生态系统服务
土地利用
环境科学
生命周期评估
环境资源管理
土壤质量
影响评估
生态系统
土地管理
土地利用、土地利用的变化和林业
土壤水分
生态学
生产(经济)
土壤科学
政治学
生物
宏观经济学
经济
公共行政
作者
Hsin‐Pei Chen,Mengshan Lee,Pei-Te Chiueh
标识
DOI:10.1016/j.scitotenv.2021.145018
摘要
Abstract Life cycle assessment (LCA) is a widely applied approach used to evaluate the environmental impacts of a product or service across its life cycle stages; however, the impacts of land use on ecosystem services are less addressed in most LCA studies. This study, therefore, aims to improve the LCA model by incorporating a new impact category of land use on ecosystem services at both midpoint and endpoint levels in the existing ReCiPe2016 impact assessment method. The impacts of land use in the LCA model included land occupation and land transformation. The soil quality-based indicator, soil organic carbon (SOC), was adopted to quantify the soil quality change in ecosystem services caused by land use. A site with contaminated soils was adopted to validate the proposed impact assessment approach and to compare the results of various remediation practices. Our results revealed that the characterization factors (CFs) varied with the type of land use intervention, with land occupation of settlements presenting the highest CFs and land occupation of forest presenting the most negative CFs and thus benefitting ecosystem services. These results were well reflected in the case study, while the type of land intervention was the key factor determining the impact level. The results suggested that long-term occupation, high contamination levels, and high material or energy use contributed to relatively higher impacts of land use on ecosystem services. The proposed approach enables the quantification of land use impacts on ecosystem services as expressed in monetary loss or benefit at the endpoint resource level. The impact assessment results indicated that the in situ bioremediation scenario contributed relatively higher impacts ($12,667 USD) than the excavation and thermal treatment scenario ($−37 USD). These monetary assessment results are informative and are expected to be used in the decision-making process towards achieving beneficial environmental outcomes.
科研通智能强力驱动
Strongly Powered by AbleSci AI