RESEARCH ARTICLE Estimation and prediction of ecological footprint using tourism development indices top tourist destination countries Ahmad Roumiani 1 | Abdul Basir Arian 2 | Hamide Mahmoodi 1 | Hamid Shayan 1 1 Department of Geography, Faculty of Letters and Humanities, Ferdowsi University of Mashhad, Mashhad, Iran 2 Department of Geography, Ferdowsi University of Mashhad, Mashhad, Iran Correspondence Ahmad Roumiani, Department of Geography, Faculty of Letters and Humanities, Ferdowsi University of Mashhad, PO Box 91775-1163, Mashhad, Iran. Email: roumiani.ah@mail.um.ac.ir Abstract During the last two decades, the ecological footprint (EF) has had various fluctuations and has been associated with an upward trend, which can be a concern. This research aims to statistically examine tourism development indices and their effect on the EF during the last two decades in eight top tourism countries (France, United States, China, Italy, Turkey, Mexico, Thailand, and Germany). For this purpose, indices (extracted from the World Bank and Global Footprint Network databases) were used. Also, repeatability models were used to check the time and place and penalized regression models were used for the fit and accuracy of tourism development indices. The research findings showed that the amount of EF in the countries of China, France, the United States of America, Mexico and Thailand had an upward trend. The predictive accuracy of the penalized regression models of Ridge, LASSO and Elastic Net were reported as 0.910, 0.908, and 0.908, respectively. The difference is that the LASSO model acted more strictly and provided a more economical model by selecting the variable. We believe that a deeper statistical look can effectively apply an efficient strategy in better management of the EF challenge. KEYWORDS ecological footprint, top tourism attraction countries, tourism development, variable selection 1 | INTRODUCTION Today, the ecological footprint (EF) is a pervasive issue, and helping to reduce the EF is important as a strategic priority for world countries. In this case, there are many discussions about the consequence of tourism development variables on the EF in the countries of the world, and researchers, managers and knowledge-based companies do not sufficiently cover these indices. But due to the changes in envi- ronmental conditions, understanding the effects of tourism develop- ment indices on the EF is challenging. In this sense, the EF is a multidimensional and comprehensive concept that can be measured not only by using economic variables (such as income, investment, gross domestic product, import, and export) (Koçak et al., 2020; Nguyen et al., 2020; Wang et al., 2020) but also by using energy and energy consumption measures (oil and natural gas, coal) (Awodumi & Adewuyi, 2020; Bildirici & Bakirtas, 2014; Saboori & Sulaiman, 2013), infrastructure variables such as hotels, restaurants, road transporta- tion (Vatan & Bildin, 2020) and tourists' expectations and demand of tourists can be measured. Researchers have carried out some statistical methods to examine the EF (Ahmed & Wang, 2019). Still, researchers have no common idea about it because each model has its advantages and disadvan- tages. In addition, academic literature about information sources and analytical techniques is relatively sparse and scarce, and the results are not comparable. Spencer (2019) believes there is a need to com- bine quantitative and qualitative data with generating knowledge about analytical techniques to predict the effects of tourism develop- ment indices on the EF. Linear regression (or ordinary least squares [OLS]) is the simplest, oldest, and most common technique in the real world, which is calculated so that statisticians can perform the calculations by hand and estimate the variables by reducing the sum of squared errors (SSE). In a linear Received: 18 February 2022 Revised: 5 October 2022 Accepted: 17 October 2022 DOI: 10.1002/sd.2442 Sustainable Development. 2022;1–17. wileyonlinelibrary.com/journal/sd © 2022 ERP Environment and John Wiley & Sons Ltd. 1 10991719, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1002/sd.2442 by Test, Wiley Online Library on [03/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License