期刊
SOCIO-ECONOMIC PLANNING SCIENCES
卷 75, 期 -, 页码 -出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.seps.2020.100837
关键词
Simulation decomposition; Monte carlo simulation; Carbon footprint; Lifecycle analysis; Environmental decision making under uncertainty
资金
- Natural Sciences and Engineering Research Council [OGP0155871]
- Finnish Strategic Research Council [313396/MFG40]
- Foundation for Economic Education, Finland [16-8940]
- Life IP on waste - Towards a circular economy in Finland (LIFE-IP CIRCWASTE-FINLAND) project [LIFE 15 IPE FI 004]
The translated content introduces the application of simulation decomposition (SD) in environmental sustainability decision-making, which enhances the visualization of the cause-effect relationships of multi-variable inputs and improves the understanding of the impacts of multi-variable inputs on outputs.
Environmental sustainability problems frequently require the need for decision-making in situations containing considerable uncertainty. Monte Carlo simulation methods have been used in a wide array of environmental planning settings to incorporate these uncertain features. Simulation-generated outputs are commonly displayed as probability distributions. Recently simulation decomposition (SD) has enhanced the visualization of the causeeffect relationships of multi-variable combinations of inputs on the corresponding simulated outputs. SD partitions sub-distributions of the Monte Carlo outputs by pre-classifying selected input variables into states, grouping combinations of these states into scenarios, and then collecting simulated outputs attributable to each multivariable input scenario. Since it is a straightforward task to visually project the contribution of the subdivided scenarios onto the overall output, SD can illuminate previously unidentified connections between the multivariable combinations of inputs on the outputs. SD is generalizable to any Monte Carlo method with negligible additional computational overhead and, therefore, can be readily extended into most environmental analyses that use simulation models. This study demonstrates the efficacy of SD for environmental sustainability decision-making on a carbon footprint analysis case for wooden pallets.
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