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Supplementary files to: Invasive Alien Species Horizon Scanning in support of implementation of Regulation 1143/2014
No full textThese files are the two Supplementary Materials of the report 'Invasive Alien Species Horizon Scanning in support of implementation of Regulation 1143/2014 - Final Study Report', as produced by IUCN together with several scientific experts from Europe in 2025. An updated methodology was put together, consisting of gathering species lists from global horizon scanning exercises and additional databases. These lists were refined, after which a rapid assessment of a subset of some species was undertaken to evaluate their potential invasiveness, followed by consensus building to achieve a final list of prioritised species through an in-person workshop.
Supplementary Material 1 refers to the lists of species that were excluded from the initial long list of candidate species due to: 1) a lack of robust distribution data and 2) their being considered widely spread in the EU, defined as occurring in three or more 50x50 km grid cells in the EU, based on GBIF data.
Supplementary Material 2 includes 1) a list of 57 species that were deemed to present the highest threats to biodiversity in the EU in the next 10 years, 2) a list of 108 species that were deemed to also be of high risk to EU biodiversity and 3) the 457 remaining species that were put forward for scoring by the experts.
This dataset represents a raw supplement to the original report. A tidy plain text file format will be made available for use in R or python linked to this dataset
Replication data: Street green space is relevant but not sufficient for adapting to growing urban heat in world cities
No full textDescription
This dataset contains all input data required to replicate the analysis workflow of the URGED (URban mitigation and adaptation strategies Gauging through Empirical functions and Data products) project. The data supports a comprehensive analysis of urban green space (UGS) impacts on heat mitigation across 357 cities globally.
Contents
The repository includes:
Urban Green Space (UGS) data: Point-level Green View Index (GVI) measurements from street-view imagery (2011-2016) for 357 cities worldwide
Local Climate Zone (LCZ) classifications: Urban form typologies (compact, open, lightweight, etc.) at spatial resolution
Climate data: Temperature, humidity, and thermal comfort metrics from URBCLIM and other sources
Geographic data: City boundaries, administrative regions, and coordinate systems from Global Human Settlement Layer (GHSL)
Köppen-Geiger climate classifications: Climate zone assignments for all analyzed cities
Land Surface Temperature (LST): Satellite-derived temperature measurements
Population data: Spatially-explicit population distributions by city and urban form
Scope
Spatial coverage: 357 cities globally across all continents (except Antarctica)
Climate zones: Tropical (A), Dry (B), Temperate (C), and Continental (D) Köppen-Geiger zones
Temporal coverage: Primary UGS data from 2011-2016; climate projections extend to 2100
Resolution: Point-level UGS data; gridded climate data at various resolutions
Related Resources
Code repository: https://github.com/giacfalk/URGED
Analysis scripts: R-based workflow for data processing, statistical analysis, and visualizatio
EU Regional Policy Payments 2014-2020
No full textThis dataset contains aggregated, temporally and spatially explicit data compiled from the public Kohesio database (download version 26 July 2023), covering the period from 2014-2020. The project-level data is presented in an aggregate form. The dataset consists of six files, each corresponding to a different level of NUTS coding (NUTS 1-3) according to the 2016 NUTS specification.
For each file, the following columns are included the following:
Identifiers:
NUTS Code: The unique identifier for the NUTS (2016) region
Year: The starting year of the projects considered for aggregation.
Category: Project category, determined with regard to the potential impact on land use.
Variable:
1. Total eligible expenditure: The monetary amount of funding that could be granted. The total eligible expenditure is usually larger than the realized policy payments. It can be interpreted as an upper bound. All values are expressed in Euro at current prices.
The temporal dimension is yearly, ranging from 2014-2020. The spatial dimension is identified by NUTS codes (2016), with granularity ranging from level 1 to level 3.-
Each project can fall into one or more categories. The projects were grouped together with regards to their potential impact on land use.
Construction: transport
Construction: energy
Construction: building
Environment, incl. N2K
Brownfield conversion
Natural risk prevention and management
The underlying project-level data on EU regional funds contains variables on the project itself (title, description, location, and project end and/or start date), the project’s beneficiary (name, location), the policy area to which the policy area contributes, and monetary information (type of fund used, co-financing rate, paid sums, eligible costs, etc.).
The dataset contains observations for all EU member states, and in case of Interreg projects, on some EU neighboring states. The UK is not included in the dataset. This is (i) due to the needs of the project, and (ii) due to data availability.
Please note that this dataset is intended for research and analysis in the fields of climatology, environmental science, and related disciplines. Users are encouraged to cite this dataset appropriately if utilized in academic or scientific publications
Harnessing Waste for Emission Cuts: the Anaerobic Digestion Potential in Qatar
No full textThe Gulf Cooperation Council (GCC) countries are on the verge of a significant shift in waste management, with governments increasingly focused on reforming the waste sector. In Qatar, which currently landfills 90% of its waste, efforts are underway to establish one of the region’s most advanced waste treatment centers, including an anaerobic digestion (AD) facility. However, the question arises whether AD solutions are justified in a fossil fuel-rich nation like Qatar. To this end, this study is among the very few ones that aim to evaluate “concomitantly” the benefits of green energy generation and fertilizer substitution– and the first to consider the specificities of GCC countries. Three organic waste streams were considered: organic fraction of municipal solid waste (OFMSW), livestock manure (LMW) and sewage sludge waste (SSW). The results indicate the potential of AD to generate 850 million m³ of biogas and 2.5 million metric tons of digestate. The biogas could produce up to 3.5 million MWh of surplus energy, equivalent to a reduction of 642 million kg CO 2 -eq. Substituting traditional fertilizers with digestate could further save 49 to 788 million kg CO₂-eq annually– reaching a total of 691-1,430 million kg CO₂-eq mitigated annually. Overall, AD of the three studied organic waste streams can potentially offset 0.7–1.4% of Qatar’s GHG emissions. The findings also highlight the importance of selecting appropriate feedstock sources to maximize GHG savings. For instance, the complementarity between OFMSW and LMW boosts both clean energy production (by OFMSW’s high biogas yield) and fertilizer replacement (by the high LMW nutrient content).
Graphical Abstract
This paper showed that three of the major organic waste streams in Qatar may be valorized, through AD, to generate two useful products: (1) biogas that can be used for combined generation of electricity and heat, and (2) digestate that can be used as soil amendment and to replace synthetic fertilizers. The outcome is 691-1,430 million kg CO₂-eq mitigated annually
Applying Google trends to analyze electoral Outcomes: A 2024 cross-national perspective
Get PDFThis study analyzes whether Google Trends data, when applied in a cross-country context, offers a consistent and meaningful indicator of electoral outcomes across different national elections. To do this, it examines how Google Trends data in national, single-round elections held in 2024 correspond to the relationship between search volumes for candidates or political parties in the week preceding elections and key electoral metrics such as vote share, winning status, and candidate ranking. The analysis demonstrates that online search behavior serves as a valuable proxy for gauging public interest and helps illustrate patterns of voter engagement. By employing adjusted Google Trends scores, which calculate each candidate's or party's proportion of the total search interest for all major contenders on a given day (so that the combined search shares for all included candidates or parties sum to 100 % of the total search volume for that day, hereafter “proportional representation”), these metrics reduce data noise and outliers. The study also demonstrates that these refined metrics exhibit stronger associations with electoral outcomes compared to the unadjusted search data. The main contribution of this study lies in its cross-country approach, offering a comparative perspective on how search interest may relate to voting behavior across diverse contexts. Moreover, the study discusses inherent limitations, including the inability of Google Trends to differentiate between positive and negative search intent and its sensitivity to demographic and regional variations in search behavior. By conducting a comprehensive cross-country analysis of multiple elections, this research contributes to the expanding literature on the application of digital data analytics in social and political research and underscores the descriptive utility of search data across different electoral contexts
Economic Coordination for Climate Policy: Optimizing Livestock Sector Emission Reduction Using Genetic Algorithms and Agent-Based Modeling in Quang Binh, Vietnam
No full textThis study presents a spatially explicit optimization framework to support subnational climate policy in Vietnam's livestock sector. By integrating a genetic algorithm with district-level emissions modeling, we have identified cost-effective allocations of eight mitigation options in Quang Binh province to help meet the national methane reduction target of 12.39% by 2030. Drawing on livestock census data, IPCC emission factors, and region-specific marginal abatement costs (MACs), the model generates scenarios that achieve reductions of 13.9-29.3% in provincial GHG emissions. The most effective measures, which are biochar supplementation for cattle and silage feeding for buffaloes, not only deliver the greatest emission reductions at the lowest MACs, but also improve livestock productivity, offering strong economic co-benefits. Other interventions such as manure separation and expanded biogas systems also yield high mitigation potential, particularly when supported by carbon revenues or external incentives. With a net abatement cost of - 7.268 million USD and economic gains of 7.627 million USD under a carbon price of 5 USD/tCO(2)eq, our approach demonstrates how targeted, locally grounded strategies can align economic development with national climate goals. While simplified in terms of its behavioral assumptions, the model offers a practical, scalable tool for low-emission planning in data-constrained contexts and is transferable to other provinces under Vietnam's climate commitments
Positive Externalities in the Polycrisis: Effectively Addressing Disaster and Climate Risks for Generating Multiple Resilience Dividends
Get PDFWith multiple risks interacting and shocks proliferating across geographies and sectors, the concept of polycrisis has come to the fore. Polycrisis describes interwoven and overlapping crises that cannot be understood or resolved in isolation. Analysts have suggested that many of the Polycrisis symptoms have been at least partially triggered by negative externalities, that is, costs arising from economic activity that are not covered by market prices and thus not internalized in national and international decision making, leading to suboptimal decisions on climate action, energy and food security, global financial stability, among others. Externalities have generally been framed as negative. Positive externalities, that is, societal benefits that indirectly arise from activities and transactions have less often been considered. International policy debate on disaster risk reduction (DRR) and climate change adaptation (CCA) over the last years, as stipulated by international compacts in 2015 (the Sendai Framework, the SDGs, and the Paris Agreement), has built on positive externality discussion, albeit not explicitly so. Disaster risk reduction and CCA analysts have emphasized the need for orienting risk management investments towards interventions that generate so-called multiple or triple resilience dividends. This means extending the focus in decision making from avoiding and reducing impacts and risks to also considering development (co-)benefits arising irrespective of disaster event occurrence. In this context, the “Triple Dividend of Resilience” (TDR) concept and framework has suggested that in addition to risk reduction benefits (dividend 1), dividends would also arise from benefits associated with unlocked development (dividend 2) as well as from co-benefits (dividend 3), for example, from investments into disaster-safe and energy efficient housing. Yet, despite the increasing burdens imposed by systemic disaster and climate risks and wide-spread recognition of this concept over a decade as well as solid evidence regarding the benefits of reducing risk, it has remained difficult to motivate sustained investment across scales into disaster and climate risk reduction. We argue that this systemic underinvestment is, at least partially, due to a lack of conceptual clarity of the TDR with regard to the framing around the dividend 2, a lack of awareness and solid evidence on the positive externalities, as well as interrelationships between resilience dividends in space and time. Based on a snowballing review of the limited literature on the TDR as well as an examination of empirical and model-based evidence, we present the state of the art on the TDR framework. We examine the various dividends in terms of epistemological and methodological contributions building on empirical and modeling methods for supporting decision making as well as evidence for decision making across scales from local to global. Overall, we suggest that there indeed can be positive externalities and solid co-benefits from disaster and climate risk reduction. Systemic risk research and practice coupled with resilience dividend reasoning may thus help to better identify those dividends for improved decision making on disaster and climate risk (reduction). We further show how analysts and decision makers may better consider those various resilience dividends beyond the reduction of losses as well as assess dependencies in risk and benefits’ creation across micro and macro scales. As we suggest, enhanced methods and better awareness for potential externalities may enable more comprehensive consideration of DRR and CCA interventions with benefits arising at various scales. This may eventually also lead to enhanced disaster risk and climate risk governance, which is key for tackling relevant risk challenges in a polycrisis context