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Agile enterprise architecture management (EAM) is a means to cope with the pressure for continuous architectural change in the context of short innovation cycles. However, the literature states a lack of practical EAM approaches. Therefore, this study aims at fostering the development of agile enterprise architecture frameworks (EAFs) by providing agile requirements and implementation concepts based on a literature review and an exploratory multiple-case study in three organizations of the logistics and construction tools industry. The multiple-case study's ranking by importance sets the focus on three requirements, for which implementation concepts are revealed. The gained insights make the concept and benefits of agile EAM tangible, indicate a shared understanding between the cases, and afford opportunities for future research.

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Albeit its importance, a large number of port authorities do not provide continuous or publicly available air emissions inventories (EIs) and thereby obscure the emissions contribution of ports. This is caused by, e.g., the economic effort generated by obtaining data. Therefore, the performance of abatement measures is not monitored and projected, which is specifically disadvantageous concerning top contributors such as container ships. To mitigate this issue, in this paper we propose port vessel EI prediction models by exploring the combination of different machine-learning algorithms, data from the one-off application of an activity-based bottom-up methodology and vessel-characteristics data. The results for this specific case show that prediction models enable acceptable trade-offs between the prediction performance and data requirements, promoting the creation of EIs.