The year of 2011 is considered the first year of big data market in China.Compared with the global scale,China's big data growth will be faster than the global average growth rate,and China will usher in the rapid...The year of 2011 is considered the first year of big data market in China.Compared with the global scale,China's big data growth will be faster than the global average growth rate,and China will usher in the rapid expansion of big data market in the next few years.This paper presents the overall big data development in China in terms of market scale and development stages,enterprise development in the industry chain,the technology standards,and industrial applications.The paper points out the issues and challenges facing big data development in China and proposes to make polices and create support approaches for big data transactions and personal privacy protection.展开更多
This article describes the development and implementations of a novel software platform that supports real-time, science-based policy making on air quality through a user-friendly interface. The software, RSM-VAT, use...This article describes the development and implementations of a novel software platform that supports real-time, science-based policy making on air quality through a user-friendly interface. The software, RSM-VAT, uses a response surface modeling（RSM） methodology and serves as a visualization and analysis tool（VAT） for three-dimensional air quality data obtained by atmospheric models. The software features a number of powerful and intuitive data visualization functions for illustrating the complex nonlinear relationship between emission reductions and air quality benefits. The case study of contiguous U.S.demonstrates that the enhanced RSM-VAT is capable of reproducing the air quality model results with Normalized Mean Bias 〈 2% and assisting in air quality policy making in near real time.展开更多
Incorporating knowledge of policy transfer into urban governance frameworks fosters cross-city learning and facilitates a transition from predictionbased to policy-guided decision-making.This approach combines data wi...Incorporating knowledge of policy transfer into urban governance frameworks fosters cross-city learning and facilitates a transition from predictionbased to policy-guided decision-making.This approach combines data with policy insights,expanding the scope of cross-city learning and fostering collaborative governance.Unlike traditional qualitative policy transfer studies,the envisioned policy-aware framework computationally translates policy contexts into quantitative representations for direct integration into machine learning,enabling automated strategy adaptation rather than relying solely on human interpretation.展开更多
Data show that carbon emissions are increasing due to human energy consumption associated with economic development. As a result, a great deal of attention has been focused on efforts to reduce this growth in carbon e...Data show that carbon emissions are increasing due to human energy consumption associated with economic development. As a result, a great deal of attention has been focused on efforts to reduce this growth in carbon emissions as well as to formulate policies to address and mitigate climate change. Although the majority of previous studies have explored the driving forces underlying Chinese carbon emissions, few have been carried out at the city-level because of the limited availability of relevant energy consumption statistics. Here, we utilize spatial autocorrelation, Markov-chain transitional matrices, a dynamic panel model, and system generalized distance estimation(Sys-GMM) to empirically evaluate the key determinants of carbon emissions at the city-level based on Chinese remote sensing data collected between 1992 and 2013. We also use these data to discuss observed spatial spillover effects taking into account spatiotemporal lag and a range of different geographical and economic weighting matrices. The results of this study suggest that regional discrepancies in city-level carbon emissions have decreased over time, which are consistent with a marked spatial spillover effect, and a ‘club' agglomeration of high-emissions. The evolution of these patterns also shows obvious path dependence, while the results of panel data analysis reveal the presence of a significant U-shaped relationship between carbon emissions and per capita GDP. Data also show that per capita carbon emissions have increased in concert with economic growth in most cities, and that a high-proportion of secondary industry and extensive investment growth have also exerted significant positive effects on city-level carbon emissions across China. In contrast, rapid population agglomeration, improvements in technology, increasing trade openness, and the accessibility and density of roads have all played a role in inhibiting carbon emissions. Thus, in order to reduce emissions, the Chinese government should legislate to inhibit the effects of factors that promote the release of carbon while at the same time acting to encourage those that mitigate this process. On the basis of the analysis presented in this study, we argue that optimizing industrial structures, streamlining extensive investment, increasing the level of technology, and improving road accessibility are all effective approaches to increase energy savings and reduce carbon emissions across China.展开更多
The paper critically evaluates the global discourses on algorithmic fairness,reviews key Western literature on artificial intelligence(AI)fairness,identifies twelve documented cases of algorithmic discrimination in We...The paper critically evaluates the global discourses on algorithmic fairness,reviews key Western literature on artificial intelligence(AI)fairness,identifies twelve documented cases of algorithmic discrimination in Western contexts,and extends them for their analytical relevance to non-Western socio-political environments.The study applies these frameworks in particular to the Indian context,and proposes that India’s entrenched socio-cultural structures—caste,religion,language,regional identity,and minority status—tends to misalign with Western paradigms of fairness.The proposed study identifies identity-specific factors unique to India that are likely to contribute to algorithmic oppression if unaddressed.A critical policy analysis of major documents shaping India’s AI and digital governance landscape reveals that these critical factors remain largely unacknowledged.Indian policy responses tend to replicate Western techno-legal models without engaging indigenous socio-structural realities.The paper concludes that ethical AI governance in India must transcend imported normative models and instead be rooted in context-sensitive approaches that accommodates the nation’s distinct social fabric,in order to prevent algorithm induced structural discrimination and ensure inclusive algorithmic justice.展开更多
Adopting the FAIR Guidelines—that data should be Findable, Accessible, Interoperable and Reusable(FAIR)—in the health data system in Nigeria will help protect data against use by unauthorised parties, while also mak...Adopting the FAIR Guidelines—that data should be Findable, Accessible, Interoperable and Reusable(FAIR)—in the health data system in Nigeria will help protect data against use by unauthorised parties, while also making data more accessible to legitimate users. However, little is known about the FAIR Guidelines and their compatibility with data and health laws and policies in Nigeria. This study assesses the governance framework for digital and health/e Health policies in Nigeria and explores the possibility of a policy window opening for the FAIR Guidelines to be adopted and implemented in Nigeria’s e Health sector. Ten Nigerian policy documents were examined for mention of the FAIR Guidelines(or FAIR Equivalent terminology) and the 15 sub-criteria or facets. The analysis found that although the FAIR Guidelines are not explicitly mentioned, 70% of the documents contain FAIR Equivalent terminology. The Nigeria Data Protection Regulation contained the most FAIR Equivalent principles(73%) and some of the remaining nine documents also contained some FAIR Equivalent principles(between 0–60%). Accordingly, it can be concluded that a policy window is open for the FAIR Guidelines to be adopted and implemented in Nigeria’s e Health sector.展开更多
Thousands of community-developed(meta)data guidelines,models,ontologies,schemas and formats have been created and implemented by several thousand data repositories and knowledge-bases,across all disciplines.These reso...Thousands of community-developed(meta)data guidelines,models,ontologies,schemas and formats have been created and implemented by several thousand data repositories and knowledge-bases,across all disciplines.These resources are necessary to meet government,funder and publisher expectations of greater transparency and access to and preservation of data related to research publications.This obligates researchers to ensure their data is FAIR,share their data using the appropriate standards,store their data in sustainable and community-adopted repositories,and to conform to funder and publisher data policies.FAIR data sharing also plays a key role in enabling researchers to evaluate,re-analyse and reproduce each other’s work.We can map the landscape of relationships between community-adopted standards and repositories,and the journal publisher and funder data policies that recommend their use.In this paper,we show how the work of the GO-FAIR FAIR Standards,Repositories and Policies(StRePo)Implementation Network serves as a central integration and cross-fertilisation point for the reuse of FAIR standards,repositories and data policies in general.Pivotal to this effort,the FAIRsharing,an endorsed flagship resource of the Research Data Alliance that maps the landscape of relationships between community-adopted standards and repositories,and the journal publisher and funder data policies that recommend their use.Lastly,we highlight a number of activities around FAIR tools,services and educational efforts to raise awareness and encourage participation.展开更多
文摘The year of 2011 is considered the first year of big data market in China.Compared with the global scale,China's big data growth will be faster than the global average growth rate,and China will usher in the rapid expansion of big data market in the next few years.This paper presents the overall big data development in China in terms of market scale and development stages,enterprise development in the industry chain,the technology standards,and industrial applications.The paper points out the issues and challenges facing big data development in China and proposes to make polices and create support approaches for big data transactions and personal privacy protection.
基金Financial and data support for this work is provided by the U.S. Environmental Protection Agency (No. GS-10F-0205T)partly supported by the funding of Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control (No. h2xj D612004 Ш )+1 种基金the funding of State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex (No. SCAPC201308)the project of Atmospheric Haze Collaboration Control Technology Design (No. XDB05030400) from Chinese Academy of Sciences
文摘This article describes the development and implementations of a novel software platform that supports real-time, science-based policy making on air quality through a user-friendly interface. The software, RSM-VAT, uses a response surface modeling（RSM） methodology and serves as a visualization and analysis tool（VAT） for three-dimensional air quality data obtained by atmospheric models. The software features a number of powerful and intuitive data visualization functions for illustrating the complex nonlinear relationship between emission reductions and air quality benefits. The case study of contiguous U.S.demonstrates that the enhanced RSM-VAT is capable of reproducing the air quality model results with Normalized Mean Bias 〈 2% and assisting in air quality policy making in near real time.
基金funded by the NSFC(nos.72401174 and 52220105001)the Independent Research Project of the State Key Laboratory of Intelligent Green Vehicle and Mobility,Tsinghua University(no.ZZ-GG-20250403)Tsinghua University(State Key Laboratory of Intelligent Green Vehicle and Mobility)-Hangzhou Airport Economic Demonstration Zone Joint Research Center for Integrated Transportation.
文摘Incorporating knowledge of policy transfer into urban governance frameworks fosters cross-city learning and facilitates a transition from predictionbased to policy-guided decision-making.This approach combines data with policy insights,expanding the scope of cross-city learning and fostering collaborative governance.Unlike traditional qualitative policy transfer studies,the envisioned policy-aware framework computationally translates policy contexts into quantitative representations for direct integration into machine learning,enabling automated strategy adaptation rather than relying solely on human interpretation.
基金National Natural Science Foundation of China,No.41601151Guangdong Natural Science Foundation,No.2016A030310149
文摘Data show that carbon emissions are increasing due to human energy consumption associated with economic development. As a result, a great deal of attention has been focused on efforts to reduce this growth in carbon emissions as well as to formulate policies to address and mitigate climate change. Although the majority of previous studies have explored the driving forces underlying Chinese carbon emissions, few have been carried out at the city-level because of the limited availability of relevant energy consumption statistics. Here, we utilize spatial autocorrelation, Markov-chain transitional matrices, a dynamic panel model, and system generalized distance estimation(Sys-GMM) to empirically evaluate the key determinants of carbon emissions at the city-level based on Chinese remote sensing data collected between 1992 and 2013. We also use these data to discuss observed spatial spillover effects taking into account spatiotemporal lag and a range of different geographical and economic weighting matrices. The results of this study suggest that regional discrepancies in city-level carbon emissions have decreased over time, which are consistent with a marked spatial spillover effect, and a ‘club' agglomeration of high-emissions. The evolution of these patterns also shows obvious path dependence, while the results of panel data analysis reveal the presence of a significant U-shaped relationship between carbon emissions and per capita GDP. Data also show that per capita carbon emissions have increased in concert with economic growth in most cities, and that a high-proportion of secondary industry and extensive investment growth have also exerted significant positive effects on city-level carbon emissions across China. In contrast, rapid population agglomeration, improvements in technology, increasing trade openness, and the accessibility and density of roads have all played a role in inhibiting carbon emissions. Thus, in order to reduce emissions, the Chinese government should legislate to inhibit the effects of factors that promote the release of carbon while at the same time acting to encourage those that mitigate this process. On the basis of the analysis presented in this study, we argue that optimizing industrial structures, streamlining extensive investment, increasing the level of technology, and improving road accessibility are all effective approaches to increase energy savings and reduce carbon emissions across China.
文摘The paper critically evaluates the global discourses on algorithmic fairness,reviews key Western literature on artificial intelligence(AI)fairness,identifies twelve documented cases of algorithmic discrimination in Western contexts,and extends them for their analytical relevance to non-Western socio-political environments.The study applies these frameworks in particular to the Indian context,and proposes that India’s entrenched socio-cultural structures—caste,religion,language,regional identity,and minority status—tends to misalign with Western paradigms of fairness.The proposed study identifies identity-specific factors unique to India that are likely to contribute to algorithmic oppression if unaddressed.A critical policy analysis of major documents shaping India’s AI and digital governance landscape reveals that these critical factors remain largely unacknowledged.Indian policy responses tend to replicate Western techno-legal models without engaging indigenous socio-structural realities.The paper concludes that ethical AI governance in India must transcend imported normative models and instead be rooted in context-sensitive approaches that accommodates the nation’s distinct social fabric,in order to prevent algorithm induced structural discrimination and ensure inclusive algorithmic justice.
基金VODAN-Africathe Philips Foundation+2 种基金the Dutch Development Bank FMOCORDAIDthe GO FAIR Foundation for supporting this research
文摘Adopting the FAIR Guidelines—that data should be Findable, Accessible, Interoperable and Reusable(FAIR)—in the health data system in Nigeria will help protect data against use by unauthorised parties, while also making data more accessible to legitimate users. However, little is known about the FAIR Guidelines and their compatibility with data and health laws and policies in Nigeria. This study assesses the governance framework for digital and health/e Health policies in Nigeria and explores the possibility of a policy window opening for the FAIR Guidelines to be adopted and implemented in Nigeria’s e Health sector. Ten Nigerian policy documents were examined for mention of the FAIR Guidelines(or FAIR Equivalent terminology) and the 15 sub-criteria or facets. The analysis found that although the FAIR Guidelines are not explicitly mentioned, 70% of the documents contain FAIR Equivalent terminology. The Nigeria Data Protection Regulation contained the most FAIR Equivalent principles(73%) and some of the remaining nine documents also contained some FAIR Equivalent principles(between 0–60%). Accordingly, it can be concluded that a policy window is open for the FAIR Guidelines to be adopted and implemented in Nigeria’s e Health sector.
基金Some of the discussion points in this article and the call for action were developed as part of the joint RDA and Force11 working group and the GO-FAIR StRePo INWe therefore gratefully acknowledge the support provided by the RDA,Force11 and GO-FAIR communities and structures.FAIRsharing is funded by grants awarded to S.-A.S.that include elements of this work+3 种基金specifically,grants from the UK BBSRC and Research Councils(BB/L024101/1,BB/L005069/1)European Union(H2020-EU.3.1,634107,H2020-EU.1.4.1.3,654241,H2020-EU.1.4.1.1,676559),IMI(116060)and NIH(U54 AI117925,1U24AI117966-01,1OT3OD025459-01,1OT3OD025467-01,1OT3OD025462-01)the new FAIRsharing award from the Wellcome Trust(212930/Z/18/Z)as well as a related award(208381/A/17/Z).S.-A.S.is funded also by the Oxford e-Research Centre,Department of Engineering Science of the University of Oxford.
文摘Thousands of community-developed(meta)data guidelines,models,ontologies,schemas and formats have been created and implemented by several thousand data repositories and knowledge-bases,across all disciplines.These resources are necessary to meet government,funder and publisher expectations of greater transparency and access to and preservation of data related to research publications.This obligates researchers to ensure their data is FAIR,share their data using the appropriate standards,store their data in sustainable and community-adopted repositories,and to conform to funder and publisher data policies.FAIR data sharing also plays a key role in enabling researchers to evaluate,re-analyse and reproduce each other’s work.We can map the landscape of relationships between community-adopted standards and repositories,and the journal publisher and funder data policies that recommend their use.In this paper,we show how the work of the GO-FAIR FAIR Standards,Repositories and Policies(StRePo)Implementation Network serves as a central integration and cross-fertilisation point for the reuse of FAIR standards,repositories and data policies in general.Pivotal to this effort,the FAIRsharing,an endorsed flagship resource of the Research Data Alliance that maps the landscape of relationships between community-adopted standards and repositories,and the journal publisher and funder data policies that recommend their use.Lastly,we highlight a number of activities around FAIR tools,services and educational efforts to raise awareness and encourage participation.
