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Global climate change has caused governments worldwide to take actions to improve their energy efficiency. This paper investigates how China's Top-1000 program, a command-and-control type of energy-saving mandate, has affected the operational choices of firms, and in turn, their profitability. We apply the propensity score matching method to find "identical twins" for the participants in the Top- 1000 program, then conduct a difference-in-differences analysis on the matched sample. Our findings suggest that the profitability of the enterprises targeted for energy savings decreased by one-third, mainly due to increased production costs. The targeted enterprises tended to increase their fixed assets per capita, which was associated with improvements in energy efficiency. Furthermore, compared to similar untargeted enterprises, there was a significant slowdown in the production growths of the targeted enterprises, raising concerns about carbon leakage due to increased production by less efficient producers. [ABSTRACT FROM AUTHOR]

The assessment of industrial-level energy efficiency's (EE) development is a critical research topic that has entrenched in the global battle against climate change. Under the Energy Efficiency Directives 2012/27/EU and 2018/2002/EU, European Commission sets specific industrial energy efficiency targets, rules and obligations for the 2020-2030 period aiming, among others, at specific energy intensity reduction and energy efficiency improvements. In this paper we use a balanced panel of fourteen European industries from twenty-seven countries for the period 1995-2011 under a metatechnology framework. The aim of this study is to evaluate, at a first stage, the industrial total factor energy efficiency (TFEE) at a national and European level by incorporating technological heterogeneity through a nonparametric approach. Reflecting the divergent views on the importance of desirable and undesirable outcomes in the pursuit of TFEE, we additionally estimate industrial performance by prioritizing either economic or environmental aspects. At the second stage of our analysis, econometric models are applied to investigate the main factors of industrial TFEE using sector specific and country characteristics while we further proceed with a ß and s-convergence analysis for our TFEE measures. The results of this study reveal that small-scale economies exhibit persistent high TFEE scores. At the same time, TFEE determinants suggest that path dependence phenomena have a strong presence, climatic characteristics occur while energy mix displays both linear and non-linear relationship. Either considering one desirable output or consolidating the undesirable output in the production function our results indicate a strong evidence of conditional and unconditional convergence in TFEE scores. [ABSTRACT FROM AUTHOR]

Since the 1990s, electricity distribution networks in many countries have been subject to incentive regulation. The sector regulators aim to identify the best performing utilities as frontier firms to determine the relative efficiency of firms. This paper develops a nested latent class (NLC) model approach where unobserved differences in firm performance are modelled using two 'zero inefficiency stochastic frontier' (ZISF) models nested in a 'latent class stochastic frontier' (LCSF) model. This captures the unobserved differences due to technology or environmental conditions. A Monte Carlo simulation suggests that the proposed model does not suffer from identification problems. We illustrate the proposed model with an application to Norwegian distribution network utilities for the period 2004-2011. We find that the efficiency scores in both LCSF and ZISF models are biased, and some firms in the ZISF model are wrongly labelled as inefficient. Conversely, inefficient firms may be wrongly labelled as being fully efficient by the ZISF model. [ABSTRACT FROM AUTHOR]