工业智能化评价与循环经济发展效果评价:2012 年至 2022 年省际数据

Jianlin Zhao
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引用次数: 0

摘要

随着人工智能和自动化技术的发展,智能制造的概念和应用得到越来越多人的认可,工业企业智能化发展趋势逐渐显著。为了提高经济体的工业智能化水平,间接促进其循环经济的发展,本研究采用模糊层次分析法和前馈神经网络算法构建经济体智能化评价模型,并通过多元线性回归建立分析模型,评价工业智能化对循环经济的作用和影响。基于2012-2022年中国省级经济年鉴,本研究设计的模糊层次分析法与前馈神经网络算法混合模型、传统层次分析法模型和人工评价法的平均绝对误差总值分别为0.14和0.31。在工业智能化-产业结构模型中,除规模以上国有企业产值比重外,其他指标均有显著的正效应,说明工业智能化、信息化建设和城镇化有利于经济规模增长。在工业智能化-环境偏技术进步模型中,规模以上国有企业产值比重、工业智能化得分、人均邮政通信量的回归系数分别为 3.846、0.8510、0.0381,可以加快经济的产业转型。在产业智能-经济规模模型中,规模以上国有企业产值占比显著影响技术进步的环境偏向,回归系数为-34.72,说明经济结构中国有企业占比越低越有利于产业智能化。该研究对辅助经济体开展工业智能化,激发循环经济发展具有一定的借鉴意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of industrial intelligence and evaluation of the effect of circular economy development: Inter-provincial data from 2012 to 2022

As artificial intelligence and automation technology develop, the concept and application of intelligent manufacturing is recognized by more and more people, and the development trend of industrial enterprises' intelligence is gradually remarkable. In order to improve the industrial intelligence of an economy and indirectly promote its circular economy, this study uses fuzzy hierarchical analysis and feed-forward neural network algorithm to construct an evaluation model of the intelligence of an economy and multiple linear regression to build an analytical model to evaluate the effect and impact of industrial intelligence on circular economy. Based on China's provincial economic yearbooks from 2012 to 2022, the total absolute difference between the average absolute error values of the hybrid fuzzy hierarchical analysis and feedforward neural network algorithm model, the traditional hierarchical analysis model and the manual evaluation method designed in this study are 0.14 and 0.31, respectively. In the industrial intelligentization - industrial structure model, except for the proportion of output value of state-owned enterprises above the scale, all other indicators have a significant positive effect, indicating that industrial intelligence, information construction and urbanization are conducive to economic scale growth. In the industrial intelligentization - environmental bias technology progress model, the regression coefficients of the proportion of output value of state-owned enterprises above the scale, industrial intelligence score, and postal communication per capita are 3.846, 0.8510, and 0.0381, respectively, which can accelerate the industrial transformation of the economy. In the industrial intelligence-economic scale model, the percentage of output value of state-owned enterprises above the scale significantly effects the environmental bias toward technological progress and the regression coefficient is −34.72, indicating that the lower percentage of state-owned enterprises in the economic structure is more conducive to industrial intelligence. This study has some reference significance for auxiliary economies to carry out industrial intelligence and stimulate the development of circular economy.

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