Decreasing environmental impacts of cropping systems using life cycle assessment (LCA) and multi-objective genetic algorithm
The environmental awareness of people has increased in recent decades, and the demand for environmentally friendly products has caused agro-scientists to give more attention to cleaner production. Life cycle assessment (LCA) has been identified as a suitable tool for assessing environmental impacts...
Saved in:
| Published in | Journal of cleaner production Vol. 86; pp. 67 - 77 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.01.2015
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0959-6526 1879-1786 |
| DOI | 10.1016/j.jclepro.2014.08.062 |
Cover
| Summary: | The environmental awareness of people has increased in recent decades, and the demand for environmentally friendly products has caused agro-scientists to give more attention to cleaner production. Life cycle assessment (LCA) has been identified as a suitable tool for assessing environmental impacts associated with a product over its life cycle. The implementation of LCA with other management tools can help LCA practitioners to evaluate agri-food systems from different viewpoints. In this study, LCA, multi-objective genetic algorithm (MOGA), and data envelopment analysis (DEA) were combined, and the pros and cons of their application were investigated. Three impact categories – global warming (GW), respiratory inorganics (RI) and non-renewable energy use (NRE) – were selected to be evaluated. The results revealed mean RI, GW and NRE in a case study of watermelon production of 10.3 kg PM2.5 eq ha−1, 9485.5 kg CO2 eq ha−1 and 186,432 MJ primary energy ha−1 respectively. The results of LCA + MOGA showed that a reduction of 27% in RI and 35% in GW and NRE can occur if an appropriate combination of resources is used in watermelon production. The use of LCA + DEA revealed that if all farmers operate on the efficient frontier (suggested values) impacts in all three categories can be reduced by 8%. |
|---|---|
| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 0959-6526 1879-1786 |
| DOI: | 10.1016/j.jclepro.2014.08.062 |