For decades, academic scholars and policy makers have commonly applied a simple average measure, energy intensity, for studying energy efficiency. In contrast, we introduce a distinctive marginal measure called energy shadow value (SV) for modeling energy efficiency drawn on economic theory. This thesis demonstrates energy SV advantages, conceptually and empirically, over the average measure recognizing marginal technical energy efficiency and unveiling allocative energy efficiency (energy SV to energy price). Using a dual profit function, the study illustrates how treating energy as quasi-fixed factor called quasi-fixed approach offers modeling advantages and is appropriate in developing an explicit model for energy efficiency. We address fallacies and misleading results using average measure and demonstrate energy SV advantage in inter- and intra-country energy efficiency comparison. Energy efficiency dynamics and determination of efficient allocation of energy use are shown through factors impacting energy SV: capital, technology, and environmental obligations.Applying the marginal measure, we also contributed to energy efficiency convergence analysis employing the delta-convergence and unconditional & conditional beta-convergence concepts, investigating economic energy efficiency differences across the four US sectors using panel data models. The results show that, in terms of technical and allocative energy efficiency, the energy-intensive sectors, SCG and textile mill products, tend to catch the energy extensive sectors, the Com and furniture & fixtures, being conditional on sector-specific characteristics. Conditional convergence results indicate that technology, capital and energy are crucial factors in determining energy efficiency differences across the US sectors, implying that environmental or energy policies, and technological changes should be industry specific across the US sectors.The main finding is that the marginal value measure conveys information on both technical and allocative energy efficiency and accounts for all costs and benefits of energy consumption including environmental and externality costs.To validate the energy SV, we applied a dual restricted cost model using KLEM dataset for the 35 US sectors stretching from 1958 to 2000 and selected a sample of the four sectors. Following the empirical results, predicted wedges between energy price and the SV growth indicate a misallocation of energy use in stone, clay and glass (SCG) and communications (Com) sectors with more evidence in the SCG compared to the Com sector, showing overshoot in energy use relative to optimal paths and cost increases from sub-optimal energy use. The results show that energy productivity is a measure of technical efficiency and is void of information on the economic efficiency of energy use. Decomposing energy SV reveals that energy, capital and technology played key roles in energy SV increases helping to consider and analyze policy implications of energy efficiency improvement.