• Energy Research

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 Using a time-varying parameter structural vector autoregression with stochastic volatility (TVP-SVAR-SV) model, we decompose the structural shocks of oil price fluctuations into four types: oil supply shocks, global demand shocks, domestic demand shocks and oil-specific demand shocks. We then analyze the time-varying effects of these oil price shocks on China's inflation at the import, production and consumption stages using monthly data from January 1999 to December 2016. The results show that the pass-through effects of the four types of oil price shocks on China's inflation at each stage are time-varying and that there are significant differences at different time horizons and points in time. The analysis of the variance decomposition shows that the effects of oil price shocks on China's inflation at each stage are incomplete and decrease along the price chain. The increase in oil prices driven by oil-specific demand shocks is the most important cause of China's inflation at the import and production stages during the full sample period, while China's inflation at the consumption stage is mainly affected by domestic demand shocks. In addition, the inflationary effects of oil price shocks have been dramatically weaker since the international financial crisis compared with before the crisis.

Abstract The correlation between Renminbi (RMB) internationalization and nonferrous metal prices was studied using the nonlinear Granger causality test and the dynamic conditional correlation-generalized autoregressive conditional heteroskedastic (DCC-GARCH) model. The results indicate that the relationship between RMB internationalization and nonferrous metal prices reflects a complex nonlinear mechanism. There was no mutual influence between RMB internationalization and nonferrous metal prices prior to the trials of the RMB settlement in the cross-border trade in July 2009. Since then, however, a bidirectional causal relationship between RMB internationalization and the price of copper and a unidirectional causal relationship from the price of aluminum to RMB internationalization were examined. In addition, due to the impact of extreme events, such as economic and financial crises, RMB internationalization and nonferrous metal prices are not always positively correlated but are rather occasionally negatively correlated.

Abstract Because of the irreversibility of financialization in nonferrous metals markets, the security of nonferrous metals resources has shifted from a supply security mode based on “production-supply” towards an economic security mode based on “trade-finance”. Thus, the pricing mechanism of nonferrous metals has become more complex and presents features such as nonlinearity, dynamic characteristics and structure dissimilation. Based on this background, we construct a theoretical framework for analysing the effects of financial factors on fluctuations in nonferrous metals prices and employ the Markov-switching vector autoregression (MS-VAR) model to conduct a nonlinear empirical analysis based on monthly data of international copper futures prices from August 2004 to October 2016. Further, we include supply and demand factors for a comparative analysis. The results show that copper futures price fluctuations present a regime switching dynamic that can be characterized as “steep drop”, “small drop” and “steady rise” periods. During each period, financial factors can fully explain the price volatility while in different mechanism, and the “Chinese factor” has apparently been exaggerated. In addition, oil price is key to monitoring the short-term volatility risks of copper futures prices in the downward cycle. The conclusions along with the established nonlinear econometric models provide a new intellectual framework and analysis tool for explaining the financialization of commodity markets.

Abstract This paper uses a time-varying parametric vector autoregressive model with random fluctuations to examine the time-varying impacts of oil prices and economic policy uncertainty (EPU) on China's nonferrous metals industry. The findings indicate the following: (a) The impact of oil price shocks on the nonferrous metals industry is positive in the short term and diverse in the long term, while fluctuation trends are heterogeneous at different positions of the industry chain. (b) The impacts of EPU shocks on the industry show fluctuating trends after 2009, while the heterogeneous impacts of EPU shocks at different chain positions exist only on output. (c) Oil prices have a positive short-term effect on EPU, while the short-term effect of EPU on oil prices has gradually turned positive since the financial crisis, highlighting the asymmetric relationship between oil prices and EPU. (d) During the global financial crisis, the impact of oil prices and EPU on industry shocks increased significantly, indicating the importance of a stable economic environment. (e) Monetary and exchange rate policy uncertainty are the main sources of EPU shocks to oil prices, and trade policy uncertainty is the cause of inflationary effects.

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.