Instituut voor Milieuvraagstukken

This study investigates windstorm impacts by combining high-resolution wind hazard data with a unique asset-level insurance loss dataset, specifically focusing on the Netherlands. We conduct statistical analyses to associate wind hazard characteristics with spatial data on windstorm losses at various spatial aggregation levels (four-digit to nationwide postal codes). Different wind hazard intensities (e.g. maximum wind gust, maximum hourly wind speed) are derived using meteorological data from 2017 to 2021 (the same period as the loss data). This data is based on station and downscaled ERA5 reanalysis data. Results show that the recorded gust has a good correlation with damage components (r = 0.41–0.61). The downscaled reanalysis data on gust and daily maximum (hourly mean) wind speed also have a good correlation (r = 0.38–0.59), albeit a bit smaller than the observed gust. When comparing different levels of aggregated data (PC4—four-digit postal code, PC2—two-digit postal code, and NL—national level), the correlation between claim and loss ratios becomes more pronounced as the level of aggregation increases. In addition, at the aggregated data level of two-digit postal codes, we see a wind speed threshold (around the 98th percentile of the records, ~ 22 m/s), where both losses and reported claims begin to rise as wind speed increases. Nevertheless, with lower wind speeds, damages and reported claims become meaningful using more aggregated data (NL). Our findings highlight the complex link between hazard and damage variables for windstorm losses, offering valuable insights for insurance portfolios, risk assessment, and management.

Drought occurs globally and can have deleterious effects on built and natural systems and societies. With the increasing human footprint on our planet, so has increased the anthropogenic influence on drought and water scarcity, leading to the development of notions of “anthropogenic drought” and “water bankruptcy”. Understanding the human dimension of drought is complex and requires a data-driven nexus approach to better understand the involved processes and address the implications of water deficits around the world. Just as it transcends scales and geographical boundaries, drought is neither restricted to a single hydrologic state in the water cycle nor are its effects confined to one sector. Drought impacts the water, energy, and food sectors, ecosystem services, socioeconomics, public policy, politics, etc. from local to regional and global scales. We argue that drought mitigation strategies and policy developments must be addressed with a multidisciplinary perspective that benefits from a nexus approach rooted in analytics, informatics, and data (AID). The United Nations University (UNU) Sustainability AID Programme employs such an approach to aid the monitoring, forecasting, and projection of drought, both from climatic and anthropogenic perspectives, and its multifaceted impacts across a variety of sectors and spatiotemporal scales. After a broad overview of this UNU Programme’s vision, and to support stakeholders and decision-makers, we present a drought resource database for drought-related information, data, and analysis tools. Our aim is not to compile an exhaustive list of all available data and tools. Instead, we prioritize mature datasets and AID tools while actively highlighting opportunities to develop new data and tools, fostering nexus research.

Agricultural abandonment has given rise to novel landscape dynamics worldwide. This paper investigates abandonment and post-abandonment dynamics in continental Portugal as a hotspot of landscape change. We mapped the spatial patterns and drivers of recent (1995–2018) land use changes in a remote mountainous region as post-abandonment trajectories, based on detailed land use/land cover data made available by the Portuguese government. We showed that ‘Revegetation’ trajectories, indicative of agricultural abandonment, were still widespread between 1995 and 2007. However, between 2007 and 2018, the landscape was much more stable with ‘Return to agriculture’ as the dominant change trajectory. To understand what drives landscape changes after abandonment, we explored the influence of a wide range of potential biogeophysical and socio-economic drivers on the observed trajectories. We contrasted different landscape outcomes in binary logistic regression models with the potential underlying drivers as independent variables. The regressions revealed that the most significant determinants of these alternating dynamics are existing land use, climate, slope, protection regime and accessibility. The results of the regressions are at times counterintuitive and give important indications of the changing spatio-temporal scales at which these variables exert influence on the landscape outcomes. However, the regression models’ limited accuracies highlight the need for deeper investigation of the socio-economic and historic context of the observed changes. Improved understanding of the (drivers of) alternative dynamics following agricultural abandonment can help inform policy decisions regarding agriculture and cultural landscape preservation.

Urbanization is a phenomenon where humans concentrate in high densities and consume more per capita energy than in rural areas, imposing high pressures on biodiversity and ecosystem services. Although Mexico is recognized as a megadiverse country and there is an understanding of ecological and evolutionary processes underlying this high diversity, only some efforts have been devoted to understanding how urban biodiversity has been shaped. Here, we compiled a set of socioeconomic and ecological variables to explore macroecological patterns in urban biodiversity across Mexican municipalities. Specifically, we tested the species-area relationships (SAR) between rural and urban areas across municipalities and evaluated the relative role of different socioeconomic and ecological variables driving urban species richness for terrestrial vertebrates. Finally, we explored the exposure of Mexican municipalities to future urban expansion, the urban heat island (UHI) effect, and climate change. Urban and rural settlements show differences in the shape of SAR models. We found that urban area, size of the network of urban protected areas, the number of ecoregions, and GDP explained the urban total species richness relatively well. Mexican cities in the northeast region may be at a higher risk than others. Based on our analyses, policymakers should identify priority urban conservation sites in cities with high species richness and low urbanization development. These actions would alleviate future urban biodiversity loss in these growing cities.

Wildfire events are driven by complex interactions of climate and anthropogenic interventions. Predictions of future wildfire events, their extremity, and their impact on the environment and economy must account for the interactions between these drivers. Economic policy and land use decisions influence the susceptibility of an area to climate extremes, the probability of burning, and future decision making. To better understand how climate-driven drought events and adaptation efforts affect burned area, agricultural production losses, and land use decisions, we developed a storyline approach centered on Indonesia’s 2015 fire events, which saw significant (>5%) production losses of palm oil. We explored analogous events under three warming conditions and two storylines (multi-model ensemble mean climate change and high impact). We employed a model chain consisting of CMIP6 climate modeling to quantify climate change impacts, a wildfire climate impacts and adaptation model (FLAM) to predict burned areas, and the Global biosphere management model (GLOBIOM) to predict the resultant production losses and socio-economic consequences in the oil palm sector in Indonesia and, by extension, the EU. FLAM is a mechanistic, modular fire model used to reproduce and project wildfires based on various scenario criteria and input variables, whereas GLOBIOM is a global economic land use model, which assesses competition for land use and provides economic impacts based on scenario data. We found that total burned area and production loss can increase by up to 25% and lead to local price increases up to 70%, with only minor differences beyond 2.5 degrees of warming. Our results highlight the importance of considering the interactions of future warming, drought conditions, and extreme weather events when predicting their impacts on oil palm losses and burned area. This study sets the stage for further exploration on the impacts of land management policies on local and international environments and economies in the context of global warming.

Climate change and socio-economic development are expected to increase flood risk. Businesses can prepare for increasing flood risk by taking flood damage mitigation (FDM) measures, or by purchasing insurance. However, little is known about the predictors of the uptake of various FDM measures by businesses. Our study uses survey data after flooding in the Netherlands in 2021 to understand the drivers of FDM actions of businesses. Our results indicate that insured businesses are neither significantly more or less likely to take FDM measures compared to uninsured businesses. Therefore, we do not see evidence of moral hazard or advantageous selection. Positive predictors of FDM uptake are receipt of early warnings, searching for information about flood risk, prior flood experience and feelings of responsibility for taking FDM measures. Our results show that insurers can stimulate FDM uptake of businesses via early warnings and information provision.

Critical infrastructure (CI) are at risk of failure due to the increased frequency and magnitude of climate extremes related to climate change. It is thus essential to include them in a risk management framework to identify risk hotspots, develop risk management policies and support adaptation strategies to enhance their resilience. However, the lack of information on the exposure of CI to natural hazards prevents their incorporation in large-scale risk assessments. This study sets out to improve the representation of CI for risk assessment studies by building a neural network model to detect CI assets from optical remote sensing imagery. We present a pipeline that extracts CI from OpenStreetMap, processes the imagery and assets’ masks, and trains a Mask R-CNN model that allows for instance segmentation of CI at the asset level. This study provides an overview of the pipeline and tests it with the detection of electrical substations assets in the Netherlands. Several experiments are presented for different under-sampling percentages of the majority class (25%, 50% and 100%) and hyperparameters settings (batch size and learning rate). The highest scoring experiment achieved an Average Precision at an Intersection over Union of 50% of 30.93 and a tile F-score of 89.88%. This allows us to confirm the feasibility of the method and invite disaster risk researchers to use this pipeline for other infrastructure types. We conclude by exploring the different avenues to improve the pipeline by addressing the class imbalance, Transfer Learning and Explainable Artificial Intelligence.

An increasing number of studies illustrate and estimate the potential of the circular economy to create new jobs, most particularly for vulnerable groups at the labor market. This creates collaborating opportunities for Work Integration Social Enterprises (WISEs) and circular economy ventures. Since a shift to a circular economy requires new visions and strategies, startups are considered as powerful engines for the innovation processes needed to support a circular transition. Nevertheless , academic literature at the crossroads of the circular economy, work integration of target groups, and startups remains quasi non-existent. In this paper we present results from survey-data of startups with varying implementation levels of circular strategies, and assess their willingness to cooperate with WISEs, or to engage in other forms of target group employment. We find a strong positive relationship between the implementation of circular strategies and work integration ambitions among startups. Circular startups who need skills on production, transportation, and logistics seek collaboration with WISEs for both inner (repair and redesign) and outer circle (recycling) strategies. Our findings suggest that the circular social economy faces specific barriers that need tailor-made enabling policies. We recommend WISEs to explicitly assess reskilling and upskilling opportunities while embracing the circular economy as a future-proof economic activity.

System innovation is a signature feature of agri-food system transformation. Such system innovation often occurs in niches. However, how the "green shoots" of transformation can be detected and appraised through time remains ambiguous. This paper proposes, applies and tests a framework that could be used as a ‘transformation assessment tool’ to evaluate the level of system innovation in a domain of change. The framework is tested against a case study of a Non-Pesticide Management initiative in South India. The framework helps to reveal how, over 20 years, the initiative triggered a number of system innovations that opened a new development pathway, more aligned to environmental sustainability, equity and social inclusion. A critical enabling factor identified for the expansionand "blossoming" of this green shoot was its capacity to flexibly respond and adapt to emergent and largely unknowable agri-food systems dynamics. In its conclusions, the paper sheds light on the ongoing tensions around the defining benchmarks or thresholds for assessing the ‘transformativeness’ of initiatives and change processes. Finding a way of combining qualitative assessments of system changes with quantitative measures of social, economic, and environmental impact could be a valuable vein of research to enhance our understanding of transformative processes and how to enable them.

To improve preparedness for natural disasters, it is imperative to understand the factors that enable individual risk‐reduction actions. This study offers such insights using innovative real‐time (N = 871) and repeated (N = 255) surveys of a sample of coastal residents in Florida regarding flood preparations and their drivers during an imminent threat posed by Hurricane Dorian and its aftermath. Compared with commonly employed cross‐sectional surveys, our methodology better represents relationships between preparedness actions undertaken during the disaster threat and their drivers derived from an extended version of Protection Motivation Theory (PMT). The repeated survey allows for examining temporal dynamics in these drivers. Our results confirm the importance of coping appraisals and show that risk perceptions relate more strongly to emergency protection decisions made during the period of the disaster threat than to decisions made well before. Moreover, we find that several personal characteristics that we add to the standard PMT framework significantly relate to undertaking preparedness actions, especially locus of control and social norms. Significant changes in key explanatory variables occur following the disaster threat, including a decline in risk perception, a potential learning effect in coping appraisals, and a decline in risk aversion. Our results confirm the advantage of the real‐time and repeated survey approach in understanding both short‐ and long‐term disaster preparedness actions.

Infrastructure systems are particularly vulnerable to climate hazards, such as flooding, wildfires, cyclones and temperature fluctuations. Responding to these threats in a proportionate and targeted way requires quantitative analysis of climate risks, which underpins infrastructure resilience and adaptation strategies. The aim of this paper is to review the recent developments in quantitative climate risk analysis for key infrastructure sectors, including water and wastewater, telecommunications, health and education, transport (seaports, airports, road, rail and inland waterways), and energy (generation, transmission and distribution). We identify several overarching research gaps, which include the (i) limited consideration of multi-hazard and multi-infrastructure interactions within a single modelling framework, (ii) scarcity of studies focusing on certain combinations of climate hazards and infrastructure types, (iii) difficulties in scaling-up climate risk analysis across geographies, (iv) increasing challenge of validating models, (v) untapped potential of further knowledge spillovers across sectors, (vi) need to embed equity considerations into modelling frameworks, and (vii) quantifying a wider set of impact metrics. We argue that a cross-sectoral systems approach enables knowledge sharing and a better integration of infrastructure interdependencies between multiple sectors.

Quantum computers hold significant promise for peaceful applications, but one of the more immediate potential applications is breaking of public key encryption technologies. This poses significant risks to the information security of global digital infrastructure in a broader sense. At the same time, the development of quantum computing is a quintessentially scientific undertaking. There is a tension in the scientific freedom required to develop these technologies, and the measures to mitigate the risks associated with quantum computers. Policy for resolving this tension must be in line with the human right to science, read together with the right to privacy and the right to freedom of expression. In this article, I apply these rights to the development of quantum computing to provide guidance for government policy on quantum computing. I conclude that states must create the conditions for scientific research to flourish, even if this research may carry significant societal risks. This applies also to research and development of quantum technologies. In the context of quantum computing, this primarily means investing in the development and uptake of alternative encryption technologies which are resistant to attacks by quantum computers. It also means regulating the use of these technologies for applications which are undesirable.

Sustainable intensification (SI) responds to the concurrent challenges of increasing food production while reducing the environmental impacts of agriculture. As an early disclosure of innovation, patents are a useful indicator of technology market potential. However, we lack understanding of the extent to which current agricultural technology patents relate to the goals of SI and which kinds of technologies can potentially address SI. Here, we analyzed the diffusion and focus of more than one million patents issued during the period 1970–2022. We explored the degree to which the patents relate to SI through the co-occurrence of efficiency and environmental friendliness targets. Our results reveal that while the rate of patent issuance has dramatically increased over the past five decades, the rate at which patents diffused to different countries had decreased over time. The USA was the biggest net exporter of patents and had produced by far the most high-impact patents (in the top 1% most-cited patents). Since 1970, only 4% of agricultural patents and 6% of high-impact patents were related to SI targets (i.e., promoting both agricultural efficiency and environmental friendliness), but the attention to SI has increased over time. The most highly cited SI-related patents had become more diverse over time, shifting from digital, machine, and energy technologies in 1980s to the current era of agroecology, information, and computer networking. Our results provide an early indication of promising technologies that may play a greater role for SI in the future, subject to the challenges of market transfer and farm adoption and complemented by non-technological innovations in farm management and institutional support.

Land use intensification favours particular trophic groups which can induce architectural changes in food webs. These changes can impact ecosystem functions, services, stability and resilience. However, the imprint of land management intensity on food‐web architecture has rarely been characterized across large spatial extent and various land uses. We investigated the influence of land management intensity on six facets of food‐web architecture, namely apex and basal species proportions, connectance, omnivory, trophic chain lengths and compartmentalization, for 67,051 European terrestrial vertebrate communities. We also assessed the dependency of this influence of intensification on land use and climate. In addition to more commonly considered climatic factors, the architecture of food webs was notably influenced by land use and management intensity. Intensification tended to strongly lower the proportion of apex predators consistently across contexts. In general, intensification also tended to lower proportions of basal species, favoured mesopredators, decreased food webs compartmentalization whereas it increased their connectance. However, the response of food webs to intensification was different for some contexts. Intensification sharply decreased connectance in Mediterranean and Alpine settlements, and it increased basal tetrapod proportions and compartmentalization in Mediterranean forest and Atlantic croplands. Besides, intensive urbanization especially favoured longer trophic chains and lower omnivory. By favouring mesopredators in most contexts, intensification could undermine basal tetrapods, the cascading effects of which need to be assessed. Our results support the importance of protecting top predators where possible and raise questions about the long‐term stability of food webs in the face of human‐induced pressures.

Social science often relies on surveys of households and individuals. Dozens of such surveys are regularly administered by the U.S. government. However, they field independent, unconnected samples with specialized questions, limiting research questions to those that can be answered by a single survey. The presented data comprise the fusion onto the American Community Survey (ACS) microdata of select donor variables from the Residential Energy Consumption Survey (RECS) of 2015, the National Household Travel Survey (NHTS) of 2017, the American Housing Survey (AHS) of 2019, and the Consumer Expenditure Survey - Interview (CEI) for the years 2015–2019. This results in an integrated microdataset of household attributes and well-being dimensions that can be analyzed to address research questions in ways that are not currently possible. The underlying statistical techniques, designed under the fusionACS project, are included in an open-source R package, fusionModel, that provides generic tools for the creation, analysis, and validation of fused microdata.

Natural hazards pose significant risks to human lives, infrastructure, and ecosystems. Understanding risks along all these dimensions is critical for effective adaptation planning and risk management. However, climate risk assessments mostly focus on population, economic asset values, and road or building infrastructure, because publicly available data on more diverse exposures are scarce. The increasing availability of crowd-sourced geospatial data, notably from OpenStreetMap, opens up a novel means for assessing climate risk to a large range of physical assets. To this end, we present a stand-alone, lightweight, and highly flexible Python-based OpenStreetMap data extraction tool: OSM-flex. To demonstrate the potential and limitations of OpenStreetMap data for risk assessments, we couple OSM-flex to the open-source natural hazard risk assessment platform CLIMADA and compute winter storm risk and event impacts from winter storm Lothar across Switzerland to forests, UNESCO heritage sites, railways, healthcare facilities, and airports. Contrasting spatial patterns of risks on such less conventional exposure layers with more traditional risk metrics (asset damages and affected population) reveals that risk hot-spots are inhomogeneously and distinctly distributed. For instance, impacts on forestry are mostly expected in Western Switzerland in the Jura mountain chain, whereas economic asset damages are concentrated in the urbanized regions around Basel and Zurich and certain train lines may be most often affected in Central Switzerland and alpine valleys. This study aims to highlight the importance of conducting multi-faceted and high-resolution climate risk assessments and provides researchers, practitioners, and decision-makers with potential open-source software tools and data suggestions for doing so.

The European Union (EU) is a frontrunner of the circular economy (CE) and has established an ambitious agenda to achieve increased circularity. This paper provides a comprehensive overview of the policies which address circularity at the national level, prior to the implementation of the EU’s new CE Action Plan in 2020. As such, this paper presents the institutional starting point of the pathway towards achieving the circularity goals. The policy overview covers 315 policy initiatives, providing the most extensive overview of policies with an impact on the CE. Each policy initiative is categorized on several parameters (e.g., lifecycle phase addressed, policy instruments used) allowing the identification of dominant policy types. Subsequently, the paper presents a co-occurrence analysis by means of a probabilistic model employing combinatorics to determine whether specific policy aspects co-occur more (or less) often together. The analysis finds that the national policy measures focus on a (too) limited number of lifecycle phases and apply a (too) limited set of different instruments to improve circularity. This calls for holistic policy action plans, expanding the focus beyond recycling. Those action plans should consist of several flanking policies and aim for systemic change and circularity throughout the entire lifecycle to improve resource efficiency. Highlights • National circular policies are currently geared towards recycling and waste phases. • Combine different policy instruments to simultaneously target multiple lifecycle phases. • Dominant policy types predominantly target specific lifecycle phases and materials. • Fiscal and enforceable policies should be applied more broadly, beyond recycling.

In 2012, soybean crops failed in the three largest producing regions due to spatially compound hot and dry weather across North and South America. Here, we present different impact storylines of the 2012 event by imposing the same seasonally evolving atmospheric circulation in a pre-industrial, present day (+1°C above pre-industrial), and future (+2°C above pre-industrial) climate. While the drought intensity is rather similar under different warming levels, our results show that anthropogenic warming strongly amplifies the impacts of such a large-scale circulation pattern on global soybean production, driven not only by warmer temperatures, but also by stronger heat-moisture interactions. We estimate that 51% (47-55%) of the global soybean production deficit in 2012 is attributable to climate change. Future warming (+2°C above pre-industrial) would further exacerbate production deficits by 58% (46-67%), compared to present-day 2012 conditions. This highlights the increasing intensity of global soybean production shocks with warming requiring urgent adaptation strategies.

Within the period 2014–2017, five hail events were reported in the city of Surabaya in Indonesia. Although deep convection commonly develops over the Maritime Continent, severe thunderstorms triggering hail events develop less frequently as specific atmospheric conditions are required. The rapid urbanization in Surabaya might have led to increased heat release to the atmosphere and to the deepening of convection, which raises the question of whether urbanization is the culprit of the recent hail events in Surabaya. Hence, for a selected hail event, we used the high‐resolution Weather Research and Forecasting model to understand the storm dynamics and to explore the role of urbanization, sea surface temperature, and aerosol concentration on the storm dynamics with a total of 11 scenarios. The control simulation reveals that low‐level convergence induced by a sea breeze creates instability. At the same time, the urban heat release enhances the energy supply to induce hail formation and retain the storm's lifetime over the city. A factor separation method revealed that the urbanization (added anthropogenic heat flux, urban aerosol, and the rise in building height) and the sea surface temperature increase contribute to the storm enhancement over Surabaya, producing two times higher updraft velocity, doubling the maximum graupel mass mixing ratio and finally resulting in 15%–30% higher accumulated precipitation over Surabaya, compared to the control simulation.