Analysis of the Driving Factors and Contributions to Carbon Emissions of Energy Consumption from the Perspective of the Peak Volume and Time Based on LEAP

2016 
Studying the driving factors and contributions of carbon emissions peak volume and time is essential for reducing the cumulative carbon emissions in developing countries with rapid economic development and increasing carbon emissions. Taking Jilin Province as a case study, four scenarios were set in this paper respectively: Business as Usual Scenario (BAU), Energy-Saving Scenario (ESS), Energy-Saving and Low-Carbon Scenario (ELS), and Low-Carbon Scenario (LCS). Furthermore, the carbon emissions were predicted according to the energy consumption based on the application of LEAP system. The research result showed that the peak time of carbon emissions would appear in 2045, 2040, 2035 and 2025 under the four different scenarios, respectively. The peak volumes would be 489.8 Mt, 395.2 Mt, 305.3 Mt and 233.6 Mt, respectively. The cumulative emissions by 2050 are respectively 15.632 Bt, 13.321 Bt, 10.971 Bt and 8.379 Bt. According to the forecasting, we analyzed the driving factors of and contributions to carbon emissions peak volume and time. On the premise of moderate economic growth, the “structural emission reduction”, namely the adjustment of industrial structure and energy structure, and “technology emission reduction”, namely the reduction of energy intensity and carbon emission coefficient could make the peak volume reduced by 20%–52% and cumulative carbon emissions (2050) reduced by 15%–46% on the basis of BAU. Meanwhile, controlling the industrial structure, energy structure and energy intensity could make carbon emissions reach the peak 5–20 years ahead of the time on the basis of BAU. Controlling GDP, industrial structure, energy structure, energy intensity and coefficient of carbon emissions is the feasible method to adjust the carbon emissions peak volume and time in order to reduce the cumulative emissions.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    33
    References
    8
    Citations
    NaN
    KQI
    []