Emission Trajectories Research Articles

Greenhouse gas contribution and emission reduction potential prediction of China's aluminum industry

The aluminum industry, with its traditionally high energy consumption, high emissions and high pollution, is facing increasing pressure to reduce greenhouse gas (GHG) emissions in China. This study analyzes the trajectory and characteristics of GHG emissions during the lifecycle of China's aluminum industry (CAI) from 2011 to 2020, and identifies key driving factors affecting the changes in GHG emissions from CAI. The results indicate that the GHG emissions of CAI mainly come from indirect emissions generated by electricity production (over 69 %). Electrolytic aluminum is the largest sub process of GHG emissions in CAI. In addition, the total energy consumption effect is the main driving factor for the increase in GHG emissions from CAI. On this basis, emission reduction measures are proposed, the economic benefits and applicability of various emission reduction measures are analyzed, and the grey prediction model GM (1,1) is used to predict the GHG emission reduction potential of CAI in 2030. According to analysis, the GHG emission reduction efficiency of CAI is expected to reach 86 % by 2030, and can produce an annual economic benefit of 2.93 × 109RMB. This study will provide a theoretical basis for GHG emission reduction in CAI and even the global aluminum industry (GAI).

Modulation of High-Order Harmonic Generation from a Monolayer ZnO by Co-rotating Two-Color Circularly Polarized Laser Fields

By numerically solving the two-dimensional semiconductor Bloch equation, we study the high-order harmonic emission of a monolayer ZnO under the driving of co-rotating two-color circularly polarized laser pulses. By changing the relative phase between the fundamental frequency field and the second one, it is found that the harmonic intensity in the platform region can be significantly modulated. In the higher order, the harmonic intensity can be increased by about one order of magnitude. Through time-frequency analysis, it is demonstrated that the emission trajectory of monolayer ZnO can be controlled by the relative phase, and the harmonic enhancement is caused by the second quantum trajectory with the higher emission probability. In addition, near-circularly polarized harmonics can be generated in the co-rotating two-color circularly polarized fields. With the change of the relative phase, the harmonics in the platform region can be altered from left-handed near-circularly polarization to right-handed one. Our results can obtain high-intensity harmonic radiation with an adjustable ellipticity, which provides an opportunity for syntheses of circularly polarized attosecond pulses.

Spatiotemporal Analysis and Prediction of Carbon Emissions from Energy Consumption in China through Nighttime Light Remote Sensing

With burgeoning economic development, a surging influx of greenhouse gases, notably carbon dioxide (CO2), has precipitated global warming, thus accentuating the critical imperatives of monitoring and predicting carbon emissions. Conventional approaches employed in the examination of carbon emissions predominantly rely on energy statistics procured from the National Bureau of Statistics and local statistical bureaus. However, these conventional data sources, often encapsulated in statistical yearbooks, exclusively furnish insights into energy consumption at the national and provincial levels, so the assessment at a more granular scale, such as the municipal and county levels, poses a formidable challenge. This study, using nighttime light data and statistics records spanning from 2000 to 2019, undertook a comparative analysis, scrutinizing various modeling methodologies, encompassing linear, exponential, and logarithmic models, with the aim of assessing carbon emissions across diverse spatial scales. A multifaceted analysis unfolded, delving into the key attributes of China’s carbon emissions, spanning total carbon emissions, per capita carbon emissions, and carbon emission intensity. Spatial considerations were also paramount, encompassing an examination of carbon emissions across provincial, municipal, and county scales, as well as an intricate exploration of spatial patterns, including the displacement of the center of gravity and the application of trend analyses. These multifaceted analyses collectively contributed to the endeavor of predicting China’s future carbon emission trajectory. The findings of the study revealed that at the national scale, total carbon emissions exhibited an annual increment throughout the period spanning 2000 to 2019. Secondly, upon an in-depth evaluation of model fitting, it was evident that the logarithmic model emerged as the most adept in terms of fitting, presenting a mean R2 value of 0.83. Thirdly, the gravity center of carbon emissions in China was situated within Henan Province, and there was a discernible overall shift towards the southwest. In 2025 and 2030, it is anticipated that the average quantum of China’s carbon emissions will reach 7.82 × 102 million and 25.61 × 102 million metric tons, with Shandong Province emerging as the foremost contributor. In summary, this research serves as a robust factual underpinning and an indispensable reference point for advancing the scientific underpinnings of China’s transition to a low-carbon economy and the judicious formulation of policies governing carbon emissions.

Predicting future global temperature and greenhouse gas emissions via LSTM model

This work is executed to predict the variation in global temperature and greenhouse gas (GHG) emissions resulting from climate change and global warming, taking into consideration the natural climate cycle. A mathematical model was developed using a Recurrent Neural Network (RNN) with Long–Short-Term Memory (LSTM) model. Data sets of global temperature were collected from 800,000 BC to 1950 AD from the National Oceanic and Atmospheric Administration (NOAA). Furthermore, another data set was obtained from The National Aeronautics and Space Administration (NASA) climate website. This contained records from 1880 to 2019 of global temperature and carbon dioxide levels. Curve fitting techniques, employing Sin, Exponential, and Fourier Series functions, were utilized to reconstruct both NOAA and NASA data sets, unifying them on a consistent time scale and expanding data size by representing the same information over smaller periods. The fitting quality, assessed using the R-squared measure, ensured a thorough process enhancing the model's accuracy and providing a more precise representation of historical climate data. Subsequently, the time-series data were converted into a supervised format for effective use with the LSTM model for prediction purposes. Augmented by the Mean Squared Error (MSE) as the analyzed loss function, normalization techniques, and refined data representation from curve fitting the LSTM model revealed a sharp increase in global temperature, reaching a temperature rise of 4.8 °C by 2100. Moreover, carbon dioxide concentrations will continue to boom, attaining a value of 713 ppm in 2100. In addition, the findings indicated that the RNN algorithm (LSTM model) provided higher accuracy and reliable forecasting results as the prediction outputs were closer to the international climate models and were found to be in good agreement. This study contributes valuable insights into the trajectory of global temperature and GHG emissions, emphasizing the potential of LSTM models in climate prediction.

Responses of globally important phytoplankton species to olivine dissolution products and implications for carbon dioxide removal via ocean alkalinity enhancement

Abstract. Anthropogenic greenhouse gas emissions are leading to global temperature increases, ocean acidification, and significant ecosystem impacts. Given current emissions trajectories, the IPCC's reports indicate that rapid abatement of CO2 emissions and development of carbon dioxide removal (CDR) strategies are needed to address legacy and difficult-to-abate emissions sources. These CDR methods must efficiently and safely sequester gigatons of atmospheric CO2. Coastal enhanced weathering (CEW) via the addition of the common mineral olivine to coastal waters is one promising approach to enhance ocean alkalinity for large-scale CDR. As olivine weathers, it releases several biologically active dissolution products, including alkalinity, trace metals, and the nutrient silicate. Released trace metals can serve as micronutrients but may also be toxic at high concentrations to marine biota, including phytoplankton, which lie at the base of marine food webs. We grew six species representing several globally important phytoplankton species under elevated concentrations of olivine dissolution products via a synthetic olivine leachate (OL) based on olivine's elemental composition. We monitored their physiological and biogeochemical responses, which allowed us to determine physiological impacts and thresholds at elevated olivine leachate concentrations, in addition to individual effects of specific constituents. We found both positive and neutral responses but no evident toxic effects for two silicifying diatoms, a calcifying coccolithophore, and three cyanobacteria. In both single and competitive co-cultures, silicifiers and calcifiers benefited from olivine dissolution products like iron and silicate or enhanced alkalinity, respectively. The non-N2-fixing picocyanobacterium could use synthetic olivine-derived iron for growth, while N2-fixing cyanobacteria could not. However, other trace metals like nickel and cobalt supported cyanobacterial growth across both groups. Growth benefits to phytoplankton groups in situ will depend on species-specific responses and ambient concentrations of other required nutrients. Results suggest olivine dissolution products appear unlikely to cause negative physiological effects for any of the phytoplankton examined, even at high concentrations, and may support growth of particular taxa under some conditions. Future studies can shed light on long-term eco-evolutionary responses to olivine exposure and on the potential effects that marine microbes may in turn have on olivine dissolution rates and regional biogeochemistry.

Ecology and risks of the global plastisphere as a newly expanding microbial habitat

Plastic offers a new niche for microorganisms, the plastisphere. The ever-increasing emission of plastic waste makes it critical to understand the microbial ecology of the plastisphere and associated effects. Here, we present a global fingerprint of the plastisphere, analyzing samples collected from freshwater, seawater, and terrestrial ecosystems. The plastisphere assembles a distinct microbial community that has a clearly higher heterogeneity and a more deterministically dominated assembly compared to natural habitats. New coexistence patterns-loose and fragile networks with mostly specialist linkages among microorganisms that are rarely found in natural habitats-are seen in the plastisphere. Plastisphere microbiomes generally have a great potential to metabolize organic compounds, which could accelerate carbon turnover. Microorganisms involved in the nitrogen cycle are also altered in the plastisphere, especially in freshwater plastispheres, where a high abundance of denitrifiers may increase the release of nitrite (aquatic toxicant) and nitrous oxide (greenhouse gas). Enrichment of animal, plant, and human pathogens means that the plastisphere could become an increasingly mobile reservoir of harmful microorganisms. Our findings highlight that if the trajectory of plastic emissions is not reversed, the expanding plastisphere could pose critical planetary health challenges.

A Preliminary Assessment of Global CO2: Spatial Patterns, Temporal Trends, and Policy Implications.

This study offers a comprehensive analysis of the distribution, evolution, and driving factors of CO2 emissions from 1990 to 2016 at multiple spatial scales. Utilizing 26 indicators encompassing various facets of CO2 emissions, it is employed principal component analysis (PCA) and empirical orthogonal functions (EOFs) to identify the dominant characteristics of global CO2 emissions. This model retained three core components, accounting for 93% of the global CO2 variation, reflecting emission trajectories and associated economic metrics, such as Gross domestic product (GDP). The analysis differentiated the effects of these components based on countries' economic standings. Using a novel aggregated index, significant national contributors to global CO2 emissions are pinpointed. Notably, the leading contributors are found among developed nations (e.g., the United States, Canada, Japan), Gulf states (e.g., Saudi Arabia, Qatar), and emerging economies (e.g., China, Brazil, Mexico). Furthermore, these results highlight that shifts in global CO2 emissions over the past 30 years are predominantly influenced by factors like industrial emissions and GDP. Results also demonstrate a distinct relationship between a country's CO2 emissions and its physical and socioeconomic factors. Specifically, the nation's coastline length, population density in coastal regions, and the diversity of its climatic conditions significantly influence its carbon footprint.

Examining climate ambition enhancement in ASEAN countries’ nationally determined contributions

With its rapidly growing population and economy, ASEAN's medium-term climate ambition is crucial to global mitigation efforts. While all ASEAN Member States (AMS) have communicated their medium-term ambitions through updated Nationally Determined Contributions (NDCs) submitted in 2020–2021, the region's emissions trajectory is still not in line with a 1.5 or 2-degree scenario. However, assessing and comparing ASEAN NDCs is a complex task given the varying types of mitigation targets and actions chosen by each country. In this article, we apply an assessment framework to examine AMS' strategies for raising mitigation ambition. Overall, AMS' economy-wide targets need to be stronger to be Paris-aligned, but the growth in sector-scale targets shows progress in the development and mainstreaming of climate strategies. By identifying similar approaches to ambition enhancement, we placed AMS in four groups, each with different needs and recommendations for the planning of successive NDCs; thus clarifying the varying stages of progress in climate ambition among AMS. Cambodia, Laos and Myanmar set or strengthened economy-wide and sectoral targets and consolidated new mitigation actions, and can explore the expansion of target coverage in future. Brunei, Singapore and Vietnam made greater efforts to strengthen, add, or expand coverage of economy-wide GHG targets, but expert and public perceptions suggest that overall ambition gaps remain. Malaysia and the Philippines enhanced their economy wide targets but could improve clarity on sector-scale implementation. Indonesia and Thailand did not add significant updates on mitigation ambition in their NDCs compared to other national planning documents and may consider including more details in future NDCs to facilitate international support.

Identifying emission sources of CH4 in East Asia based on in-situ observations of atmospheric δ13C-CH4 and C2H6

Methane (CH4) is the second most important greenhouse gas influenced by human activity. The increase in atmospheric CH4 concentrations contributed ~23 % to the anthropogenic radiative forcing (Saunois et al., 2020). The current anthropogenic CH4 emissions trajectory implies that large emissions reductions are needed to meet the target of the Paris Agreement (Nisbet et al., 2019). For effective regulation of CH4, it is important to identify spatiotemporal emission sources, in particular those from East Asia – one of the largest CH4 emitters. In this study, we present in-situ observations of atmospheric CH4 concentrations (i.e., dry air mole fractions in part per billion (ppb)) and carbon isotopic compositions of CH4 made during 2017–2020 at the Gosan station (GSN, 33.3°N, 126.2°E, 72 m a.s.l) which is representative of regional background conditions in East Asia. The annual growth rate of the observed CH4 baseline concentrations was 11 ± 1 ppb yr−1. The enhanced pollution concentrations of CH4 showed seasonally distinctive correlations with the corresponding δ13C-CH4. The CH4 source isotopic signature for winter derived based on both the Keeling and Miller-Tans approaches was −40.7 ± 3.4 ‰, suggesting dominant thermogenic sources (e.g., coal and/or gas combustion), whereas the source signature for summer was estimated as −54.1 ± 1.2 ‰, which seemed to represent both microbial sources (e.g., rice paddies) and fossil fuel sources of CH4 emissions. Based on the δ13C-CH4 source signatures, we were able to infer that the proportional contribution of microbial sources to CH4 summer emissions was ranges from 45 to 79 %. The finding indicates that microbial sources account for a substantial portion of CH4 summer emissions, consistent with estimates of 74–80 % derived from the observed correlation between CH4 and C2H6, which serves as a complementary tracer for fossil fuel sources.

Kenya’s Low Carbon Futures: An Assessment Using the KCERT Model

KCERT 2050 is a modelling tool designed to assist in the identification and evaluation of synergies and trade-offs within sectoral decarbonization pathways for Kenya. KCERT 2050 is positioned as a user-friendly and dynamic tool that bridges complex energy systems and emissions models with integrated impact assessment tools, aimed at aiding decision making towards carbon neutrality in both public and private sectors. The tool considers greenhouse gas emissions from various economic sectors and is validated through a collaborative process involving experts from diverse backgrounds. This study uses KCERT 2050 to examine the prospects of achieving a net−zero emissions pathway by 2050. In the baseline scenario, a significant emission trajectory is observed, with the transport sector emerging as the largest contributor. Transitioning to the net−zero pathway reveals substantial reductions across key sectors, such as transport, industry, and land use, driven by strategies including electrification, waste reduction, and afforestation. The sensitivity analysis underscores the potential for emission mitigation through various levers, including land use optimization and the adoption of cleaner transportation modes. In conclusion, our findings emphasize the potential and feasibility of Kenya’s ambitious net−zero emissions target. To attain this goal, it is imperative to prioritize sustainable land use and innovative waste management strategies.

China can peak its energy-related CO2 emissions before 2030: Evidence from driving factors

China has pledged to achieve the CO2 emissions peak by 2030. This study aims to ascertain whether China can meet its commitment by predicting CO2 emissions. Based on industrial structure, energy structure, and energy intensity data forecasted by appropriate time series models, this study uses the Monte Carlo method to simulate the pathway of GDP growth rates and predict the trajectory of China's CO2 emissions from 2021 to 2035. The results reveal a 43.60% probability that China's CO2 emissions have peaked at 10.55 Gt in 2021. Furthermore, it is highly probable that China will achieve its CO2 emissions peak between 2023 and 2028. As the peak year shifts from 2023 to 2028, the mean value of CO2 emissions peak increases from 10.56 Gt to 10.71 Gt. Finally, the study employs the LMDI method to analyze the contributions of different driving factors to the CO2 emissions peak. The results indicate that energy structure transformation will be the primary factor driving the CO2 emissions peak, demonstrating the importance of transiting to a low-carbon energy system.

Optimizing Freight Vehicle Routing in Dynamic Time-Varying Networks with Carbon Dioxide Emission Trajectory Analysis

In this study, we formulate a freight vehicle path-planning model in the context of dynamic time-varying networks that aims to capture the spatial and temporal distribution characteristics inherent in the carbon dioxide emission trajectories of freight vehicles. Central to this model is the minimization of total carbon dioxide emissions from vehicle distribution, based on the comprehensive modal emission model (CMEM). Our model also employs the freight vehicle travel time discretization technique and the dynamic time-varying multi-path selection strategy. We then design an improved genetic algorithm to solve this complicated problem. Empirical results vividly illustrate the superior performance of our model over alternative objective function models. In addition, our observations highlight the central role of accurate period partitioning in time segmentation considerations. Finally, the experimental results underline that our multi-path model is able to detect the imprint of holiday-related effects on the spatial and temporal distribution of carbon dioxide emission trajectories, especially when compared to traditional single-path models.

Understanding the challenge of decoupling transport-related CO2 emissions from economic growth in developing countries

The transition to net zero requires full decarbonization of the transport sector, currently one of the leading sources of emissions globally. Transport-related carbon emissions are growing fastest in the developing world. This makes it particularly critical to understand whether low- and middle-income countries are making progress towards decoupling transport sector emissions from economic growth, as well as the extent to which their current emissions trajectory is being shaped by structural factors versus more amenable policy choices. This paper assembles and analyzes a comprehensive dataset on transport-related emissions with a uniquely broad coverage of developing countries. The paper employs the Tapio decoupling model over the period 1990–2018 to demonstrate that high-income countries are almost twice as likely to have reached relative decoupling as low and middle-income countries (70 vs 36 percent, while the latter are almost twice as likely to be in a state of negative decoupling as the former (17 versus 41 percent). This paper conducts index-decomposition and econometric analysis to shed light on the factors driving transport-related carbon emissions. Index decomposition reveals that there have been only relatively modest reductions in the transport emissions intensity of GDP since 1990 and that these have not been large enough to offset economic growth in middle-income countries and demographic growth in low-income countries. Regression analysis further shows that urbanization and industrialization are important correlates of transport-related emissions, while the correlation of policy choices with reduced emissions is rather weak.

Evolution characteristics of rural carbon emissions in Northwest China from 2006 to 2019

China is faced with significant challenges in simultaneously promoting rural development and reducing carbon emissions. However, the issue of quantifying and addressing carbon emissions in rural areas has not been adequately addressed. Accurately quantifying these emissions is crucial for developing effective strategies to reduce carbon output. In this study, the historical evolution and spatial distribution of rural carbon emissions in northwestern China from 2006 to 2019 were evaluated across five key sectors: residential energy consumption, agricultural machinery, solid waste management, planting practices, and breeding industry activities. During this period, total carbon emissions in rural areas of northwest China steadily increased from 60.15Mt to 83.49Mt at an annual growth rate of 2.55%. Given the complex interplay between economic and social factors driving these changes, the future trajectory of rural carbon emissions remains uncertain. To analyze the underlying drivers behind regional variations in carbon emissions over time, we constructed an LMDI model which revealed that economic growth primarily contributed to regional increases in carbon output. Furthermore, due to a remarkable annual growth rate of 35.17% in renewable energy generation (such as photovoltaic and wind power), it can be inferred that if renewable electricity were included within our calculations for carbon emission statistics, northwest China’s rural areas achieved a state of being effectively ‘carbon-neutral’ by 2019 solely from a production-based perspective.

Global methane pledge versus carbon dioxide emission reduction

Methane (CH4) is a potent greenhouse gas whose contribution to anthropogenic radiative forcing of the climate system is second only to carbon dioxide (CO2). CH4 emission reduction has become critical to global climate mitigation policy, resulting most notably in the global methane pledge (GMP), pledging a 30% reduction of CH4 emissions by 2030. Methane is, however, much shorter-lived in the atmosphere than CO2, so emissions reductions may have different impacts on global warming over time. We quantify the difference over time in global annual mean surface temperature of the GMP versus the equivalent amount of CO2 emission reduction. The avoidance of CH4 emissions in the 2020s due to the GMP initially results in greater relative cooling than the avoidance of the equivalent amount of CO2 emissions over the same period, but less relative cooling after ∼2060, when almost all CH4 emitted during the 2020s has been removed from the atmosphere but much of the CO2 emitted during the 2020s remains. However, if the GMP places the world on a lower CH4 emissions trajectory after 2030, this results in a persistently and substantially greater reduction to global warming than the equivalent change in the CO2 emissions trajectory, with a maximum difference of 0.22 ± 0.06 ∘C in 2055 and relative cooling for well over a century. This equates to a large difference in avoided climate change damages if momentum in CH4 emission reduction from the GMP can be sustained after the 2020s. While the greatest reduction in warming is obtained by reducing both CH4 and CO2 emissions, our results underscore the striking global societal benefits of sustained reduction in CH4 emissions.

Prediction of CO2 emission peak and reduction potential of Beijing-Tianjin-Hebei urban agglomeration

Forecasting the future emission trajectories and the relating sensitive driving factors of emissions for cities is of great significance to formulate realizable CO2 mitigation policies. To proceed the forecasting, studies on peak prediction and quantification of reduction potential at the city level are essential. However, the studies in the area are very limited. Selecting the Beijing-Tianjin-Hebei urban agglomeration (BTH) as the study region, this paper aims to contribute to the research area and provides implications for other cities or urban agglomerations. The Kaya identity and multi-scenario simulation were employed to predict the dynamic evolution pathways of CO2 emissions from 2021 to 2035 and explore the differential CO2 peak time, peak value, and reduction potential for 13 cities in BTH. Monte Carlo simulation, Mann-Kendall trend test and Sen's slope estimation method are jointly used to reduce uncertainties in estimation. The Monte Carlo simulation results show that most cities in BTH have already reached their CO2 emissions peak, while Tianjin, Langfang, Cangzhou and Tangshan are expected to reach their peaks between 2025 and 2030. Among them, 5 and 8 cities have the risk of not reaching their peak before 2035 in the high consumption scenario (HCS) and extensive development scenario (EDS) respectively. Comparative analysis reveals that low-carbon scenario (LCS) and sustainable development scenario (SDS) have significant effects on emissions reductions. The top three cities in terms of accumulative emission reduction in 2021–2035 are Tianjin, Tangshan and Cangzhou, estimated as 117.82–250.75 Mt CO2 in LCS and 217.77–454.10 Mt CO2 in SDS, respectively. The results of sensitivity analysis reveal that the most critical driver of CO2 emissions in Beijing is population, while that is GDP per capita for Tianjin. Langfang and Hengshui showed the highest sensitivity to energy intensity. Accordingly, these cities have differentiated concerns and priorities to achieve their carbon peak goal as scheduled.

From political pledges to quantitative mapping of climate mitigation plans: Comparison of two European cities

BackgroundUrban agglomerates play a crucial role in reaching global climate objectives. Many cities have committed to reducing their greenhouse gas emissions, but current emission trends remain unverifiable. Atmospheric monitoring of greenhouse gases offers an independent and transparent strategy to measure urban emissions. However, careful design of the monitoring network is crucial to be able to monitor the most important sectors as well as adjust to rapidly changing urban landscapes.ResultsOur study of Paris and Munich demonstrates how climate action plans, carbon emission inventories, and urban development plans can help design optimal atmospheric monitoring networks. We show that these two European cities display widely different trajectories in space and time, reflecting different emission reduction strategies and constraints due to administrative boundaries. The projected carbon emissions rely on future actions, hence uncertain, and we demonstrate how emission reductions vary significantly at the sub-city level.ConclusionsWe conclude that quantified individual cities’ climate actions are essential to construct more robust emissions trajectories at the city scale. Also, harmonization and compatibility of plans from various cities are necessary to make inter-comparisons of city climate targets possible. Furthermore, dense atmospheric networks extending beyond the city limits are needed to track emission trends over the coming decades.

Comparing the impacts of ozone-depleting substances and carbon dioxide on Arctic sea ice loss

The rapid decline of Arctic sea ice is widely believed to be a consequence of increasing atmospheric concentrations of greenhouse gases (GHGs). While carbon dioxide (CO2) is the dominant GHG contributor, recent work has highlighted a substantial role for ozone-depleting substances (ODS) in Arctic sea ice loss. However, a careful analysis of the mechanisms and relative impacts of CO2 versus ODS on Arctic sea ice loss has yet to be performed. This study performs this comparison over the period 1955–2005 when concentrations of ODS increased rapidly, by analyzing a suite of all-but-one-forcing ensembles of climate model integrations, designed to isolate the forced response to individual forcing agents in the context of internal climate variability. We show that ODS have played a significant role in year-round Arctic sea ice extent and volume trends over that period, accounting for 64% and 32% of extent and volume trends, respectively. These impacts represent 50% and 38% of the impact from CO2 forcing, respectively. We find that ODS act via similar physical processes to CO2, causing sea ice loss via increased summer melt, and not sea ice dynamics changes. These findings imply that the future trajectory of ODS emissions will play an important role in future Arctic sea ice evolution.

How do logistics and financial ındicators contribute to carbon emissions in Turkiye?

Increasing carbon emissions (CO2) due to factors such as energy consumption (enco), industrialization, increase in world population, and decrease in green areas with the industrial revolution is one of the main causes of both climate change and global warming. In this context, due to the increasing commercial activities in Turkiye, the rapid growth of energy consumption and greenhouse gas (GHG) emissions in the logistics sector alert the government. However, there is a lack of standard measures for evaluating GHG emissions generated from freight transport operations. To improve this situation, Turkiye's policymakers need to evaluate GHG emissions for energy saving and pollution reduction. This background leads us to examine the GHG emission trajectories and features of Turkiye's freight transport patterns in the last three decades. In this context, it is aimed to determine the impacts of financial development (findev), GDP per capita, energy consumption, and amount of freight carried by rail and road on CO2 emissions within the framework of 1990-2021 time-series data for Turkiye. By doing so, the ARDL bound testing cointegration test is employed and observes that independent variables have similar and different effects on CO2 emissions. Energy consumption, findev, and per capita income variables have a positive effect on CO2 emissions in Turkiye. According to these results, it is seen that the environmental Kuznets curve (EKC) is valid in Turkiye. However, the effect of rail and road freight transport (FT) on CO2 emissions is negative. The unexpected finding is related to road FT. The amount of freight transported by road has a decreasing effect on CO2 emissions in Turkiye. This paradoxical situation in Turkiye may be due to the developments in the transportation infrastructure, which has enabled the convergence of space and time in recent years, young and modern vehicle fleets, and the efficiency provided through logistics companies. The findings will assist in formulating specific and effective policies for Turkiye's transport sector.

Climate uncertainty impacts on optimal mitigation pathways and social cost of carbon

Emissions pathways used in climate policy analysis are often derived from integrated assessment models. However, such emissions pathways do not typically include climate feedbacks on socioeconomic systems and by extension do not consider climate uncertainty in their construction. We use a well-known cost-benefit integrated assessment model, the Dynamic Integrated Climate-Economy (DICE) model, with its climate component replaced by the Finite-amplitude Impulse Response (FaIR) model (v2.1). The climate uncertainty in FaIR is sampled with an ensemble that is consistent with historically observed climate and Intergovernmental Panel on Climate Change (IPCC) assessed ranges of key climate variables such as equilibrium climate sensitivity (ECS). By varying discounting assumptions, three scenarios are produced: a pathway similar to the ‘optimal welfare’ scenario of DICE that has similar warming outcomes to current policies, and pathways that limit warming to ‘well-below’ 2 and 1.5 with a short-term overshoot, aiming to meet Paris Agreement long-term temperature goals. Climate uncertainty alone is responsible for a factor of five variation (5%–95% range) in the social cost of carbon (SCC) in the 1.5 overshoot scenario, with the spread in SCC increasing in relative terms with increasing stringency of climate target. CO2 emissions trajectories resulting from the optimal level of emissions abatement in all pathways are also sensitive to climate uncertainty, with 2050 emissions ranging from −12 to +14 GtCO2 yr−1 in the 1.5 scenario. ECS and the strength of present-day aerosol effective radiative forcing are strong determinants of SCC and mid-century CO2 emissions. This shows that narrowing climate uncertainty leads to more refined estimates for the social cost of carbon and provides more certainty about the optimal rate of emissions abatement. Including climate and climate uncertainty in integrated assessment model derived emissions scenarios would address a key missing feedback in scenario construction.