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Within the Paris agreement, Europe has adopted ambitious climate targets. Achieving these targets through appropriate low-carbon investments is thus key. This study aims at providing new insight into this issue by considering the DNE21 + model, an optimization model that assesses global energy systems, and the Investment Preference Index model, a simulation model where decision-making is based on technology preferences from a utility's perspective. We evaluate the impact of a climate-policy scenario on the European electricity sector using these models with harmonized assumptions. The resulting investment choices provide insight into the effectiveness of a low-carbon investment policy. We find that various types of incentives are required before companies abandon their historical preferences and low-carbon technologies can flourish. Testing various options has revealed that a negative constraint on coal is more efficient than adding further positive incentives for low-carbon technologies (such as carbon pricing and support schemes). However, we also expect these preferences to shift over time. [ABSTRACT FROM AUTHOR]

Life cycle assessment (LCA) is a standardized, holistic, and multi‐criteria approach to estimate the environmental impacts of a system over its entire lifecycle. Modeling the environmental impacts of future scenarios and evolving systems in LCA is challenging due to lack of knowledge around technological evolutions and changing backgrounds influencing the foreground system. The method proposed here tackles these issues by combining parameterized life cycle inventories (LCIs) of foreground and background systems to assess how background changes, for example in the energy sector, affect the environmental impacts of the foreground. First, parameterized LCIs for the heating and energy sector were developed to flexibly estimate the environmental impacts of heating, gas, and electricity trajectories. Assessing their environmental impacts is essential to avoid any burden shifting to other impacts. Thanks to their parameterization, these models can represent different geographical, temporal, or technological contexts. Second, the combination of these parameterized LCIs was implemented using the Python library lca_algebraic, designed to enhance computational efficiency and calculation speed of LCAs. lca_algebraic's features were extended to link parameters to specific LCIs and, hence, ease their import and export and facilitate teamwork. In a final step, the developed LCIs and lca_algebraic library were used to estimate the environmental impacts of French heating scenarios from 2018 until 2050, as a test of the approach. The parameterized models developed within this work are available on a Git together with published reference datasets for comparison, so that the method can be adapted to evaluate similar energy trajectories for other countries or regions. This article met the requirements for a gold–gold JIE data openness badge described at http://jie.click/badges. [ABSTRACT FROM AUTHOR]

Buildings are key in supporting human activities and well-being by providing shelter and other important services to their users. Buildings are, however, also responsible for major energy use and greenhouse gas (GHG) emissions during their life cycle. Improving the quality of services provided by buildings while reaching low energy demand (LED) levels is crucial for climate and sustainability targets. Building sector models have become essential tools for decision support on strategies to reduce energy demand and GHG emissions. Yet current models have significant limitations in their ability to assess the transformations required for LED. We review building sector models ranging from the subnational to the global scale to identify best practices and critical gaps in representing transformations toward LED futures. We focus on three key dimensions of intervention (socio-behavioral, infrastructural, and technological), three megatrends (digitalization, sharing economy, and circular economy), and decent living standards. This review recommends the model developments needed to better assess LED transformations in buildings and support decision-making toward sustainability targets. [ABSTRACT FROM AUTHOR]

Abstract Integrated assessment models are extensively used in the analysis of climate change mitigation and are informing national decision makers as well as contribute to international scientific assessments. This paper conducts a comprehensive review of techno-economic assumptions in the electricity sector among fifteen different global and national integrated assessment models. Particular focus is given to six major economies in the world: Brazil, China, the EU, India, Japan and the US. The comparison reveals that techno-economic characteristics are quite different across integrated assessment models, both for the base year and future years. It is, however, important to recognize that techno-economic assessments from the literature exhibit an equally large range of parameters as the integrated assessment models reviewed. Beyond numerical differences, the representation of technologies also differs among models, which needs to be taken into account when comparing numerical parameters. While desirable, it seems difficult to fully harmonize techno-economic parameters across a broader range of models due to structural differences in the representation of technology. Therefore, making techno-economic parameters available in the future, together with of the technology representation as well as the exact definitions of the parameters should become the standard approach as it allows an open discussion of appropriate assumptions. Highlights • Comparison of technology representation and techno-economic parameters of 15 IAMs. • Structural differences in representing technology in IAMs are identified. • Methods of projecting techno-economic parameters differ across IAMs. • Strict harmonization of techno-economic parameters across models is difficult. • Recommendation to publish techno-economic parameters and technology representation. [ABSTRACT FROM AUTHOR]