Low-carbon scenario analysis on urban transport of one metropolitan in China in 2020

Xiaofei Chen, Zijia Wang

Abstract


Purpose: This paper discussed possible ways of implementing effective energy conservation and GHG emission reduction measures by providing: the forecasts of mid-to-long term citywide carbon emission rate; and the analysis of potential low-carbon transport solutions.

Design/methodology/approach: According to the characteristics of the transport system in China, based on the review and application analysis of existing transport energy and GHG emission calculation models, the comprehensive carbon emission calculation model established. Existing data were utilized with regression analysis to project the prospective traffic data in the baseline scenario at the target year of 2020 to calculate the emission amount. Four low-carbon scenarios were set in accordance with the goal of “low carbon transportation, green trip”, and the effectiveness of each low-carbon scenario was evaluated by comparing them with the baseline scenario in terms of the respective GHG emission rate.

Findings: Under the current developing trend in policy environment and technical specifications, the total projected GHG (CO2) emissions from transport sector in 2020 of the city will reach 30.085 million ton CO2; private-vehicles are the major contributor among all transport modes at 16.89 million ton CO2.

Practical implications: Limiting the growth in private-vehicle ownership, reducing the frequency of mid-to-long range travel and the average trip distance, and prompting the public transit oriented policies are all possible solutions to reduce carbon emission. The most effective practice involves a shift in public travel behavior.

Originality/value: This paper presents a method to forecast the mid-to-long term city-wide carbon emission rate; and provides some potential low-carbon transport solutions


Keywords


Low carbon transport; Carbon emission; Scenario analysis; Forecasting; Energy conservation and emission reduction

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DOI: http://dx.doi.org/10.3926/jiem.640


Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2019

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience