Modern environmental science relies heavily on the accurate computational modeling of complex molecular systems, as it is necessary to solve such pressing problems as climate change and sustainable energy. Nevertheless, these systems have an inherent quantum-mechanical nature which is a fundamental barrier to scalability of classical computers. In this chapter, the research will discuss how quantum computing has the potential to transform to eliminate this limitation. It gives an in-depth overview of quantum algorithms such as the Variational Quantum Eigensolver (VQE) to finding the electronic structure of quantum systems on noisy intermediate-scale quantum (NISQ) machines. A new, more efficient hybrid quantum-classical scheme, Quantum-Classical Density Functional Embedding (QC-DFE) scheme is presented and described. The strategy divides a large system in a quantum treated active system and a classically treated environment, which is a way of reducing the amount of quantum resources required by a large system. The quality of QC-DFE is evidenced by a simulated experiment on the OH + CH4 to H2O + CH3 troposphere reaction, in which the quantum simulation with QC-DFE is chemically accurate (error < 1 kcal/mol) with a significantly smaller quantum footprint than an all-system quantum simulation. The chapter also broadens the horizon by talking about wider applications in photocatalytic CO2 reduction, battery material design and making of atmospheric aerosols and also talks of the economic and environmental consequences. This work provides a specific process by which quantum computing can find its way into the arsenal of the environmental scientist, by reconciling the potential of quantum computing with its actual implementation.