Temporal-Spatial Characteristics and Driving Factors of Total Factor Carbon Productivity in the Yangtze River Economic Belt

Abstract

Under the policy background of carbon neutralization and carbon peaking, how to promote total factor carbon productivity (TFCP) has become an important part of promoting green and low-carbon development of the Yangtze River Economic Belt. This paper uses SBM model to measure the TFCP of the Yangtze River Economic Belt, and uses empirical analysis methods such as Dagum Gini coefficient and geographical detector to measure the spatio-temporal evolution characteristics and driving factors of the TFCP of the Yangtze River Economic Belt. The research finds that: First, the development trend of TFCP in the Yangtze River Economic Belt shows a downward trend no matter from the overall regional or subregional level. There are differences in the number and spatial agglomeration patterns of different levels of cities. Second, the overall difference of TFCP in the Yangtze River Economic Belt is gradually decreasing, and the regional difference has become the main factor of spatial difference. The dispersion of TFCP has decreased, and the regional gap has gradually narrowed. TFCP is characterized by non -normal distribution, with local agglomeration development trend, and its focus gradually shifted to the southwest. Third, technological innovation, infrastructure construction and economic growth are the key drivers of the Yangtze River Economic Belt TFCP. The dominant driving factors of TFCP are different in different regions.

Evaluating the tourism green productivity and its driving factors in the context of climate change

“…Third, the results of the quantile regression suggested TGP is affected by various driving factors such as tourism economic scale, industrial structure, scientific and technological innovation, traffic conditions, and urbanization level, which, to some extent, echoes the findings of Long et al (2019) and Zhang et al (2021). However, the quantile regression method used in this study further deepens and refines the driving mechanism of each influencing factor, thereby producing more comprehensive analytical results.…”

“…Research on driving factors. In previous studies, the fixed-effect OLS model, Tobit model, and Geo-Detector were applied to explore the comprehensive influence of regional tourism economic scale, the level of opening up, tourism education, the urbanization level, science and technology, and other factors on the TGP (Wang et al, 2020;Zhang et al, 2021;Xia et al, 2018;Long et al, 2019). Furthermore, given the maturity of research on the driving factors of green productivity in agriculture (Liu and Feng, 2019), industry (Gao and Yuan, 2022), logistics (Li and Wang, 2021), transportation (Yang et al, 2021), and other industries, factor analysis-based practical guidelines have been formulated, providing an important reference for the empirical study of the driving mechanism and improvement path of TGP, as in this study.…”

“…promoting technological progress in tourism and improving the efficiency of tourism resource allocation. Referring to Zhang et al (2021), research and development (R&D) expenditure intensity was used to quantify STI. 4.…”

“…Second, with an increase in the quantile, the effect of TO on TGP was polarized, reaching a maximum at the 0.9 quantile and passing the significance test at the 1% level. Third, the driving direction of TO on TGP changed from negative to positive, and the estimated coefficients' sign changed at the 0.75 quantile, implying that foreign investment only had a positive promotion effect on high-TGP areas, indicating a certain threshold effect on the impact of TO on TGP, suggesting that only when the knowledge and technology spillover and demonstration effects of foreign investment, etc., are fully exerted can regional TGP be significantly promoted (Zhang et al, 2021). 5.…”

“…Taking this into account, this study systematically examined the TGP's heterogeneous response to various driving factors using quantile regression models. Compared with ordinary regression models (Zhang et al, 2021), the quantile regression method offers more informative mining, which is an effective method for revealing the changing trends of heterogeneous driving factors (Akram et al, 2021). Specifically, when the independent variables differentially affect different conditional distributions of the dependent variable, quantile regression can more comprehensively characterize the conditional distribution, thus avoiding the idealized model of mean regression and identifying the optimal improvement or control factors corresponding to different conditional quantile levels of TGP, so as to provide important directions for the formulation of green tourism policies.…”