Using Deep Learning Techniques to Forecast Environmental Consumption Level

Donghyun Lee,Suna Kang,Jungwoo Shin

doi:10.3390/su9101894

Abstract

Artificial intelligence is a promising futuristic concept in the field of science and technology, and is widely used in new industries. The deep-learning technology leads to performance enhancement and generalization of artificial intelligence technology. The global leader in the field of information technology has declared its intention to utilize the deep-learning technology to solve environmental problems such as climate change, but few environmental applications have so far been developed. This study uses deep-learning technologies in the environmental field to predict the status of pro-environmental consumption. We predicted the pro-environmental consumption index based on Google search query data, using a recurrent neural network (RNN) model. To verify the accuracy of the index, we compared the prediction accuracy of the RNN model with that of the ordinary least square and artificial neural network models. The RNN model predicts the pro-environmental consumption index better than any other model. We expect the RNN model to perform still better in a big data environment because the deep-learning technologies would be increasingly sophisticated as the volume of data grows. Moreover, the framework of this study could be useful in environmental forecasting to prevent damage caused by climate change.

Highlights

The seriousness of environmental pollution—such as that of air and water—has become more evident
In order to compare the prediction performance for the pro-environmental consumption index and investigate the possibility of applying artificial intelligence to environment studies, this study considers the traditional regression analysis model, artificial neural network (ANN), and recurrent neural network (RNN)
We proposed a pro-environmental consumption index using big data queries to measure the environmental consumption level for each country, and predicted the proposed index using deep-learning technology in the context of its application to environmental studies

Summary

Introduction

The seriousness of environmental pollution—such as that of air and water—has become more evident. Environmental pollution is a global problem, rather than a national one To solve such problems, governments need to control the actions of businesses and individuals to reduce their environmental pollution and embrace sustainable consumption. Governments need to control the actions of businesses and individuals to reduce their environmental pollution and embrace sustainable consumption Such encouragements are typically made through policy, but they do not lead to pro-environmental consumption practices. In the case of South Korea, over 800 government agencies spent 2.2 trillion Korea Won on eco-products in 2014 [1]; green products are rarely purchased outside these agencies This phenomenon occurs because there is a gap between consumer attitudes and behavior [2]—that is, environmental attitude is a major factor in decision-making vis-à-vis the consumption of “green” goods and services [3]. It is necessary to understand those consumer attitudes that will lead to sustainability-conducive behavior and consumption

Objectives

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Conclusion