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Transition road maps – an investigative approach to map the daily life consumption of individuals
(2014)
The present paper aims at investigating an innovative approach to guide consumers’ daily life choices in Germany towards a more sustainable way of acting. This should be achieved by introducing a new concept: transition road maps. Transition road maps bear the capability of illustrating courses of consumption behaviour without being prohibitive. These schemes foster self-determined behaviour and encourage the consumer to rethink and restructure his or her habits of consumption, with a focus on sustainability. The innovative thought is, not to simply stick to the usual triad of spheres of activity, consisting of nutrition, mobility and housing. Instead further aspects of consumers’ daily routines are considered, such as leisure activities, time usage or financial activities. Moreover the transition road maps are based on a new ideology of combining and connecting the qualitative algorithm of time use, financial spending and resource impact of social practices in the area of private consumption. In the long-term, the transition road maps could e.g. be used in sustainability communication or consumer counselling.
Response of petunia to wood fibre amended peat substrate under ebb-and-flow irrigation (Abstract)
(2024)
Test von Schnellverfahren zur Bestimmung der Benetzungseigenschaften von Kultursubstraten (Abstract)
(2024)
Artificial intelligence (AI) promises transformative impacts on society, industry, and agriculture, while being heavily reliant on diverse, quality data. The resource-intensive "data
problem" has initialized a shift to synthetic data. One downside of synthetic data is known as the "reality gap", a lack of realism. Hybrid data, combining synthetic and real data, addresses this. The paper examines terminological inconsistencies and proposes a unified taxonomy for real, synthetic, augmented, and hybrid data. It aims to enhance AI training datasets in smart agriculture, addressing the challenges in the agricultural data landscape. Utilizing hybrid data in AI models offers improved prediction performance and adaptability.