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

Liu, Xue; Ma, Shoufeng; Tian, Junfang; Jia, Ning; Li, Geng

doi:10.1016/j.enpol.2015.06.007

Cited by 119 publications

(39 citation statements)

References 66 publications

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“…Moreover, in most cases, any effort on carbon emissions caused by changes in the road transport sector's energy consumption lasts longer than effects brought about due to economic growth. In addition, many scholars have also studied the CO 2 reduction potential in the transport sector at the national level [2,33,[38][39][40][41][42][43][44][45][46][47][48][49][50]. For instance, Xu et al [46,51] introduced the vector auto-regression model and the dynamic non-parametric additive regression model as a means to analyze the factors that influenced the CO 2 emissions of China's transportation sector.…”

Section: Introductionmentioning

confidence: 99%

Moving Low-Carbon Transportation in Xinjiang: Evidence from STIRPAT and Rigid Regression Models

Dong

Deng

et al. 2016

Sustainability

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Abstract:With the rapid economic development of the Xinjiang Uygur Autonomous Region, the area's transport sector has witnessed significant growth, which in turn has led to a large increase in carbon dioxide emissions. As such, calculating of the carbon footprint of Xinjiang's transportation sector and probing the driving factors of carbon dioxide emissions are of great significance to the region's energy conservation and environmental protection. This paper provides an account of the growth in the carbon emissions of Xinjiang's transportation sector during the period from 1989 to 2012. We also analyze the transportation sector's trends and historical evolution. Combined with the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model and ridge regression, this study further quantitatively analyzes the factors that influence the carbon emissions of Xinjiang's transportation sector. The results indicate the following: (1) the total carbon emissions and per capita carbon emissions of Xinjiang's transportation sector both continued to rise rapidly during this period; their average annual growth rates were 10.8% and 9.1%, respectively; (2) the carbon emissions of the transportation sector come mainly from the consumption of diesel and gasoline, which accounted for an average of 36.2% and 2.6% of carbon emissions, respectively; in addition, the overall carbon emission intensity of the transportation sector showed an "S"-pattern trend within the study period; (3) population density plays a dominant role in increasing carbon dioxide emissions. Population is then followed by per capita GDP and, finally, energy intensity. Cargo turnover has a more significant potential impact on and role in emission reduction than do private vehicles. This is because road freight is the primary form of transportation used across Xinjiang, and this form of transportation has low energy efficiency. These findings have important implications for future efforts to reduce the growth of transportation-based carbon dioxide emissions in Xinjiang and for any effort to construct low-carbon and sustainable environments.

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Section: Introductionmentioning

confidence: 99%

Moving Low-Carbon Transportation in Xinjiang: Evidence from STIRPAT and Rigid Regression Models

Dong

Deng

et al. 2016

Sustainability

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“…Structural and behavioral validation tests were performed for model validation in the research, which compared the simulated values with the real values and examined the sensitivity of the SD model's behavior under the changed values of some major parameters. Xue Liu [28] proposed an SD approach to scenario analysis for energy consumption and CO2 emissions of urban passenger transport and built a Beijing urban passenger transport carbon model. The SD model simulated different policy scenarios under different conditions.…”

Section: Research On System Dynamics In Traffic Problems Such As Tranmentioning

confidence: 99%

“…SD models are found to be valid when they can be used with confidence [39,40]. For SD model validation, it is common to compare the simulation values and actual data [22][23][24][25][26][27][28][29][30][31][32][38][39][40][41]. Sun [41] chose two variables (public transport vehicles and public transport demand) to test the SD model of the public transport price strategy.…”

Section: Model Verificationmentioning

confidence: 99%

System Dynamics Analysis of the Relationship between Transit Metropolis Construction and Sustainable Development of Urban Transportation—Case Study of Nanchang City, China

et al. 2020

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In order to systematically analyze the benefits of transit metropolis construction, the system dynamics (SD) theory was used to construct the transit metropolis SD simulation model from the four subsystems of economy, society, environment, and transportation supply and demand. The validity of the SD model was verified by the social and economic data of Nanchang City and the operational data of the bus company, and the quantitative simulation analysis was carried out by taking the construction of the transit metropolis in Nanchang as an example. The simulation results show that, in 2020, the number of motor vehicles in Nanchang will reach 1.13 million and the urban population will reach 5.71 million. It is necessary to build a transit metropolis for the sustainable development of urban transportation. In order to complete the transit metropolis creation goal of 60% of the public transit mobility sharing rate, the proportion of public transport investment in the total transportation investment needs to be adjusted from 0.25 to 0.35. As a result, Nanchang City will improve after the peak traffic congestion in 2022, indicating that the construction of the transit metropolis will have a positive effect on Nanchang. By developing new energy vehicles and low-emission vehicles, vehicle emissions will drop from 0.05 tons/year to 0.04 tons/year, and overall nitrogen oxide emissions will fall by 70%, which is significant for urban environments. The research results provide theoretical support for the significance of transit metropolis construction, and promote the sustainable development of urban transportation.

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“…It was applied to four training programs in building vocational skills for marginalised youth in Uganda and to a strategic planning framework for land consolidation in China (Tukundane et al, 2015;Yan et al, 2015). SD, on other hand, is a mathematical modelling strategy used in the analysis of relationships among various factors, simulation of quantitative data, and acquisition of information on the system's behaviour (Liu et al, 2015). It has been used in evaluating alternatives towards green industrial transformation in a resource-based city and strategic capacity planning for the recycling industry (Kuai et al, 2015) and in analysing urban passenger transport energy consumption and CO2 emissions in Beijing (Liu et al, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Supply input-output economics in process prioritisation of interdependent manufacturing systems

Noel

Omega

Masbad

et al. 2017

IJMDM

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Currently, manufacturing firms strive not only to survive but also to remain competitive in their industries. To sustain competitive advantage, firms adopt various approaches to aid in their decision making processes. A crucial decision making area is on process prioritisation which attempts to identify areas that need to be given higher attention in terms of policy development, improvement efforts and resource allocation. Previous approaches addressing this concern have drawbacks which may lead to less reliable decisions. Thus, this study attempts to develop a methodological approach based on supply-driven input-output analysis that addresses process prioritisation in manufacturing process system level. The motivation behind the adoption of such approach lies in the strength of input-output analysis in understanding systems with interdependent components. This approach was applied to two manufacturing systems in different case firms. Results reported in this work include the identified key processes of both firms together with the process prioritisation ranking of other processes.

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

Cited by 119 publications

References 66 publications

Moving Low-Carbon Transportation in Xinjiang: Evidence from STIRPAT and Rigid Regression Models

Moving Low-Carbon Transportation in Xinjiang: Evidence from STIRPAT and Rigid Regression Models

System Dynamics Analysis of the Relationship between Transit Metropolis Construction and Sustainable Development of Urban Transportation—Case Study of Nanchang City, China

Supply input-output economics in process prioritisation of interdependent manufacturing systems

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