behavioural climate change mitigation - from individual energy choices to demand-side potential
Leila Niamir is a PhD student in Department of Governance and Technology for Sustainability (CSTM). Her supervisors are prof.dr. T. Filatova and prof.dr. J.T.A. Bressers from the Faculty of Behavioural, Management and Social sciences (BMS).
Climate change is one of the major global environmental challenges faced by humanity in the 21st century. Global carbon emissions from fossil fuels stand at almost 37 GtCO2 per year and have grown by an average of 2.4% per year so far this century. Based on the latest the global carbon budget report, in 2018 CO2 emissions are still on track to rise by 2.7% (range: 1.8% to 3.7%). Among these, households – directly and indirectly – are responsible for more than 70% of carbon emissions. Hence, decarbonization of the economy requires massive worldwide efforts and a strong involvement of regions, cities, businesses, and individuals in addition to commitments at national levels. While climate mitigation is expanding, UN confirms that we need to urgently and sharply bend the emissions curve by accelerating these efforts to keep the temperature increase to 1.5°C above pre-industrial levels. In the last few years, the discussions about mitigation strategies stress the importance of demand-side solutions and shifts to transdisciplinary and bottom-up approaches in assisting climate mitigation efforts worldwide. The IPCC Special Report on 1.5 degrees names ‘behavioural and lifestyle changes’ as a vital climate change mitigation strategy complimentary to technological measures. Yet, despite behavioural change being emphasized as a crucial component of mitigation strategies worldwide, empirical studies on individual energy-related choices and behavioural factors impacting them are scarce. Individual energy behaviour, especially when amplified through social context, shapes energy demand and, consequently, carbon emissions. By changing their behaviours, individuals can play an essential role in the transformation process towards a low-carbon society and global emissions reduction. However, explaining and affecting human behaviour is a difficult task since human nature is complex and heterogeneous. As a result, quantitative tools to assess cumulative household emissions, given the diversity of behaviour and a variety of psychological and social factors influencing it beyond purely economic considerations, are scarce.
This dissertation highlights the potential of behavioural changes among heterogeneous households regarding energy use and their role in mitigating climate change. To do so, (a) a comprehensive household survey is designed and conducted to explore how individuals choose to change their energy behaviour and what factors trigger or inhibit these choices; (b) simulation tools are designed and developed to aggregate these insights and quantitatively assess regional and national impacts of individual choices on carbon emissions; and (c) a novel method to upscale individual energy behaviour for climate change mitigation strategies is presented.
The determinants of the main types of households’ energy behaviour: investments in house insulation, installation of solar panels, and energy-efficient appliances; conservation of energy by changing energy-use habits (e.g., switching off unnecessary devices, turning down the heat, and using less energy); and switching between energy suppliers are studied based on the unique data from an extensive survey (N=1,790) from two provinces in EU member states. By employing correlation and probit regression analyses the relationships between individual household attributes (socioeconomic, structural and behavioural factors) and the likelihood of choosing one of the energy actions that contribute to climate change mitigation are quantitatively assessed. The empirical analysis demonstrates that behavioural factors, next to structural factors and education, play at least as important role as do monetary factors, such as income.
An agent-based simulation model is designed and developed to quantify the cumulative impacts of household behavioural changes on regional dynamics of saved energy and CO2 emissions. This model builds up on the advances in agent-based modelling applied in the energy domain, and adds theoretically and empirically-grounded individual behavioural rules that drive households’ energy-related choices. The results of this novel model indicate that accounting for the demand-side heterogeneity provides better insights into possible transition pathways to a low-carbon economy and into potential of behavioural changes as a climate change mitigation strategy. In order to facilitate this transition, the broader view on the social environment, cultural practices, public knowledge, producers technologies and services, and the facilities used by consumers are needed to design implementable and politically feasible policy options. Accordingly, the policy mix should also aim at encouraging and facilitating social interactions between individuals/households and promoting and diffusing information that they need. Such accompanying information and value-based policy instruments have the potential to greatly contribute to the effectiveness of conventional price-based and technology-effectiveness policies.
Aggregating behavioural changes of heterogeneous individuals: this dissertation brought attention to the potential of heterogeneous individual energy behavioural changes in terms of the transition to a low-carbon economy at national and EU levels by presenting a novel method for the systematic upscaling of individual heterogeneity and social dynamics (micro-macro models integration). This tool is ideal for studying the dynamic effects of climate change mitigation policy measures targeted at changes in individual energy use practices. The result shows that this transition varies from one region to another. Some regions are lagging behind and others are moving ahead due to heterogeneity in individual sociodemographic (e.g., education and age) and structural characteristics (e.g., type and size of dwellings), behavioural and social traits, and spatial characteristics (e.g., urban vs. rural).
