مقاله انگلیسی رایگان در مورد آنالیز پایایی زنجیره تامین به وسیله پیش‌بینی انتشار ذرات معلق – تیلور و فرانسیس ۲۰۲۲

taylorandfrancis

 

مشخصات مقاله
ترجمه عنوان مقاله تجزیه و تحلیل پایداری زنجیره تامین از طریق پیش‌بینی انتشار ذرات معلق در چین
عنوان انگلیسی مقاله Sustainability analysis of supply chain via particulate matter emissions prediction in China
انتشار  مقاله سال ۲۰۲۲
تعداد صفحات مقاله انگلیسی  ۱۴ صفحه
هزینه  دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده  نشریه تیلور و فرانسیس – Taylor & Francis
نوع نگارش مقاله مقاله پژوهشی (Research article)
مقاله بیس این مقاله بیس میباشد
نمایه (index) JCR – Master Journal List – Scopus
نوع مقاله
ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
۶٫۰۷۸ در سال ۲۰۲۰
شاخص H_index ۳۸ در سال ۲۰۲۲
شاخص SJR ۱٫۰۵۶ در سال ۲۰۲۰
شناسه ISSN ۱۴۶۹-۸۴۸X
شاخص Quartile (چارک) Q1 در سال ۲۰۲۰
فرضیه ندارد
مدل مفهومی دارد
پرسشنامه ندارد
متغیر دارد
رفرنس دارد
رشته های مرتبط مهندسی صنایع – مدیریت – مهندسی محیط زیست – مهندسی فناوری اطلاعات
گرایش های مرتبط لجستیک و زنجیره تامین – مهندسی مالی و ریسک – مدیریت کسب و کار – آلودگی هوا – هوش مصنوعی
نوع ارائه مقاله
ژورنال
مجله / کنفرانس مجله بین المللی تحقیقات و کاربردهای لجستیک – International Journal of Logistics Research and Applications
دانشگاه School of Management, Hefei University of Technology, PRC
کلمات کلیدی تجزیه و تحلیل پایداری – مدیریت زنجیره تامین – پیش بینی انتشار ذرات – تقویت شبکه عصبی
کلمات کلیدی انگلیسی  Sustainability analysis – supply chain management – particulate matter emission prediction – boosting neural network
شناسه دیجیتال – doi https://doi.org/10.1080/13675567.2020.1870674
کد محصول e16635
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
دانلود رایگان مقاله دانلود رایگان مقاله انگلیسی
سفارش ترجمه این مقاله سفارش ترجمه این مقاله

 

فهرست مطالب مقاله:

Abstract

۱٫ Introduction

۲٫ Literature review

۳٫ Integrated analytical framework for sustainability analysis of supply chain management

۴٫ Results

۵٫ Conclusions

Disclosure statement

References

 

بخشی از متن مقاله:

Abstract

     With the increasing importance of air pollutant emissions to the platform economy and green supply chain management, it is essential to analyse the trend and correlation between particulate matter emissions and supply chain statistics. Typical approaches do not integrate particulate matter prediction with the sustainability analysis, and suffer from common issues such as low classification accuracy and unstable prediction performance. In this study, we propose an integrated analytical framework for sustainability analysis of supply chain management through particulate matter emissions prediction. Specifically, we performance trend and correlation analysis between particulate matter emissions (PM2.5 and PM10) and supply chain statistics in Beijing of China. We combine the boosting algorithm and neural network method to predict particulate matter emissions. Experimental results show that our prediction model achieved high performance. Sustainability analysis shows that the steady growth of the supply chain operations is accompanied by decreasing air pollutant emissions in China.

Introduction

     Today, human society is facing very severe environmental and resource problems. The green and sustainable supply chain are the concrete manifestations of sustainable development strategies that comprehensively consider these two issues in platform economy management. In recent years, continued urbanisation and industrialisation, particularly in developing countries, has led to severe deterioration in air quality and a rapid increase in the degree of contamination (Shi and Yu 2020). Air pollution has substantially affected the living environment of the human population and endangered health (Stieb et al. 2019). The United Nations (UN) issued Sustainable Development Goals (SDGs) in 2015, serving as a blueprint for building a more sustainable future across the globe. There are totally 17 goals in SDGs, addressing issues in economic, environmental, and social areas. These goals are interrelated and expected to be fulfilled by 2030 (2015). PM2.5 emissions have an adverse effect on public health (Yin, Pizzol, and Xu 2017). At present, many countries attach great importance to inhalable total suspended particulates that cannot be blocked by the human upper respiratory tract, especially inhalable aerosol particles with sizes less than 2.5 μm. It is necessary to arouse the stakeholder

Conclusions

     In this paper, an integrated analytical framework targeting sustainability analysis of supply chain management through particulate matter emissions prediction has been proposed. Specifically, we firstly collect and preprocess the particulate matter emission data and supply chain statistics in Beijing of China, then we conduct correlation analysis between air pollutant concentrations and supply chain statistics to uncover the intrinsic associations. Secondly, we combine the boosting ensemble meta-algorithm and neural network method for particulate matter concentration forecasting. The boosting algorithm is used to train a set of individual subnets to improve the efficiency of prediction, and afterwards, the output results of individual subnets are weighted for acquiring the final prediction results. Finally, we perform an analysis on sustainability between air pollutant emissions and supply chain statistics after obtaining the particulate matter prediction results.

     This analytical framework is able to perform sustainability analysis with accuracy and provide reliable insights for the legislation and implementation of green supply chain management. The forecasting of particulate matter emissions conveys basis and justifications for policy-makers to enforce the environmental protection measures associated with air pollution and design efficient policies with the aim of improving the air pollutant management of the supply chain, which will further lead to an improved public health environment, especially in economically developed areas. Because this study mainly targets the prediction of PM2.5 and PM10 concentrations, it cannot comprehensively reveal the geographical distribution of air pollution. There are various factors that affect air quality, and a regression model can be established to predict PM2.5 and PM10 distributions more comprehensively and to determine the key influential factors, which would be important for formulating economic development strategies and implementing environmental protection mechanisms.

ارسال دیدگاه

نشانی ایمیل شما منتشر نخواهد شد.