• Energy Research
• 11. Sustainability close
• 9. Industry and infrastructure close

Abstract Based on the manufacturing processes panel data for 42 iron and steel enterprises (ISEs) in China from 2005 to 2014, a data envelopment analysis (DEA) model with undesirable output and a Malmquist-Luenberger (ML) index model are used to calculate the pollution control efficiency (PCE) and its variation trend of China's 42 ISEs. The results show that the comprehensive PCE of the 42 ISEs is relatively low, and the PCE of short-process enterprises is better than that of long-process enterprises. The total factor pollution control efficiency (TFPCE) of China's ISEs with a short process presents an increasing trend, which is mainly attributed to the increase of the technical change, while the efficiency change has a slight negative effect. In contrast, the TFPCE of ISEs with a long process is in the trend of decreasing, which is mainly affected by the decline of the technical change, while the efficiency change has a slight positive effect. In addition, the PCE varies significantly across regions and ISEs. Among 42 ISEs, the TFPCE of 18 enterprises shows an increasing trend, while that of 24 enterprises presents a decreasing trend.

Abstract China's mining and quarrying industry is characterized by “high pollution, high energy consumption, and high emissions.” Improving this sector's green total factor productivity (TFP) is of great importance for furthering the sustainable development of China's economy. Using a global data envelopment analysis (DEA), this paper analyzes the green TFP of China's mining and quarrying industry for the period of 1991–2014 with regard to technology, scale, and management. The following results are found. First, during the sample period, the green TFP of China's mining and quarrying industry increased by 71.7%. Technological progress was the most important contributor, and the decline in scale efficiency and management efficiency were two inhibitors. Fortunately, in recent years, management efficiency has gradually improved and become a new impetus for green TFP growth. Second, the characteristics of the green TFPs in the sub-industries vary considerably. During the sample period, the green TFPs of the mining and processing of ferrous metal ores (MPFMO), the mining and processing of non-ferrous metal ores (MPNFMO), and the mining and processing of nonmetal ores (MPNO) grew rapidly and became the benchmarks, whereas those of the mining and washing of coal (MWC) and the extraction of petroleum and natural gas (EPNG) remained very low. Third, the returns to scale of the sub-industries also varied. EPNG, MPNFMO, MPNO were in the stage of increasing returns to scale or constant returns to scale during the entire period, whereas MWC and MPFMO have recently entered the stage of decreasing returns to scale.

Abstract Cities are the main carbon dioxide (CO 2 ) emitters in China, and it is important to explore both the characteristics and reduction potential of their CO 2 emissions. This paper calculates the industrial energy-related CO 2 emissions (IECEs) of 17 cities in China's Yangtze River Delta (YRD) region from 2005 to 2014 and analyses the driving factors of CO 2 emissions using the Logarithmic Mean Divisia Index (LMDI). In addition, this paper predicts the CO 2 reduction potential of this group of cities for the period from 2015 to 2020. The results show that (1) during the sample period, industrial CO 2 emissions in the studied cities increased by a factor of 1.61. Economic output is the greatest contributor, followed by population size. Energy intensity and energy structure are the two main emission mitigation factors; (2) the driving factors of CO 2 emissions in the group of cities exhibit distinct spatial characteristics, indicating that future analyses of cities in this area should have distinctly different foci; and (3) the forecasting results show that under moderate and aggressive scenarios, CO 2 emissions in the studied cities can be reduced by 281.59 Mt and 711.90 Mt, respectively, by 2020.

Abstract Taking the four-stage SBM-DEA as the theoretical tool, this paper evaluates the total factor waste gas treatment efficiency (TFWGTE) of 65 Chinese iron and steel enterprises (CISEs) from 2005 to 2014. Then, after the influences of external environmental factors and statistical noise are eliminated, TFWGTE is decomposed into total factor waste gas managerial efficiency (TFWGME) and total factor waste gas environmental efficiency (TFWGEE), and the differences between TEWGTE, TEWGME and TEWGEE are systematically analysed from regional and enterprise perspectives. The results reveal that: (1) TFWGTE and TFWGEE showed an upward trend from 2005 to 2014, whereas TFWGME showed a downward trend from 2005 to 2014. The elimination of external environmental factors had provincial specific effects on TFWGTE; (2) By comparing the dynamic trends of TFWGTE in eastern, central and western regions, it is found that there are differences among different regions, which indicates that the external environmental factors in the region have an impact on TFWGTE; (3) In 4 provinces, TFWGME increased compared with TFWGTE, while in 23 provinces, TEWGME decreased compared with TFWGTE; (4) The Tobit regression results show that the command-and-control environmental regulation, environmental awareness and manufacturing process have significant effects on the input slacks of waste gas treatment, indicating that the strengthening of command-and-control environmental regulation, the improvement of environmental awareness, and the decline of the proportion of the short process can effectively reduce the input slacks of waste gas treatment.

Abstract To achieve sustainable and green development, it is imperative for China, as the world's major metal consumer, to improve utilization efficiency and conservation of metal resources. Thus, understanding the industrial endogenous growth patterns of China regarding metal utilization and technical change becomes the prerequisite. Therefore, this paper regards metal as one of production factors nested with capital and labor, employs normalized supply-side system approach to estimate the elasticity of substitution in constant elasticity of substitution (CES) function and analyzes technical change bias based on the analytical framework built in this paper. Results show that: the optimal nesting structure for most metal-intensive industries are metal-capital composite nested with labor. And the metal input is less efficient than capital or labor in all metal-intensive industries. The technical change biases for metal-intensive industries are diversified, but only metal mining industries and heavy machinery manufacturing industries tend to save metal when expanding the production effectively. Targeted suggestions are made per industry according to the heterogeneity of production factors allocation and technical change biases.