(2013) Development of Greenhouse Gas Estimation Method for a Local Government Level Using Traffic Demand Model

The industrial revolution has brought rapid increase in fossil energy consumption. The transportation sector represents the highest growth in emissions in all sectors which accounts for all significant environmental impacts like climate change (IEA,2012). The United Nations Framework Convention on Climate Change (UNFCCC) in cooperation with different countries has put best efforts to support all necessary undertakings to prevent global warming by reduction of GHGs. Estimation of road traffic emissions and fuel consumption is becoming necessary for the evaluation of environmental policies. Korea’s rapid economic growth for the past decades have ensued growing pressure on the environment due to increased energy consumption. The country’s living standards and extremely fast expansion have made it one of the most rigorous economies in the OECD area. Environmental impacts have been one of the negative attributes of urbanization. According to the report from the Korea Energy Economics Institute, the total net greenhouse gas emissions in Korea is 182.1 Mt in 2009, this denotes 142.4% increase compared to 1990. CO2 was responsible for the highest proportion of green house gas emitted in 2009 which is about 89% of the total emissions which was the result of the country’s heavy dependence on oil and gas followed by CH4 (4.6%), SF6 (3.1%), N2O (2.1%), HFCs (1.0%) and PFCs (0.4%). The transportation sector accounted the largest increase in energy consumption, representing a ten-fold escalation from 1980-2009. The road transportation comprised 79.1% of transport energy consumption in 2007. Road transport is the highest contributor to GHG emissions since it is the most convenient mode of transportation. (Singh,2008) Although fuel specifications and end-of-pipe technologies have decreased the emissions per kilometer of some pollutants, increased travel demand and congestion have sternly offset the beneficial effect on air quality. In the past years, different real-time traffic policy measures have been developed to manage travel demand and influence driver behavior. According to (Chiquetto, 1997) although traffic demands were primarily designed for network efficiency improvement and to reduce congestion, they also have significant effects on urban traffic pollution. Different types of traffic data were essential in the emission modeling process like vehicle volume, fleet composition, average speed and infrastructure characteristics (road type, length, speed limit, number of lanes). Traffic models were frequently used to generate the required traffic data to input to emission models. The process to generate traffic data however increases with the road network size (Smit et al., 2008). Traffic models usually generate macroscopic traffic data for each road link in a large urban network. Macroscopic simulation models based on average speed have been the most common methodology for estimating road emissions for the past years. In Europe, most inventories for exhaust emissions for a city are still calculated using average speed model like Copert IV (Panis et al, 2006). According to (Andre and Hammarstrom, 2000) average speed is also an important variable in emission modeling because traffic emissions were clearly dependent on speed in a non-linear manner. Inaccurate speed predictions may strongly affect emission estimation. Given that emission modeling is sensitive to speed, improvement on estimates of mean link speed from static macroscopic traffic models using traffic data that is relatively easy to obtain by considering Emission Specific Characteristics (ESC). (Nesamani et al., 2006) This study proposed a traffic demand-based model in calculating the greenhouse gas emission for the local government in Korea. The results of traffic assignment (average speed and mileage per facility type) from the travel demand forecast serve as input to the emission model that calculates the total emissions for the transportation network. The required traffic data for the emission models was generated using Visum Public Transit software. Visum was also equipped with environmental impact calculation utilizing the Pollution-emission and the HBEFA. The next section of this paper presents the proposed methodology to estimate emission, discussion of the results based on local government traffic demand and technique validation for GHG emission estimation. The last section concludes the paper.

 

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