• Energy Research

Abstract Rising affluence and a warming climate mean that the demand for air conditioning (AC) is rising rapidly, as society adapts to climate extremes. Here we present findings from a new methodological framework to flexibly couple and emulate these growing demands into a global integrated assessment model (IAM), subsequently representing the positive feedbacks between rising temperatures, growth in cooling demand, and carbon emissions. In assessing global and regional climate change impacts on cooling energy demand, the emulator incorporates climate model uncertainties and can explore behavioural and adaptation-related assumptions on setpoint temperature and access to cooling. It is also agnostic to the emissions and climate warming trajectory, enabling the IAM to run new policy-relevant scenarios (Current Policies, 2 °C and 1.5 °C) with climate impacts that do not follow Representative Concentration Pathways. We find that climate model uncertainty has a significant effect, more than doubling the increase in electricity demand, when comparing the 95th percentile cases to the median of the climate model ensemble. Residential AC cooling energy demands are expected to increase by 150% by 2050 whilst providing universal access to AC would result in the order of a 400% increase. Depending on the region, under current policies and limited mitigation, climate change could bring in the order of 10%–20% higher cooling-related electricity demands by 2050, and approximately 50% by 2100. Set point temperature has an important moderating role—increasing internal set-point from 23 °C to 26 °C, approximately halves the growth in electricity demand, for the majority of scenarios and regions. This effect is so strong that the change in set point temperature to both residential and commercial sectors outweighs the growth in demand that would occur by providing universal access to AC by 2050 to the 40% of the global population who would otherwise not afford it.

AbstractThe building sector in China is responsible for 40% of total energy‐related CO2 emissions, driven by its large population, continuous economic growth, and construction boom. In addition to greenhouse gas (GHG) emissions from energy use, buildings drive significant emissions for construction activities and production of energy‐intensive materials, such as steel and cement. While supply‐side energy strategies have been extensively explored, a demand‐side perspective that considers stock dynamics and circularity improvements is essential to assess sustainable pathways for the buildings sector. Here, we explore a set of decarbonization scenarios for the building sector in China considering a range of circular strategies and their interplay with different climate policies. The strategies include lifetime extension of buildings, switch to wood‐based construction, reduction of per‐capita floorspace, and a combination of all three strategies. We use the building sector model MESSAGEix‐Buildings soft linked to the integrated assessment model (IAM) MESSAGEix‐GLOBIOM and prospective life cycle assessment (LCA) to assess the effects of these circular strategies on building material and energy demands, and operational and embodied emissions. We find that the three strategies could reduce building material demand up to 60% on mass basis by 2060 compared to a reference scenario with continuation of current policies. This translates into a reduction of embodied and total GHG emissions of 62% and 24%, respectively, significantly contributing to achieving decarbonization targets. Integrating industrial ecology methods in IAMs, as demonstrated in this study, can provide valuable insights to inform national policy decisions on mitigation strategies accounting for both demand and supply sides.

Abstract Decarbonization of energy-using sectors is essential for tackling climate change. We use an ensemble of global integrated assessment models to assess CO2 emissions reduction potentials in buildings and transport, accounting for system interactions. We focus on three intervention strategies with distinct emphases: reducing or changing activity, improving technological efficiency and electrifying energy end use. We find that these strategies can reduce emissions by 51–85% in buildings and 37–91% in transport by 2050 relative to a current policies scenario (ranges indicate model variability). Electrification has the largest potential for direct emissions reductions in both sectors. Interactions between the policies and measures that comprise the three strategies have a modest overall effect on mitigation potentials. However, combining different strategies is strongly beneficial from an energy system perspective as lower electricity demand reduces the need for costly supply-side investments and infrastructure.

Buildings are responsible for 40% of total final energy consumptions in Europe. Numerous bottom-up models were recently developed to support local authorities in assessing the energy consumption of large building stocks and reduction potentials. However, current models rarely consider uncertainty associated to building usage and characteristics within the stock, resulting in potentially biased results. This study presents a generic model simplification approach using uncertainty propagation and stochastic sensitivity analysis to derive fast simplified (surrogate) models to estimate the current building stock energy use for improved urban planning. The methodology includes an engineering-based energy model as input to global sensitivity analysis (GSA) using the elementary effects (EE) screening and Sobol’ method for key parameters identification and regression analysis to derive simplified models for entire building stocks. The application to the housing stock of Esch-sur-Alzette (Luxembourg) showed that the parameters explaining most of the variability in final energy use for heating and domestic hot water are floor area, set-point temperature, external walls U-values, windows and heating system type. Results of the simplified models were validated against measured data and confirmed the validity of the approach for a simple yet robust assessment of the building stock energy use considering uncertainty and variability.

Cities are critical to meeting our sustainable energy goals. Informal settlement redevelopment programs represent an opportunity to improve living conditions and curb increasing demand for active cooling. We introduce an energy modeling framework for informal settlements to investigate how building design decisions influence the onset of heat stress and energy-intensive cooling demand. We show that occupants of tropically-located informal settlements are most vulnerable to prolonged heat stress year-round. Up to 98% of annual heat stress exposure can be mitigated by improving the building envelope. We find a universal solution (cool roofs) that reduces up to 91% of annual heat stress exposure. Finally, we show how proposed redevelopment building schemes could worsen thermal conditions of dwellers and further increase urban energy demand. Our results underscore how building design affects human well-being and highlight potential near-term and long-term pathways for reducing energy-intensive cooling demand for 800+ million informal settlement dwellers worldwide.

The MESSAGE Integrated Assessment Model (IAM) developed by IIASA has been a central tool of energy-environment-economy systems analysis in the global scientific and policy arena. It played a major role in the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC); it provided marker scenarios of the Representative Concentration Pathways (RCPs) and the Shared Socio-Economic Pathways (SSPs); and it underpinned the analysis of the Global Energy Assessment (GEA). Alas, to provide relevant analysis for current and future challenges, numerical models of human and earth systems need to support higher spatial and temporal resolution, facilitate integration of data sources and methodologies across disciplines, and become open and transparent regarding the underlying data, methods, and the scientific workflow. In this manuscript, we present the building blocks of a new framework for an integrated assessment modeling platform; the \ecosystem" comprises: i) an open-source GAMS implementation of the MESSAGE energy++ system model integrated with the MACRO economic model; ii) a Java/database backend for version-controlled data management, iii) interfaces for the scientific programming languages Python & R for efficient input data and results processing workflows; and iv) a web-browser-based user interface for model/scenario management and intuitive \drag-and-drop" visualization of results. The framework aims to facilitate the highest level of openness for scientific analysis, bridging the need for transparency with efficient data processing and powerful numerical solvers. The platform is geared towards easy integration of data sources and models across disciplines, spatial scales and temporal disaggregation levels. All tools apply best-practice in collaborative software development, and comprehensive documentation of all building blocks and scripts is generated directly from the GAMS equations and the Java/Python/R source code.

See the "What's New" page in the message_ix documentation for a list of all changes.