Paper by Yoshio Kamijo et al: “People to be born in the future have no direct influence on current affairs. Given the disconnect between people who are currently living and those who will inherit the planet left for them, individuals who are currently alive tend to be more oriented toward the present, posing a fundamental problem related to sustainability.
In this study, we propose a new framework for reconciling the disconnect between the present and the future whereby some individuals in the current generation serve as an imaginary future generation that negotiates with individuals in the real-world present. Through a laboratory-controlled intergenerational sustainability dilemma game (ISDG), we show how the presence of negotiators for a future generation increases the benefits of future generations. More specifically, we found that when faced with members of an imaginary future generation, 60% of participants selected an option that promoted sustainability. In contrast, when the imaginary future generation was not salient, only 28% of participants chose the sustainable option…(More)”.
Jonathan D. Paul in Frontiers in Earth Science: “Pluvial flooding can have devastating effects, both in terms of loss of life and damage. Predicting pluvial floods is difficult and many cities do not have a hydrodynamic model or an early warning system in place. Citizen science and crowdsourcing have the potential for contributing to early warning systems and can also provide data for validating flood forecasting models. Although there are increasing applications of citizen science and crowdsourcing in fluvial hydrology, less is known about activities related to pluvial flooding. Hence the aim of this paper is to review current activities in citizen science and crowdsourcing with respect to applications of pluvial flooding.
Based on a search in Scopus, the papers were first filtered for relevant content and then classified into four main themes. The first two themes were divided into (i) applications relevant during a flood event, which includes automated street flooding detection using crowdsourced photographs and sensors, analysis of social media, and online and mobile applications for flood reporting; and (ii) applications related to post-flood events. The use of citizen science and crowdsourcing for model development and validation is the third theme while the development of integrated systems is theme four. All four main areas of research have the potential to contribute to early warning systems and build community resilience. Moreover, developments in one will benefit others, e.g., further developments in flood reporting applications and automated flood detection systems will yield data useful for model validation….(More)”.
Victoria Moody at the UK Data Service: “The 2015 Lancet Commission on Health and Climate Change—which assessed responses to climate change with a view to ensuring the highest attainable standards of health for populations worldwide—concluded that “tackling climate change could be the greatest global health opportunity of the 21st century”. The Commission recommended that more accurate national quantification of the health co-benefits and economic impacts of mitigation decisions was essential in promoting a low-carbon transition.
The partnership comprises 24 academic institutions from every continent, bringing together individuals with a broad range of expertise across disciplines (including climate scientists, ecologists, mathematicians, geographers, engineers, energy, food, and transport experts, economists, social and political scientists, public health professionals, and physicians).
Four of the indicators developed for Working Group 3 (Mitigation actions and health co-benefits) uses International Energy Agency (IEA) data made available by the the IEA via the UK Data Service for use by researchers, learners and teaching staff in UK higher and further education. Additionally, two of the indicators developed for Working Group 4 (Finance and economics) also use IEA data.
Oliver Wearn, RobinFreeman and David Jacoby in Nature: “Machine learning (ML) is revolutionizing efforts to conserve nature. ML algorithms are being applied to predict the extinction risk of thousands of species, assess the global footprint of fisheries, and identify animals and humans in wildlife sensor data recorded in the field. These efforts have recently been given a huge boost with support from the commercial sector. New initiatives, such as Microsoft’s AI for Earth and Google’s AI for Social Good, are bringing new resources and new ML tools to bear on some of the biggest challenges in conservation. In parallel to this, the open data revolution means that global-scale, conservation-relevant datasets can be fed directly to ML algorithms from open data repositories, such as Google Earth Engine for satellite data or Movebank for animal tracking data. Added to these will be Wildlife Insights, a Google-supported platform for hosting and analysing wildlife sensor data that launches this year. With new tools and a proliferation of data comes a bounty of new opportunities, but also new responsibilities….(More)”
Jason Samenow in the Washington Post: “In a month, the National Weather Service plans to launch its “next generation” weather prediction model with the aim of “better, more timely forecasts.” But many meteorologists familiar with the model fear it is unreliable.
The introduction of a model that forecasters lack confidence in matters, considering the enormous impact that weather has on the economy, valued at around $485 billion annually.
The Weather Service announced Wednesday that the model, known as the GFS-FV3 (FV3 stands for Finite Volume Cubed-Sphere dynamical core), is “tentatively” set to become the United States’ primary forecast model on March 20, pending tests. It is an update to the current version of the GFS (Global Forecast System), popularly known as the American model, which has existed in various forms for more than 30 years….
A concern is that if forecasters cannot rely on the FV3, they will be left to rely only on the European model for their predictions without a credible alternative for comparisons. And they’ll also have to pay large fees for the European model data. Whereas model data from the Weather Service is free, the European Center for Medium-Range Weather Forecasts, which produces the European model, charges for access.
But there is an alternative perspective, which is that forecasters will just need to adjust to the new model and learn to account for its biases. That is, a little short-term pain is worth the long-term potential benefits as the model improves….
The Weather Service’s parent agency, the National Oceanic and Atmospheric Administration, recently entered an agreement with the National Center for Atmospheric Research to increase collaboration between forecasters and researchers in improving forecast modeling.
In addition, President Trump recently signed into law the Weather Research and Forecast Innovation Act Reauthorization, which establishes the NOAA Earth Prediction Innovation Center, aimed at further enhancing prediction capabilities. But even while NOAA develops relationships and infrastructure to improve the Weather Service’s modeling, the question remains whether the FV3 can meet the forecasting needs of the moment. Until the problems identified are addressed, its introduction could represent a step back in U.S. weather prediction despite a well-intended effort to leap forward….(More).
Paper by Ariane L.Beck et al in Energy Research & Social Sciences: “In order to help mitigate climate change and reduce the health-related consequences of air pollution, consumers need to be empowered to make better and more effective decisions regarding energy use. Utilities, government, and commercial entities offer numerous programs and consumer products to help individuals set or reach goals related to energy use.
Many of these interventions and products have related apps that use gamification in some capacity in order to improve the user experience, offer motivation, and encourage behavior change. We identified 57 apps from nearly 2400 screened apps that both target direct energy use and employ at least one element of gamification.
We evaluated these apps with specific focus on gamification components, game elements, and behavioral constructs. Our analysis shows that the average energy related app heavily underutilizes search engine optimization, gamification components, and game design elements, as well as the behavioral constructs known to impact energy-related decision-making and behavior. Our findings offer several insights for the design of more effective energy apps….(More)”.
Blogpostby Majken Brahe and Ellegaard Christensen: “What does the blue whale have in common with the Bengal tiger and the green turtle? They share the risk of extinction and are classified as endangered species. There are multiple reasons for species to die out, and climate changes is among the main reasons.
The risk of extinction varies from species to species depending on how individuals in its populations reproduce and how long each animal survives. Understanding the dynamics of survival and reproduction can support management actions to improve a specie’s chances of surviving.
Mathematical and statistical models have become powerful tools to help explain these dynamics. However, the quality of the information we use to construct such models is crucial to improve our chances of accurately predicting the fate of populations in nature.
Colchero’s research focuses on mathematically recreating the population dynamics by better understanding the species’s demography. He works on constructing and exploring stochastic population models that predict how a certain population (for example an endangered species) will change over time.
These models include mathematical factors to describe how the species’ environment, survival rates and reproduction determine to the population’s size and growth. For practical reasons some assumptions are necessary.
Two commonly accepted assumptions are that survival and reproduction are constant with age, and that high survival in the species goes hand in hand with reproduction across all age groups within a species. Colchero challenged these assumptions by accounting for age-specific survival and reproduction, and for trade-offs between survival and reproduction. This is, that sometimes conditions that favor survival will be unfavorable for reproduction, and vice versa.
For his work Colchero used statistics, mathematical derivations, and computer simulations with data from wild populations of 24 species of vertebrates. The outcome was a significantly improved model that had more accurate predictions for a species’ population growth.
Despite the technical nature of Fernando’s work, this type of model can have very practical implications as they provide qualified explanations for the underlying reasons for the extinction. This can be used to take management actions and may help prevent extinction of endangered species….(More)”
Larry Dignan at ZDN: “IBM is hoping that mobile barometric sensors from individuals opting in, supercomputing ,and the Internet of Things can make weather forecasting more local globally.
Big Blue, which owns The Weather Company, will outline the IBM Global High-Resolution Atmospheric Forecasting System (GRAF). GRAF incorporates IoT data in its weather models via crowdsourcing.
While hyper local weather forecasts are available in the US, Japan, and some parts of Western Europe, many regions in the world lack an accurate picture of weather.
Mary Glackin, senior vice president of The Weather Company, said the company is “trying to fill in the blanks.” She added, “In a place like India, weather stations are kilometers away. We think this can be as significant as bringing satellite data into models.”
For instance, the developing world gets forecasts based on global data that are updated every 6 hours and resolutions at 10km to 15km. By using GRAF, IBM said it can offer forecasts for the day ahead that are updated hourly on average and have a 3km resolution….(More)”.
Jennifer Valentino-DeVries and Natasha Singer in The New York Times: “The Weather Channel app deceptively collected, shared and profited from the location information of millions of American consumers, the city attorney of Los Angeles said in a lawsuit filed on Thursday.
One of the most popular online weather services in the United States, the Weather Channel app has been downloaded more than 100 million times and has 45 million active users monthly.
The government said the Weather Company, the business behind the app, unfairly manipulated users into turning on location tracking by implying that the information would be used only to localize weather reports. Yet the company, which is owned by IBM, also used the data for unrelated commercial purposes, like targeted marketing and analysis for hedge funds, according to the lawsuit…
In the complaint, the city attorney excoriated the Weather Company, saying it unfairly took advantage of its app’s popularity and the fact that consumers were likely to give their location data to get local weather alerts. The city said that the company failed to sufficiently disclose its data practices when it got users’ permission to track their location and that it obscured other tracking details in its privacy policy.
“These issues certainly aren’t limited to our state,” Mr. Feuer said. “Ideally this litigation will be the catalyst for other action — either litigation or legislative activity — to protect consumers’ ability to assure their private information remains just that, unless they speak clearly in advance.”…(More)”.
By Alexandra Shaw, Michelle Winowatan, Andrew Young, and Stefaan Verhulst
The Living Library Index – inspired by the Harper’s Index – provides important statistics and highlights global trends in governance innovation. This installment focuses on open data and was originally published in 2018.
Value that open data can help unlock in economic value annually across seven sectors in the United States:$3-5 trillion
Public Views on and Use of Open Government Data
Number of Americans who do not trust the federal government or social media sites to protect their data: Approximately 50%
Key findings from The Economist Intelligence Unit report on Open Government Data Demand:
Percentage of respondents who say the key reason why governments open up their data is to create greater trust between the government and citizens: 70%
Percentage of respondents who say OGD plays an important role in improving lives of citizens: 78%
Percentage of respondents who say OGD helps with daily decision making especially for transportation, education, environment: 53%
Percentage of respondents who cite lack of awareness about OGD and its potential use and benefits as the greatest barrier to usage: 50%
Percentage of respondents who say they lack access to usable and relevant data: 31%
Percentage of respondents who think they don’t have sufficient technical skills to use open government data: 25%
Percentage of respondents who feel the number of OGD apps available is insufficient, indicating an opportunity for app developers: 20%
Percentage of respondents who say OGD has the potential to generate economic value and new business opportunity: 61%
Percentage of respondents who say they don’t trust governments to keep data safe, protected, and anonymized: 19%
Efforts and Involvement
Time that’s passed since open government advocates convened to create a set of principles for open government data – the instance that started the open data government movement: 10 years
