Roy Sarkis | Searcharxiv

arXiv2026

Climate Policy and The Energy Transition

This paper studies the macroeconomic dynamics of climate policy in a multi-sector dynamic general equilibrium model with renewable and non-renewable energy, sector-specific capital adjustment frictions, household energy demand, and endogenous fossil resource dynamics. The central mechanism is that decarbonization requires reallocating energy use and installed capital: fossil energy demand can contract immediately, while renewable capacity and abatement adjust only gradually. The analysis delivers four results. First, gradual policy implementation sharply reduces transition costs: relative to immediate implementation, gradual emissions caps improve welfare by 2.26 percentage points under comprehensive regulation and by 5.06 percentage points under firm-only regulation. Second, renewable energy subsidies and non-renewable energy taxes support renewable capital accumulation and reduce, but do not eliminate, the welfare cost of front-loaded tightening. Third, sectoral coverage changes the welfare ranking across implementation speeds. Firm-only regulation performs better under gradual implementation because it shields utility-relevant household energy services, but becomes nearly as costly as the carbon-price-only transition under immediate implementation. Fourth, endogenous fossil exploration and stock-dependent extraction costs transmit climate policy into lower extraction, fewer discoveries, and a declining shadow value of reserves, providing a structural mechanism for stranded fossil assets. The results show that deep decarbonization can be achieved at substantially lower macroeconomic cost when policy manages the speed and incidence of energy-capital reallocation.

econ.GN↗

arXiv2025

Solar Multimodal Transformer: Intraday Solar Irradiance Predictor using Public Cameras and Time Series

Accurate intraday solar irradiance forecasting is crucial for optimizing dispatch planning and electricity trading. For this purpose, we introduce a novel and effective approach that includes three distinguishing components from the literature: 1) the uncommon use of single-frame public camera imagery; 2) solar irradiance time series scaled with a proposed normalization step, which boosts performance; and 3) a lightweight multimodal model, called Solar Multimodal Transformer (SMT), that delivers accurate short-term solar irradiance forecasting by combining images and scaled time series. Benchmarking against Solcast, a leading solar forecasting service provider, our model improved prediction accuracy by 25.95%. Our approach allows for easy adaptation to various camera specifications, offering broad applicability for real-world solar forecasting challenges.

cs.CV↗