基于人工神经网络的数字经济碳减排及其条件效应研究
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1.湖南工商大学经济与贸易学院;2.香港恒生大学商学院;3.悉尼科技大学澳中关系研究所

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国家社会科学基金后期资助项目(24FJYB051);国家社会科学基金重大项目(23&ZD067);湖南省自然科学基金面上项目(2024JJ5117);湖南省教育厅科学研究重点项目(23A0487)


Research on carbon emission reduction and its conditional effects of digital economy based on Artificial Neural NetworksWU Weiping1, LIU Yuning1, SU Leyan1,*, Tsun Se Cheong2,3, Shuaiyi Liu 2
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Hunan University of Technology and Business

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    摘要:

    数字经济如何在可持续发展框架下推动碳排放双控正引发社会各界广泛关注和深度探讨。为精准识别数字经济与碳排放之间的内在联系,创新性地运用人工神经网络模型系统探究数字经济对碳排放总量、碳排放强度、人均碳排放量的净效应与条件依赖性。结果表明:①数字经济发展推进了碳排放总量减排且速率随经济发展水平上升而增强;数字经济对碳排放强度、人均碳排放量的净效应分别表现为负偏态倒“U”型趋势、先下降再上升再下降的“S”型态势,且对二者的减排效应直到数字经济发展水平达到较高门槛值时才显著。②人工神经网络模型相比较传统计量估计更具适用性和精准度,在拟合数字经济对碳排放量、碳排放强度、人均碳排放量的净效应时,精准度相比较传统计量估计分别提升了65.58%、67.86%、56.57%。③数字经济对碳排放的影响表现出城市规模、城市行政等级和数字经济试验区异质性。超大和特大城市数字经济发展的总量减排效应最显著,但在碳排放强度和人均碳排放量减排方面仍需优化;行政等级较高城市数字经济发展有助于碳排放强度和人均碳排放量减排,但在总量减排上未体现优势;数字经济试验区充分展现了绿色发展和碳减排的制度优势,尤其是在碳排放强度和人均碳排放量减排方面。④数字经济对碳排放的净效应因经济发展水平、外商直接投资、技术创新能力和技术关注度等条件变化而变化,意味着推进数字经济的碳排放双控可以从上述四个方面发力。

    Abstract:

    How the digital economy can promote dual control of carbon emissions and intensity within the framework of sustainable development is attracting widespread attention and in-depth exploration from all sectors of society. With the increasing integration of digital technologies into various industries, understanding their potential to contribute to environmental sustainability is crucial. To gain a deeper understanding of the intrinsic connection between the digital economy and carbon emissions, this paper innovatively applies an artificial neural network model. This model systematically explores the net effects and conditional dependencies of the digital economy on total carbon emissions, carbon intensity, and per capita carbon emissions, providing a comprehensive analysis of how digital transformation impacts carbon reduction efforts.The results show that: ①The digital economy has played a crucial role in reducing total carbon emissions, with the reduction rate accelerating as economic development levels increase. Its impact on carbon intensity and per capita carbon emissions follows distinct patterns, exhibiting a negatively skewed inverted "U" shape and an "S"-shaped trend, respectively. Notably, the significant reduction effects on both carbon intensity and per capita emissions become evident only when the digital economy reaches a sufficiently high threshold, highlighting the importance of sustained digital advancement.②Compared to traditional econometric methods, the artificial neural network model demonstrates significantly superior accuracy in capturing the complex relationships between digital economy development and carbon emissions. When fitting the net effects on total carbon emissions, carbon intensity, and per capita emissions, the model’s accuracy increased by 65.58%, 67.86%, and 56.57%, respectively, demonstrating its enhanced predictive power. ③The impact of the digital economy on emissions varies significantly across different urban contexts, including city size, administrative level, and the presence of digital economy pilot zones. The most substantial emission reductions occur in super-large and mega-cities, although further efforts are needed to optimize reductions in carbon intensity and per capita emissions. In cities with higher administrative levels, digital economy development helps lower carbon intensity and per capita emissions, but does not significantly affect total emissions. Pilot zones show strong institutional advantages, particularly in green development and emission reduction efforts.④The net effect of the digital economy on carbon emissions is influenced by various conditions, including economic development, foreign direct investment, innovation, and technology focus. These factors play a crucial role in shaping how digital economy development impacts carbon emissions, suggesting that the dual control of emissions can be effectively promoted through strategic advancements in these four aspects.

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吴伟平,刘雨宁,苏乐言,张俊狮,刘帅跇.基于人工神经网络的数字经济碳减排及其条件效应研究.生态学报,,(). http://dx. doi. org/10.5846/stxb202411242877

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