Big data and environmental sustainability based integrated framework for isotope hydrology applications in India

2021 ◽  
pp. 101889
Author(s):  
Tirumalesh Keesari ◽  
Manish Kumar Goyal ◽  
Brij Gupta ◽  
Nikhil Kumar ◽  
Anndasankar Roy ◽  
...  
Keyword(s):  
Big Data ◽  
Environmental Sustainability ◽  
Isotope Hydrology ◽  
Integrated Framework

scholarly journals Big data for climate action or climate action for big data?

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172098203
Author(s):  
Maria I Espinoza ◽  
Melissa Aronczyk
Keyword(s):  
Big Data ◽  
Environmental Sustainability ◽  
Global Climate ◽  
Intergovernmental Organizations ◽  
Address Climate Change ◽  
Data Practices ◽  
Climate Action ◽  
User Data ◽  
Climate Crisis ◽  
Depth Interviews

Under the banner of “data for good,” companies in the technology, finance, and retail sectors supply their proprietary datasets to development agencies, NGOs, and intergovernmental organizations to help solve an array of social problems. We focus on the activities and implications of the Data for Climate Action campaign, a set of public–private collaborations that wield user data to design innovative responses to the global climate crisis. Drawing on in-depth interviews, first-hand observations at “data for good” events, intergovernmental and international organizational reports, and media publicity, we evaluate the logic driving Data for Climate Action initiatives, examining the implications of applying commercial datasets and expertise to environmental problems. Despite the increasing adoption of Data for Climate Action paradigms in government and public sector efforts to address climate change, we argue Data for Climate Action is better seen as a strategy to legitimate extractive, profit-oriented data practices by companies than a means to achieve global goals for environmental sustainability.

Vehicle to Cloud

2016 ◽  
pp. 182-201
Author(s):  
Alper Ozpinar ◽  
Serhan Yarkan
Keyword(s):  
Big Data ◽  
Environmental Sustainability ◽  
Traffic Management ◽  
Human Beings ◽  
Standard Equipment ◽  
Political Views ◽  
Iot Devices ◽  
The One ◽  
Social Applications ◽  
Use Of Internet

The population of humanity has become more than seven billion. Daily used devices, machines, and equipment, are also increasing quicker than the human population. The number of mobile devices in use like phones, tablets and IoT devices already passed the two billion barrier and even more than one billion as vehicles are also on the roads. Combining these two will make the one of the biggest Big Data Environment about the daily life of human beings after the use of internet and social applications. For the newly manufactured vehicles, internet operated entertainment and information Systems are becoming a standard equipment delivering such an information to the manufacturers but most of the current vehicles do not have a system like that. This chapter explains the combined version of IoT and vehicles to create a V2C vehicle to cloud system that will create the big data for environmental sustainability, energy and traffic management by different technical and political views and aspects.

scholarly journals Use of Big Data to Improve Environmental Sustainability in Developing Countries

2017 ◽  
Vol 12 (11) ◽  
pp. 249 ◽  
Author(s):  
Maged Adel Abdo Mukred ◽  
Zheng Jianguo
Keyword(s):  
Big Data ◽  
Multinational Corporations ◽  
Environmental Sustainability ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Government Sector ◽  
Wide Range ◽  
Private And Public ◽  
The Government ◽  
Set Up

Big data inhibits the ability to significantly impact a wide range of fields in an economy, from the government sector to commercial sectors like retail and healthcare. Not only has it altered the way companies assess their product’s demand and supply patterns but has also phenomenally helped in making the environment healthier in recent years. It carries the ability to identify valuable data from a huge dataset with exceptional parallel processing. This study presents the general introduction of big data bringing forth its various features and advantages along with the challenges which organizations face while using with respect to environmental sustainability. Observations have also been made on the findings of various researches, and studies and surveys performed by some international organizations in the recent years on the urgent need of taking necessary measures and initiatives to prevent further depletion of natural resources thus making the environment sustainable. Making the issue the study aim, future studies must intend to explore how multinational corporations can enhance environmental sustainability through big data analytics. Lastly, recommendations have been made to organisations– private and public in hiring adequate expertise and set-up, thereby making big data analytics more efficient and reliable.

scholarly journals Environmentally data-driven smart sustainable cities: applied innovative solutions for energy efficiency, pollution reduction, and urban metabolism

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Simon Elias Bibri ◽  
John Krogstie
Keyword(s):  
Energy Efficiency ◽  
Big Data ◽  
Real Time ◽  
Environmental Sustainability ◽  
Smart Cities ◽  
Data Driven ◽  
Urban Metabolism ◽  
Pollution Reduction ◽  
Illustrative Case ◽  
Sustainable Cities

AbstractThe IoT and big data technologies have become essential to the functioning of both smart cities and sustainable cities, and thus, urban operational functioning and planning are becoming highly responsive to a form of data-driven urbanism. This offers the prospect of building models of smart sustainable cities functioning in real time from routinely sensed data. This in turn allows to monitor, understand, analyze, and plan such cities to improve their energy efficiency and environmental health in real time thanks to new urban intelligence functions as an advanced form of decision support. However, prior studies tend to deal largely with data-driven technologies and solutions in the realm of smart cities, mostly in relation to economic and social aspects, leaving important questions involving the underlying substantive and synergistic effects on environmental sustainability barely explored to date. These issues also apply to sustainable cities, especially eco-cities. Therefore, this paper investigates the potential and role of data-driven smart solutions in improving and advancing environmental sustainability in the context of smart cities as well as sustainable cities, under what can be labeled “environmentally data-driven smart sustainable cities.” To illuminate this emerging urban phenomenon, a descriptive/illustrative case study is adopted as a qualitative research methodology§ to examine and compare Stockholm and Barcelona as the ecologically and technologically leading cities in Europe respectively. The results show that smart grids, smart meters, smart buildings, smart environmental monitoring, and smart urban metabolism are the main data-driven smart solutions applied for improving and advancing environmental sustainability in both eco-cities and smart cities. There is a clear synergy between such solutions in terms of their interaction or cooperation to produce combined effects greater than the sum of their separate effects—with respect to the environment. This involves energy efficiency improvement, environmental pollution reduction, renewable energy adoption, and real-time feedback on energy flows, with high temporal and spatial resolutions. Stockholm takes the lead over Barcelona as regards the best practices for environmental sustainability given its long history of environmental work, strong environmental policy, progressive environmental performance, high environmental standards, and ambitious goals. It also has, like Barcelona, a high level of the implementation of applied data-driven technology solutions in the areas of energy and environment. However, the two cities differ in the nature of such implementation. We conclude that city governments do not have a unified agenda as a form of strategic planning, and data-driven decisions are unique to each city, so are environmental challenges. Big data are the answer, but each city sets its own questions based on what characterize it in terms of visions, policies, strategies, pathways, and priorities.

scholarly journals Optimizing Bus Passenger Complaint Service through Big Data Analysis: Systematized Analysis for Improved Public Sector Management

2016 ◽  
Author(s):  
Weng-Kun Liu ◽  
Chia-Chun Yen
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Public Sector ◽  
Environmental Sustainability ◽  
Research Area ◽  
Big Data Analysis ◽  
The Public ◽  
Demand Chain ◽  
Customer Complaint ◽  
The Government

With the advances in industry and commerce, passengers have become more accepting of environmental sustainability issues; thus, more people now choose to travel by bus. Government administration constitutes an important part of bus transportation services as the government gives the right-of-way to transportation companies allowing them to provide services. When these services are of poor quality, passengers may lodge complaints. The increase in consumer awareness and developments in wireless communication technologies have made it possible for passengers to easily and immediately submit complaints about transportation companies to government institutions, which has brought drastic changes to the supply-demand chain comprised of the public sector, transportation companies, and passengers. This study proposed the use of big data analysis technology including systematized case assignment and data visualization to improve management processes in the public sector and optimize customer complaint services. Taichung City, Taiwan was selected as the research area. There, the customer complaint management process in public sector was improved, effectively solving such issues as station-skipping, allowing the public sector to fully grasp the service level of transportation companies, improving the sustainability of bus operations, and supporting the sustainable development of the public sector-transportation company-passenger supply chain.

scholarly journals Big data analytics and sustainable textile manufacturing

2020 ◽  
Vol 58 (8) ◽  
pp. 1699-1714 ◽  
Author(s):  
Dieu Hack-Polay ◽  
Mahfuzur Rahman ◽  
Md Morsaline Billah ◽  
Hesham Z. Al-Sabbahy
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Environmental Sustainability ◽  
Textile Industry ◽  
Data Analytics ◽  
New Technologies ◽  
Developing World ◽  
Big Data Analytics ◽  
Content Type ◽  
Member Exchange

PurposeThe purpose of this article is to discuss issues associated with the application big data analytics for decision-making about the introduction of new technologies in the textile industry in the developing world.Design/methodology/approachThe leader–member exchange theoretical framework to consider the nature of the relationships between owners and followers to identify the potential issues that affect decision-making was used. However, decisions to adopt such environmentally friendly biotechnologies are hampered by the lack of awareness amongst owners, intergenerational conflict and cultural impediments.FindingsThe article found that the limited use of this valuable technological resource is linked to several factors, mainly cultural, generational and educational factors. The article exposes two key new technologies that could help the industry reduce its carbon footprint.Originality/valueThe study suggests more awareness raising amongst plant owners and greater empowerment of new generations in decision-making in the industry. This study, therefore, bears significant implications for environmental sustainability in the developing world where the textile industry is one of the major polluting industries affecting water quality and human health.

scholarly journals An Advanced Operation Mode with Product-Service System Using Lifecycle Big Data and Deep Learning

2021 ◽  
Author(s):  
Shan Ren ◽  
Yingfeng Zhang ◽  
Tomohiko Sakao ◽  
Yang Liu ◽  
Ruilong Cai
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Environmental Sustainability ◽  
Service System ◽  
Operation Mode ◽  
Fault Prediction ◽  
Cnc Machine ◽  
Product Service System ◽  
Operation And Maintenance ◽  
Product Service

AbstractAs a successful business strategy for enhancing environmental sustainability and decreasing the natural resource consumption of societies, the product-service system (PSS) has raised significant interests in the academic and industrial community. However, with the digitisation of the industry and the advancement of multisensory technologies, the PSS providers face many challenges. One major challenge is how the PSS providers can fully capture and efficiently analyse the operation and maintenance big data of different products and different customers in different conditions to obtain insights to improve their production processes, products and services. To address this challenge, a new operation mode and procedural approach are proposed for operation and maintenance of bigger cluster products, when these products are provided as a part of PSS and under exclusive control by the providers. The proposed mode and approach are driven by lifecycle big data of large cluster products and employs deep learning to train the neural networks to identify the fault features, thereby monitoring the products’ health status. This new mode is applied to a real case of a leading CNC machine provider to illustrate its feasibility. Higher accuracy and shortened time for fault prediction are realised, resulting in the provider’s saving of the maintenance and operation cost.

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