Cargando…

A system dynamics approach to scenario analysis for urban passenger transport energy consumption and CO(2) emissions: A case study of Beijing

With the accelerating process of urbanization, developing countries are facing growing pressure to pursue energy savings and emission reductions, especially in urban passenger transport. In this paper, we built a Beijing urban passenger transport carbon model, including an economy subsystem, populat...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Xue, Ma, Shoufeng, Tian, Junfang, Jia, Ning, Li, Geng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7116959/
https://www.ncbi.nlm.nih.gov/pubmed/32287871
http://dx.doi.org/10.1016/j.enpol.2015.06.007
Descripción
Sumario:With the accelerating process of urbanization, developing countries are facing growing pressure to pursue energy savings and emission reductions, especially in urban passenger transport. In this paper, we built a Beijing urban passenger transport carbon model, including an economy subsystem, population subsystem, transport subsystem, and energy consumption and CO(2) emissions subsystem using System Dynamics. Furthermore, we constructed a variety of policy scenarios based on management experience in Beijing. The analysis showed that priority to the development of public transport (PDPT) could significantly increase the proportion of public transport locally and would be helpful in pursuing energy savings and emission reductions as well. Travel demand management (TDM) had a distinctive effect on energy savings and emission reductions in the short term, while technical progress (TP) was more conducive to realizing emission reduction targets. Administrative rules and regulations management (ARM) had the best overall effect of the individual policies on both energy savings and emission reductions. However, the effect of comprehensive policy (CP) was better than any of the individual policies pursued separately. Furthermore, the optimal implementation sequence of each individual policy in CP was TP→PDPT→TDM→ARM.