Abstract
Energy shortage and atmospheric pollution problems are getting more serious in China, and transportation is the main source of energy consumption, pollutants, and carbon emissions. This study combined the activity-based analysis method with emission models, and investigated the influence mechanism of people’s activity travel scheduling on transportation energy consumption and emissions on holidays. Based on the holiday travel behavior survey data, the multinomial logistic regression model was first applied to explore the decision mechanisms of individual travel-mode choices in holidays. Next, the emission model was integrated with an activity-based travel demand model to calculate and compare transportation energy consumption and emissions under different policy scenarios. The results showed that socio-demographic characteristics had significant effects on holiday activity–travel patterns, and combined mode chains had a larger number of activity points than single mode chains. With an increase in the trip time of cars, and decrease of travel distance and the number of activity points, transportation energy consumption and emissions could be reduced greatly with an adjustment of holiday activity–travel patterns. The reduced portion is mainly attracted by slow traffic and public transport. However, the effects of a single policy strategy are very limited, thus portfolio policies need to be considered by policy makers.
Highlights
In China, energy consumption and pollutant emissions have increased annually with rapid urbanization, which has had a great impact on the global ecological environment and people’s living conditions
This study focuses on transportation energy consumption and emissions, as transportation is the largest emitter of greenhouse gases (GHGs) and the main cause of air pollution in cities [14,15]
To investigate the internal influence mechanism of the travel-mode chain choices and explore effective ways to reduce energy consumption, pollutants and carbon emissions in a metropolis, a multinomial logistic regression model was built based on the activity analysis method
Summary
In China, energy consumption and pollutant emissions have increased annually with rapid urbanization, which has had a great impact on the global ecological environment and people’s living conditions. As the bad atmospheric environment sparked reflections for future development, the Chinese government and policy makers have paid more attention to energy conservation and emissions reduction. (compared to 2005), and non-fossil energy consumption should account for around 15% of the primary energy consumption [2]. Transportation, with its high-energy consumption and emissions intensity, has become the main source of air pollution in the metropolis [3]. Beijing had more than 20 million permanent residents with a total of 5.6 million vehicles in 2013. The energy consumption for transportation is million tons of standard coal per year, accounting for 27% of the total energy consumption, and 31.1%
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