Influencing Factors of Carbon Emission in China's Road Freight Transport

Wang, Tianyi; Li, Hongqi; Zhang, Jun; Lu, Yue

doi:10.1016/j.sbspro.2012.04.077

Cited by 35 publications

(18 citation statements)

References 6 publications

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“…This section built a forecasting model and conducted experimental analysis based on the amount of CO 2 emissions in Taiwan, and considered the factors that affect the amount of CO 2 emissions. Among the factors that affect the amount of CO 2 emissions mentioned in the literature review in Section 2 [1][2][3][4], the factors that had a higher correlation with the amount of emissions include population, GDP per capita, total energy consumption, number of registered motor vehicles, and foreign investment.…”

Section: Resultsmentioning

confidence: 99%

“…Wang et al [3] analyzed how the policies with different restrictions to the number of private vehicles influence the CO 2 emission amount in Beijing. In their study, first the impact of this policy on private vehicle application quantity was analyzed.…”

Section: Previous Studies On Factors That Affect Co 2 Emission Amountmentioning

confidence: 99%

“…Previous studies on the amount of CO 2 emissions include factors that affect the amount of emissions [1][2][3][4] and carbon emission amount forecasting [2,5]. Previous studies on factors that affect the amount of carbon emissions considered different factors.…”

mentioning

confidence: 99%

“…However, the urban population does not represent the impact of a nation's total population on the amount of CO 2 emissions. Wang et al [3] primarily explored the impact of China's road cargo transportation on carbon emissions and used the least squares regression model and multiple linear regression analysis to build a forecast model. However, their study did not first conduct a correlation analysis between carbon emission pollution from road transportation, and only considered transportation.…”

mentioning

confidence: 99%

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Considering Multiple Factors to Forecast CO2 Emissions: A Hybrid Multivariable Grey Forecasting and Genetic Programming Approach

Lin

Liu

2018

Energies

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Development of technology and economy is often accompanied by surging usage of fossil fuels. Global warming could speed up air pollution and cause floods and droughts, not only affecting the safety of human beings, but also causing drastic economic changes. Therefore, the trend of carbon dioxide emissions and the factors affecting growth of emissions have drawn a lot of attention in all countries in the world. Related studies have investigated many factors that affect carbon emissions such as fuel consumption, transport emissions, and national population. However, most of previous studies on forecasting carbon emissions hardly considered more than two factors. In addition, conventional statistical methods of forecasting carbon emissions usually require some assumptions and limitations such as normal distribution and large dataset. Consequently, this study proposes a two-stage forecasting approach consisting of multivariable grey forecasting model and genetic programming. The multivariable grey forecasting model at the first stage enjoys the advantage of introducing multiple factors into the forecasting model, and can accurately make prediction with only four or more samples. However, grey forecasting may perform worse when the data is nonlinear. To overcome this problem, the second stage is to adopt genetic programming to establish the error correction model to reduce the prediction error. To evaluating performance of the proposed approach, the carbon dioxide emissions in Taiwan from 2000 to 2015 are forecasted and analyzed. Experimental comparison on various combinations of multiple factors shows that the proposed forecasting approach has higher accuracy than previous approaches.

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Section: Resultsmentioning

confidence: 99%

Section: Previous Studies On Factors That Affect Co 2 Emission Amountmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Considering Multiple Factors to Forecast CO2 Emissions: A Hybrid Multivariable Grey Forecasting and Genetic Programming Approach

Lin

Liu

2018

Energies

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“…Production specialization and concentration and greater separation between the stages of value creation, as well as the increasing internationalization of companies' economic activities, have boosted transportation volumes globally and average transportation distances have increased. A country's economic growth and industrial structure are the most important drivers of national freight activity (Bennathan et al 1992;Wang 2004;Wang et al 2012;Li et al 2013;Tavasszy and De Jong 2014;Luo et al 2016).…”

mentioning

confidence: 99%

The Impacts of Industrialization on Freight Movement in China

Chen¹,

Bhatt²

2019

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China could reduce the volume of its freight transport by transforming its process of industrialization through coordinated urban planning, using new materials, developing its high-tech industries and expanding the share of the service sector in its economy. This paper finds that these measures could result in a reduction of 2.6 trillion tonne-kilometer of freight transport, 6% lower than the business-asusual model of urban and industrial development. Changes to the country's economic structure may also lead to structural changes in modes of transportation, such as an increased share for railroads, the growing use of automotive transportation, and the increased use of containers in China's integrated freight transport system. A change in the structure of the Chinese economy could trigger further investment in railway networks and improvements to internal waterways. This could lead to an increased share of rail in freight traffic. The increased requirement for doorto-door delivery may also increase the use of short-distance automotive transport. Improved connectivity between railways, waterways and highways will increase the use of container transportation for higher value-added industrial products and consumer goods.

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Low-Carbon Transportation for the Residential Sector in China

Wang

Zhang

2020

Low-Carbon Consumption in China: Residential Behavior, Corporate Practices and Policy Implication

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Influencing Factors of Carbon Emission in China's Road Freight Transport

Cited by 35 publications

References 6 publications

Considering Multiple Factors to Forecast CO2 Emissions: A Hybrid Multivariable Grey Forecasting and Genetic Programming Approach

Considering Multiple Factors to Forecast CO2 Emissions: A Hybrid Multivariable Grey Forecasting and Genetic Programming Approach

The Impacts of Industrialization on Freight Movement in China

Low-Carbon Transportation for the Residential Sector in China

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