NEURAL NETWORK ANALYSIS AND GAUSSIAN MIXTURE MODELS IN PREDICTING ENVIRONMENTAL PSYCHOLOGICAL IMPACTS ON ECONOMIC GROWTH
DOI:
https://doi.org/10.51594/ijae.v4i5.335Abstract
The study aimed to examine the environmental psychological impact on economic growth. Specifically, the study explored how individuals’ choices/selections influence the trade-offs of economic growth and environmental quality. The cross-sectional survey data were randomly sampled from 211 individuals in two regions in Tanzania. The data analysed by using neural network analysis and Gaussian Mixture models. This study found that the environment has a negative impact on economic growth. The study concluded that the nature of the relationship between economic growth and environmental quality is preliminary (fundamentally) determined by both levels of income (economic growth) and environmental psychological well-being of the individual imposed by the available environmental instruments such as policies and regulations. The study recommended that to achieve both environmental quality and economic growth; the government and other stakeholders should design and implement the environmental instruments that maximise the environmental psychological well-being of the individual.
Keywords: Environmental Psychological Impacts, Economic Growth, Neural Network Analysis and Gaussian Mixture Models.
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