The phenomena of “large energy consumption, high carbon emission, and serious environmental pollution” are against the goals of “low energy consumption, low emissions” in China’s industrial sector. The key to solving the problem lies in improving total factor energy efficiency (TFEE) and carbon emission efficiency (TFCE). Considering the heterogeneity of different sub-industries, this paper proposes a three-stage global meta-frontier slacks-based measure (GMSBM) method for measuring TFEE and TFCE, as well as the technology gap by combining meta-frontier technology with slacks-based measure (SBM) using data envelopment analysis (DEA). DEA can effectively avoid the situation where the technology gap ratio (TGR) is larger than unity. This paper uses the three-stage method to empirically analyze TFEE and TFCE of Anhui’s 38 industrial sub-industries in China from 2012 to 2016. The main findings are as follows: (1) Anhui’s industrial sector has low TFEE and TFCE, which has great potential for improvement. (2) TFEE and TFCE of light industry are lower than those of heavy industry under group-frontier, while they are higher than those of heavy industry under meta-frontier. There is a big gap in TFEE and TFCE among sub-industries of light industry. Narrowing the gap among different sub-industries of light industry is conducive to the overall improvement in TFEE and TFCE. (3) The TGR of light industry is significantly higher than that of heavy industry, indicating that there are sub-industries with the most advanced energy use and carbon emission technologies in light industry. And there is a bigger carbon-emitting technology gap in heavy industry, so it needs to encourage technology spillover from light industry to heavy industry. (4) The total performance loss of industrial sub-industries in Anhui mainly comes from management inefficiency, so it is necessary to improve management and operational ability. Based on the findings, some policy implications are proposed.

Performance or efficiency evaluation is of great importance for effective supply chain management, especially in the context of sustainable development and platform economy. In existing literature, two-stage network data envelopment analysis (DEA) has been widely used for supply chain efficiency evaluation. Although existing supply chain DEA usually assumes two-stage systems resolve the inherent conflicts between two stages, e.g. the supplier and manufacturer in a two-echelon supply chain, the relation of supply chain DEA to traditional ‘black box' DEA remains unclear. Moreover, the coordination effect between the two stages is ignored. In this paper, we define the coordination efficiency and propose a DEA-based approach for measuring the coordination effect of supply chain systems. We prove that the proposed multiplier DEA approach is equivalent to the envelopment DEA one. The proposed approach in this paper is verified via a numerical example from a supplier–manufacturer sustainable supply chain on resin companies finally.

Based on a panel data of China’s iron and steel (IS) industry from 2000 to 2014, this paper explores the impact of environmental regulations on CO₂ emissions in the industry. The results show that there is a clear inverted-U relationship between environmental regulations and CO₂ emissions in the IS industry. Additionally, there are regional heterogeneity and regulatory intensity on the impact of environmental regulations on CO₂ emissions. The results in the eastern region are consistent with the whole sample results, while the upward trend in the central region and the downward trend in the western region together lay the basis for the inverted-U shape of the whole sample. High environmental regulations affect CO₂ emissions in an inverted-U shape, while low environmental regulations present a U shape. The mechanisms of environmental regulations affecting CO₂ emissions are synergy effect and technological innovation effect. Finally, this paper proposes some policy recommendations according to the above findings.