This dissertation consists of three chapters studying the effects of climate change, climate change adaptation, and impact of renewable energy policies on the economy. The first chapter explores the consequence of choosing an arbitrary temporal window to aggregate weather conditions. Timing of weather inputs is critical for agricultural production. For instance, hot conditions can be beneficial or detrimental to yields depending on the crop stage. However, the empirical literature studying weather effects on agricultural outcomes has mostly struggled to capture these fine-scale temporal nuances. Researchers tend to arbitrarily choose a “season”, or simply employ the whole year to aggregate the weather conditions from high to low frequency. We show in simulations that imposing imprecise seasons introduces nonclassical measurement error in weather regressors, causing biases in unknown directions. To address this concern, we propose ana approach to recover the “true” underlying season and apply it to a US state-level panel of agricultural Total Factor Productivity. We find that accounting for seasonality can lead to substantially different estimates. The second chapter examines the potential for adaptation to climate risks in the corporate sector and firms’ private adaptive responses using the evidence from wildfires in the US. I link the high-resolution large wildfire data and establishment-level data of all publicly and privately held manufacturing firms in the US from 1997 to 2017 to examine the causal impact of wildfire exposure on financial performance. The wildfire data includes annual burn scar grids at the 1km-by-1km resolution (nearly 8 million grids over the contiguous US). Exploiting quasi-random variation in burned area within a given municipality over two decades, I find that wildfire exposure reduces financial performance. Then I explore the scope for adaptation to future wildfire events by comparing the differences in short-run marginal impacts between areas that are prone or less prone to wildfires. I find that wildfires are substantially less damaging to sales of establishments in wildfire-prone areas, suggesting the role of adaptation in mitigation. The third chapter quantifies the impact of shared renewable policy on farmland values by using the parcel-level farmland transaction data from 2007 to 2022 and the uniform timing of the Shared Renewables Program (SRP) launched by New York state in 2015. Our main empirical framework leverages the fact that developers seek farmland within proximity to electrical infrastructure for solar development. We examine how the SRP affects farmland prices across locations that are close or far away from transmission lines or substations. We observe a 15% higher price increase for farmlands within a 2-mile radius of electric grids compared to those situated farther away after 2019 when there was a substantial upsurge in community solar projects. Moreover, we show that this effect is concentrated in regions with higher electric rates, making them more lucrative for solar development. Our results suggest that solar development potential might emerge as a novel driver for rising farmland prices during the state's transition to renewable energy.