<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>With the rapid development of economic globalisation, global economic and trade activities are escalating. However, environmental problems and the emergence of green economy, a response to these problems, has led to the widespread introduction of green trade barriers. These barriers implicitly limit the development of trade activities. This paper focuses on the export difficulties caused by green trade barriers and proposes a method to quantify discrete product characteristics, explore the internal characteristics of commodities and decide optimally on intended export regions. Firstly, the discrete feature of products is quantified by quantitative transformation method. Secondly, the quantitative data are used to derive the best decision for export regions through support vector regression (SVR) method. Particle swarm optimisation is used in optimising SVR parameters to achieve high-precision decision making. Comparison with historical data from the industry park shows the identification accuracy of the optimised SVR model to be better than that of the traditional regression model. This finding presents a novel perspective for developing import and export under the background of green trade barriers.</p>
</abstract>

With the rapid development of economic globalisation, global economic and trade activities are escalating. However, environmental problems and the emergence of green economy, a response to these problems, has led to the widespread introduction of green trade barriers. These barriers implicitly limit the development of trade activities. This paper focuses on the export difficulties caused by green trade barriers and proposes a method to quantify discrete product characteristics, explore the internal characteristics of commodities and decide optimally on intended export regions. Firstly, the discrete feature of products is quantified by quantitative transformation method. Secondly, the quantitative data are used to derive the best decision for export regions through support vector regression (SVR) method. Particle swarm optimisation is used in optimising SVR parameters to achieve high-precision decision making. Comparison with historical data from the industry park shows the identification accuracy of the optimised SVR model to be better than that of the traditional regression model. This finding presents a novel perspective for developing import and export under the background of green trade barriers.

Intelligent Analysis of Import and Export in Green Trade Barrier Based on Big Data Analysis

Science and Technology Department, Xianning Vocational Technical College

Ecological Chemistry and Engineering S

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

With the rapid development of economic globalisation, global economic and trade activities are escalating. However, environmental problems and the emergence of...

Intelligent Analysis of Import and Export in Green Trade Barrier Based on Big Data Analysis

Data publikacji: 10 kwi 2024

Zakres stron: 117 - 126

DOI: https://doi.org/10.2478/eces-2024-0009

Słowa kluczowe
green trade barrier, intelligent decision, big data analysis

© 2024 Yu Mao, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Intelligent Analysis of Import and Export in Green Trade Barrier Based on Big Data Analysis

Data publikacji: 10 kwi 2024

Zakres stron: 117 - 126

DOI: https://doi.org/10.2478/eces-2024-0009

Słowa kluczowegreen trade barrier, intelligent decision, big data analysis

© 2024 Yu Mao, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
green trade barrier, intelligent decision, big data analysis