Interactions between social learning and technological learning in electric vehicle futures

The transition to electric vehicles is an important strategy for reducing greenhouse gas emissions from passenger cars. Modelling transition pathways helps identify critical drivers and uncertainties. Global integrated assessment models (IAMs) have been used extensively to analyse climate mitigation policy. IAMs emphasise technological change processes but are largely silent on important social and behavioural dimensions to technological transitions. Here, we develop a novel conceptual framing and empirical evidence base on social learning processes relevant for vehicle adoption. We then implement this formulation of social learning in IMAGE, a widely-used global IAM. We apply this new modelling approach to analyse how technological learning and social learning interact to influence electric vehicle transition dynamics. We find that technological learning and social learning processes can be mutually reinforcing. Increased electric vehicle market shares can induce technological learning which reduces technology costs while social learning stimulates diffusion from early adopters to more risk-averse adopter groups. In this way, both types of learning process interact to stimulate each other. In the absence of social learning, however, the perceived risks of electric vehicle adoption among later adopting groups remains prohibitively high. In the absence of technological learning, electric vehicles remain relatively expensive and therefore only for early adopters an attractive choice. This first-of-its-kind model formulation of both social and technological learning is a significant contribution to improving the behavioural realism of global IAMs. Applying this new modelling approach emphasises the importance of market heterogeneity, real-world consumer decision-making, and social dynamics as well as technology parameters, to understand climate mitigation potentials

Decarbonising the critical sectors of aviation, shipping, road freight and industry to limit warming to 1.5–2°C

Limiting warming to well below 2°C requires rapid and complete decarbonisation of energy systems. We compare economy-wide modelling of 1.5°C and 2°C scenarios with sector-focused analyses of four critical sectors that are difficult to decarbonise: aviation, shipping, road freight transport, and industry. We develop and apply a novel framework to analyse and track mitigation progress in these sectors. We find that emission reductions in the 1.5°C and 2°C scenarios of the IMAGE model come from deep cuts in CO2 intensities and lower energy intensities, with minimal demand reductions in these sectors’ activity. We identify a range of additional measures and policy levers that are not explicitly captured in modelled scenarios but could contribute significant emission reductions. These are demand reduction options, and include less air travel (aviation), reduced transportation of fossil fuels (shipping), more locally produced goods combined with high load factors (road freight), and a shift to a circular economy (industry). We discuss the challenges of reducing demand both for economy-wide modelling and for policy. Based on our sectoral analysis framework, we suggest modelling improvements and policy recommendations, calling on the relevant UN agencies to start tracking mitigation progress through monitoring key elements of the framework (CO2 intensity, energy efficiency, and demand for sectoral activity, as well as the underlying drivers), as a matter of urgency

Improving future travel demand projections: a pathway with an open science interdisciplinary approach

Transport accounts for 24% of global CO2 emissions from fossil fuels. Governments face challenges in developing feasible and equitable mitigation strategies to reduce energy consumption and manage the transition to low-carbon transport systems. To meet the local and global transport emission reduction targets, policymakers need more realistic/sophisticated future projections of transport demand to better understand the speed and depth of the actions required to mitigate greenhouse gas emissions. In this paper, we argue that the lack of access to high-quality data on the current and historical travel demand and interdisciplinary research hinders transport planning and sustainable transitions toward low-carbon transport futures. We call for a greater interdisciplinary collaboration agenda across open data, data science, behaviour modelling, and policy analysis. These advancemets can reduce some of the major uncertainties and contribute to evidence-based solutions toward improving the sustainability performance of future transport systems. The paper also points to some needed efforts and directions to provide robust insights to policymakers. We provide examples of how these efforts could benefit from the International Transport Energy Modeling Open Data project and open science interdisciplinary collaborations

Long term, cross-country effects of buildings insulation policies

Building codes are an effective policy instrument to reduce energy consumption, but their impact depends on local building construction, renovation and demolition cycles, affected by economic and demographic development. In this research a unique global building stock model, with country level detail, is developed to understand the impact of building codes on global energy scenarios. The model shows that the majority of buildings standing in 2050 will be built after 2015, mostly outside of the OECD. In these regions despite growing space cooling demand due to projected economic development, insulation levels of new buildings remain low. New construction policies could thereby have a significant impact. In Africa and China the model shows that if all new buildings would be near zero-energy buildings in 2050 this would save respectively 64% and 43% of space heating and cooling energy demand. In OECD countries, on the contrary, the slower stock turn-over results in renovation policies being more effective, but also more vulnerable to delays. Delaying policy implementation by only 10 years drops global annual emission savings in 2050 by approximately 1 Gt CO2, showing the necessity of a fast and ambitious ramp up of building codes for achieving the Paris climate agreement

Interactions between social learning and technological learning in electric vehicle futures

The transition to electric vehicles is an important strategy for reducing greenhouse gas emissions from passenger cars. Modelling future pathways helps identify critical drivers and uncertainties. Global integrated assessment models (IAMs) have been used extensively to analyse climate mitigation policy. IAMs emphasise technological change processes but are largely silent on important social and behavioural dimensions to future technological transitions. Here, we develop a novel conceptual framing and empirical evidence base on social learning processes relevant for vehicle adoption. We then implement this formulation of social learning in IMAGE, a widely-used global IAM. We apply this new modelling approach to analyse how technological learning and social learning interact to influence electric vehicle transition dynamics. We find that technological learning and social learning processes can be mutually reinforcing. Increased electric vehicle market shares can induce technological learning which reduces technology costs while social learning stimulates diffusion from early adopters to more risk-averse adopter groups. In this way, both types of learning process interact to stimulate each other. In the absence of social learning, however, the perceived risks of electric vehicle adoption among later-adopting groups remains prohibitively high. In the absence of technological learning, electric vehicles remain relatively expensive and therefore is only an attractive choice for early adopters. This first-of-its-kind model formulation of both social and technological learning is a significant contribution to improving the behavioural realism of global IAMs. Applying this new modelling approach emphasises the importance of market heterogeneity, real-world consumer decision-making, and social dynamics as well as technology parameters, to understand climate mitigation potentials

Mitigating energy demand sector emissions : The integrated modelling perspective

Limiting climate change below a given temperature will require fundamental changes in the current energy system, both in the energy supply and the energy demand sectors. Previous global model-based analyses, however, have focused mostly on energy supply transformations. Therefore, in this study we respond to this knowledge gap by analysing the future energy demand projections in both baseline and climate policy scenarios of global models in detail. We examine the projections for the industry, transport and buildings sectors across four models and three different reference scenarios from the Shared-Socioeconomic Pathway framework by applying a decomposition analysis. We compare the projected demand side mitigation efforts to a more detailed, sector-specific, technology-oriented assessment of demand-side abatement potential for the year 2030. Without climate policy, model-based projections show that baseline emissions can grow rapidly in industry and transport sectors, but are also highly uncertain across models. The decomposition analysis shows that the key uncertainty across the global scenarios is the projected final energy per capita. For modellers therefore there lies a challenge to better understand drivers of future energy efficiency and service demand, that contribute to the projected energy demand. This model enhancement would moreover allow to evaluate policy measures that can impact this relation. The technology assessment estimates that in particular in the transport and buildings sector there is a higher potential to reduce demand-side emissions through energy efficiency improvements than implemented in the scenarios. Improved insulation, higher electric vehicle penetration rates and modal shift for example could reduce final energy demand to lower levels in the short term than currently projected, reducing the dependency on fuel switching required in current scenarios to meet the stringent climate targets

Transport electrification : the effect of recent battery cost reduction on future emission scenarios

Although the rapid fall in the costs of batteries has made electric vehicles (EVs) more affordable and boosted their sales, EVs still account for only a fraction of total car sales. In the last years, the battery costs of electric vehicles have dropped faster than previously estimated in the empirical literature. As a result, future cost projections have been adjusted. The larger than expected reduction in costs also shows the uncertainty of battery cost development, which will affect the success of a transition to low-carbon transport. Integrated assessment models show that reducing greenhouse gas emissions is more challenging in the transport sector than in other sectors. Switching to EVs could significantly reduce passenger road-transport emissions. In this study, we test the sensitivity of the projected sales of EVs to different battery costs and climate policy futures. The model suggests that the effectiveness of policy incentives will strongly depend on the battery floor costs, as EVs only gain significant shares (15% or more) of global car sales within our model framework when battery costs reach 100 $/kWh or less. We therefore conclude that besides the pace of the battery costs decline, which has been rapid in the last years, it is important to understand the lower boundary of battery costs for modelling long-term global energy transitions

Transport electrification: the effect of recent battery cost reduction on future emission scenarios

Although the rapid fall in the costs of batteries has made electric vehicles (EVs) more affordable and boosted their sales, EVs still account for only a fraction of total car sales. In the last years, the battery costs of electric vehicles have dropped faster than previously estimated in the empirical literature. As a result, future cost projections have been adjusted. The larger than expected reduction in costs also shows the uncertainty of battery cost development, which will affect the success of a transition to low-carbon transport. Integrated assessment models show that reducing greenhouse gas emissions is more challenging in the transport sector than in other sectors. Switching to EVs could significantly reduce passenger road-transport emissions. In this study, we test the sensitivity of the projected sales of EVs to different battery costs and climate policy futures. The model suggests that the effectiveness of policy incentives will strongly depend on the battery floor costs, as EVs only gain significant shares (15% or more) of global car sales within our model framework when battery costs reach 100 $/kWh or less. We therefore conclude that besides the pace of the battery costs decline, which has been rapid in the last years, it is important to understand the lower boundary of battery costs for modelling long-term global energy transitions

Transport electrification : the effect of recent battery cost reduction on future emission scenarios

Although the rapid fall in the costs of batteries has made electric vehicles (EVs) more affordable and boosted their sales, EVs still account for only a fraction of total car sales. In the last years, the battery costs of electric vehicles have dropped faster than previously estimated in the empirical literature. As a result, future cost projections have been adjusted. The larger than expected reduction in costs also shows the uncertainty of battery cost development, which will affect the success of a transition to low-carbon transport. Integrated assessment models show that reducing greenhouse gas emissions is more challenging in the transport sector than in other sectors. Switching to EVs could significantly reduce passenger road-transport emissions. In this study, we test the sensitivity of the projected sales of EVs to different battery costs and climate policy futures. The model suggests that the effectiveness of policy incentives will strongly depend on the battery floor costs, as EVs only gain significant shares (15% or more) of global car sales within our model framework when battery costs reach 100 $/kWh or less. We therefore conclude that besides the pace of the battery costs decline, which has been rapid in the last years, it is important to understand the lower boundary of battery costs for modelling long-term global energy transitions

Interactions between social learning and technological learning in electric vehicle futures

The transition to electric vehicles is an important strategy for reducing greenhouse gas emissions from passenger cars. Modelling future pathways helps identify critical drivers and uncertainties. Global integrated assessment models (IAMs) have been used extensively to analyse climate mitigation policy. IAMs emphasise technological change processes but are largely silent on important social and behavioural dimensions to future technological transitions. Here, we develop a novel conceptual framing and empirical evidence base on social learning processes relevant for vehicle adoption. We then implement this formulation of social learning in IMAGE, a widely-used global IAM. We apply this new modelling approach to analyse how technological learning and social learning interact to influence electric vehicle transition dynamics. We find that technological learning and social learning processes can be mutually reinforcing. Increased electric vehicle market shares can induce technological learning which reduces technology costs while social learning stimulates diffusion from early adopters to more risk-averse adopter groups. In this way, both types of learning process interact to stimulate each other. In the absence of social learning, however, the perceived risks of electric vehicle adoption among later-adopting groups remains prohibitively high. In the absence of technological learning, electric vehicles remain relatively expensive and therefore is only an attractive choice for early adopters. This first-of-its-kind model formulation of both social and technological learning is a significant contribution to improving the behavioural realism of global IAMs. Applying this new modelling approach emphasises the importance of market heterogeneity, real-world consumer decision-making, and social dynamics as well as technology parameters, to understand climate mitigation potentials