Wyniki wyszukiwania - BazTech

1

The eco-efficiency of fisheries in EU countries

Piwońska Kalina, Kryszak Łukasz

Economics and Environment

|

2024

|

No. 2

art. no. 691

EN

The main goal of this article was to (1) assess the dynamics of eco-efficiency of fisheries in EU countries and its components and (2) identify potential sources of inefficiencies and efficiency surpluses through slack analysis. The hybrid data envelopment analysis (DEA) model was used for the 2008-2019 period. Progress in eco-efficiency was found among 11 countries (out of 23), but the average eco-efficiency index for the sample was 0.988. Differences in the levels and dynamics of eco-efficiency between the studied countries were mainly driven by the efficiency change component, i.e. internal factors. The largest input-saving potential was found in relation to number of employees and gross tonnage of the vessel, suggesting that sample countries deal with the problem of overinvestment and overstaffing. We also found that greenhouse gas emissions could be reduced by approximately a third.

PL

Głównym celem niniejszego artykułu była (1) ocena dynamiki eko-efektywności rybołówstwa w krajach UE i jej komponentów oraz (2) identyfikacja potencjalnych źródeł nieefektywności i nadwyżek efektywności poprzez analizę luzu. Zastosowano hybrydowy model analizy obwiedni danych (DEA) dla okresu 2008-2019. Postęp w zakresie eko-efektywności stwierdzono wśród 11 krajów (z 23), ale średni wskaźnik eko-efektywności dla próby wyniósł 0,988. Różnice w poziomach i dynamice eko-efektywności pomiędzy badanymi krajami wynikały głównie z komponentu zmiany efektywności, tj. czynników wewnętrznych. Największy potencjał w zakresie redukcji nakładów stwierdzono w odniesieniu do liczby pracowników i pojemności brutto statku, co sugeruje, że badane kraje borykają się z problemem przeinwestowania i nadmiernego zatrudnienia. Stwierdziliśmy również, że emisję gazów cieplarnianych można zmniejszyć o około jedną trzecią.

2

Coastal and marine issues and their relation to ecosystem survey

Elentably A., Fisher K., Holger S., Alghanmi A., Alhrbi S.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

|

2022

|

Vol. 16 No. 3

449--454

EN

Protecting and preserving the environment and marine resources is a constant concern of countries. The seas and oceans face increasing threats to their flora and fauna from pollution, both from land and sea sources. Overexploitation of marine resources and overfishing pose serious threats to biodiversity and the balance of marine ecosystems. Especially for countries that rely on fisheries resources to feed their populations in closed or semi-closed seas. It is unusual to highlight overfishing by ships, as coastal states' resources do not allow for effective safety controls and as a result, there are a number of severely depleted fisheries worldwide. It is therefore vital that conservation and management measures for straddling fish stocks and highly migratory fish stocks continue and increase, as it is a resource that has transcended many national jurisdictions. According to the priorities of the current research project, which include alignment and adaptation to the regulations of the Saudi marine environment, the research group of the current marine ecosystem project tries to analyze the variables contained in maritime transport and shipping and to measure the impact of these variables on the marine ecosystem, by focusing on four national priority areas: 1) reliable and long-term seafood supply; 2) thriving coastal ecosystems; 3) sustainable coastal development; and 4) risk resilience in coastal communities. Prioritizing coastal issues and gathering desired outcomes from.

3

Exploration and mining learning robot of autonomous marine resources based on adaptive neural network controller

Pan L.

Polish Maritime Research

|

2018

|

S 3

78--83

EN

To study the autonomous learning model of the learning robot for marine resource exploration, an adaptive neural network controller was applied. The motion characteristics of autonomous learning robots were identified. The mathematical model of the multilayer forward neural network and its improved learning algorithm were studied. The improved Elman regression neural network and the composite input dynamic regression neural network were further discussed. At the same time, the diagonal neural network was analysed from the structure and learning algorithms. The results showed that for the complex environment of the ocean, the structure of the composite input dynamic regression network was simple, and the convergence was fast. In summary, the identification method of underwater robot system based on neural network is effective.