Exploring potential pathways towards fossil energy-related GHG emission peak prior to 2030 for China: An integrated input-output simulation model
Exploring potential pathways towards fossil energy-related GHG emission peak prior to 2030 for China: An integrated input-output simulation model
复制标题
探索中国在2030年之前实现化石能源相关温室气体排放峰值的潜在路径:综合投入产出模拟模型
DOI:
10.1016/j.jclepro.2018.01.062
复制
发表时间:
2018-03
影响因子:
11.1
通讯作者:
Fang Kai
中科院分区:
文献类型:
--
作者:
Song Junnian;Yang Wei;Wang Shuo;Wang Xian'en;Higano Yoshiro;Fang Kai
This study develops a dynamic integrated input-output simulation model to explore potential pathways towards GHG emission peak prior to 2030 for China. Dynamic energy consumption intensities and GHG emission intensities (GHGEIs) of sectors (household), as well as various levels of economic growth are set in 4 scenarios (each containing 4 sub-scenarios). The impacts of changes in the added value (reflected as industrial restructuring) and changes in GHGEIs (reflected as technological advancement and intensified policies) of 10 target sectors including both promoted and constrained ones on the peak are elaborated. In the Business-as-Usual scenario, no emission peak could appear before 2040 along the historical trends without taking further intensified emission reduction policies. In Scenario 1 and 2, when economic growth is maintained at higher levels, sole dependence on changes in either added value or GHGEIs of sectors could curb GHG emissions, however without contributing to a peak timing before 2030. The peak timing could be advanced to 2026 (10.85 × 109t CO2-e), 2025 (10.77 × 109t CO2-e), 2024 (10.69 × 109t CO2-e) and 2023 (10.65 × 109t CO2-e) corresponding to different levels of economic growth in Scenario 3, where industrial restructuring and intensified energy and GHG emission reduction policies are involved. The results are expected to provide references to future planning of energy utilization and GHG emission reduction from the perspective of both the country and sectors.
登录
查看更多内容
影响因子:
11.2
作者:
Bin Ye;Jingjing Jiang;Changsheng Li;Lixin Miao;Jie Tang
通讯作者:
Jie Tang
影响因子:
3.9
作者:
Zheng Haitao;Fang Qi;Wang Cheng;Wang Huiwen;Ren Ruoen
通讯作者:
Ren Ruoen
影响因子:
11.1
作者:
Ming-Chung Chang
通讯作者:
Ming-Chung Chang
影响因子:
11.1
作者:
Mi, Zhi-Fu;Pan, Su-Yan;Wei, Yi-Ming
通讯作者:
Wei, Yi-Ming
影响因子:
6.9
作者:
Kang, Yan-Qing;Zhao, Tao;Yang, Ya-Yun
通讯作者:
Yang, Ya-Yun