Coupled Coordination and Drivers of Green Technology Innovation and Carbon Emission Efficiency
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2. Literature Review
Green technological innovation is an inevitable choice to realize low-carbon transition. Now, the relevant research results mainly focus on evaluation measurement and influencing factors. First, in terms of the evaluation of green technology innovation, most scholars choose a single indicator to measure the level of green technology innovation, such as Bode E using R&D expenditure to represent the level of green technological innovation [2]; Hamamoto, Yang et al. measuring the level of green technological innovation and using part of the increase in R&D investment caused by environmental regulation [3,4]; the authorized amount of green technology patents [5]; and urban patent applications [6]. A small number of scholars measure the level of green technology innovation by constructing an indicator system to measure the comprehensive value, such as Sun, etc., [7,8,9,10]. Han measures the level of green technological innovation by using input–output indicators [7]. Lin Yan constructs the indicator system to measure the comprehensive value of green technology innovation level from the three dimensions of innovation input, innovation output, and support level of green technology, respectively [11]. Secondly, in terms of influencing factors, the studies mainly include financial R&D and education expenditures [12], digital transformation [13], level of economic development and population size [14], science and technology finance [15], government environmental attention [16], and environmental regulation [17].
Since the “dual carbon” goal was proposed, more and more scholars have been researching carbon emission efficiency, and combing through the relevant literature at home and abroad, we can see that it mainly focuses on the three aspects of concept definition, measurement methodology, and influencing factors. For concept definition, early scholars used the ratio of GDP to carbon emissions during the study period [18], the ratio of unit energy consumption to GDP [19], and other single-factor indicators to measure carbon emission efficiency. Yang Hongliang et al. [20] believe that the measurement of carbon emission efficiency with single-factor indicators is simple and easy to understand, but there are a lot of shortcomings. Therefore, in recent years, more and more scholars have constructed the indicator system to measure carbon emission efficiency from a multi-factor perspective, i.e., to obtain the smallest carbon dioxide emission and the largest economic output without increasing the inputs of labor, capital, and energy [21]. The measurement methods of multifactor indicators are mainly divided into parametric methods represented by stochastic frontier analysis (SFA) [22,23] and nonparametric methods represented by data envelopment analysis (DEA). The DEA model is often used in the measurement of carbon emission efficiency, but because the traditional DEA model does not introduce slack variables and ignores the influence of the external environment and stochastic perturbation factors, the results of the measurement deviate from the actual situation. Therefore, relevant improvement models are mostly adopted at this stage; for example, Wang Yong et al. [24], Wang Xinping et al. [25], and Xu Yingqi et al. [26] measured the carbon emission efficiency of entire China, its regions and cities, by using the improvement models. In terms of influencing factors, it is found that the level of economic development, energy intensity, industrial structure, urbanization level, and technological innovation [25,26,27] are closely related to the improvement in carbon emission efficiency.
From the existing literature, the relevant research on the relationship between green technological innovation and carbon emission efficiency mainly focuses on the relationship between traditional technological innovation and carbon emission efficiency, which is specifically categorized into the following three aspects. Firstly, technological innovation is considered to be able to reduce carbon emissions and improve carbon emission efficiency. Sun Zhenqing et al. divided the level of technological innovation into the input and output of technological innovation and found that the input of technological innovation is more helpful in carbon emission reduction than the output of technological innovation [28]. When Li Jianbao et al. and Xu et al. studied the factors affecting the efficiency of carbon emissions, they found that technological progress and technological innovation were important ways to improve the efficiency of carbon emissions [29,30]. Secondly, it is believed that carbon emission reduction can promote technological innovation. Fan Decheng et al. conducted a study based on the market perspective, and the results show that carbon emission reduction alliance can promote enterprise low-carbon technological innovation [31]. Thirdly, there was a study on the coupling relationship between technological innovation and carbon emission efficiency. Feng Junhua et al. used the coupling coordination and relative development degree model to analyze the coupling coordination relationship between the technological innovation and carbon emission efficiency of Chinese industrial enterprises, and the study showed that technological innovation and carbon emission efficiency present the characteristics of mutual promotion in the early stage and gradual inhibition in the late stage [32].
In summary, a large number of scholars have carried out a lot of research on green technological innovation, carbon emission efficiency, and the impact of technological innovation on carbon emission efficiency and have achieved rich results, but few scholars have studied the coupling relationship between green technological innovation and carbon emission efficiency. Based on this, this paper will take the Yangtze River Economic Belt as the research area to explore the coupling coordination degree and driving factors of green technology innovation and carbon emission efficiency. Compared with the existing research, the research results of this paper are mainly reflected in the following three aspects: ① Existing research on how to construct the indicator system of green technological innovation level is not yet a unified standard, and most scholars still use a single indicator to measure the level of green technological innovation. In this paper, through the construction of a green technology innovation evaluation index system for a comprehensive level of measurement, the indicator system is enriched to measure the level of green technology innovation. ② There is existing research on the relationship between traditional technological innovation and carbon emission efficiency. In recent years, some scholars have begun to explore the impact of green technological innovation on carbon emission efficiency, but the research results are fewer, and no scholars have made a relevant study on the coupling and coordination of green technological innovation and carbon emission efficiency yet. In this paper, research on the coupling and coordination of the two has enriched the theory of coupling and coordinated development of green technological innovation and carbon emission efficiency. ③ There are only studies on the driving factors of the coupling and coordinated development of traditional technological innovation and carbon emission efficiency in the existing research, and there is no analysis of the driving factors of the coordinated development of green technological innovation and carbon emission efficiency. This paper analyzes the driving role of selected factors on the coupled and coordinated development of green technological innovation and carbon emission efficiency by reading the literature, selecting some factors from outside, and introducing the panel Tobit model.
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