可再生能源与产业结构优化的动态互动——基于VAR模型的实证研究

A Dynamic Interaction between Renewable Energy and Industrial Structural Optimization Based on VAR Model

摘要

基于2011—2024年相关数据，采用向量自回归（VAR）模型，系统考察可再生能源发展水平与产业结构优化之间的动态互动关系。研究目的在于揭示两者间是否存在长期均衡关系，识别其相互影响的传导路径与作用强度，为协调推进能源转型与产业升级提供实证依据。在研究方法上，构建多变量VAR模型，选取可再生能源消费占比、产业结构高度化指数等关键变量，利用单位根检验、协整分析等方法验证数据的平稳性与长期均衡关系，并通过脉冲响应函数与方差分解技术，深入刻画变量间的动态交互机制。研究结果显示，可再生能源发展水平的提升对产业结构优化具有持续且显著的正向促进作用，且该效应在长期呈现逐步增强趋势。脉冲响应分析进一步表明，可再生能源发展水平的提高在初期可能对现有产业结构带来一定程度的适应性冲击，但随着时间的推移，该冲击逐步转化为推动产业结构向高级化、绿色化方向调整的积极动力。与此同时，产业结构优化也为可再生能源技术的推广应用与产业规模的扩大创造了更为有利的市场环境和政策条件，二者形成良性的双向促进机制。方差分解分析从波动贡献角度佐证了上述互动关系：随着预测期的延长，可再生能源发展水平对产业结构优化波动的解释力度显著提升，说明其在推动产业结构持续调整中的作用日益凸显；产业结构优化对可再生能源发展的波动亦具有不可忽视的影响，进一步印证两系统间的内生互动特征。综上，本文的主要结论为：可再生能源发展与产业结构优化之间存在显著的动态关联与正向反馈机制，二者通过相互促进、协同演进的路径，共同推动经济体系向低碳、高效、可持续方向转型。这一结论对于制定兼顾能源安全与产业竞争力的综合政策具有参考价值。

Abstract

This paper, based on 2011 to 2024 data, uses vector auto-regression (VAR) model to study the dynamic interaction between renewable energy and industrial structural optimization aiming at revealing if there is a long-term balance, recognizing the conducting path and intensity of their mutual influence, which can provide references for advancing energy transformation and industrial upgrade. A VAR model with multiple variables is established to explore their dynamic interaction among key variables like renewable energy consumption proportion, industrial structural advancing index via impulse response function and variance decomposition, with unit root test and co-integration methods are used to verify the data stability and long-term balance. Improvements of renewable energy development dramatically improve industrial structural optimization, continuously rising in the long term. Impulse response analysis indicates that it may strike the existing industrial structures at the beginning but will convert to a drive to advancing and greening industrial structures over time. Meanwhile, industrial structural optimization can return favorable marketing environment and supportive policies for spreading renewable energy technologies and expanding industrial scales. Both form a mutual improving mechanism, verified by variance decomposition from a perspective of fluctuation contribution. As forecasting period goes longer, development of renewable energy largely contributes to the fluctuation of industrial structural optimization, suggesting it is a rising driver for upgrading industrial structures, and industrial structural optimization also impacts the fluctuation of renewable energy development. In conclusion, renewable energy development has a dynamic connection and positive response with industrial structural optimization through mutual improvement and co-integrated evolution, advancing economy to a low carbon, effective and sustainable way, offering references for making comprehensive policies covering energy security and industrial competitiveness.

关键词

可再生能源 / 产业结构优化 / 向量自回归模型

Key words

renewable energy / industrial structural optimization / vector auto-regression model

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