Tracking the Dynamics and Uncertainties of Soil Organic Carbon in Agricultural Soils Based on a Novel Robust Meta-Model Framework Using Multisource Data

Monitoring and estimating spatially resolved changes in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at assisting land degradation neutrality and climate change mitigation, improving soil fertility and food production, maintaining water quality, and enhancing renewable energy and ecosystem services. In this work, we report on the development and application of a data-driven, quantile regression machine learning model to estimate and predict annual SOC stocks at plow depth under the variability of climate. The model enables the analysis of SOC content levels and respective probabilities of their occurrence as a function of exogenous parameters such as monthly temperature and precipitation and endogenous, decision-dependent parameters, which can be altered by land use practices. The estimated quantiles and their trends indicate the uncertainty ranges and the respective likelihoods of plausible SOC content. The model can be used as a reduced-form scenario generator of stochastic SOC scenarios. It can be integrated as a submodel in Integrated Assessment models with detailed land use sectors such as GLOBIOM to analyze costs and find optimal land management practices to sequester SOC and fulfill food–water–energy–-environmental NEXUS security goals

Water circles—a tool to assess and communicate the water cycle

‘Water circles’ are presented as flexible water cycle diagrams aggregating the flows through a system for a specific region and time period, categorized by flow type and organized by magnitude. Water circles for an entire system and separate storage components can be interpreted as water cycle speedometers and can help compare and communicate different climate and human impacts on different regions, time periods, and storage components. Water circles can facilitate comparisons between hydrological models and other methods for deriving water balances

Substantial reductions in non-CO2 greenhouse gas emissions reductions implied by IPCC estimates of the remaining carbon budget

Carbon budgets are quantifications of the total amount of carbon dioxide that can ever be emitted while keeping global warming below specific temperature limits. However, estimates of these budgets for limiting warming to 1.5 °C and well-below 2 °C include assumptions about how much warming can be expected from non-CO2 emissions. Here, we uncover the non-CO2 emissions assumptions that underlie the latest remaining carbon budget estimates by the Intergovernmental Panel on Climate Change and quantify the implication of the world pursuing alternative higher or lower emissions. We consider contributions of methane, nitrous oxide, fluorinated gases, and aerosols and show how pursuing inadequate methane emission reductions causes remaining carbon budgets compatible with the Paris Agreement temperature limits to be exhausted today, effectively putting achievement of the Paris Agreement out of reach

Exploring fishing impacts on the structure and functioning of the Yellow Sea ecosystem using an individual-based modeling approach

The Yellow Sea is a marginal sea in the Northwestern Pacific where the fishery resources have been overfished and the community structure has greatly changed over the past six decades. Ecosystem modeling approaches are valuable tools to uncover potential mechanisms behind the ecosystem changes. Here, we developed ‘OSMOSE-YS’, an individual-based multi-species OSMOSE model that includes important commercial pelagic and demersal fish and invertebrates in the Yellow Sea. Simulations were carried out under three fishing scenarios to investigate how different levels of fishing pressure may have impacted the Yellow Sea ecosystem. Results indicate that the biomass of demersal fish continued to decline during 1970–2014, while the biomass of pelagic fish and invertebrates fluctuated periodically. Long-term fishing pressure has led to the reduction of total biomass, body sizes, and longevity of the modelled species. Under low-fishing condition, the ecosystem biomass is restored and the proportion of elder and larger individuals increases. On the contrary, high-fishing condition further decreases the proportion of high-trophic-level species. OSMOSE-YS serves as a baseline model to investigate ecosystem responses to different fishing strategies, in support of ecosystem-based fisheries management in the Yellow Sea

Translocal social resilience dimensions of migration as adaptation to environmental change

There is growing recognition of the potential of migration to contribute to climate-change adaptation. Yet, there is limited evidence to what degree, under what conditions, for whom, and with which limitations this is effectively the case. We argue that this results from a lack of recognition and systematic incorporation of sociospatiality—the nested, networked, and intersectional nature of migration-as-adaptation. Our central objective is to utilize the translocal social-resilience approach to overcome these gaps, to identify processes and structures that shape the social resilience of translocal livelihood systems, and to illustrate the mechanisms behind the multiplicity of possible resilience outcomes. Translocal livelihood constellations anchored in rural Thailand as well as in domestic and international destinations of Thai migrants serve as illustrative empirical cases. Data were gathered through a multisited and mixed-methods research design. This paper highlights the role of the distinct but interlinked situations and operational logics at places of origin and destination, as well as the different positionalities and resulting vulnerabilities, roles, commitments, and practices of individuals and households with regard to resilience. Based on the empirical results, the paper distills a generalized typology of five broad categories of resilience outcomes, which explicitly considers sociospatiality. Our approach helps to grasp the complexity of migration-as-adaptation and to avoid simplistic conclusions about the benefits and costs of migration for adaptation—both of which are necessary for sound, evidence-based, migration-as-adaptation policymaking

Fertility decline and age-structure in China and India

China and India, two Asian countries that experienced a rapid decline in fertility since the middle of the twentieth century, are the focus of this paper. Although there is no doubt that lower fertility levels have many positive effects on the economy, development and sustainability, little is known about the optimal transition from high to medium or even low levels of fertility. Firstly, implementing policies that have the potential to reduce fertility is costly. Secondly, additional costs arise from adapting the infrastructure to a population that fluctuates quickly not only in terms of size but also with respect to the age structure. We apply an intertemporal optimisation model that takes the costs and benefits of fertility decline into account. The optimal time path depends on the cost structure, the planning horizon and the initial conditions. In the case of a long planning horizon and high initial fertility, it may even be optimal to reduce fertility temporarily below replacement level in order to slow down population growth at an early stage. A key finding of our formal investigation is that, under the same plausible parameter settings, the optimal paths for China and India differ substantially. Moreover, our analysis shows that India, where the fertility decline emerged as a consequence of societal and economic developments, followed a path closer to the optimal fertility transition than China, where the fertility decline was state-imposed. The mathematical approach deployed for this analysis provides insights into the optimal long-term development of fertility and allows for policy conclusions to be drawn for other countries that are still in the fertility transition process

Tracing fossil-based plastics, chemicals and fertilizers production in China

Phasing down fossil fuels is crucial for climate mitigation. Even though 80-90% of fossil fuels are used to provide energy, their use as feedstock to produce plastics, fertilizers, and chemicals, is associated with substantial CO2 emissions. However, our understanding of hard-to-abate chemical production remains limited. Here we developed a chemical process-based material flow model to investigate the non-energy use of fossil fuels and CO2 emissions in China. Results show in 2017, the chemical industry used 0.18 Gt of coal, 88.8 Mt of crude oil, and 12.9 Mt of natural gas as feedstock, constituting 5%, 15%, and 7% of China's respective total use. Coal-fed production of methanol, ammonia, and PVCs contributes to 0.27 Gt CO2 emissions ( ~ 3% of China's emissions). As China seeks to balance high CO2 emissions of coal-fed production with import dependence on oil and gas, improving energy efficiency and coupling green hydrogen emerges as attractive alternatives for decarbonization

Ecosystem Resilience Monitoring and Early Warning Using Earth Observation Data: Challenges and Outlook

As the Earth system is exposed to large anthropogenic interferences, it becomes ever more important to assess the resilience of natural systems, i.e., their ability to recover from natural and human-induced perturbations. Several, often related, measures of resilience have been proposed and applied to modeled and observed data, often by different scientific communities. Focusing on terrestrial ecosystems as a key component of the Earth system, we review methods that can detect large perturbations (temporary excursions from a reference state as well as abrupt shifts to a new reference state) in spatio-temporal datasets, estimate the recovery rate after such perturbations, or assess resilience changes indirectly from stationary time series via indicators of critical slowing down. We present here a sequence of ideal methodological steps in the field of resilience science, and argue how to obtain a consistent and multi-faceted view on ecosystem or climate resilience from Earth observation (EO) data. While EO data offers unique potential to study ecosystem resilience globally at high spatial and temporal scale, we emphasize some important limitations, which are associated with the theoretical assumptions behind diagnostic methods and with the measurement process and pre-processing steps of EO data. The latter class of limitations include gaps in time series, the disparity of scales, and issues arising from aggregating time series from multiple sensors. Based on this assessment, we formulate specific recommendations to the EO community in order to improve the observational basis for ecosystem resilience research

Economic and labour market impacts of migration in Austria: an agent-based modelling approach

This study examines the potential economic and labour market impacts of a hypothetical but plausible migration scenario of 250,000 new migrants inspired by Austria’s experience in 2015. Using the agent-based macroeconomic model developed by Poledna et al. (Eur Econ Rev, 151:104306, 2023. 10.1016/j.euroecorev.2022.104306, the study explores the detailed labour market outcomes for different groups in Austria’s population and the macroeconomic effects of the migration scenario. The analysis suggests that Austria’s economy and labour market have the potential to be resilient to the simulated migration influx. The results indicate a positive impact on GDP due to increased aggregate consumption and investment. The labour market experiences an increase in the unemployment rates of natives and previous migrants. In some industries, the increase in the unemployment rates is more significant, potentially indicating competition among different groups of migrants. This research provides insights for policymakers and stakeholders in Austria and other countries that may face the challenge of managing large-scale migration in the near future

Modelling the impacts of policy interventions for food systems transformation in Indonesia – Governance and policy support: Report.

The Government of Indonesia and FAO have recognized the need for thorough analysis and modelling of Indonesia’s food systems to support food systems transformation efforts in the country. This is needed to provide a better understanding of the governance context in food systems, including the political economy dynamics influencing performance, as well as to identify synergies and trade-offs across different policy goals and optimal policy mixes for achieving multiple policy objectives. In this regard, FAO facilitated a project to pilot an innovative approach to modelling for food systems transformation. This modelling approach was developed and implemented by a team of researchers from IFPRI, IIASA, IISD and Christian-Albrechts-University of Kiel. It makes use of three different economic models to generate insights that can assist Indonesian policymakers in developing technically sound and politically feasible policy interventions for food systems transformation. This report provides context for food systems transformation in Indonesia and describes the overall modelling approach before synthesizing the results of the individual modelling activities and distilling these into the overall findings of the modelling. It concludes with implications from these findings for policymaking for food systems transformation in Indonesia and some suggestions for next steps. The results of this modelling and the insights drawn from these results are expected to support efforts to translate Indonesia’s commitments on food systems transformation into concrete policy interventions and to inform medium- and long-term development planning by the Indonesian Government