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The electricity market transitions from a market clearing in which flexible generation meets inflexible demand to one in which inflexible generation meets flexible demand. Consequently, new types of flexibility are needed to balance generation and demand. This paper mainly aims to analyse competition and synergy between various flexibility options in the electricity market. The developed European electricity market model maximises the joint socio-economic welfare of the electricity and the hydrogen market. Transmission grid,- storage expansion, electric vehicle demand side flexibility and hydrogen sector coupling are flexibility options. This work examines the implementation of different flexibility options in the bidding zones Austria and Germany. Austria, with a very high storage capacity in the alpine arc and Germany, with massive wind turbine capacities serve as representative case study. Results show that the storage location heavily influences the corresponding socio-economic welfare increase. Hence, location-dependent synergy and competition between storage and transmission line expansion can occur. In contrast, sector coupled hydrogen production and storage expansion compete with each other as both will reduce very low electricity prices. Demand-side flexibility primarily benefits end-users while producer surplus reduces. In combination with storage expansion, further positive synergistic effects for end-users occur. The combination of storage expansion and demand-side flexibility can replace transmission line expansion and vice versa if used comprehensively. Further efforts could address the evaluation of these effects if the individual flexibility providers aim for something other than a system-wide but individual optimum. • Implementation of several flexibility options into the European electricity market. • Storage location and transmission capacity have a huge influence on the results. • Consumers benefit from combined storage and demand-side flexibility use. • Additional electricity demand by sector coupling increases renewable energy revenue. • Storage and sector coupling compete with each other as both reduce price volatility. [ABSTRACT FROM AUTHOR]

Small and medium-sized enterprises (SMEs) are said to engage significantly less with corporate sustainability (CS) than their larger peers, because they are more reluctant to voluntarily engage in CS beyond regulatory thresholds. However, the mechanisms for changing the reluctance of SMEs with regard to CS are poorly understood. By drawing on self-determination and stakeholder theory from a social proximity perspective, this paper argues that stakeholders play a key role in influencing the controlled CS motivation of SMEs, and that SMEs will consider the claims of 'proximate' stakeholders as being more salient than 'distant' regulatory pressure, with the latter even potentially exercising a negative effect on SMEs' controlled CS motivation. The hypotheses are empirically tested using survey-based data from a sample of 344 privately-held SMEs operating in Germany and Austria. Results of the structural equation model confirm that 'proximate' employee and community pressure reduces controlled CS motivation and ultimately increases overall CS performance, whilst 'distant' regulatory pressure has precisely the opposite effect, ultimately reducing the CS performance of SMEs. These findings help to clarify that the close attachment of SMEs to their employees, and their deep embeddedness in the local community might be important catalysts with regard to CS improvements of SMEs, whilst regulatory pressure reduces their willingness to engage in CS, since this is often perceived as an unfair, demotivating, external imperative, which compromises their self-determination. Lastly, theoretical and managerial implications are provided. [Display omitted] • We analyze the influence of stakeholder pressure on corporate sustainability (CS). • Social proximity (close vs. distant) is a driver of stakeholder salience in SMEs. • Close employee and community pressure reduces controlled CS motivation. • Distant regulatory pressure increases controlled CS motivation. • Policy-makers should be cautious to apply regulatory pressure to spur CS. [ABSTRACT FROM AUTHOR]

Abstract: The Folded Molasse zone of the Northern Alps consists of clastic sedimentary rocks that are usually not considered to be karstifiable. However, large areas within this zone are composed of carbonatic conglomerates. Numerous karst landforms have recently been discovered but are not recorded on official maps and in the literature. Therefore, a research programme was initiated at the Hochgrat site (Austria/Germany) that included geomorphological mapping and characterisation of the karst phenomena. Both fracture-controlled and hydrodynamically-controlled karren were observed on conglomerate outcrops. The predominant karst landforms, dolines, are typically circular, funnel shaped, most often 2 to 9m in diameter, 1 to 6m deep, and frequently act as swallow holes. Poljes that are atypically small (~1ha) occur in either glacial cirques or syncline depressions, are flat floored and lined with sediment and soil, and drain underground via swallow holes. Short caves, springs with marked discharge variations and estavelles are further evidence for karst development. Karstic landforms are widespread in carbonatic conglomerate terrains, but their dimensions are smaller than in typical limestone karst. The practical implications of these findings are also briefly mentioned in this paper. [Copyright &y& Elsevier]

Abstract: Background: Acceptance by citizens seems to be crucial for the future success of an electronic health record (EHR) in Germany and Austria. We analyzed citizens’ knowledge and expectations about the concept and contents of an EHR. We also addressed possible fears and barriers, and we investigated desired EHR functionalities relevant to citizens in the Austrian and German population. Methods: Standardized interviews of a convenience sample of 203 Austrian and 293 German citizens recruited in two metropolises. Results: Up to three-quarter of the interviewed citizens already collect and store medical documents at home, mostly in paper-based form. No respondents had already used an Internet-based personal health record. Between 80% and 90% of respondents were supportive of the idea of an electronic exchange of health-related data between health care providers as core functionality of an EHR. However, many respondents formulated concerns with regard to data protection and data security within an EHR. The EHR functionalities most supported by respondents included the electronic vaccination record, online information on doctors and hospitals, and the administration of appointments and reminders. Conclusion: The results indicate a generally positive attitude towards the EHR. However, the study shows that data protection is an issue for many citizens, and that despite strong media discussion, there are information deficits with regard to the national EHR initiatives. [Copyright &y& Elsevier]

One month-ahead electricity prices open up a great potential for manufacturing companies that aim at reducing energy costs. On the machine level, an energy cost-oriented scheduling can be realized through determining the least electricity cost-intensive launch times for job processing during a day. To obtain reliable price information at production planning stage in advance, well-performing forecast models are required. In this paper, we apply a Weighted Nearest Neighbor (WNN) approach, a TBATS approach, and a Deep Feedforward Neural Network (DFNN) approach to forecast EPEX Spot day-ahead electricity prices of the European Energy Exchange in Germany/Austria, while accounting for a forecast horizon of up to one month. Model performances are compared by means of established error measures as well as by adjusted error measures to better interpret results from a production planner's point of view. For evaluation purposes, 1357 different forecasts were computed for each of the methods, allowing valid conclusions as to their performance. The results show that all approaches lead to acceptable accuracies. However, the DFNN outperforms WNN and TBATS, providing well-fitting forecasts even 29 days ahead. Image 1 • Artificial Neural Networks outperform TBATS and Weighted Nearest Neighbor. • Forecast results are highly applicable for mid-term production planning purposes. • Neural Network forecasts for longer terms are almost as precise as for shorter ones. • On average, only 5% of the adjusted forecasts exceed the limit of a 10.00 EUR error. [ABSTRACT FROM AUTHOR]