MODEL NEURONSKE MREŽE POVEZANOSTI FINANCIRANJA ZELENE EKONOMIJE I EKONOMSKOG RASTA
NEURAL NETWORK MODEL OF THE CONNECTION BETWEEN FINANCING THE GREEN ECONOMY AND ECONOMIC GROWTH

Author(s): Marko Markić, Brano Markić
Subject(s): Energy and Environmental Studies, Economic development, Socio-Economic Research
Published by: Finrar d.o.o Banja Luka
Keywords: green economy financing; sustainable development; multilayer neural network; R language;
Summary/Abstract: Green economy, sustainable development and economic growth are a research thread that attracts the scientific community, institutes, research centers, but also the creators of national economic policy. Their goal is to find the necessary balance between ecology and economic development, while developing is preserving the natural environment. Therefore, it requires to find a fiscal model that balances economic development and protection of the natural human environment. Approaches to explaining the connection between finance and the green economy are different. They can be classified in two directions. The first direction claims that financing the green economy is a direction that has a negative impact on economic development, and the second that financing the green economy promotes economic growth. The aim of this paper is to investigate the connection between financing the green economy and economic growth using a multilayer neural network. It is hypothesized that it is possible to build a neural network model that allows forecasting economic growth by increasing funding for the green economy from public sources. At the input layer of the neural network are data on exports of products and services, gross investment, inflation, total public sector investment in the green economy and sustainable development. At the output layer of the neural network is the gross domestic product per capita of the observed countries. The neural network model is extensible with new variables at the input layer such as personal consumption and imports. The neural network was developed using R language software packages.

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