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Highlights • A nonlinear relationship between industrial agglomeration and energy efficiency. • A threshold effect exists when industrial agglomeration affects energy efficiency. • When agglomeration degree is less 1.3865, it will promote energy efficiency. • Future policy for energy efficiency in China's textile industry is suggested. Abstract Based on provincial panel data on the textile industry in China, this paper calculates the total-factor energy efficiency of this industry as the dependent variable. Additionally, based on linear panel analysis of the relationship between industrial agglomeration and energy efficiency, in-depth analysis of the industry is performed at different industrial agglomeration levels. The paper identifies different impacts of industrial agglomeration on energy efficiency, uses the threshold regression model to extend the research to a nonlinear framework, and constructs a double threshold regression model in which the threshold of the textile industry agglomeration level serves as the threshold variable. The results show, first, a threshold effect occurs when industrial agglomeration affects total-factor energy efficiency. Second, a significant positive correlation exists among the degree of economic development, energy prices, research and development investment (R&D), enterprise scale, and total factor energy efficiency of the textile industry. Third, a non-linear relationship exists between industrial agglomeration and energy efficiency in the industry. When industrial agglomeration is low, promoting it improves energy efficiency. However, when industrial agglomeration reaches a certain level, agglomeration and energy efficiency show a negative relationship. Finally, based on the empirical results, ways of improving energy efficiency in the industry are suggested. [ABSTRACT FROM AUTHOR]

• The impact of economic growth pressure on energy efficiency improvement is discussed. • Economic growth pressure has hindered the improvement of energy efficiency. • Heterogeneity effects exist in individual dimension and temporal dimension. Economic growth target management has been a typical feature of Chinese governments at all levels for a long time in the past decades. Based on a panel data of 188 Chinese cities, this paper examines the impact of pressure brought by economic growth target on energy efficiency improvement. The results suggest that economic growth pressure has hindered the improvement of energy efficiency. For every unit of increase in economic growth pressure, the improvement indicator of energy efficiency will decrease by 3.3%. This finding is robust, after replacing explained variables and core explanatory variables, no substantial changes are found. Sub-sample regression confirms the existence of heterogeneity effects in individual dimension and temporal dimension. Finally, by summarizing existing studies, this paper finds that the investment imbalance effect, the hindrance effect on industrial structure upgrading, and the crowding-out effect on innovation are three important ways for economic growth pressure to restrain the improvement of energy efficiency. Our findings are helpful to understand the role of the government's economic growth target management in resource allocation and energy utilization and provide a certain practical reference for China to promote high-quality economic development. [ABSTRACT FROM AUTHOR]

This paper attempts to explore the effect of energy construction adjustment on the energy-saving and emission-reduction (ESER) dynamical evolution system. Based on the nonlinear dynamics theory, the dynamic behavior of the novel system is discussed. The quantitative coefficients of the actual system are identified with the aid of genetic algorithm-back propagation neural network. Scenario analysis results show that, energy construction adjustment could effectively control energy intensity. To clarify this further, an example of 4D ESER system with new energy constraints is demonstrated. Investigation results show that, government control can effectively control energy intensity, while brings inhibiting impact on economic growth and people’s livelihood. Economic investment is the key variable affecting energy construction adjustment and ESER, the ESER system will crash when the investment is too low. Energy construction adjustment could effectively reduce energy intensity. However, the ESER system will also crash if the development of energy construction adjustment is too fast. The interesting thing is that the ESER system should be pulled back to steady state as the investment getting bigger. Energy intensity could be controlled in expected range by taking adequate measures. Full use of the role of energy structure adjustment should be made to promote the development of new energy, while government control is used only when necessary. [ABSTRACT FROM AUTHOR]

The current mode of economic development in China is typified by high growth, high energy consumption, and high pollution characteristics and this has caused great stress on both energy consumption and the environment. This paper focuses on a historical analysis of China’s energy, environmental, and economic (‘E3’) efficiency and the sources of E3 productivity growth therein. A developed slacks-based measure is utilized to evaluate the performance of E3 efficiency and decompose the performance fluctuations into three components: energy, economy, and environmental efficiency fluctuations. By applying a method based on global data envelopment analysis, we also analyze the key factors responsible for the change in E3 productivity during 2002–11 from the point of view of technical progress, production scale, and management level. The results show that China performs well on the economic front, while the energy and environmental performances are not optimistic. Fortunately, energy and environmental efficiency have gradually improved in recent years. Further analysis shows that the trend in E3 productivity in China has begun to follow an ascending path. Technical progress is the most powerful contributor to China’s E3 productivity growth, while falling scale and management efficiency are the two main obstacles preventing improvement in E3 productivity. [ABSTRACT FROM AUTHOR]