While capital, labor, and material shares are of similar magnitude in all three subsets, the high energy-use subset exhibits a mean energy cost share of 4.0%, more than six times higher than the mean energy cost share in the low energy-use subset (0.6%).
A minimum energy efficient subset of network resources can be calculated through a resource optimization technique and implemented through SDN as demonstrated in [57].
They indicate that the share of capital increases from 21.6 to 24.2% when we exclude the less energy-intensive firms from the high energy-use subset.
Excluding the least energy-intensive firms from the high energy-use subset tends to strengthen the complementarity result in both modeling specifications: The magnitude of the elasticities increases as well as the t-values.
At the significance level of 10%, for example, 83% of all CPEs are significant both in the full sample (TL 75%) and in the low energy-use subset (TL 50%), while 50% in the medium energy-use subset (TL 50% and 1000% in the high energy-use subset (TL 50%).
Most CPEs estimated from the high energy-use subset are similar to those shown for firms with low and medium energy use.
High energy-use firm sample Tables 9 and 10 display the OPEs and the CPEs for the high energy-use subset.
For the high energy-use subset, 90% of all concavity restrictions are met.
We subject this result to various sensitivity tests and find that it holds under different modeling strategies as well as different definitions of the high energy-use subset.
The specification we will use in the empirical analysis for each modeling approach and each of the four samples is shaded in gray: Using the LL model, 100% of the concavity restrictions are met for the full sample as well as the low and medium energy-use subsets.
A matched termination, absorbing all impinging energy, is a subset of the optimal, non-causal solution.
