Systematic Sensitivity Analysis of the Full Economic Impacts of Sea Level Rise

Abstract

The potential impacts of sea level rise (SLR) due to climate change have been widely studied in the literature. However, the uncertainty and robustness of these estimates has seldom been explored. Here we assess the model input uncertainty regarding the wide effects of SLR on marine navigation from a global economic perspective. We systematically assess the robustness of computable general equilibrium (CGE) estimates to model's inputs uncertainty. Monte Carlo (MC) and Gaussian quadrature (GQ) methods are used for conducting a Systematic sensitivity analysis (SSA). This design allows to both explore the sensitivity of the CGE model and to compare the MC and GQ methods. Results show that, regardless whether triangular or piecewise linear Probability distributions are used, the welfare losses are higher in the MC SSA than in the original deterministic simulation. This indicates that the CGE economic literature has potentially underestimated the total economic effects of SLR, thus stressing the necessity of SSA when simulating the general equilibrium effects of SLR. The uncertainty decomposition shows that land losses have a smaller effect compared to capital and seaport productivity losses. Capital losses seem to affect the developed regions GDP more than the productivity losses do. Moreover, we show the uncertainty decomposition of the MC results and discuss the convergence of the MC results for a decomposed version of the CGE model. This paper aims to provide standardised guidelines for stochastic simulation in the context of CGE modelling that could be useful for researchers in similar settings.

Quasi-Monte Carlo application in CGE systematic sensitivity analysis

“…In the rMC method, the error is estimated from the generated data, whereas in the QG more global measures of error estimation are required such as the Chebyshev's inequality for the confidence bounds. The Chebyshev's inequality, will produce confidence bounds that are extremely conservative compared to the Central Limit Theorem which provides narrower confidence intervals if the available number of data points is sufficiently large[...] ' Chatzivasileiadis et al (2017); Villoria, Preckel et al (2017).…”

“…The difference between QG and rMC is in the way w n is defined. While in rMC all realizations have equal weight 1/N, in QG we choose the most appropriate points within the interval [a, b] and associated weights w n , such that, the crude moments of the approximating distribution equals the moments of the true distribution from zero to some specified order Chatzivasileiadis et al (2017). Thus the GQ method is able to economise the computational requirements of the SSA (i.e small number of simulations are required).…”

“…Moreover, the number of points required by the GQ is based on the shocks evaluated and the degree of the quadratures. For example in the case of Chatzivasileiadis et al (2017), 4096 simulations were required by the Liu quadrature based on the GEMPACK software. Thus, in that case, the advantage of GQ that economises the SSA is questioned.…”

“…Additional information about the differences between GQ and rMC can be found inChatzivasileiadis et al (2017) andVilloria, Preckel et al (2017).…”

Model-based planning for post-conflict reconstruction: The case of Yemen

“…For example, Systematic Sensitivity Analysis is used byWebster et al (2008) to address the uncertainty in projections of emissions and costs of atmospheric stabilization for five climate scenarios Phimister and Roberts (2017). investigate the implications of allowing uncertainty in exogenous shocks when modeling a new onshore wind sector in northeast Scotland Chatzivasileiadis et al (2019). conduct SSA to address model input uncertainty when analyzing the effects of sea-level rise on the global economy.3 Because some of the entries of the original SAM represent specific computational limitations for our CGE model, we perform some data adjustments and amendments.…”