This study compiles and updates the findings of 16 comprehensive state climate action plans and extrapolates the results to the nation. The study then takes those results and using a widely accepted econometric model projects the national impact of these policies on employment, incomes, gross domestic product (GDP) and consumer energy prices. Finally, using the bottom-up data developed by the states and aggregated here, the study models the national impact of major features of the Kerry-Lieberman climate bill under consideration in Congress in 2010. These state action plans and supporting assessments were proposed by over 1,500 stakeholders and technical work group experts appointed by 16 governors and state legislatures to address climate, energy and economic needs through comprehensive, fact-based, consensus-driven, climate action planning processes conducted over the past five years with facilitative and technical assistance by the Center for Climate Strategies. Findings show potential national improvements from implementation of a top set of 23 major sector-based policies and measures drawn from state plans.

Evaluating the impact of learning from climate data, in addition to bridge data, on the performance of concrete deck condition rating prediction is critical for identifying the right data needed to enhance bridge maintenance decision making. Few studies have considered such an evaluation and utilized a small size of samples that prevent revealing the knowledge hidden within the big size of data. Although, such evaluation over big data seems quite necessary, class imbalance problem makes it challenging. To alleviate such a problem, five states, including Alabama, Iowa, New York, Pennsylvania, and South Carolina, were selected as the case study. Not only are the states located in three different climatically consistent regions defined by the National Ocean and Atmospheric Administration (NOAA), but also their concrete deck conditions ratings are somewhat balanced. To conduct the evaluation, this research developed the bridge data set pertaining to 56,288 bridges across the afore-mentioned states through employing the GIS technology. The bridge data set contains bridge data derived from National Bridge Inventory (NBI), and climate data derived from Parameter-elevation Relationships on Independent Slopes Model (PRISM) climate maps and NOAA. Then, two machine learning algorithms, including random forest and GBM, were trained - with and without climate …