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The implementation of asymmetric emission reduction policies can not only increase the cost of reducing emissions but also reduce the effectiveness of climate policies themselves, leading to policy inefficiencies such as carbon leakage. This paper investigates the impact of asymmetric emission reduction policies on the cost-effectiveness and efficiency of climate strategies in North America. Using a model inter-comparison approach, which combines two bottom-up global models and one top-down global model, this study assesses the effects of such policies on fuel substitution, global fossil fuel trade, and emissions in North America and globally. It is the first work where a multi-model approach is used for exploring how different energy systems react to asymmetric carbon policies. This provides critical insights into regional policy design within a global emissions framework. Quantitatively, the study reveals that asymmetric carbon pricing can lead to more than 60% global emissions reduction in certain models, but can also drive trade distortions, where U.S. exemptions result in emissions rising by more than 10% compared to reference scenarios. Qualitatively, significant fuel substitution patterns across Canada, Mexico, and the U.S. demonstrate increased coal consumption when carbon prices are unevenly applied. While no global emission increase was observed, asymmetric policies result in inefficiencies between local policy costs and emissions reduction outcomes, such as rising fossil fuel trade in non-abating regions. The findings suggest that harmonising carbon policies across regions would reduce inefficiencies and minimise carbon leakage.

Low carbon energy transitions are of paramount importance to achieve climate goals. These transitions are not only technical and economical, but also deeply social and gendered. In this paper, we reviewed the academic literature to understand: firstly, what gender vulnerabilities have been discussed in the literature and how they have been embedded in structural dynamics. Secondly, what socio-cultural and socio-economic drivers may lead to these gendered vulnerabilities? Based on content analysis, four key themes emerged from this literature survey: land use change, gender-neutral energy policies, access to resources , and green practices, gender, and culture . These four themes indicate that there are several enabling mechanisms arising from social and structural inequalities, indicative that vulnerabilities ought not to be considered in isolation, but in relationship with others. We also explored dimensions of vulnerability (exposure, sensitivity, adaptative capacity) based on Carley et al (2018 Nat. Energy 3 621–7) to contextualise components of vulnerability in relation to gender. The main finding suggests considering further intersectional approaches to low carbon energy transitions, emphasising acknowledging, and lessening societal inequalities.

The role of different technologies in a future low carbon energy system is determined by numerous factors, many of which are highly uncertain. Their deployment may be a function of dependency on other technologies, or competition, or wider system effects. In this paper, using a UK example, we explore patterns of interdependency between technologies using a hierarchical clustering approach across multiple scenarios. We find that technologies compete in some instances, often on costs, cluster because they co-depend on each other, or emerge under all conditions, as robust options. Crucially, the broader scenario framing around carbon capture and storage (CCS) availability and climate policy stringency strongly influences these interdependencies. Keywords: Energy system models, Scenario clustering, Technology interdependency, Low carbon pathways

Integrated assessment models (IAMs) form a prime tool in informing about climate mitigation strategies. Diagnostic indicators that allow comparison across these models can help describe and explain differences in model projections. This increases transparency and comparability. Earlier, the IAM community has developed an approach to diagnose models (Kriegler (2015 Technol. Forecast. Soc. Change 90 45–61)). Here we build on this, by proposing a selected set of well-defined indicators as a community standard, to systematically and routinely assess IAM behaviour, similar to metrics used for other modeling communities such as climate models. These indicators are the relative abatement index, emission reduction type index, inertia timescale, fossil fuel reduction, transformation index and cost per abatement value. We apply the approach to 17 IAMs, assessing both older as well as their latest versions, as applied in the IPCC 6th Assessment Report. The study shows that the approach can be easily applied and used to indentify key differences between models and model versions. Moreover, we demonstrate that this comparison helps to link model behavior to model characteristics and assumptions. We show that together, the set of six indicators can provide useful indication of the main traits of the model and can roughly indicate the general model behavior. The results also show that there is often a considerable spread across the models. Interestingly, the diagnostic values often change for different model versions, but there does not seem to be a distinct trend.