| Title |
Comprehensive Review on Waste Generation Modeling |
| ID_Doc |
15634 |
| Authors |
Somplák, R; Smejkalová, V; Rosecky, M; Szásziová, L; Nevrly, V; Hrabec, D; Pavlas, M |
| Title |
Comprehensive Review on Waste Generation Modeling |
| Year |
2023 |
| Published |
Sustainability, 15, 4 |
| DOI |
10.3390/su15043278 |
| Abstract |
Strategic plans for waste management require information on the current and future waste generation as a primary data source. Over the years, various approaches and methods for waste generation modeling have been presented and applied. This review provides a summary of the tasks that require information on waste generation that are most frequently handled in waste management. It is hypothesized that there is not currently a modeling approach universally suitable for forecasting any fraction of waste. It is also hypothesized that most models do not allow for modeling different scenarios of future development. Almost 360 publications were examined in detail, and all of the tracked attributes are included in the supplementary. A general step-by-step guide to waste generation forecasting, comprising data preparation, pre-processing, processing, and post-processing, was proposed. The problems that occurred in the individual steps were specified, and the authors' recommendations for their solution were provided. A forecasting approach based on a short time series is presented, due to insufficient options of approaches for this problem. An approach is presented for creating projections of waste generation depending on the expected system changes. Researchers and stakeholders can use this document as a supporting material when deciding on a suitable approach to waste generation modeling or waste management plans. |
| Author Keywords |
waste generation modeling; waste production; waste prediction and forecasting; projection; short time series |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000942059100001 |
| WoS Category |
Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies |
| Research Area |
Science & Technology - Other Topics; Environmental Sciences & Ecology |
| PDF |
https://www.mdpi.com/2071-1050/15/4/3278/pdf?version=1676286727
|