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Meeting European Union biodiversity targets under future land-use demands
No full textThe European Union is committed to achieving ambitious area-based conservation and restoration targets in the upcoming decade. However, there is concern that these targets risk conflicting with socioeconomic needs, particularly for food, timber and bioenergy production. Here we develop an integrated spatial planning approach to identify where restoration, conservation and production allocation could maximize benefits to species conservation and climate mitigation, while acknowledging future land demands of the bio-economy. We show that, while changing production demands risk driving further biodiversity loss by 2030, when these demands are met alongside strategic restoration measures, as outlined by the EU Nature Restoration Regulation, future landscapes could improve the conservation status of populations for more than 20% of species of conservation concern while also increasing terrestrial carbon stocks. Our analysis demonstrates how critical the Nature Restoration Regulation is to achieving biodiversity targets and how integrated planning can align biodiversity policy objectives with future socioeconomic demands
Indicators of Global Climate Change 2024: annual update of key indicators of the state of the climate system and human influence
Get PDFIn a rapidly changing climate, evidence-based decision-making benefits from up-to-date and timely information. Here we compile monitoring datasets (published here https://doi.org/10.5281/zenodo.15327155 Smith et al., 2025a) to produce updated estimates for key indicators of the state of the climate system: net emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, the Earth's energy imbalance, surface temperature changes, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. This year, we additionally include indicators for sea-level rise and land precipitation change. We follow methods as closely as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report.
The indicators show that human activities are increasing the Earth’s energy imbalance and driving faster sea-level rise compared to the AR6 assessment. For the 2015–2024 decade average, observed warming relative to 1850–1900 was 1.24 [1.11 to 1.35] °C, of which 1.23 [1.0 to 1.5] °C was human-induced. The 2024 observed record in global surface temperature (1.52°C best estimate) is well above the best estimate of human-caused warming (1.36°C). However, the 2024 observed warming can still be regarded as a typical year, considering the human induced warming level and the state of internal variability associated with the phase of El Niño and Atlantic variability. Human-induced warming has been increasing at a rate that is unprecedented in the instrumental record, reaching 0.27 [0.2–0.4] °C per decade over 2015–2024. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 53.6 ± 5.2 GtCO2e per year over the last decade (2014–2023), as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that the rate of increase in CO2 emissions over the last decade has slowed compared to the 2000s, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track decreases or increases in the rate of the climatic changes presented here
Simulating plant functional acclimation and trait evolution using an eco-evolutionary vegetation model (PlantFATE)
Get PDFIn the face of ongoing global crises, such as climate change and biodiversity loss, we urgently need to understand dynamic and complex responses of global forest ecosystems. To do so, we need to develop modeling frameworks that account for multiple temporal and organizational scales, and therefore capture functional adaptations of individuals, species, and ecosystems in response to the environment.
Here we present Plant-FATE (Plant Functional Acclimation and Trait Evolution) an eco-evolutionary vegetation model that embodies functional diversity by representing plant life-history strategies, and adaptations by accounting for short-term physiological acclimation, mid-term demographic shifts, and long-term trait evolution.
Tested with data obtained from an hyperdiverse site in the Amazon Forest, our model predicts a nonlinear response of tropical forests to increasing atmospheric CO2 due to diverse aspects of the growth-mortality tradeoff. At moderately elevated CO2, we found that evolution towards higher wood density increases vegetation C sequestration. By contrast, under highly elevated CO2 levels, a darkening understorey rather triggers lower wood densities, thus reversing gains from the proposed CO2 fertilization effect.
Our results suggest that competition for resources may modulate community-level eco-evolutionary dynamics of forest ecosystems, such that competition-induced changes in wood density may render forests more vulnerable to future climatic extreme events. Our study highlights the importance of accounting for eco-evolutionary dynamics when simulating the functional response of forest ecosystems to projected climate change
AERO-MAP: a data compilation and modeling approach to understand spatial variability in fine- and coarse-mode aerosol composition
Get PDFAerosol particles are an important part of the Earth climate system, and their concentrations are spatially and temporally heterogeneous, as well as being variable in size and composition. Particles can interact with incoming solar radiation and outgoing longwave radiation, change cloud properties, affect photochemistry, impact surface air quality, change the albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. High particulate matter concentrations at the surface represent an important public health hazard. There are substantial data sets describing aerosol particles in the literature or in public health databases, but they have not been compiled for easy use by the climate and air quality modeling community. Here, we present a new compilation of PM2.5 and PM10 surface observations, including measurements of aerosol composition, focusing on the spatial variability across different observational stations. Climate modelers are constantly looking for multiple independent lines of evidence to verify their models, and in situ surface concentration measurements, taken at the level of human settlement, present a valuable source of information about aerosols and their human impacts complementarily to the column averages or integrals often retrieved from satellites. We demonstrate a method for comparing the data sets to outputs from global climate models that are the basis for projections of future climate and large-scale aerosol transport patterns that influence local air quality. Annual trends and seasonal cycles are discussed briefly and are included in the compilation. Overall, most of the planet or even the land fraction does not have sufficient observations of surface concentrations – and, especially, particle composition – to characterize and understand the current distribution of particles. Climate models without ammonium nitrate aerosols omit ∼ 10 % of the globally averaged surface concentration of aerosol particles in both PM2.5 and PM10 size fractions, with up to 50 % of the surface concentrations not being included in some regions. In these regions, climate model aerosol forcing projections are likely to be incorrect as they do not include important trends in short-lived climate forcers
Realizing Recognition Justice in Flood Risk Management Policy: A Case Study on Implementation Gaps and Legitimacy Gaps in Austria
Get PDFFlood risk is often unequally distributed. These inequalities highly depend on socio‐political decisions. The recognition of the needs of individuals within a floodplain needs to be considered as a precondition for reaching justice in flood risk management, especially as people differ in their vulnerabilities and capacities to deal with floods. This paper addresses the question of how vulnerable population groups are recognized in flood risk management used in the federal state of Upper Austria. We use a qualitative research method, which is based on policy, legal documents and strategies and on 32 semi‐structured interviews conducted at different levels. Even though clearly stating the overall policy goal of reducing social vulnerability and inequality, most risk reduction strategies neglect these aspects, which creates an implementation gap regarding recognition justice. Strict adherence to the principle of equality leads to, among others, uniform design levels and cost contributions that undermine the notion of differentiated vulnerability. By contrast, disaster aid payments do use eligibility criteria that recognize social inequalities. However, even if justice principles are implemented, they lack transparency and accountability, which creates a legitimacy gap. Restricting the role of civil servants in the public administration through hybrid governance may narrow implementation and legitimacy gaps
Causal pathway from AMOC to Southern Amazon rainforest indicates stabilising interaction between two climate tipping elements
Get PDFDeclines in resilience have been observed in several climate tipping elements over the past decades, including the Atlantic Meridional Overturning Circulation (AMOC) and the Amazon rainforest (AR). Large-scale nonlinear and possibly irreversible changes in system state, such as AMOC weakening or rainforest-savanna transitions in the Amazon basin, would have severe impacts on ecosystems and human societies worldwide. In order to improve future tipping risk assessments, understanding interactions between tipping elements is crucial. The AMOC is known to influence the Intertropical Convergence Zone, potentially altering precipitation patterns over the AR and affecting its stability. However, AMOC-AR interactions are currently not well understood. Here, we identify a previously unknown stabilising interaction pathway from the AMOC onto the Southern AR, applying an established causal discovery and inference approach to tipping element interactions for the first time. Analysing observational and reanalysis data from 1982-2022, we show that AMOC weakening leads to increased precipitation in the Southern AR during the critical dry season, in line with findings from recent Earth system model experiments. Specifically, we report a 4.8% increase of mean dry season precipitation in the Southern AR for every 1 Sv of AMOC weakening. This finding is consistent across multiple data sources and AMOC strength indices. We show that this stabilising interaction has offset 17% of dry season precipitation decrease in the Southern AR since 1982. Our results demonstrate the potential of causal discovery methods for analysing tipping element interactions based on reanalysis and observational data. By improving the understanding of AMOC-AR interactions, we contribute toward better constraining the risk of potential climate tipping cascades under global warming
A Carbon Wealth Tax: Modelling, Empirics, and Policy
Get PDFAlthough economists widely advocate carbon pricing as an effective solution to reduce carbon emissions, this mechanism has had so far limited effects. This paper proposes a new type of tax to help finance (and accelerate) the green transition. A carbon wealth tax (CWT) is proposed to be levied on carbon-intensive (brown) wealth rather than primarily on carbon-intensive goods. We consider tax implementation issues such as tax base, incidence, and efficiency. Moreover, we analyze the impacts of such a tax scheme by setting up a model of asset pricing and dynamic portfolio decisions. Green and carbon-intensive returns used in the model are calibrated with low-frequency returns on stock prices between 2010 and 2021. We find that such a tax and subsidy scheme is a feasible and effective instrument in speeding up the transition to a greener economy, particularly in protracted periods of economic contraction. Our approach also brings a new perspective to the wealth inequality discussions
Anthropogenic emissions of CH4, N2O, F-gases and BC from GAINS, for EU-countries plus CH, NO, UK developed under the EYE-CLIMA project - March 2025 update
No full textAs part of the EYE-CLIMA project, GAINS emission data for CH4, N2O, BC and selected F-gases (HFC-125, HFC-134a, HFC-143a, HFC-23, HFC-32 and SF6) were released for all EU-27 countries plus UK, Switzerland, and Norway for the period 1990 to 2020 (with exception of F-gases, from 2005 only, and BC/CH4 emissions from agricultural waste burning, from 2000). Results have been documented in EYE-CLIMA deliverable D2.8 (http://folk.nilu.no/~rthompson/eyeclima_reports/EYECLIMA_D2.8.pdf), and they are publicly available at the Zenodo repository under https://doi.org/10.5281/zenodo.11032177. All data is available on a 0.1°x0.1° grid and in monthly resolution. Emissions are attributed to the respective source categories according to GNFR.
The motivation of an update resulted from the need to extending the emission data time series to 2023. With underlying statistics and national emission data currently available till 2022 only (the latter submitted to UNFCCC only by December 2024), the historical data series also could only be established for 2022. Here we use the GAINS scenario feature to extrapolate between 2022 historical data and the first scenario point, 2025 which is based on IEA’s Word Energy Outlook 2023 (https://www.iea.org/reports/world-energy-outlook-2023). Obviously, this also means that emission results for 2023 are not any more based on robust statistics but represent an extrapolation.
Extrapolation of spatially explicit data is only possible when the spatial resolution conveys a realistic signal. For the sector “agricultural waste burning” (files with “AWB” as sector, see notation below) spatial allocation is based on actual observation from satellites. As such data products on agricultural fires have been made available until 2022 only, no spatial or temporal signal exists for 2023. The time series provided thus has to end in 2022. No recommendation can be given to modellers, other than to either use 2022 also for 2023 (understanding that the pattern will be strikingly different) or to use a five-year average (which will remove a lot of spatial specificity)
Methane emissions from fossil fuel extraction and embodied in fossil fuel trade in China
No full textThe fossil fuel sector significantly contributes to methane emissions, a potent driver of climate change. Previously, assessments of methane emissions from fossil fuel extraction were primarily conducted at the national and provincial levels, while the heterogeneities in methane emissions across fossil fuel sites and fuel types (coal, oil, gas) have not yet been fully understood. Here, we present a detailed evaluation of methane emissions from China's fossil fuel extraction using an inventory of 994 coal mines, 761 oil fields, and 896 gas fields. The results show that in 2017, fossil fuel extraction emitted 23.2 Mt of methane emissions, with coal, oil, and gas contributing 95 %, 2.7 %, and 2.3 %, respectively. Further, we quantify the methane emissions embodied in intercity fossil fuel trade by integrating the emission assessment with an environmentally extended intercity input-output model. We find that 17.1 Mt (74 % of methane emissions from extraction) is embodied in the intercity fossil fuel trade, with the remaining 26 % not embodied in intercity trade but driven by the local fossil fuel demand of cities. Compared with the production-based perspective, 243 cities result in more methane emissions from the consumption perspective, while 70 cities see the opposite. This study shows the heterogeneity in methane emissions across regions and opportunities for intercity collaborations on emission mitigation