Research on resource allocation optimization based on multilevel model in green economy driven by data elements

Jingwen Fang1,2, Mengyu Ruan1, Zhenghao Chang1
1School of Business Administration, Zhongnan University of Economics and Law, Wuhan, Hubei, 430073, China
2School of E-commerce, Wuhan Technology and Business University, Wuhan, Hubei, 430065, China

Abstract

Green economy is an important factor to measure the quality of economic development. In order to explore the current green economy resource allocation, this paper embeds methods such as DEAMalmquist model and Tobit regression model into the study of green economy resource allocation, explores the green economy resource allocation efficiency of 30 provinces in China by constructing a multilevel model of green economy resource allocation, and analyzes China’s green economy during the period of 2021-2023 through the results of the measurement of the Static, dynamic and level changes of resource allocation efficiency. Tobit regression analysis of the influencing factors of green economy resource allocation efficiency is carried out to optimize the current resource allocation based on the influencing factors. The green economy resource allocation efficiency increases year by year in 2021-2023, and the resource allocation effect improves continuously, with the mean value of the comprehensive efficiency of 0.712, 0.762, and 0.809, respectively. The green economy resource allocation efficiency in Beijing, Shanghai, Jiangsu, and Zhejiang is the highest, and the allocation structure is the most reasonable. Chongqing, Gansu, Qinghai, Ningxia and Xinjiang are less efficient in green economy resource allocation. The per capita GDP and the ratio of education expenditure to GDP have a positive impact on the effect of green economy resource allocation, with an impact of 1.246 and 0.489, respectively.

Keywords: DEA-Malmquist model, Tobit regression, multilevel model, green economy, resource allocation