Inequities blocking the path to circular economies: A bio-inspired network-based approach for assessing the sustainability of the global trade of waste metals

Considering the importance of waste metals for the transition to circular economies, this study follows a bioinspired approach to evaluate their material and monetary global trade patterns for sustainability and equity. Between 2000 and 2022, the global trade grew by 5 % in trading countries, by 37 % in trade links, by 71 % in material flows, and by 569 % in economic flows. Driven by indirect effects, the average circulation of material and monetary flows ranged between 21.8-34.9 - 34.9 % depending on the demand or supply perspective but showed a declining trend. Due to homogenization, high network redundancy, and low network efficiency the trade remained robust yet outside the "window of vitality" characterizing natural ecosystems. A few, mostly high-income countries dominated the market, consolidating imports of high-value metal waste mostly from low- and middle- income exporters. Policies should address circularity and trade inequities, accounting for environmental and social ramifications throughout the lifecycle of products and materials

High with low: Harnessing the power of demand-side solutions for high wellbeing with low energy and material demand

The authors are all devoted energy system and sustainability transformation scholars, who collaborate regularly and actively at global and local levels to advance the knowledge space of demand-side solutions and policies. They are members of a growing bottom-up initiative, the Energy Demand Changes Induced by Technological and Social Innovations (EDITS) network (https://iiasa.ac.at/projects/edits), which builds on various research disciplines to facilitate advances in modeling, data compilation, and analysis of the scope and breadth of the potential contributions of demand-side solutions for climate change mitigation, improved wellbeing for all, and sustainability, complementing supply-side solutions for decarbonizing the energy and material systems

Realizing climate resilient development pathways in forestry: A focus on carbon management in Republic of Korea

Overcoming the climate crisis and achieving the 1.5 °C target requires the exploration of climate-resilient development pathways (CRDPs), as emphasized in the intergovernmental panel on climate change (IPCC) AR6 report. Republic of Korea has aligned itself with the international context by setting nationally determined contributions (NDC) and long-term low greenhouse gas emission development strategies (LEDS) goals. In addition, the country has announced plans to enhance carbon sink in the forestry sector. This study explored the CRDP in the forestry sector using an advanced Korean forest dynamic growth model (AKO-G-Dynamic model) with refined management algorithms. We utilized this model and applied various options for forest management based on the available detailed data, including climate change scenarios and policies reflecting possible CRDPs in the Republic of Korea. As a result, CO2 sequestration in the 2050s was predicted to be 23.08 million tCO2 year−1 if climate change SSP 5–8.5 and the current forest management level are maintained and 28.49 million tCO2 year−1 if climate change SSP 1–2.6 and resilient level of forest management are applied. Furthermore, from the perspective of the age class of the forest, the proportion of over-matured forests decreased, leading to an improvement in the imbalance of age classes as climate change mitigation and sustainable forest management were implemented. Therefore, this study demonstrated realizable CRDPs and their implementation in decision-making concerning the NDC and LEDS. This comprehensive analysis of climate change and forest management, exploring the CRDP from various perspectives, can contribute to the development of forest management policies for climate adaptation strategies and carbon sink enhancement, thereby influencing the allocation of the carbon budget

Improving the representation of smallholder farmers’ adaptive behaviour in agent-based models: Learning-by-doing and social learning

Computational models have been used to investigate farmers’ decision outcomes, yet classical economics assumptions prevail, while learning processes and adaptive behaviour are overlooked. This paper advances the conceptualisation, modelling and understanding of learning-by-doing and social learning, two key processes in adaptive (co-)management literature. We expand a pre-existing agent-based model (ABM) of an agricultural social-ecological system, RAGE (Dressler et al., 2018). We endow human agents with learning-by-doing and social learning capabilities, and we study the impact of their learning strategies on economic, ecological and social outcomes. Methodologically, we contribute to an under-explored area of modelling farmers’ behaviour. Results show that agents who employ learning better match their decisions to the ecological conditions than those who do not. Imitating the learning type of successful agents further improves outcomes. Different learning processes are suited to different goals. We report on conditions under which learning-by-doing becomes dominant in a population with mixed learning approaches

The Tail End of Migration: Assessing the Climate Resilience of Migrant Households in Ethiopia

Climate change is associated with increasing frequencies and intensities of extreme weather events. These can, directly and indirectly, shape human (im)mobility. While most research on migration in the context of climate change focuses on climate as a migration driver in origin areas, there is a gap in knowledge on the role of migration for climate resilience in the destination areas. Consequently, this paper aims to study differences in resilience (resistance and recovery) to climatic shocks between migrant and non-migrant households at migration destination areas in Ethiopia, a country that is highly exposed and vulnerable to climate change. We use longitudinal data from the Living Standards Measurement Study (LSMS) conducted by the World Bank to construct a comprehensive Well-Being Index, which is used to analyze the impacts of climatic shocks and identify households that are more or less able to resist and recover from shocks. We use fixed effect panel regression approaches to model the impacts of climatic shocks on well-being over time for migrant and non-migrant households. Further explorative mediation analyses yield insights into mechanisms explaining differences between households. We find that migrant households have an overall lower climate resistance as they experience double as high well-being impacts when exposed to climatic shocks compared to non-migrant households. Climatic shocks significantly reduce the food security of all affected households and, in addition, negatively impact access to basic infrastructures and health for migrant households. Mediation analyses suggest that these differential climatic impacts are mainly driven by characteristics of migrant-origin regions, including poverty. Migrant households originating from less prosperous regions still face disadvantages even if they now reside in more prosperous regions. This contrasts the experience of non-migrant households whose resilience benefits from increased prosperity in their region of residence. While migrant households show a lower resistance to climate shocks, they recover faster from climatic shocks, which can be associated with diversified livelihoods and remittances that take time to unfold. This research is highly relevant to policy as it improves the understanding of underlying factors shaping differential vulnerability to climate change impacts and supports targeted interventions to increase the resilience of affected households

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

A harmonized database of European forest simulations under climate change

Process-based forest models combine biological, physical, and chemical process understanding to simulate forest dynamics as an emergent property of the system. As such, they are valuable tools to investigate the effects of climate change on forest ecosystems. Specifically, they allow testing of hypotheses regarding long-term ecosystem dynamics and provide means to assess the impacts of climate scenarios on future forest development. As a consequence, numerous local-scale simulation studies have been conducted over the past decades to assess the impacts of climate change on forests. These studies apply the best available models tailored to local conditions, parameterized and evaluated by local experts. However, this treasure trove of knowledge on climate change responses remains underexplored to date, as a consistent and harmonized dataset of local model simulations is missing. Here, our objectives were (i) to compile existing local simulations on forest development under climate change in Europe in a common database, (ii) to harmonize them to a common suite of output variables, and (iii) to provide a standardized vector of auxiliary environmental variables for each simulated location to aid subsequent investigations. Our dataset of European stand- and landscape-level forest simulations contains over 1.1 million simulation runs representing 135 million simulation years for more than 13,000 unique locations spread across Europe. The data were harmonized to consistently describe forest development in terms of stand structure (dominant height), composition (dominant species, admixed species), and functioning (leaf area index). Auxiliary variables provided include consistent daily climate information (temperature, precipitation, radiation, vapor pressure deficit) as well as information on local site conditions (soil depth, soil physical properties, soil water holding capacity, plant-available nitrogen). The present dataset facilitates analyses across models and locations, with the aim to better harness the valuable information contained in local simulations for large-scale policy support, and for fostering a deeper understanding of the effects of climate change on forest ecosystems in Europe

Integration of energy system and computable general equilibrium models: An approach complementing energy and economic representations for mitigation analysis

Energy system and computable general equilibrium (CGE) models play vital roles in climate change mitigation studies. These models have advantages and disadvantages, and attempts have been made to integrate them. This study aimed to describe the method for integrating energy system and CGE models and demonstrate the new model that captures the strengths of both models. The method developed in this study ensured the detailed convergence of the energy system by exchanging the results iteratively. We demonstrated the model integration by adopting the method to MESSAGEix-GLOBIOM and AIM/Hub and estimating a mitigation scenario that limits the temperature rise to below 2 °C under the middle-of-the-road socioeconomic projection in Shared Socioeconomic Pathways. As a result of the integration, the index showing the difference between the two models proposed in this study decreased from 1.0 to 0.066. Therefore, we confirmed that these models estimated consistent scenarios. The diagnostic indicators showed that compared to its counterpart CGE model, the newly-developed model was characterized by a higher contribution of demand-side reductions, a lesser alteration in the primary energy supply composition, and lower abatement costs. Given the convergence and advantages of the integrated framework, the proposed method is useful for further application to mitigation studies

Ambitious nitrogen abatement is required to mitigate future global PM2.5 air pollution toward the World Health Organization targets

Nitrogen oxides (NOx) and ammonia (NH3) contribute substantially to current global fine particulate matter (PM2.5) pollution. Their future role remains unclear and is complicated by interactions with background emissions. Here, we show that under climate mitigation scenarios, by 2050, a hypothetical phaseout of anthropogenic NH3 emissions would reduce PM2.5 by 20%–60% locally and be more effective than phasing out NOx. Reducing NH3 by 25%, instead, would be less effective than 25% NOx reduction for many regions. Future reductions of NOx and sulfuric dioxides from clean energy transitions would shift the nonlinear chemical regime of secondary inorganic aerosol formation toward NH3 saturation. The later NH3 controls are installed, the deeper the required reductions will be to be effective, although for many regions such levels are still within technical feasibility, while NOx controls will always remain effective. Nitrogen reductions remain useful for achieving the World Health Organization guideline target for PM2.5, and NH3 controls need to happen sooner rather than later

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