Journal articles on the topic 'Industrial emission'
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1
Duan, Haiyan, Xize Dong, Pinlei Xie, Siyan Chen, Baoyang Qin, Zijia Dong, and Wei Yang. "Peaking Industrial CO2 Emission in a Typical Heavy Industrial Region: From Multi-Industry and Multi-Energy Type Perspectives." International Journal of Environmental Research and Public Health 19, no. 13 (June 26, 2022): 7829. http://dx.doi.org/10.3390/ijerph19137829.
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Peaking industrial carbon dioxide (CO2) emissions is critical for China to achieve its CO2 peaking target by 2030 since industrial sector is a major contributor to CO2 emissions. Heavy industrial regions consume plenty of fossil fuels and emit a large amount of CO2 emissions, which also have huge CO2 emissions reduction potential. It is significant to accurately forecast CO2 emission peak of industrial sector in heavy industrial regions from multi-industry and multi-energy type perspectives. This study incorporates 41 industries and 16 types of energy into the Long-Range Energy Alternatives Planning System (LEAP) model to predict the CO2 emission peak of the industrial sector in Jilin Province, a typical heavy industrial region. Four scenarios including business-as-usual scenario (BAU), energy-saving scenario (ESS), energy-saving and low-carbon scenario (ELS) and low-carbon scenario (LCS) are set for simulating the future CO2 emission trends during 2018–2050. The method of variable control is utilized to explore the degree and the direction of influencing factors of CO2 emission in four scenarios. The results indicate that the peak value of CO2 emission in the four scenarios are 165.65 million tons (Mt), 156.80 Mt, 128.16 Mt, and 114.17 Mt in 2040, 2040, 2030 and 2020, respectively. Taking ELS as an example, the larger energy-intensive industries such as ferrous metal smelting will peak CO2 emission in 2025, and low energy industries such as automobile manufacturing will continue to develop rapidly. The influence degree of the four factors is as follows: industrial added value (1.27) > industrial structure (1.19) > energy intensity of each industry (1.12) > energy consumption types of each industry (1.02). Among the four factors, industrial value added is a positive factor for CO2 emission, and the rest are inhibitory ones. The study provides a reference for developing industrial CO2 emission reduction policies from multi-industry and multi-energy type perspectives in heavy industrial regions of developing countries.
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Xiao, Sun Xi, and Lin Wu. "Carbon Emissions Measurement of Jiangsu Province Industrial Energy Consumption Based on LMDI Method." Advanced Materials Research 1010-1012 (August 2014): 1932–36. http://dx.doi.org/10.4028/www.scientific.net/amr.1010-1012.1932.
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Energy consumption is the major source of industrial carbon emissions. Energy consumption carbon emission factor method and LMDI (Logarithmic Mean Divisia Index) method was used to analyze the carbon emission evolution of industrial economy energy consumption in Jiangsu Province with collected data on industrial energy consumption in 1995-2012. Results showed that Jiangsu province economic industrial carbon emissions keep increasing in 1995-2012 years. The results of carbon emission increase analysis of energy consumption structure effects, industrial energy consumption intensity effects and output scale effects in 1999-2012 showed that energy consumption intensity effect has the maximum contribution to carbon emissions in industrial carbon emissions Jiangsu Province. Therefore, the main way to control carbon emissions of industrial energy consumption in Jiangsu Province is reasonably control the growth of energy consumption.
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Tong, Qing, Sheng Zhou, Yuefeng Guo, Yang Zhang, and Xinyang Wei. "Forecast and Analysis on Reducing China’s CO2 Emissions from Lime Industrial Process." International Journal of Environmental Research and Public Health 16, no. 3 (February 11, 2019): 500. http://dx.doi.org/10.3390/ijerph16030500.
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China greenhouse gas inventories show that CO2 emissions from the lime industrial process are large scales and closely related to the development of its downstream industries. Therefore, there is high importance to analyze and forecast on reducing China’s CO2 emissions from lime industrial process. The aims of this paper are to make up the research gaps in China and provide a quantitative reference for related authorities to formulate relevant policies. The prediction method in this paper is consistent with the published national inventory, which is an activity data based method to predict carbon dioxide emissions from the industrial process of four categories of lime products. Three future scenarios are assumed. The business as usual scenario (BAU) is a frozen scenario. There are two emission reduction scenarios (ERS and SRS) assumed under different emission reduction strength considering combined industrial process CO2 emission reduction approaches from both the production side and the consumption side. The results show that between 2020 and 2050, China’s lime industrial process has an increasingly significant CO2 emission reduction potential, enabling both emission intensity reductions and total emission reductions to be achieved simultaneously. Based on the simulation results from emission reduction scenarios, compared with 2012 level, in 2050, the emission intensity can be reduced by 13–27%, the total lime production can be reduced by 49–78%, and the CO2 emissions in the lime industrial process can be reduced by 57–85%.
4
Cheng, Xiao, Yanping Pu, and Ran Gu. "Effect of Shanxi pilot emission trading scheme on industrial soot and dust emissions: A synthetic control method." Energy & Environment 31, no. 3 (September 19, 2019): 461–78. http://dx.doi.org/10.1177/0958305x19876682.
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To launch the nationwide emission trading scheme, some provinces in China were approved to design their pilot work for emission trading scheme according to local circumstances. Shanxi Province is the only pilot area with provincial trading market for industrial soot and dust emissions. This paper investigates the effect of Shanxi Pilot emission trading scheme on industrial soot and dust emissions by using the synthetic control method. The idea behind the synthetic control approach is to construct a combination of comparison cities to approximate the emission paths that the cities in Shanxi would have experienced in the absence of the pilot emission trading scheme. We demonstrate that, following Shanxi Pilot emission trading scheme, industrial soot and dust emissions fell markedly in Taiyuan, Datong, and Linfen relative to the synthetic counterparts. The finding that emission trading scheme can help achieve emission reduction targets is shown to be robust to the reduction in the number of control units, placebo tests, and difference-in-differences estimation.
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Shen, Zijie, and Liguo Xin. "Characterizing Carbon Emissions and the Associations with Socio-Economic Development in Chinese Cities." International Journal of Environmental Research and Public Health 19, no. 21 (October 23, 2022): 13786. http://dx.doi.org/10.3390/ijerph192113786.
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Reducing carbon emissions in cities is crucial for addressing climate change, while the city-level emissions of different compositions and their relationships with socio-economic features remain largely unknown in China. Here, we explored the city-level emission pattern from the industrial, transportation, and household sectors and the emission intensity, as well as their associations with socio-economic features in China, using the up-to-date (2020) CO2 emissions based on 0.1° grid (10 × 10 km) emission data. The results show that: (1) CO2 emissions from the industrial sector were considerably dominant (78%), followed by indirect (10%), transportation (8%), and household (2%) emissions on the national scale; (2) combining total emissions with emission intensity, high emission–high intensity cities, which are the most noteworthy regions, were concentrated in the North, while low emission–low intensity types mainly occurred in the South-West; (3) cities with a higher GDP tend to emit more CO2, while higher-income cities tend to emit less CO2, especially from the household sector. Cities with a developed economy, as indicated by GDP and income, would have low emissions per GDP, representing a high emission efficiency. Reducing the proportion of the secondary sector of the economy could significantly decrease CO2 emissions, especially for industrial cities. Therefore, the carbon reduction policy in China should focus on the industrial cities in the North with high emission–high intensity performance. Increasing the income and proportion of the tertiary industry and encouraging compact cities can effectively reduce the total emissions during the economic development and urbanization process.
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Song, Li, and Xiaoliang Zhou. "Does the Green Industry Policy Reduce Industrial Pollution Emissions?—Evidence from China’s National Eco-Industrial Park." Sustainability 13, no. 11 (June 3, 2021): 6343. http://dx.doi.org/10.3390/su13116343.
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As a regional green industrial policy, the construction of national eco-industrial parks is of great significance to the realization of industrial green transformation, while its environmental effects and mechanisms have not yet been clarified. Using panel data from 308 prefecture-level cities in China from 2003 to 2017, this study takes the establishment of 3 national-level ecological industrial parks as a quasi-natural experiment, also using a time-varying difference in difference model to examine how green industrial policies affect industrial pollution emissions. The study found that the establishment of a national eco-industrial park has significantly reduced industrial sulfur dioxide emissions, and the emission reduction effect has a lag effect and long-term impact. In cities with strong environmental regulations, provincial capitals and municipalities, and cities with a high degree of marketization, eco-industrial parks have better emission reduction effects, while, in cities with greater economic growth incentives and fiscal pressures, eco-industrial parks are difficult to achieve emission reduction effects. The establishment of national eco-industrial parks can reduce industrial pollution emissions by improving pollution treatment efficiency and energy efficiency, as well as promoting industrial agglomeration. China should continue to promote the implementation of green industrial policies, to strengthen the construction of national-level eco-industrial parks at this stage.
7
Ye, Anning, Yuguo Ji, and Min Zhang. "Research on Carbon Emissions of Industrial Clusters in China." Academic Journal of Science and Technology 3, no. 2 (October 28, 2022): 65–70. http://dx.doi.org/10.54097/ajst.v3i2.2094.
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At present, the global warming problem is becoming more and more serious, and effective carbon emission reduction is urgent, and the cooperation between industries within a specific supply chain can provide a new method to reduce emissions. Whith 2017 year as the research period, 30 industrial sectors in China as the research object, using the new method proposed by Kanemoto et al. to identify high carbon emission industrial clusters. Combined with modified normalized cut function, we find out high carbon emission industrial clusters among 30 industrial sectors from the supply chain perspective with multiple clustering methods, and based on this, the relative position of each industrial sector in the industrial chain is studied through minimum spanning tree to find the key industrial chain. The results show that the clustering effect performs best at k=7, where cluster 1 accounts for 89% of the total carbon emissions of all clusters, indicating that this industrial cluster has more potential for emission reduction compared with other industrial clusters and is the focus of future emission reduction efforts, while the upstream and downstream industrial chains with the construction industry as the core are the key industrial chains of this cluster as shown by the minimum spanning tree.
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Zhang, Lu, Yan Yan, Wei Xu, Jun Sun, and Yuanyuan Zhang. "Carbon Emission Calculation and Influencing Factor Analysis Based on Industrial Big Data in the “Double Carbon” Era." Computational Intelligence and Neuroscience 2022 (February 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/2815940.
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The arrival of the “double carbon” era indicates that industrial carbon reduction with high energy consumption and high carbon emission is imperative. From the perspective of carbon emission driving factors, industrial carbon emission is decomposed into five influencing factors: energy intensity, energy structure, industrial structure, economic efficiency, and employee scale. Taking the data of 41 subindustries of industrial industry in Liaoning Province from 2010 to 2019 as the research sample, the carbon emission is calculated. The LMDI model is used to analyze and point out the difference in the driving contribution of carbon emissions of each subindustry. The results show that the total carbon emission of Liaoning province gradually decreases, decreases for the first time in 2014, and gradually turns from flat to upward. Economic efficiency is the only and most important reason to drive the increase of industrial carbon emissions in Liaoning Province, and energy efficiency is the primary factor to curb carbon emissions. High carbon industries play a significant role in promoting the increase of carbon emissions, while the medium and low carbon industries have a better effect on restraining carbon emissions. It provides reference and theoretical basis for the government to adjust the industrial structure, control industrial overcapacity, and realize the “double carbon” goal as soon as possible. It is of great significance for the country to optimize energy layout, ensure energy security, and implement the new energy strategy.
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Ma, Lei, and Mei Song. "Approaches to Carbon Emission Reductions and Technology in China’s Chemical Industry to Achieve Carbon Neutralization." Energies 15, no. 15 (July 26, 2022): 5401. http://dx.doi.org/10.3390/en15155401.
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Based on China’s goal of achieving carbon neutrality by 2060, this study focused on its coal gasification in 2010–2019. Carbon emissions were calculated from industrial data, and an LMDt model was established to analyze the influencing factors of carbon emissions. Through scenario analysis, the paths of carbon emission reductions in the chemical industry were analyzed, and their emission reduction potential was estimated. The results showed that the carbon emissions in the chemical industry increased rapidly in 2010–2019, reaching 196 million tons in 2019. The emission structure was the most important factor in mitigating carbon emissions, and the emission intensity, industrial structure, economic development level, and labor force scale had different degrees of promotion effects, of which emission intensity was the strongest. The chemical industry can reach a carbon peak before 2030 under the three analyzed scenarios, and the emission reduction potential is the largest under the landing policy scenario. The results showed that carbon capture, usage, and storage (CCUS) technology is key for carbon emission reductions and that it is necessary to adjust the industrial structure, reduce emission intensity, and increase forest carbon sink to achieve carbon neutrality in the chemical industry.
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Li, Ya Li, and Yao Chen Qin. "Study on Dynamic Change of Carbon Emission in Zhengzhou." Applied Mechanics and Materials 291-294 (February 2013): 1353–58. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.1353.
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In order to explore the impact of the fossil energy consumption,industrial production and population on regional carbon cycle , this paper estimated the dynamic changes of carbon emissions from 2000 to 2009 in Zhengzhou based on the quantitative emission model proposed by IPCC and ORNL. The results show that the total carbon emissions in Zhengzhou was 48944.2 ×104t during 2000~ 2009, among which 83.3% came from fossil fuel combustion,7.7% from industrial production process and 9% came from population. The carbon emissions of fossil energy consumption and industrial production increases gradually.The carbon emission of coal was the highest among all kinds of fossil fuels,occupying 97.1% of the total emission of fossil fuel consumption. The carbon emissions increase progressively and surpass the national average level from 2000 to 2009 in China. And the carbon emissions for 100 million yuan GDP is increasing.Finally, some measures are proposed for the carbon emission reduction in Zhengzhou
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Guo, Xiurui, Yaqian Shen, Wenwen Liu, Dongsheng Chen, and Junfang Liu. "Estimation and Prediction of Industrial VOC Emissions in Hebei Province, China." Atmosphere 12, no. 5 (April 21, 2021): 530. http://dx.doi.org/10.3390/atmos12050530.
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The study of industrial volatile organic compound (VOC) emission inventories is essential for identifying VOC emission levels and distribution. This paper established an industrial VOC emission inventory in 2015 for Hebei Province and completed an emission projection for the period 2020–2030. The results indicated that the total emissions of industrial VOCs in 2015 were 1017.79 kt. The use of VOC products accounted for more than half of the total. In addition, the spatial distribution characteristics of the industrial VOC emissions were determined using a geographic information statistics system (GIS), which showed that the VOCs were mainly distributed the central and southern regions of Hebei. Considering the future economic development trends, population changes, related environmental laws and regulations, and pollution control technology, three scenarios were defined for forecasting the industrial VOC emissions in future years. This demonstrated that industrial VOC emissions in Hebei would amount to 1448.94 kt and 2203.66 kt in 2020 and 2030, with growth rates of 42.36% and 116.51% compared with 2015, respectively. If all industrial enterprises took the control measures, the VOC emissions could be reduced by 69% in 2030. The analysis of the scenarios found that the most effective action plan was to take the best available control technologies and clean production in key industries, including the chemical medicine, coke production, mechanical equipment manufacturing, organic chemical, packaging and printing, wood adhesive, industrial and construction dye, furniture manufacturing, transportation equipment manufacturing, and crude oil processing industries.
12
Li, Ying, Lei Li, Ming Na, and Shengjiang Zhao. "Analysis on the Efficiency of Anhui’s Industrial Sectors under the Carbon Emission Constraints." Journal of Finance Research 3, no. 1 (April 29, 2019): 33. http://dx.doi.org/10.26549/jfr.v3i1.1363.
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This paper uses the SBM-DDF model to measure the green inefficiency of all kinds of industries in Anhui Province under the constraint of carbon emissions from 2006 to 2014. The results show that whether from the perspective of the overall industries in Anhui Province or from the perspective of separate industry groups, the sources of the green inefficiency are mainly from insufficient industrial output, followed by excessive emissions of CO2. The green inefficiency values of each group (from big to small) are sized down by high-emission industries, medium-emission industries and low-emission industries respectively. During the period of research, the effect of the emission reduction in high-emission industries was not significant, and the potential of reducing the green inefficiency in the medium-emission and low-emission industries by increasing the output was not large.
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Li, Xiao Fen, Rui Peng, and Min Xing Yang. "Analysis of Industrial Carbon Emissions Assessment and Spatial Distribution." Advanced Materials Research 1073-1076 (December 2014): 2745–50. http://dx.doi.org/10.4028/www.scientific.net/amr.1073-1076.2745.
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Under the background of ecological civilization, the energy saving and emission reduction, low carbon eco-city construction is booming. At present, China industrial carbon emissions account for more than half of total carbon emissions in the whole society. The analysis of industrial carbon emissions and its spatial distribution is quite important. Consider the Park of Shenzhen international low-carbon city as the research object; based on the enterprise data from the 2011 industrial census, it combines both the top-down and bottom-up methods to evaluate its current industrial carbon emission levels and then discusses the relationship between the industrial structure and carbon emissions. Comprehensive utilization of the spatial distribution function of GIS, the spatial distribution characteristics of industrial carbon emissions and carbon intensity is evaluated, so as to provide the references for the related policy-making of low-carbon ecological city.
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Wang, Lian Long. "Study on Industrial Carbon Emission of Qinhuangdao City." Advanced Materials Research 926-930 (May 2014): 4365–68. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.4365.
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The amounts of carbon emissions from energy consumption of industry in Qinhuangdao from year 2005 to 2010 are calculated with the methodology recommended by intergovernmental panel on climate change (IPCC) .The main driving factors of carbon emission and carbon emission intensity include low industrial benefits, large proportion of heavy industry and energy structure dominated by coal. Some suggestions are raised such as, transform and promote the traditional leading industry, enhance the equipment manufacturing industry, speed up the industrial park construction, perfect industrial chain, and adjust energy structure.
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Wang, Bing, Yifan Wang, and Yuqing Zhao. "Collaborative Governance Mechanism of Climate Change and Air Pollution: Evidence from China." Sustainability 13, no. 12 (June 15, 2021): 6785. http://dx.doi.org/10.3390/su13126785.
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Since entering the industrialized era, China’s greenhouse gas emissions and air pollutant emissions have increased rapidly. China is the country with the most greenhouse gas emissions, and it is also facing serious local air pollution problems. China’s industrial sector is the largest contributor to CO2 and air pollutants. The resulting climate change and air pollution issues have caused China to face double pressures. This article uses the CO2 and comprehensive air pollutant emission data of China’s industrial sector as a starting point and uses econometric research methods to explore the synergy between China’s industrial carbon emission reduction and industrial comprehensive air pollutant emission reduction. The synergistic effect between industrial carbon emissions and industrial comprehensive air pollutant emissions has been quantified, and the transmission path of the synergistic effect has been explored. The empirical results show that there are benefits of synergistic governance between climate change and air pollution in China’s industrial sector. Every 1000 tons of carbon reduction in the industrial sector will result in 1 ton of comprehensive air pollutant reduction. The increase in R&D expenditure in the energy and power sector can significantly promote the reduction of air pollutants in the industrial sector. Increasing the intensity of environmental regulations is the main expansion path for synergy. However, in eastern, central, and western China, the synergy is not the same. Therefore, it is necessary to formulate regionally differentiated emission reduction policies. The research conclusions of this article can provide policy references for the coordinated governance of climate change and air pollution in China.
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Zhou, Aishuang, Jinsheng Zhou, Jingjian Si, and Guoyu Wang. "Study on Embodied CO2 Emissions and Transfer Pathways of Chinese Industries." Sustainability 15, no. 3 (January 25, 2023): 2215. http://dx.doi.org/10.3390/su15032215.
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Industries with low direct CO2 emissions downstream in the industry chain have significant carbon emissions upstream, which is similar to how carbon leakage in interprovincial regions and international commerce affects these regions. Due to the interchange and transit of goods, there are intermediate production and consumption processes across industrial sectors. The CO2 emissions produced by each sector are insufficient to satisfy the sector’s ultimate demand. It will also move along with the industrial chain. Investigating embodied carbon transfer across industrial sectors is crucial to strike a balance between economic growth and greenhouse gas emissions. Locating the key sectors to reduce carbon emissions provides a basis for formulating resource conservation and environmental protection policies. In this study, the industrial sector divides into 24 subsectors, and the embodied CO2 emissions and carbon transfer pathways of each are examined from the viewpoint of the industrial chain using the Economic Input–Output Life Cycle Assessment (EIO-LCA) and the Hypothetical Extraction Method (HEM). The indirect CO2 emissions downstream of the industrial chain are higher than the direct carbon dioxide emissions, and the intersectoral carbon transfer constitutes a significant part of the total carbon emissions of the industrial sector. The upstream sector of the industry chain has a significantly higher direct carbon emission intensity than the indirect CO2 emission intensity, while the downstream sector is the opposite. The production and supply of electricity, gas and water, and raw material industries transfer significant CO2 to other sectors. The manufacturing industry is mainly the inflow of CO2. CO2 flows from the mining industry to the raw material industry and from the raw material industry to the manufacturing industry constitute the critical pathway of carbon transfer between industries. A study on the embodied carbon emissions and transfer paths of various industrial sectors is conducive to clarifying the emission reduction responsibilities and providing a basis for synergistic emission reduction strategies.
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CHEN, Zi, Changyi LIU, and Shenning QU. "China’s Industrialization and the Pathway of Industrial CO2 Emissions." Chinese Journal of Urban and Environmental Studies 03, no. 03 (September 2015): 1550019. http://dx.doi.org/10.1142/s2345748115500190.
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Industrial sector is the largest CO2 emission sector in China, thus the peak of China’s total CO2 emissions relies heavily on its industrial sector. After rapid industrialization during the last three decades, China now is between the intermediate and the late industrialization stage in general. Looking at the production and emission structures of China’s industries, especially the heavy and chemical industrial sectors which are energy- and emission-intensive industries, we claim that the output of these heavy and chemical industries will peak at around 2020, the industrialization process will complete at around 2025 and after that, China will enter the post-industrialization era. According to the CO2 emission pathways of developed countries during their industrialization, i.e. the so-called “Carbon Kuznets Curve”, and based on the characteristics of China’s industrialization and urbanization process, it is estimated that the CO2 emissions from the industrial sector will keep rising over time and reach its peak at around the year 2040 in the business-as-usual scenario; while in the low-carbon scenario, it will peak between 2025 and 2030 and decline after the year 2040.
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Lam, Yun Fat, Chi Chiu Cheung, Xuguo Zhang, Joshua S. Fu, and Jimmy Chi Hung Fung. "Development of a new emission reallocation method for industrial sources in China." Atmospheric Chemistry and Physics 21, no. 17 (September 1, 2021): 12895–908. http://dx.doi.org/10.5194/acp-21-12895-2021.
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Abstract. An accurate emission inventory is a crucial part of air pollution management and is essential for air quality modelling. One source in an emission inventory, an industrial source, has been known with high uncertainty in both location and magnitude in China. In this study, a new reallocation method based on blue-roof industrial buildings was developed to replace the conventional method of using population density for the Chinese emission development. The new method utilized the zoom level 14 satellite imagery (i.e. Google®) and processed it based on hue, saturation, and value (HSV) colour classification to derive new spatial surrogates for province-level reallocation, providing more realistic spatial patterns of industrial PM2.5 and NO2 emissions in China. The WRF-CMAQ-based PATH-2016 model system was then applied with the new processed industrial emission input in the MIX inventory to simulate air quality in the Greater Bay Area (GBA) area (formerly called Pearl River Delta, PRD). In the study, significant root mean square error (RMSE) improvement was observed in both summer and winter scenarios in 2015 when compared with the population-based approach. The average RMSE reductions (i.e. 75 stations) of PM2.5 and NO2 were found to be 11 µg m−3 and 3 ppb, respectively. Although the new method for allocating industrial sources did not perform as well as the point- and area-based industrial emissions obtained from the local bottom-up dataset, it still showed a large improvement over the existing population-based method. In conclusion, this research demonstrates that the blue-roof industrial allocation method can effectively identify scattered industrial sources in China and is capable of downscaling the industrial emissions from regional to local levels (i.e. 27 to 3 km resolution), overcoming the technical hurdle of ∼ 10 km resolution from the top-down or bottom-up emission approach under the unified framework of emission calculation.
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Zheng, Kun, Hongbing Deng, Kangni Lyu, Shuang Yang, and Yu Cao. "Market Integration, Industrial Structure, and Carbon Emissions: Evidence from China." Energies 15, no. 24 (December 11, 2022): 9371. http://dx.doi.org/10.3390/en15249371.
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Against the backdrop of China’s carbon emission reduction targets and the promotion of the construction of a unified domestic market, what kind of carbon emission effect has market integration had in weakening regional barriers and optimizing resource allocation? This paper adopts a two-way fixed effects analysis based on China’s provincial panel data from 2003 to 2019. It uses a mediation model to explore the relationship between market integration and carbon emissions. Furthermore, industrial rationalization and upgrade are the basis for examining whether a competitive or cooperative relationship exists between the carbon emission effects generated and promoting market integration between regions. The study finds a negative relationship between market integration and carbon emissions. In addition, there is significant heterogeneity in the effect of market integration on carbon emissions, and the influence effect is mainly in central China; industrial rationalization can play an enhanced role in the process of the negative impact of market integration on carbon emissions, further enhancing the negative contribution of market integration to carbon emissions. However, market integration can weaken its negative impact on carbon emissions with the industrial upgrade, and there may still be invisible barriers between regions in promoting market integration barriers.
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Chontanawat, Jaruwan, Paitoon Wiboonchutikula, and Atinat Buddhivanich. "Decomposition Analysis of the Carbon Emissions of the Manufacturing and Industrial Sector in Thailand." Energies 13, no. 4 (February 12, 2020): 798. http://dx.doi.org/10.3390/en13040798.
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Since the 1990s, CO2 emissions have increased steadily in line with the growth of production and the use of energy in the manufacturing sector in Thailand. The Logarithmic Mean Divisia Index Method is used for analysing the sources of changes in CO2 emissions as well as the CO2 emission intensity of the sector in 2000–2018. On average throughout the period, both the amount of CO2 emissions and the CO2 emission intensity increased each year relative to the baseline. The structural change effect (effect of changes of manufacturing production composition) reduced, but the intensity effect (effect of changes of CO2 emissions of individual industries) increased the amount of CO2 emissions and the CO2 emission intensity. The unfavourable CO2 emission intensity change came from the increased energy intensity of individual industries. The increased use of coal and electricity raised the CO2 emissions, whereas the insignificant change in emission factors showed little impact. Therefore, the study calls for policies that decrease the energy intensity of each industry by limiting the use of coal and reducing the electricity used by the manufacturing sector so that Thailand can make a positive contribution to the international community’s effort to achieve the goal of CO2 emissions reduction.
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Teng, Xiangyu, Liang Lu, and Yung-ho Chiu. "Considering Emission Treatment for Energy-Efficiency Improvement and Air Pollution Reduction in China’s Industrial Sector." Sustainability 10, no. 11 (November 21, 2018): 4329. http://dx.doi.org/10.3390/su10114329.
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China has one of the most serious air quality conditions in the world, with the main energy consumption and air pollution emissions coming from its industrial sector. Since 2010, the Chinese government has strengthened the governance requirements for industrial sector emissions. This study uses emission treatment as a new input on the basis of past literature, and employs the dynamic SBM model to evaluate the energy and emission-reduction efficiencies of the country’s industrial sector from 2011 to 2015. The study finds that the improvement in industrial sector efficiency is not only due to the optimization of the energy consumption structure and reduction of energy intensity, but also from investing inemission treatment methods that help cut emissions as an undesirable output. The end result is a positive effect on the improvement and sustainability of energy and emission-reduction efficiencies.
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Ma, Zhuo, Xiao Gang He, Xun Zhou Tong, Hai Yan Duan, Xian En Wang, and De Ming Dong. "The Study on Carbon Emission Influencing Factors of Industrial Energy Consumption of Changchun City." Applied Mechanics and Materials 164 (April 2012): 302–5. http://dx.doi.org/10.4028/www.scientific.net/amm.164.302.
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To make great efforts for energy saving and promote low-carbon transition of industrial development pattern have been the most crucial tasks for Changchun industrial developmen. Using Logarithmic Mean Divisia Index (LMDI) mode decomposes the carbon emission influencing factors of the industrial department in Changchun, and study on the effects of factors on the carbon emissions of industrial energy consumption. The result shows that the major factors for carbon emissions of industrial energy consumption in Changchun are economic development, the population size and the industrialization rate, and the key factors for the carbon emission changes in industrial department of Changchun are the energy consumption structure and the energy intensity.
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Cheng, Pengfei, Xingang Huan, and Baekryul Choi. "The Comprehensive Impact of Outward Foreign Direct Investment on China’s Carbon Emissions." Sustainability 14, no. 23 (December 2, 2022): 16116. http://dx.doi.org/10.3390/su142316116.
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Outward foreign direct investment (OFDI), as an important carrier of global technology and industrial transfer, will significantly impact the home country’s environment. Therefore, using data from 30 Chinese provinces gathered between 2004 and 2019, we empirically analyze the impact of OFDI on China’s carbon emissions across two dimensions: total carbon emissions and carbon emission efficiency. In addition, when the previous studies explored the impact of OFDI on carbon emissions, there were few studies on the synergistic emission reduction effect of OFDI. Therefore, based on sorting out previous research, we incorporated OFDI, technological progress, industrial structure upgrading, international trade, and carbon emissions into the same analytical framework. Based on the classic fixed model, we introduce the interaction term further to explore the synergistic emission reduction effect of OFDI. Our model suggests that OFDI has increased total carbon emissions, but the associated reverse technology spillover has improved carbon emission efficiency. We also found a synergistic emission reduction effect between OFDI and technological progress, international trade, and industrial structure upgrading. This synergistic effect suppresses the growth of total carbon emissions and improves carbon emissions efficiency. Robustness testing confirmed these results. This research also provides a relatively novel perspective for China to achieve the goals of “carbon peaking” and “carbon neutrality”.
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Zhu, Xiaoqing, Tiancheng Zhang, Weijun Gao, and Danying Mei. "Analysis on Spatial Pattern and Driving Factors of Carbon Emission in Urban–Rural Fringe Mixed-Use Communities: Cases Study in East Asia." Sustainability 12, no. 8 (April 13, 2020): 3101. http://dx.doi.org/10.3390/su12083101.
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Urban-intensive areas are responsible for an estimated 80% of greenhouse gas emissions, particularly carbon dioxide. The urban–rural fringe areas emit more greenhouse gases than urban centers. The purpose of this study is to analyze the spatial pattern and driving factors of carbon emissions in urban–rural fringe mixed-use communities, and to develop planning methods to reduce carbon emissions in communities. This study identifies mixed-use communities in East Asian urban–rural fringe areas as industrial, commercial, tourism, and rental-apartment communities, subsequently using the emission factor method to calculate carbon emissions. The statistical information grid analysis and geographic information systems spatial analysis method are employed to analyze the spatial pattern of carbon emission and explore the relationship between established space, industrial economy, material consumption, social behavior, and carbon emission distribution characteristics by partial least squares regression, ultimately summing up the spatial pattern of carbon emission in the urban–rural fringe areas of East Asia. Results show that (1) mixed-use communities in the East Asian urban–rural fringe areas face tremendous pressure to reduce emissions. Mixed-use community carbon emissions in the late urbanization period are lower than those the early urbanization. (2) Mixed-use community carbon emission is featured by characteristics, such as planning structure decisiveness, road directionality, infrastructure directionality, and industrial linkage. (3) Industrial communities produce the highest carbon emissions, followed by rental-apartment communities, business communities, and tourism communities. (4) The driving factor that most affects the spatial distribution of carbon emissions is the material energy consumption. The fuel consumption per unit of land is the largest driver of carbon emissions. Using the obtained spatial pattern and its driving factors of carbon emissions, this study provides suggestions for planning and construction, industrial development, material consumption, and convenient life guidance.
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Wang, Jianshi, Shangkun Yu, Mengcheng Li, Yu Cheng, and Chengxin Wang. "Study of the Impact of Industrial Restructuring on the Spatial and Temporal Evolution of Carbon Emission Intensity in Chinese Provinces—Analysis of Mediating Effects Based on Technological Innovation." International Journal of Environmental Research and Public Health 19, no. 20 (October 17, 2022): 13401. http://dx.doi.org/10.3390/ijerph192013401.
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Global warming caused by greenhouse gas emissions seriously threatens a region's sustainable environmental and socioeconomic development. Promoting industrial restructuring and strengthening technological innovation have become an important path to achieving pollution and carbon reduction as well as the green transformation of economic structure. This paper explored the mechanism of the mediating effect of technological innovation on industrial restructuring and carbon reduction while accounting for the direct effect of industrial restructuring on carbon emissions. Then, based on China's provincial panel data from 2001 to 2019, we estimated the carbon emission intensity using the Intergovernmental Panel on Climate Change (IPCC)'s methods and analyzed its spatiotemporal evolution characteristics. Finally, we constructed a fixed-effect model and a mediating effect model to empirically analyze how industrial restructuring and technological innovation affect carbon emission intensity. The results are as follows: (1) From 2001 to 2019, China's carbon emission intensity showed a continuous downward trend, with a pronounced convergence trend; there were obvious differences in carbon emission intensity between eastern, central, and western regions (western region > central region > eastern region) due to the unbalanced industrial structure. (2) In terms of direct effects, industrial restructuring can significantly reduce carbon emission intensity. The intensity of the effect is inversely proportional to the level of industrial restructuring, and the results of sub-regional tests are similar. Nevertheless, there is an obvious regional difference in the size of the carbon emission reduction effect of industrial restructuring in the east, central, and western regions. (3) In terms of indirect effects, industrial restructuring can reduce carbon emission intensity by enhancing technological innovation, and it acts as a mediating variable in the process of industrial restructuring to reduce carbon emission. Finally, we put forward recommendations for promoting industrial restructuring, strengthening green technological innovation, and properly formulating carbon reduction measures to provide a reference for countries and regions to achieve the goals of carbon neutrality, carbon peaking, and high-quality economic development.
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Li, Jingyuan, Jinhua Cheng, Beidi Diao, Yaqi Wu, Peiqi Hu, and Shurui Jiang. "Social and Economic Factors of Industrial Carbon Dioxide in China: From the Perspective of Spatiotemporal Transition." Sustainability 13, no. 8 (April 12, 2021): 4268. http://dx.doi.org/10.3390/su13084268.
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The reduction of CO2 emission has become one of the significant tasks to control climate change in China. This study employs Exploratory Spatial Data Analysis (ESDA) to identify the provinces in China with different types of spatiotemporal transition, and applies the Logarithmic Mean Divisia Index (LMDI) to analyze the influencing factors of industrial CO2 emissions. Spatial autocorrelation of provincial industrial CO2 emissions from 2003 to 2017 has been demonstrated. The results are as follows: (1) 30 provinces in China are categorized into 8 types of spatiotemporal transition, among which 24 provinces are characterized by stable spatial structure and 6 provinces show significant spatiotemporal transition; (2) For all types of spatiotemporal transition, economic scale effect is mostly contributed to industrial CO2 emission, while energy intensity effect is the most crucial driving force to reduce industrial carbon dioxide emission; (3) provinces of type HH-HH, HL-HL and HL-HH are most vital for CO2 emission reduction, while the potential CO2 emission increase of developing provinces in LL-LL, LH-LH and LL-LH should also be taken into account. Specific measures for CO2 emission reduction are suggested accordingly.
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Liu, Shao Bo. "Energy Consumption and Structural Reformation in Chinese Northeast Old Industrial Base." Applied Mechanics and Materials 448-453 (October 2013): 4281–84. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.4281.
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Using IPCC methodology, the carbon emissions of Chinese Northeast Old Industrial Base is calculated, and the energy's synthesized impact on carbon emissions intensity is presented. The resulting shows that the carbon emissions in the three northeast provinces decreased 52.87% from 2000 to 2010, of which, Liaoning, Jilin and Heilongjiang are individually 60.09%, 45.47% and 54.14% lower. The implications are that the energy structure is one of the main factors in carbon emission in the Old Industrial Base of Northeast China, and its industrial structure is changing greatly due to energy consumption carbon emission. To adjust optimally the energy and industrial structure, and to develop the energy technology to promote energy utilization are recommended.
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Mahmud, Ashique, and Ataul Gani Osmani. "Investigating the relationship between CO2 emission and industrial production of bangladesh through the unrestricted vector auto regression methods." Journal of Management and Science 11, no. 2 (March 31, 2021): 25–34. http://dx.doi.org/10.26524/jms.11.11.
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Industrial production units discharge large amounts of CO2 as manufacturing facilities directly use fossil fuels and more electricity than any other sector. Although the per capita emissions in the industrialized countries are typically as much as ten times the average in the developing countries, this study is aimed at analyzing the long-run association between CO2 emissions and industrial production in Bangladesh using an Unrestricted Vector Auto Regression approach. For this purpose, the study uses secondary data for the periods of 1960 to 2016 from world development indicators. The variables of interest are co2 emission and industrial production. In general CO2 data are measured in metric tons per capita and the industrial production index is used as the proxy of industrial production. Other econometric techniques, such as unit root test-ADF, Johanson Co-integration test, and OLS techniques are also applied. Firstly, a descriptive analysis finds that there has been a rapid fall in industrial output and co2 emission in 1971 which can be denoted as an adverse effect of the Independence war of Bangladesh. Despite that, industrial production and co2 emission are intended to increase at a positive slope till 2016. But the increasing rate of industrial production is significantly higher than the increasing rate of co2 emission in Bangladesh. Secondly, the Johanson Co-integration test results reveal that there is no long-run relationship between industrial production and CO2 emission in Bangladesh. But the results from Unrestricted VAR and Ordinary Least Square estimation confirm that CO2 emission one period lag has a negative and significant impact on the industrial production of Bangladesh, where the value of the coefficient is -16.01059. This means that if in the last year, co2 emission increased by 1 metric ton per capita, industrial production will be decreased by 16% in the current period. The study concludes that Bangladesh is running conscious industrial production considering energy conservation policies.
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Yang, Na, Zilong Zhang, Bing Xue, Junxia Ma, Xingpeng Chen, and Chenyu Lu. "Economic Growth and Pollution Emission in China: Structural Path Analysis." Sustainability 10, no. 7 (July 23, 2018): 2569. http://dx.doi.org/10.3390/su10072569.
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The relationship between economic growth and environmental pollution has long been a controversial topic. However, simply the detection of the existence of environmental Kuznets curve (EKC) is not enough to understand how economic growth induced environmental pollution. This study investigated the path and mechanism of the effect of economic growth on the emission of two types of environmental pollutants, that is, industrial smoke and dust and sulfur dioxide, by using a structural equation model and a sample of 283 prefecture-level cities in China in 2005 and 2015. The research results show that economic growth exerted both direct and indirect effects on the emission of the two environmental pollutants. In addition to a direct impact through the economic scale effect, economic growth also indirectly impacted the two environmental pollutants emissions through three mediators, that is, industrial structure, technological innovations and environmental regulations. For different pollutants, the effect paths of economic growth on their emission showed both similarities and differences. First, with regards to industrial smoke and dust emissions and sulfur dioxide emissions, the effects of economic growth on the amount of these two emissions through environmental regulations and the industrial structure were negative inhibitory effects and positive promoting effects, respectively. This means that in prefectural-level cities in China, environmental regulation factors have produced some effects in reducing the emissions of these two pollutants while the industrial structure (level of industrialization) can increase the emissions of these two pollutants. However, the effect strength of these two paths shows a gradual weakening. Second, these two paths differ in effect strength and its changes. The positive promoting effects of the industrial structure on pollutant emission are significantly higher than the inhibitory effects of environmental regulation. In addition, our study also found that the direct impact path of economic growth on environmental pollution also passed significance testing, particularly in 2015. This shows that other reasons affect pollutant emission, such as system factors, spatial migration of industries and so forth.
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Baran, Tomasz. "The use of waste and industrial by-products and possibilities of reducing CO2 emission in the cement industry – industrial trials." Cement Wapno Beton 26, no. 3 (2021): 169–84. http://dx.doi.org/10.32047/cwb.2021.26.3.1.
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This article aims to present the possibility of reducing CO2 emission in the composite cement production, by using large amounts of industrial by-products and to present the possibility of reducing CO2 emission in the process of Portland clinker synthesis. The last one will be the result of using raw materials containing calcium compounds other than carbonates and the use of alternative fuels containing biomass for the synthesis of clinker, the combustion of which is not included in the CO2 emission balance. Replacing one mass % of CaO in the raw mix as limestone, reduces the emission by 8 kg CO2 per Mg of clinker. The reduction of CO2 emissions was evaluated and confirmed by industrial production trial. Clinker was produced using raw materials containing carbide lime or limestone fly ash. The results of the trial showed, that the use of 2%÷5% of Bełchatów calcareous fl y ash in the composition of the raw mix, allows of reducing the emission by 4.0÷10.3 kg of CO2 per Mg of Portland clinker. The use of 2%÷5% of carbide lime in the composition of the raw mix leads to emission reduction by 9.5÷23.9 kg of CO2 per Mg of Portland clinker. On the other hand, the development of composite cement production with a large amount of industrial by-products, seems to be the basic solution for the coming years, allowing a significant reduction of CO2 emission in the cement industry and in the concrete production.
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Fan, Jia Feng, Hao Xu, Juan Yuan, and Bang Zhu Zhu. "Cluster Analysis of Industrial Transfer Park Based on Carbon Emission Intensity." Applied Mechanics and Materials 291-294 (February 2013): 1550–55. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.1550.
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The carbon emission control of Industrial Transfer Park is analyzed from four aspects. Metrics of these four aspects are energy consumption per industrial value added of leading industry, carbon emissions intensity of buildings, carbon emissions intensity of transportation and carbon sinks. On this basis, 36 industrial transfer parks in Guangdong province are analyzed with the method of Hierarchical cluster in order to explore practical measures to reduce carbon emissions in the parks.
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Zhang, Guoxing, and Mingxing Liu. "The Changes of Carbon Emission in China’s Industrial Sectors from 2002 to 2010: A Structural Decomposition Analysis and Input-Output Subsystem." Discrete Dynamics in Nature and Society 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/798576.
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Based on 2002–2010 comparable price input-output tables, this paper first calculates the carbon emissions of China’s industrial sectors with three components by input-output subsystems; next, we decompose the three components into effect of carbon emission intensity, effect of social technology, and effect of final demand separately by structure decomposition analysis; at last, we analyze the contribution of every effect to the total emissions by sectors, thus finding the key sectors and key factors which induce the changes of carbon emissions in China’s industrial sectors. Our results show that in the latest 8 years five departments have gotten the greatest increase in the changes of carbon emissions compare with other departments and the effect of final demand is the key factor leading to the increase of industrial total carbon emissions. The decomposed effects show a decrease in carbon emission due to the changes of carbon emission intensity between 2002 and 2010 compensated by an increase in carbon emissions caused by the rise in final demand of industrial sectors. And social technological changes on the reduction of carbon emissions did not play a very good effect and need further improvement.
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Tian, Guiliang, Suwan Yu, Zheng Wu, and Qing Xia. "Study on the Emission Reduction Effect and Spatial Difference of Carbon Emission Trading Policy in China." Energies 15, no. 5 (March 6, 2022): 1921. http://dx.doi.org/10.3390/en15051921.
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To cope with huge carbon emission pressure, China has implemented a carbon emissions trading pilot policy that aims to provide reasonable suggestions for the smooth operation of the national carbon market. This paper selects the provincial panel data in China from 2005 to 2019 and uses the propensity score matching-difference in difference (PSM-DID) method to evaluate the carbon emission policy’s reduction effect. Based on carbon emissions (CE) and carbon emission intensity (CI), provinces and cities are divided into four regions, and each region is verified by spatial difference analysis. Furthermore, the mediating effects of carbon emission reduction through the dual aspects of technological progress and industry structure are also discussed. Results verified that, (1) under the carbon emission trading policy, regional carbon emissions and carbon emission intensity are both significantly reduced. (2) Technological progress helps to reduce carbon emissions, while industrial structure shows no obvious contribution. (3) The four regions all show ideal emission reduction effects, of which the High CE-High CI region shows the best, but is greatly restricted by techniques. The industrial structure of the High CE-Low CI region needs to be further optimized for carbon reduction. In the Low CE-High CI region, the carbon emissions brought by economic development fail to effectively improve per capita GDP. The Low CE-Low CI region contributes greatly to carbon emission reduction with technical advantages.
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Le, Dong, Fei Ren, Yiding Tang, and Yuke Zhu. "The Effect of Environmental Policy Uncertainty on Enterprises’ Pollution Emissions: Evidence from Chinese Industrial Enterprise." International Journal of Environmental Research and Public Health 19, no. 16 (August 10, 2022): 9849. http://dx.doi.org/10.3390/ijerph19169849.
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In response to the global call for emission reduction, China has assumed international responsibility for energy conservation and emission reduction by enacting several environmental policies to save energy and reduce consumption. However, it is debatable whether the increased uncertainty in environmental policies negatively affects firms’ emission reduction. Few studies have examined this relationship based on micro-level data. Therefore, this study constructs a theoretical framework of environmental policy uncertainty affecting firms’ pollution emissions. Based on comprehensive data from the Chinese Industrial Enterprise Database, the Chinese Industrial Enterprise Pollution Emission Database, and the Chinese Patent Database from 2002 to 2014, we empirically analyzed the impact of environmental policy uncertainty on firms’ pollution emissions. The results show that (1) environmental policy uncertainty significantly aggravates the pollution emission intensity of industrial enterprises; (2) environmental policy uncertainty inhibits the improvement of enterprises’ innovation capacity, reduces their human capital stock and foreign investment, and aggravates their pollution emission; (3) environmental policy uncertainty has significant heterogeneity on enterprise pollution emissions, that is, environmental policy uncertainty has a greater impact on non-export enterprises, large enterprises, young enterprises, capital-intensive enterprises, state-owned enterprises, and enterprises in polluting industries and central regions. This study provides a useful reference for the improvement of environmental policy and the green transformation of enterprises.
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Zhang, Xiao, Meng Li, Qiao Li, Yanan Wang, and Wei Chen. "Spatial Threshold Effect of Industrial Land Use Efficiency on Industrial Carbon Emissions: A Case Study in China." International Journal of Environmental Research and Public Health 18, no. 17 (September 5, 2021): 9368. http://dx.doi.org/10.3390/ijerph18179368.
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China’s industry is still in the middle of industrialization. Land use activities are crucial to the growth of carbon emissions. However, few scholars focus on the influence mechanism between industrial land use efficiency (ILUE) and industrial carbon emissions. In this paper, the threshold model and the spatial Durbin model are used to investigate the spatial threshold effect of industrial land use efficiency on industrial carbon emission from 2003 to 2018. The results show that ILUE of China’s provinces basically shows an improvement trend, with little difference in spatial distribution, showing a pattern of high in the eastern region and low in the western region. When economic development level (A) and technical level (T) are taken as the threshold variable, ILUE has a single threshold effect on industrial carbon emissions in the eastern region. In the central region, with a as the threshold variable, ILUE shows a double threshold effect on industrial carbon emission. Under the 0–1 geographical proximity weight matrix, the indirect spillover effect of ILUE on reducing regional carbon emissions is significant, and the indirect effect is even greater than that on regional carbon emissions. The spatial spillover effect is not significant in the eastern region. These findings have important practical significance for promoting regional industrial transformation and upgrading, optimizing land space and realizing high-quality economic development.
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Huang, Liang Xiong, and Yong Hui Han. "Industrial Restructuring and Emission Reduction." Advanced Materials Research 869-870 (December 2013): 777–80. http://dx.doi.org/10.4028/www.scientific.net/amr.869-870.777.
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The most essential way to realize the goals in emission reduction is to speed up industrial restructuring and promote the transformation of economic growth pattern. This article collected four-digit indexes concerning industrial restructuring in 30 provinces (cities, regions) in China from 1999 to 2007 to show the extent of industrial restructuring in that area. And then it matched these figures to those of environmental pollution on provincial level. This way, it would be able to see how manufacturing industrys inner restructuring affects the emission. The results showed that Chinas industrial restructuring had led to a significant decrease in the emission per unit of GDP.
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Volkodaeva, M. V., O. A. Taranina, and V. A. Kuznecov. "Measuring of industrial emission parameters." IOP Conference Series: Earth and Environmental Science 194 (November 15, 2018): 062035. http://dx.doi.org/10.1088/1755-1315/194/6/062035.
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Prasad, R. "Acoustic emission - its industrial applications." NDT & E International 25, no. 6 (December 1992): 302. http://dx.doi.org/10.1016/0963-8695(92)90753-4.
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Prasad, R. "Acoustic emission — its industrial applications." NDT & E International 27, no. 4 (January 1994): 221. http://dx.doi.org/10.1016/0963-8695(94)90585-1.
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Liu, Jianhua, Tianle Shi, and Liangchao Huang. "A Study on the Impact of Industrial Restructuring on Carbon Dioxide Emissions and Scenario Simulation in the Yellow River Basin." Water 14, no. 23 (November 24, 2022): 3833. http://dx.doi.org/10.3390/w14233833.
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Based on a detailed analysis of the impact mechanism of industrial restructuring on carbon dioxide emissions in the Yellow River Basin, this paper first calculated the carbon dioxide emission data of 57 prefecture-level cities in the Yellow River Basin from 2009 to 2019 and constructed indicators from two dimensions: the advancement and the rationalization of the industrial structure. Then, the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model was used to empirically analyze the influencing factors of industrial structure adjustments on carbon dioxide emissions in the Yellow River Basin. Consequently, changing carbon dioxide emission trends in the Yellow River Basin under various scenarios were predicted. The research observed the following: (1) the eastern part of the Shandong Peninsula Urban Agglomeration and the Energy Golden Triangle have higher carbon dioxide emissions; (2) the advancement of industrial structures in the Yellow River Basin has a better emission reduction effect than the rationalization of industrial structures; (3) increased foreign investment will lead to an increase in carbon dioxide emissions in the Yellow River Basin, and a “Pollution Refuge Effect” will emerge; (4) accelerated industrial transformations and upgrades, high-quality economic development, and a moderate population growth rate are consistent with future development trends.
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Peng, Hui, Yifan Wang, Yisha Hu, and Hong Shen. "Agglomeration Production, Industry Association and Carbon Emission Performance: Based on Spatial Analysis." Sustainability 12, no. 18 (September 4, 2020): 7234. http://dx.doi.org/10.3390/su12187234.
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Current emission reduction policies have struggled to adapt to the reality of industrial spatial agglomeration and increasing industrial linkages. In response, this paper incorporates new economic geography factors such as agglomeration production and industrial (trade) association into the analysis framework of carbon emission performance factors through China’s provincial panel data and conducts empirical research. It has been found that large-scale industrial production under economic agglomeration is conducive to improving carbon emission performance and that different forms of agglomeration at different degrees of agglomeration correspond to different carbon emission performances. As the degree of agglomeration increases, the effect of reducing emissions by specialized agglomeration decreases while the effect of reducing emissions by diversified agglomeration increases. Specialized agglomeration externalities and diversified agglomeration externalities can coexist at the same time, depending on the appropriate degree of agglomeration. There is a strong negative environmental efficiency effect in the provinces with close commodity trade links, which has triggered environmental dumping and pollution transfer between provinces. In the work of energy conservation and emission reduction, we must attach great importance to the hidden carbon in domestic merchandise trade and the resulting intergovernmental environmental game, and furthermore, give full play to the “self-purification” effect of aggregate production on energy conservation and emission reduction.
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Saraswati Rudianto, Ines. "Characteristics of Particulate Emissions from Co-Firing in An Industrial Boiler." Jurnal Ecolab 15, no. 1 (May 1, 2021): 23–29. http://dx.doi.org/10.20886/jklh.15.1.23-29.
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Characteristics of Particulate Emissions from Co-Firing in An Industrial Boiler. PT. X is a textile industry that consumes a massive amount of coal for its boiler operation. It requires substantial costs to obtain coal from Sumatra and Kalimantan. An alternative solid biofuel (briquette) was developed to combine bottom ash and biomass made from municipal solid waste called Biomass Coal Fuel (BCF) briquette. The purpose of this study is to measure the total concentration of particulate matter and emission factor (PM) emitted from two burning experiments: only coal (100%) and mixed coal fuel with 10% of BCF (co-firing). Mixed coal and BCF burning are carried out in the fire-tube boiler where the PM emission is released through the stack. The Center for Pulp and Paper measured particulate emission with methodology referring to SNI 7117.17-2009. Particulate matter concentration emitted from only coal-burning was 12,1 mg/Nm3,but when mixed BCF and coal were used, the higher concentration was emitted 70,9 mg/Nm3. The addition of BCF briquettes affects the particulate matter emission, even though the emission does not exceed the regulated quality standard. The increase of particulate concentration is due to the BCF briquette characteristics, which have a low heating value and high ash content. The boiler has already been equipped with cyclone and wet scrubber; therefore, PM emissions presented here are treated emissions. The controlled PM emission factor of BCF was 4,46 g/kg, which is higher than only coal which was 0,51 g/kg. BCF briquette can still be used as co-fuel for the boiler, but further effort is still required to reduce the ash content of the BCF and increase the calorific value of the BCF.
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Zhang, Shiyue, Alan R. Collins, Xiaoli L. Etienne, and Rijia Ding. "The Environmental Effects of International Trade in China: Measuring the Mediating Effects of Technology Spillovers of Import Trade on Industrial Air Pollution." Sustainability 13, no. 12 (June 18, 2021): 6895. http://dx.doi.org/10.3390/su13126895.
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China is in a strategic phase of an industrial green transformation. Industrial air pollution is a key environmental target for governance. Because import trade is a core channel through which advanced environmental protection technology is absorbed, the question of whether technology spillovers brought about by import trade can reduce industrial air pollution emissions is a topic worth exploring. This paper uses a generalized spatial two-stage least-square (GS2SLS) model to explore the impact of import trade technology spillovers on industrial air pollution emission intensities using panel data from 30 provinces and cities between 2000 and 2017. Economic scale, industrial structure, and technological innovation are used as intermediary variables to test whether they play mediating effects. The results show that: (1) capital and intermediate goods technology spillovers directly reduce industrial air pollution emission intensity and (2) import trade technology spillovers indirectly reduce emission intensities by expanding economic scale, optimizing industrial structure, and enhancing technological innovation through mediating variables. Furthermore, industrial structure optimization and technological innovation have the largest mediating effects on industrial SO2, while economic expansion has the most significant mediating effect on industrial smoke and dust. The mediating effects of technology spillovers from intermediate goods exceed those of capital technology spillovers. Finally, industrial air pollution emission intensity demonstrates both spatial agglomeration and time lag effects. Environmental regulations and energy structure are shown to increase industrial air pollution emissions, while urbanization and foreign direct investment reduce industrial air pollution. Based upon these research results, some pertinent policy implications are proposed for China.
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Yang, Junliang, and Haiyan Shan. "Identifying Driving Factors of Jiangsu’s Regional Sulfur Dioxide Emissions: A Generalized Divisia Index Method." International Journal of Environmental Research and Public Health 16, no. 20 (October 19, 2019): 4004. http://dx.doi.org/10.3390/ijerph16204004.
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The Chinese government has made some good achievements in reducing sulfur dioxide emissions through end-of-pipe treatment. However, in order to implement the stricter target of sulfur dioxide emission reduction during the 13th “Five-Year Plan” period, it is necessary to find a new solution as quickly as possible. Thus, it is of great practical significance to identify driving factors of regional sulfur dioxide emissions to formulate more reasonable emission reduction policies. In this paper, a distinctive decomposition approach, the generalized Divisia index method (GDIM), is employed to investigate the driving forces of regional industrial sulfur dioxide emissions in Jiangsu province and its three regions during 2004–2016. The contribution rates of each factor to emission changes are also assessed. The decomposition results demonstrate that: (i) the factors promoting the increase of industrial sulfur dioxide emissions are the economic scale effect, industrialization effect, and energy consumption effect, while technology effect, energy mix effect, sulfur efficiency effect, energy intensity effect, and industrial structure effect play a mitigating role in the emissions; (ii) energy consumption effect, energy mix effect, technology effect, sulfur efficiency effect, and industrial structure effect show special contributions in some cases; (iii) industrial structure effect and energy intensity effect need to be further optimized.
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Yang, Yize, Xiujian Wei, Jie Wei, and Xiang Gao. "Industrial Structure Upgrading, Green Total Factor Productivity and Carbon Emissions." Sustainability 14, no. 2 (January 17, 2022): 1009. http://dx.doi.org/10.3390/su14021009.
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Carbon emission reduction is becoming a global issue. Methods of reducing carbon emissions in developing countries have become a hot topic of discussion. Based on the obvious structural transformation in developing countries, this paper discusses the logical mechanisms among industrial structure upgrading, green total factor productivity improvements, and carbon emission reduction. In addition, this paper empirically tests these relationships with provincial data from 2000 to 2017 in China. The conclusions are as follows: (1) industrial structure upgrades have a significant impact on carbon emissions. The industrial structure rationalization remains a noteworthy inhibition on carbon emissions. The industrial structure’s advancement has obvious features of development at the current stage, and its effect on carbon emissions shows an inverted “V” trend, which is initially accelerating but then restraining. (2) Upgrades to industrial structures will decrease carbon emissions by raising green total factor productivity. (3) The rise of green total factor productivity in a certain region will have a relatively obvious inhibitory effect on carbon emissions, but it will exhibit a negative spatial spillover effect on the adjacent areas.
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Jiang, Hongtao, Jian Yin, Yuanhong Qiu, Bin Zhang, Yi Ding, and Ruici Xia. "Industrial Carbon Emission Efficiency of Cities in the Pearl River Basin: Spatiotemporal Dynamics and Driving Forces." Land 11, no. 8 (July 22, 2022): 1129. http://dx.doi.org/10.3390/land11081129.
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In the context of green and high-quality development, effectively enhancing industrial carbon emission efficiency is critical for reducing carbon emissions and achieving sustainable economic growth. This study explored this research area using three models: the super-efficient SBM model was used to measure the industrial carbon emission efficiency of 48 cities in the Pearl River Basin from 2009 to 2017; the exploratory spatiotemporal data analysis method was used to reveal the spatiotemporal interaction characteristics of industrial carbon emission efficiency; and the geographical detectors and geographically weighted regression model were employed to explore the influencing factors. The results are as follows: (1) The Pearl River Basin’s industrial carbon emission efficiency steadily increased from 2009 to 2017, with an average annual growth rate of 0.18 percent, but the industrial carbon emission efficiency of some sites remains low; (2) The local spatiotemporal pattern of industrial carbon emission efficiency is solitary and spatially dependent; (3) The spatial variation of industrial carbon emission efficiency is influenced by a number of factors, including the industrialization level, openness to the outside world, the science and technology level, energy consumption intensity, and productivity level, with the productivity level, industrialization level, and openness to the outside world being the most important. Among these factors, the productivity level, science and technology level, openness to the outside world, and industrialization level all have a positive correlation with industrial carbon emission efficiency, but energy consumption intensity has a negative correlation. This study provides an integrated framework using exploratory spatiotemporal analysis and geographically weighted regression to examine carbon emission efficiency among cities. It can serve as a technical support for carbon reduction policies in cities within the Pearl River Basin, as well as a reference for industrial carbon emission studies of other regions of the world.
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Wang, Xian’en, Tingyu Hu, Junnian Song, and Haiyan Duan. "Tracking Key Industrial Sectors for CO2 Mitigation through the Driving Effects: An Attribution Analysis." International Journal of Environmental Research and Public Health 19, no. 21 (November 7, 2022): 14561. http://dx.doi.org/10.3390/ijerph192114561.
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The heavy pressure to improve CO2 emission control in industry requires the identification of key sub-sectors and the clarification of how they mitigate CO2 emissions through various actions. Focusing on 30 Chinese provincial regions, this study quantifies the contribution of each industrial sector to regional CO2 mitigation by combining the logarithmic mean Divisia index with attribution analysis and extract the key sectors of CO2 mitigation for each region. Results indicate that during 2010–2019, significant emission reduction was achieved through energy intensity (74%) in Beijing, while emission reductions were attained through industrial structure changes for Anhui (50%), Henan (45%), and Chongqing (45%). The contribution to emission reduction through energy structures is not significant. The production and supply of power and heat (PSPH) is a central factor in CO2 mitigation through all three inhibitive factors. Petroleum processing and coking (PPC) generally contributes to emission reduction through energy structures, while the smelting and pressing of ferrous metals (SPMF) through changes in industrial structures and energy intensity. PSPH and SPMF, in most regions, have not achieved the emission peak. Except in the case of coal mining and dressing (CMD), CO2 emissions in other key sectors have almost been decoupled from industrial development. CMD effectively promotes CO2 mitigation in Anhui, Henan, and Hunan, with larger contribution of PPC in Tianjin, Xinjiang, Heilongjiang, and that of smelting and pressing of nonferrous metals in Yunnan and Guangxi. The findings help to better identify key sectors across regions that can mitigate CO2 emissions, while analyzing the critical emission characteristics of these sectors, which can provide references to formulating region- and sector-specific CO2 mitigation measures for regions at different levels of development.
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Yue, Jiachen, Huasheng Zhu, and Fei Yao. "Does Industrial Transfer Change the Spatial Structure of CO2 Emissions?—Evidence from Beijing-Tianjin-Hebei Region in China." International Journal of Environmental Research and Public Health 19, no. 1 (December 29, 2021): 322. http://dx.doi.org/10.3390/ijerph19010322.
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As an important cause of global warming, CO2 emissions have become a research hotspot in recent years. Industrial transfer impacts regional CO2 emissions and is related to the low-carbon development of regional industries. Taking the Beijing-Tianjin-Hebei region (BTH region) as an example, this study analysed industrial transfer’s direct and indirect impacts on CO2 emissions based on a mediating model and two-way fixed effect panel regression. The results obtained indicate that industrial transfer-in has promoted CO2 emissions to a small extent, and the positive impact of industrial transfer-in on CO2 emissions wanes over time. Industrial transfer affects CO2 emissions by acting on the economic level, on population size, and on urbanisation level, but the indirect effect is weaker than the direct effect. Industrial transfer does not lead to technological upgrading, but the latter is an effective means of carbon emission reduction. Industrial transfer-in has shown a positive effect on CO2 emissions for most cities, but there are exceptions, such as Cangzhou. In the future, the BTH region should maintain coordinated development among cities and improve the cooperative innovation mechanism for energy conservation and emission reduction.
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Choi, Hyojeong, and Young Sunwoo. "Environmental Benefits of Ammonia Reduction in an Agriculture-Dominated Area in South Korea." Atmosphere 13, no. 3 (February 25, 2022): 384. http://dx.doi.org/10.3390/atmos13030384.
Full textAbstract:
Agricultural activity greatly contributes to the secondary PM2.5 concentrations by releasing relatively large amounts of ammonia emissions. Nonetheless, studies and air quality policies have traditionally focused on industrial emissions such as NOx and SOx. To compare them, this study used a three-dimensional modeling system (e.g., WRF/CMAQ) to estimate the effects of emission control policies of agricultural and industrial emissions on PM2.5 pollution in Chungcheong, an agriculturally active region in Korea. Scenario 1 (S1) was designed to estimate the effect of a 30% reduction in NH3 emissions from the agro-livestock sector on air pollution. Scenario 2 (S2) was designed to show the air quality under a mitigation policy on NOx, SOx, VOCs, and primary PM2.5 from industrial sources, such as power plants and factories. The results revealed that monthly mean PM2.5 in Chungcheong could decrease by 3.6% (1.1 µg/m3) under S1 with agricultural emission control, whereas S2 with industrial emission control may result in only a 0.7~1.1% improvement. These results indicate the importance of identifying trends of multiple precursor emissions and the chemical environment in the target area to enable more efficient air quality management.
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Zhou, Jianguo, Yushuo Li, Xuejing Huo, and Xiaolei Xu. "How to Allocate Carbon Emission Permits Among China’s Industrial Sectors Under the Constraint of Carbon Intensity?" Sustainability 11, no. 3 (February 11, 2019): 914. http://dx.doi.org/10.3390/su11030914.
Full textAbstract:
With the official launch of China’s national unified carbon trading system (ETS) in 2017, it has played an increasingly important role in controlling the growth of carbon dioxide emissions. One of the core issues in carbon trading is the allocation of initial carbon emissions permits. Since the industry emits the largest amount of carbon dioxide in China, a study on the allocation of carbon emission permits among China’s industrial sectors is necessary to promote industry carbon abatement efficiency. In this study, industrial carbon emissions permits are allocated to 37 sub-sectors of China to reach the emission reduction target of 2030 considering the carbon marginal abatement cost, carbon abatement responsibility, carbon abatement potential, and carbon abatement capacity. A hybrid approach that integrates data envelop analysis (DEA), the analytic hierarchy process (AHP), and principal component analysis (PCA) is proposed to allocate carbon emission permits. The results of this study are as follows: First, under the constraint of carbon intensity, the carbon emission permits of the total industry in 2030 will be 8792 Mt with an average growth rate of 3.27%, which is 1.57 times higher than that in 2016. Second, the results of the carbon marginal abatement costs show that light industrial sectors and high-tech industrial sectors have a higher abatement cost, while energy-intensive heavy chemical industries have a lower abatement cost. Third, based on the allocation results, there are six industrial sub-sectors that have obtained major carbon emission permits, including the smelting and pressing of ferrous metals (S24), manufacturing of raw chemical materials and chemical products (S18), manufacturing of non-metallic mineral products (S23), smelting and pressing of non-ferrous metals (S25), production and supply of electric power and heat power (S35), and the processing of petroleum, coking, and processing of nuclear fuel (S19), accounting for 69.23% of the total carbon emissions permits. Furthermore, the study also classifies 37 industrial sectors to explore the emission reduction paths, and proposes corresponding policy recommendations for different categories.