A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Assessing the Drivers of Carbon Intensity Change in China: A Dynamic Spatial–Temporal Production-Theoretical Decomposition Analysis Approach
As carbon intensity (CI) can better reflect the coordinated relationship between carbon emissions and economic growth, the related research has gradually increased in recent years. To better explore the influence of production technology and spatial variations on CI disparities in China, this paper constructs a dynamic spatial–temporal production-theoretical decomposition analysis (DST-PDA) model to explore the dynamic spatial disparities and temporal variations of driving factors on CI in different regions. Moreover, this paper further investigates the impact of production-related factors, such as carbon emission technology’s change with regard to carbon intensity, and explores the benchmarking catch-up effect and the effort on reducing CI by setting benchmarks and dynamic comparative analysis, which could provide guidance for some underperforming regions. The main results are as follows: (1) The overall trends of CI increased from 2007–2019, and the northwest region had the largest growth rate. (2) Energy intensity was the dominant driver to reduce CI, and technological changes also played a great role in decreasing CI. Conversely, carbon emissions efficiency had negative effects on reducing CI. (3) The spatial variations of the contributions in factors to reduce CI have gradually increased. Resource-dependent development areas have great potential to reduce carbon intensity by improving energy and carbon emission efficiencies. The northwest has great potential to reduce CI by introducing advanced technologies. Some policies are proposed based on the results.
Assessing the Drivers of Carbon Intensity Change in China: A Dynamic Spatial–Temporal Production-Theoretical Decomposition Analysis Approach
As carbon intensity (CI) can better reflect the coordinated relationship between carbon emissions and economic growth, the related research has gradually increased in recent years. To better explore the influence of production technology and spatial variations on CI disparities in China, this paper constructs a dynamic spatial–temporal production-theoretical decomposition analysis (DST-PDA) model to explore the dynamic spatial disparities and temporal variations of driving factors on CI in different regions. Moreover, this paper further investigates the impact of production-related factors, such as carbon emission technology’s change with regard to carbon intensity, and explores the benchmarking catch-up effect and the effort on reducing CI by setting benchmarks and dynamic comparative analysis, which could provide guidance for some underperforming regions. The main results are as follows: (1) The overall trends of CI increased from 2007–2019, and the northwest region had the largest growth rate. (2) Energy intensity was the dominant driver to reduce CI, and technological changes also played a great role in decreasing CI. Conversely, carbon emissions efficiency had negative effects on reducing CI. (3) The spatial variations of the contributions in factors to reduce CI have gradually increased. Resource-dependent development areas have great potential to reduce carbon intensity by improving energy and carbon emission efficiencies. The northwest has great potential to reduce CI by introducing advanced technologies. Some policies are proposed based on the results.
Assessing the Drivers of Carbon Intensity Change in China: A Dynamic Spatial–Temporal Production-Theoretical Decomposition Analysis Approach
Xiaolei Liu (author) / Heng Chen (author) / Cheng Peng (author) / Mingqiu Li (author)
2022
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Decomposition and Attribution Analysis of Industrial Carbon Intensity Changes in Xinjiang, China
| DOAJ | 2017
Drivers of Spatial and Temporal Dynamics in Water Turbidity of China Yangtze River Basin
| DOAJ | 2023