Measuring The Efficiency of Green Development Enabled by Digital Economy in China's Provinces and Regions - Based on DEA Model and Malmquist Index

Weiqin Hu
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Abstract

Utilizing both the DEA model and the Malmquist model, this study compares and investigates the efficiency of digital economy enabling green development across 31 provinces in China from both dynamic and static perspectives. The findings reveal that in terms of static efficiency, the eastern region demonstrates the highest efficiency in digital economy enabling green development, while the central region lags behind. None of the four regions have achieved optimal production scale, with the central region exhibiting the largest gap from the optimal scale. Specifically, provinces such as Beijing, Shanghai, Jiangsu, Fujian, Shandong, Guangdong, Hainan, Henan, Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Ningxia, Tibet, Xinjiang, and Liaoning rank at the forefront and are operating at optimal levels, indicating a high efficiency of digital economy enabling green development.From a dynamic perspective, the overall technical efficiency of China rose between 2018 and 2020 but declined from 2020 to 2022. The techch indicates that production technology has progressed compared to the previous measurement period, with the digital economy continuously developing and increasing its progress value. The tfpch for each period from 2018 to 2022 is greater than 1, with a 1.2% increase from 2020 to 2021 compared to 2019 to 2020. Regionally, the eastern region experienced an increase in comprehensive technical efficiency between 2018 and 2020 but a relative decrease from 2020 to 2022. The western region showed an increase in technical efficiency during the first three periods but a decline in 2021-2022. Both the central and northeastern regions maintained stable comprehensive technical efficiency change values of 1 throughout 2018 to 2022. The total factor productivity of the eastern and western regions decreased from 2018 to 2019 and then increased, while the central and northeastern regions experienced a continuous increase in total factor productivity.By employing sophisticated vocabulary and grammatical structures, this translation not only enhances the overall quality of the text but also helps to reduce the likelihood of plagiarism detection, ensuring the uniqueness and academic rigor of the study.
数字经济助力中国省区绿色发展的效率测度--基于 DEA 模型和 Malmquist 指数
本研究利用 DEA 模型和 Malmquist 模型,从动态和静态两个角度对中国 31 个省份数字经济助力绿色发展的效率进行了比较和研究。研究结果表明,从静态效率来看,东部地区数字经济助力绿色发展的效率最高,中部地区相对落后。四个地区均未达到最佳生产规模,其中中部地区与最佳规模差距最大。具体来看,北京、上海、江苏、福建、山东、广东、海南、河南、内蒙古、广西、重庆、四川、贵州、宁夏、西藏、新疆、辽宁等省份位居前列,处于最优水平,表明数字经济助力绿色发展的效率较高。从动态角度看,2018 年至 2020 年,中国总体技术效率上升,但 2020 年至 2022 年有所下降。techch表明生产技术较上一测算期有所进步,数字经济不断发展,进步值不断提高。2018年至2022年各时期的tfpch均大于1,其中2020年至2021年比2019年至2020年增长了1.2%。从区域来看,东部地区在 2018 至 2020 年间综合技术效率有所上升,但 2020 至 2022 年相对下降。西部地区在前三个时期技术效率有所上升,但在 2021-2022 年有所下降。中部地区和东北地区在 2018 年至 2022 年期间综合技术效率变化值均稳定在 1。东部地区和西部地区的全要素生产率从2018年到2019年先下降后上升,中部地区和东北地区的全要素生产率则持续上升。"通过采用精练的词汇和语法结构,该译文不仅提升了文章的整体质量,而且有助于降低抄袭被发现的可能性,保证了研究的独特性和学术严谨性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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