با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
Applying emergy and decoupling analysis to assess the sustainability of China’s coal mining area
استفاده از تحلیل اضطراری و جداسازی برای ارزیابی پایداری منطقه استخراج زغال سنگ چین-2020
The sustainable development of coal mining area continues to be one of the most topical issues in the world. Taking Shainxi Province as a case, this study applies emergy and decoupling analysis to build a multi-index sustainability evaluation system and constructs an emergy decoupling index to investigate the sustainability of a coal mining area in China during 2006e2015. It overcomes the problem of the unification of the traditional evaluation index system and integrates the influence of economic development, resources, the environment, and energy. The study finds that the coal mining area still depends on its coal resources. The sustainability of the coal mining area is still at a low level, and it is not sustainable in the long term. The economic growth still has a strong negative decoupling from the environmental loss. Energy management system and circular economic system should be built to improve the coal mining area’s sustainability. In the long run, the coal mining industry should gradually be abandoned. Based on China’s growing energy consumption, the findings of this study may not only serve as a reference for management to improve the sustainability of the coal mining areas but also to address China’s energy shortage problem.
Keywords: Sustainability | Emergy analysis | Decoupling | Coal mining area
Economic feasibility valuing of deep mineral resources based on risk analysis: Songtao manganese ore - China case study
ارزیابی امکان سنجی اقتصادی منابع معدنی عمیق بر اساس ریسک تجزیه و تحلیل: سنگ معدن منگنز Songtao - مطالعه موردی چین-2020
The exploitation of deep mineral resources is an inevitable choice under economic development and resource shortage. Assessing the economic feasibility of deep mineral resource exploit projects is a prerequisite for resource industry development. Mining industry have some problems influence its economic feasibility, including long mining period, high infrastructure investment and lack flexibility, and have risks of geology instability and economic reserve degrade. On the other hand, with the increase of the buried depth of mineral resources, some problems have intensified the uncertainty of the profit of deep resource utilization project, such as high stress, high lithology, high temperature environment, and increase of upgrading cost. Net Present Value (NPV) and Internal Rate of Return (IRR) are traditional economic evaluation means which difficult to identify and assess risks precisely. Decoupled Net Present Value (DNPV) provides an efficiency tool to separate the time value and risk cost which is helpful to finds the real value of projects. A manganese mining project which is located Guizhou province, China is analyzed, paper choices several mainly risks of influence expected revenue to analysis project feasibility based on the DNPV technology, which includes the thickness of ore body, ore grade, market price, operation cost and nature disaster. The cost of potential environmental risk (carbon emission cost) also is analyzed. Paper constructs a risk management framework by risk identify, assess and classification, and analyzes the corresponding measures to reduce risk costs. The mainly risk cost of study case from market price shock and unexpected ore grade decline, which accounting for 80% of the total risk cost. In the process of deep mineral resources exploit, effective cost control measures can reduce the risk cost to a certain extent, including improving productivity, reducing unit cost of ore, improving mine sustainability and exploration accuracy. Green mineral construction is a feasible direction of deep resource utilization. For improve the accuracy of economic feasibility evaluation of deep mineral resources utilization, further improvement is needed in the selection and construction of different risk assessment model.
Keywords: Deep mining | Risk value assess | DNPV | Risk management | Songtao manganese
Divergent agricultural water governance scenarios: The case of Zayanderud basin, Iran
سناریوهای حاکم بر آب کشاورزی واگرا: پرونده حوضه زاینده رود ، ایران-2020
There is an urgent need to consider adaptation strategies for agricultural water resources in response to the evergrowing demand for freshwater around the world. This is especially poignant in arid and semi-arid regions, like the Middle East and North Africa (MENA) where water resources have been extremely limited historically. Today, water resources are declining due to a variety of factors, including climate change, population growth and changing food preferences. Research on this topic typically seeks to assess the impact of discreet alternative interventions in isolation. However, it is necessary to analyze the broader factors affecting agricultural water management as interconnected components of a complex water governance system within a specific geographic context. This research uses an exploratory, formative scenario planning approach to a) identify important adaptation strategies, b) use those adaptation strategies to construct a small set of coherent, plausible and diverse regional agricultural water governance scenarios, and c) analyze future scenarios of the Zayandehroud watershed in Iran in the year 2040. The research shares five scenarios that exemplify divergent adaptation and mitigation approaches to agriculture water demand in Zayandehroud watershed, including adhering to the status quo. Each scenario embodies different economic and political priorities to reveal how those priorities impact the ecological, social, and economic sustainability of this watershed. These scenarios provide insights into the longterm implications of near-term decisions about water and food security, resilience of local communities and the ecological integrity of the regional watershed. This research explores the conceptual relationships between components of the water governance system and demonstrates an approach to analyzing alternative constellations of factors that will impact agricultural water management. Policy-makers can make more effective policies if they consider how to transform the broader system of regional water governance, rather than only evaluating discrete agricultural water management projects on a project-by-project basis.
Keywords: Adaptive governance | Scenario planning | Water market | Rural development | Local governance | Land use planning
Manufacturing big data ecosystem: A systematic literature review
ساخت اکوسیستم داده های بزرگ: مروری بر ادبیات سیستماتیک-2020
Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business. Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware. Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security. Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to realtime big data analytics and cybersecurity.
Keywords: Smart manufacturing | Big data | Cloud computing | Cloud manufacturing | Internet of things | NoSQL
System architecture for blockchain based transparency of supply chain social sustainability
معماری سیستم برای شفافیت مبتنی بر بلاکچین پایداری اجتماعی زنجیره تأمین-2020
Social sustainability is a major concern in global supply chains for protecting workers from exploitation and for providing a safe working environment. Although there are stipulated standards to govern supply chain social sustainability, it is not uncommon to hear of businesses being reported for noncompliance issues. Even reputable firms such as Unilever have been criticized for production labor exploitation. Consumers now increasingly expect sellers to disclose information on social sustainability, but sellers are confronted with the challenge of traceability in their multi-tier global supply chains. Blockchain offers a promising future to achieve instant traceability in supply chain social sustainability. This study develops a system architecture that integrates the use of blockchain, internet-of-things (IoT) and big data analytics to allow sellers to monitor their supply chain social sustainability efficiently and effectively. System implementation cost and potential challenges are analyzed before the research is concluded.
Keywords: Blockchain | Social sustainability | Multi-tier supply chain | Supply chain sustainability | Traceability
Designing a short-term load forecasting model in the urban smart grid system
طراحی یک مدل پیش بینی بار کوتاه مدت در سیستم شبکه هوشمند شهری-2020
The transition of the energy system from fossil fuel towards renewable energy (RE) is rising sharply, which provides a cleaner energy source to the urban smart grid system. However, owing to the volatility and intermittency of RE, it is challenging to design an accurate and reliable short-term load forecasting model. Recently, machine learning (ML) based forecasting models have been applied for short-term load forecasting whereas most of them ignore the importance of characteristics mining, parameters fine-tuning, and forecasting stability. To dissolve the above issues, a short-term load forecasting model is proposed that incorporates thorough data mining and multi-step rolling forecasting. To alleviate the chaos of short-term load, a de-noising method based on decomposition and reconstruction is used. Then, a phase space reconstruction (PSR) method is employed to dynamically determine the train-test ratios and neurons settings of the artificial neural network (ANN). Further, a multi-objective grasshopper optimization algorithm (MOGOA) is applied to optimize the parameters of ANNs. Case studies are conducted in the urban smart grid systems of Victoria and New South Wales in Australia. Simulation results show that the proposed model can forecast short-term load well with various measurement metrics. Multiple criterion and statistical evaluation also show the good performance of the proposed forecasting model in terms of accuracy and stability. To conclude, the proposed model achieves high accuracy and robustness, which will provide references to RE transitions and smart grid optimization, and offer guidance to sustainable city development.
Keywords: Smart grid | Short-term load forecasting | Neural networks | Multi-objective optimization algorithm | Urban sustainability
The on-paper hydropower boom: A case study of corruption in the hydropower sector in Bosnia and Herzegovina
رونق نیروگاه برق روی کاغذ: یک مطالعه موردی از فساد در بخش برق در بوسنی و هرزگوین-2020
The demand for hydropower production, as a prominent sustainable development strategy, has created a boom in the number of planned hydropower projects, especially small ones. These projects are mainly located in postsocialist transition and developing countries. However, emerging evidence suggests that most of the projects remain on paper. One reason for this is prevalent corruption. In the literature, corruption has been identified in megaprojects but a significant number of hydropower projects are smaller in size. This leaves a literature gap and a subsequent lack of understanding regarding corruption and its potential connection to the absence of hydropower construction. We argue that it also creates a ‘safe space’ for corrupt actors who use the sector for personal gain. In this paper, we address this nexus of unfinished hydropower projects, sustainable development in transition countries, corruption, and lack of scholarly attention by presenting empirical evidence from Bosnia and Herzegovina. We highlight that sustainability goals associated with hydropower might be distorted, especially in transition countries as they are intermingled with hydropower projects that are embedded in complex bureaucratic structures. We conclude that focusing on the sector might improve its management, thus contributing to sustainable development, and also help to decrease the corruption risk.
Keywords: Hydropower boom | Small hydropower projects | Unbuilt projects | Corruption | Transition countries | Sustainable development
Role of big data and social media analytics for business to business sustainability: A participatory web context
نقش تجزیه و تحلیل داده های بزرگ و رسانه های اجتماعی برای پایداری تجارت از مشاغل: زمینه وب مشارکتی-2020
The digital transformation is an accumulation of various digital advancements, such as the transformation of the web phenomenon. The participatory web that allows for active user engagement and gather intelligence has been widely recognised as a value add tool by organisations of all shapes and sizes to improve business productivity and efficiency. However, its ability to facilitate sustainable business-to-business (B2B) activities has lacked focus in the business and management literature to date. This qualitative research is exploratory in nature and fills this gap through findings arising from interviews of managers and by developing taxonomies that highlight the capability of participatory web over passive web to enable different firms to engage in business operations. For this purpose, two important interrelated functions of business i.e. operations and marketing have been mapped against three dimensions of sustainability. Consequently, this research demonstrates the ability of big data and social media analytics within a participatory web environment to enable B2B organisations to become profitable and remain sustainable through strategic operations and marketing related business activities. The research findings will be useful for both academics and managers who are interested in understanding and further developing the business use of participatory web tools to achieve business sustainability. Hence, this may be considered as a distinct way of attaining sustainability.
Keywords: Participatory web | Marketing and operations | Big data | Social media analytics | Business sustainability | Business-to-business (B2B)
Blockchain-based life cycle assessment: An implementation framework and system architecture
ارزیابی چرخه زندگی مبتنی بر بلاکچین: چارچوب پیاده سازی و معماری سیستم-2020
Life cycle assessment (LCA) is widely used for assessing the environmental impacts of a product or service. Collecting reliable data is a major challenge in LCA due to the complexities involved in the tracking and quantifying inputs and outputs at multiple supply chain stages. Blockchain technology oﬀers an ideal solution to overcome the challenge in sustainable supply chain management. Its use in combination with internet-of-things (IoT) and big data analytics and visualization can help organizations achieve operational excellence in con- ducting LCA for improving supply chain sustainability. This research develops a framework to guide the im- plementation of Blockchain-based LCA. It proposes a system architecture that integrates the use of Blockchain, IoT, and big data analytics and visualization. The proposed implementation framework and system architecture were validated by practitioners who were experienced with Blockchain applications. The research also analyzes system implementation costs and discusses potential issues and solutions, as well as managerial and policy implications.
Keywords: Blockchain | Life cycle assessment | Supply chain sustainability | Environmental sustainability | Operational excellence
Big data analytics as an operational excellence approach to enhance sustainable supply chain performance
تجزیه و تحلیل داده های بزرگ به عنوان یک رویکرد برتری عملیاتی برای افزایش عملکرد پایدار زنجیره تأمین-2020
Operations management is a core organizational function involved in the management of activities to produce and deliver products and services. Appropriate operations decisions rely on assessing and using information; a task made more challenging in the Big Data era. Effective management of data (big data analytics; BDA), along with staff capabilities (the talent capability in the use of big data) support firms to leverage big data analytics and organizational learning in support of sustainable supply chain management outcomes. The current study uses dynamic capability theory as a foundation for evaluating the role of BDA capability as an operational excellence approach in improving sustainable supply chain performance. We surveyed mining executives in the emerging economy of South Africa and received 520 valid responses (47% response rate). We used Partial Least Squares Structural Equation Modelling (PLS-SEM) to analyze the data. The findings show that big data analytics management capabilities have a strong and significant effect on innovative green product development and sustainable supply chain outcomes. Big data analytics talent capabilities have a weaker but still significant effect on employee development and sustainable supply chain outcomes. Innovation and learning performance affect sustainable supply chain performance, and supply chain innovativeness has an important moderating role. A contribution of the study is identifying two pathways that managers can use to improve sustainable supply chain outcomes in the mining industry, based on big data analytics capabilities.
Keywords: Big data analytics | Operational excellence | Dynamic capability view | Supply chain sustainability | Learning performance