This report recognizes the interdependence of climate, ecosystems and biodiversity, and human societies and integrates knowledge more strongly across the natural, ecological, social and economic sciences than earlier IPCC assessments. The assessment of climate change impacts and risks as well as adaptation is set against concurrently unfolding non-climatic global trends e.g., biodiversity loss, overall unsustainable consumption of natural resources, land and ecosystem degradation, rapid urbanisation, human demographic shifts, social and economic inequalities and a pandemic.
The vulnerability of exposed human and natural systems is a component of risk, but also, independently, an important focus in the literature. Approaches to analysing and assessing vulnerability have evolved since previous IPCC assessments. Vulnerability is widely understood to differ within communities and across societies, regions and countries, also changing through time.
Source: IPCC.ch
In December 2021 the United Nations Institute for Disarmament Research (UNIDIR) published a report on deepfakes, trust and international security.
According to the institute, the report "provides a summary of the key themes, issues, and takeaways that emerged from the 2021 Innovations Dialogue on Deepfakes, Trust and International Security. Bringing together 20 expert speakers from government, international organizations, academia, and industry and nearly 1,000 (virtual and in-person) participants from around the world, the Dialogue illuminated how algorithmically generated synthetic media is created and disseminated, and how it could erode trust and present novel risks for international security and stability. The discussions also explored the key governance issues concerning deepfakes and the technical countermeasures and policy responses by which the technology’s dangers could be addressed. Finally, the Dialogue reflected on how the international community can preserve and foster trust in the digital ecosystem moving forward."
Authors: Alisha Anand & Belen Bianco
Date of publication: 22 December 2021
The COVID-19 pandemic has painfully confirmed what experts have warned against since the 2009 H1N1 and 2014-2016 Ebola pandemics: the world has been gravely under-prepared for large outbreaks of emerging infectious diseases.
The EU is drawing lessons from the COVID-19 crisis, with new policy initiatives brought forward by the European Commission on better preparedness for future health threats. To support and inform that process, we as science and ethics advisors have examined evidence on the responses to the COVID-19 and, in part, to previous pandemics – which has revealed important lessons learned and to be learned.
Under the strategic guidance of the High-Level Group of Personalities, the Africa-Europe Foundation’s flagship report proposes a vision for the future of global healthcare. The report reaffirms the Foundation’s call for action on vaccine equity, the interdependence between health, climate and development, as well as the role of digital technologies for a better resilience in the future healthcare ecosystems.
The report outlines how a comprehensive partnership between Africa and Europe, based on mutual recognition of shared challenges and mutual learning, could strengthen the international response to future crises. Key recommendations include the necessity to restructure the finance, manufacturing and supply chains; to enact the health-climate-development nexus; and to foster the strategic implementation of digital technologies in healthcare systems.
Source: www.africaeuropefoundation.org
Autors
Paul Walton Report Director
Wuleta Lemma Digital Healthcare Ambassador
Tamsin Rose Senior Fellow for Health
Rahul Chawla Coordinating Editor
Charles Ebikeme Researcher
Rasna Warah Strategic Contents Advisor
Matjaz Krmelj Graphic Designer
The WHO European Centre for Environment and Health has been closely following the research on green and blue spaces because of their importance in addressing human and ecosystem health in urban planning, especially in the context of climate change. Particular attention has been paid to the mental health effects of such spaces.
The EKLIPSE Expert Working Group on Biodiversity and Mental Health conducted two systematic reviews on the types and characteristics of green and blue spaces, in relation to a broad set of mental health aspects. The reviews demonstrated the overall positive relationship between green and blue spaces and mental health.
This report summarizes the key findings of the systematic reviews, briefly looks at the relevant WHO tools and strategies, and reflects on future needs for research and action. The comparisons of the different green space types and characteristics produced mixed results, indicating that there is no one single space type or characteristic that is a “gold standard” that works best for everyone, everywhere and at any time. For blue spaces, few high-quality papers were available, with little systematic variation in the type of blue space exposure. This prevented the formulation of firm conclusions and recommendations.
Finally, the role of access to green and blue spaces, as a refuge for people to relax and socially interact, in the context of the COVID-19 pandemic is discussed.
The purpose of this study was to assess the impact of Artificial Intelligence (AI) on education. Premised on a narrative and framework for assessing AI identified from a preliminary analysis, the scope of the study was limited to the application and effects of AI in administration, instruction, and learning. A qualitative research approach, leveraging the use of literature review as a research design and approach was used and effectively facilitated the realization of the study purpose.
Artificial intelligence is a field of study and the resulting innovations and developments that have culminated in computers, machines, and other artifacts having human-like intelligence characterized by cognitive abilities, learning, adaptability, and decision-making capabilities. The study ascertained that AI has extensively been adopted and used in education, particularly by education institutions, in different forms. AI initially took the form of computer and computer related technologies, transitioning to web based and online intelligent education systems, and ultimately with the use of embedded computer systems, together with other technologies, the use of humanoid robots and web-based chatbots to perform instructors’ duties and functions independently or with instructors. Using these platforms, instructors have been able to perform different administrative functions, such as reviewing and grading students’ assignments more effectively and efficiently, and achieve higher quality in their teaching activities. On the other hand, because the systems leverage machine learning and adaptability, curriculum and content has been customized and personalized in line with students’ needs, which has fostered uptake and retention, thereby improving learners experience and overall quality of learning.
Source: Chen, L., Chen, P., & Lin, Z. 2020. Artificial Intelligence in Education: A Review. IEEE Access. DOI: 10.1109/ACCESS.2020.2988510
The field of Artificial Intelligence in Education (AIED) has undergone significant developments over the last twenty-five years. As we reflect on our past and shape our future, we ask two main questions: What are our major strengths? And, what new opportunities lay on the horizon?
We analyse 47 papers from three years in the history of the Journal of AIED (1994, 2004, and 2014) to identify the foci and typical scenarios that occupy the field of AIED. We use those results to suggest two parallel strands of research that need to take place in order to impact education in the next 25 years: One is an evolutionary process, focusing on current classroom practices, collaborating with teachers, and diversifying technologies and domains. The other is a revolutionary process where we argue for embedding our technologies within students’ everyday lives, supporting their cultures, practices, goals, and communities.
Source: Roll, I., & Wylie, R. 2016. Evolution and Revolution in Artificial Intelligence in Education. International Artificial Intelligence in Education Society, 26. p. 582-599. DOI: 10.1007/s40593-016-0110-3
This paper proposes that the field of AIED is now mature enough to break away from being delivered mainly through computers and pads so that it can engage with students in new ways and help teachers to teach more effectively. Mostly, the intelligent systems that AIED has delivered so far have used computers and other devices that were essentially designed for businesses or personal use, and not specifically for education.
The future holds the promise of creating technologies designed specifically for learning and teaching by combining the power of AIED with advances in the field of robotics and in the increasing use of sensor devices to monitor our surroundings and actions. The paper assumes that Bschools^ (i.e., a place where children will gather to learn) will still exist in some shape or form in 25 years and that teachers will continue to oversee and promote learning among the students. It proposes that there will be educational cobots assisting teachers in the classrooms of tomorrow and provides examples from current work in robotics. It also envisions smart classrooms that make use of sensors to support learning and illustrates how they might be used in new ways if AIED applications are embedded into them.
Source: Timms, M. J. 2016. Letting Artificial Intelligence in Education Out of the Box: Educational Cobots and Smart Classrooms. International Artificial Intelligence in Education Society, 26. p. 701-712. DOI: 10.1007/s40593-016-0095-y
The rapid advancement of computing technologies has facilitated the implementation of AIED (Artificial Intel-ligence in Education) applications. AIED refers to the use of AI (Artificial Intelligence) technologies or applicationprograms in educational settings to facilitate teaching, learning, or decision making. With the help of AI tech-nologies, which simulate human intelligence to make inferences, judgments, or predictions, computer systems canprovide personalized guidance, supports, or feedback to students as well as assisting teachers or policymakers inmaking decisions.
Although AIED has been identified as the primary research focus in thefield of computers andeducation, the interdisciplinary nature of AIED presents a unique challenge for researchers with different disci-plinary backgrounds. In this paper, we present the definition and roles of AIED studies from the perspective ofeducational needs. We propose a framework to show the considerations of implementing AIED in differentlearning and teaching settings. The structure can help guide researchers with both computers and educationbackgrounds in conducting AIED studies. We outline 10 potential research topics in AIED that are of particularinterest to this journal. Finally, we describe the type of articles we like to solicit and the management of thesubmissions.
Source: Hwang, W., Xie, H., Wah, B. W., & Gasevic, D. 2020. Vision, challenges, roles and research issues of Artificial Intelligence in Education. Elsevier, Computers and Education: Artificial Intelligence (1). https://doi.org/10.1016/j.caeai.2020.100001
We discuss the new challenges and directions facing the use of big data and artificial intelligence (AI) in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research. The convenience and embeddedness of data collection within educational technologies, paired with computational techniques have made the analyses of big data a reality.
We are moving beyond proof-of-concept demonstrations and applications of techniques, and are beginning to see substantial adoption in many areas of education. The key research trends in the domains of big data and AI are associated with assessment, individualized learning, and precision education. Model-driven data analytics approaches will grow quickly to guide the development, interpretation, and validation of the algorithms. However, conclusions from educational analytics should, of course, be applied with caution. At the education policy level, the government should be devoted to supporting lifelong learning, offering teacher education programs, and protecting personal data. With regard to the education industry, reciprocal and mutually beneficial relationships should be developed in order to enhance academia-industry collaboration. Furthermore, it is important to make sure that technologies are guided by relevant theoretical frameworks and are empirically tested. Lastly, in this paper we advocate an in-depth dialog between supporters of “cold” technology and “warm” humanity so that it can lead to greater understanding among teachers and students about how technology, and specifically, the big data explosion and AI revolution can bring new opportunities (and challenges) that can be best leveraged for pedagogical practices and learning.
Source: Luan H, Geczy P, Lai H, Gobert J, Yang SJH, Ogata H, Baltes J, Guerra R, Li P and Tsai C-C (2020) Challenges and Future Directions of Big Data and Artificial Intelligence in Education. Front. Psychol. 11:580820. doi: 10.3389/fpsyg.2020.580820
“Be water”. This is the evocative and enigmatic phrase of the current mask-wearing protestors in Hong-Kong. It seems to represent the fight of citizens for the right to be shapeless and anonymous among the crowd, including when exercising the right to protest, versus surveillance by the state authorities.
It is undeniable that facial recognition, the biometric application used to identify or verify a person’s identity, has become increasingly present in many aspects of daily life. It is used for ‘tagging’ people on social media platforms and to unlock smart phones. In China it is used for airport check-in, for monitoring the attentiveness of pupils at school and even for dispensing paper in public latrines.
In the general absence of specific regulation so far, private companies and public bodies in both democracies and authoritarian states have been adopting this technology for a variety of uses. There is no consensus in society about the ethics of facial recognition, and doubts are growing as to its compliance with the law as well as its ethical sustainability over the long term.
The purposes that triggered the introduction of facial recognition may seem uncontroversial at a first sight: it seems unobjectionable to use it to verify a person’s identity against a presented facial image, such as at national borders including in the EU. It is another level of intrusion to use it to determine the identity of an unknown person by comparing her image against an extensive database of images of known individuals.
In your face
There appear to be two big drivers behind this trend.
Firstly, politicians react to a popular sense of insecurity or fear that associates the movements of foreigners across borders with crime and terrorism. Facial recognition presents itself as a force for efficient security, public order and border control. Facial recognition is a key component of the general surveillance apparatus deployed to control the Uighur population in Xinjiang, justified by the government on grounds of combating terrorism.
The second justification is the lure of avoiding physical and mental efforts - ‘convenience’: some people would prefer to be able to access to an area or a service without having to produce a document.
France aims to be the first European country to use such technology for granting a digital identity. Meanwhile the Swedish data protection authority recently imposed a fine on a school for testing facial recognition technology to track its students’ attendance.
Although there was no great debate on facial recognition during the passage of negotiations on the GDPR and the law enforcement data protection directive, the legislation was designed so that it could adapt over time as technologies evolved.
Face/Off
The privacy and data protection issues with facial recognition, like all forms of data mining and surveillance, are quite straightforward.
First, EU data protection rules clearly cover the processing of biometric data, which includes facial images: ‘relating to the physical, physiological or behavioural characteristics of a natural person, which allow or confirm the unique identification of that natural person’ (GDPR Art. 2(14)). The GDPR generally forbids the processing of biometric data for uniquely identifying purposes unless one can rely on one of the ten exemptions listed in Art. 9(2).
Second, any interference in fundamental rights under the Article 52 of the Charter must be demonstrably necessary. The bar for this test becomes higher the deeper the interference. Is there any evidence yet that we need the technology at all? Are there really no other less intrusive means to achieve the same goal? Obviously, ‘efficiency’ and ‘convenience’ could not stand as sufficient.
Third, could there be a valid legal basis for the application of such technology given that it relies on the large-scale processing of sensitive data? Consent would need to be explicit as well as freely-given, informed and specific. Yet unquestionably a person cannot opt out, still less opt in, when they need access to public spaces that are covered by facial recognition surveillance. Under Article 9(2)(g) the national and EU legislators have the discretion to decide the cases where the use of this technology guarantees a proportionate and necessary interference with human rights.
Fourth, accountability and transparency. The deployment of this technology so far has been marked by obscurity. We basically don’t know how data is used by those who collect it, who has access and to whom it is sent, how long do they keep it, how a profile is formed and who is responsible at the end for the automated decision-making. Furthermore, it is almost impossible to trace the origin of the input data; facial recognition systems are fed by numerous images collected by the internet and social media without our permission. Consequently, anyone could become the victim of an algorithm’s cold testimony and be categorised (and more than likely discriminated) accordingly.
Finally, the compliance of the technology with principles like data minimisation and the data protection by design obligation is highly doubtful. Facial recognition technology has never been fully accurate, and this has serious consequences for individuals being falsely identified whether as criminals or otherwise. The goal of ‘accuracy’ implies a logic that irresistibly leads towards an endless collection of (sensitive) data to perfect an ultimately unperfectible algorithm. In fact, there will never be enough data to eliminate bias and the risk of false positives or false negatives.
Saving face
It would be a mistake, however, to focus only on privacy issues. This is fundamentally an ethical question for a democratic society.
A person’s face is a precious and fragile element her identity and sense of uniqueness. It will change in appearance over time and she might choose to obscure or to cosmetically change it - that is her basic freedom. Turning the human face into another object for measurement and categorisation by automated processes controlled by powerful companies and governments touches the right to human dignity - even without the threat of it being used as a tool for oppression by an authoritarian state.
Moreover, it tends to be tested on the poorest and most vulnerable in society, ethnic minorities, migrants and children.
Where combined with other publicly available information and the techniques of Big Data, it could obviously chill individual freedom of expression and association. In Hong Kong the face has become a focal point. The wearing of masks has been a reaction to the use of facial recognition and in turn has been prohibited under a new law.
Does my face look bothered?
It seems that facial recognition is being promoted as a solution for a problem that does not exist. That is why a number of jurisdictions around the world have moved to impose a moratorium on the use of the technology.
We need to assess not only the technology on its own merits, but also the likely direction of travel if it continues to be deployed more and more widely. The next stage will be pressure to adopt other forms of objectification of the human being, gait, emotions, brainwaves. Now is the moment for the EU, as it discusses the ethics of AI and the need for regulation, to determine whether- if ever - facial recognition technology can be permitted in a democratic society. If the answer is yes, only then do we turn questions of how and safeguards and accountability to be put in place.
Independent DPAs will be proactive in these discussions.
The various economic and social implications of “digitalisation” have been discussed in many scientific disciplines and regarding manifold aspects. For instance, early analyses on the digital economy began with Tapscott (1994) and Rochet and Tirole (2003), while publications on digital capitalism date from Schiller (1999) to Staab (2019). Yet, ecological economy research has only marginally touched upon the issue of digitalisation so far. Despite a surge in publications regarding Green IT already in the 2000s and attempts to research ICT for Sustainability from a comprehensive and interdisciplinary perspective more recently, a particular focus on challenges related to governing economic activities linked to digitalisation in a way that these promote sustainability, is still emerging.
Automated decision-making based on Artificial Intelligence is associated with growing expectations and is to contribute to sustainable development goals. Which opportunities and risks for the environment, economy and society are associated with Artificial Intelligence-based applications and how can they be governed?
For the whole article see: Digitalisation and Sustainability. 2021. Ökologisches Wirtschaften.
Can digitalisation be part of the solution to pressing sustainability challenges? Or are current developments going to impede a socio-ecological transformation? The answer is not black and white; it is complex and cross-cutting. We analyse key problems and give an outlook on possible solutions.
For the whole article see: Digitalisation and Sustainability. 2021. Ökologisches Wirtschaften.
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.
The effects of climate change are increasingly visible. Storms, droughts, fires, and flooding have become stronger and more frequent. Global ecosystems are changing, including the natural resources and agriculture on which humanity depends. The 2018 intergovernmental report on climate change estimated that the world will face catastrophic consequences unless global greenhouse gas emissions are eliminated within thirty years. Yet year after year, these emissions rise. Addressing climate change involves mitigation (reducing emissions) and adaptation (preparing for unavoidable consequences). Both are multifaceted issues. Mitigation of greenhouse gas (GHG) emissions requires changes to electricity systems, transportation, buildings, industry, and land use. Adaptation requires planning for resilience and disaster management, given an understanding of climate and extreme events. Such a diversity of problems can be seen as an opportunity: there are many ways to have an impact.
In recent years, machine learning (ML) has been recognized as a broadly powerful tool for technological progress. Despite the growth of movements applying ML and AI to problems of societal and global good, there remains the need for a concerted effort to identify how these tools may best be applied to tackle climate change. Many ML practitioners wish to act, but are uncertain how. On the other side, many fields have begun actively seeking input from the ML community. This paper aims to provide an overview of where machine learning can be applied with high impact in the fight against climate change, through either effective engineering or innovative research. The strategies we highlight include climate mitigation and adaptation, as well as meta-level tools that enable other strategies. In order to maximize the relevance of our recommendations, we have consulted experts across many fields (see Acknowledgments) in the preparation of this paper.
Source: Rolnick et al. 2019. Tackling Climate Change with Machine Learning. Cornell University.
Artificial intelligence (AI) is a powerful concept still in its infancy that has the potential,
if utilised responsibly, to provide a vehicle for positive change that could promote sustainable transitions to a more resource-efficient livability paradigm. AI with its deep learning functions and capabilities can be employed as a tool which empowers machines to solve problems that could reform urban landscapes as we have known them for decades now and help with establishing a new era; the era of the “smart city”. One of the key areas that AI can redefine is transport. Mobility provision and its impact on urban development can be significantly improved by the employment of intelligent transport systems in general and automated transport in particular. This new breed of AI-based mobility, despite its machine-orientation, has to be a user-centred technology that “understands” and “satisfies” the human user, the markets and the society as a whole.
Source: Nikitas, A., Michalakopoulou, K., Njoya, E.T., & Karampatzakis, D. Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era. Sustainability 2020, 12, 2789. https://doi.org/10.3390/su12072789
Developments in Artificial Intelligence are inspired by systems found in the the natural world. Such as artificial neural networks, genetic algorithms or swarm intelligence. The development of Artificial Intelligence benefits from the fact that nature has already refined a lot of the steps to make them as efficient as possible.
Since the development of AI and the systems found in nature are closely related, it is important to know how and which aspects of AI are inspired by nature. Also, by relating AI to nature the digital world of AI becomes less abstract and better to understand.
Swarm intelligence is group of optimization algorithms that takes inspiration from how collective intelligence work. Some of the very effective results of evolution are insects like bees, termites and ants. While individually any of these insects may not appear to be very intelligent, they cooperate and work as a group to find solutions to complex problems like finding an optimal route to the source of food.
Consider how ant colonies find the best route to the source of food. Ants roam around until one of them has found a source of food. The ant which found the food source leaves a trace of a chemical called pheromone while coming back to its colony. This pheromone trace works as a guiding mechanism for other wandering ants using which they reach to the same food source. These ants also leave a trace of pheromone while returning. As a result, the pheromone trail becomes stronger and stronger with more ants using the same path to reach to the food source and return. Eventually all ants start following the same path to reach to the food source.
An example of Swarm Intelligence include Ant-based routing. Ant-based routing is a technique in which multiple paths are set up between the source and destination of a data session or a graph. The aim of AI is then to find the best path by testing existing paths and exploring new ones.
Genetic algorithms are optimization algorithms that takes inspiration from evolution in nature. Nature optimizes the fitness of a species over succeeding generations through propagation of genes. Similarly genetic algorithms work by evolving successive generations of genomes which get progressively more and more fit over the generations. Just like nature has them for evolution, the key processes involved in genetic algorithms are Selection, Crossover and Mutation.
Genetic algorithms find application in for example optimization problems. The goal of the optimization problem technique is simply to find the best solution from all possible solutions. The more data the AI has, the more assumptions it can make and thus the bigger the chance that it will find better solutions .
Artificial Neural Networks (ANNs) take their inspiration from how brain functions. Just like the human brain, neural networks also make use of neurons, axons, synapses and dendrites to transfer signal from the input layer of the network to the output layer. Neurons can be considered as simple computing cells and are fundamental to the operation of ANNs. ANNs use a large interconnection of neurons for optimal performance. Axons are the transmission lines or the nerves through which the signal travels between the neurons. Synapses are the nerve endings and Dendrites are the receptive zones of the neurons.
The key function of a neuron is to apply weights to all the inputs signals received from other neurons, add them and apply a bias and an activation function to limit the amplitude of the output. Once the signal travels through the network and reaches to the output layer, it is compared with the desired output using a cost function and an error signal is fed back to the network. The network then adjusts the weights applied to the signals and re-transmits an updated signal towards the output layer. This process continues until the error gets reduced to acceptable limits. Once this is done, the network training completes and it becomes ready to make predictions on the unseen data.
A large number of ANN variants have been developed with specific use cases. For example, Convolution Neural Networks (CNNs) take inspiration from visual cortex and are primarily used for image classification. Image classification involves teaching an Artificial Intelligence how to detect objects in an image based on their unique properties. An example of image classification is an AI that detects how likely an object in an image is to be an apple, orange or pear.
The natural world has always designed intelligent systems. Everywhere you look, the natural world bursts with examples of complex adaptive systems. However, nature has a significant advantage on its side: time. The majority of these systems are the result of years and years of evolution. Years during which they went from one configuration to the next until they found the best way to solve the task. Technology does not have the luxury of perfecting a solution over millions of years nor can we afford catastrophes. With all their differences, nature and technology should not be excluding each other. We should pay close attention to the natural world. Start by finding out if a biological system has not already solved the problem. If it has, then there is no point in reinventing the wheel, extract the fundamental principles and methods and transfer them to the problem we are trying to solve. Nature is a source of inspiration, while technology is the engine for creation
Source:
Rastogi, N. 2019. AI - Taking inspiration from nature. Firing Neurons, Medium.
Dacian Consulting - The Natural World Helps Design Intelligent Systems
Computer Vision & Image Classification in AI. Study.
Rohrer, B. How optimization for machien learning works.
Today, Artificial Intelligence (AI) is another, however, strong technological wave that is flattening the world by providing the ability for a machine to perform cognitive functions, such as perceiving, reasoning, learning and interacting. AI has rapidly entered our lives by solving business problems due to three technological developments that have reached enough maturity and convergence: advancement in algorithms, massive data, and increasing computational power and storage at low cost.
Source: Ergen, M. 2019. What is Artificial Intelligence? Technical Considerations and Future Perception. Department of Electrical and Electronics Engineering, Istanbul Technical University, 22. p 5-7. DOI: 10.14744/AnatolJCardiol.2019.79091
For more information on AI see:
Scientific advice has never been in greater demand; nor has it been more contested. From climate change to cyber-security, poverty to pandemics, food technologies to fracking, the questions being asked of scientists, engineers, social scientists and other experts by policymakers, the media, and the public continue to multiply. At the same time, in the wake of the financial crisis and controversies such as 'Climategate', the authority and legitimacy of those same experts are under greater scrutiny.
To mark the transition in April 2013 to Sir Mark Walport as the UK's chief scientific adviser, this collection brings together new essays by more than 20 leading thinkers and practitioners, including Sir John Beddington, Sheila Jasanoff, Geoff Mulgan, Roger Pielke Jr., Jill Rutter, Mike Hulme and Sir Bob Watson.
In the context of the UK government agenda for Whitehall reform, and a growing emphasis on the use of evidence in policy, these contributors chart future directions for the politics and practice of scientific advice.
The first in a two-part collection of essays, Future Directions for Scientific Advice in Whitehall focuses on scientific advice in the United Kingdom. The collection was published following a series of seminars on that topic held in 2013, and was launched at the 2013 CSaP Annual Conference.
The project was a collaborative initiative of five partners: University of Cambridge’s Centre for Science and Policy; Science Policy Research Unit (SPRU) and ESRC STEPS Centre at the University of Sussex; Alliance for Useful Evidence; Institute for Government; and Sciencewise-ERC.
Part two of the collection, Future Directions for Scientific Advice in Europe, was published in April 2015. A free digital copy can be downloaded here.
Source and more information: website University of Cambridge
According to the UN, over two-thirds of the world’s population will be living in urban areas by 2050. Today the planet counts an estimated 4.2 billion urbanites; this number is set to rise to 5 billion by 2030 and 6.7 billion by 2050. Cities lie at the heart of financial and migratory flows, and shape global societal values and lifestyles. They generate both economic wealth and technological innovation, and are the drivers of social and environmental transformation. They are windows on a rapidly changing world. Since the beginning of the third millennium, city centres have been regenerated and intensified, while suburban areas have continued to spread. But the long-term development of cities raises many key issues. As victims of their own success, will they end up becoming unliveable? How environmentally and socially sustainable will these urban areas be? How can attractiveness and quality of life for all be effectively interconnected?
From New York to Paris, from Tokyo to Copenhagen, and from Singapore to Medellín, cities are inventing new development trajectories on a range of different scales, combining economic competitiveness, urban regeneration, social inclusion, energy frugality and climate resilience. By doing this, they are changing the world.
This issue of Les Cahiers highlights particularly inspiring strategies and initiatives that respond to the fundamental challenges faced by the Wider Grand Paris.
Source: website L'institute Paris Region
Nowadays, homelessness is predominantly a local responsibility. The policy challenges that local authorities face in dealing with this issue are complex or, according to some commentators, can even be described as “wicked”. Until recently, local authorities have had limited success in addressing homelessness for reasons including a lack of information and fragmentation of services, to name but two.
In a new attempt to face up to these challenges, several northern European metropolises have published similar strategic approaches to ending homelessness. By studying their policy, structure and management style, this volume focuses on the impacts and outcomes of these new governance arrangements on the quality of service provision. By comparing and evaluating the different approaches in governance, the author provides deeper insight into exactly which elements of administrative and political approaches, or which governance arrangements, are most effective in this respect and how social results can be improved in general.
In this way this study makes an important contribution to the academic debate on the optimum organization of governance arrangements. This volume also provides a critical perspective on current decentralising trends and contains a plea for a corporate, instrumental approach towards governance arrangements on homelessness. The author concludes that the social relief sector should be functioning as a trampoline, not as a last resort.
Auteur: Nienke Boesveldt, VUmc
Bron: website G4-USER
The multifaceted challenges of contemporary governance demand a complex account of the ways in which those who are subject to laws and policies should participate in making them. This article develops a framework for understanding the range of institutional possibilities for public participation. Mechanisms of participation vary along three important dimensions: who participates, how participants communicate with one another and make decisions together, and how discussions are linked with policy or public action. These three dimensions constitute a space in which any particular mechanism of participation can be located. Different regions of this institutional design space are more and less suited to addressing important problems of democratic governance such as legitimacy, justice, and effective administration.
Fung, A. (2006). Varieties of participation in complex governance. Public administration review, 66, 66-75.
Literature suggests there is a need to build more theoretically-informed understandings of the social processes implicated in participatory IT planning and implementation (Jakku & Thorburn, 2010). In this study, we explore the value of Archon Fung’s (2006) “democracy cube” as a framework for qualitatively examining the process we undertook for planning a community-based IT strategy. Our planning process involved consultations with multiple stakeholder groups across five different communities, as well as from other entities involved in disaster management, with the aim of surfacing factors that shaped local communities’ abilities to participate in disaster management activities. These factors, drawn from qualitative interviews and categorized using a SWOT framework, were subsequently translated into an IT strategy. In this paper, we revisit this process and examine it using Fung’s (2006) three dimensions of democratic participation as a lens: participant selection (our use of multiple stakeholder groups); communication and decision (our consultation process); and authority and power (how participant input drove our strategy). We use the framework to identify the specific practices that made IT planning participative, as well as those that made it nonparticipative. We also use our empirical data to explore ways that the framework can be enhanced.
Pablo, Z., Ona, S., Roxas, R. E., Cheng, C., Borra, A., & Oco, N. (2013). The Democracy Cube as a Framework for Guiding Participatory Planning for Community-Based IT Initiatives. Democracy, 6, 18-2013.
Bron: link
Using a unique analytical framework based on host-stranger relations, this book explores the response of cities to the arrival and settlement of labour immigrants. Comparing the local policies of four cities - Paris, Amsterdam, Rome and Tel Aviv - Michael Alexander charts the development of migrant policies over time and situates them within the broader social context. Grounded in multi-city, multi-domain empirical findings, the work provides a fuller understanding of the interaction between cities and their migrant populations. Filling a gap in existing literature on migrant policy between national-level theorizing and local-level study, the book will provide an important basis for future research in the area.
Alexander, M. (2007). Cities and Labour Immigration. London: Routledge, https://doi.org/10.4324/9781351161725
Across Western Europe, the emphasis has shifted from physical manufacturing to the development of ideas, new products and creative processes. This has become known as the knowledge economy. While much has been written about this concept, so far there has been little focus on the role of the city. Bringing together comparative case studies from Amsterdam, Dortmund, Eindhoven, Helsinki, Manchester, Munich, Münster, Rotterdam and Zaragoza, this volume examines the cities' roles, as well as how the knowledge economy affects urban management and policies. In doing so, it demonstrates that the knowledge economy is a trend that affects every city, but in different ways depending on the specific local situation. It describes a number of policy options that can be applied to improve cities' positions in this new environment.
Berg, L. (2005). European Cities in the Knowledge Economy. London: Routledge, https://doi.org/10.4324/9781351158725
The high density of built-up areas and resulting imperviousness of the land surface makes urban areas vulnerable to extreme rainfall, which can lead to considerable damage. In order to design and manage cities to be able to deal with the growing number of extreme rainfall events, rainfall data are required at higher temporal and spatial resolutions than those needed for rural catchments. However, the density of operational rainfall monitoring networks managed by local or national authorities is typically low in urban areas. A growing number of automatic personal weather stations (PWSs) link rainfall measurements to online platforms. Here, we examine the potential of such crowdsourced datasets for obtaining the desired resolution and quality of rainfall measurements for the capital of the Netherlands. Data from 63 stations in Amsterdam (∼ 575km2) that measure rainfall over at least 4 months in a 17-month period are evaluated. In addition, a detailed assessment is made of three Netatmo stations, the largest contributor to this dataset, in an experimental setup. The sensor performance in the experimental setup and the density of the PWS network are promising. However, features in the online platforms, like rounding and thresholds, cause changes from the original time series, resulting in considerable errors in the datasets obtained. These errors are especially large during low-intensity rainfall, although they can be reduced by accumulating rainfall over longer intervals. Accumulation improves the correlation coefficient with gauge-adjusted radar data from 0.48 at 5min intervals to 0.60 at hourly intervals. Spatial rainfall correlation functions derived from PWS data show much more small-scale variability than those based on gauge-adjusted radar data and those found in similar research using dedicated rain gauge networks. This can largely be attributed to the noise in the PWS data resulting from both the measurement setup and the processes occurring in the data transfer to the online PWS platform. A double mass comparison with gauge-adjusted radar data shows that the median of the stations resembles the rainfall reference better than the real-time (unadjusted) radar product. Averaging nearby raw PWS measurements further improves the match with gauge-adjusted radar data in that area. These results confirm that the growing number of internet-connected PWSs could successfully be used for urban rainfall monitoring.
de Vos, L., Leijnse, H., Overeem, A., and Uijlenhoet, R. (2017) The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam, Hydrol. Earth Syst. Sci., 21, 765-777, https://doi.org/10.5194/hess-21-765-2017
Amsterdam has the ambition to develop as a competitive and sustainable European metropolis. The flows of energy, water and resources within the urban environment have a large potential to contribute to this ambition. Through a transition from a linear usage of resources and waste production towards a sustainable management of urban resources with circular flows of resources, the sustainability of cities can be increased. This Urban Harvesting Concept may be applied in Amsterdam. The challenge is how to operationalize this concept in practice. For two municipal companies in Amsterdam, Waternet (responsible for the water management) and AEB (the waste-to-energy company), initiatives were identified on how to do this. The focus is on water, energy, waste and material flows. Circular flows result in economic benefits and sustainability benefits, either expressed as Ecopoints or CO2-emissions. The integration of these flows is especially beneficial.
Cities are hotspots of air pollution and heat stress, resulting in nuisance, health risks, cost of medication, reduced labour productivity and sick leave for citizens. Yet the air pollution and heat stress are spatially and temporally unevenly distributed over the city, depending on pollutant emissions, street design and atmospheric turbulent mixing and radiation. This spatiotemporal variation allows pedestrians and bikers to choose alternative routes to minimize their exposure, if the distribution is known. In this project, we develop a route planner for bicyclists and pedestrians for Amsterdam (NL), that proposes routes and departure times based on model simulations of weather and air quality.We use the WRF-Chem atmosphere and air quality model at unprecedented grid spacing of 100-m (Ronda et al, 2015), with an underlying urban canopy model and NOx and PM10 emissions. The emissions by traffic are calculated based on observed traffic intensities and emission factors. An urban land use map will characterize urban density and street configuration to estimate urban heat storage (Attema et al, 2015). WRF-Chem runs will be issued daily for a lead time of 48 hours, resulting in forecast maps of temperature and pollutant concentrations that will be uniquely expressed in a metric that combines both treats The hourly fields of this metric are provided to the route planner based on the open source routing library pgRouting to identify more healthy routes on the route network of Amsterdam. The objectives of the healthy urban route planner are to raise awareness of heat and air quality issues in Amsterdam, to provide an innovative adaptation tool for citizens and tourists, to locate the most important bottlenecks in (the exposure to) air pollution and heat stress, and ultimately to test the readiness of the travellers to use the information and adapt the route.
| Author(s) | Steeneveld, G.J.; Vreugdenhil, L.C.; Molen, M.K. van der; Ligtenberg, A. |
|---|---|
| Source | EMS annual meeting abstracts 14 (2017). - ISSN 1812-7053 - 1 p. |
| Event | Annual Meeting European Meteorological Society, Dublin, Dublin, 2017-09-04/2017-09-08 |
| Department(s) | Meteorology and Air Quality WIMEK Laboratory of Geo-information Science and Remote Sensing PE&RC |
| Publication type | Abstract in scientific journal or proceedings |
| Publication year | 2017 |
This paper aims to address the effects of labour market mutations on housing through an analysis of “spatial hybridization”, focusing on the qualitative comparison of Brussels and Amsterdam. The objective is to provide first elements of context, methodology and results of a wider on-going research. In the first section, we highlight underlying trends, in particular the emergence of NWoW in a context of new economy, and current issues on the housing market (flexibilisation, commodification, gentrification). Then, we discuss the relevance of applying path dependence in our research, before presenting our two case-study cities from a historical perspective and pointing out innovative practices and the current public discourse. Finally, we discuss differences and similarities through four elements of comparison: functional mix at the block level, service-oriented housing, economic and housing paths. The discussion is based on our literature review, early policy analysis and interviews with key-stakeholders.
Uyttebrouck, Constance, and Jacques Teller (2017). Presented at ENHR Conference 2017 in Tirana, Albania.
As part of the research project Renewal of Urban Renewal the master specialisation Hybrid Build-ings2 launched several Design Research Graduation Studios proposing as main theme of investigation the role of architecture in contemporary urban renewal. The research project is starting from the observation that most urban renewal approaches until now have been focusing very much on the scale of the housing block, concentrating on the relationships within the neighbourhood itself. Considering the Dutch scene, despite a rather convincing renewal of the housing stock, this neighbourhoodbased approach does not seem to deliver the desired results. In addition, this subject deserves particular attention in the current postcrisis period, in which the official authorities too are clearly switching their position, from a leading role to almost an undefined player seeking new operational strategies and opportunities. Therefore, starting from the acknowledgment that urban renewal is no longer driven by a predefined economic, social and spatial agenda, the Renewal of Urban Renewal research project propagated the need of finding new balance, consensus and perspectives in the approach to urban transformations. Instead of massive restructuring processes characterising the first urban regeneration flow, nowadays reality argues for a more bottomup driven urban renewal, for more limited interventions involving facilities, infrastructures and/or public space, in short, for new challenges and opportunities.
CAVALLO, Roberto. Urban renewal: matter of opportunities; Design projects for East Amsterdam. OverHolland, [S.l.], p. 5-29, mar. 2017. ISSN 1574-3160. Available at: <https://journals.library.tudelft.nl/index.php/overholland/article/view/1664>.
Date accessed: 30 jan. 2019. doi: https://doi.org/10.7480/overholland.2014.14-15.1664.
Amsterdam Southeast is characterized by a high ethnic diversity and high percentage of citizens with low-socioeconomic status (SES). Health promotion strategies on how to improve health and lives in this area is an enormous challenge for all parties involved. The aim of the present study was to identify and structure needs and strengths of citizens with low-SES of multi-ethnic backgrounds with respect to their health and living environment.
Methods:
Semi-structured interviews were conducted with 25 citizens. The concept of Positive health was used to structure interviews into six health related domains: physical functioning, mental wellbeing, purpose, quality of life, participation, daily functioning. Data were analyzed using an inductive content analysis approach with open coding resulting in themes and sub-themes. An environmental scan was carried out to assess interactions of living environment with health of citizens.
Results:
Main themes were: physical environment, social environment and available services. The physical environment had improved, green space allowed people to relax and exercise. Biggest issue was litter in the streets, lack of parking space and availability of healthy food. Sense of community was strong and the neighborhood felt safe. Community centers played a large role in promoting health of citizens. More places to interact, like benches, were desired. Citizens generally did not ask for help when ill and some were lonely. Some citizens experienced a strong sense of purpose from being active in the neighborhood and a core group of active citizens in the district played a large role in social cohesion.
Conclusions:
Citizens generally felt a strong sense of social cohesion. Litter and lack of parking space was a concern. Physical places and community centers were important for health of citizens. A core group of active citizens could be approached in order to enhance social cohesion and health.
M Jong, H van Wietmarschen, S Staps; Needs and strengths of citizens in Amsterdam regarding improving their health and living environment, European Journal of Public Health, Volume 28, Issue suppl_4, 1 November 2018, cky213.103, https://doi.org/10.1093/eurpub/cky213.103
In the city of Amsterdam, the bicycle is rapidly becoming the most popular method of transportation, and is seen as the solution to congestion, pollution and health problems. A number of improvements have been proposed by the city of Amsterdam, to increase the safety and capacity of the bicycle transportation network. The effect of these improvements will be measured in surveys and path width percentages, creating a knowledge gap on the actual impact of the proposed changes in capacity and safety. Therefor, this thesis research proposes an additional feedback method in the form of an Agent Based Model application of the bicycle network, to calculate the effects of cyclist behavior on the bicycle network load distribution under varying influence of external parameters.
First, the bicycle network is translated to a conceptual Agent Based Model, defining the main components and their interactions of a bicycle network. This conceptual model is implemented in a case study for the city of Amsterdam, which is translated to a model in the GAMA simulation environment.
The simulation model is able to simulate the behavior of 60.000 cyclist agents in the Centrum district of Amsterdam, for a time period between 05:00 and 23:00. Environmental changes are simulated with external parameters on road safety class and occupation of network segments. A change in cyclist behavior in the simulation affects distribution of cyclists on the network.
The accuracy of these results relies heavily on assumed values derived from census data, as data on migration patterns within the city of Amsterdam is non-existent. Comparison of the simulation results with observation based data from the Fietstelweek does however show similarities in cyclist distribution. Agent Based Modeling can therefore be considered an additional tool to investigate the effects of infrastructural changes on a cyclist network, within the boundaries of the available information
on cyclist behavior.
Luc Oude Veldhuis
Thesis Report GIRS-2018-15
March 29, 2018
Bron: website WUR
