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The sustainable development of China's economy is bottlenecked by resource shortage and environmental pollution. As the leading resource consumer and pollutant source, the industrial sector needs to improve its energy efficiency. This paper establishes a super epsilon-based measure (Super-EBM) model with bad outputs like environmental cost and evaluates the industrial green total-factor energy efficiencies (IGTFEEs) of 30 provinces in China during 2000–2017. Unlike previous research, the main contribution of this paper is to choose four environmental pollutants as bad outputs (industrial carbon dioxide, industrial sulfur dioxide, industrial chemical oxygen demand, industrial solid waste). By contrast, the previous studies mostly only take one environmental pollutant as bad output, i.e., the bad outputs are not fully measured. Then, the spatiotemporal dynamics and spatial correlations of the IGTFEEs were analyzed, and the influencing factors of IGTFEE were examined empirically with a spatial econometric model. Finally, this paper adopts generalized method of moments (GMM) to solve the endogenous problem, trying to assure the robustness of estimation results. The results show significant provincial differences in IGTFEE. Most eastern coastal provinces achieved satisfactory IGTFEEs, while most inland provinces had undesirable IGTFEEs. Eastern region achieved the highest IGTFEE, followed by central region; western region had the lowest IGTFEE. The IGTFEE improved over time in some provinces while worsened greatly in some provinces. The IGTFEE in most provinces need to be further improved. Global Moran's I values indicate that the provincial IGTFEEs were clustered in space, rather than randomly distributed. Local indication of spatial association (LISA) map reflects significant local spatial clustering of provincial IGTFEEs. In addition, IGTFEE is significantly promoted by industrial structure, technological innovation, human capital, opening-up, and energy structure yet significantly suppressed by ownership structure and environmental regulation. Considering the endogeneity, GMM results show that the estimation results of the model were robust. Specific policy recommendations include vigorously developing high-tech industries, deepening state-owned enterprises reform, diverting more funds to research and development, cultivating versatile talents, introducing environmentally-friendly foreign capital, accelerating the implementation of clean energy development strategy, and widening the fund channels of pollution control investment. [ABSTRACT FROM AUTHOR]

The production and consumption of energy are important driving forces for the economic and social development of a country, and environmental pollution caused by the consumption of energy especially for traditional energy has become a global focus in recent years. This paper analyzes the progress of energy production and consumption in China from 1957 to 2017 and characterizes the correlation between energy consumption and environmental pollution in China. Statistical methods, such as fit regression and gray relevance based on distance, are employed to describe their relations. It is shown that the total amount of energy production and consumption in China has steadily risen. Energy consumption has a strong link with environmental pollution, as demonstrated by its high correlations with the emission of waste gas and the generation of solid waste. The corresponding determination coefficients are 0.99 and 0.98, respectively. The results of the distance-based gray relevancy analysis indicate that, among all the correlations between different industrial wastes, the relation between discharged industrial solid waste and coal combustion is the largest (R = 0.79), while the relation between industrial solid waste and crude oil consumption is the smallest (R = 0.50). Compared to the other two wastes of the industry, energy consumption has the most impact on industrial wastewater, of which the average correlation coefficient is 0.61. The influence of coal combustion on environmental quality is the largest among the various energy consumption activities, with an average correlation coefficient of 0.76. Finally, the paper uses case analysis of remote sensing monitoring for environmental pollution to further discuss environmental problems. This study can provide necessary support for optimizing the domestic structure of energy consumption to further reduce pollution and improve environmental quality in China. [ABSTRACT FROM AUTHOR]

Improving the accuracy of the green total factor productivity (GTFP) calculation has certain theoretical and practical value for sustainable development. We use a slack-based directional distance function (DDF) proposed by Färe and Grosskopf (European Journal of Operational Research 200:320-322, Färe and Grosskopf, European Journal of Operational Research 200:320–322, 2010) to combine with Malmquist-Luenberger Productivity Index (MLPI) to re-examine the GTFP. This new GTFP index takes inputs such as energy consumption, labor, and capital stock into account, while considering the comprehensive outputs of real GDP and three types of industrial wastes (gas, solid, water). The proposed slack-based MLPI is applied to access GTFP changes and compare the results under the slack-based DDF measure and original DDF measure over the period 2005–2012. The primary results in this paper present the average growth of GTFP for these 30 Chinese provinces shows a downward trend until 2010 suggesting that most provinces pay less attention on the productivity growth. The downward trend results primarily from no significant improvement in technical change. We also discover that although the distributions of the slack-based measure are roughly similar with that of the original DDF, the decomposed component, efficiency change, shows a significant difference between them. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to analyze the environmental impact of the viscose fiber/lyocell fiber manufacturing industry and obtain improvement information from China's analysis results. Our main research question is: How will the production of viscose/lyocell fiber affect the environment? Which raw material or process has the most significant impact on the environment? Is it possible for lyocell fiber to replace viscose fiber that has a greater impact on the environment? Methods: For the life cycle assessment (LCA) method, we use the analysis from the raw materials (pulp) to the end of the production (fiber) process. The paper mainly introduces the water consumption, energy utilization, and the production of three industrial wastes in producing the two fibers. The life cycle inventory input and output data were collected from two fiber manufacturers in China (manufacturing viscose fiber and lyocell fiber, respectively). Six impact categories were calculated using eFootprint online platform, including primary energy consumption (PED), non-living resource consumption potential (ADP), Resource depletion–water (WU), global warming potential (GWP), acidification potential (AP), and ecotoxicity (ET). Results and discussion: There is no significant difference between the ADP and WU values of the two processes from a comprehensive comparison. In PED, the value of lyocell fiber is approximately three times that of viscose fiber. Because viscose fiber uses more sulfide; its AP value is about twice that of lyocell fiber. The GWP of lyocell fiber is about two of that of viscose fiber. The ET value of viscose fiber is an order of magnitude higher than that of lyocell fiber. In the viscose fiber process, steam, pulp, ZnSO4, and sulfuric acid are the main contributors to the process's environmental impact. The main contribution rates of steam to PED, WU, GWP, and AP are 23.6%, 16.76%, 43.33%, and 18.99%, respectively. The use of ZnSO4 in viscose fiber resulted in an ET value of more than 90%. Alternatives can be found to reduce the environmental impact further. It is of great significance to study materials that can replace ZnSO4 as additives. In the lyocell fiber process, electricity, pulp, and steam are the main contributors to the process. The main contribution rates of electricity to PED, ADP, GWP, AP, and ET are 57.5%, 62%, 51%, 59.9%, and 69.3%, respectively. At present, this process mainly uses thermal power generation, which makes the environmental impact caused by electricity consumption great. If more economical or cleaner power supply can be considered, the environmental impact will be further improved. [ABSTRACT FROM AUTHOR]

Reducing exhaust emissions, promoting economic development, and realizing clean energy utilization have always been concerns in China. To measure the relationship between them, this study selects the data related to energy consumption, economic growth, and exhaust emissions from 2000 to 2019 in 30 Chinese provinces. By constructing a panel vector autoregressive (PVAR) model, the dynamic relationship between them in China is quantitatively analyzed. The results show that there is a long-term interaction between energy consumption, economic growth, and exhaust emissions. Among them, economic growth is highly dependent on energy consumption, but it can promote the reduction of exhaust emissions. However, energy consumption will produce a large amount of industrial waste, such as sulfur dioxide and carbon dioxide emissions. Specifically, the industrial structure and energy structure have the most obvious impact on reducing industrial sulfur dioxide emissions and carbon emissions, with the proportion between 0.071–0.090 and 0.031–0.032, respectively. Therefore, the adjustment of industrial structure and energy structure is the key to exhaust emission reduction. [ABSTRACT FROM AUTHOR]

Nowadays, as China has become the factory of the world, China's industrial sectors have become the country's main source of energy consumption and emissions including CO2, SO2, NOx and soot. This article will use the non-radial directional dispersion function model (DDF) to explore ways to improve both the energy efficiency and the emission efficiency of China's industrial sectors. Based on current research, this article proposes a new projected input for the treatment of industrial waste gas, and optimizes the projected input and output settings. The data analysis shows that there is room for improvement in China's energy efficiency, and its regional imbalances have expanded. The Chinese government is urged to pay more attention to the improvement of energy efficiency in the central and western regions, and to provide more policy support. Simultaneously, the paper attemtps to establish measurement indexes to improve the efficiency of emission reduction. As a result, the authors suggest that increasing government expenditure can play a positive role in the overall improvement of emission reduction efficiency. [ABSTRACT FROM AUTHOR]

The development of the energy economy in Northeastern China is unbalanced. In this paper, some energy structures such as oil, coal, and electricity are chosen as input indexes, while per capita gross domestic product (GDP) and total retail sales of consumer goods are desirable output indexes. The main causes of haze are industrial waste gas emission, industrial wastewater discharge, and solid waste production, which are regarded as undesirable output indexes. Environmental variables include industrial structure, urbanization, per capita GDP, and government planning. The proportion of the tertiary industry's output value to its total output value is one of the important indexes to measure economic development in one area. Therefore, industrial structure plays a vital role in a region's economic development. The tertiary industry oriented by service sector is the main force to promote economic transformation. The development of the urban economy will change from high to low as the process of urbanization, which can be characterized by green coverage of the urban built-up area. The higher per capita GDP, the higher the economic development can be achieved. Government planning refers to the total number of construction projects and completed projects in the year the government takes measures to control pollution. It shows that the total factor productivity of energy green efficiency has been stable in the last decades but fluctuated wildly in special periods, in which technical progress has played a great role, as well as the increase of technical efficiency. [ABSTRACT FROM AUTHOR]

The mechanism between financial agglomeration and environmental pollution is an important concern for both academia and policymaking. The main objective of this paper is to study the nonlinear impacts of financial agglomeration on environmental pollution. A theoretical framework was first constructed based on the scale effect, structure effect, and technology innovation effect of financial agglomeration and a Copeland-Taylor endogenous growth model. Using the panel data of 281 Chinese prefectural-level cities from 2003 to 2019, a panel threshold regression model was introduced to estimate the nonlinear association between financial agglomeration and environmental pollution. Industrial smoke (dust) emissions and industrial wastewater discharge were adopted to quantify current environmental pollution in China. The results show that financial agglomeration had a significant effect on improving the environment characterized by gradient thresholds; also notable is that 68.64% of the cities crossed the threshold value, entering the decelerating phase of financial agglomeration inhibiting environmental pollution. Both upgrading industrial structure and enhancing marketization could reduce environmental pollution, whereas increasing human capital, environmental regulation, and energy consumption had a deteriorating effect. The three channels for financial agglomeration to reduce environmental pollution were revealed to be financial scale, financial structure, and financial technology innovation. Our findings provide strong evidence for policymaking in sustainable development. [Display omitted] • Financial agglomeration (FA) is a crucial way to improve environment pollution (EP). • We built the theoretical framework of FA and EP based on the Copeland-Taylor model. • We tested the nonlinear impacts of FA on EP using the panel threshold model. • We revealed the transmission mechanism between FA and EP. [ABSTRACT FROM AUTHOR]