Evaluation of Green Industry Innovation Efficiency Based on Three-Stage DEA Model: A Case Study of Chinese Information Technology Industry

Abstract

The information technology industry as a new engine driving China’s economy has made more and more contributions to Chinese sustainable development. At present, it has overtaken real estate as the new cradle of Chinese billionaires. The information technology industry not only has its own characteristics of high economic, social benefits, and small impact on the ecological environment, but also can enable the green development of the economy and society. So it is the core industry to support the realization of the “double carbon” goal. This paper evaluated the innovation efficiency of 80 enterprises in the software and information technology service from 2017 to 2018 by constructing a three-stage DEA model. It puts forward countermeasures, which points out the direction for the development of environmental protection and green low-carbon industry. Empirical results show that environmental variables have different effects on innovation efficiency. After excluding the influence of environmental and random factors, the increase in innovation efficiency, while generally significant, is not high. Low innovation efficiency is caused by both pure technical efficiency and scale efficiency, especially pure technical efficiency. Enterprises’ adjusted scale returns are mostly increasing; the innovation investment scale is not optimal. Regional differences of enterprise innovation exist; the East and Midwest have obvious polarization both in quantity and quality. These results quantify the effect of the factors affecting enterprise innovation efficiency and put forward policies and suggestions for promoting the development of China’s information technology industry accordingly.

The Driving Factors of Green Technology Innovation Efficiency—A Study Based on the Dynamic QCA Method

“…Xu et al [26] measured the GTI efficiency of 19 wastewater treatment companies in the same way. Zhang et al [27] measured the efficiency of the IT industry through a three-stage DEA model. Du et al [28] regarded industrial carbon emissions and "three wastes" contaminants as unexpected outputs and used the network DEA to estimate the GTI efficiency of industrial enterprises.…”

Spatiotemporal evolution and driving factors of urban green technology innovation efficiency in the Chengdu-Chongqing Economic Circle of China

“…Some scholars focus on the efficiency measurement of GTI. In terms of research objects, scholars mainly focus on GTIE at the industry level, such as measuring GTIE in manufacturing, high-tech industries, and more broadly in the industrial sector (Wang et al, 2017;Zhu et al, 2018;Yang et al, 2020;Xu et al, 2023;Zhang et al, 2023), while some scholars focus on GTIE at the provincial level in China (Luo and Liang, 2016;Li, 2017;Shen et al, 2022). It is not difficult to find that existing studies lack attention to urban GTIE.…”

“…Existing studies mainly focus on the efficiency measurement of GTI, and the research content mainly focuses on GTIE at industry and enterprise levels. At the industry level, existing studies have measured the GTIE of industrial, manufacturing, and high-tech industries (Wang et al, 2017;Xu et al, 2023;Zhang et al, 2023); at the firm level, existing studies have measured the GTIE of energybased firms (Jiang et al, 2021). It is not difficult to find that existing studies have paid less attention to urban GTIE.…”

Assessing Manufacturing Efficiency in Central Plains Cities: A Three-Stage DEA and Malmquist Index Approach

“…Cui and Li [4] used the DEA-BBC and AHP models to conduct an empirical analysis of 21 specific industries in Qinghai's manufacturing industry, concluding that Qinghai's manufacturing industry overall lacks comparative advantages and exhibits significant internal developmental disparities. Zhang et al [5] used the DEA-BCC and Malmquist index models to calculate the green innovation technology efficiency of 22 sub-industries in Shanxi's manufacturing industry, finding that the green technology innovation efficiency of various sub-industries in Shaanxi's manufacturing industry is low, but the overall trend is upward. Many scholars mainly use the first-stage DEA model in analyzing manufacturing efficiency, but the first-stage DEA model does not consider the impact of environmental variables on the efficiency evaluation of decision-making units [6], which may lead to the efficiency values being underestimated or overestimated, and the model's assumptions are inappropriate [7].…”