The Impact Relationships between Scientific and Technological Innovation, Industrial Structure Advancement and Carbon Footprints in China Based on the PVAR Model

Abstract

As one of the three major engines of economic growth in China, the Beijing–Tianjin–Hebei (BTH) urban agglomeration has become one of the regions with the highest energy consumption intensity. To investigate the dynamic relationships between scientific and technological innovation, industrial structure advancement and carbon footprints, panel data in BTH from 2006 to 2019 was selected, and a Panel Vector Auto-Regressive (PVAR) model was established to conduct an empirical study. The conclusions show that there is a causal relationship between the industrial structure advancement and carbon footprints, and the influence of each on the other is significant. The impact of scientific and technological innovation on carbon footprints has a “rebound effect”. Scientific and technological innovation can accelerate the process of industrial structure advancement. Carbon footprints have a significant backward forcing effect on both industrial structure advancement and scientific and technological innovation, with impact coefficients of 0.0671 and 0.2120, respectively. Compared with scientific and technological innovation, the industrial structure advancement has a greater impact on carbon footprints, with a variance contribution of 25.4%. The research findings are conducive to providing policy support for the coordinated development of BTH and promoting the realization of the Double Carbon goal.

Tourism Transport-Related CO2 Emissions and Economic Growth: A Deeper Perspective from Decomposing Driving Effects

“…This approach reduces the need for specific causal assumptions and enhances the flexibility and freedom of the model. Compared to the VAR model, the PVAR model has several distinctive advantages [40]. Firstly, it fully accounts for individual fixed effects and time effects, ensuring better control of individual heterogeneity.…”

“…Cointegration test: If two or more variables are found to be non-stationary, then it is necessary to test for cointegration among them. Cointegration implies a long-run relationship between the variables, and it is tested using methods such as the Pedroni test [40].…”

The Influencing Effect of Tourism Economy on Green Development Efficiency in the Yangtze River Delta

“…The panel vector auto regression (PVAR) model was proposed by Sims, which uses the lagged values of variable systems as endogenous variables to analyze and predict the dynamic interrelationship, impact intensity, positive and negative shocks, and duration between the time series variable systems [35,36]. It can be expressed as follows:…”

The impact of the digital economy on carbon emission intensity: Evidence from China

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“…This study chose the PVAR and Gaussian mixture model (GMM) methods to analyze the relationship between the digital economy level and carbon emission intensity. The PVAR model not only follows the advantages of the vector autoregressive (VAR) model but also effectively solves the endogenous and individual heterogeneity problems and accurately reflects the influence of one variable and its lag term on other variables in the model (Dai et al, 2022). The formula is as follows:…”