System Dynamics Model of Co2 Emissions From Urban Transportation in Chiang Mai City

Abstract

The transportation sector has significant economic, social, and environmental impacts on cities. It represents the key contributor to energy consumption and CO2 emissions which cause global warming and climate change. The energy consumption and CO2 emissions data at city level are important for policy makers to develop sustainable urban transportation. In Thailand, most earlier studies have emphasized at country level. In addition, urban transportation system is a complex system with multiple variables and feedback behaviors. Therefore, the objective of this study is to develop a model for estimate and analyze energy consumption and CO2 emissions from urban transportation systems using the system dynamics approach. To demonstrate the application of the model, Chiang Mai city has been taken as a practical case. The proposed model comprised of five subsystems including population, economy, transport supply, vehicle, and energy consumption and CO2 emissions. The validation of a proposed model revealed that this model was reliable to estimate CO2 emissions and analyze the reduction policy for the city level. The proposed model can be used as a key decision support system (DSS) for urban transportation planning for the sustainability of the city.

Evaluation of the Transport Environmental Effects of an Urban Road Network in a Medium-Sized City in a Developing Country

“…In addition, the TEESs can be applied to specify the likely causes of transportrelated environmental problems of such road segments. For instance, as shown in Figure 8e, segments 48, 49, 56, 62, 75, and 80 had very high (VL) scores, and segments 3,11,13,17,20,28,37,50,55,58,59,60,61,63,58, and 79 had high (H) scores for the pedestrian accident risk (PAR) criterion. These road segments can be considered as problematic locations.…”

“…The transport sector is the second largest producer of carbon dioxide (CO 2 ) in Thailand after the power generation sector. It contributes approximately 26% of energy-related CO 2 emissions [55]. In addition, most CO 2 emissions are generated by road transport (approximately 97% of total CO 2 emissions from the total transport sector) [56].…”

Exploring Sustainable Planning Strategies for Carbon Emission Reduction in Beijing’s Transportation Sector: A Multi-Scenario Carbon Peak Analysis Using the Extended STIRPAT Model

“…In terms of the prediction of transportation CO 2 emission trends, researchers have built prediction models based on the abovementioned influencing factors to predict transportation CO 2 emission trends under different scenarios and provide targeted strategies. Relevant model building methods include forecasting models based on environmental economics principles [30][31][32][33], grey prediction models based on grey system theory [34][35][36][37], the linear programming-based LEAP model [38][39][40][41][42][43], system dynamics models [44][45][46], combined applications of various machine learning models [47][48][49][50]. The objects of empirical research cover different scales such as countries, regions, provinces, and cities.…”

An Addition of Ethanol and Patchouli Oil on Pertamax: The Case Study of Their Effect on Exhaust Gas Emission for 4-stroke Engine

“…However, this case brought up the disadvantages for the environment. An uncontrolled vehicle number day by day will increase the emission of harmful gases and it causes global warming [2]. Hence, many people in every sector that consist of engineers or researchers have been focused on controlling the vehicles combustion due to their emission, including carbon monoxide (CO) and carbon dioxide (CO2) [3].…”