Results 211 to 220 of about 24,019 (309)

Food waste prevention in Denmark:Identification of hotspots and potentials with Life Cycle Assessment [PDF]

open access: yes, 2017
Astrup, Thomas Fruergaard   +2 more
core  

Economic and Environmental Tradeoffs in Cultivating Short Food Supply Chains With Urban Indoor Agriculture

open access: yesAgribusiness, EarlyView.
ABSTRACT This study advances the literature on sustainable urban agriculture and alternative sustainable food production systems, which have gained momentum due to the need to strengthen regional food supply chains and meet the growing urban demand for fresh food. Indoor agriculture (IA) holds promise for year‐round cultivation of fresh produce even in
Joseph Seong   +2 more
wiley   +1 more source

Composition of municipal solid waste in Denmark [PDF]

open access: yes, 2016
Edjabou, Maklawe Essonanawe
core  

A Hazard-Based Approach Enables the Efficient Identification of Chemicals of Concern in Plastics. [PDF]

open access: yesEnviron Sci Technol
Hader JD   +8 more
europepmc   +1 more source

Does ESG Matter for Unlisted Companies in the Agri‐Food Industry? Evidence From Japan's Unlisted Agri‐Food Companies

open access: yesAgribusiness, EarlyView.
ABSTRACT While ESG (environmental, social, and governance) is emphasized among listed companies for their stakeholders and ESG disclosures, ESG engagement among unlisted companies has been rarely examined due to data limitations. This is particularly problematic for the agri‐food industry that has significant impacts on the environment and consists ...
Ying Wang, Satoru Shimokawa
wiley   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

open access: yesAdvanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia   +3 more
wiley   +1 more source

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