Autor: Richter, Martin - Prolib Integro

Skocz do pozycji: 1.

Tytuł:: Regional eco-efficiency of the agricultural sector in V4 regions, its dynamics in time and decomposition on the technological and pure technical eco-efficiency change
Autorzy:: Richterová, Eva
Richter, Martin
Sojková, Zlata
Tematy:: agricultural eco-efficiency
Visegrad regions
Malmquist index
technological change
pure technical eco-efficiency; Pokaż więcej
Wydawca:: Instytut Badań Gospodarczych
Powiązania:: https://bibliotekanauki.pl/articles/22444326.pdf Link otwiera się w nowym oknie
Opis:: Research background: Agriculture plays a vital role in producing food to ensure food security, but it is one of the biggest contributors to environmental pollution. One of the main goals of the new CAP is to set higher ambitions for environmental actions, which brings into the front the concept of agricultural eco-efficiency. The notion of eco-efficiency includes the economic and also ecological dimensions of sustainable agriculture. Purpose of the article: The main goal of this paper is to evaluate the eco-efficiency of agricultural production and its dynamics during the years 2013, 2015, and 2017 of NUTS 2 regions within the Visegrad 4 (V4), i. e. The Czech Republic, Slovakia, Hungary, and Poland. The part of the main goal is to verify the research hypothesis that all the biggest agriculture producers are eco-efficient. Methods: V4 regional eco-efficiency of the agricultural sector is expressed by the Malmquist productivity index and is estimated using the output-oriented Data envelopment analysis (DEA) model, under the assumption of constant return to scale (CRS). The Malmquist index is decomposed to technical eco-efficiency change (EC) and technological change (TC). Based on the eco-efficiency, technological and pure technical eco-efficiency change, V4 regions are classified into three groups: the most progressive regions, the progressive regions, and the regressive regions. Findings & value added: CZ02: Central Bohemia, CZ04: Northwest, HU33: Dél-Alföld, HU31: Észak-Magyarország, HU32: Észak-Alföld, PL21: Malopolskie, PL41: Wielkopolskie, SK01: Bratislava region, and SK02: Western Slovakia have an eco-effective agricultural sector, the remaining V4 regions have eco-ineffective agricultural sector. The research hypothesis that all the biggest agricultural producers are eco-effective is not confirmed. During the analyzed years, 19 V4 regions improve their agricultural eco-efficiency. The main contributor to eco-efficiency improvement is technological progress, which indicates that producers implement innovations that lead to more eco-efficiency agricultural production.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 2.

Tytuł:: Enabling federated learning services using OPC UA, linked data and GAIA-X in cognitive production
Autorzy:: Friedrich, Christian
Vogt, Stefan
Rudolph, Franziska
Patolla, Paul
Grützmann, Jossy Milagros
Hohmeier, Orlando
Richter, Martin
Wenzel, Ken
Reichelt, Dirk
Ihlenfeldt, Steffen
Tematy:: Industry 4.0
digital manufacturing
data space
cognitive production; Pokaż więcej
Wydawca:: Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Powiązania:: https://bibliotekanauki.pl/articles/59124054.pdf Link otwiera się w nowym oknie
Opis:: Value creation in production is based on collaboration of different stakeholders and requires the secure and sovereign exchange of knowledge. Today, knowledge has mostly been built up individually and is only exchanged in a proprietary manner. This paper presents an exemplary pipeline for federated services in cross-domain and cross-company value creation networks for cognitive production. On the example of collaboratively training of a federated machine learning model, machine tool lifetime is predicted in industrial manufacturing for high-end operating resources (high-quality cutting tools). From the shop floor to the cloud, all service relevant information is structured using existing digital twin standards and a linked data approach. In particular, the Industry 4.0 Asset Administration Shell (AAS) and OPC UA are used for collecting and referencing operational and engineering data. GAIA-X connectors transfer the service relevant data through a shared data space. The solution enables intelligent analysis and decision-making under the prioritization of data sovereignty and transparency and, therefore, acts as an enabler for future collaborative, data-driven manufacturing applications.
Dostawca treści:: Biblioteka Nauki