Multi-Variable Simulation Decomposition in Environmental Planning: An Application to Carbon Capture and Storage

Abstract

Environmental decision-making commonly involves multifaceted problems that demonstrate considerable uncertainty. Monte Carlo simulation approaches have been employed in a variety of environmental planning venues to address these uncertain aspects. Simulation-based outputs are frequently presented in the form of probability distributions. Recently an approach referred to as simulation decomposition (SD) has been introduced that extends the analysis of Monte Carlo results by enhancing the explanatory power of the cause-effect relationships between the multi-variable combinations of inputs and the simulated outputs. SD constructs sub-distributions of the simulation output by pre-classifying some of the uncertain input variables into states, clustering the various combinations of these different states into scenarios, and then collecting simulated outputs attributable to each multi-variable input scenario. Since the contribution of subdivided scenarios to the overall output is easily portrayed visually, SD can highlight and disclose previously unidentified connections between the multi-variable combinations of inputs on the outputs. An SD approach is generalizable to any Monte Carlo model with negligible additional computational overhead and, hence, can be readily used for environmental analyses that employ simulation models. This study illustrates the efficacy of SD in environmental analysis using a carbon capture and storage project from China.

Carbon footprint of an EUR-sized wooden and a plastic pallet

“…The method is based on decomposition and colour-coding distributions into groups of outcomes. The method helps to effectively represent the contribution of uncertain parameters on the results which were studied with the example of carbon capture and sequestration [13].…”

Monte Carlo Enhancement via Simulation Decomposition: A “Must-Have” Inclusion for Many Disciplines

“…Because the contribution of the state combinations to the overall output is easy to portray visually, SimDec can reveal previously unidentified connections between the multivariable combinations of inputs on the outputs. The decomposition has been shown to provide deeper insights into the uncertainty surrounding the problem, to assist decision making when actionable variables are chosen for decomposition, and to better understand the interplay of different sources of uncertainty on the distribution of outcomes (Kozlova et al 2016, Kozlova andYeomans 2019). The SimDec approach is completely generalizable to any Monte Carlo model with negligible additional computational overhead.…”

“…Although stacked histograms and bar charts have been widely used across all domains of academia and industry, the idea of decomposing Monte Carlo simulation results has remained largely overlooked. To the best of our knowledge, multivariable simulation decomposition has only been applied to a couple of investment cases (Kozlova et al 2016, Kozlova andYeomans 2019); single-factor decomposition has been used in memristor performance analysis (García-Redondo et al 2012) and has been alluded to sparingly in some very narrowly focused commercial software products.…”

A GIS-based Decision-Making Support System for Wind Power Plant Site Selection, Case Study for Saskatchewan

“…Renewable energy, especially wind power energy, plays a key role in reducing the GHG emissions. Wind energy is considered to be a clean, renewable energy due to its few emissions, both in construction and operation periods (Global Wind Energy Council, 2018;Kozlova and Yeomans, 2019;. Thus, it is necessary to develop wind energy to mitigate GHG emissions.…”