual analysi

Unravelling Collective Action Frames Through a Temporal Lens: A Case Study of an Environmental Movement in Germany

Organizing collective action in the face of climate change is one of the grand challenges of our time. Social movements and their approach to framing climate change are pivotal, as they are tasked with the role of challenging and redirecting dominant beliefs and narratives. Recent research suggests that time is at the core of framing and sustainability. However, there is scant research at the intersection of social movements and time. This study responds to this gap by examining how the framing of the environmental movement Letzte Generation in Germany constructs temporality. My findings reveal how the movement frames climate change as a catastrophe, representing itself as a fire alarm to create a shared sense of urgency and advocate for a crisis mode. Temporally, the framing constructs a clear chronology between a dominant past and an undesirable future and aims to redirect the focus to the present. As a result, the movement had to actively orchestrate a balance between disruptive strategies aimed at attention and polarization, and alignment strategies to foster resonance and support. By conceptualizing temporality in framing processes my study illustrates the pivotal role of time in research on social movements and framing. Moreover, it contributes to the discourse on time and sustainability by showing how actors emphasize a present-time perspective. Keywords: climate crisis; polarization; social movements; strategic framing; time and temporalityOrganizing collective action in the face of climate change is one of the grand challenges of our time. Social movements and their approach to framing climate change are pivotal, as they are tasked with the role of challenging and redirecting dominant beliefs and narratives. Recent research suggests that time is at the core of framing and sustainability. However, there is scant research at the intersection of social movements and time. This study responds to this gap by examining how the framing of the environmental movement Letzte Generation in Germany constructs temporality. My findings reveal how the movement frames climate change as a catastrophe, representing itself as a fire alarm to create a shared sense of urgency and advocate for a crisis mode. Temporally, the framing constructs a clear chronology between a dominant past and an undesirable future and aims to redirect the focus to the present. As a result, the movement had to actively orchestrate a balance between disruptive strategies aimed at attention and polarization, and alignment strategies to foster resonance and support. By conceptualizing temporality in framing processes my study illustrates the pivotal role of time in research on social movements and framing. Moreover, it contributes to the discourse on time and sustainability by showing how actors emphasize a present-time perspective. Keywords: climate crisis; polarization; social movements; strategic framing; time and temporalit

Analyzing the Retail Gasoline Market in Germany: Impact of Spatial Competition and Market Concentration on Prices

Given the changing landscape of fuel retailing, this study explores the impact of spatial competition and market concentration on diesel prices in Germany. The question of how population density and gas station density, i.e. the equilibrium pattern of locations of firms, are related is examined. In addition, the impact of gas station density, as a spatial measure of competition, and market concentration on diesel prices is investigated. Based on theory, population density should have a positive impact on gas station density. Gas station density should have a negative and market concentration a positive influence on the diesel price in a district. Using 2022 data on German gas stations and diesel prices, a positive effect of population density and on gas station density, a negative effect of gas station density on diesel price, and a positive effect of market concentration on diesel price were each found at the district level. The effects of gas station density and market concentration, however, were relatively small. The results show that fuel prices at gas stations are influenced by spatial competition and market concentration. Keywords: diesel prices; gas station density; market concentration; retail gasoline market; spatial competitionGiven the changing landscape of fuel retailing, this study explores the impact of spatial competition and market concentration on diesel prices in Germany. The question of how population density and gas station density, i.e. the equilibrium pattern of locations of firms, are related is examined. In addition, the impact of gas station density, as a spatial measure of competition, and market concentration on diesel prices is investigated. Based on theory, population density should have a positive impact on gas station density. Gas station density should have a negative and market concentration a positive influence on the diesel price in a district. Using 2022 data on German gas stations and diesel prices, a positive effect of population density and on gas station density, a negative effect of gas station density on diesel price, and a positive effect of market concentration on diesel price were each found at the district level. The effects of gas station density and market concentration, however, were relatively small. The results show that fuel prices at gas stations are influenced by spatial competition and market concentration. Keywords: diesel prices; gas station density; market concentration; retail gasoline market; spatial competitio

Predicting Stock Returns With Machine Learning: Global Versus Sector Models

Recent studies highlight the superior performance of non-linear machine learning models, such as neural networks, over traditional linear models in predicting cross-sectional stock returns. These models are capable of capturing complex non-linear interactions between predictive signals and future returns. This thesis researches whether sector-specific neural networks can detect sector-related relationships to outperform a global neural network. It evaluates the predictive power of these models at the stock level and in portfolios based on return forecasts, constructing long-short portfolios from the networks’ sorted predictions. A global neural network model trained on the full sample of stocks dominates neural networks trained on individual GICS sectors in predicting the cross-section of US stock returns. Sector-specific neural networks fail to gain an advantage by capturing complex sector-specific interactions. They underperform the global neural network especially in the early out-of-sample period. The smaller sample size for each GICS sector requires a trade-off between model complexity and robust model estimation. Pooling the data for the global model solves this problem and supports the predictive power of neural networks for stock returns. Keywords: cross-section of stock returns; machine learning; neural networks; return prediction; sector modelsRecent studies highlight the superior performance of non-linear machine learning models, such as neural networks, over traditional linear models in predicting cross-sectional stock returns. These models are capable of capturing complex non-linear interactions between predictive signals and future returns. This thesis researches whether sector-specific neural networks can detect sector-related relationships to outperform a global neural network. It evaluates the predictive power of these models at the stock level and in portfolios based on return forecasts, constructing long-short portfolios from the networks’ sorted predictions. A global neural network model trained on the full sample of stocks dominates neural networks trained on individual GICS sectors in predicting the cross-section of US stock returns. Sector-specific neural networks fail to gain an advantage by capturing complex sector-specific interactions. They underperform the global neural network especially in the early out-of-sample period. The smaller sample size for each GICS sector requires a trade-off between model complexity and robust model estimation. Pooling the data for the global model solves this problem and supports the predictive power of neural networks for stock returns. Keywords: cross-section of stock returns; machine learning; neural networks; return prediction; sector model

Diversity Within Top Management Teams: The Effects of Diversity Within Boards Towards Managerial Attention on Digital Transformation

Digital transformation (DT) is crucial for firms to stay competitive, yet few fully embrace it. DT goes beyond moving from analogue to digital; it necessitates a complete restructuring of business models, including customer experiences and internal structures. Leadership significantly impacts strategic decision-making, as Hambrick (2007) notes. A board’s diversity and composition affect a firm’s decisions. Diversity in TMT can enhance innovation and creativity or increase friction and conflicts. While much research exists on these topics, examining managerial focus on DT and TMT diversity using Harrison and Klein’s (2007) framework is new. As DT becomes more important, firms must understand TMT diversity’s role. I argue that top management’s demographic characteristics are positively influenced by diverse education, tenure, and network, with a negative moderating effect of age and gender heterogeneity. This study found that in cumulative DT efforts, there are effects between age and tenure, and gender and network. Age separation decreases tenure’s positive effect, and gender separation diminishes the positive effect of diverse networks, suggesting inconsistencies with Hambrick’s (2007) theory. Keywords: Blue’s Index; digital transformation; diversity; top management teams; Upper Echelon TheoryDigital transformation (DT) is crucial for firms to stay competitive, yet few fully embrace it. DT goes beyond moving from analogue to digital; it necessitates a complete restructuring of business models, including customer experiences and internal structures. Leadership significantly impacts strategic decision-making, as Hambrick (2007) notes. A board’s diversity and composition affect a firm’s decisions. Diversity in TMT can enhance innovation and creativity or increase friction and conflicts. While much research exists on these topics, examining managerial focus on DT and TMT diversity using Harrison and Klein’s (2007) framework is new. As DT becomes more important, firms must understand TMT diversity’s role. I argue that top management’s demographic characteristics are positively influenced by diverse education, tenure, and network, with a negative moderating effect of age and gender heterogeneity. This study found that in cumulative DT efforts, there are effects between age and tenure, and gender and network. Age separation decreases tenure’s positive effect, and gender separation diminishes the positive effect of diverse networks, suggesting inconsistencies with Hambrick’s (2007) theory. Keywords: Blue’s Index; digital transformation; diversity; top management teams; Upper Echelon Theor

Energy-Aware Production Planning with Renewable Energy Generation Considering Combined Battery- and Hydrogen-Based Energy Storage Systems

This study investigates the capacity of a developed production planning model to coordinate energy management within a hybrid energy system. The specific focus is on energy-intensive manufacturing firms utilizing renewable energy generation and energy storage. Unlike prior research in the field of energy-aware production planning, which revealed considerable cost saving potentials for the consideration of energy storage, this study considers a combined battery- and hydrogen-based energy storage with more realistic technology modeling. A formal mathematical model is developed as a mixed-integer linear program. Moreover, the cost saving potential of the combined energy storage system in energy-aware production planning is investigated based on numerical experiments. The experiments reveal that the implementation of the proposed planning approach saves significant costs compared to a baseline scenario. Up to 29.3 % cost saving potentials can be reached. In particular, the battery storage achieves significant energy cost savings while the hydrogen storage improves independence from fluctuating energy tariffs and availability of renewable energy. Possible model extensions are suggested to enhance the utilization of the proposed planning approach. Keywords: energy-aware production planning; energy storage systems; hydrogen; mixed-integer linear programming; renewable energy generationThis study investigates the capacity of a developed production planning model to coordinate energy management within a hybrid energy system. The specific focus is on energy-intensive manufacturing firms utilizing renewable energy generation and energy storage. Unlike prior research in the field of energy-aware production planning, which revealed considerable cost saving potentials for the consideration of energy storage, this study considers a combined battery- and hydrogen-based energy storage with more realistic technology modeling. A formal mathematical model is developed as a mixed-integer linear program. Moreover, the cost saving potential of the combined energy storage system in energy-aware production planning is investigated based on numerical experiments. The experiments reveal that the implementation of the proposed planning approach saves significant costs compared to a baseline scenario. Up to 29.3 % cost saving potentials can be reached. In particular, the battery storage achieves significant energy cost savings while the hydrogen storage improves independence from fluctuating energy tariffs and availability of renewable energy. Possible model extensions are suggested to enhance the utilization of the proposed planning approach. Keywords: energy-aware production planning; energy storage systems; hydrogen; mixed-integer linear programming; renewable energy generatio

Sustainability in the Corporate Sector: A News Textual Analysis Approach to Measuring ESG Performance

Sustainability has become a crucial factor in the financial sector, making the assessment of a company’s sustainability performance essential for informed decision-making. Recognizing the media’s power to shape public perception of corporate sustainability issues, this study examines the use of news analysis to evaluate companies’ performance against Environmental, Social, and Governance (ESG) criteria. Leveraging OpenAI’s models, this research parses unstructured data within news articles and introduces a machine learning pipeline to score companies’ ESG performance based on their media representation. The study uncovers several key findings: firstly, it demonstrates that a less costly, fine-tuned model can surpass the zero-shot capabilities of a more expensive model in classifying ESG content. Secondly, it identifies discrepancies in media coverage across industries, leading to unequal assessments of companies. Thirdly, it reveals a media tendency to underreport companies’ environmental efforts. Finally, the study highlights areas where companies face media criticism, suggesting potential improvements in their ESG practices. These insights contribute to the understanding of how machine learning can assist in the critical evaluation of sustainability in the business domain. Keywords: ESG; machine learning; natural language processing; news; NLP; sustainabilitySustainability has become a crucial factor in the financial sector, making the assessment of a company’s sustainability performance essential for informed decision-making. Recognizing the media’s power to shape public perception of corporate sustainability issues, this study examines the use of news analysis to evaluate companies’ performance against Environmental, Social, and Governance (ESG) criteria. Leveraging OpenAI’s models, this research parses unstructured data within news articles and introduces a machine learning pipeline to score companies’ ESG performance based on their media representation. The study uncovers several key findings: firstly, it demonstrates that a less costly, fine-tuned model can surpass the zero-shot capabilities of a more expensive model in classifying ESG content. Secondly, it identifies discrepancies in media coverage across industries, leading to unequal assessments of companies. Thirdly, it reveals a media tendency to underreport companies’ environmental efforts. Finally, the study highlights areas where companies face media criticism, suggesting potential improvements in their ESG practices. These insights contribute to the understanding of how machine learning can assist in the critical evaluation of sustainability in the business domain. Keywords: ESG; machine learning; natural language processing; news; NLP; sustainabilit

Understanding Emergent Leadership Across Cultural Levels: A Theoretical Framework

Emergent leadership literature emphasises identifying and nurturing leaders at all organisational levels to foster team harmony and align efforts toward shared goals. Since past studies focused largely on individual traits predicting leadership emergence, the interplay of different cultural levels, such as national culture, organisational culture and team culture in relation to individuals emerging as emergent leaders remains unexplored. This study extends beyond discussing the antecedents and outcomes of emergent leadership and provides an in-depth understanding of the phenomenon through different cultural levels. It introduces an overarching theoretical framework proposing that a) the unfolding of emergent leadership occurs at four levels, which are organic emergence, non-normative emergence, conditional emergence and non-emergence, based upon the type of interaction between cultural levels and potential emergent leaders, b) for emergent leadership to occur, potential emergent leaders must have or display some of the compatible antecedents, c) the approval of higher-level authority figures at the organisational or national level is a precondition for the occurrence of emergent leadership in stratified teams. Keywords: emergent leadership; individual traits; national culture; organisational culture; team cultureEmergent leadership literature emphasises identifying and nurturing leaders at all organisational levels to foster team harmony and align efforts toward shared goals. Since past studies focused largely on individual traits predicting leadership emergence, the interplay of different cultural levels, such as national culture, organisational culture and team culture in relation to individuals emerging as emergent leaders remains unexplored. This study extends beyond discussing the antecedents and outcomes of emergent leadership and provides an in-depth understanding of the phenomenon through different cultural levels. It introduces an overarching theoretical framework proposing that a) the unfolding of emergent leadership occurs at four levels, which are organic emergence, non-normative emergence, conditional emergence and non-emergence, based upon the type of interaction between cultural levels and potential emergent leaders, b) for emergent leadership to occur, potential emergent leaders must have or display some of the compatible antecedents, c) the approval of higher-level authority figures at the organisational or national level is a precondition for the occurrence of emergent leadership in stratified teams. Keywords: emergent leadership; individual traits; national culture; organisational culture; team cultur