20253 research outputs found
Sort by
Assessing future impacts of tropical cyclones on global banana production
Get PDFTropical cyclones (TCs) are projected to increase in intensity globally, impacting human lives; infrastructure; and important agricultural activities, such as banana production. Banana production is already impacted by TCs in several parts of the world, leading to price volatility and impacted livelihoods of banana producers. While many potential impacts on banana production have already been quantified on a local scale, it remains unclear how bananas could be impacted by TCs across the globe under present and future climate conditions. To address this, we first looked at the documented impacts of TCs on banana production from different places all around the world. Using spatially explicit data on banana-producing regions and future TC occurrence and magnitude, we then identified the spatial distribution and extent of areas where TCs could impact banana production. Our results suggest that considerable portions of global banana production are at risk of being impacted by TCs under present and future climate conditions, and we show this for different return periods.
Globally, 24.3 % of all banana-producing areas are projected to suffer major or complete (>84 %) damage under current climate conditions, increasing to 26.5 % under future climate scenarios at the 100-year wind speed return period. The regions experiencing the most notable increases in majorly damaged area under future conditions are the Caribbean (9.3 %), the Middle East and North Africa (36 %), and Southeast Asia (21.9 %). The most profound decreases in majorly damaged area are found in Central America (−35.8 %) and East Asia (−7.6 %). The most substantial change in completely damaged area is observed in East Asia, Southeast Asia and Oceania.
Additionally, we estimate that 30.1 % of global production under current conditions and 31.1 % under future conditions will be majorly or completely damaged at the 100-year return period. The regions predominantly affected in the future are Asia and the Caribbean, potentially experiencing substantial disruption in banana production. Our results therefore indicate that considerable efforts in climate adaptation are essential to ensure the stability of global banana supply chains
Reviewing and benchmarking ecological modelling practices in the context of land use
Get PDFDespite habitat loss and degradation are the primary drivers of biodiversity loss, different conclusions have been drawn about the importance of land‐use or land‐cover (LULC) change for biodiversity. Differences may be due to the difficulty of framing a coherent model design to assess LULC effects. Recommendations have previously been identified for the design of statistical models and failing to follow them can risk misidentification of drivers, misinterpretation of predictions, overconfidence, high uncertainty, and incorrect management recommendations. We review modelling practices in statistical models assessing biodiversity responses to LULC, and investigated relationships between modelling practices and citations by scientific articles and policy documents. We benchmarked practices across model approaches, political extents, and objectives. From 346 model applications, we found that more than half of the model applications have justified ecologically‐relevant predictors, have used 1 km² or lower LULC spatial resolution, have used fine LULC thematic resolutions, performed validation or communicated uncertainty. However, we found that the model approach and political extent were strong determinants of the misuse of modelling recommendations. Top–down models followed less frequently three recommendations out of six, compared to other model approaches. Global studies used coarser LULC thematic and spatial resolution than studies at other extents, and thus potentially underestimated the relationships between LULC and biodiversity. Global studies were however more frequently cited by both scientific studies and policy documents. Modelling recommendations are not universally applied, especially because of methodological tradeoff, technical difficulties in their applications and data requirements. However, the multiples risks associated with the misuse of modelling recommendations, particularly in large‐scale modelling exercises, raise concerns on model interpretation and policy support from science, regarding the impacts of LULC on biodiversity
Challenges and opportunities in climate risk assessment: future directions for assessing complex climate risks
Get PDFAs climate change impacts intensify worldwide, assessing climate risks comprehensively is essential for guiding effective disaster risk management and adaptation strategies. This systematic literature review examines the latest developments in Climate Risk Assessment (CRA), focusing on how climate risks are framed and assessed. It explores advancements, ongoing challenges, and emerging opportunities to guide future generations of CRAs. Key findings highlight a more nuanced risk framework that incorporates climate responses, modulating the three risk determinants (exposure, vulnerability, and hazards), as outlined in the latest IPCC assessment. The state-of-the-art concentrates on the temporal and spatial characteristics of hazards, while exposure and vulnerability are increasingly understood as dynamic concepts influenced by socioeconomic changes. Recent developments, such as multi-hazard approaches, risk tolerance integration, and the concept of Climatic Impact-Drivers (CID), provide new perspectives on assessing climate risks. However, managing complexity and uncertainty remain the main operational challenges, underscoring the need for improved CRA methodologies and models, as well as consistent, interoperable datasets. The paper discusses avenues to advance CRA, emphasizing the importance of bridging the gap between academic advancements and practical implementation. Conceptual recommendations include adopting a systemic approach to, for example, better account for the cascading and compounding risks, hazard thresholds, adaptation limits, and risk amplifiers, as well as using storylines to improve CRA communication. Technical recommendations include leveraging emerging technologies such as artificial intelligence, machine learning methods and big data analytics to improve real-time risk prediction and modeling. To enhance the CRA practice, the study advocates for greater stakeholder involvement and inclusive governance to ensure that CRAs remain context-specific and relevant. These recommendations, together with strengthened interdisciplinary collaboration and knowledge-sharing, are expected to pave the way for more effective climate risk management, adaptation, and resilience-building strategies
Under which conditions can shocks stimulate transformative recovery: the Strategy Shock Implementation Reaction (SSIR) framework
Get PDFFuture shocks from climate change impacts will likely overstretch current individual coping capacities. Integrated policy strategies could foster sustainable and resilient reactions of households and businesses by rebuilding for transformative recovery instead of bouncing back to the pre-shock status. We present the Strategy Shock Implementation Reaction (SSIR) framework as a conceptual framework for bridging the design of policy strategies to their implementation after a shock and the following reactions of the affected households and businesses. We illustrate the SSIR framework using examples of climate resilience pathways that integrate climate change adaptation and mitigation policy: planned relocation and building renovation. The framework details how a shock converts a strategy and how this conversion influences the strategy’s effect on individual reactions. It thus re-conceptualizes shocks from mere policy windows to policy filters. We discuss how the framework may be operationalized in research on how policy strategies evolve and function over time
Promoting health through climate change mitigation in Europe
Get PDFSeveral EU climate change mitigation policies have the potential to deliver health co-benefits. However, existing frameworks guiding research in this area lack important details that are needed to understand how evidence of health co-benefits can be used to support the ambition and acceptability of EU climate policy. In this Personal View, we propose an integrated framework for advancing the state-of-the-science on health co-benefits of climate change mitigation and realising the societal effect of evidence documenting co-benefits. We apply this framework to the EU context. Our framework spans multiple economic sectors-including land use, land-use change, and forestry and health systems-and provides details on the different types of mitigation actions, levers of change, and societal actors with the agency to implement specific mitigation actions. This framework aims to inform future research on the magnitude of health co-benefits of climate change mitigation, and provide strategies to communicate health co-benefits to support increases in mitigation ambition and societal acceptance of mitigation actions
The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Get PDFThe Multi-Compartment Hg (mercury) Modeling and Analysis Project (MCHgMAP) is an international multimodel research initiative intended to simulate and analyze the geospatial distributions and temporal trends of environmental Hg to inform effectiveness evaluations of two multilateral environmental agreements (MEAs): the Minamata Convention on Mercury (MC) and the Convention on Long-Range Transboundary Air Pollution (LRTAP). This MCHgMAP overview paper presents its science objectives, background, and rationale; experimental design (multimodel ensemble (MME) architecture, inputs and evaluation data, simulations, and reporting framework); and methodologies for the evaluation and analysis of simulated environmental Hg levels. The primary goals of the project are to facilitate detection and attribution of recent (observed) and future (projected) spatial patterns and temporal trends of global environmental Hg levels and identification of key knowledge gaps in Hg science and modeling to improve future effectiveness evaluation cycles of the MEAs. The current advances and challenges of Hg models, emission inventories, and observational data are examined, and an optimized multimodel experimental design is introduced to address the key policy questions of the MEAs. A common set of emissions, environmental conditions, and observation datasets is proposed (where possible) to enhance the MME comparability. A novel harmonized simulation approach between atmospheric, land, oceanic, and multimedia models is proposed to account for the short- and long-term changes in secondary Hg exchanges and to achieve mechanistic consistency of Hg levels across environmental matrices. A comprehensive set of model experiments is proposed and prioritized to ensure systematic analysis and participation of a variety of models from the scientific community
Peri-urban ammonia emissions in Beijing-Tianjin-Hebei decrease with restructuring of China's livestock industry
No full textCONTEXT
Ammonia (NH3) emissions from livestock production significantly contribute to formation of PM2.5, posing a major environmental concern. Over the past two decades, compared to other regions in China, Beijing, Tianjin and Hebei (BTH) region, has been suffering from high NH3 emissions due to increased urbanization and resulting intensified agricultural production. Uneven distribution of livestock leads to separation of crop and livestock, resulting in the waste of manure resources and environmental pollution.
OBJECTIVE
This study systematically investigates the 2000–2019 temporal-spatial pattern of NH3 emissions in the BTH region, with three focuses on: (1) analyzing the spatial distribution of livestock, the efficiency of livestock manure utilization, and the characteristics of NH3 emissions; (2) elucidating impact of restructuring livestock industry on NH3 emissions; (3) developing scenarios analysis to propose potential optimization pathways for reducing livestock NH3 emissions.
METHODS
We utilized the NUFER model to analyze livestock NH3 emissions in the BTH region. The distribution of livestock was further elucidated through a panel analysis, which identified key factors shaping their spatial arrangement. Scenarios analysis (S1: coupling crop and livestock, S2: spatial planning) was applied to explore optimization pathways of reducing NH3 emission.
RESULTS AND CONCLUSIONS
The study reveals an initial increasing trend in livestock NH3 emissions, peaking in 2015 at 511 kt NH3, followed by a sharp decline to 401 kt NH3 in 2019. Spatially, emission hotspots concentrated in the northeastern BTH region. Based on restructuring of China's livestock industry, the number of livestock started to decrease in areas designated as “urban sprawl” or for “ecological protection”, while the number of livestock in other land (land except those for urban sprawl and ecological protection) increased. The livestock moving outside of functional areas (urban sprawl areas and ecological protection area) has triggered a corresponding displacement of NH3 emission to non-functional areas (the other land) where population density was low. Scenario simulations demonstrate that optimized scenarios S1 and S2 achieved an increase in the recycling rate of manure nitrogen to 51 % and 52 %, reducing NH3 emissions by 200,000 and 240,000 tons. Optimizing the livestock distribution can increase the rate of manure returning to the field by enhanced coupling of crop and livestock.
SIGNIFICANCE
This study significantly advances our understanding of NH3 emissions dynamics, providing critical insights into the spatial-temporal patterns and key drivers of NH3 emission. By proposing and evaluating three optimization scenarios, it offers policymakers and stakeholders evidence-based pathways for effective environmental management
Robust Statistical Estimation and Two-Stage Stochastic Optimization: Quantile Regression EPIC Meta-Model of Soil Organic Carbon for Robust Decision Making with GLOBIOM
No full textThe paper discusses the connections between two-stage stochastic optimization and robust statistical estimation. Main question related to statistical predictions is how to use the predictions to optimize the overall decisions and how current decisions can affect predictions. In general problems of decision-making, feasible solutions, concepts of optimality and robustness are characterized from the context of decision-making situations, i.e., systems structure, goals, security constraints, safety norms, supply-demand relationships, thresholds. Robust statistical approaches can be effectively combined with disciplinary or interdisciplinary models, e.g., land use model GLOBIOM, for effective decision-making in the conditions of uncertainty, increasing interdependencies and systemic risks. We discuss a quantile- regression EPIC meta-model for tracking dynamics and uncertainties of Soil Organic Carbon (SOC), which is an important agri-environmental indicator. SOC levels (quantiles) can be controlled with GLOBIOM, to analyze the costs of achieving robust land management practices to sequester SOC and fulfill food-energy-water-environmental nexus security goals. Quantiles identify critical SOC levels signaling how close is a threshold or a targeted level. The SOC-EPIC meta-model is developed using historical observations and results of the bio-physical model EPIC. It enables the analysis of SOC content and respective probabilities as a function of exogenous parameters such as monthly temperature and precipitation and endogenous, decision-dependent parameters, which can be altered by the land management decisions computed with GLOBIOM
Third-Best Carbon Taxation: Trading Off Emission Cuts, Equity, and Efficiency
No full textWe analyze carbon taxes, lump-sum climate dividends, and changes to the level and progressivity of the income tax system that optimally trade off carbon emissions, equity, and efficient raising of public revenue while preserving budgetary neutrality and not using individualized lump-sum transfers. Such “third-best” policies include a carbon tax that exceeds the Pigouvian level and recycling of all carbon tax revenue via climate dividends for high (and our preferred) degrees of inequality aversion, even if this implies higher income taxes to meet existing revenue requirements. The carbon tax, climate dividends, and the progressivity of the income tax rise with the degree of inequality aversion. Our results are derived from a microsimulation model estimated from German data, which includes heterogeneous households, an exact affine Stone index demand system, and endogenous labor supply. We decompose the welfare effects of policy into emissions, equity, and efficiency components for different degrees of inequality aversion and climate damages
Subtropical vegetation damage and recovery dynamics after the great 2008 Chinese ice storm
No full textIn early 2008, an extreme ice storm struck southern China, including the subtropical Guangdong Province, causing substantial ecological damage and economic losses. Previous evaluations of vegetation damage during this event primarily focused on the immediate physical structure damage caused by ice, overlooking the delayed physiological damage from extreme low-temperature stress, especially in adjacent non-frozen regions. Combining remotely-sensed (i.e., MODIS Gross Primary Productivity (GPP) and Enhanced Vegetation index (EVI)) and in-situ data, we conducted a more complete assessment of subtropical vegetation damage and recovery following the 2008 Chinese ice storm by considering time-lag effects. We found vegetation damage and subsequent recovery exhibited distinct spatial patterns correlated to ice storm severity. Assessments that accounted for time-lag effects were more aligned with ground truth, revealing that vegetation damage signal typically lagged the event onset by 1–2 months. The time-lag effect showed distinct patterns in non-frozen regions experiencing secondary low-temperature stress (without direct ice storm exposure). Physiological damage dominated these areas, reducing GPP by 62 %. In contrast, physical structural damage caused a comparatively smaller decline (51 %) in EVI. We also found a positive correlation between frozen time and the severity of vegetation damage, with 37 % of vegetation damaged in less frozen zone versus 70 % in severe frozen zone. Subsequent recovery of GPP and EVI to pre-ice storm conditions took 4–9 months, with GPP recovering faster than EVI, especially in severe frozen forests. Such positive correlation also existed between damage severity (or recovery time) and elevation and slope, but the pattern varied across different freezing zones. Our findings highlight the delayed physiological damage from extreme low-temperature stress and provide new insights into subtropical vegetation dynamics following extreme ice storms