Greenhouse Gas Implications of Fleet Electrification Based on Big Data-Informed Individual Travel Patterns

2013 ◽  
Vol 47 (16) ◽  
pp. 9035-9043 ◽  
Author(s):  
Hua Cai ◽  
Ming Xu
2017 ◽  
Vol 142 ◽  
pp. 926-935 ◽  
Author(s):  
Xiao Luo ◽  
Liang Dong ◽  
Yi Dou ◽  
Ning Zhang ◽  
Jingzheng Ren ◽  
...  

2020 ◽  
Vol 14 (4) ◽  
pp. 593-604
Author(s):  
Francesco Mureddu ◽  
Juliane Schmeling ◽  
Eleni Kanellou

Purpose This paper aims to present pertinent research challenges in the field of (big) data-informed policy-making based on the research, undertaken within the course of the European Union-funded project Big Policy Canvas. Technological advancements, especially in the past decade, have revolutionised the way that both every day and complex activities are conducted. It is, thus, expected that a particularly important actor such as the public sector, should constitute a successful disruption paradigm through the adoption of novel approaches and state-of-the-art information and communication technologies. Design The research challenges stem from a need, trend and asset assessment based on qualitative and quantitative research, as well as from the identification of gaps and external framework factors that hinder the rapid and effective uptake of data-driven policy-making approaches. Findings The current paper presents a set of research challenges categorised in six main clusters, namely, public governance framework, privacy, transparency, trust, data acquisition, cleaning and representativeness, data clustering, integration and fusion, modelling and analysis with big data and data visualisation. Originality/value The paper provides a holistic overview of the interdisciplinary research challenges in the field of data-informed policy-making at a glance and shall serve as a foundation for the discussion of future research directions in a broader scientific community. It, furthermore, underlines the necessity to overcome isolated scientific views and treatments because of a high complex multi-layered environment.


2021 ◽  
Vol 40 (12) ◽  
pp. 2035-2047
Author(s):  
De TONG ◽  
Xincan ZHOU ◽  
Yongxi GONG ◽  
Keyword(s):  
Big Data ◽  

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