“绿色矿业”的逻辑机理、实践探索与推进策略

发展“绿色矿业”是人类进入生态文明社会的必然选择，绿色发展理念指导下的“绿色矿业”发展具有主旨统一性和实践多样性、国家主导性和企业渐进性，需要完善的理论指导、体系构建和政策支持。“绿色矿业”的运行体系是由相关主体（绿色政府、绿色企业、绿色社会、绿色市场）及其功能共同形成的网络结构，各主体多元化、多维度的相互作用共同促进“绿色矿业”的有序发展，最终实现“矿业经济发展、生态环境良好、发展成果共享”的目标。我国发展“绿色矿业”的实践取得了显著成效，各地不同的发展模式既有目标的同一性，又有方式的差异性。当前，我国“绿色矿业”发展还存在标准体系不健全、政策法规不完善、发展格局不协调、协同监管不到位等问题，需要进一步发挥政府的主导作用、企业的主体作用、行业协会及社会的协调支撑作用，并构建和优化“绿色矿业链”和“绿色矿业区域板块网络”，推动“绿色矿业”健康有序发展。     

Discovering bank risk factors from financial statements based on a new semi‐supervised text mining algorithm

This paper aims to comprehensively uncover bank risk factors from qualitative textual risk disclosures reported in financial statements, which contain a huge amount of information on bank risks. We propose a new semi‐supervised text mining approach named naive collision algorithm to analyse the textual risk disclosures, which can more accurately identify bank risk factors compared with the typical unsupervised text mining approach. We identified 21 bank risk factors in total, which is far more than identified in previous studies. We further analyse the importance of each bank risk factor and how the importance of each risk factor changes over time.     

Employing structural topic modelling to explore perceived service quality attributes in Airbnb accommodation

This study employs the structural topic model to extract service quality attributes from 242,020 Airbnb reviews in Malaysia. 22 service related topics were extracted from the corpus and four topics have not appeared in previous Airbnb studies. A widely used modified SERVQUAL questionnaire (MSQ) is cross-validated in this study by comparing its service quality attributes with the results of the topic modelling, which indicates that this MSQ can cover general Airbnb service quality attributes. This study also examines the different preferences of Malaysian and international Airbnb users and the changing patterns of the top six service quality attributes during a five-year period. The findings reveal that Malaysian Airbnb users care more about the appearance and location of the property, and international Airbnb users pay more attention to whether the property can accommodate a group of people. In addition, communication with the host is found to play an increasingly important role in Airbnb users’ lodging experiences.     

Impact mitigation or ecological restoration? Examining the environmental sustainability practices of multinational mining companies

This paper examines the environmental sustainability practices of multinational mining companies in addressing their impacts and promoting the sustainable development of local communities in Ghana. Although large-scale mining companies have embraced environmental sustainability, the drivers and the mechanisms for addressing their impacts throughout the mine life cycle is not fully understood because of the limited research in this area. The focus in this study involves an examination of the drivers for environmental sustainability in a weak and non-enabling institutional context and the mechanisms for addressing impacts on biodiversity, water quality and quantity, and ambient climate. The findings show that the environmental sustainability practices of multinational mining companies are determined by regulatory compliance and corporate environmental responsibility based on perceived ethical obligation. Additionally, we find gaps in mine closure planning and rehabilitation because of the limited requirement for biodiversity restoration in the domains of flora repopulation and active fauna reintroduction. This paper provides empirical and theoretical insights for academics and practitioners in industry and policymaking.     

Assessing qualitative similarities between financial reporting frameworks using visualization and rules: COREP vs. pillar 3

Financial institutions are struggling with larger volume, more specific and greater frequency of regulatory reporting after the global financial crisis in 2008, especially those that need to report to multiple jurisdictions. To help to improve reporting efficiency, this paper aims to assess the existence of similarities between templates related to credit and counter party credit risk of COREP and Pillar 3 regulatory reporting frameworks by applying Correspondence Analysis and Association Rules Mining. Our results suggest a high degree of overlap between these reporting frameworks, more prominently the three business functions as Front office, Finance and Risk. These patterns can be used as guidance for financial institutions to reshape their reporting architecture.     

时空大数据支持的土地储备智能决策体系与应用研究

研究目的:设计土地储备智能决策体系,将土地储备决策环节进行科学化、定量化和模型化,为政府合理开展土地储备提供决策依据。研究方法:遵循科学性、实用性、先进性、开放性原则,以数据为驱动,人工智能为核心,通过基于Hadoop/Spark的大数据处理技术、多源异构数据融合技术、智能决策模型集对多源时空大数据挖掘分析。研究结果:提出了土地储备智能决策体系,研究了一套面向土地储备的决策方法集,实现对现状条件的精准刻画,对土地储备规模、结构、时序等内容的科学决策;在此基础上,研发了智能决策支持平台,将数据资源共享、现状条件评估、智能决策专题等功能集成,构建土地储备智能决策"一张图",并实现平台的自我学习和更新。研究结论:应用于宁波市实例验证,该体系有效地促进了土地储备科学智慧决策,对优化土地资源配置具有参考意义。     

Multinational mining enterprises and artisanal small-scale miners: From confrontation to cooperation

In many developing countries, multinational enterprises (MNEs) in mining operate alongside fast-growing communities of informal artisanal, small-scale miners. This has led to direct conflict and competition for mineral resources. This paper introduces the Salience and Institutional Analysis and Design framework as a means to analyze the MNE strategies used to address informal miners across different governance levels in the gold mining sector of Ghana and discusses the implications of these strategies for sustainability. We identify the emergence of a cooperative strategy with informal miners as a sustainable alternative to the political strategy of reliance on the state to protect tenure.     

A gossip based information fusion protocol for distributed frequent itemset mining

The computational complexity, huge memory space requirement, and time-consuming nature of frequent pattern mining process are the most important motivations for distribution and parallelization of this mining process. On the other hand, the emergence of distributed computational and operational environments, which causes the production and maintenance of data on different distributed data sources, makes the parallelization and distribution of the knowledge discovery process inevitable. In this paper, a gossip based distributed itemset mining (GDIM) algorithm is proposed to extract frequent itemsets, which are special types of frequent patterns, in a wireless sensor network environment. In this algorithm, local frequent itemsets of each sensor are extracted using a bit-wise horizontal approach (LHPM) from the nodes which are clustered using a leach-based protocol. Heads of clusters exploit a gossip based protocol in order to communicate each other to find the patterns which their global support is equal to or more than the specified support threshold. Experimental results show that the proposed algorithm outperforms the best existing gossip based algorithm in term of execution time.     

A proposed configurable approach for recommendation systems via data mining techniques

This study presents a configurable approach for recommendations which determines the suitable recommendation method for each field based on the characteristics of its data, the method includes determining the suitable technique for selecting a representative sample of the provided data. Then selecting the suitable feature weighting measure to provide a correct weight for each feature based on its effect on the recommendations. Finally, selecting the suitable algorithm to provide the required recommendations. The proposed configurable approach could be applied on different domains. The experiments have revealed that the approach is able to provide recommendations with only 0.89 error rate percentage.     

Shifts in consumer behavior towards organic products: Theory-driven data analytics

Consumer behavior is key in shifts towards organic products. A diversity of factors influences consumer preferences, driving planned, impulsive, and unplanned purchasing decisions. We study choices among organic and conventional wine using an extensive survey among Australian consumers (N = 1003). We integrate five behavioral theories in the survey design, and use supervised and unsupervised machine learning algorithms for analysis. We quantify a gap between intention and behavior, and emphasize the importance of cognitive factors. Findings go beyond correlation to the causation of behavior when combining predictive prowess with explanatory power. Results reveal that affective factors and normative cues may prompt unplanned and spontaneous purchasing behavior, causing consumers to act against their beliefs.