Hamish Beath

The Paris Agreement establishes that Parties’ Nationally Determined Contributions (NDCs) will represent a progression, a country’s highest possible ambition (HPA), and an equitable contribution to the collective long-term temperature goal. However, the agreement provides no guidance on how the HPA standard should be interpreted, assessed, or operationalised in practice. In response to this gap, initial scholarship has proposed frameworks to interpret HPA based on the treaty’s text and the wider international environmental law context, which includes the 2025 Advisory Opinion on Obligations of States in Respect of Climate Change from the International Court of Justice. Altogether, this has clarified how HPA is part of states’ due diligence obligations and sets the stage for the next challenge: how can the theoretical understanding of HPA be practically applied to assess whether NDCs reflect this norm? Here, we build on existing reporting obligations under the United Nations Framework Convention on Climate Change (UNFCCC), emerging approaches reflected in the UNFCCC NDC synthesis report, and best practices from the research community. We describe expectations for the process, conduct, presentation, and documentation of NDCs of HPA. These expectations provide qualitative benchmarks and criteria for assessing whether Parties have undertaken a rigorous and reasoned process to identify their highest possible mitigation efforts, and whether the evidence provided is sufficient to make that reasoning open to scrutiny. This promotes transparency and consistency in the formulation and assessment of NDCs, whilst intentionally not assessing targets against quantified mitigation benchmarks. Finally, the proposed expectations are contextualised and illustrated with examples from the latest round of NDC submissions. This practical translation of theoretical obligations can serve as a starting point for a deeper policy conversation on how HPA of NDCs can be evidenced, assessed or challenged.

Digitalisation is a double-edged sword for energy transitions and climate change mitigation. Digital applications can improve the energy efficiency of processes and systems, but can also induce demand for energy-hungry activity. Digital infrastructure like data centres also has a large energy footprint. AI is amplifying and accelerating these impacts, both for better and for worse. How does this affect the feasibility of long-term climate targets? We use a global integrated modelling framework to quantify scenarios to 2050 describing alignment or misalignment between digitalisation and climate goals. We combine empirical estimates of the energy impacts of digital and AI applications in buildings, transport, and industry sectors with digitalisation’s role in the integration of renewables, storage, and demand flexibility in electricity systems. We show digitalisation uncertainties for future energy demand and CO ₂ emissions are large - similar in magnitude to the effect of all global demographic, economic, and technological uncertainties quantified in future climate pathways. We also show that available near-term assessments of AI impacts within this uncertainty space are optimism biased. Energy transition and emission risks from digital and AI applications in transport and buildings are a factor five of higher than from data centres and other infrastructure. Climate policy is the most effective driver of emission reductions to meet Paris targets but climate-aligned digitalisation reduces energy investment needs by a factor of two down to $35 trillion and brings the global net-zero year forward to as early as 2047. Conversely, climate-misaligned digitalisation drives up energy demand and sees lost opportunities to build a reliable and renewable future electricity system. Resulting increases in near-term cumulative CO ₂ emissions mean the probability of limiting warming to 2°C almost falls below the ‘likely’ threshold, increasing risk of temperature overshoot. Digital transformation can undermine or enable the feasibility and affordability of long-term climate targets delivered by decarbonised energy systems. Entwining digital and low-carbon energy transitions requires strategic governance and institutional innovation to link climate and digital concerns.

Long-term global emissions scenarios underpin climate policy by providing quantitative benchmarks for near-term actions consistent with long-term temperature goals. However, scenario benchmarks can quickly become outdated as real-world emissions, policies, technologies and even geopolitics evolve. Here we present an updated and curated ensemble of global integrated assessment model (IAM) scenarios developed since the IPCC Sixth Assessment Report. We use a continuous “live” collection and vetting framework designed for regular updating - with planned annual releases - to derive up-to-date policy-relevant benchmarks. Of more than 1,500 collected scenarios, only a minority remains consistent with recent trends between 2015 and 2025. After assessing feasibility, plausibility, and sustainability, 162 scenarios form the basis for updated benchmarks. These indicate that pathways limiting peak warming to around 1.7 °C above pre-industrial levels represent the lowest warming outcomes currently supported in the recent literature, requiring global greenhouse gas (GHG) emissions reductions of around 60% by 2035 and 90% by 2050 relative to 2019, with net-zero CO₂ emissions reached around 2050. All Paris-consistent benchmarks require a rapid transitioning away from unabated fossil fuels, an unprecedented scale-up of renewable energy, and limited but necessary deployment of carbon dioxide removal (CDR). While limiting peak warming to 1.5 °C with low or no overshoot is no longer feasible, returning warming to 1.5 °C this century (after overshooting to about 1.7 °C) remains possible.

Integrated assessment models produce large ensembles of socioeconomic scenarios that are used profusely in climate change research. The Intergovernmental Panel on Climate Change (IPCC), non-governmental organizations or national climate committees often rely on ensemble statistics to identify mitigation strategies and set climate targets. A limitation of such evidence is the opportunistic nature of scenario ensembles: they are an unstructured, serendipitous collection of evidence. Drawing on concepts from physical climate science and ensemble analysis, we present an approach for the flexible, multidimensional weighting of emission scenario data that accounts for relevance, quality and diversity. Our illustrative application to the latest IPCC scenario database demonstrates a reduction in dominance of highly represented models and studies, and sees net-zero emission milestones differ to those originally reported. Our framework formalizes decisions otherwise made in an ad hoc manner, providing a tool contributing to the broader challenge of assessing ensembles of opportunity.

Artificial Intelligence (AI) may impact greenhouse gas emissions in both positive and negative ways, yet these impacts are not represented in recent assessments of climate change mitigation. Uncertainty surrounding future AI capability and adoption, its impacts on energy systems, and limited data availability complicates the inclusion of AI impacts. We conduct an expert-led process, ascertaining plausible impacts, and quantify ranges in their magnitude to 2040. We present a new scenario framework for AI and climate change that considers interactions between AI growth, climate policy, and AI policy. We translate elicited insights into illustrative scenarios and assess their implications using an integrated assessment model. Cumulative global CO2 emissions by 2040 range from +11% above to 1.4% below a baseline without AI impacts, depending on the scale of AI growth and policy intervention. Our results highlight emissions risks and opportunities from AI and the need for explicit representation in mitigation scenarios.

Agrivoltaic systems offer the potential to boost land productivity, reduce water consumption and improve access to energy: an energy-food-water nexus solution. Agrivoltaic systems are promising for regions with relatively low land productivity and both energy- and water-access challenges, such as sub-Saharan Africa. However, there is a lack of research exploring the use of agrivoltaics in such contexts. This study addresses that gap. It uses both energy-system and crop modelling to explore optimal agrivoltaic system design and deployment for a medium-sized farm, located in Zimbabwe, where electricity is expensive and unreliable. We compare conventional, ground-mounted PV mini-grid systems with three agrivoltaic system configurations. We model fixed-tilt, single-axis tracking and vertical bifacial configurations installed above wheat and maize. We calculate the optimal required system sizes at 100% and 95% reliability levels. We find that the cheapest agrivoltaic system (on a unit cost basis) is single-axis tracking, with a unit cost only 11% higher than the ground-mounted PV at an equivalent 95% reliability level. Under our base-case scenario, high electricity costs from the grid mean that the farm is operating with a deficit (when considering crop revenues). However, with agrivoltaic systems, the deficit is significantly reduced and at 95% reliability, the farm becomes profitable with crop revenue exceeding upkeep costs. Of the agrivoltaic systems, all were profitable though this varied depending on the ratio between module spacing and height (P-by-H ratio). We found greater system profitability at higher P-by-H ratios, with the largest improvement when switching from a P-by-H ratio of 2 to 3. All agrivoltaic system types can achieve land-equivalent ratios (LERs) greater than 1, depending on the P-by-H ratio, indicating improved efficiency. Our calculation of the price-performance ratio (PPR) found that all system types were economical.

Despite recent improvements to electricity access in lower-income countries, reliability remains low for many. Local renewable electricity infrastructure supplementing the national grid offers a promising route to improved reliability for rural communities. However, improvements in the reliability of national grids create risks for investors including the possibility of “stranded” renewable assets. We use energy-system modelling to explore ways in which solar photovoltaic (PV)-based mini-grids could be interconnected with national grids. We explore the impact of reduced electricity demand to quantify the investment risks of losing customers. Our results indicate that national grid–connected mini-grids can reduce the unit electricity costs for communities whilst also increasing reliability and reducing the carbon intensity of electricity in line with Sustainable Development Goal (SDG) 7. Reductions in demand have a minimal impact at lower levels but at moderate levels are likely to undermine economic viability. Finally, we discuss policy interventions to facilitate and protect investing in national grid-connected mini-grids.

Approximately 80% of the global population without access to electricity live in sub-Saharan Africa (SSA) with rural areas disproportionately affected. Solar-powered mini-grids are being used to increase rural electrification. However, their deployment is hindered by high costs, low electricity demand, and limited incomes in rural households. Agriculture constitutes 70% of rural incomes in SSA and faces threats due to a reliance on rainfall coupled with low irrigation levels in the context of climate change. Irrigation loads have been suggested as a strategy to enhance the economic viability of solar mini-grids by boosting electricity demand: reducing the levelised cost of used electricity (LCUE) whilst improving farmer yields and incomes. This study investigates a case study in Tanzania, selected based on the amount of population without access to electricity, the potential for irrigation, and the prevalence of photovoltaic solutions in the least-cost pathways for rural electrification. The seasonality and timing of irrigation demand, as well as crop mixes, are varied to optimise for the lowest LCUE using the open-source modelling framework continuous lifetime optimisation of variable electricity resources. Evapo-transpiration and energy-system modelling are used to estimate energy demand. We find that adding irrigation loads has the potential to increase the LCUE and lower asset utilisation (load factor) when compared to residential loads but that, by selecting crops with longer growth periods, optimising irrigation timing, and implementing multiple planting seasons, the LCUE can be up to 7% less than residential-only systems with an increase seen in asset utilisation. LCUE values of 0.54–1.30 $/kWh were found: higher costs were associated with shorter crop-growth periods, single planting seasons, and poorly timed irrigation loads whilst lower costs were observed with bimodal cropping and solar-coordinated irrigation times. The research highlights that, when incorporating irrigation loads into rural-electrification strategies, specific factors should be considered to enhance their viability within mini-grids in SSA. We recommend to both policymakers and mini-grid developers to promote multiple planting seasons (bimodal cropping) and encourage optimal crop mixes where relevant alongside irrigation schemes and rural electrification projects as well as adopting the use of flexible tariffs for irrigation loads.

Ensembles of climate change mitigation scenarios present users with a collection of strategies for limiting global warming. These strategies may differ in their associated feasibility challenges, mitigation co-impacts, and ultimately their relative societal desirability. Understanding these scenario characteristics is therefore crucial when scenarios are used to inform strategic decisions. One approach to enhance this understanding is to establish scenario archetypes and select contrasting illustrative scenarios from a larger ensemble. We present a new multidimensional framework for the systematic comparison of scenarios at the global or regional level. We illustrate the framework with comparisons in seven dimensions: economic feasibility, mineral resource availability, impacts on societal resilience, near-term scenario robustness, environmental sustainability, interregional fairness, and speed of societal transformation. Using cluster analysis, the framework can be used to select a group of illustrative scenarios with contrasting scores across the dimensions. Beyond the selection of scenarios, our exploration and evaluation framework also allows the identification of gaps in the scenario space that may be of interest but are not covered by the literature. We demonstrate these use cases by applying our framework to a set of mitigation scenarios that limit warming to 1.5 °C. Our results show our framework systematically selects contrasting scenarios, with our illustrative pathways having diverging energy mixes and uses of carbon dioxide removal. Further, we highlight considerable regional differences in the distribution of indicator and dimension scores as a key area for further investigation.

We compare the performance of photovoltaic (PV), flat-plate and evacuated-tube solar-thermal (ST), and hybrid photovoltaic-thermal (PV-T) collectors to meet the energy demands of multi-effect distillation (MED) desalination plants across four locations. We consider three scales: 1700 m³day⁻¹, 120 m³day⁻¹ and 3 m³day⁻¹. We find a strong dependence of the capacity and configuration of the solar collectors on both the cost of sourcing electricity from the grid and the specific collector employed. We find specific costs as low as 7.8, 3.4 and 3.7 USDm⁻³ for the three plant capacities. We find that solar-driven systems optimised for the lowest specific cost result in CO₂eq emissions equal to, or higher than, those from grid-driven reverse osmosis (RO) and in line with PV-RO. This highlights the need to consider the environmental footprint of these systems to ensure that desalination is in line with the United Nations’ Sustainable Development Goal 6.