A Haavelmo grey model based on economic growth and its application to energy industry investments

经济 增长模型 能量(信号处理) 经济体制 自然资源经济学 产业组织 微观经济学 数学 统计
作者
Hui Li,Weige Nie,Huiming Duan
出处
期刊:Chaos Solitons & Fractals [Elsevier BV]
卷期号:181: 114669-114669
标识
DOI:10.1016/j.chaos.2024.114669
摘要

The energy industry is a major source of greenhouse gas emissions, and energy investment is an important regulatory tool to encourage the energy industry to actively respond to climate change and achieve low-carbon development. Therefore, it is of great practical significance to correctly understand the important role of the energy industry, to predict energy investments objectively and accurately, to achieve scientific and rational investment, and make policy recommendations for the energy production and consumption revolution. In this paper, the Haavelmo model of economic growth is introduced into the energy system, using the characteristics of the continuous form of the model to establish the differential equations for the dynamics of fixed asset investment in the energy industry, and Haavelmo's grey prediction model using the grey difference information principle. Meanwhile, the Python program is used to solve the parameters of the new model, and the mathematical transformation is used to find the time response equation of the new model, and the modeling steps and the modeling flow chart of the model are obtained. Finally, the new model will be applied to two types of energy investments in China: total energy industry investment and investment in electricity, steam, hot water production, and supply industry. Both types of energy use the same modeling object and forecast object, and six cases are compared with three grey forecasting models from different perspectives, and their results show that they are much better than the other three grey forecasting models, demonstrating the effectiveness of the new model to effectively forecast energy investments and improve the efficiency of energy industry investments, cultivate healthy and environmentally friendly energy consumption habits.
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