Historical Emissions Research Articles

Deploying human and non-human ecosystem engineers to rebuild nature for people

Protecting and rebuilding nature can deal with 30% of historic carbon dioxide emissions and prevent ecosystem collapse that will otherwise imperil human society. Our collective climate resilience can be increased by restoring bigger, more dynamic ecosystems. Traditional conservation approaches cannot do this. Human and non-human ‘ecosystem engineers’ need to work alongside one another to ensure that keystone species can range across our landscapes (‘re-animating’ them) to increase their long-term carbon dioxide lock-up value, maximise their ongoing biodiversity value and concurrently increase the other ecosystem services that they provide and on which people ultimately depend. However, ecosystem engineer species such as large herbivores cannot move across landscapes that have been severed by infrastructure many other species need to be enables to move as the climate warms. This implies the need for a strategic nature network, linking ecosystems under restoration by way of physical crossings that overcome barriers such as roads. Human engineers can play a leading role in enabling all these key elements of rebuilding nature – from working on individual structures and projects, to applying nature-positive systems-thinking in their day-to-day jobs and advocating for ambitious nature recovery. We can all help engineer a more nature-positive future. We need to all become ecosystem engineers.

Climate Change, Historical Emissions, and Unjust Benefits: A Comment on Axel Gosseries’ Account of Climate Justice

One of the claims Axel Gosseries makes in What is Intergenerational Justice? is that greenhouse gas emissions produced before 1990 are morally unimportant for present climate duties. This article challenges that claim by drawing on the idea that present people may have special duties to combat climate change in virtue of benefiting from past emissions. 

Forecasting nitrous oxide emissions from a full-scale wastewater treatment plant using LSTM-based deep learning models

Nitrous oxide (N2O) emissions from wastewater treatment plants (WWTPs) exhibit significant seasonal variability, making accurate predictions with conventional biokinetic models difficult due to complex and poorly understood biochemical processes. This study addresses these challenges by exploring data-driven alternatives, using long short-term memory (LSTM) based encoder-decoder models as basis. The models were developed for future integration into a model predictive control framework, aiming to reduce N2O emissions by forecasting these over varying prediction horizons. The models were trained on 12 months and tested on 3 months of data from a full-scale WWTP in Amsterdam West, the Netherlands. The dataset encompasses seasonal peaks in N2O emissions typical for winter and spring months. The best performing model, featuring a 256-256 LSTM architecture, achieved the highest accuracy with test R2 values up to 0.98 across prediction horizons spanning 0.5 to 6.0 hours ahead. Feature importance analysis identified past N2O emissions, influent flowrate, NH4+, NOx, and dissolved oxygen (DO) in the aerobic tank as most significant inputs. The observed decreasing influence of historical N2O emissions over extended prediction horizons highlights the importance and significance of process variables for the model's performance. The model's ability to accurately forecast short-term N2O emissions and capture immediate trends highlights its potential for operational use in controlling emissions in WWTPs. Further research incorporating diverse datasets and biochemical process inputs related to microbial activities in the N2O production pathways could improve the model's accuracy for longer forecasting horizons. These findings advocate for hybridising deep learning models with biokinetic and mechanistic insights to enhance prediction accuracy and interpretability.

Large scale study on PFASs levels in fruits, vegetables and soil from allotments and gardens contaminated by atmospheric deposition from a Dutch fluorochemical production plant

Citizens grow their own fruits and vegetables in allotment gardens in the vicinity of a fluorochemical production plant (FCPP) in The Netherlands. Historic emissions and the subsequent atmospheric deposition of perfluorooctanoic acid (PFOA) and GenX (hexafluoropropylene oxide-dimer acid / HFPO-DA) from the FCPP have resulted in the nearby environment being contaminated with per- and polyfluoroalkyl substances (PFASs). This research aimed to investigate the levels of PFASs in garden produce and whether a gradient can be observed in relation to distance from the FCPP. Furthermore, differences between certain types of fruits and vegetables were explored, as well as a potential relation between the measured concentrations in garden produce and soil. 737 fruit and vegetable samples were collected from 17 allotments and 4 gardens up to 20 kilometres from the FCPP, along with soil and water samples. Garden produce included fruits, potatoes, fruiting vegetables, brassicas, leafy vegetables, root vegetables, bulb vegetables, legumes and stem vegetables. PFASs concentrations in the samples were quantified using a very sensitive UPLC-MS/MS method. PFASs were detected in most samples above the analytical limit of detection (0.3 to 12.5 pg/g ww). PFOA and GenX were found in the highest concentrations (up to 5280 pg/g ww GenX and 3020 pg/g ww PFOA) in garden produce sampled downwind and close to the FCPP. Other PFASs were also found, but at (much) lower levels. Field-derived bioaccumulation factors (BAFs) were calculated for PFOA and GenX. The BAFs for PFOA were shown to be approximately 1 order of magnitude lower than BAFs from other studies. This may be explained by aging of the PFASs contamination and the intense cultivation of the garden plots. This study shows that PFOA and GenX can end up in garden produce and this will result in human exposure when the garden produce is consumed.

An adaptive prediction method for carbon emissions of power systems containing new energy based on least-squares support vector machine

Aiming at the problem of high volatility and intermittency of new energy-containing power systems, which affects the prediction accuracy of carbon emissions, we study the adaptive prediction method of carbon emissions of a new energy-containing power system based on a least-squares support vector machine. The carbon emission coefficients of the nodes of the new energy-containing power system are determined based on the trend analysis method. Historical carbon emissions and carbon emission coefficients of the power system are collected, and the rough set method is used to simplify the carbon emission attributes and obtain a simplified attribute set for carbon emission prediction. The least-squares support vector mechanism is used to construct the carbon emission prediction model, and the simplified attribute set is used as the input of the carbon emission prediction model. The whale optimization algorithm was selected to determine the optimal parameters of the least-squares support vector machine through the process of encircling prey, hunting behavior, searching for predation, and adaptively optimizing the least-squares support vector machine to output carbon emission prediction results. The experimental results show that the method can accurately predict the carbon emissions of the power system, and the mean square error of the carbon emissions prediction is lower than 7.

Global carbon transition in the passenger transportation sector over 2000–2021

The high-emitting and hard-to-abate passenger transport sector plays a crucial role in global deep decarbonization. To lead an equitable and rapid transition in the passenger transportation, this work is the first to develop a bottom-up modeling framework integrated with the latest decomposing structural decomposition methodology to assess and compare historical emission patterns and decarbonization efforts of 28 countries over the past two decades. Results indicate: (1) Carbon emissions from the global passenger transport sector increased between 2000 and 2021, peaking in 2019, with GDP per capita and population size being key drivers of rising carbon emissions across countries. (2) The decarbonization efforts of the global passenger transport sector varied by traffic mode. The largest contributors were passenger buses [−0.46 megatons of carbon dioxide per year (Mt CO2/yr)], followed by trains (−0.4 Mt CO2/yr), and airplanes (−0.28 Mt CO2/yr), while passenger cars (1.04 Mt CO2/yr) hindered the decarbonization process. (3) Although the global passenger transport sector has cumulatively decarbonized 3005.9 Mt CO2 and achieved a decarbonization rate of 5.1 %, regional performance varied significantly, exhibiting uneven and inadequate progress. Overall, the study provides an effective data-driven assessment framework for reviewing and comparing global and national passenger transport decarbonization performance, which will facilitate the planning of decarbonization pathways by global emitters and the early achievement of zero-carbon transport.

Allocating inter-provincial CEA in China based on the utility perspective --a method for improving the variable weight function

Introduction:At different times, China has pursued different carbon emission reduction targets, so it is crucial to develop a reasonable and flexible allocation scheme for Chinese carbon emissions quotas, referred to as Chinese Emission Allowance (CEA), in order to achieve carbon reduction goals. As important responsible entities for carbon reduction, each province needs to rely on a well-designed CEA allocation scheme to help achieve their emission reduction goals.Methods:Therefore, based on the utility perspective, this paper constructs allocation principles and methods to formulate the inter-provincial CEA allocation scheme for China in 2030. Specifically, the entropy method, SBM model, improved variable weight function, and ARIMA time series model are sequentially adopted to simulate the re-allocation scheme, examine its rationality, and develop CEA allocation schemes under different principles.Results and Discussion:The following conclusions are drawn: 1) The allocation scheme formulated based on historical emission simulation methods, industry benchmark methods, and other current CEA allocation methods has certain irrationality, and future CEA allocation should not follow the original methods; 2) The improved variable weight function is better suited for allocation in CEA than the current original allocation method. The allocation scheme developed under this method, which balances fairness and efficiency principles, is more appropriate for the actual reduction of carbon emissions in China.

Integrating remotely sensed imagery in a forest gap model to study North American boreal forests in a changing world

Abstract Boreal forests of Alaska and Western Canada are experiencing rapid climate change characterized by higher temperatures, more extreme droughts, and changing disturbance regimes, resulting in forest mortality and composition changes. Mechanistic models are increasingly important for predicting future forest trends as the region experiences novel environmental change. Previously, many process-based models have generated starting conditions by ‘spinning up’ to equilibrium. However, setting appropriate initial conditions remains a persistent challenge in using mechanistic forest models, where stochastic events and latent parameters governing tree establishment have long-lasting impacts on simulation outcomes. Recent advances in remote sensing analysis provide information that can help address this issue. We updated an individual-based gap model, the University of Virginia Forest Model Enhanced (UVAFME), to include initial conditions derived from aerial and satellite imagery at two locations. Following these updates, material legacies (e.g., trees, seed banks, soil organic layer) allowed new forest types to persist in UVAFME simulations, landscape-level forest heterogeneity increased, and forest-wide biomass estimates increased. At both study sites, initialization from remotely sensed data had a strong impact on forest cover and volume. Climate change impacts were simulated decades earlier than when the model was ‘spun up'. In Alaska’s Tanana Valley State Forest, warmer climate scenarios drove deciduous expansion, increased drought stress, and resulted in a 28% decrease in overall biomass by 2100 between historical and high emissions climate scenarios. At a lowland site in Northern British Columbia, lodgepole pine (Pinus contorta) remained dominant and became more productive with exogenous climate forcing as temperature, nutrient, and flooding limitations decreased. These case studies demonstrate a new framework for forest modeling and emphasize the advantages of integrating remotely sensed data with mechanistic models, thereby laying groundwork for future research that explores near-term impacts of non-stationary ecological change.

A novel deep ensemble-based model with outlier removal and order-invariant ranking for carbon dioxide emission prediction.

Excessive carbon dioxide ( ) emissions pose a formidable challenge, driving global climate change and necessitating urgent attention. Striking a balance between curbing emissions and fostering economic growth hinges upon the ability to reliably forecast emissions. Such forecasts are indispensable for policymakers as they endeavor to make informed decisions and proactively implement mitigation measures. In this research, we introduce an innovative deep ensemble prediction model for emissions. This model is constructed around four parallel Long Short-Term Memory (LSTM) neural networks, complemented by a novel Multi-Layer Perception (MLP)-based ensemble framework, equipped with an outlier detection mechanism and an order-invariant ranking module. To enhance prediction accuracy and stability, a k-nearest neighbor (KNN)-based outlier detection module is employed to identify non-outliers and reasonable predictions for the ensemble models. Additionally, a novel feature ranking module is proposed to mitigate prediction fluctuations. The performance evaluation of our model is conducted using historical emission data spanning from 1971 to 2021, encompassing six representative countries. Our findings demonstrate that the proposed methodology outperforms existing approaches across various evaluation metrics, offering considerably reduced prediction variances and greater stability. Moreover, long-term emission predictions for the corresponding six countries have been provided, which might offer policymakers some basis for making decisions.

Historical exposure to metals in contaminated areas and its impact on cardio- and cerebrovascular health

Environmental pollution is of global concern. In Southeast Sweden, historical glass production has contaminated communities with toxic metals. Long-term residency in these communities and high consumption of local foods may constitute a risk for cardiovascular disease (CVD) or stroke.The current study investigates if residency in these contaminated sites and long-term consumption of local foods is associated with self-reported CVD and stroke. In addition, the body burden of the toxic metals arsenic, cadmium, and lead, as well as inflammatory protein markers, were studied for association with CVD and stroke.From an existing questionnaire cohort and biomonitoring sub cohort (n = 2290/882) of people living in the contaminated areas, self-reported CVD cases (n = 366/166) and stroke cases (n = 78/25) were identified. Individuals were grouped based on their residency within a 2 km radius of glassworks with historical high, moderate, or low air-borne lead emissions. Body burden of arsenic, cadmium, and lead was analyzed using ICP-MS. Inflammatory markers were investigated using electrochemiluminescence.Long-term residency near glassworks with historically high levels of lead emissions, and high consumption of local foods, were associated with CVD. The risk was increasing for each year of residency in high emission areas. Increased body burden of arsenic in blood, and lead in urine, were associated with stroke. Five and two inflammatory markers, respectively, were elevated in CVD and stroke cases after adjusting for confounders.An increased risk for CVD was found in areas with historically high emissions of lead but possibly also other toxic metals. Interestingly, there was an indication of a dose-response relationship with increasing risk for CVD per year of residency time. Inhalation and consumption of local food may constitute major pathways for this association. The study shows that long-term exposure to toxic metals in these contaminated areas is associated with CVD and that there is a need to limit exposure in the general population.

Threatening the Poor? The economic impacts of carbon border adjustment mechanism on developing countries

It is highly necessary to conduct a more detailed assessment of the economic impacts caused by the Carbon Border Adjustment Mechanism (CBAM), especially for numerous developing countries. This study utilizes the input-output (IO) model to evaluate the inequality consequence of CBAM. Results indicate that 1) the overall impact of output decline and production cost increase on non-OECD countries is greater than that on OECD countries; 2) regionally, African countries experience a more severe output decline, while Asian countries see a higher increase in production costs. Apart from BRICS, other developing countries demonstrate low reduction efficiency; 3) a refund mechanism based on historical emission responsibility results in larger benefits for the LDCs. Conversely, green innovation contribution and emission reduction efforts primarily benefit developed economies and large developing countries. We recommend 1) prioritizing assessments for developing countries before designing cross-border climate policy; 2) considering both fairness and efficiency when designing a refund mechanism.

Per- and polyfluoroalkyl substances (PFAS) in homegrown crops: Accumulation and human risk assessment

Homegrown crops can present a significant exposure source of per- and polyfluoroalkyl substances (PFAS) to humans. Field studies studying PFAS accumulation in multiple vegetable food categories and examining the potential influence of soil characteristics on vegetable bioavailability under realistic exposure conditions are very scarce. Crop PFAS accumulation depends on a complex combination of factors. The physicochemical differences among the numerous PFAS makes risk assessment very challenging. Thus, simplification of this complexity into key factors that govern crop PFAS accumulation is critical. This study analyzed 29 targeted legacy, precursor and emerging PFAS in the vertical soil profile (0–45 cm depth), rainwater and edible crop parts of 88 private gardens, at different distances from a major fluorochemical plant. Gardens closer to the plant site showed higher soil concentrations which could be linked with historical and recent industrial emissions. Most compounds showed little variation along the soil depth profile, regardless of the distance from the plant site, which could be due to gardening practices. Annual crops consistently accumulated higher sum PFAS concentrations than perennials. Highest concentrations were observed in vegetables, followed by fruits and walnuts. Single soil-crop relationships were weak, which indicated that other factors (e.g., porewater) may be better measures of bioavailability in homegrown crop accumulation. Regression models, which additionally considered soil characteristics showed limited predictive power (all R2 ≤ 35%), possibly due to low variability in crop concentrations. Human intake estimations revealed that the PFAS exposure risk via crop consumption was similar nearby and remotely from the plant site, although the contribution to the overall dietary exposure can be relatively large. The tolerable weekly intake was frequently exceeded with respect to fruit and vegetable consumption, thus potential health risks cannot be ruled out.

Carcinogenic industrial air pollution and postmenopausal breast cancer risk in the National Institutes of Health AARP Diet and Health Study

BackgroundChemicals emitted from industrial facilities include known or suspected mammary carcinogens and endocrine disruptors, but epidemiologic studies are limited. We evaluated associations between air emissions of multiple carcinogenic chemicals and postmenopausal breast cancer risk in a large prospective U.S. cohort. MethodsWe used the U.S. Environmental Protection Agency’s Toxics Release Inventory to estimate historical airborne emissions (1987–1995) of 19 known and probable carcinogens for participants enrolled (1995–1996) in the NIH-AARP Diet and Health Study. Among 170,402 women, 15,124 breast cancers were diagnosed through 2018. We constructed inverse distance- and wind-weighted average emissions metrics within 1, 2, 5, and 10 km of the enrollment address for each chemical. We estimated multivariable adjusted HRs and 95 % CIs for categories (quartiles, tertiles, medians) of each chemical in association with breast cancer overall and separately by type (invasive, ductal carcinoma in situ) and estrogen receptor (ER) status. ResultsWe observed an association between benzene emissions and breast cancer risk that was strongest at 1 km (HRQ4 vs. non-exposed = 2.06, 95 %CI: 1.34–3.17; p-trend = 0.001). The magnitude of the association weakened with increasing distance (2 km HRQ4 vs. non-exposed = 1.17, 95 %CI=0.92–1.49; p-trend = 0.19; 5 km HRQ4 vs. non-exposed = 1.05, 95 %CI=0.94–1.16; p-trend = 0.37; 10 km HRQ4 vs. non-exposed = 0.95, 95 %CI=0.89–1.02; p-trend = 0.19) and appeared to be most relevant for invasive rather than intraductal disease. Overall risk was also elevated for vinyl chloride at 5 km (HR≥median vs. non-exposed = 1.20, 95 %CI=1.01–1.43; p-trend = 0.04), but not 2 km or 10 km. We observed suggestive associations for asbestos, trichloroethylene, and styrene in different subgroup analyses, but risk patterns were not clear across distances. Associations with other chemicals were generally null, with limited evidence of heterogeneity by disease type or ER status. ConclusionsAn increased risk of breast cancer associated with relatively high levels of industrial benzene emissions warrants additional study, particularly among participants with diverse sociodemographic characteristics that live in areas with higher density of industrial facilities.

Can intelligent manufacturing drive green development in China's pharmaceutical industry? -- Evidence from listed enterprises

The pharmaceutical industry's historical extensive pollution emissions have caused severe environmental issues. In the context of the evolving intelligent age, it is urgent to investigate the potential of intelligent manufacturing in improving the environmental performance of the pharmaceutical industry. Our study employs panel data from 110 publicly listed pharmaceutical enterprises covering the period from 2012 to 2020 to assess the impact of intelligent manufacturing on the environmental performance of the pharmaceutical sector. The results reveal a substantial positive effect of intelligent manufacturing on the environmental performance of these enterprises. Additionally, we identify three primary mechanisms for this enhancement: supply chain optimization, the emergence of technological innovations, and structural optimization. A notable observation is that enterprises not certified in environmental management systems or operating within highly competitive markets exhibit a greater potential for enhancing their environmental performance. Drawing upon these insights, our study proposes detailed policy recommendations to leverage intelligent manufacturing for the sustainable progression of the pharmaceutical industry.

Impact of fiscal regulation on regional carbon intensity: assessing the carbon emission reduction effect of transparent government revenue and expenditure

ABSTRACT Fiscal regulations enhance fiscal compliance and reduce undue government intervention in economic performance, which may have important implications for low-carbon development. However, few empirical studies have investigated the impact of fiscal regulations on carbon intensity. Based on data from 278 cities in China from 2005 to 2020, this study constructed a difference-in-differences model using the release of the new Budget Law to investigate the impact of fiscal revenue and expenditure regulation on regional carbon emission intensity. The empirical results indicated that fiscal regulations can significantly reduce the intensity of carbon emissions. The mechanism tests indicate that fiscal regulation can optimize the allocation of local governments’ fiscal revenues and expenditures and improve infrastructure and innovation. Heterogeneity analyses indicate that the effect of fiscal regulation on reducing carbon emission intensity is more obvious in regions with high historical emissions and a high proportion of polluting industries. Based on the empirical results, the government should continuously improve fiscal transparency and the efficiency of fiscal fund use to reduce the impact of unreasonable fiscal revenue and expenditure on carbon emissions intensity.

A tracking control method for electricity-carbon emission forecasting

This paper introduces a novel carbon emission prediction method based on tracking control, leveraging historical CO2 emission prediction errors and feed-forward integration of electricity consumption data to enhance forecasting accuracy and minimize lag. Comparative analysis with pre-trained models such as LSTM and ARDL using Python showcases the proposed method's substantial reduction in prediction errors compared to singular reliance on electricity data, while also significantly reducing computational time in contrast to LSTM models. The findings establish a valuable reference for policymakers and researchers in refining carbon emission prediction methodologies and formulating effective carbon reduction policies.

Tibetan lake sediment records reveal historical emission and long-range atmospheric transport of chlorinated paraffins

The Tibetan Plateau, a recognized global sink for Persistent Organic Pollutants (POPs), lies adjacent to two major emitting regions, inland China and India. This unique geographical setting makes it a pivotal site for examining the presence and compositional evolution of POPs following their long-range atmospheric transport (LRAT). This study focuses on the current predominant POPs, chlorinated paraffins (CPs). We comprehensively screened 675 homologues of the very short- (vSCCPs), short- (SCCPs), medium- (MCCPs), and long-chain CPs (LCCPs) in six dated sediment cores across the extensive Tibetan area. The findings unveiled pronounced temporal disparities in CP concentrations and compositions between Tibet's southern and eastern sectors, reflecting divergent usage and emission chronicles of inland China and India. Notably, a market shift in China from regulated SCCPs to the in-use MCCPs and LCCPs was observed in the 21st century, contrasting with India's unregulated production of SCCPs. The Organization for Economic Cooperation and Development (OECD) Screening Tool, developed to assess the overall persistence (POV) and long-range transport potential (LRTP) of organic chemicals, elucidated the erosion of CP source signatures induced by fractionation, a process that intensifies with transport distance from the source regions. This study enhances our understanding of the emission inventories and LRAT behavior of these transitional regulatory contaminants, highlighting the Tibetan Plateau's crucial role as an environmental sentinel in global pollution dynamics.

Long-term changes of methane emissions from rice cultivation during 2000 – 2060 in China: Trends, driving factors, predictions and policy implications

Regional budget assessments of methane (CH4) are critical for future climate and environmental management. CH4 emissions from rice cultivation (CH4-rice) constitute one of the most significant sources. However, previous studies mainly focus on historical emission estimates and lack consideration of future changes in CH4-rice under climate change or anthropogenic policy intervention, which hampers our understanding of long-term trends and the implementation of targeted emission reduction efforts. This study investigates the spatiotemporal variations of CH4-rice over the past two decades, using an integrated method to identify the major drivers and predict future emissions under climate change scenarios and policy perspectives. Results indicate that the CH4-rice emissions in China ranged between 6.21 and 6.57 Tg yr−1 over the past two decades, with a spatial distribution characterized by decreases in the south and increases in the north, associated with economic development, dietary shifts, technological advancements, and climate change. Factors such as the rate of straw added (RSA), fertilization, soil texture, temperature, and precipitation significantly influence CH4 emissions per unit rice production (CH4-urp), with RSA identified as the most significant tillage management factor, explaining 32 % of the variance. Lowering RSA to 8 % is beneficial for reducing CH4-urp. Scenario analysis indicates that under policies focusing on production or demand, CH4-rice is expected to increase by 0.3 % to 5.6 %, while adjusting RSA can reduce CH4-rice by 9.4 % to 10.0 %. Structural adjustments and regional cooperation serve as beneficial starting points for controlling and reducing CH4-rice in China, while optimizing industrial layouts contributes to regional development and CH4-rice control. Implementing policies related to maintaining field and crop yields can achieve a balance between rice supply and demand ahead of schedule. Dynamic adjustment of rice cultivation based on supply–demand balance can effectively reduce CH4-rice from excess rice production. By 2060, the reduction effect could reach 8.95 %–12.01 %. Introducing policy-driven tillage management measures as reference indicators facilitates the reduction of CH4-rice.

Aerosol-Induced Changes in Atmospheric and Oceanic Heat Transports in the CESM2 Large Ensemble

Abstract The total poleward energy transport (PET) is set by the top of atmosphere radiation flux, and is therefore sensitive to any process which can alter those fluxes, particularly in the shortwave. One example is the direct and indirect effects of anthropogenic aerosols, which increase the local reflection of solar radiation back to space. The historic emission of sulphur dioxide, which peaked in the northern midlatitudes during the 1980s, has been proposed as a primary contributor to historic anomalies in cross-equatorial energy transport, as well as related processes such as a shift in the tropical rainband. In this study, we analyze simulations from the CESM2 large ensemble and single-forcing projects to better understand the forced response of PET to historical forcings. First, analysis of the single-forcing project reveals that the position of the ITCZ responds in a non-linear manner to greenhouse gas forcing in CESM2. This type of non-linearity has been found previously in the context of the aerosol-only simulations in the CESM2 single-forcing project, but may be the first identification of a similar effect in the greenhouse gas-only simulations. Second, through analysis of the full CESM2 large ensemble simulations, we find that anomalous heat transport occurred in both the atmosphere (through the mean meridional circulation and atmospheric eddies) and in the oceans (through the Atlantic and Indo-Pacific sectors) due to a variety of related processes including the Hadley cells, the midlatitude storm tracks, the Atlantic Meridional Overturning Circulation (AMOC), and the Pacific wind-driven subtropical gyre.

Historical volatile organic compound emission performance, drivers, and mitigation potential for automobile manufacturing in China

The rising emission trend of volatile organic compound (VOC) from automobile manufacturing (AMVOC) in China has been effectively curbed. However, the effect and driving force of this reduction remains unclear, posing a challenge of further deep emission mitigation for better air quality. Herein, we analyzed the spatiotemporal emission characteristics of AMVOC, identified the key drivers, and predicted the mitigation potential under different scenarios. The results indicated that China's AMVOC emissions followed an increasing-then-decreasing trend from 2001 to 2020, from 44.7 Gg in 2001, peaking at 211.8 Gg in 2012 and declining to 81.6 Gg in 2020. The most notable factor that positively drove AMVOC emissions was economic development, contributing to 88.3% of the increase. Conversely, the reduction in AMVOC emissions was primarily attributed to the internal structure of the automotive industry and the emission intensity, contributing to 44.7% and 43.8% of the emission decrease, respectively. Furthermore, scenario analysis results show that China's AMVOC emissions in 2035 were reduced by 55.5% through a combination of the whole process control measures. Overall, our study confirms the effectiveness of China's AMVOC control measures and offers valuable insights for future policymakers in China and other countries facing similar challenges.