|Technological innovation and provincial carbon abatement in China
|Wang, Shuaian (LMS)
|Carbon dioxide mitigation -- China
Technological innovations -- Environmental aspects
Air -- Pollution -- China
Hong Kong Polytechnic University -- Dissertations
|Department of Logistics and Maritime Studies
|xii, 125 pages : color illustrations
|Human production and living activities have an increasing demand for natural resources, causing a large amount of carbon dioxide-based (CO2) greenhouse gas emissions and ultimately harming the earth's ecological environment. The increased atmospheric CO2 concentration has led to a severe greenhouse effect, which has caused tremendous damage to global agriculture and animal husbandry, ecosystems, water resources, coastal zones, and social economy. In 1992, the United Nations Framework Convention on Climate Change (UNFCCC) was established; in 1997, the Kyoto Protocol was signed; in 2016, the Paris Agreement entered into force, which has laid the political foundation and legal framework for countries around the world to work together and address climate change. The international community reached a consensus on carbon peaking and carbon neutrality. Many countries have emphasized the role of technological innovation in their core strategies to deal with climate change.
However, previous literature has found that technological innovation can increase and inhibit carbon emissions. The research on the relationship between technological innovation and carbon emission reduction is not systematic, and the view that technological innovation promotes is the core of carbon emission reduction lacks empirical research and testing. Limited research has explored the optimization and simulation of the carbon emission reduction path. At present, China is the largest carbon emitter. It is necessary to examine the spatial characteristics of carbon emissions in China's provinces in-depth and systematically and study technological innovation's effect and paths on carbon abatement to help achieve the "dual carbon" goal.
Based on spatial autocorrelation, system dynamics, game theory, innovation theory, sustainable development, and circular economy theory, this research used literature induction, statistical analysis, computer simulation, and scenario analysis to study the effect and path of technological innovation in promoting provincial carbon abatement in China.
The IPCC method was used to calculate the provincial carbon emissions, and the Moran's I, and the Moran scatts plots were used to explore the spatial characteristics and spatial agglomeration of the provincial carbon emissions. The spatial β convergence model and spatial Durbin model (SDM) were established to investigate provincial carbon emission's conditional and absolute convergence trends.
The Moran's I and Moran scatter plots were used to identify the spatial autocorrelation of technological innovation. SDM and quantile regression were used to explore the spatial effect of technological innovation on provincial carbon emissions. The moderation effect of environmental regulation was tested at the national and province levels. Hansen's threshold model was used to identify the threshold value and threshold effect of environmental regulation on the relationship between technological innovation and provincial carbon emissions.
A multiple mediation model was established using industrial structure upgrade and energy structure adjustment as the mediation variables to identify mechanisms through which technological innovation influences carbon emission reduction. The moderating effect of the environmental regulation on the mediating variable was tested using Bootstrap methods.
A system dynamics model was established to simulate the technological innovation-driven carbon abatement system. The Vensim PLS software was used to test the correctness and effectiveness of the basic system dynamics model. To acquire the quantitative feedback loop of the system dynamics model, the evolutional game model was integrated into the system. The optimal scenarios and carbon abatement strategy were identified for both inland and coastal regions based on sensitivity analysis of technology investment structure and intensity of environmental regulation. The system was simulated under different scenarios and the results of the static and dynamic simulations were compared to identify the optimal parameter configurations under different scenarios.
The major conclusion of this dissertation includes the following.
(1) This dissertation constructed a system dynamics model, obtained the optimal configuration of variables in the system through dynamic simulation, and established optimal paths for carbon emission reduction under different scenarios. This dissertation provides a new perspective and points out the strategic direction for the coordinated and unified development of society, economy, and ecological environment. The results can effectively help with the urgent challenges brought by global climate change.
(2) China's provincial carbon emissions show a significant spatial agglomeration effect and conditional and absolute β-convergence. From 2008 to 2019, provincial carbon emissions in China continued to increase. The increasing trend of carbon emissions in coastal provinces slowed down, and the carbon emissions in inland provinces showed a nonlinear trend. Regional carbon emissions showed spatial dependence. The spatial absolute β convergence coefficient is −0.161 on the national level, and that in inland and coastal provinces are −0.141 and −0.235, respectively, indicating absolute spatial β convergence, and the degree of convergence in coastal regions is more significant than that in the inland areas. The spatial conditional β convergence coefficient is −0.353 on the national level, and that in inland and coastal provinces are −0.372 and −0.473, respectively, indicating conditional spatial β convergence, and the degree of convergence in coastal regions is more significant than that in the inland areas. Technological innovation is one of the main factors that affect the amount of provincial carbon emission and increases the convergence speed.
(3) Technological innovation has a significant promoting effect on carbon emission reduction and shows spatial-temporal heterogeneity. For every 1% increase in technological innovation, provincial carbon emissions decrease by 0.086%. The promoting effect of technological innovation on carbon emission reduction in inland regions is greater than that of coastal areas (the coefficients are −0.145 and −0.114), and the inhibiting effect further strengthened after 2013 (coefficient is −0.197 in 2013 and after, −0.060 before 2013). Environmental regulation boosts the promoting impact of technological innovation on carbon emission reduction in the inland regions. When environmental regulation is above the threshold value of 11.964, the coefficient of technological innovation on provincial carbon emissions changes from −0.102 to −0.099, showing a decrease in the inhibiting effect.
(4) Industrial structure change and energy structure adjustment moderate the relationship between technological innovation and provincial carbon emissions. Three mediating paths are identified: path 1 is technological innovation → industrial structure upgrades → carbon emissions (effect value −0.072), path 2 is technological innovation → energy structure adjustment → carbon emissions (effect value −0.059), and path 3 is technological innovation → industrial structure upgrades → energy structure adjustment→ carbon emission (effect value 0.024). Environmental regulation has a moderating effect on the mediating effects. In path 1, only when the environmental regulation is greater than −0.725, the negative impact of technological innovation on carbon emissions is significant. In Path 3, when the environmental regulation is within ( − 1.33, − 0.12), technological innovation promotes provincial carbon emissions, and when environmental regulation is greater than 0.55, the promoting effect of technological innovation on provincial carbon emissions reduction is more substantial.
(5) The optimal path for inland provinces should address short-term dynamic adjustments, and the technological investment in clean energy is the key to carbon emission reduction; the optimal path for coastal provinces should address long-term static stability, and the technological investment in the upgrade of industrial structure is the key to carbon emission reduction. Based on the system dynamics model, this research used sensitivity analysis of the technology investment structure (i.e., the proportion of technology innovation investment in energy structure optimization, industrial structure upgrade, and green technology innovation) and intensity of environmental regulation to adjust the dynamic and static optimization path of carbon control through technological innovation. The optimal path for inland provinces in the simulation model: the ratio of technology investment in clean energy structure optimization, industrial structure upgrade, and green technology innovation is 0.57:0.2:0.23, with the highest environmental regulation intensity, which has an estimated carbon peaking time of 2024 with the peak value 91.56 million tons. The optimization path in the inland regions is sensitive to the structure of science and technology investment, and the proportion of technology investment in clean energy structure optimization is the key to carbon emissions control. The optimal path for coastal provinces in the simulation model: the ratio of technology investment in clean energy structure optimization, industrial structure upgrade, and green technology innovation should stabilize at 0.02:0.18:0.8 in the long run, which has an estimated carbon peaking time of 2023 with a peak value of 152.96 million tons. The simulation of dynamic and static paths in the coastal regions shows similar results, emphasizing long-term technology investments in industrial structure upgrades and green technology innovation.
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