Transition to low-carbon economy: Assessing cumulative impacts of individual behavioral changes

© 2018 The Authors Changing residential energy demand can play an essential role in transitioning to a green economy. Environmental psychology suggests that behavioral changes regarding energy use are affected by knowledge, awareness, motivation and social learning. Data on various behavioral drivers of change can explain energy use at the individual level, but it provides little information about implications for macro energy demand on regional or national levels. We address this challenge by presenting a theoretically-based and empirically-driven agent-based model to track aggregated impacts of behavioral changes among heterogeneous households. We focus on the representation of the multi-step changes in individual energy use behavior and on a quantitative assessment of their aggregated impacts on the regional level. We understand the behavioral complexity of household energy use as a dynamic process unfolding in stages, and explore the barriers for utilizing the full potential of a region for emissions reduction. We suggest a policy mix that facilitates mutual learning among consumers

Demand-side solutions for climate mitigation: Bottom-up drivers of household energy behavior change in the Netherlands and Spain

© 2019 The Authors Households are responsible for 70% of CO2 emissions (directly and indirectly). While households as agents of change increasingly become a crucial element in energy transitions, bottom-up mechanisms facilitating behavioral change are not fully understood. A scientific understanding of individual energy use, requires eliciting factors that trigger or inhibit changes in energy behavior. This paper explores individual energy consumption practices and behavioral aspects that affect them. We quantitatively study the determinants of three energy actions: (1) investments in house insulation, solar panels and/or energy-efficient appliances, (2) conservation of energy by changing energy-use habits like switching off unused devices or adjusting house temperature, and (3) switching to green(er) electricity sources. To address this goal, we conduct a comprehensive survey among households (N = 1790) in two EU regions: Overijssel, the Netherlands and Navarre, Spain. We use probit regression to estimate how behavioral factors, households’ socioeconomic characteristics and structural attributes of dwellings influence energy related actions. Our analysis demonstrates that awareness and personal and social norms are as important as monetary factors. Moreover, education and structural dwelling factors significantly affect households’ actions. These results have implications for governmental policies aimed at reducing residential CO2 footprints and facilitating demand-side solutions in a transition to low-carbon economy

Economy-wide impacts of behavioral climate change mitigation: Linking agent-based and computable general equilibrium models

© 2020 The Author(s) Households are responsible for a significant share of global greenhouse emissions. Hence, academic and policy discourses highlight behavioral changes among households as an essential strategy for combating climate change. However, formal models used to assess economic impacts of energy policies face limitations in tracing cumulative impacts of adaptive behavior of diverse households. The past decade has witnessed a proliferation of agent-based simulation models that quantify behavioral climate change mitigation relying on social science theories and micro-level survey data. Yet, these behaviorally-rich models usually operate on a small scale of neighborhoods, towns, and small regions, ignoring macro-scale social institutions such as international markets and rarely covering large areas relevant for climate change mitigation policy. This paper presents a methodology to scale up behavioral changes among heterogeneous individuals regarding energy choices while tracing their macroeconomic and cross-sectoral impacts. To achieve this goal, we combine the strengths of top-down computable general equilibrium models and bottom-up agent-based models. We illustrate the integration process of these two alien modeling approaches by linking data-rich macroeconomic with micro-behavioral models. Following a three-step approach, we investigate the dynamics of cumulative impacts of changes in individual energy use under three behavioral scenarios. Our findings demonstrate that the regional dimension is important in a low-carbon economy transition. Heterogeneity in individual socio-demographics (e.g. education and age), structural characteristics (e.g. type and size of dwellings), behavioral and social traits (e.g. awareness and personal norms), and social interactions amplify these differences, causing nonlinearities in diffusion of green investments among households and macro-economic dynamics

Exploring low-carbon futures: A web service approach to linking diverse climate-energy-economy models

© 2019 by the authors. The use of simulation models is essential when exploring transitions to low-carbon futures and climate change mitigation and adaptation policies. There are many models developed to understand socio-environmental processes and interactions, and analyze alternative scenarios, but hardly one single model can serve all the needs. There is much expectation in climate-energy research that constructing new purposeful models out of existing models used as building blocks can meet particular needs of research and policy analysis. Integration of existing models, however, implies sophisticated coordination of inputs and outputs across different scales, definitions, data and software. This paper presents an online integration platform which links various independent models to enhance their scope and functionality. We illustrate the functionality of this web platform using several simulation models developed as standalone tools for analyzing energy, climate and economy dynamics. The models differ in levels of complexity, assumptions, modeling paradigms and programming languages, and operate at different temporal and spatial scales, from individual to global. To illustrate the integration process and the internal details of our integration framework we link an Integrated Assessment Model (GCAM), a Computable General Equilibrium model (EXIOMOD), and an Agent Based Model (BENCH). This toolkit is generic for similar integrated modeling studies. It still requires extensive pre-integration assessment to identify the ‘appropriate’ models and links between them. After that, using the web service approach we can streamline module coupling, enabling interoperability between different systems and providing open access to information for a wider community of users

Social tipping dynamics in the energy system

The fast growth in renewables has led to an economic tipping point for the adoption of renewables. However, we do not observe a corresponding reduction in fossil fuel demand. The tipping point has not led to a system-wide energy transition. This paper reviews how the cost tipping point in renewables can initiate other social tipping dynamics in the energy transition and it presents energy communities as a promising and fast-growing niche environment that can exploit and foster such tipping dynamics

Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions

© 2019, The Author(s). In the last decade, instigated by the Paris agreement and United Nations Climate Change Conferences (COP22 and COP23), the efforts to limit temperature increase to 1.5 °C above pre-industrial levels are expanding. The required reductions in greenhouse gas emissions imply a massive decarbonization worldwide with much involvement of regions, cities, businesses, and individuals in addition to the commitments at the national levels. Improving end-use efficiency is emphasized in previous IPCC reports (IPCC 2014). Serving as the primary ‘agents of change’ in the transformative process towards green economies, households have a key role in global emission reduction. Individual actions, especially when amplified through social dynamics, shape green energy demand and affect investments in new energy technologies that collectively can curb regional and national emissions. However, most energy-economics models—usually based on equilibrium and optimization assumptions—have a very limited representation of household heterogeneity and treat households as purely rational economic actors. This paper illustrates how computational social science models can complement traditional models by addressing this limitation. We demonstrate the usefulness of behaviorally rich agent-based computational models by simulating various behavioral and climate scenarios for residential electricity demand and compare them with the business as usual (SSP2) scenario. Our results show that residential energy demand is strongly linked to personal and social norms. Empirical evidence from surveys reveals that social norms have an essential role in shaping personal norms. When assessing the cumulative impacts of these behavioral processes, we quantify individual and combined effects of social dynamics and of carbon pricing on individual energy efficiency and on the aggregated regional energy demand and emissions. The intensity of social interactions and learning plays an equally important role for the uptake of green technologies as economic considerations, and therefore in addition to carbon-price policies (top-down approach), implementing policies on education, social and cultural practices can significantly reduce residential carbon emissions