Higher education for sustainable development plays a crucial role in the transformation of society towards a more sustainable pathway of development. The new trends in society and technology experienced in the course of the fourth industrial revolution come with challenges, but also provide opportunities. In this paper, we reflect on the conceptual basis of education for sustainable development as approached at the University of Graz, Austria, and contrast this basis with the expectations stated by students. The results showed that students acknowledged the high importance of digital competencies and found it highly important to be confronted with future-oriented topics and contents. Interestingly, students seemed skeptical about online course formats and digitalization of teaching and clearly preferred the interactive classroom experience. Students also rated international topics and transdisciplinary thinking as fairly important. Interestingly, a relatively high share of students only saw mediocre added value in experiencing international classrooms, and in having international teachers, when it comes to meeting their top priority, which is being competitive in the job market. Education for sustainable development in the future will not only need to prepare students for international, interdisciplinary, and digital environments, but also will need to meet the expectations of demanding and ambitious students and provide them with bright career prospects.

Several authors have pointed out the importance of systems thinking, and have considered both environmental and social aspects (holistic perspective) of sustainability assessment in the past. Sustainability assessment tools which integrate different aspects (e.g., environmental/social aspects) in order to identify negative impacts have already been developed. Common tools used to assess environmental, social, or economic impacts include the life cycle assessment (LCA), social life cycle assessment (S-LCA), life cycle costing (LCC) and life cycle sustainability assessment (LCSA) approaches. The goal of the present study was to investigate how and to what extent the three dimensions of sustainability (environmental, social, economic; holistic sustainability perspective) have been integrated into the field of LCA. A topic modeling method was applied to examine whether the emphasis placed on integrating environmental, social, and economic aspects in sustainability assessment has resulted in a more comprehensive application of the LCA approach. The results show that topics related to energy and infrastructure are currently prevailing, and that topics related to methods have been decreasing since 1997. A minor discussion of social aspects and a lack of discussion on economic aspects were identified in the present study. These results do not support the predicted “decade of life cycle sustainability assessment.” Consequently, a new period of LCA extension and application is predicted, namely, the third wave of LCA as the “decade of consolidation.” During this period, the LCA framework will be enhanced to reduce existing practical and methodological difficulties and integrate environmental and social aspects in a sustainability assessment to support global sustainable development.

Many systems in various scientific fields like medicine, ecology, economics or climate science exhibit so-called critical transitions, through which a system abruptly changes from one state to a different state. Typical examples are epileptic seizures, changes in the climate system or catastrophic shifts in ecosystems. In order to predict imminent critical transitions, a mathematical apparatus called early warning signals has been developed and this method is used successfully in many scientific areas. However, not all critical transitions can be detected by this approach (false negative) and the appearance of early warning signals does not necessarily proof that a critical transition is imminent (false positive). Furthermore, there are whole classes of systems that always show early warning signals, even though they do not feature critical transitions. In this study we identify such classes in order to provide a safeguard against a misinterpretation of the results of an early warning signal analysis of such systems. Furthermore, we discuss strategies to avoid such systematic false positives and test our theoretical insights by applying them to real world data.

Our current labour market is affected by massive changes like digitalization, automation and globalization, which gives rise to completely new forms of generating income. One such innovative idea is crowdworking, where many people (a so-called crowd) work on individual tasks for a firm in a way similar to a self-employed freelancer. This form of occupation is a recent development but gains acceptance, esteem and relevance quite rapidly. The risk potential for wage dumping and (self-) exploitation is still unknown. A crucial, but often neglected fact about crowdworking is that it exists in many variants which have completely different properties. We investigate how much these distinct versions of crowdworking differ by using an agent-based computer simulation. Wages, job security, workforce composition and other relevant indicators are calculated by simulating the micro scale to gain aggregated information on the macro-scale. We find that there is a significant difference between the versions of crowdworking. Our main finding is that especially variants where the crowdworkers are able to set their own wages are susceptible to wage dumping. Simulations suggest that this phenomenon is independent of the specifics of the labour market but rather a fundamental property of those variants of crowdworking.

Scanning a system's dynamics for critical transitions, i.e. sudden shifts from one system state to another, with the methodology of Early Warning Signals has been shown to yield promising results in many scientific fields. So far however, such investigations focus on aggregated system dynamics modeled with equation-based methods. In this paper the methodology of Early Warning Signals is applied to critical transitions found in the context of Cooperation Games. Since equation-based methods are not well suited to account for interactions in game theoretic settings, an agent-based model of a repeated Cooperation Game is used to generate data. We find that Early Warning Signals can be detected in agent-based simulations of such systems.

Motorized transport is one of the main contributors to anthropogenic CO${}_2$ emissions, which cause global warming. Other emissions, like nitrogen oxides or carbon monoxide, are detrimental to human health. A prominent way to understand and thus be able to minimize emissions is by using traffic simulations to evaluate different scenarios. In that way, one can find out which policies, technical innovations, or behavioral changes can lead to a decrease in emissions. Since the effect of CO${}_2$ is on a global scale, a macroscopic model is often enough to find reasonable results. However, NO${}_{\mathrm{x}}$ emissions can also have a direct, local effect. Therefore, it is interesting to investigate these emissions on a mesoscopic scale, to gain insight into the local distribution of this pollutant. In this study, we used a traffic model that, contrary to most other state-of-the-art traffic simulations, does not require an origin–destination matrix as an input, but calculates it from mobility behavior extracted from a survey. We then generated agents with realistic mobility behavior that perform their daily trips and calculate key features like congestion and emissions for every edge of the road network. Our approach has the additional advantage of allowing to investigate technical, juridical, as well as behavioral changes, all within the same framework. It is then possible to identify strategies that minimize NO${}_{\mathrm{x}}$ emissions caused by private motorized transport. Evaluation showed good agreement with reality in terms of local and temporal resolution. Especially when looking at the sum of emissions, the main feature for evaluating policies, and deviations between our simulation and available statistics were negligible. We found that, from all scenarios we investigated, the ban of old diesel cars is the most promising policy: By replacing all diesel cars built in 2005 or earlier with petrol cars of the same age, NO${}_{\mathrm{x}}$ emissions could drop by roughly a third.