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Eco-efficiency is of great significance in facilitating a region’s sustainable development. However, there are some inconsistencies among the concepts and also relationships of eco-efficiency and its relevant efficiencies in recent studies. This paper integrates eco-efficiency and three related-efficiencies (economic efficiency, energy efficiency, and environmental efficiencies) into a total-factor analysis framework, regarding them as composite eco-efficiency indicators. Employing a new data envelopment analysis approach, named Meta-US-SBM, which considers meta-frontier, undesirable outputs, super efficiency, and slacks simultaneously, the measure system of these four efficiencies is established. An empirical study is conducted using Chinese provincial data from 2001 to 2014. Results indicate that, overall, the regional economic efficiency is better than other related efficiency indicators. Furthermore, different provinces are adopting different development modes, as denoted by significant heterogeneity among eco-efficiency indicators. [ABSTRACT FROM AUTHOR]

This paper adopts data envelopment analysis method to calculate the regional energy efficiency from the perspective of total-factor energy efficiency. The Markov chain and spatial Markov chain are the common methods to test the club convergence of regional energy efficiency in China. Results indicate that the regional energy efficiency in China has been globally characterized by “club convergence” since 1999, and the energy efficiency transitions in China are closely connected with regional characteristics. A high level of energy efficiency has a positive influence on a region, whereas a low level of energy efficiency has a negative influence. This empirical analysis provides a spatial explanation to the “convergence clubs” of regional energy efficiency in China. [ABSTRACT FROM AUTHOR]

• We measure the green growth degree of China's industrial sectors by considering the constraint of total energy consumption. • We proposed a new common equilibrium efficient frontier, which considers each DMU's own adjustment strategy on fixed-sum energy input. • The proposed approaches are applied to analyze the green growth and energy and environmental efficiency of 36 of China's industrial sectors. Tight energy constraints and severe environmental pollution are two main problems affecting economic growth and sustainable development. As an important economic growth mode, green growth seeks to achieve both economic growth and environmental sustainability. Previous studies on the efficiency evaluation of green growth ignored fixed-sum energy consumption; this is a significant omission because this constraint is general in China: the government has set targets to reduce energy consumption by 20% and 16% per unit of GDP during its the eleventh (2006-2010) and twelfth (2011-2015) five-year plans. To fill this gap, we propose a new data envelopment analysis (DEA) model to analyze the green growth degree of China's industrial sectors. The proposed DEA model considers the constraint of total energy consumption in order to conform to the policy of energy and emission reduction. Previous theoretical studies on fixed-sum output construct the common equilibrium efficient frontier (CEEF) only by considering the minimal reduction strategy for the fixed-sum output of all decision making units (DMUs), while ignoring each DMU's adjustment strategy for fixed-sum input/output. In this paper, we construct a new CEEF by considering each industrial sector's adjustment of energy consumption. The new CEEF not only guides the industrial sectors to alter their energy consumption structure to achieve green growth but also allows a more accurate evaluation of the energy and environmental efficiency (EEE) of industrial sectors. Last, our proposed model is applied to analyze the green growth and EEE of 36 of China's industrial sectors. [ABSTRACT FROM AUTHOR]