Electrifying light-duty passenger transport for CO2 emissions reduction: A stochastic-robust input–output linear programming model

2021 ◽  
pp. 105623
Author(s):  
Jidong Kang ◽  
Tsan Sheng Ng ◽  
Bin Su ◽  
Alexandre Milovanoff
Keyword(s):  
Linear Programming ◽  
Co2 Emissions ◽  
Programming Model ◽  
Linear Programming Model ◽  
Emissions Reduction ◽  
Passenger Transport ◽  
Input Output ◽  
Light Duty

scholarly journals Identifying industrial restructuring strategy for CO2 emissions reduction in China: A multi-region input-output linear programming model

2019 ◽  
pp. 12-25
Author(s):  
Jidong Kang ◽  
Tsan Sheng Ng
Keyword(s):  
Linear Programming ◽  
Co2 Emissions ◽  
Programming Model ◽  
Linear Programming Model ◽  
Industrial Restructuring ◽  
Emissions Reduction ◽  
Input Output ◽  
Optimal Sequence ◽  
Negative Effect ◽  
Restructuring Strategy

The current paper combines multi-regional input-output model and linear programming model to identify industrial shift strategies for CO2 emissions reduction in China. As a supplement to the previous studies, the optimal sequence of demand regulation for various products is explored. The results show that demand side regulation would pose negative effect on both GDP and CO2 emissions. However, certain strategies can be adopted to decrease CO2 emissions at the minimum decrease in GDP. According to the optimal sequence analysis, a group of key final products, such as the metallurgy products, the nonmetal products, the metal products, and the chemical products should be firstly regulated. Most of these key products concentrate in the eastern and coastal regions in China. Our model can be used to aid policy makers in design of effective industrial restructuring policy to achieve the national emissions targets.

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