From integrated to integrative: Delivering on the paris agreement

In pursuit of the drastic transformations necessary for effectively responding to climate change, the Paris Agreement stresses the need to design and implement sustainable, robust, and socially acceptable policy pathways in a globally coordinated and cooperative manner. For decades, the scientific community has been carrying out quantitative modelling exercises in support of climate policy design, primarily by means of energy systems and integrated assessment modelling frameworks. Here, we describe in detail the context of a hitherto ineffective scientific contribution to policymaking, highlight the available means to formulate a new paradigm that overcomes existing and emerging challenges, and ultimately call for change. In particular, we argue that individual modelling exercises alone widen the gap between formal representation and real-life context in which decisions are taken, and investigate major criticisms to which formalised modelling frameworks are subject. We essentially highlight the importance of employing diverse modelling ensembles, placing the human factor at the core of all modelling processes, and enhancing the robustness of model-driven policy prescriptions through decision support systems. These altogether compose a truly integrative approach to supporting the design of effective climate policy and sustainable transitions and, therefore, strengthen the modelling-policymaking interface. © 2018 by the authors

Implementation of stochastic multi attribute analysis (SMAA) in comparative environmental assessments

The selection of an alternative based on the results of a comparative environmental assessment such as life cycle assessment (LCA), environmental input-output analysis (EIOA) or integrated assessment modelling (IAM) is challenging because most of the times there is no single best option. Most comparative cases contain trade-offs between environmental criteria, uncertainty in the performances and multiple diverse values from decision makers. To circumvent these challenges, a method from decision analysis, namely stochastic multi attribute analysis (SMAA), has been proposed instead. SMAA performs aggregation that is partially compensatory (hence, closer to a strong sustainability perspective), incorporates performance uncertainty in the assessment, is free from external normalization references and allows for uncertainties in decision maker preferences. This paper presents a thorough introduction of SMAA for environmental decision-support, provides the mathematical fundamentals and offers an Excel platform for easy implementation and access

A multiple-uncertainty analysis framework for integrated assessment modelling of several sustainable development goals

This research introduces a two-level integration of climate-economy modelling and portfolio analysis, to simulate technological subsidisation with implications for multiple Sustainable Development Goals (SDGs), across socioeconomic trajectories and considering different levels of uncertainties. We use integrated assessment modelling outputs relevant for progress across three SDGs namely air pollution-related mortality (SDG3), access to clean energy (SDG7) and greenhouse gas emissions (SDG13) calculated with the Global Change Assessment Model (GCAM) for different subsidy levels for six sustainable technologies, across three Shared Socioeconomic Pathways (SSPs), feeding them into a portfolio analysis model. Optimal portfolios that are robust in the individual socioeconomic scenarios as well as across the socioeconomic scenarios are identified, by means of an SSP-robustness score. A second link between the two models is established, by feeding portfolio analysis results back into GCAM. Application in a case study for Eastern Africa confirms that most SSP-robust portfolios show smaller output ranges among scenarios

Identifying optimal technological portfolios for European power generation towards climate change mitigation: A robust portfolio analysis approach

Here, an integrative approach is proposed to link integrated assessment modelling results from the GCAM model with a novel portfolio analysis framework. This framework comprises a bi-objective optimisation model, Monte Carlo analysis and the Iterative Trichotomic Approach, aimed at carrying out stochastic uncertainty assessment and enhancing robustness. The approach is applied for identifying optimal technological portfolios for power generation in the EU towards climate change mitigation until 2050. The considered technologies include photovoltaics, concentrated solar power, wind, nuclear, biomass and carbon capture and storage, for which different subsidy curves for emissions reduction and energy security are considered. © 2019 Elsevier LtdThe most important part of this research is based on the H2020 European Commission Project “Transitions pathways and risk analysis for climate change mitigation and adaptation strategies—TRANSrisk” under grant agreement No. 642260

The technological and economic prospects for CO2 utilization and removal

The capture and use of carbon dioxide to create valuable products might lower the net costs of reducing emissions or removing carbon dioxide from the atmosphere. Here we review ten pathways for the utilization of carbon dioxide. Pathways that involve chemicals, fuels and microalgae might reduce emissions of carbon dioxide but have limited potential for its removal, whereas pathways that involve construction materials can both utilize and remove carbon dioxide. Land-based pathways can increase agricultural output and remove carbon dioxide. Our assessment suggests that each pathway could scale to over 0.5 gigatonnes of carbon dioxide utilization annually. However, barriers to implementation remain substantial and resource constraints prevent the simultaneous deployment of all pathways

Considering Life Cycle Greenhouse Gas Emissions in Power System Expansion Planning for Europe and North Africa Using Multi-Objective Optimization

We integrate life cycle indicators for various technologies of an energy system model with high spatiotemporal detail and a focus on Europe and North Africa. Using multi-objective optimization, we calculate a pareto front that allows us to assess the trade-offs between system costs and life cycle greenhouse gas (GHG) emissions of future power systems. Furthermore, we perform environmental ex-post assessments of selected solutions using a broad set of life cycle impact categories. In a system with the least life cycle GHG emissions, the costs would increase by ~63%, thereby reducing life cycle GHG emissions by ~82% compared to the cost-optimal solution. Power systems mitigating a substantial part of life cycle GHG emissions with small increases in system costs show a trend towards a deployment of wind onshore, electricity grid and a decline in photovoltaic plants and Li-ion storage. Further reductions are achieved by the deployment of concentrated solar power, wind offshore and nuclear power but lead to considerably higher costs compared to the cost-optimal solution. Power systems that mitigate life cycle GHG emissions also perform better for most impact categories but have higher ionizing radiation, water use and increased fossil fuel demand driven by nuclear power. This study shows that it is crucial to consider upstream GHG emissions in future assessments, as they represent an inheritable part of total emissions in ambitious energy scenarios that, so far, mainly aim to reduce direct CO$_{2}$ emissions

Emissions of electric vehicle charging in future scenarios: The effects of time of charging

Electrification of transport is an important option to reduce greenhouse gas emissions. Although many studies have analyzed emission implications of electric vehicle charging, time-specific emission effects of charging are inadequately understood. Here, we combine climate protection scenarios for Europe for the year 2050, detailed power system simulation at hourly time steps, and life cycle assessment of electricity in order to explore the influence of time on the greenhouse gas emissions associated with electric vehicle charging for representative days. We consider both average and short-term marginal emissions. We find that the mix of electricity generation technologies, and thus, also the emissions of charging, vary appreciably across the 24-h day. In our estimates for Europe for 2050, an assumed day-charging regime yields one-third-to-one-half lower average emissions than an assumed night-charging regime. This is owing to high fractions of solar PV in the electricity mix during daytime and more reliance on natural gas electricity in the late evening and night. The effect is stronger during summer months than during winter months, with day charging causing one-half-to-two-thirds lower emissions than night charging during summer. Also, when short-term marginal electricity is assumed, emissions tend to be lower with day charging because of contributions from nuclear electricity during the day. However, the results for short-term marginal electricity have high uncertainty. Overall, our results suggest a need for electric vehicle charging policies and emission assessments to take into consideration variations in electricity mixes and time profiles of vehicle charging over the 24-h day

When the Background Matters: Using Scenarios from Integrated Assessment Models in Prospective Life Cycle Assessment

Prospective life cycle assessment (LCA) needs to deal with the large epistemological uncertainty about the future to support more robust future environmental impact assessments of technologies. This study proposes a novel approach that systematically changes the background processes in a prospective LCA based on scenarios of an integrated assessment model (IAM), the IMAGE model. Consistent worldwide scenarios from IMAGE are evaluated in the life cycle inventory using ecoinvent v3.3. To test the approach, only the electricity sector was changed in a prospective LCA of an internal combustion engine vehicle (ICEV) and an electric vehicle (EV) using six baseline and mitigation climate scenarios until 2050. This case study shows that changes in the electricity background can be very important for the environmental impacts of EV. Also, the approach demonstrates that the relative environmental performance of EV and ICEV over time is more complex and multifaceted than previously assumed. Uncertainty due to future developments manifests in different impacts depending on the product (EV or ICEV), the impact category, and the scenario and year considered. More robust prospective LCAs can be achieved, particularly for emerging technologies, by expanding this approach to other economic sectors beyond electricity background changes and mobility applications as well as by including uncertainty and changes in foreground parameters. A more systematic and structured composition of future inventory databases driven by IAM scenarios helps to acknowledge epistemological uncertainty and to increase the temporal consistency of foreground and background systems in LCAs of emerging technologies

MESSAGEix-Materials v1.0.0: Representation of Material Flows and Stocks in an Integrated Assessment Model

Extracting and processing raw materials into products in industry is a substantial source of CO2 emissions, which currently lacks process detail in many integrated assessment models (IAMs). To broaden the space of climate change mitigation options and to include circular economy and material efficiency measures in IAM scenario analysis, we developed MESSAGEix-Materials module representing material flows and stocks within the MESSAGEix-GLOBIOM IAM framework. With the development of MESSAGEix-Materials, we provide a fully open-source model that can assess different industry decarbonization options under various climate targets for the most energy and emissions-intensive industries: Aluminium, iron and steel, cement, and petrochemicals. We illustrate the model’s operation with a baseline and mitigation (2 degrees) scenario setup and validate base year results for 2020 against historical datasets. We also discuss the industry decarbonization pathways and material stocks of the electricity generation technologies resulting from the new model features

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