Science-based climate targets for aviation fall short on mitigation and implementation

Electrified vehicles can substantially reduce emissions from light-duty vehicles (LDVs), but large-scale deployment remains challenging due to their associated demands for critical materials in batteries. The challenge is further complicated by trade-offs among greenhouse gas emissions, costs, and critical materials. We develop an optimization model to explore the cost and technical feasibility of meeting climate targets for U.S. LDVs under various material supply scenarios. To meet a sectoral target consistent with 2 °C, global lithium supply would need to grow by 35%/yr to 2035 or 50%/yr to 2030, assuming: (1) no recycling, (2) the U.S. can access a share of global supply proportionate to its population, and (3) medium- and heavy-duty vehicles electrify as fast as LDVs. Recycling or greater U.S. material allocation (proportionate to gross domestic product) can reduce the necessary growth rates to 30-45%/yr or 5-10%/yr, respectively. In low material supply scenarios, the 2 °C target is sometimes still attainable with preference to hybrid vehicles in the early years, later transitioning to a mix of fully electric and plug-in hybrid vehicles (PHEVs) from the 2030s onward. To hedge against future material supply uncertainty, PHEVs can act as transitional technologies in the short term and remain an important technology in the long term.

China's steel sector is pivotal to national and global climate goals, yet decarbonization is constrained by entrenched coal-based processes and regional disparities. Top-down and bottom-up models often neglect firm-level heterogeneity and behavioral dynamics. Here, we develop an agent-based model linking policy signals-carbon caps, pollution standards, and resource limits-with firm-level investment decisions. We evaluate four scenarios combining current and strengthened climate and air targets with neutral or accelerated technology progress. All scenarios achieve deep CO2 reductions (90 to 95% by 2060), but only the integrated policy-plus- technology pathway eliminates blast furnaces before midcentury, achieving the steepest declines. Policy- or technology-only pathways entail trade-offs, including lock-in to carbon capture and storage or delayed reductions. Our results show that synchronizing stringent climate policy with rapid technology cost declines is essential to steer China's steel industry toward a carbon-neutral trajectory.

Abstract Under Article 6(4) of the European Climate Law, the Commission must assess the consistency of all new legislative proposals with European Union (EU) climate targets. Despite its significant potential for climate governance, the provision has been inadequately applied to date. This contribution proposes an interpretation of what Article 6(4) requires, drawing guidance from other impact assessments, and considers recent case law on climate impacts in environmental impact assessments to examine what lessons can be learnt for Article 6(4) as a climate impact assessment. Considering the Commission’s persistent failure to comply with Article 6(4), the article further explores possible avenues to challenge the lack of (adequate) climate consistency assessments through strategic litigation. The EU Common Agricultural Policy is used as an illustration throughout, given the long-standing criticism of its inconsistency with EU climate and environmental objectives.

The rapid deployment of renewable energy creates urgent trade-offs with biodiversity conservation. The uneven distribution of renewable energy potential and biodiversity creates a critical governance challenge: at which administrative level should goal setting and spatial planning decisions be set to best mitigate these conflicts? Here we model the expansion of solar and wind energy in China through 2060 to reach its carbon neutrality goal, to compare a centralized national planning scenario against the current provincial (more decentralized) planning approach. We find that national sitings' spatial conflict with richness-based conservation priorities is 4-7% less and overlaps 11-33% fewer species on average than the provincial planning. However, these gains are offset by greater conflicts with functional diversity and with open or dry biomes, including associated species such as the endangered marbled polecat (Vormela peregusna). By contrast, decentralized planning disproportionately overlapped more with the habitats of small-ranged forest species. Such divergence reveals the limitations of any single governance level in addressing these trade-offs, showing the imperative for integrated, multilevel siting frameworks. Effectively meeting dual climate and biodiversity targets requires strategies that synthesize strengths across administrative scales.

Translating global climate targets into national decarbonization roadmaps is profoundly uncertain. To navigate this uncertainty for China, we employ a national-scale energy system model developed in the MESSAGEix framework─calibrated to China's energy balances─that uniquely combines provincial-level resolution for key sectors with a high-granularity representation of intra-annual (48 time slices) power system dynamics. Across three temperature targets (1.5, 1.6, 2.0 °C) and six allocation principles, our analysis reveals that wind and solar consistently emerge as "no-regret" pillars, CCS is essential for heavy-industry abatement, and hydrogen's sourcing shifts with budget stringency. A critical systemic codependency exists across scenarios with stringent emissions constraints: the power sector must transform into a net carbon sink to enable the decarbonization of heavy industry, creating stark path dependencies across technology choices. The 1.6 °C pathway under the Grandfathering principle presents a pragmatic alignment with China's 2060 neutrality pledge and offers a detailed blueprint for this transition. Our provincial-level analysis distinguishes high-stakes decisions from robust "no-regret" investments, offering a framework to guide China's journey to carbon neutrality.

Aligning nature sustainability reporting requirements across national, business and farm scale: lessons from Ireland.

Accurate greenhouse gas (GHG) emissions estimations from hydropower reservoirs are critical for ensuring that this renewable energy source effectively contributes to climate mitigation. In this paper, we critically review and compare a range of methodologies, including direct field measurements (e.g. floating chambers and eddy covariance), empirical/statistical models (like G-res and HydroCalculator), process-based simulations, satellite remote sensing, machine learning (ML) techniques, and hybrid modelling frameworks that integrate these components. Our analysis evaluates each approach against key criteria: accuracy and uncertainty, scalability and transferability, and data requirements and transparency. Direct measurements remain the gold standard for site-specific validation; however, they are limited by spatial and temporal coverage and demand substantial resources. Empirical models offer simplicity but struggle to capture dynamic environmental drivers, often leading to under- or overestimation of emissions. Process-based models provide critical mechanistic insights but require extensive input data and computational resources. While satellite observations and ML enhance spatial and temporal coverage and predictive capability, explainable AI can overcome the “black-box” nature of ML Hybrid approaches that combine in-situ data, remote sensing, ML, and process-based elements show the most significant promise for an accurate and scalable emissions estimation. As the European Union and other regions work to meet stringent climate targets, robust reservoir GHG accounting is essential for guiding investments and driving mitigation actions in genuinely low-carbon hydropower. Our findings highlight the necessity of integrated monitoring networks, open-access data, and interdisciplinary collaboration to develop next-generation tools that bridge precise measurement with large-scale modelling for informed climate and energy policy. • Critical review of GHG estimation methods for hydropower reservoirs conducted. • Traditional and emerging approaches are systematically analysed and compared. • Evaluation focuses on accuracy, scalability, uncertainty and data needs. • Hybrid models with ML and remote sensing show highest potential. • Research gaps and future directions identified for policymakers and stakeholders.

Meeting global climate targets requires cost-effective strategies for reducing carbon emissions, especially in the waste management sector. Waste-to-energy (WtE) technologies are widely recognized for their greenhouse gas mitigation potential by diverting waste from disposal sites and displacing fossil fuels with energy recovered from waste. However, their cost-effectiveness for carbon abatement remains underexplored. This study introduces the Levelized Cost of Carbon Mitigation (LCOCM) as a novel metric to assess the economic feasibility of WtE technologies in Iran. The LCOCM is particularly valuable in countries where carbon pricing mechanisms such as emission trading schemes (ETS) are not yet in place, thereby serving as an alternative decision-support tool for policymakers. The results show LCOCM values of $32/ton CO₂-eq for incineration, $14/ton CO₂-eq for anaerobic digestion (AD), and $7/ton CO₂-eq for landfill gas (LFG) recovery. Financial indicators confirm that LFG and AD are economically viable options for carbon-emission reduction, with NPVs of $1.9 billion and $1.1 billion, IRRs of 28 % and 13 %, and payback periods of 3 and 7 years, respectively, whereas incineration is not viable under current conditions. Sensitivity analyses further underscore the critical role of operational optimization—such as extended lifespans, improved energy yields, and minimized downtimes—in reducing the LCOCM. This study demonstrates the cost-effectiveness of WtE technologies for carbon mitigation and provides a replicable framework for sustainable energy and climate policy in emerging economies. • Introduces LCOCM as a novel metric for assessing WtE carbon mitigation costs. • AD and LFG show strong economic feasibility with positive NPV and high IRR. • Incineration offers highest mitigation but remains financially unviable. • WtE systems could achieve up to 54 % of Iran’s 2030 emission reduction target. • Sensitivity analysis identifies CAPEX, OPEX, and efficiency as key cost drivers.

This study examines degrowth pathways as an alternative socio-economic approach that challenges the assumption that continuous economic growth is a prerequisite for human well-being and environmental sustainability. The central question is whether an intentional reduction in production and consumption could be compatible with a sustainable and equitable society. However, despite rising academic interest, quantitative tools to assess degrowth remain limited. To address this gap, this study compares three macroeconomic models based on different modelling typologies: International Futures (IFs), LowGrow SFC, and EUROGREEN. Each model’s structure, assumptions, and capacity to simulate degrowth-oriented policies were analysed in the context of the energy sector and energy transition. In IFs, a tailored degrowth scenario was created by adjusting 60 parameters obtained from the degrowth literature. In LowGrow SFC, a smaller but highly specific set of parameters was modified through its interactive interface. EUROGREEN directly enabled degrowth simulations via predefined policy packages, emphasizing sufficiency, redistribution, and behavioural change. The analysis shows that the models capture degrowth in distinct ways, reflecting their structural foundations. While IFs struggle with conceptual coherence, LowGrow SFC offers a consistent post-growth framework, and EUROGREEN aligns most closely with degrowth principles. Across models, degrowth scenarios consistently reduced CO 2 emissions to meet climate targets. The results suggest that abandoning a growth-oriented paradigm can support both social well-being and the energy transition, provided it is accompanied by robust redistributive and labour policies.

Comparative prospective life cycle assessment of decarbonization strategies in the Saudi cement industry

The increasing electrification of the heating and mobility sectors, in particular an increasing penetration of building-sector heat pumps, battery electric vehicles and rooftop photovoltaic, necessitates a spatially differentiated analysis of additional loads in order to understand when and where the electricity distribution grid might need to be extended. While numerous studies define national ramp up trajectories for heat pumps, rooftop photovoltaic systems, and battery electric vehicles, district level analyses remain scarce. This study addresses this gap by translating climate target based national pathways into development trajectories for seven representative neighborhood archetypes up to 2045. Building upon existing energetic archetype definitions, a classification of residential areas into seven archetypes is first developed to serve as the basis for scaling. National ramp-up trajectories from several studies are converted into annual growth curves and assigned to the seven neighborhood archetypes using census data and structural indicators such as building type, geometric rooftop potentials, and current as well as projected vehicle densities. The outcome is a set of archetype-specific, temporally resolved projections of building-sector heat pump, photovoltaic, and battery electric vehicle penetration until 2045 which show pronounced differences in the required ramp-up across neighborhood archetypes, with single- and two-family house areas driving most of the early adoption, while dense urban and commercial archetypes follow with delayed but steep increases. The presented work provides a robust and scalable framework applicable across Germany for simulating future energy and infrastructure developments across neighborhood structures aligned with national climate goals. • Climate-based tech ramp-ups downscaled to neighborhood archetypes to 2045. • Seven archetypes reflect Germany’s diverse energy-relevant building stock. • Yearly 2025–2045 HP, rooftop-PV and BEV penetration projections by archetype. • Method applicable across Germany.

The power sector is a major contributor to global energy use and greenhouse gas emissions. In 2020, China’s power sector accounted for over 40% of national carbon emissions, underscoring the importance of its decarbonization for achieving global climate targets. Effective emission-reduction policies require detailed and reliable carbon data. Given China’s “top-down” target-setting system, accurate regional data are essential for designing differentiated local policies and evaluating their effectiveness. In this study, we systematically estimate CO2 emissions from the power sector in 352 Chinese cities from 2000 to 2019, providing broader spatial and temporal coverage than existing datasets. Using a particle swarm optimization–back propagation algorithm, we established relationships among provincial and prefectural power generation, socioeconomic indicators, and operating revenue for the electricity and heat production and supply sectors. City-level power generation was derived based on provincial training results, combined with the 2021 electricity CO2 emissions factor published by the Ministry of Ecology and Environment and the National Bureau of Statistics, providing a valuable foundation for regional low-carbon research and policymaking.

Rice production is a major contributor to greenhouse gas (GHG) emissions. Reducing GHG emissions in this sector is essential for achieving the climate targets. However, little is known about the effectiveness of monetary incentives for promoting low-emission practices. This study investigated whether conditional monetary incentives could drive behavioral changes and GHG emission reductions among rice farmers in the Mekong Delta, Vietnam. A field experiment was conducted with 200 farmers, applying difference-in-differences regression and inverse-probability-weighted regression adjustment models alongside behavioral indicators, namely, a change scale and a best practice scale. The results showed that monetary incentives had a limited effect on reducing GHG emissions as no statistically significant differences were observed between the treatment and control groups. However, moderate improvements were observed in low-emission practices, such as reducing the seed rate, nitrogen fertilizer, and pesticide spraying within the treatment group. The findings suggest that incentive-based programs should be combined with technical support, local stakeholder engagement, behavioral nudges, and robust monitoring systems. This study provides valuable insights into the limitations and design considerations of incentive interventions for low-emission rice production. • Behavioral changes measured using change and best practice scales • Farmers in the treatment group reduced seed, nitrogen, and pesticide use • Monetary incentives had limited effects on reducing GHG emissions • Effective incentives require support, nudges, and strong monitoring

Green loans and energy-efficient technology adoption: Evidence on sectoral heterogeneity in SMEs

Achieving the ambitious climate targets set by the Paris Agreement requires a rapid transition to defossilised energy systems. The Intergovernmental Panel on Climate Change (IPCC) is instrumental in providing comprehensive assessments and policy guidance, with Integrated Assessment Models (IAM) and associated climate change mitigation scenarios being central to these evaluations. This study conducts a systematic analysis of global climate change mitigation scenarios in the IPCC Sixth Assessment Report targeting at most 1.5°C warming levels to evaluate the role of renewable energy and electrification in their mitigation pathways. Remarkably, 8.5% of scenarios analysed a target near the safe and just planetary boundaries of 1.0°C/350 ppm, essential to ensure a safe climate for civilisation. Furthermore, most IAM scenarios overestimate the role of bioenergy and fossil fuels with carbon capture and sequestration, and do not anticipate the expected growth of renewable energy technologies due to the largely missing concept of power-to-X in modelling structures. Of the 143 scenarios targeting warming to 1.5°C, only 30% project a 55% or higher share of primary renewable electricity in primary energy demand and 97% exceed the global sustainable bioenergy potential in 2100. The findings highlight the critical need for greater input assumption transparency in IAMs and to incorporate insights from a wider array of modelling perspectives to ensure credible policy recommendations regarding renewable energy and power-to-X deployment. Such advancements, via improvements to IAMs or collaboration with advanced energy system models, can guide more informed investment strategies, ultimately accelerating the transition towards safe and just climate conditions. • IAM scenarios tend to favour bioenergy instead of primary renewable electricity. • The limited power-to-X routes constrain the growth of renewables. • Greater transparency and inclusion of diverse perspectives are needed in ARs. • Cost assumptions regarding the key renewable technologies should be re-evaluated. • There are severe inconsistencies in reporting practices of models.

Energy-sector decarbonisation requires large-scale investment in low-carbon technologies, yet only a limited share flows to low- and middle-income countries, partly due to higher financing costs and perceived risks. Most modelling exercises do not fully account for how the cost of capital may vary across regions and technologies, potentially influencing policy insights. We examine how plausible, expert-informed long-term trends in de-risking clean energy and increasing risks for fossil fuels could shape decarbonisation pathways, using an empirical dataset differentiated by country and technology. We also evaluate a "corrective justice" policy that taxes corporate windfall profits and redistributes revenues to support low-carbon investments in higher-risk regions. Results suggest that incorporating differentiated cost-of-capital trajectories may improve mitigation outcomes and help narrow the gap between current commitments and long-term climate targets, while indicating potential underestimation of risks associated with bioenergy-based negative emissions technologies in mitigation scenarios for high-income nations.

Corporate transition plans (TPs) are increasingly recognized as crucial for evaluating the credibility of corporate climate commitments. However, robust and transparent methods to assess the alignment of disclosed TPs with carbon budgets remain limited. Here, we introduce an open-source methodology-the production asset-based planning approach (APA)-which uses asset-level data to estimate potential CO2 emission trajectories. We apply APA to ten electric utilities and ten steel companies and find that only three companies' disclosed TPs appear broadly compatible with Paris-aligned benchmarks. Furthermore, our estimates suggest that 12% of electric utility assets and 42% of steel assets may require reinvestment before 2030, indicating a risk of carbon lock-in. Applying this method can support more robust evaluation of TPs and enable stakeholders to hold corporations accountable to reduce CO2 emissions in line with the Paris climate targets even if corporate disclosure is not available.

Increasing global demand for nickel, an essential metal in low-carbon technologies and stainless steel, is driving a surge in mining in strongholds of tropical biodiversity. We use a global mine-by-mine supply scenario model to quantify the trade-off between meeting future nickel demand for decarbonization and conserving areas critical for achieving biodiversity and climate targets. Nickel laterites-near-surface deposits often found beneath tropical forests-account for 78 to 83% of modelled supply between 2025 and 2050. Over this timeframe, half of mined nickel threatens the top 10% of global land areas most critical for conserving biodiversity and storing carbon, but avoiding mining in these areas increases the risk of supply shortfalls. In addition, 53 to 60% of future supply comes from coastal mines, which threaten the top 10% of global priority areas for conserving marine biodiversity. While deep-sea resource development remains controversial, we show that a moratorium may increase reliance on nickel sourced from high-priority areas for conserving terrestrial and coastal marine biodiversity. Securing ecologically responsible nickel supply requires integrating terrestrial and marine conservation priorities to inform sourcing and mine development decisions, alongside efforts to mitigate unavoidable impacts, increase resource exploration and reduce long-term demand.

Offshore wind farms (OWFs) play a crucial role in reducing carbon emissions and fossil fuel dependence, and their expansion is essential for meeting European energy and climate targets. Understanding their ecological impact is therefore essential. While many environmental impacts of OWFs are systematically monitored, chemical emissions remain largely overlooked. To address this gap, sediment samples were collected within and around OWFs in the Belgian and German parts of the North Sea, along with reference samples from three types of reference sites. Non-target screening using GC-MS-EI and LC-HRMS-ESI± detected over 8000 compounds. Most of these compounds were omnipresent in the whole study area, but more than 1000 showed significantly higher abundance inside OWFs. Tentative identification suggests that some of these compounds originate from epoxy and polyurethane coatings. These findings suggest that OWFs contribute to the release of chemicals into the environment. However, the impact and effects of these chemicals are still unknown.