Behavioural changes for climate policy

Introduction

This study aims to examine the economic impacts of behavioral shifts and social innovations in the context of the energy transition towards climate change mitigation goals. It highlights the social value of behavioral change as a policy outcome, rather than focusing only on its cost or effectiveness. The study addresses questions from the Policy Response Mechanism, such as the economic effects of behavioral change and the role of varying discount rates across consumer categories in clean technology adoption. The goal of the study is to explore how behavioral change and social innovation can drive climate action, with objectives like creating storylines, quantifying policies, and modeling multi-scenario impacts on climate efforts. Initially focused on transport at the EU level, the study assesses the effects on GDP, GHG emissions, investments, employment, and the potential for regional uniformity in the impacts of behavioral change.

Background

The terms "behavioral change" and "lifestyle" have varying interpretations depending on context and discipline, which can lead to misunderstandings. A common understanding is needed (van den Berg et al., 2019). According to Akengi & Chen (2016), a sustainable lifestyle involves habits shaped by societal institutions, norms, and infrastructures, aiming to reduce resource use and waste while fostering fairness and prosperity. The IPCC views behavioral change as a demand-side strategy for sustainable development (Shukla et al., 2022), encompassing a range of actions from simple adjustments to transformative shifts that challenge existing development trends. Categorizing behavioral change is complex, as different frameworks exist. The IPCC’s ASI framework classifies strategies as Avoid, Shift, or Improve, focusing on reducing resource use, providing alternative services, and enhancing efficiency, respectively. The motivation for behavioural change varies with the socio-cultural shifts, influencing into “Avoid” measures, infrastructure affecting “Shift” options, and technology closely tied to “Improve” actions. However, such decisions may face psychological barriers and societal costs. Other classifications, such as efficiency, consistency, and sufficiency, provide additional perspectives. Definitions often overlap, highlighting the complexity of categorising behavioural changes.

  Figure 1:  Different behavioural change decisions categorised in the different frameworks. Source: (van den Berg et al.,
                   2019)

As illustrated in Figure 1, different behavioural change decisions are categorised in various frameworks (van den Berg et al., 2019). This figure provides a visual representation of the relationships between different behavioural change domains. 

Despite the extensive research on promoting environmentally friendly behaviour, the potential benefits are underrepresented in Integrated Assessment Models (IAMs). Drivers and barriers influencing climate-related behavioural changes are multifaceted, including socio-demographic, economic, and psychological factors. The individual motivation, along with capacity and infrastructure, is crucial for an effective change. The IPCC calls for more research in understanding the causal mechanisms, narratives associated with technologies, the impact of social media, the effects of social movements, the dynamic feasibility of low-carbon transitions, as well as the effects of shocks and disasters on willingness and capacity to change. In summary, the representation of behavioural change in IAMs can contribute to increase the social awareness and, ultimately, enable behavioural change, addressing challenges in low-carbon transition feasibility and governance (Goldberg et al., 2020; Shukla
et al., 2022).

Scenario Protocol

In Study 7, four scenarios, “NDC_LTT”, “NDC_LTT_Trans”, “NDC_LTT_LED” and “NDC_LTT_LIFE”, have been defined and proposed for use in the IAM COMPACT models. For this first modelling cycle, three models, namely WILIAM and GCAM, are employed to generate results for selected outcomes, including GDP, sectorial emissions, employment, and total investment in the energy sector. Further details about the four scenarios and their narratives can be found in Table 1.