The substantial risks posed to Venice and its lagoon by ongoing and projected sea-level rise (SLR) require unprecedented long-term adaptation strategies. We map the evolution of development pathways and the progressive shrinking of the solution space as SLR advances, identifying adaptation tipping points and analysing the relative pros and cons of alternative measures. The analysis highlights trade-offs among environmental quality, heritage preservation, social well-being and relevant Sustainable Development Goals, and costs increasing with SLR. With present insufficient greenhouse gas mitigation policies, the current open lagoon strategy, with mobile barriers and multiple accommodation measures, is likely to encounter hard limits within the current century. Follow-up strategies include ring-dikes isolating the city from the rest of the lagoon, or a closed lagoon with permanent coastal dams, each preserving different combinations of values while entailing major ecological and socio-cultural transitions. Under extreme SLR, relocation of monuments to suitable inland areas and abandonment would be the only remaining strategy, which might become unavoidable in the 22nd century under current climate policies and an Antarctic ice-sheet collapse. Rapid mitigation could still avoid the most disruptive long-term outcomes.

In the absence of comprehensive federal greenhouse gas mitigation policy, state-led strategies may play a pivotal role, particularly following the 2024 United States presidential election. Using a detailed energy system optimization model, we examine the outcomes of 23 climate-minded states pursuing net-zero emissions targets compared to a federal carbon cap achieving equivalent CO2-eq reductions. Here we show that state-led decarbonization results in distinct technology choices, a 0.7% increase in system costs, and nationwide emissions reduction of 46% — substantial, but insufficient for ambitious climate goals. This pathway relies more on electrification, with 952 terawatt-hours more generation in 2050, reallocating 17.2% of emissions to the power sector. Some regions favor solar, wind, and storage, while direct air capture emerges as critical, particularly in California and the Northeast. Inter-regional trading supports and complicates mitigation efforts, underscoring the need for careful policy design. Overall, our findings highlight how state-led and federal decarbonization approaches can yield differing energy portfolios to achieve similar emissions reductions.

Under the "Double Carbon" target, the development of low-carbon agriculture requires a holistic comprehension of spatially and temporally explicit greenhouse gas (GHG) emissions associated with agricultural products. However, the lack of systematic evaluation at a fine scale presents considerable challenges in guiding localized strategies for mitigating GHG emissions from crop production. Here, we analyzed the county-level carbon footprint (CF) of China's rice production from 2007 to 2018 by coupling life cycle assessment and the DNDC model. Results revealed a significant annual increase of 74.3 kg CO2-eq ha-1 in the average farm-based CF (FCF), while it remained stable for the product-based CF (PCF). The CF exhibited considerable variations among counties, ranging from 2324 to 20,768 kg CO2-eq ha-1 for FCF and from 0.36 to 3.81 kg CO2-eq kg-1 for PCF in 2018. The spatiotemporal heterogeneities of FCF were predominantly influenced by field CH4 emissions, followed by diesel consumption and soil organic carbon sequestration. Scenario analysis elucidates that the national total GHG emissions from rice production could be significantly reduced through optimized irrigation (48.5%) and straw-based biogas production (18.0%). Moreover, integrating additional strategies (e.g., advanced crop management, optimized fertilization, and biodiesel application) could amplify the overall emission reduction to 76.7% while concurrently boosting the rice yield by 11.8%. Our county-level research provides valuable insights for the formulation of targeted GHG mitigation policies in rice production, thereby advancing the pursuit of carbon-neutral agricultural practices.

Methane (CH4) is a matter of environmental concern; however, global methane isotopologue data remain inadequate. This is due to the challenges posed by high-resolution testing technology and the need for larger sample volumes. Here, worldwide methane clumped isotope databases (n = 465) were compiled. We compared machine-learning (ML) models and used random forest (RF) to predict new Δ12CH2D2 distributions, which cover valuable and hard-to-replicate methane clumped isotope experimental data. Our RF model yields a reliable and continuous database including ruminants, acetoclastic methane, multiple pyrolysis, and controlled experiments. We showed the effectiveness of utilizing a new data set to quantify isotopologue fractionations in biogeochemical methane processes, as well as predicting the steady-state atmospheric methane clumped isotope composition (Δ13CH3D of +2.26 ± 0.71‰ and Δ12CH2D2 of +62.06 ± 4.42‰) with notable biological contributions. Our measured summer and winter water emitted gases (n = 6) demonstrated temperature-driven seasonal microbial community evolution determined by atmospheric clumped isotope temporal variations (Δ 13CH3D ∼ -0.91 ± 0.25 ‰ and Δ12CH2D2 ∼ +3.86 ± 0.84 ‰), which in turn is relevant for future models quantifying the contribution of methane sources and sinks. Predicting clumped isotopologues translates our methane geochemical understanding into quantifiable variables for modeling that can continue to improve predictions and potentially inform global greenhouse gas emissions and mitigation policy.

ObjectiveClimate change is expected to increase global temperatures. How temperature-related mortality risk will change is not completely understood, and how future demographic changes will affect temperature-related mortality needs to be clarified. We evaluate temperature-related mortality across Canada until 2099, accounting for age groups and scenarios of population growth.MethodsWe used daily counts of non-accidental mortality for 2000 to 2015 for all 111 health regions across Canada, incorporating in the study both urban and rural areas. A two-part time series analysis was used to estimate associations between mean daily temperatures and mortality. First, current and future daily mean temperature time series simulations were developed from Coupled Model Inter-Comparison Project 6 (CMIP6) climate model ensembles from past and projected climate change scenarios under Shared Socioeconomic Pathways (SSPs). Next, excess mortality due to heat and cold and the net difference were projected to 2099, also accounting for different regional and population aging scenarios.ResultsFor 2000 to 2015, we identified 3,343,311 non-accidental deaths. On average, a net increase of 17.31% (95% eCI: 13.99, 20.62) in temperature-related excess mortality under a higher greenhouse gas emission scenario is expected for Canada in 2090–2099, which represents a greater burden than a scenario that assumed strong levels of greenhouse gas mitigation policies (net increase of 3.29%; 95% eCI: 1.41, 5.17). The highest net increase was observed among people aged 65 and over, and the largest increases in both net and heat- and cold-related mortality were observed in population scenarios that incorporated the highest rates of aging.ConclusionCanada may expect net increases in temperature-related mortality under a higher emissions climate change scenario, compared to one assuming sustainable development. Urgent action is needed to mitigate future climate change impacts.

Greenhouse gas emission budgets and policies for zero-Carbon road transport in Europe

Climate change represents one of the major drivers of habitat modification that is affecting a wide variety of environments. In coastal environments, great effort is being put in trying to understand and forecast the possible effects of such processes, and the Sea-Level Rise (SLR) is one of the most investigated phenomena. This paper describes the possible effects of different 2100 sea-level scenarios related to greenhouse gas mitigation policies (Representative Concentration Pathways - RCPs). This work was conducted on a supralittoral habitat situated in Genova (Ligurian Sea), and has covered an eventual change of environmental conditions driven by SLR, which might impact the Culicid Acartomyiamariae, a resident species. The wave run-up stemming from the different RCPs was simulated using the XBeach model, and to infer SLR effects on A. mariae life cycle; the results were coupled with data obtained from field surveys. The model outputs highlighted a variation in the wave run-up oscillations under common wave conditions, which might affect the supralittoral area in terms of water input and hydric balance, and the A. mariae life cycle, which is highly dependent on temperature and salinity.

When it comes to greenhouse gas (GHG) mitigation, both bottom-up and top-down policies have limitations. Bottom-up policies are region-specific and cannot be applied at the national level. Top-down policies may not balance the considerations of economic growth and the environment. Therefore, a combined approach is necessary. This Vietnamese case study investigates optimal GHG mitigation options for both economic development and emission reduction by simulating four scenarios characterized by the different carbon tax and subsidy rates. Interventions, like replacing old buses with low-carbon buses and conventional electricity generation with solar power, are considered in a dynamic input–output framework. The objective function is Green GDP—industries’ total value added reflecting GHG emissions’ social cost. The simulation model comprises four cases: business as usual, low subsidy rate (up to 10%), medium subsidy rate (up to 20%), and high subsidy rate (up to 30%), which are analyzed on parameters, including economic development, GHG emissions, and development of innovative sectors, like transportation and electricity. In three cases with different subsidy rates, the optimal carbon tax is simulated at the rate of USD 1/tCO2 equivalent, the lowest rate among the world’s current carbon prices. In addition, the medium subsidy (up to 20%) option yields the most competent scheme, with the highest GHG emission reduction and economic development effectiveness.

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

Greenhouse gas emissions in British Columbia: Production versus consumption accounting from 2010 to 2015

A credible assessment of a city’s greenhouse gas (GHG) mitigation policies requires a valid account of a city’s emissions. However, questions persist as to whether cities’ ‘self-reported inventories’ (SRIs) are accurate, precise, and consistent enough to track progress toward city mitigation goals. Although useful for broad policy initiatives, city SRIs provide annual snapshots that may have limited use to city managers looking to develop targeted mitigation policies that overlap with other issues like equity, air quality, and human health. An emerging approach from the research community that integrates ‘bottom-up’ hourly, street-level emission data products with ‘top-down’ GHG atmospheric observations have begun to yield production-based (scope 1) GHG estimates that can track changes in emissions at annual and sub-annual timeframes. The use of this integrated approach offers a much-needed assessment of SRIs: the atmospheric observations are tied to international standards and the bottom-up information incorporates multiple overlapping socio-economic data. The emissions are mapped at fine scales which helps link them to attribute information (e.g. fuel types) that can further facilitate mitigation actions. Here, we describe this approach and compare results to the SRI from the City of Indianapolis which shows a yearly difference of 35% in scope 1 emissions. In the City of Baltimore, we show that granular emission information can help address multiple issues, e.g. GHG emissions, air pollution, and inequity, at the sub-zip code scale where many roots and causes for each issue exist. Finally, we show that the incorporation of atmospheric concentrations within an integrated system provides rapid, near-real-time feedback on CO2 emissions anomalies that can uncover important behavioral and economic relationships. An integrated approach to GHG monitoring, reporting and verification can ensure uniformity, and provide accuracy to city-scale GHG emissions, scalable to states and the nation—ultimately helping cities meet stated ambitions.

Numerical simulations of the global climate system provide inputs to integrated assessment modeling for estimating the impacts of greenhouse gas mitigation and other policies to address global climate change. While essential tools for this purpose, computational climate models are subject to considerable uncertainty, including intermodel "structural" uncertainty. Structural uncertainty analysis has emphasized simple or weighted averaging of the outputs of multimodel ensembles, sometimes with subjective Bayesian assignment of probabilities across models. However, choosing appropriate weights is problematic. To use climate simulations in integrated assessment, we propose, instead, framing climate model uncertainty as a problem of partial identification, or "deep" uncertainty. This terminology refers to situations in which the underlying mechanisms, dynamics, or laws governing a system are not completely known and cannot be credibly modeled definitively even in the absence of data limitations in a statistical sense. We propose the min-max regret (MMR) decision criterion to account for deep climate uncertainty in integrated assessment without weighting climate model forecasts. We develop a theoretical framework for cost-benefit analysis of climate policy based on MMR, and apply it computationally with a simple integrated assessment model. We suggest avenues for further research.

Addressing Partial Identification in Climate Modeling and Policy Analysis

World agriculture needs to find the right balance to cope with the trilemma between feeding a growing population, reducing its impact on biodiversity and minimizing greenhouse gas (GHG) emissions. In this paper, we evaluate a broad range of scenarios that achieve 4.3 GtCO2,eq/year GHG mitigation in the Agriculture, Forestry and Other Land-Use (AFOLU) sector by 2100. Scenarios include varying mixes of three GHG mitigation policies: second-generation biofuel production, dietary change and reforestation of pasture. We find that focusing mitigation on a single policy can lead to positive results for a single indicator of food security or biodiversity conservation, but with significant negative side effects on others. A balanced portfolio of all three mitigation policies, while not optimal for any single criterion, minimizes trade-offs by avoiding large negative effects on food security and biodiversity conservation. At the regional scale, the trade-off seen globally between biodiversity and food security is nuanced by different regional contexts.

Economic potential for substitution of fossil fuels with liquefied biomethane in Swedish iron and steel industry – Synergy and competition with other sectors

Modelling future patterns of urbanization, residential energy use and greenhouse gas emissions in Dar es Salaam with the Shared Socio-Economic Pathways

Multi-Dimensional Hypothetical Fuzzy Risk Simulation model for Greenhouse Gas mitigation policy development

Limiting the magnitude of climate change via stringent greenhouse gas (GHG) mitigation is necessary to prevent further biodiversity loss. However, some strategies to mitigate GHG emission involve greater land-based mitigation efforts, which may cause biodiversity loss from land-use changes. Here we estimate how climate and land-based mitigation efforts interact with global biodiversity by using an integrated assessment model framework to project potential habitat for five major taxonomic groups. We find that stringent GHG mitigation can generally bring a net benefit to global biodiversity even if land-based mitigation is adopted. This trend is strengthened in the latter half of this century. In contrast, some regions projected to experience much growth in land-based mitigation efforts (i.e., Europe and Oceania) are expected to suffer biodiversity loss. Our results support the enactment of stringent GHG mitigation policies in terms of biodiversity. To conserve local biodiversity, however, these policies must be carefully designed in conjunction with land-use regulations and societal transformation in order to minimize the conversion of natural habitats.

The impact of green house gas mitigation policy for land use and the forestry sector in Indonesia: Applying the computable general equilibrium model

This study analyzed the greenhouse gas (GHG) emissions from the transportation sector in Korea from 1990 to 2013 using Logarithmic Mean Divisia Index (LMDI) factor decomposition methods. We decomposed these emissions into six factors: The population effect, the economic growth effect due to changes in the gross domestic product per capita, the energy intensity effect due to changes in energy consumption per gross domestic product, the transportation mode effect, the energy mix effect, and the emission factor effect. The results show that some factors can cause an increase in GHG emissions predominantly influenced by the economic growth effect, followed by the population growth effect. By contrast, others can cause a decrease in GHG emissions, predominantly via the energy intensity effect. Even though the transportation mode effect has contributed to a reduction of GHG emissions, it remains relatively small compared to other factors. The energy mix and emission factor effects contributed to the reduction of GHG emissions in the early 2000s, however the effects have led to an increase of GHG emissions since the mid-2000s. Altogether, based on these results, this study suggests some GHG mitigation policies aimed at achieving the national target for this sector.