20253 research outputs found
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Endogenizing long-term material and energy demand in response to power capacity changes by model soft-linking: application to Japan
No full textJapan's decarbonization transition towards carbon neutrality by 2050 will be more dependent on the long-term development of renewables. However, the renewable power generation technologies themselves are highly material- and energy-intensive. We estimated such materials and energy demands in response to power capacity changes. Our main results show that: (1) achieving a 100% reduction of GHG emissions requires enormous and urgent investment during 2020-2030; (2) the largest gap of material demands would show in 2020-2030, especially for cement-related products, petrochemical products, cables, wood products, and steel products, but with different degrees of dispersion; (3) the largest gap of industrial energy demands would show later in 2030-2040 as a result of early investment (inter-period iterations). Increasing material efficiency and benefiting more and earlier from the increasingly low-carbon energy supply would be the key to Japan's industrial decarbonization
Incorporating grid development in capacity expansion optimisation - a case study for Indonesia
Get PDFCapacity expansion optimisation is a widely used techno-economic analysis particularly on topics related to climate change mitigation and renewable energy transition. Using optimisation models to investigate capacity expansion in regions that potentially require significant grid infrastructure development requires incorporation of grid expansion problem within the optimisation. This study presents the development of SELARU, a spatially explicit optimisation model that incorporates the economies of scale of grid expansion using contextualized geographical feature to form the model's high-resolution spatial units. The model is used to investigate the case study of Indonesia using various spatial treatments to demonstrate the impact of detailed spatial depiction of grid expansion. Results reveal significant difference in renewable energy deployment trajectory (up to 2272 % increase in new generation capacity) between high-resolution spatial depiction of grid expansion vis-à-vis non spatially explicit energy system optimisation. Due to its high-resolution, SELARU also generates detailed information on the geographical extent of grid expansion requirement, which provides more realistic insights on governance challenges of renewable energy transition. Careful consideration of spatial representation is crucial when optimisation model is used to evaluate scenarios that concern technology selection such as renewable energy deployment or climate change mitigation
Socio-economic, environmental and health impacts of dietary transformation in Bangladesh
Get PDFThe transition to healthier diets might be accompanied by trade-offs that occur in other parts of the food system. In this study the trade-offs between socio-economic, environmental, and health indicators were analyzed in different dietary scenarios for Bangladesh between 2022 and 2050. We used a global economic simulation model with updated national food consumption data, extended with a footprint module to track environmental impacts through the food value chain in Bangladesh and its trading partners. This study compares a business-as-usual (BAU) diet with the EAT-Lancet diet and the Bangladesh food-based dietary guidelines (FBDGs). The BAU diet has a higher intake of animal products and sugar, and a lower intake of vegetables, fruits, legumes, and nuts than the EAT-Lancet and FBDG diets. We found that promoting a diet with more plant-based proteins has a strong positive impact on dietary health and an overall positive impact on the environment compared to the BAU scenario. This is due to the reduced impact of animal protein production on greenhouse gas emissions and the reduced impact of rice production on water use and nitrogen application. In addition, the transition to sustainable and healthy diets had minor impacts on the wages of low-skilled workers, Bangladesh's self-sufficiency, and the affordability of food and cereals. In particular, the FDBG diet scenario scored best on diet and cereal affordability, as well as freshwater use compared to the other two scenarios. The decrease in the self-sufficiency ratio was comparable to the BAU diet scenario and smaller compared to the EAT-Lancet diet
Content analysis of multi-annual time series of flood-related Twitter (X) data
Get PDFSocial media can provide insights into natural hazard events and people's emergency responses. In this study, we present a natural language processing analytic framework to extract and categorize information from 43 287 textual Twitter (X) posts in German since 2014. We implement bidirectional encoder representations from transformers in combination with unsupervised clustering techniques (BERTopic) to automatically extract social media content, addressing transferability issues that arise from commonly used bag-of-words representations. We analyze the temporal evolution of topic patterns, reflecting behaviors and perceptions of citizens before, during, and after flood events. Topics related to low-impact riverine flooding contain descriptive hazard-related content, while the focus shifts to catastrophic impacts and responsibilities during high-impact events. Our analytical framework enables the analysis of temporal dynamics of citizens’ behaviors and perceptions, which can facilitate lessons-learned analyses and improve risk communication and management
Impact of the EU biodiversity strategy for 2030 on the EU wood-based bioeconomy
Get PDFThe EU Biodiversity Strategy (EUBDS) for 2030 aims to conserve and restore biodiversity by protecting large areas throughout the European Union. A target of the EUBDS is to protect 30 % of the EU's land area by 2030, with 10% being strictly protected (including all primary and old growth forests) and 20% being managed 'closer to nature'. Even though this will have a positive impact on biodiversity, it may negatively impact the EU's woodbased bioeconomy. In this study, we analyze how alternative interpretations and distributions of the EU's protection targets may affect future woody biomass harvest levels, exports of wood commodities, and the spatial distribution of managed areas under wood demands aligned with SSP2-RCP1.9. Using the model GLOBIOMForest, we simulate scenarios representing a variety of interpretations and geographic distributions of the EUBDS targets. The EUBDS targets would have a limited impact on EU harvest levels since the EU can still increase its wood harvest between 21 % and 24 % by 2100. With strict protection of 30 % of the area, the EU harvest level can still be increased by 10 %. Moreover, the most likely scenario (10 %/20 % protection within each MS) will result in increased net exports in the coming decades, but a slight decline after 2050. However, if protection is intended to also represent site productivity or to re-establish a green infrastructure, then EU net exports will also decline before 2050. With the decreased EU roundwood harvest, increased harvest will occur in other biomes and mostly leaking into boreal regions
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
Get PDFClimate policies evolve quickly, and new scenarios designed around these policies are used to illustrate how they impact global mean temperatures using simple climate models (or climate emulators). Simple climate models are extremely efficient, although some can only provide global estimates of climate metrics such as mean surface temperature, CO2 concentration and effective radiative forcing. Within the Intergovernmental Panel on Climate Change (IPCC) framework, understanding of the regional impacts of scenarios that include the most recent science is needed to allow targeted policy decisions to be made quickly. To address this, we present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), a new flexible probabilistic framework which aims to provide an efficient mechanism to run new scenarios without the significant overheads of larger, more complex Earth system models (ESMs). PRIME provides the capability to include features of the most recent ESM projections, science and scenarios to run ensemble simulations on multi-centennial timescales and include analyses of many key variables that are relevant and important for impact assessments. We use a simple climate model to provide the global temperature response to emissions scenarios. These estimated temperatures are used to scale monthly mean patterns from a large number of CMIP6 ESMs. These patterns provide the inputs to a “weather generator” algorithm and a land surface model. The PRIME system thus generates an end-to-end estimate of the land surface impacts from the emissions scenarios. We test PRIME using known scenarios in the form of the shared socioeconomic pathways (SSPs), to demonstrate that our model reproduces the ESM climate responses to these scenarios. We show results for a range of scenarios: the SSP5–8.5 high-emissions scenario was used to define the patterns, and SSP1–2.6, a mitigation scenario with low emissions, and SSP5–3.4-OS, an overshoot scenario, were used as verification data. PRIME correctly represents the climate response (and spread) for these known scenarios, which gives us confidence our simulation framework will be useful for rapidly providing probabilistic spatially resolved information for novel climate scenarios, thereby substantially reducing the time between new scenarios being released and the availability of regional impact information
The Ocean System Pathways (OSPs): A New Scenario and Simulation Framework to Investigate the Future of the World Fisheries
Get PDFThe Fisheries and Marine Ecosystems Model Intercomparison Project (FishMIP) has dedicated a decade to unraveling the future impacts of climate change on marine animal biomass. FishMIP is now preparing a new simulation protocol to assess the combined effects of both climate and socio-economic changes on marine fisheries and ecosystems. This protocol will be based on the Ocean System Pathways (OSPs), a new set of socio-economic scenarios derived from the Shared Socioeconomic Pathways (SSPs) widely used by the Intergovernmental Panel on Climate Change (IPCC). The OSPs extend the SSPs to the economic, governance, management and socio-cultural contexts of large pelagic, small pelagic, benthic-demersal and emerging fisheries, as well as mariculture. Comprising qualitative storylines, quantitative model driver pathways and a "plug-in-model" framework, the OSPs will enable a heterogeneous suite of ecosystem models to simulate fisheries dynamics in a standardised way. This paper introduces this OSP framework and the simulation protocol that FishMIP will implement to explore future ocean social-ecological systems holistically, with a focus on critical issues such as climate justice, global food security, equitable fisheries, aquaculture development, fisheries management, and biodiversity conservation. Ultimately, the OSP framework is tailored to contribute to the synthesis work of the IPCC. It also aims to inform ongoing policy processes within the United Nations Food and Agriculture Organization (FAO). Finally, it seeks to support the synthesis work of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), with a particular focus on studying pathways relevant for the United Nations Convention on Biological Diversity
Central banks, climate risks, and energy transition—a dynamic macro model and econometric evidence
No full textA growing body of literature proposes a climate-oriented monetary and financial policy for Central Banks (CBs). However, other literature defends a market-neutral monetary policy to keep CB independence and avoid addressing other than conventional objectives. However, if the CBs’ market-neutral policy is only targeting inflation rates and employment, it could amplify the macro impacts of negative economic externalities, while also neglecting positive externalities in the long-run. Even if climate-related policy goals appear advisable, the actions of CBs reveal significant delayed impacts on macro and climate-risk variables. We propose a non-linear dynamic macro model of finite horizon with multiple targets, including macro imbalances and climate risks arising from a trend in carbon emissions. This non-stationary emission dynamic has feedback effects on stationary and non-stationary macro variables and the multiple (possibly conflicting) objectives of the CBs. In this context, we first explore to what extent CBs can impact emission trends with and without delays. Second, given the mix of stationary and non-stationary dynamic variables, we explore the responses to policy and economic and financial shocks using a mixed Vector Error Correction Model (VECM) with stationary and non-stationary variables. Third, in the face of multiple objectives—and macroeconomic concerns that CBs face—we are motivated by Kaya and Maurer (2023) to construct a Pareto front that introduces weights for the multiple objectives and permits target prioritization
The evidence gap index: mapping evidence where it matters for climate change impacts
No full textClimate change impacts are already evident and projected to worsen throughout the 21st century, even with mitigation efforts. Systematic mapping is key to organizing scientific evidence and identifying gaps, but current methods lack geographical context in relation to climate impact risk. In this study, we leverage machine learning to scale up systematic mapping and use automatic geolocation to track place-based research. We then enhance conventional systematic mapping by integrating location-based climate risk components—hazard, exposure, and vulnerability—to create an evidence gap index. This identifies high-risk regions that lack sufficient scientific study. We demonstrate this method using fluvial floods, combining research distribution with a flood-risk indicator (hazard), population density (exposure), and the Human Development Index (vulnerability). Our novel approach refines evidence mapping, supporting data-driven policymaking and directing research resources to the most urgent areas