Aligning sustainability and regional earthquake hazard mitigation planning: integrating greenhouse gas emissions and vertical equity

Abstract

Concerns about the potential economic consequences of earthquakes have increased in recent years as scientifically based probabilities of future earthquakes in many large urban areas have risen. These hazards disproportionately impact low-income communities as wealth disparities limit their capacity to prepare and recover from potentially disastrous events. In addition to major economic losses, the activities related to building recovery result in significant greenhouse gas emissions contributing to climate change. This article develops a framework that quantifies the complex relationships between pre-earthquake retrofit activities and their economic, environmental and equity implications to promote informed decision-making, using the city of San Francisco, California as a case study. This research consists of two sections. In the first section, a bi-objective optimization model is proposed to identify optimal earthquake risk mitigation policies to minimize total earthquake-related economic and environmental costs, simultaneously. Decisions entail the seismic retrofit, combined seismic and energy retrofit or complete reconstruction of building-type groups. The benefits of increased energy efficiency of the upgraded buildings are incorporated to evaluate decisions from a holistic perspective. In the second section, the model is extended to address the issue of inequitable budget allocation from a public-sector perspective. Vertical equity considerations are incorporated as an optimization constraint to distribute available resources aiming to limit the discrepancy of expected losses as a fraction of income between households across income groups. The tradeoff between equity and economic efficiency is explored. Results show that life-cycle environmental impacts constitute an informative performance metric to regional risk mitigation decision-makers, in addition to the more customarily used monetary losses. Although construction costs primarily dictate optimal decisions from an economic perspective, energy considerations largely impact optimal decisions from an environmental perspective.

Assessing uncertainty in building material emissions using scenario-aware Monte Carlo simulation

“…It can even be extended to provide confidence intervals for the carbon storage potential of different sequestering materials such as concrete and biomass-based materials, especially given their large potential for mitigating the carbon impacts of the built environment [20,87]. The use of a quantitative uncertainty framework that integrates scenario analysis such as this becomes especially significant when comparing and choosing between alternatives, as this study reaffirms that deterministic analyses are insufficient for drawing robust conclusions [37,64,65,68,71,73,88].…”

Exploring Regional Fine Particulate Matter (PM2.5) Exposure Reduction Pathways Using an Optimal Power Flow Model: The Case of the Illinois Power Grid

“…Next steps should also incorporate equity considerations into the reduction strategies. Previous studies ,,, have used optimization techniques to assess and reduce the exposure disparity for disadvantaged groups, and the OPF framework can be augmented with such capabilities. Equity consideration should be incorporated into exposure mitigation plans of all infrastructure to ensure that reducing absolute exposure does not come at the expense of increasing the exposure disparity for disadvantaged groups. ,,,− …”

Exploring Regional Reduction Pathways for Human Exposure to Fine Particulate Matter (PM2.5) Using a Traffic Assignment Model

“…However, the UET and SOI objective functions can be combined to generate a set of trade-off optimal solutions known as Pareto-optimal solutions. This new formulation can be solved using a weighted-sum method, where both objective functions are merged into a single formulation by multiplying each by a weight, forming a convex combination of objectives. − Weights are parametrically varied to obtain a Pareto front. Since a large magnitude difference exists between the travel costs and exposure damages (travel costs are valued 10 times higher in this instance, as shown later), each objective is normalized by the interval of its variation over the Pareto-optimal set.…”