Abstract
This study was undertaken to forecast the waste generation rates of the accommodation sector in North Cyprus. Three predictor models, multiple linear regression (MLR), artificial neural networks (ANNs) and central composite design (CCD), were applied to predict the waste generation rate during the lean and peak seasons. ANN showed highest prediction performance, specifically, lowest values of the standard error of prediction (SEP = 2.153), mean absolute error (MAE = 1.378) and highest R2 value (0.998) confirmed the accuracy of the model. The analysed waste was categorised into recyclable, general waste and food residue. The authors estimated the total waste generated during the lean season at 2010.5 kg/day, in which large hotels accounted for the largest fraction (66.7%), followed by medium-sized hotels (19.4%) and guesthouses (2.6%). During the peak season, about 49.6% increases in waste generation rates were obtained. Interestingly, 45% of the waste was generated by British tourists, while the least waste was generated by African tourists (7.5%). The ANN predicted that small and large hotels would produce 5.45 and 22.24 tons of waste by the year 2020, respectively. The findings herein are promising and useful in establishing a sustainable waste management system.
Highlights
In recent decades, tourism has become one of the most important and significant sectors in the economies of many countries
Even though tourism can sustain high levels of employment, the sector is a source of environmental impacts with consequent public health concerns [2]
One of the most significant impacts of tourism is the generation of municipal waste, which increases as the seasonal population of tourists rises [3,4]
Summary
Tourism has become one of the most important and significant sectors in the economies of many countries. Even though tourism can sustain high levels of employment, the sector is a source of environmental impacts with consequent public health concerns [2]. One approach to the reduction of the threatening environmental and health impacts from hospitality generated wastes is the conversion to useful value-added or alternative products. In this regard, several governments have launched policies to promote the conversion of municipal waste to green precursors or products, many with a specific focus on green fertilizers, bioelectricity, biofuels or bioadsorbents [5,6,7,8,9]
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