A new approach to predicting carbon emissions at tourist attractions is discussed in the International Journal of Environment and Pollution. The tool developed by Xiumei Feng of Northeast Petroleum University in Qinhuangdao, China, uses a fuzzy support vector machine (SVM) to peak emissions in a way that improves significantly on conventional methods. The work could offer more precise and reliable forecasts of carbon emissions and so allow stakeholders to improve on how they manage the environmental impact of tourism.
Tourism is a major industry and a major contributor to carbon emissions, not least because of the energy-intensive activities such as transportation, energy consumption, and the operation of infrastructure that are involved. As we attempt to address the problem of climate change, there is a pressing need to manage and mitigate against the so-called carbon footprint of tourism. Peak carbon emissions represent the highest levels of emissions from a given tourist site and are an important measure of the potential impact of a given tourist trap on the environment. Understanding these peaks might allow stakeholders to devise more effective strategies to shrink the carbon footprint.
The models commonly used to predict carbon emissions often struggle with issues such as low stability, poor sensitivity, and inaccurate predictions. This, of course, limits their capacity to support effective climate action. To address the shortcomings, Feng has turned to an advanced statistical technique known as fuzzy SVM. This method is an enhancement of the traditional support vector machine model, which is widely used in machine learning to classify and predict data. The “fuzzy” aspect refers to a system that accounts for uncertainty, allowing the model to handle ambiguous or incomplete data more effectively.
Feng has applied the new approach to data on carbon emissions, tourist numbers, meteorological data, and resource usage and demonstrated that the model can make more accurate predictions regarding peak emissions. Understanding peak emission times will allow for better planning in terms of energy use, transportation schedules, and waste management—factors that collectively contribute to emissions at tourist destinations.
Feng, X. (2024) ‘Peak carbon emission prediction of tourist attractions based on fuzzy support vector machine’, Int. J. Environment and Pollution, Vol. 74, Nos. 1/2/3/4, pp.66–78.