Rolling Processing Industry Research Articles

Characterization of Industrial Carbon Emission Dynamics and Network Structure Evolution in China

Given that industry is the major source of carbon emissions, clarifying the carbon transfer regularity triggered by industry production linkage is of great importance in realizing the synergistic development of industry to reduce pollution and carbon emissions. Based on the perspective of responsibility allocation, the study accounted for the industry carbon emissions and constructed a carbon transfer weighted directed network model and applied the social network analysis method to explore the structure of the industry carbon transfer network and its evolution characteristics from 2005 to 2020. The results showed that： ① The overall carbon emissions of industries from 2005 to 2020 presented a rapid growth trend and large differences occurred in emissions between industries. ② The scale of the carbon transfer network was expanding rapidly and the characteristics of the small-world network were significant. ③ The community structure of the carbon transfer network was obvious, including four optimal associations： high carbon emission, carbon transmission, carbon endogenous, and high spillover. ④ The construction industry and the metal smelting and rolling processing industry were the core of the carbon transfer network structure, with strong network control. In view of the above characteristics, we proposed countermeasures and suggestions in terms of implementing the allocation of carbon emission reduction responsibilities and differentiated emission reduction measures for associations as well as focusing on the management of core nodes of the network, with the intent of providing cross-sectoral synergistic emission reduction ideas for the development of sustainable low-carbon transformation in China's industry sector.

RETRACTED ARTICLE: Digital transformation, green innovation, and carbon emission reduction performance of energy-intensive enterprises

Digital transformation and green innovation are powerful initiatives to achieve carbon peaking, carbon neutrality targets and high-quality economic development. Using a sample of high energy-consuming listed enterprises from 2012 to 2021, a double fixed-effect model is constructed to verify the effect of green innovation on the carbon emission reduction performance of high energy-consuming enterprises, and digital transformation is used as a moderating variable to analyze the inner mechanism of green innovation affecting the carbon emission reduction performance of high energy-consuming enterprises under the effect of digital transformation. The empirical results show that green innovation can significantly improve the carbon emission reduction performance of energy-consuming enterprises, while digital transformation positively moderates the effect of green innovation on the carbon emission reduction performance of energy-consuming enterprises. When considering the industry heterogeneity, the moderation effect of digital transformation is significant in the chemical raw materials and chemical products manufacturing industry and the electricity and heat production and supply industry, but the petroleum processing and coking and nuclear fuel processing industry, the non-metallic mineral products industry, the ferrous metal smelting and rolling processing industry and the non-ferrous metal smelting and rolling processing industry are not yet significantly affected by green innovation and digital transformation. The findings of the study provide empirical evidence to promote the improvement of carbon emission reduction performance of energy-intensive enterprises in China and to achieve the "double carbon" target.

The impact of green finance on the optimization of industrial structure: Evidence from China.

Green finance promotes the optimization of industrial structure and continuous improvement of ecological environment by supporting the development of green industries. Based on the panel data of 30 provinces in China from 2012 to 2020, this paper uses the entropy weight TOPSIS method to measure the development level of green finance and the level of industrial structure optimization in China, and constructs a panel data model to empirically test the impact of green finance on the upgrading of China's industrial structure. The study finds that there is still an imbalance and insufficiency in the development of green finance and industrial structure optimization in China. From 2012 to 2020, the development level of green finance and the level of industrial structure optimization in China have been continuously rising, but there is obvious heterogeneity, showing an eastern>central>western spatial pattern. Empirical analysis results show that at the significance level of 1‰, the development of green finance has a significant promoting effect on the rationalization and upgrading of the industrial structure. However, there is significant heterogeneity in the impact of green finance on industrial structure optimization. In terms of regional heterogeneity, at the significance level of 1‰, the role of green finance in promoting the optimization of industrial structure in central and western China is higher than that in eastern China, and the impact of green finance on China's industrial structure shows a spatial pattern of western>central>eastern China. In terms of industry heterogeneity, at the significance level of 1‰, green finance has a significant promoting effect on the development of green industries, and a significant inhibiting effect on the development of high-energy-consuming industries. Specifically, in the green industry, green finance has the greatest promoting effect on the communication and other electronic equipment manufacturing industry; in the high-energy-consuming industry, green finance has the greatest inhibiting effect on the black metal smelting and rolling processing industry, and the smallest impact on the petroleum, coal and other fuel processing industry. Finally, based on this, policy suggestions for green finance to support the optimization of industrial structure are proposed from two dimensions: government and financial institutions.

Effects of the green finance policy on the green innovation efficiency of the manufacturing industry: A difference-in-difference model

This study examined the impact of green finance policy on the green innovation efficiency (GIE) of the manufacturing industry in China based on data between 2005 and 2019 using a difference-in-difference model. The results indicate that implementing the green finance policy reduced the GIE of heavy-polluting industries compared with that of non-heavy-polluting industries. In terms of industry-specific heterogeneity, the green finance policy had a significant negative effect on the GIE of the textile industry, non-metallic mineral products industry, ferrous and non-ferrous metal smelting and rolling processing industry, chemical fiber manufacturing industry, metal product industry, leather and other related industries, and pharmaceutical manufacturing industry. However, it had a significant positive effect on the GIE of the chemical raw material and chemical product manufacturing industry, agricultural and sideline food processing industry, and papermaking and paper product industry. Furthermore, it also promotes the GIE of environment-friendly industries. In terms of factor heterogeneity, the green finance policy has had a positive effect on GIE improvement in technology- and capital-intensive industries. Moreover, it has inhibited GIE improvement in labor-intensive industries. To promote the GIE in manufacturing, a series of policy recommendations have been put forward.

Research on Spatial Distribution Characteristics of High Haze Pollution Industries Such as Thermal Power Industry in the Beijing-Tianjin-Hebei Region

The Beijing-Tianjin-Hebei region is subject to the most severe haze condition in China. Against the backdrop of the coordinated development of Beijing, Tianjin, and Hebei, it is of great significance to explore the space-time distribution characteristics of high haze pollution industries in the above region. The purpose of this article is to find high haze pollution industries scientifically, analyze the spatial distribution characteristics of high haze pollution industries in Beijing-Tianjin-Hebei correctly, and formulate effective reduction measures for the spatial distribution of high haze pollution industries in different regions of Beijing-Tianjin-Hebei. It hopes that the measures provide a basis for effective to reduce high haze pollution in the Beijing-Tianjin-Hebei region. Using the Gini coefficient and location entropy, this paper explores the space-time characteristics of high haze pollution industries in 13 cities of the Beijing-Tianjin-Hebei region. The results show that those aforesaid high haze pollution industries present an obvious clustering trend in Beijing, Tianjin, Shijiazhuang, Handan, and some areas along the economic axis of China, overall flooding into Eastern coastal and Southern inland areas. By industry, the petroleum processing and coking processing industry, chemical raw materials and chemical products industry, manufacture of non-metallic mineral products, and ferrous metal smelting and rolling processing industry witness a declining clustering trend, as opposed to the electricity, heat production, and supply industry. Meanwhile, core cities of the Beijing-Tianjin-Hebei region have developed high haze pollution industries, and the hinterland lags in economic development. It is necessary to face up to the differences in economic development among areas in the Beijing-Tianjin-Hebei region, promote the staggered development of high haze pollution industries, and build a high haze pollution industry chain with coordinated economic and environmental development, thus reducing the economic gap with each area, and realizing the coordinated development of economy, industry, and environment.

Measuring structural characteristics and evolutionary patterns of an industrial carbon footprint network: A social network analysis approach

AbstractThe formation of an industrial network is inevitable because an industrial structure evolves to a higher level. The continuous material exchange among sectors creates a path for the embodied carbon in products to flow freely among industries. With the gradual complication of industrial networks, however, the emission reduction behavior of sectors not only becomes rooted in individual conduct, but also originates from the interaction forces between individuals. Therefore, carbon management in industries in the real economy needs to be explored from the perspective of these complex networks. On the basis of these considerations, this study utilized a hybrid model, the Economic Input–Output Life Cycle Assessment model, to construct the carbon footprint network of China's industries in 2002, 2007, 2012, and 2017. We also applied social network analysis to quantify the structural characteristics and evolutionary patterns of the network. Results showed that the transmission efficiency of resources and the subgroup‐integrated degree of the actors within the carbon footprint network were excellent, which was conducive to coordinated carbon emission reduction of these sectors from a network perspective. Agriculture and services were the engines driving sector‐wide carbon emission reduction through links within the network. Furthermore, some sectors, such as transportation and the warehousing industry, basic chemicals and chemical products manufacturing, and metal smelting and rolling processing industry, act as bridges and brokers. These sectors are keys to ensuring network cohesion and reducing the risk of network fragmentation. Finally, the industrial block network presented a hierarchical structure of a core–edge network. The network followed a trend of structural changes in which the network core became increasingly clear, and the network distribution pattern gradually concentrated on a single block. This finding revealed a new pathway for collaborative carbon mitigation in the industry from the perspective of the industrial community.

Performance Prediction of the Ferrous Metal Smelting and Rolling Processing Industry in Supply-Side Structural Reform in China

Supply-side structural reforms and environmental protection policies have a great impact on the ferrous metal smelting and rolling processing industry. This paper uses a grey model that introduces a fractional-order cumulative generating operator to study the development of ferrous metal smelting and rolling processing enterprises under the influence of supply-side structural reform in order to derive the future development trend of the industry. The forecast results show that from 2018 to 2022, the number of enterprises and substitute enterprises, inventory, finished products, and assets and liabilities decreases; the scale of income of metal smelting and rolling processing industry increases. The results can serve as a reference for policy makers and industry investors.

Emission Performance Quantitative Evaluation and Application of Industrial Air Pollution Sources

Treatment of industrial atmospheric emission sources is an important way to improve air quality, but accurate pollution control remains still an urgent challenge. Taking Xiqing District of Tianjin as an example, based on the second national pollution source census, this study carried out a quantitative evaluation of the pollutant emission performance of industrial enterprises and explored the significance, feasibility, and challenges facing emission performance evaluation. The results show that the emission performance of various industries in Xiqing District vary greatly. The pollutant emission performance level is closely related to an industry's own attributes, development scale, and management level. On the whole, the emission performance level of industries with high production process emission coefficients and a high proportion of small and medium-sized enterprises (such as furniture manufacturing, the metal products industry, ferrous metal smelting, and the rolling processing industry) is worse, while the emission performance of high-end industries represented by computer communication and other electronic equipment manufacturing and automobile manufacturing is generally better. The emission performance of different enterprises in the same industry also varies greatly. For example, the 11 enterprises with the worst performance in the metal machinery manufacturing industry only contributed 0.06% of industrial output yet their PM emission contribution reached 8.50%. The 19 worst-performing enterprises in the rubber and plastic industry contributed 4.76% of industrial output yet their VOCs emissions accounted for 43.59% of the total. At the same time, this study presents an emissions reduction plan according to the relevant technical guidelines of the Ministry of Ecology and Environment. Based on this, the cost of emissions reduction could be cut by as much as 90% when the pollutant emissions reductions of the same scale are reduced. The gap in the pollutant emissions performance of various industries and enterprises, the incongruity between economic benefits and environmental costs, and the important guiding role of emission performance evaluation for emissions reductions demonstrate the necessity of performance evaluation. Overall, this research shows that pollutant emission performance evaluation can effectively support macro-industrial structure adjustment and the environmental governance of meso-micro industrial enterprises, providing an important reference for pollution control interventions.

Epidemiological characteristics of new pneumoconiosis in different industries in Tianjin

Objective: To analyze the distribution characteristics of new pneumoconiosis in different industries in Tianjin from 2009 to 2018, in order to provide a theoretical basis for the prevention and treatment of pneumoconiosis. Methods: In November 2019, the data of new pneumoconiosis cases in Tianjin from 2009 to 2018 were collected and classified according to difference industries. The epidemiological characteristics of new pneumoconiosis in different industries were analyzed based on time, type of pneumoconiosis, type of work, age, and working age. Results: A total of 4657 new cases of pneumoconiosis were reported in Tianjin from 2009 to 2018, 4640 cases (99.63%) in the first stage, 13 cases (0.28%) in the second stage, and 4 case (0.09%) in the third stage. The number of new cases increased with time and then decreased. Among them, there were 3482 males (74.77%) and 1175 females (25.23%) . The proportion of women with new pneumoconiosis in the non-metallic mineral products industry was the highest, and the differences were statistically significant (P<0.05) . There are differences in the distribution of new pneumoconiosis species in different industries (χ(2)=4920.11, P<0.05) . Silicosis is mainly distributed in non-metallic mineral products industry, metal smelting and rolling processing industry, petroleum processing coking and nuclear fuel processing industries. Foundry worker's pneumoconiosis is mainly distributed in the general equipment manufacturing industry and special equipment manufacturing industry, and cement pneumoconiosis is mainly distributed in the non-metallic mineral products industry. The types of work are mainly distributed in smelting, casting, raw materials and forming workers, and the sum of the three accounts for 36.46% (1689/4657) . The M (P(25), P(75)) of diagnosis age was 56.0 (50.0, 63.0) years, and the M (P(25), P(75)) of working age was 19.0 (16.0, 26.0) years. The working age of new pneumoconiosis patients in the petroleum processing, coking and nuclear fuel processing industries is 19.0 (16.0, 26.0) years, which is shorter than that of other industries (P<0.05) ; The diagnostic age of the general equipment manufacturing industry is 54.0 (49.0, 59.0) years, which is less than that of other industries (P<0.05) . New pneumoconiosis is mainly distributed in large and medium-sized enterprises. New cases of pneumoconiosis in non-metallic mineral products industry, metal smelting, calendar processing industry and general equipment manufacturing industry are mainly distributed in large and medium-sized enterprises. New cases of pneumoconiosis in the metal products industry, special equipment manufacturing industry and petroleum processing, coking and nuclear fuel processing industries are mainly distributed in large enterprises. Conclusion: The work of preventing and controlling pneumoconiosis in Tianjin has a long way to go. We should implement targeted measures according to the characteristics of pneumoconiosis industry, strengthen the supervision of hazard industries, and effectively control the occurrence of pneumoconiosis.

Decomposition of driving factors of industry-related CO2 emissions and its decoupling with economic growth in Fujian Province, China

The emission of CO2 from major sectors and key industries are the predominant sources of regional CO2 emissions. It is the prerequisite to promote sectoral carbon emissions reduction, to cla-rify their influencing factors and investigate their relationship with regional economic growth. It is also of great significance for the implementation of regional total carbon emissions control. Using the Logarithmic mean Divisia index method (LMDI) and the Tapio decoupling model, we analyzed the driving factors, and decoupling status with economic growth of 13 major carbon emissions industries in Fujian Province from 1997 to 2017. The results showed that the electricity and heat production and supply industry was the major source of CO2 emissions in Fujian Province, with an increase of 101.74 Mt (from 18.89 Mt to 120.63 Mt) during the period 1997 to 2017. The top three industries with the fastest annual growth rate in CO2 emissions were non-ferrous metal smelting and rolling processing industry (18.1%), textile industry (12.1%), and ferrous metal smelting and rolling processing industry (12.1%). Among the influence factors for the changes in carbon emissions in 13 major industries, economic growth effect and population scale effect were the main positive driving factors, while the restraining effects of energy structure, energy intensity, and industrial structure were continuously increasing. In terms of decoupling relationship, the decoupling index between economic growth and industry-related CO2 emissions showed a downward trend on the whole. Since the 11th Five-Year-Plan period, some industries had begun to show strong decoupling to some extent. The farming, forestry, animal husbandry, fishery and water conservancy industry exhibited expansive negative decoupling, whereas the electricity and heat production and supply industry exhibited weak negative decoupling during 13th Five-Year Plan period. The effects of energy structure and energy intensity had substantial impacts on the decoupling with economic growth for various industries. The industrial structure effect had a smaller impact on the decoupling with economic growth, while the population scale effect had almost no impact.

Environmental regulations, energy and environment efficiency of China’s metal industries: A provincial panel data analysis

Improving the energy and environmental efficiency (EEE) of China’s metal industries is vital for China’s sustainable development. Based on provincial panel data from 2006 to 2016, this study first calculates the unified efficiency index (UEI) and energy-environmental performance index (EEI) of Chinese metal sub-industries. Then, the effects of different types of environmental regulations on the EEE of Chinese metal industries are examined. The results show that the EEE of China’s metal sub-industries was extremely low between 2006 and 2016. The EEE of the ferrous metal smelting and rolling processing industry (S3) was the highest; the EEE of the non-ferrous metal smelting and rolling processing industry (S4) was the lowest. Although the EEE of the metal sub-industries in the central and western regions was lower than in the eastern region during the sample period, it increased faster than in the eastern region, especially after 2012. The regression results show a significant inverted U-shaped relationship between environmental regulations and EEE in the metal industries, indicating that although command-and-control environmental regulation (CER) and market-based environmental regulation (MER) can improve these industries’ EEE in the short term, excessive intensity will backfire. In addition, MER is more effective than CER in improving EEE. Therefore, diversified environmental regulations are needed to promote sustainable development.

Environmental regulation policies, local government enforcement and pollution-intensive industry transfer in China

Pollution-intensive industries are the focus of regulatory targets in environmental regulation policies, which are the basis for the enforcement of environmental regulation by governments at all levels. Based on the “pollution haven” hypothesis, we select Beijing-Tianjin-Hebei and the surrounding regions (Henan, Shandong, Shanxi) as the objective areas. By constructing spatial panel measurement models, we study the impact of environmental regulation policies on the transfer of the overall industry and five pollution-intensive industries, and explore the possible roles of the local government environmental regulation enforcement. The results show that environmental regulation policies have not caused an obvious phenomenon of industrial transfer out of Beijing-Tianjin-Hebei and the surrounding regions, and the intervention of local government environmental regulation enforcement will not change this situation. Environmental regulation policies have different impacts on the transfer of different pollution-intensive industries. The interaction effect of the enforcement of local government environmental regulation will lead to industrial transfer in the ferrous metal smelting and rolling processing industry and the petroleum processing, coking and nuclear fuel processing industry. The transfer of pollution-intensive industries in Beijing-Tianjin-Hebei and the surrounding regions is more affected by economic development, infrastructure construction, R&D investment and other factors.

The epidemiological characteristics of silicosis cases in Tianjin from 2001 to 2015

Objective: To provide scientific evidence for the prevention and control measures of silicosis by analyzing the epidemiological characteristics of silicosis cases reported in Tianjin city during 2001-2015. Methods: The database of silicosis cases reported in Tianjin from 2001 to 2015 was established and analyzed by SPSS 24.0 software. Results: 2 213 cases of silicosis were reported during 2001~2015 in Tianjin, 2015 of them were new cases, and fewer reports of promoting stage cases and death cases, which were129 and 69 cases. The new cases were increasing by years, mainly stage I silicosis accounting for 99.2%, the years of dust exposure were concentrated from 1970 to 1989, accounting for 69.58%. From 2001 to 2015, the median length of service at the onset of silicosis was 19.30 years, the age of the onset of silicosis is decreasing. The median of onset age in new cases of silicosis was 53.28 years, and increased year by year. There were significant differences in indirect dust working age and diagnostic age in different reporting years. Most cases were distributed in non-mental mineral product industry, and ferrous metal smelting and rolling processing industry. The work types of these cases are various. Conclusion: The increasing incidence of silicosis and the shortening of dust-exposed working years suggest that the form of silicosis hazards is still severe in Tianjin. According to the epidemic characteristics of silicosis, we should strengthen the supervision and management of key industries and special investigation, carry out silicosis census and silicosis screening of dust-removal workers, and take pertinent measures to prevent and control the occurrence of silicosis.

Decomposition of Electricity Consumption Intensity of High Energy-consuming Industries in Shanxi Province Based on LMDI Method

In this paper, the electricity consumption intensity of the high energy-consuming industries in Shanxi province from2007 -2012 is decomposed based on LMDI method, in which the affecting factors involve structure effect and intensity effect. The results show that the structure adjustment of Shanxi province is the main driver which declines the electricity consumption intensity for high energy-consuming industries. For most industries, the structure effect declines the intensity with a high contribution proportion. Meanwhile, the contribution proportions of efficiency effect for most industries are less than the corresponding structure effect. The totla effect of each high energy-consuming industry is shown in table 4, in which the totoal effect of Non-ferrous metal smelting and rolling processing industry is relative higher than other industries, and the effect of Black metal smelting and rolling processing industry is just smaller than it.

A Study on the Carbon Emissions Calculation Model of Iron and Steel Products Based on EIO-LCA

Carbon emission has become a global focus. The construction of carbon emissions calculation model is helpful for its control. Currently, there is still no uniform method about accounting on the carbon emissions of steel products. The common calculation models are not totally suitable for China. To make up for the shortcomings of them, this paper defines the life cycle system of the iron and steel products based on EIO-LCA, measures the quantity of the direct, indirect carbon emissions and carbon emission deduction in various stages of this life cycle, identifies the hotspot and department which contributes most in carbon emission, and takes Hunan Valin Xiangtan Iron and Steel Co., Ltd (abbreviated Xiang Gang) as an example to validate it. It shows that 2103.87kg of carbon in total would be emitted when one tonne of steel is produced by Xiang Gang. Among the total, the quantity of direct, indirect and deductible carbon emission are 2033.5kg, 216.75kg and 146.38kg respectively, namely carbon emissions of producing per ton of steel is 2.1 tons. Direct carbon emissions from all stages of the life cycle of steel products mainly exist in the stage of steel production and transportation. And ferrous metal smelting and rolling processing industry are the largest emissions industries of the total indirect emissions. Converting by-product gas, heat, and pressure into electrical energy use can reduce carbon dioxide emissions by 146kg, which is the equivalent of reducing carbon dioxide emissions per ton of steel 0.15 tons. Therefore, in order to make the carbon dioxide emissions reach the advanced domestic level of 1.7 tons per ton steel, the iron and steel enterprises can meet emissions reduction targets by strengthening control of carbon emission and improving the efficiency of the utilization of secondary energy from small and large scale.

Prediction of Wastewater Emission in China

A model to predict the wastewater emission in China was established with comprehensive factor analysis, combining the ARMA and OLS methods. Applied the model, the wastewater emission from 2013 to 2015 in China was predicted. Industry characteristics of wastewater emission between the whole nation and Haihe River basin were analyzed using the 1999 and 2002 water conservancy input-occupancy-output (IOO) tables. The results showed that the national wastewater emission of 2013-2015 will reach 72998, 76897 and 81071 million tons respectively, and industries like paper printing and educational goods manufacturing, chemical industry, discharged a large amount of wastewater and their wastewater emission by unit value added were also very high. In the meantime, wastewater by unit value added of metal smelting and rolling processing industry went down largely from 1999 to 2002, which revealed that the strict supervision had positive effects on reducing wastewater emission.

A Grey Neural Network and Input-Output Combined Forecasting Model and Its Application in Primary Energy Related CO2 Emissions Estimation by Sector in China

In this paper, a Grey Neural Network and Input-Output Combined Forecasting Model (GNF-IO) was built to forecast by sector primary energy related CO2 emissions. Applied the GNF-IO model, the coal, crude oil and natural gas consumption volume and the related CO2 emissions volume by China's 42 sectors in 2010 were estimated. The total energy-related CO2 emissions volume in China in 2010 was forecasted as 7508.56 million tons, in which 80.2 percent was from coal consumption, 17.6 percent was from oil consumption. By sector CO2 emissions results showed that the energy efficiency work of coal can be focused on electricity, heat production and supply, ferrous metal smelting and rolling processing industry, petroleum processing, coking and nuclear fuel processing industry. The energy efficiency work of crude oil can be emphasized on petroleum processing, coking and nuclear fuel processing industry, raw chemical materials and chemical products.