Analysis of GHG Emission Verification of the Iron-Steel Industry - Case Study of an Iron-Steel Company in Hebei Province

The situation of global warming is becoming more and more serious. As an important field of total carbon emission control, it is of great significance to study the power industry for achieving global climate goals and sustainable development. Firstly, the related concepts of carbon emissions, carbon peaking, carbon neutralization and emission reduction potential in the power industry are introduced. Secondly, from the international and domestic perspectives, the latest research progress of carbon emissions accounting, carbon emissions influencing factors, carbon emissions prediction and emission reduction potential in the power industry are summarized and sorted out. Finally, the existing research on carbon emissions in the power industry is analyzed from four aspects.

Addressing climate change and improving air quality are prominent tasks facing China’s ecological environment. The synergistic emission reduction in greenhouse gases (GHGs) and air pollutants has become an important task of environmental governance in different provinces. In this study, Hebei Province was taken as the research object. Firstly, the emission factors of GHGs (CO2, CH4, and NO2) and air pollutants (SO2, NOX, and smoke & dust) in Hebei Province from 2011 to 2020 were calculated and analyzed. Seven socio-economic indicators were selected to analyze the trend during the study period. The Spearman rank correlation coefficient method was used to analyze the correlation between GHG and air pollutant emissions. Finally, the synergistic control effect coordinate system and the cross-elasticity coefficient of emission reduction were used to study the synergistic emission reduction effect of GHGs and air pollutants. The results showed that the total amount of GHG emissions fluctuated slightly from 2011 to 2020, and energy activities were the main source of total GHG emissions. The total emissions of air pollutants decreased year by year, and decreased by 71.13% in 2020 compared with 2011. During the study period, the emission synergy between smoke & dust and GHG was better than that between SO2, NOX, and GHG. GHG and SO2, NOX, and smoke & dust achieved synergistic emission reduction in most years, but the overall emission reduction synergy was poor.

Currently, Tangshan confronts the dual challenge of elevated carbon emissions and substantial pollution discharge from the iron and steel industries (ISIs). While significant efforts have been made to mitigate air pollutants and carbon emissions within the ISIs, there remains a gap in comprehending the control of carbon emissions, air pollutant emissions, and their contributions to air pollutant concentrations at the enterprise level. In this study, we devised the Air Pollutant and Carbon Emission and Air Quality (ACEA) model to identify enterprises with noteworthy air pollution and carbon emissions, as well as substantial contributions to air pollutant concentrations. We constructed a detailed inventory of air pollutants and CO2 emissions from the iron and steel industry in Tangshan for the year 2019. The findings reveal that in 2019, Tangshan emitted 5.75 × 104 t of SO2, 13.47 × 104 t of NOx, 3.55 × 104 t of PM10, 1.80 × 104 t of PM2.5, 5.79 × 106 t of CO and 219.62 Mt of CO2. The ACEA model effectively pinpointed key links between ISI enterprises emitting air pollutants and carbon dioxide, notably in pre-iron-making processes (coking, sintering, pelletizing) and the Blast furnace. By utilizing the developed air pollutant emission inventory, the CALPUFF model assessed the impact of ISI enterprises on air quality in the Tangshan region. Subsequently, we graded the performance of air pollutant and CO2 emissions following established criteria. The ACEA model successfully identified eight enterprises with significant air pollution and carbon emissions, exerting notable influence on air pollutant concentrations. Furthermore, the ACEA outcomes offer the potential for enhancing regional air quality in Tangshan and provide a scientific instrument for mitigating air pollutants and carbon emissions. The effective application of the ACEA model in Tangshan’s steel industry holds promise for supporting carbon reduction initiatives and elevating environmental standards in other industrial cities across China.

To achieve the goal of "carbon peak and neutrality," the strict requirements for greenhouse gas (GHG) emissions control in the agricultural sector were recommended in relevant plans for Beijing during the 14th Five-Year Plan period. Through collecting agricultural activity data and calculating and screening the emission factors, the amount and emission characteristics of agricultural GHG emissions in Beijing in 2020 were estimated and set as the baseline condition. On this basis, the GHG emissions in 2025 with optimized measurements implemented, which were selected in combination with the natural conditions and planting-breeding mode of Beijing, were set as the reduction condition. The emission reduction potential and its distribution during the 14th Five-Year Plan Period were predicted simultaneously. Meanwhile, the reduction effects on the GHG emissions of optimized measurements were evaluated. In addition, relevant policy recommendations on GHG reduction were proposed accordingly. The results revealed that the total agricultural GHG emissions in Beijing were estimated to be 456000 t (CO2-eq) in 2020, primarily from sources of animal intestinal fermentation and manure management, with contribution rates of 50.7% and 26.7%, respectively. Spatially, it was mainly distributed in districts with large livestock and poultry breeding scales, such as Shunyi District, Miyun District, and Yanqing District, etc. It was predicted that in 2025, the total agricultural GHG emissions would be 349000 t (CO2-eq), and the emission reduction potential in the 14th Five-Year Plan period would be 107000 t (CO2-eq). Animal intestinal fermentation would be the emission source with the largest reduction potential (60000 tons, CO2-eq), followed by the emission source of animal manure management (37000 tons, CO2-eq). Adjusting fodder composition and optimizing manure management were analyzed to be the most effective optimized measurements for agricultural GHG emission reduction. Moreover, the emission reduction potential of CH4 would be greater than that of N2O. The emission reduction potential would be mainly distributed in Miyun District, Shunyi District, Yanqing District, Fangshan District, Tongzhou District, and other suburbs with large livestock and poultry breeding scales, accounting for more than 10% of the total emission reduction potential for each. These regions with large emission reduction potential should be prioritized and then the assessments should be extended to the whole city. The measurements were recommended as follows:① the research and promotion of technologies such as fodder optimization and the efficient treatment of manure should be strengthened, ② the scope of the combination of planting and breeding model should be expanded to promote the development of circular agriculture, and ③ relevant standards, guidelines, and specifications for green and low-carbon agriculture should be formulated, and the regulatory and policy system for synergy reduction of agricultural pollution and GHG should be developed.

China's CO2 emissions of a critical sector: Evidence from energy intensive industries

Spatial and temporal dynamics of air-pollutant emission inventory of steel industry in China: A bottom-up approach

As global warming intensifies, reducing carbon emissions has become a global common mission. Tourism transportation is one of the important sources of carbon emissions, and reducing its carbon emissions is a key part of achieving China’s carbon reduction goals. Based on the panel data of various provinces and cities in North China from 2000 to 2022, this paper calculates the carbon emissions of tourism transportation by using the carbon emission coefficients of different transportation modes in different segments. Moreover, the temporal and spatial evolution of the tourism economy is systematically analyzed. The Tapio decoupling model and LMDI addition decomposition model are used to analyze the relationship between carbon emissions and tourism economic growth and the effects of 11 influencing factors on carbon emissions. The results show that: (1) The carbon emission of tourism transportation in North China has experienced four stages: a steady growth period, a transitional adaptation period, a stable equilibrium period, and a drastic decline period. The overall carbon emission level of tourism transportation is as follows: Hebei Province > Shanxi Province > Inner Mongolia Autonomous Region > Beijing City > Tianjin City. (2) The decoupling coefficient between tourism traffic carbon emissions and economic development fluctuates but mainly shows a weak decoupling state. (3) In terms of influencing factors, passenger size and passenger density have the greatest impact on the carbon emissions of tourism transportation.

Sustainable development of China's energy intensive industries: From the aspect of carbon dioxide emissions reduction

The assessment of energy-related greenhouse gas emissions in China's chemical industry

In view of the world carbon emission problem, the current status of low carbon economy research is reviewed. Based on the historical data of industrial carbon emissions in Hebei Province from 2008 to 2017, based on the historical data of industrial carbon emissions in Hebei Province, the Kaya decomposition model was used to find the industry affecting Hebei Province. The driving factors of carbon emission levels and the trends of various factors, and then combined with the LMDI model, the absolute contribution of each driving factor to the economic development of Hebei Province, and the specific analysis of carbon emission levels, and finally put forward rationalized emission reduction measures and suggest.

The standard of living has significantly risen along with ongoing economic progress, but CO2 emissions have also been rising. The reduction in CO2 resulting from the daily activities of residents has become a crucial priority for every province. A relevant study on the carbon emissions of Hebei Province residents was conducted for this publication, aiming to provide a theoretical basis for the sustainable development of Hebei Province. The first part of the article calculates the carbon emissions of Hebei Province people from 2005 to 2020 using the emission factor method and the Consumer Lifestyle Approach (CLA). Secondly, the Logarithmic Mean Divisia Index (LMDI) decomposition approach is used to assess the components that influence both direct and indirect carbon emissions. Finally, the scenario analysis approach is employed in conjunction with the LEAP model to establish baseline, low-carbon, and ultra-low-carbon scenarios to predict the trend of residents’ carbon emissions in Hebei Province from 2021 to 2040. The results show that the total carbon emissions of residents in Hebei Province from 2005 to 2020 rose, from 77.45 million tons to 153.35 million tons. Income level, energy consumption intensity, and population scale are factors that contribute to the increase in direct carbon emissions, while consumption tendency factors have a mitigating effect on direct carbon emissions. Economic level, consumption structure, and population scale factors are factors that contribute to the increase in indirect carbon emissions, while energy consumption intensity and energy structure factors have a mitigating effect on indirect carbon emissions. The prediction results show that under the baseline scenario, the cumulative residents’ carbon emissions in Hebei Province will not reach a zenith from 2021 to 2040. However, under the low-carbon situation, the carbon emissions of residents in Hebei Province will peak in 2029, with a peak of 174.69 million tons, whereas under the ultra-low-carbon scenario, it will peak in 2028, with a peak of 173.27 million tons.

The collaborative pollutants and carbon dioxide emission reduction and cost of ultra-low pollutant emission retrofit in China's cement kiln

Hebei Province in China is facing a serious water shortage, which is further aggravated by the pressure of industrial transfer and the unreasonable structure of industrial water use. To explore the relationship between industrial water use and carbon emissions, in this study, a refined logarithmic mean divisia index (LMDI) decomposition method was developed to analyze the driving factors of industrial water use in Hebei Province during 2008–2019 from carbon emission and sectoral perspectives. The results show that the carbon emission effect, the water–carbon effect, and the industrial structure effect were the main factors contributing to the decrease in industrial water use during the study period. The carbon emission effect made a great contribution to its decline. The cumulative contributions of these factors were −1425, −533, and −763 million m3 from 2008 to 2019. The contribution of the industrial structure effect was −106.93%, with a large potential for water saving. According to the sectoral analysis, the 32 sectors in Hebei Province exhibited significant sectoral heterogeneity, and the strong promoting industries were identified as the main sectors contributing to the increase in the promotion of industrial water use. This paper provides a reference for the scientific formulation of water-saving and emission-reduction policies and research on the water–carbon relationship in Hebei Province.

PDF HTML阅读 XML下载 导出引用 引用提醒 基于净生产力生态足迹模型的工业碳排放效应、影响因素与情景模拟 DOI: 10.5846/stxb201603310591 作者: 作者单位: 浙江大学土地科学与不动产研究所,浙江大学土地科学与不动产研究所,浙江大学土地科学与不动产研究所,浙江大学农业遥感与信息技术应用研究所,中国地质大学(武汉)公共管理学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家重点研发计划重点专项(2016YFD0201200);浙江省教育厅重点项目(Z201121260) Analysis of the industrial carbon emission effect based on the Net Primary Productivity Model, its influencing factors and scene simulation Author: Affiliation: School of Public Affairs, Institute of Land Science and Property Management, Zhejiang University,School of Public Affairs, Institute of Land Science and Property Management, Zhejiang University,School of Public Affairs, Institute of Land Science and Property Management, Zhejiang University,Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University,School of Public Administrations, China University of Geosciences, Wuhan Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:以不同类型城市东营和滨州为例,采用基于净生产力的生态足迹模型测度2005-2014年两市工业碳排放效应,利用弹性系数模型对工业碳排放生态足迹及其影响因素进行对比,通过情景模拟分析了基准和低碳情景下两市的可持续低碳发展潜力。研究结果显示:(1)东营碳排放总量和碳排放强度明显高于滨州,两市的碳排放生态足迹总体上都处于上升趋势,年均增长率分别为12.79%和6.16%,这与两市工业化发展阶段不同有关;(2)2005-2008、2008-2011和2011-2014,东营工业碳排放生态足迹当量主导影响因素组合变化为"耕地面积-土地城镇化率-能源结构系数"转化为"耕地面积-人口规模-能源结构系数"到"耕地面积-人口规模-第二产业比重";滨州2005-2014年的主导因素组合一直为"人口规模-土地城镇化率-能源结构系数";(3)通过情景模拟分析2020年东营、滨州的低碳发展潜力:基准和低碳情景下,滨州生态赤字分别为东营的10倍和2.6倍;就"减排"潜力而言,滨州远远高于东营,但实现低碳情景是工业GDP增长从现阶段20.6%骤降到6.5%为代价,对产业结构调整升级要求很高。对东营而言,低碳情景的实现不仅要将能源利用效率提高一倍,更要保证大量重要"碳汇"资源的恢复与重建。 Abstract:At present, there are serious environmental problems caused by global warming, primarily resulting from the interaction of industrial fossil fuel emissions and Land-Use and Land-Cover Change (LUCC). According to IPCC reports, the industrial sector is the most important source of fossil fuel consumption, which accounts for 78.75% of the carbon emission to the atmosphere. In China, the annual growth rate of carbon emissions from fossil fuel combustion has been 5.2% since 1978, and the future growth trend is difficult to reverse. Moreover, a lack of ecological security in the implementation of the cultivated land balance policy, the principle of "occupying one up one", can easily lead local governments to incorrectly understand infinite cultivated land reserve resources. Thus, to explore how natural resources, industrial development, and restricted cultivated land protection policies affect the ecological pressure of industrial carbon emission in different cities, the present study uses Dongying and Binzhou as examples. Both cities, located in the Yellow River delta, were used to research the ecological pressure of industrial carbon emission based on the Net Primary Productivity Model (NPPM); the elastic coefficient model was applied to analyze changes of influencing factors from 2005 to 2014; and finally, the potential of low-carbon sustainable development using the scene simulation method was measured. Net Primary Productivity (NPP) is employed as a common indicator of biological productivity and the Net Primary Productivity Model (NPPM) can illustrate the interaction between carbon emissions and land carbon sequestration. The following conclusions were reached: (1) carbon emissions and carbon emission intensities of Dongying were significantly higher than that of Binzhou, and the ecological footprint of carbon emissions annually increased by 12.79 and 6.16%, respectively. This is related to the difference of industrial development between the two cities. (2) After analyzing the results of the elastic coefficient model, we found the combination of critical factors of the industrial carbon emission ecological footprint of Dongying changed from a "cultivated land-land urbanization rate-energy structure coefficient" to "cultivated land-population size-energy structure coefficient" to "cultivated land-population size-the proportion of the second industry" from 2005-2008 to 2008-2011 and 2011-2014; that of Binzhou remained a "population size-land urbanization rate-energy structure coefficient" from 2005 to 2014. (3) Through the situational simulation analysis until 2020, we found that under the baseline scenario, the carbon emission ecological deficit of Binzhou was approximately ten times than that of Dongying; under the low carbon scenario, that of Binzhou was only 2.6 times that of Dongying. Regarding the emission reduction potential (the distance between the carbon emission ecological deficit under the baseline scenario and low carbon scenario), the potential of Binzhou was significantly higher than that of Dongying. However, the low carbon scenario of Binzhou is at the expense of a serious slowdown from 20.6 to 6.5% in industry GDP, which needs to forcibly eliminate high energy-consuming enterprises, and economic growth mainly relies on the completion of the third industry. Therefore, there is a very high demand for the readjustment of the industrial structure. Regarding Dongying, the low carbon scenario needs to improve the energy use efficiency by double, and ensure the restoration of large numbers of "carbon sink" resources. 参考文献 相似文献 引证文献

In China, the county is not only an important component of industrial areas and a large contributor of carbon emissions, but also a key administrative unit for the implementation of carbon peak and carbon neutrality goals and policies. The spatiotemporal variations and structural characteristics of carbon emissions at the county scale are of great significance to China's dual goals of regional carbon policy implementation and low carbon spatial planning. Thus, it is important and insightful to conduct an in-depth and detailed examination of these characteristics while focusing on a typical iron and steel industry county-level city in North China. This study systematically calculated the carbon emissions of the county-level city of Wu'an from 2008 to 2017, and explored their structural characteristics and spatiotemporal variations. The results showed that (1) under the influence of macroeconomic and national policies, the carbon emissions of county-level cities dominated by the iron and steel industry show obvious phased characteristics; (2) there is a significant negative correlation between industry carbon emission concentrations and industrial carbon emissions; (3) within the steel industry system, sintering, iron smelting, steelmaking, and metal product processing are the main sources of carbon emissions, and the coal-based production process of the iron and steel industry needs a fundamental reformation; and (4) the carbon emission of Wu'an City shows obvious spatial differentiation characteristics. The geographic distribution of carbon emissions in Wu'an City is very unbalanced and tended to cluster together in urban areas, industrial and mining areas, and major towns. Taking 2014 as the turning point, the spatial pattern of carbon emissions in Wu'an City presents different variation characteristics.