Environmental and economic sustainability of key sectors in China's steel industry chain: An application of the Emergy Accounting approach

Abstract Increasing urbanization day–by–day requires new housing and transportation infrastructures. As a consequence, demand for steel – a basic material for buildings construction as well as for vehicles and railroads – would also increases. This study applies Emergy Accounting (EMA) to assess the Chinas steel industry environmental performance and to identify key application sectors. Subsequently, this study calculates emergy–based indicators capable to assess the present economic performance, environmental sustainability, and land resource appropriate utilization. Building on these indicators, changes of sustainability scenarios in key application sectors are also investigated, with special focus on increased use of recycled steel. The results show that the environmental impacts of steel use in downstream sectors, specially in the Housing and Vehicles Sectors, are significantly higher. Furthermore, the downstream sectors also have a very large requirement for embodied land. Additionally, the Emergy Benefit Ratio (EBR) shows non-negligible advantages to China derived from importing raw iron from abroad at international market prices. Finally, when the recycling rate of scrap steel increases, the performance of downstream sectors improves, with the Vehicle sector showing the most significant changes. Although the benefits of steel-based economy to society are clear, multidimensional sustainability concerns and international competition for primary resources necessitate a transition towards increased recycling and innovative materials within a strictly enforced "circular economy" policy

Research on improving development effect of high-saturated reservoir in the late stage of water-flooding

High-saturated reservoir is characterized by high saturation pressure and high gas-oil ratio. The effects of water flooding are easily influenced by the formation pressure and GOR, especially at the late stage. This article presents the relationship between the reasonable pressure maintenance level and GOR as well as water cut based on the actual characteristics of high-saturated reservoir. Then, the reservoir numerical simulation method is used to analyse the influence of pressure recovery rate and water cut rise under different injection-production ratios and injection-production methods. Research results show that the pressure maintenance level of high-saturated reservoir is larger than normal reservoirs. Bigger injection-production ratio results in not only faster pressure recovery rate but also higher water cut. Cyclic injection and production method under the maximum injection rate and liquid extraction amount can enhance oil recovery rate and control water cut rise at the same time, which plays a significant role in improving the development effect of water-flooding in high-saturated reservoirs at the late stage

Fluctuation patterns of financial multi–time series.

The daily fluctuation patterns of four well-known stock indices, the NASDAQ Composite (COMP), the S&P 500 Index, the Dow Jones Industrial Average and the Russell 1000 Index. from January 22, 2003 to January 21, 2015

Global anti-dumping incident against China Steel

The global steel anti-dumping incidents, which are aimed at China. From September 1, 2010 to December 31, 201

Evaluating the Structural Robustness of Large-Scale Emerging Industry with Blurring Boundaries

The present large-scale emerging industry evolves into a form of an open system with blurring boundaries. However, when complex structures with numerous nodes and connections encounter an open system with blurring boundaries, it becomes much more challenging to effectively depict the structure of an emerging industry, which is the precondition for robustness evaluation. Therefore, this study proposes a novel framework based on a data-driven percolation process and complex network theory to depict the network skeleton and thus evaluate the structural robustness of large-scale emerging industries. The empirical data we used are actual firm-level transaction data in the Chinese new energy vehicle industry in 2019, 2020, and 2021. We applied our method to explore the transformation of structural robustness in the Chinese new energy vehicle industry in pre-COVID (2019), under-COVID (2020), and post-COVID (2021) eras. We unveil that the Chinese new energy vehicle industry became more robust against random attacks in the post-COVID era than in pre-COVID

A NOVEL MICRO-BLOG SENTIMENT ANALYSIS APPROACH BY LONGEST COMMON SEQUENCE AND K-MEDOIDS

This paper introduced a clustering-based Chinese sentiment analysis approach which is a new method to sentiment analysis appropriated for short text such as Sina Weibo. By building Sentiment Sequence from Weibo text, we apply the Longest Common Sequence algorithms to measure the sentiment different from two Sentiment Sequence, and K-Medoids clustering method to break a mass of Sentiment Sequences into groups. It has great advantages over the existing sentiment analysis method such as classification by supervised learning algorithms. The experiment result shows the sentiment distribution and groups of a mass of Weibo data aggregated by a given topic, and in a specific period. The method is well performed, efficient, and non-human participating, and appropriated for Chinese short text

RAPID UNDERSTANDING OF HOT-KEYWORDS OF PAPERS ON A GIVEN THEME: BASED ON TWO-MODE AFFILIATION NETWORK

How to keep up with the tendency of the literature and grasp the key-points of them from the holistic perspective rapidly is a new challenge both for the literature research and text mining. Most of current theories and tools are directed at finding one paper or a small amount of sample, not gaining a rapid understanding of the hot-keywords of all the papers about one given theme or topic. This paper presents an effort to integrate statistics, text mining, complex networks and visualization to analyze all the papers of one theme-complex networks. we extracted all the 5944 papers about complex networks on Web of Science (http://apps.webofknowledge.com/) from 1990 to 2013. Based on the two-mode affiliation network theory, a new frontier of complex network, we took the keywords of the papers as nodes, took the co-occurrence relationships as the edges and the times of the co-occurrence at the same time as the weight to construct the keywords’ co-occurrence equivalence networks in different year. Then we put forword the integrated way to analyze the evolution and the stability of the keywords’ co-occurrence equivalence networks, and analyze the topological features of the networks about complex networks to find out the hot-keywords and its’ trendency in different time.This paper procvide a useful tool and process to realize rapid understanding of the trend and the hotwords of a large amount of literature successfully

Words analysis of online Chinese news headlines about trending events: a complex network perspective.

Because the volume of information available online is growing at breakneck speed, keeping up with meaning and information communicated by the media and netizens is a new challenge both for scholars and for companies who must address public relations crises. Most current theories and tools are directed at identifying one website or one piece of online news and do not attempt to develop a rapid understanding of all websites and all news covering one topic. This paper represents an effort to integrate statistics, word segmentation, complex networks and visualization to analyze headlines' keywords and words relationships in online Chinese news using two samples: the 2011 Bohai Bay oil spill and the 2010 Gulf of Mexico oil spill. We gathered all the news headlines concerning the two trending events in the search results from Baidu, the most popular Chinese search engine. We used Simple Chinese Word Segmentation to segment all the headlines into words and then took words as nodes and considered adjacent relations as edges to construct word networks both using the whole sample and at the monthly level. Finally, we develop an integrated mechanism to analyze the features of words' networks based on news headlines that can account for all the keywords in the news about a particular event and therefore track the evolution of news deeply and rapidly

Correlation evaluation of ion adsorption-based rare earth leaching performance based on zeta potential drop leaching

Rare earth elements are indispensable raw materials for advanced aero-engines, special optical materials, and high-performance electronic products. With the development of social economy, the global demand for rare earth resources is increasing, and rare earths have become a key metal for the development of new industries and frontier technologies that are highly valued both at home and abroad. ion-adsorbed rare earth ores are an important source of rare earths, so the efficient green leaching of ion-adsorbed rare earths is important. Researchers found that the selection of efficient green leaching agent for ion-adsorbed rare earths is based on the zeta potential of tailing clay minerals in addition to leaching efficiency, and both zeta potential and leaching ion concentration are related to mineral acidity and alkalinity, and the pH of tailing water suspension is a direct indicator of environmental water quality requirements. Therefore, the efficiency of the leaching process is closely integrated with the environmental evaluation, and the characteristics and correlation of the changes in zeta potential, pH, conductivity and pollutant concentration of the pulp of clay mineral content during the leaching process of ore leaching and tailings aqueous electrolyte solution leaching are studied by evaluating the leaching system, and a set of correlation leaching efficiency and environmental impact evaluation method is established based on the results of the above analysis, which is of scientific development of ion adsorption rare earth resources It has important theoretical significance and practical application value