The CO2 reduction potential for the European industry via direct electrification of heat supply (power-to-heat)

The decarbonisation of industry is a bottleneck for the EU's 2050 target of climate neutrality. Replacing fossil fuels with low-carbon electricity is at the core of this challenge; however, the aggregate electrification potential and resulting system-wide CO2 reductions for diverse industrial processes are unknown. Here, we present the results from a comprehensive bottom-up analysis of the energy use in 11 industrial sectors (accounting for 92% of Europe's industry CO2 emissions), and estimate the technological potential for industry electrification in three stages. Seventy-eight per cent of the energy demand is electrifiable with technologies that are already established, while 99% electrification can be achieved with the addition of technologies currently under development. Such a deep electrification reduces CO2 emissions already based on the carbon intensity of today's electricity (∼300 gCO2 kWhel−1). With an increasing decarbonisation of the power sector IEA: 12 gCO2 kWhel−1 in 2050), electrification could cut CO2 emissions by 78%, and almost entirely abate the energy-related CO2 emissions, reducing the industry bottleneck to only residual process emissions. Despite its decarbonisation potential, the extent to which direct electrification will be deployed in industry remains uncertain and depends on the relative cost of electric technologies compared to other low-carbon options

Robust CO2-abatement from early end-use electrification under uncertain power transition speed in China's netzero transition

Decarbonizing China's energy system requires both greening the power supply and end-use electrification. While the latter speeds up with the electric vehicle adoption, a rapid power sector transformation can be technologically and institutionally challenging. Using an integrated assessment model, we analyze the synergy between power sector decarbonization and end-use electrification in China's net-zero pathway from a system perspective. We show that even with a slower coal power phase-out, reaching a high electrification rate of 60% by 2050 is a robust optimal strategy. Comparing emission intensity of typical end-use applications, we find most have reached parity with incumbent fossil fuel technologies even under China's current power mix due to efficiency gains. Since a 10-year delay in coal power phase-out can result in an additional cumulative emission of 28% (4%) of the global 1.5{\deg}C (2{\deg}C) CO2 budget, policy measures should be undertaken today to ensure a power sector transition without unexpected delays.Comment: 26 pages 4 figure

FAIR Data in Energy Systems Analysis

Modelling of future energy systems requires large amounts of heterogeneous input data and also generates heterogeneous result data across a number of scientific disciplines such as e.g. meteorology, engineering, geography, social-sciences and economy. As this modelling work supports the transition to a more sustainable society, open and transparent modelling is important to inform the public debate. The domain of energy systems analysis is therefore moving towards FAIR (Findable, Accessible, Interoperable and Reuseable) data. With this poster we want to show an infrastructure we developed around the dbpedia databus, the open energy platform, open energy metadata, and the open energy ontology which eases open data sharing, semantic data searches, data discovery and interoperable data reuse within our modelling domain. Meteorological data is important in planning and operation of future energy systems. To ease the use of this data and to improve the integration of meteorological data into the data pipelines, energy relevant meteorological data should also use these infrastructures for sharing and annotation of data

Implementing FAIR through a distributed data infrastructure

Within the research project LOD-GOESS (https://lod-geoss.gitub.io ) we are developing a distributed data architecture for sharing and improved discovery of research data in the domain of energy systems analysis. A central element is the databus (https://databus.dbpedia.org ) which acts as a central searchable metadata catalog. Research data can be registered to the databus. The metadata improves the findability of the data, direct links to the data sources accessibility. If the metadata is annotated with an ontology (e.g. the open energy ontology), semantic searches can be performed to find suitable research data. This improves interoperability and reusability of the data. Currently we are developing several demonstrators which show the benefit of open and transparent data handling for the publication of scenario data, model coupling and shared technology data bases. The infrastructure can also be used to track the provenance of data which is used in energy systems analysis. With our presentation we want to show how this infrastructure can be used to improve transparency and traceability of the analysis of future energy systems

REMIND2.1: transformation and innovation dynamics of the energy-economic system within climate and sustainability limits

This paper presents the new and now open-source version 2.1 of the REgional Model of INvestments and Development (REMIND). REMIND, as an integrated assessment model (IAM), provides an integrated view of the global energy–economy–emissions system and explores self-consistent transformation pathways. It describes a broad range of possible futures and their relation to technical and socio-economic developments as well as policy choices. REMIND is a multiregional model incorporating the economy and a detailed representation of the energy sector implemented in the General Algebraic Modeling System (GAMS). It uses non-linear optimization to derive welfare-optimal regional transformation pathways of the energy-economic system subject to climate and sustainability constraints for the time horizon from 2005 to 2100. The resulting solution corresponds to the decentralized market outcome under the assumptions of perfect foresight of agents and internalization of external effects. REMIND enables the analyses of technology options and policy approaches for climate change mitigation with particular strength in representing the scale-up of new technologies, including renewables and their integration in power markets. The REMIND code is organized into modules that gather code relevant for specific topics. Interaction between different modules is made explicit via clearly defined sets of input and output variables. Each module can be represented by different realizations, enabling flexible configuration and extension. The spatial resolution of REMIND is flexible and depends on the resolution of the input data. Thus, the framework can be used for a variety of applications in a customized form, balancing requirements for detail and overall runtime and complexity

Short term policies to keep the door open for Paris climate goals

Climate policy needs to account for political and social acceptance. Current national climate policy plans proposed under the Paris Agreement lead to higher emissions until 2030 than cost-effective pathways towards the Agreements’ long-term temperature goals would imply. Therefore, the current plans would require highly disruptive changes, prohibitive transition speeds, and large long-term deployment of risky mitigation measures for achieving the agreement’s temperature goals after 2030. Since the prospects of introducing the cost-effective policy instrument, a global comprehensive carbon price in the near-term, are negligible, we study how a strengthening of existing plans by a global roll-out of regional policies can ease the implementation challenge of reaching the Paris temperature goals. The regional policies comprise a bundle of regulatory policies in energy supply, transport, buildings, industry, and land use and moderate, regionally differentiated carbon pricing. We find that a global roll-out of these policies could reduce global CO2 emissions by an additional 10 GtCO2eq in 2030 compared to current plans. It would lead to emissions pathways close to the levels of cost-effective likely below 2 °C scenarios until 2030, thereby reducing implementation challenges post 2030. Even though a gradual phase-in of a portfolio of regulatory policies might be less disruptive than immediate cost-effective carbon pricing, it would perform worse in other dimensions. In particular, it leads to higher economic impacts that could become major obstacles in the long-term. Hence, such policy packages should not be viewed as alternatives to carbon pricing, but rather as complements that provide entry points to achieve the Paris climate goals

Environmental co-benefits and adverse side-effects of alternative power sector decarbonization strategies

A rapid and deep decarbonization of power supply worldwide is required to limit global warming to well below 2 °C. Beyond greenhouse gas emissions, the power sector is also responsible for numerous other environmental impacts. Here we combine scenarios from integrated assessment models with a forward-looking life-cycle assessment to explore how alternative technology choices in power sector decarbonization pathways compare in terms of non-climate environmental impacts at the system level. While all decarbonization pathways yield major environmental co-benefits, we find that the scale of co-benefits as well as profiles of adverse side-effects depend strongly on technology choice. Mitigation scenarios focusing on wind and solar power are more effective in reducing human health impacts compared to those with low renewable energy, while inducing a more pronounced shift away from fossil and toward mineral resource depletion. Conversely, non-climate ecosystem damages are highly uncertain but tend to increase, chiefly due to land requirements for bioenergy

Deriving life cycle assessment coefficients for application in integrated assessment modelling

The fields of life cycle assessment (LCA) and integrated assessment (IA) modelling today have similar interests in assessing macro-level transformation pathways with a broad view of environmental concerns. Prevailing IA models lack a life cycle perspective, while LCA has traditionally been static- and micro-oriented. We develop a general method for deriving coefficients from detailed, bottom-up LCA suitable for application in IA models, thus allowing IA analysts to explore the life cycle impacts of technology and scenario alternatives. The method decomposes LCA coefficients into life cycle phases and energy carrier use by industries, thus facilitating attribution of life cycle effects to appropriate years, and consistent and comprehensive use of IA model-specific scenario data when the LCA coefficients are applied in IA scenario modelling. We demonstrate the application of the method for global electricity supply to 2050 and provide numerical results (as supplementary material) for future use by IA analysts

Water demand for electricity in deep decarbonisation scenarios : a multi-model assessment

This study assesses the effects of deep electricity decarbonisation and shifts in the choice of power plant cooling technologies on global electricity water demand, using a suite of five integrated assessment models. We find that electricity sector decarbonisation results in co-benefits for water resources primarily due to the phase-out of water-intensive coal-based thermoelectric power generation, although these co-benefits vary substantially across decarbonisation scenarios. Wind and solar photovoltaic power represent a win-win option for both climate and water resources, but further expansion of nuclear or fossil- and biomass-fuelled power plants with carbon capture and storage may result in increased pressures on the water environment. Further to these results, the paper provides insights on the most crucial factors of uncertainty with regards to future estimates of water demand. These estimates varied substantially across models in scenarios where the effects of decarbonisation on the electricity mix were less clear-cut. Future thermal and water efficiency improvements of power generation technologies and demand-side energy efficiency improvements were also identified to be important factors of uncertainty. We conclude that in order to ensure positive effects of decarbonisation on water resources, climate policy should be combined with technology-specific energy and/or water policies