Study on industrial carbon emissions in China based on GDIM decomposition method and two decoupling effects

Area of Science:

• Environmental Science
• Economics
• Industrial Ecology

Background:

• China's industrial sector is a major contributor to global carbon emissions.
• Understanding the drivers of these emissions is crucial for effective climate policy.

Purpose of the Study:

• To decompose the driving factors of China's industrial carbon emissions.
• To analyze the decoupling of industrial carbon emissions from economic growth.
• To investigate the relationship between economic development and carbon emissions using the Environmental Kuznets Curve (EKC).

Main Methods:

• Generalized Divisia Index Method (GDIM) for factor decomposition.
• Tapio decoupling elasticity and emission reduction effort function for speed decoupling.
• Environmental Kuznets Curve (EKC) for quantity decoupling.

Main Results:

• Positive drivers: output size, industrial energy consumption, population size.
• Negative drivers: investment carbon emission, output carbon intensity, per capita output, economic efficiency, energy intensity.
• Emission reduction elasticity exceeds energy conservation elasticity, indicating reduction potential.
• Decoupling effect: undecoupling-strong decoupling-undecoupling.
• EKC analysis shows a "U" shaped quadratic curve and an "N" shaped cubic curve, supporting the EKC hypothesis.

Conclusions:

• China's industrial carbon emissions are influenced by multiple factors, with output size and energy consumption being key positive drivers.
• While progress in decoupling emissions from economic growth has been observed, further efforts are needed for sustained reduction.
• The study confirms the EKC hypothesis for China's industrial sector, suggesting emissions may eventually decrease with further economic development.