Using system dynamics approach to build policy scenario for reducing CO2 emission resulted from tourism travel to Karimunjawa

Abstract

Purpose This study aims to create the causal relationship between transportation behavior to Karimunjawa, the number of tourists and the amount of CO2 produced; calculate the reduction of CO2 emissions from the transportation to Karimunjawa based on several proposed policy scenarios; and formulate the managerial implication and recommendation to support the implementation of several proposed policy scenarios. Design/methodology/approach This study develops a system dynamics‐based model by using three sub-systems, i.e. “the number of tourist sub-system,” “the switching behavior of tourist travel sub-system” and “the CO2 emission sub-system.” Findings The simulation results have shown that, under the current situation, tourist travel behavior should be changed to maximum condition to get the minimum CO2 emission. Improvement of the behavior of tourist in selecting the mode of transportation and the departure point of mini-tour bus and ferry are an effective way to reduce the CO2 emission. Research limitations/implications This study only considers limited variables as the driver of the level of change of the capacity of Karimunjawa and the road as well as the variables as the driver of tourism growth. This study only focuses on CO2 emission from the direct impacts of tourist travel; this study does not consider the indirect impact of tourism activity on CO2 emissions. International air travel is not included in the present study. Practical implications From a managerial perspective, this study demonstrates that change in the tourist travel behavior is generally not effective in triggering CO2 emission reduction, unless it is accompanied by the strict restriction policy related to the tourist route. Social implications This study has the potential to raise societal awareness that the causality of tourist growth and CO2 emissions should be seen as the impact of tourist travel behavior. In this case, to modify the travel behavior, tourist needs to change their mode of transportation to more sustainable transportation. Originality/value This paper intends to fill the literature gap of the effect of tourism growth from two perspectives, namely, tourist travel behavior and environmental. The modeling of tourist transport and CO2 emission will provide an overview of the selection of the problem-solving mode for tourist transport that can give a significant contribution to the greenhouse gas emissions reduction to the environmental.

Post-disaster tourism development and scenario planning for the quality of life of residents

“…Referring to the studies of Wang et al (2020) and Zhang and Cheng (2019a, 2019b), we used a behavior test to check the validity and reasonability of the SD in comparison with the actual and predictive data. Four variables, including the GDP, total population, tourist arrivals and tourism revenue (Susanty et al , 2021; Mai and Smith, 2018; Wang et al , 2020), were used to validate the model (Table 3). The absolute average error values of all the key variables in the three selected years were less than 10% (Barlas, 1989).…”

“…To describe the dynamic changes of the six key indicators in different policy scenarios, we considered nine indicators that influence tourism-related policies as representative indicators because they may be potentially important drivers contributing to the evolution of the SD model. Referring to the studies of Wang et al (2020), Susanty et al (2021) and Mai and Smith (2018), three scenarios are provided in Table 4 by adjusting their parameters. The current scenario predicts future trends of various variables based on a simulation of the current situation.…”

“…The SD approach developed by Forrester (1961) has been used to model the behavior of complex systems and forecast the trends of variables in a single system based on the causal effect among elements (Susanty et al , 2021). Compared with traditional techniques, the SD model can effectively address nonlinear, high-level, multifeedback and complex time-varying system problems.…”

“…The shortcoming of CLD is that it cannot reveal the quantitative relationship between system variables because it offers a qualitative description of the SD. A stock-and-flow model is a quantitative approach that can show a system’s quantitative relationship, feedback characteristics and control law and consists of stocks, flows (Mai and Smith, 2018), auxiliary variables (Susanty et al , 2021) and feedback loops.…”

“…They found that current tourism development was not sustainable because of water shortages, environmental pollution and tourist overcrowding. Susanty et al (2021) harnessed the SD approach to examine the causality between traffic behavior, tourist numbers and the level of carbon dioxide in Karimunjawa National Park, Indonesia. Wang et al (2020) established a dynamic tourist carrying capacity model using the SD approach to investigate how tourism investment, economic growth, environmental protection and infrastructure level affected tourism development.…”