From individual energy behavioral changes to carbon emissions and public health: A new integrated framework

Defining feasible and cost-effective low-emission pathways are crucial in the transition to sustainable societies. In this context, scenario-based model projections play an important role in evaluating different mitigation options. Current macroeconomic models are widely used to develop climate change mitigation scenarios by finding cost-optimal solutions for total societal costs. Yet, decision-making tools to assess the impact of individual behavioral and lifestyle changes on energy consumption and carbon emissions are scarce. This is surprising, given the importance of diversity/heterogeneity in personal and social factors that influence decisions beyond purely economic considerations. The macroeconomic impacts of individual energy behavioral changes on carbon emissions are explored by using agent-based modelling techniques. We further extend the framework to include the impact of air pollutants from fossil fuel-fired electricity generating units (EGUs) and residential combustion on public health. Air pollution-related public health benefits of these strategies can be appreciable, with thousands of lives saved per year and an array of morbidity benefits from CO2 control strategies that include residential energy efficiency. In this (ongoing) research, we present a novel integrated modular framework to capture climate-health benefits of residential energy behavioural change, considering EGUs and residential combustion. This framework consists of several analysis components and two main models: a bottom-up energy behavioral agent-based model (BENCH model); and an air pollution and climate integrated assessment model (GAINS model). These two models are linked systematically. The BENCH model introduces set of end-user behavioral and social scenarios. As a result, the aggregated changes in residential energy updates GAINS residential energy demand, where ambient air pollution and health impact are determined at the macro scale. This framework supports policy-makers’ decision on climate mitigation options by launching bottom-up (behavioral and social) end-user policies into top-down (monetary incentives) climate-energy-economy macro policies

Macro scale impacts of behavioral climate change mitigation: linking agent-based and computable general equilibrium models

Agent-based models (ABMs) are the key method to formalize adaptive human behavior in computational models of human-environment systems. It allows for a detailed representation of diverse behavioral strategies of actors grounded in different theories and data, explicit modeling of their learning and adaptation behavior and their social interactions driving information and innovation diffusion. However, ABMs are originally designed to be small scale. As computer science literature highlights, ABMs are difficult to scale up. While the technical part of the challenge could be resolved by more (distributed) computational power, the architectural solutions regarding heterogeneity, interactions, coordination and synchronization of actions of a much larger population are also in demand. Importantly, as ABMs scale up, it becomes insufficient to just multiply agents (e.g. households) in numbers. It implies that new institutional decisions and processes relevant at larger scales have to be modelled endogenously. This paper proposes an innovative methodological approach for scaling up behavioral changes among heterogeneous individuals regarding energy choices while tracing macroeconomic and cross-sectoral impacts of these changes. To achieve this goal, we combine the strengths of top-down computable general equilibrium (CGE) models and bottom-up ABMs. Our ABM simulates behavioral changes among households with respect to energy use and climate mitigation actions, which are grounded in theories from psychology and survey data. CGE estimates economy-wide impacts of behavioural changes through a step-wise aggregation procedure going from regional to national and EU scales. Following this approach, we investigate the dynamics of cumulative impacts of changes in individual energy use under three behavioral scenarios. We find that differences in education and age among different EU regions lead to an uneven distribution of benefits of a green economy transition. Heterogeneity among household agents and presence of social interactions amplify these differences, causing nonlinearities in diffusion of green investments among households and macro-economic dynamic

Modeling low energy demand futures for buildings: current state and research needs

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

Social tipping dynamics in the energy system

This paper reviews evidence on how the fast growth in renewable energy technologies can trigger social tipping dynamics that potentially accelerate a system-wide energy transition. It does so by reviewing a variety of literature across several disciplines addressing socio-technical dimensions of energy transitions. The tipping dynamics in wind and solar power create potential for cascading effects to energy demand sectors, including household energy demand. These most likely start with shift actions and adoption of household-scale batteries and heat pumps. Key enablers are strong regulations incentivising reductions in demand and setting minimum efficiency levels for buildings and appliances. While there is evidence of spillovers to more environmentally friendly behaviour, the extent of these and the key leverage points to bring them about present a knowledge gap. Moreover, these behavioural feedback loops require strong additional policy support to “make them stick”. Understanding the economic and social tipping dynamics in a system can empower decision-makers, fostering realistic energy transition policies. This paper highlights energy communities as a promising niche for leveraging tipping dynamics. Ultimately, bridging the gap between these tipping dynamics and institutional reforms is crucial for unlocking the full potential of sustainable energy systems

A dynamic systems approach to harness the potential of social tipping

Social tipping points are promising levers to achieve net-zero greenhouse gas emission targets. They describe how social, political, economic or technological systems can move rapidly into a new state if cascading positive feedback mechanisms are triggered. Analysing the potential of social tipping for rapid decarbonization requires considering the inherent complexity of social systems. Here, we identify that existing scientific literature is inclined to a narrative-based account of social tipping, lacks a broad empirical framework and a multi-systems view. We subsequently outline a dynamic systems approach that entails (i) a systems outlook involving interconnected feedback mechanisms alongside cross-system and cross-scale interactions, and including a socioeconomic and environmental injustice perspective (ii) directed data collection efforts to provide empirical evidence for and monitor social tipping dynamics, (iii) global, integrated, descriptive modelling to project future dynamics and provide ex-ante evidence for interventions. Research on social tipping must be accordingly solidified for climate policy relevance

Energy Systems

The tipping dynamics in wind and solar power create potential for cascading effects to energy demand sectors, including household energy demand. These most likely start with shiftactions and adoption of household-scale batteries and heat pumps. Key enablers are strong regulations incentivising reductions in demand and setting minimum efficiency levels for buildings and appliances. While there is evidence of spillovers to more environmentally friendly behaviour, the extent of these and the key leverage points present a knowledge gap. Moreover, these behavioural feedback loops require strong additional policy support to ‘make them stick

Energy Systems

The tipping dynamics in wind and solar power create potential for cascading effects to energy demand sectors, including household energy demand. These most likely start with shiftactions and adoption of household-scale batteries and heat pumps. Key enablers are strong regulations incentivising reductions in demand and setting minimum efficiency levels for buildings and appliances. While there is evidence of spillovers to more environmentally friendly behaviour, the extent of these and the key leverage points present a knowledge gap. Moreover, these behavioural feedback loops require strong additional policy support to ‘make them stick

Agent-based modeling to integrate elements from different disciplines for ambitious climate policy

Ambitious climate mitigation policies face social and political resistance. One reason is that existing policies insufficiently capture the diversity of relevant insights from the social sciences about potential policy outcomes. We argue that agent-based models can serve as a powerful tool for integration of elements from different disciplines. Having such a common platform will enable a more complete assessment of climate policies, in terms of criteria like effectiveness, equity and public support. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models The Carbon Economy and Climate Mitigation > Policies, Instruments, Lifestyles, Behavior Policy and Governance > Multilevel and Transnational Climate Change Governance

How to Keep it Adequate: A Validation Protocol for Agent-Based Simulation

Agent-based models are used in a huge diversity of contexts, which complicates the establishment of a shared understanding of model validity and adequate methods for model construction, inference and validation. Starting from the tenet that model validity can only be judged with respect to a well-defined purpose and context, we conceptualise validation as systematically substantiating the premises on which conclusions from simulation analysis for a specific context are built. We revisit the premises of empirical and structural validation and argue that validation should not be understood as an isolated step in the modelling process. Rather, sound conclusions from simulation analysis require context-adequate choices at all steps of simulation analysis. To facilitate communication, we develop a protocol of guiding questions to analyse the modelling context, choose appropriate methods at each step, document the premises involved in a specific simulation analysis, and demonstrate the adequacy of the model for its context