1,196 research outputs found
Low-carbon transition is improbable without carbon pricing
Full text linkUnidad de excelencia María de Maeztu CEX2019-000940-MThis Letter has a Reply and related content. Please see: Reply to van den Bergh and Botzen: A clash of paradigms over the role of carbon pricing (https://doi.org/10.1073/pnas.2014350117) - Opinion: Why carbon pricing is not sufficient to mitigate climate change-and how "sustainability transition policy" can help (https://doi.org/10.1073/pnas.2004093117)
Value of urban nature
No full textBockarjova M., W.J.W. Botzen, M.J. Koetse “” Nieuwsbrief Milieu & Economie, 19 februari 202
Digging through the dirt: a general method for abstract discrete state estimation with limited prior knowledge
No full textAutonomous robots are often successfully deployed in controlled environments. Operation in uncontrolled situations remains challenging; it is hypothesized that the detection of abstract discrete states (ADS) can improve operation in these circumstances. ADS are high-level system states that are not directly detectable and influence system dynamics. An example of a typical ADS problem that is used in this thesis is that of a wheeled robot driving through puddles of mud that, when entered, alters the velocity of the robot. When the robot is in such a puddle, it is in an ADS 'mud', and when it is not, it is in an ADS 'free'. ADS can be indirectly inferred through the analysis of lower-level data such as the velocity of the robot. The goal of this thesis is to design a general abstract discrete state estimator (ADSE) operating with limited prior knowledge. An ADSE is a hierarchical system for detecting changes in ADS. The ADSE should be general; applicable to multiple ADSE problems. The ADSE should further operate under limited prior knowledge: only assuming that the amount of ADS and the ADS that describes the regular operation are known. The basis for the ADSE designed in this thesis is a Gaussian hidden Markov model (GHMM), a hidden Markov model enhanced with Gaussian emissions. Randomly generated experiments are done on a simple but general ADSE problem. Two unsupervised learning methods derived from Expectation Maximization are evaluated, namely Baum-Welch (BW) and forward extraction (FWE). FWE is introduced in this thesis and is a simpler implementation of Viterbi extraction, leveraging assumptions of ADSE to in theory gain computational efficiency. We found that both BW and FWE exhibit superior performance compared to a likelihood-based baseline estimator when the maximum score of the learning curve is considered. When the final score is considered, in some cases, FWE displays a deteriorating learning curve, resulting in worse final scores compared to the baseline. Furthermore, it was found that the lower the overlap coefficient (therefore the less similar the ADS), the higher the maximum reached score. It was further shown that BW exhibits better convergence than FWE to the true model parameters. Besides this, FWE obtained comparable or in some cases even superior scores compared to BW. In general, from the results, the diversity of the experiments conducted, and the assumptions made we can conclude that the GHMM can be a general method for an ADSE with limited prior knowledge. To quantify the suitability of the GHMM for ADSE, further research should include the evaluation of different ADSE methods on the same problem. There exists a tradeoff between the lower computational cost FWE and the more stable but more computationally intensive BW learning. Therefore, future research can include a combination of these methods. Other extensions include extending the GHMM to a Gaussian mixture hidden Markov model to allow for the modeling of more complex distributions, or the application to multiple states or a changing environment.https://github.com/Wouter-deBoer/adseMechanical Engineering | Vehicle Engineering | Cognitive Robotic
Example damage function.xlsx
No full textThis is an example of implementation of the damage function proposed by Estrada F, Tol RSJ and Botzen WJW in EXTENDING INTEGRATED ASSESSMENT MODELS' DAMAGE FUNCTIONS TO INCLUDE
ADAPTATION AND DYNAMIC SENSITIVITY. </p
embalming and reperfusion of porcine kidneys
No full text<p>These are the data of the following article:</p>
<p>Understanding Thiel embalming in pig kidneys to develop a new circulation model</p>
<p>First author: Wouter Willaert</p
Nederland op een kantelpunt: Interview met Wouter Veldhuis over het Stedelijk Netwerk Nederland en het sociaal netwerk van woonwijken
Full text linkDe stedenbouwkundige en architect Wouter Veldhuis en landschapsarchitect Jannemarie de Jonge zijn per 1 december 2020 Rijksadviseur voor de fysieke leefomgeving. Later in september 2021 komt daar de architect Francesco Veenstra bij als Rijksbouwmeester en dan is het nieuwe trio College van Rijksadviseurs weer compleet. De uitdagingen voor het college zijn groot. De ruimteclaims die er liggen in stad en land, de hooggestemde ambities om klimaatneutraal en circulair te zijn in 2050, de roep om een minister voor de fysieke leefomgeving en of wonen en weer een echt ministerie met budget. Het enorme probleem op de woningmarkt en de druk om één miljoen woningen ergens bij te bouwen. Op 24 april sprak het team van 1M Homes initiative van de TU Delft met de nieuw benoemde rijksadviseur voor de fysieke leefomgeving Wouter Veldhuis over de aanstaande veranderingen
Perceptions of Catastrophic Climate Risks
Full text linkMany climate change-related risks, such as more frequent and severe natural disasters, can be characterised as low-probability/high-consequence (LP/HC) events. Perceptions of LP/HC risks are often associated with biases which hamper taking action to limit these risks, such as underestimation of risk, myopia, and the adoption of simplified decision heuristics. This chapter discusses these biases and outlines key elements of policies to overcome them in order to enhance climate action
DIFI: Dynamic Integrated Flood Insurance model
No full text<p>This database contains the software and necessary datafiles to run the simulation developed for the study titled "Flood insurance is a driver of population growth in European floodplains" (Nature Communications (2023, provisionally accepted).</p><p>Earlier applications of the model can be found here:</p><p>Hudson et al. (2019). Global Environmental Change</p><p>Tesselaar et al. (2020). Atmosphere</p><p>Tesselaar et al. (2020). Sustainability</p><p>Tesselaar et al. (2022). Ecological Economics</p>
Uncovering the veil of night light changes in times of catastrophe
Full text linkNatural disasters have large social and economic consequences. However, adequate economic and social data to study subnational economic effects of these negative shocks are typically difficult to obtain especially in low-income countries. For this reason, the use of night light data is becoming increasingly popular in studies which aim to estimate the impacts of natural disasters on local economic activity. However, it is often unclear what observed changes in night lights represent exactly. In this paper, we examine how changes in night light emissions following a severe hurricane relate with local population, employment, and income statistics. We do so for the case of Hurricane Katrina, which struck the coastline of Louisiana and Mississippi in August 2005. Hurricane Katrina is an excellent case for this purpose as it is one of the biggest hurricanes in recent history in terms of human and economic impacts, it made landfall in a country with high-quality sub-national socioeconomic data collection, and it is covered extensively in the academic literature. We find that overall night light changes reflect the general pattern of direct impacts of Katrina as well as indirect impacts and subsequent population and economic recovery. Our results suggest that change in light intensity is mostly reflective of changes in resident population and the total number of employed people within the affected area and less so but positively related to aggregate income and real GDP.</p
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