Big-Data Mechanism and Energy-Policy Design

Author(s):  
M. Pandiyan ◽  
R. Venkadesh
Keyword(s):  
Big Data ◽  
DYNA ◽  
2015 ◽  
Vol 82 (194) ◽  
pp. 160-169 ◽  
Author(s):  
Carlos Jaime Franco Cardona ◽  
Mónica Castañeda Riascos ◽  
Alejandro Valencia Arias ◽  
Jonathan Bermúdez Hernández

The energy "Trilemma" seeks to develop an electricity market which simultaneously ensures environmental quality, security of supply, and economic sustainability. The objective of this paper is to present the "Trilemma" energy as the latest trend in the design of energy policy. For this, a theoretical framework is presented in sections 2 and 3, in section 4 and 5 the importance of security of supply and economic sustainability are discussed, respectively. In section 6 the energy "trilemma" is presented, in section 7 a brief state of the art is showed. Finally in section 8, it is approached three different electricity markets. It is concluded that the regulator has passed in recent years from encouraging a liberalized market scheme, to promote a scheme based on intervention through policies that affect the market competitiveness but allow achieving its environmental goals.


Author(s):  
Kawa Nazemi ◽  
Martin Steiger ◽  
Dirk Burkhardt ◽  
Jörn Kohlhammer

Policy design requires the investigation of various data in several design steps for making the right decisions, validating, or monitoring the political environment. The increasing amount of data is challenging for the stakeholders in this domain. One promising way to access the “big data” is by abstracted visual patterns and pictures, as proposed by information visualization. This chapter introduces the main idea of information visualization in policy modeling. First abstracted steps of policy design are introduced that enable the identification of information visualization in the entire policy life-cycle. Thereafter, the foundations of information visualization are introduced based on an established reference model. The authors aim to amplify the incorporation of information visualization in the entire policy design process. Therefore, the aspects of data and human interaction are introduced, too. The foundation leads to description of a conceptual design for social data visualization, and the aspect of semantics plays an important role.


Big Data ◽  
2016 ◽  
pp. 139-180
Author(s):  
Kawa Nazemi ◽  
Martin Steiger ◽  
Dirk Burkhardt ◽  
Jörn Kohlhammer

Policy design requires the investigation of various data in several design steps for making the right decisions, validating, or monitoring the political environment. The increasing amount of data is challenging for the stakeholders in this domain. One promising way to access the “big data” is by abstracted visual patterns and pictures, as proposed by information visualization. This chapter introduces the main idea of information visualization in policy modeling. First abstracted steps of policy design are introduced that enable the identification of information visualization in the entire policy life-cycle. Thereafter, the foundations of information visualization are introduced based on an established reference model. The authors aim to amplify the incorporation of information visualization in the entire policy design process. Therefore, the aspects of data and human interaction are introduced, too. The foundation leads to description of a conceptual design for social data visualization, and the aspect of semantics plays an important role.


Author(s):  
Lamyaa El Bassiti

At the heart of all policy design and implementation, there is a need to understand how well decisions are made. It is evidently known that the quality of decision making depends significantly on the quality of the analyses and advice provided to the associated actors. Over decades, organizations were highly diligent in gathering and processing vast amounts of data, but they have given less emphasis on how these data can be used in policy argument. With the arrival of big data, attention has been focused on whether it could be used to inform policy-making. This chapter aims to bridge this gap, to understand variations in how big data could yield usable evidence, and how policymakers can make better use of those evidence in policy choices. An integrated and holistic look at how solving complex problems could be conducted on the basis of semantic technologies and big data is presented in this chapter.


Author(s):  
Bryant Hawthorne ◽  
Zhenghui Sha ◽  
Jitesh H. Panchal ◽  
Farrokh Mistree

This is the second paper in a four-part series focused on a competency-based approach for personalized education in a group setting. In the first paper, we focus on identifying the competencies and meta-competencies required for the 21st century engineers. In this paper, we provide an overview of an approach to developing competencies needed for the fast changing world and allowing the students to be in charge of their own learning. The approach fosters “learning how to learn” in a collaborative environment. We believe that two of the core competencies required for success in the dynamically changing workplace are the abilities to identify and manage dilemmas. In the third paper, we discuss our approach for helping students learn how to identify dilemmas in the context of an energy policy design problem. The fourth paper is focused on approaches to developing the competency to manage dilemmas associated with the realization of complex, sustainable, socio-techno-eco systems. The approach is presented in the context of a graduate-level course jointly offered at University of Oklahoma, Norman and Washington State University, Pullman during Fall 2011. The students were asked to identify the competencies needed to be successful at creating value in a culturally diverse, distributed engineering world at the beginning of the semester. The students developed these competencies by completing various assignments designed to collaboratively answer a Question for Semester (Q4S). The Q4S was focused on identifying and managing dilemmas associated with energy policy and the next generation bridging fuels. A unique aspect of this course is the collaborative structure in which students completed these assignments individually, in university groups and in collaborative university teams. The group and team structures were developed to ultimately aid individual learning. The details of the answer to the Q4S are elaborated in the other three papers which address identifying and managing dilemmas, specifically related to Feed-In-Tariff (FIT) policy and bridging fuels. The fundamental principles of our approach include a shift in the role of the instructor to orchestrators of learning, shift in the role of students to active learners, providing opportunities to learn, shift in focus from lower levels to upper levels of learning, creation of learning communities, embedding flexibility in courses, leveraging diversity, making students aware of the learning process, and scaffolding. Building on our experience in the course, we discuss specific ways to foster the development of learning organizations within classroom settings. Additionally, we present techniques for scaffolding the learning activities in a distributed classroom based on systems thinking, personal mastery, mental models, a shared vision, and team learning. The approach enables personalized learning of individuals in a group setting.


2021 ◽  
pp. 117-120
Author(s):  
Francesca Giambona ◽  
Adham Kahlawi ◽  
Lucia Buzzigoli ◽  
Laura Grassini ◽  
Cristina Martelli

Economists and social scientists are increasingly making use of web data to address socio-economic issues and to integrate existing sources of information. The data produced by online platforms and websites could produce a lot of useful and multidimensional information with a variety of potential applications in socio-economic analysis. In this respect, with the internet growth and knowledge, many aspects of job search have been transformed due to the availability of online tools for job searching, candidate searching and job matching. In European countries there is growing interest in designing and implementing real labour market information system applications for internet labour market data in order to support policy design and evaluation through evidence-based decision-making. The analysis of labour market web data could provide useful information for policy-makers to define labour market strategies as big data, jointly with official statistics, support policy makers in a pressing policy question namely “How to tackle the mismatch between jobs and skills?”. In this regard, the topic of skills gap, how to measure it and how to bridge it with education and continuous training have been tackled by using the big data collection, such as the Cedefop (European Center for the Development of Vocational Training) initiative and the Wollybi Project (made by Burning Glass). In this framework, this contribution focuses on the issues arising from the use (and the usefulness) of on-line job vacancy data to analyse the Italian labour market by using the Wollybi data available for the years 2019 and 2020. Furthermore, the availability of data for the year 2020, will allow us to evaluate whether there has been an impact of COVID19 in terms of needed skills and required occupations in the online job vacancies.


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