Emission Patterns Research Articles

Assessing the impact of green transformation on carbon emissions: a dynamic spatial Durbin model

IntroductionChina's environmental policies and the green transformation of its industries have attained paramount significance in the wake of intensifying climate change and soaring carbon emissions, critically shaping sustainable development at both national and international scales. As the exploration of the drivers and outcomes of this green revolution on carbon emissions gains traction as a focal point of research, this study endeavors to assess the effects and their underlying causes.MethodsUtilizing a systems thinking methodology, a dynamic spatial Durbin model was constructed, analyzing statistical data spanning from 2004 to 2021, encompassing 30 Chinese provinces.ResultsChina's green transformation significantly influences the spatial distribution patterns of carbon emissions across various regions.DiscussionEnvironmental Protection investment has been effective in curbing carbon emissions from local businesses, yet it may also prompt a relocation of these businesses to neighboring provinces, inadvertently elevating their carbon emissions. Green innovation and the adoption of green energy technologies have proven instrumental in reducing carbon emissions in a region, with positive spillover effects extending to nearby areas. The dynamic spatial Durbin model analysis further underscores the inertia of carbon emissions, indicating that achieving carbon reduction is a process requiring consistent and sustained efforts over the long term. The findings of this study offer valuable insights and recommendations for the government in devising and implementing green transformation policies that align with the dual-carbon target, aiming to achieve a greener and more sustainable future.

Comprehensive Assessment, Intelligent Prediction, and Precise Mitigation Strategies for Greenhouse Gas Emissions in Full-Scale Wastewater Treatment Plants.

Wastewater treatment plants (WWTPs) are major contributors to global anthropogenic greenhouse gas (GHG) emissions, with China ranks among the leading emitters. In the context of China's "dual-carbon" journey, precision quantification and predictive forecasting of GHG fluxes, particularly methane (CH4) and nitrous oxide (N2O)-are crucial for developing advanced mitigation strategies of WWTPs. To accurately assess GHG emissions, this study firstly introduced customized emission factors (EFs) to precisely evaluate the GHG emissions of a full - scale A2O - based WWTP in Beijing. This approach addressed the overestimation of emissions when using the IPCC's standard EFs. Additionally, the study proposed machine learning (ML) techniques to predict GHG fluxes based on routine wastewater quality parameters. Specifically, Long Short-Term Memory (LSTM) and Random Forest (RF) models showed the strong performance in predicting CH4 and N2O emissions, respectively. Moreover, our findings revealed distinct spatiotemporal patterns of GHG emission: CH4 emissions peak during the summer solstice, while N2O emissions rise during the winter months. For the first time, this study identified the nitrification biofilter in the advanced treatment unit as a significant direct source of N2O emissions. Eventhough, indirect CO2 emissions account for a dominant 57%-90% of the total GHG emissions. Scenario analyses revealed a strategic mitigation approach. Energy conservation emerged as the most effective measure, capable of reducing emissions by 23.41%, followed by heat recovery, which could cut emissions by 10.15%. In practical applications, improving energy efficiency is of utmost importance in real - world mitigation strategies. This highlights the significance of integrated approaches for achieving the sustainable development of WWTPs in the "dual - carbon" background.

Global impacts of land use on terrestrial carbon emissions since 1850.

Since the Industrial Revolution, significant changes in global land-use patterns have occurred, which have disrupted terrestrial carbon emissions. However, the disturbance processes, change trends, and distribution patterns are not clear. Therefore, the changes in terrestrial carbon emissions (Eluc) caused by land-use change (LUC) since 1850 were analyzed in this study. The results showed that, owing to the sharp decrease in forestland (-13.39%; 84.26×105km2) and significant increases in built-up land (+1360.4%; 7.21×105km2), cropland (+175.8%; 130.88×105km2), and grassland (+162.6%; 239.73×105km2), the global Eluc increased from 0.42 Pg C in 1850 to 11.05 Pg C in 2018, with an average annual increase of approximately 3.42 Pg C yr-1, while the average annual carbon emissions after the 21st century reached 9.65 Pg C yr-1. Among them, direct Eluc increased by approximately 0.80 Pg C yr-1 and indirect Eluc increased by 2.62 Pg C yr-1. In addition, from 1850 to 2018, global Eluc was approximately 578.26 Pg C, with North America, Europe, and Asia being the largest regional sources. Our results highlight the changing trend and distribution pattern of global terrestrial carbon emissions under the influence of LUC since the Industrial Revolution and provide a scientific basis for regional and sectoral formulation of low-carbon emission-reduction policies and planning of low-carbon land-use patterns.

Moderate grazing reduces while mowing increases greenhouse gas emissions from a steppe grassland: Key modulating function played by plant standing biomass.

Grassland represents one of the most expansive terrestrial ecosystems, exerting a profound influence on atmospheric greenhouse gas (GHG) levels within the broader context of global change. Both climate and land use changes play important roles in modulating grassland GHG emissions by directly or indirectly altering soil physical and chemical properties, especially soil temperature and inorganic nitrogen content. The optimal grassland management practices need to simultaneously meet the requirements of reducing GHG emissions, maintaining biological biodiversity, and ensuring productivity. However, the information on the management effects on GHG emissions from natural grasslands is still insufficient. Here we conducted a six-year grazing and mowing experiment in a semi-arid steppe grassland in central Inner Mongolia, and employed the static chamber method to investigate the effects of three major management measures, fencing, grazing and mowing, on ecosystem respiration (CO2 emission), methane uptake (CH4), and nitrous oxide emission (N2O) patterns in the experimental grassland. The results demonstrated that: (i) moderate grazing reduced plant aboveground standing biomass and CO2 emissions, but promoted belowground nutrient cycling and CH4 uptake; (ii) mowing enhanced plant biomass production, increased soil carbon and nitrogen content, and also increased CO2 emission; (iii) reducing grazing frequency reduced plant biomass loss and N2O emissions. We conclude that grazing at a moderate intensity and frequency is the best for mitigating GHG emissions while maintaining grassland production, and that mowing enhancement of plant production and GHG emissions should be considered in optimizing grassland management.

Crediting peatland rewetting for carbon farming: some considerations amidst optimism

Crediting peatland rewetting for carbon farming: some considerations amidst optimism

Declines in anthropogenic mercury emissions in the Global North and China offset by the Global South.

Human activities have emitted substantial mercury into the atmosphere, significantly impacting ecosystems and human health worldwide. Currently, consistent methodologies to evaluate long-term mercury emissions across countries and industries are scant, hindering efforts to prioritize emission controls. Here, we develop a high-spatiotemporal-resolution dataset to comprehensively analyze global anthropogenic mercury emission patterns. We show that global emissions increased 330% during 1960-2021, with declines in developed Global North countries since the 1990s and China since the 2010s completely offset by rapid growth in Global South countries (excluding China). Consequently, global emissions have continued to rise slightly since the 2013 Minamata Convention. In 2021, Global South countries produced two-thirds of global emissions, despite comprising only one-fifth of the global economy. We predict that, although large uncertainties exist, continued emission growth in Global South countries under a business-as-usual scenario could increase 10%-50% global mercury emissions by 2030. Our findings demonstrate that global control of anthropogenic mercury emissions has reached a critical juncture, highlighting the urgent need to target reductions in Global South countries to prevent worsening health and environmental impacts.

Bottom-up Analysis for Carbon Emissions from Passenger and Freight Transport Sectors in Xi’an: A City-Level Study Using MOVES and LMDI Models

Abstract Road transportation is identified as one of the primary contributors to urban carbon emissions. This study employed a localized MOVES model, integrating high-resolution urban vehicle data, along with a categorical LMDI decomposition model, to conduct a detailed bottom-up analysis of carbon emissions from passenger and freight vehicles in Xi'an. The MOVES model was utilized to evaluate the inter annual variability of carbon emissions from road transportation sources. The LMDI models assessed the impact of six factors, focusing on microscopic traffic activities such as passenger/freight volume and turnover, vehicle ownership, mileage, carbon emission rate, vehicle types, and fuel types. The research indicates that carbon emissions in Xi'an increased significantly between 2012 and 2021, rising from 21.95 to 51.15 million tons, with an average annual growth rate of 9.9%. Passenger vehicles are the primary contributors to this increase. The emission patterns of passenger and freight vehicles differ notably due to various factors. For passenger transport, which includes passenger cars and school buses accounting for 97% of emissions, per capita vehicle turnover is the primary influencing factor. In contrast, for freight transport, combination long-haul trucks dominate emissions, comprising 65% of total freight emissions, with freight volume being the key influencing factor. By analyzing the factors driving carbon emissions from passenger and freight vehicles, this study provides valuable insights for reducing road traffic emissions in Xi'an and similar regions.

Nitrogen and Water Additions Affect N2O Dynamics in Temperate Steppe by Regulating Soil Matrix and Microbial Abundance

Elucidating the effects of nitrogen and water addition on N2O dynamics is critical, as N2O is a key driver of climate change (including nitrogen deposition and shifting precipitation patterns) and stratospheric ozone depletion. The temperate steppe is a notable natural source of this potent greenhouse gas. This study uses field observations and soil sampling to investigate the seasonal pattern of N2O emissions in the temperate steppe of Inner Mongolia and the mechanism by which nitrogen and water additions, as two different types of factors, alter this seasonal pattern. It explores the regulatory roles of environmental factors, soil physicochemical properties, microbial community structure, and abundance of functional genes in influencing N2O emissions. These results indicate that the effects of nitrogen and water addition on N2O emission mechanisms vary throughout the growing season. Nitrogen application consistently increase N2O emissions. In contrast, water addition suppresses N2O emissions during the early growing season but promotes emissions during the peak and late growing seasons. In the early growing season, nitrogen addition primarily increased the dissolved organic nitrogen (DON) levels, which provided a matrix for nitrification and promoted N2O emissions. Meanwhile, water addition increased soil moisture, enhancing the abundance of the nosZ (nitrous oxide reductase) gene while reducing nitrate nitrogen (NO3−-N) levels, as well as AOA (ammonia-oxidizing archaea) amoA and AOB (ammonia-oxidizing bacteria) amoA gene expression, thereby lowering N2O emissions. During the peak growing season, nitrogen’s role in adjusting pH and ammonium nitrogen (NH4+-N), along with amplifying AOB amoA, spiked N2O emissions. Water addition affects the balance between nitrification and denitrification by altering aerobic and anaerobic soil conditions, ultimately increasing N2O emissions by inhibiting nosZ. As the growing season waned and precipitation decreased, temperature also became a driver of N2O emissions. Structural equation modeling reveals that the impacts of nitrogen and water on N2O flux variations through nitrification and denitrification are more significant during the peak growing season. This research uncovers innovative insights into how nitrogen and water additions differently impact N2O dynamics across various stages of the growing season in the temperate steppe, providing a scientific basis for predicting and managing N2O emissions within these ecosystems.

Analyzing Travel and Emission Characteristics of Hazardous Material Transportation Trucks Using BeiDou Satellite Navigation System Data

Road hazardous material transportation plays a critical role in road traffic management. Due to the dangerous nature of the cargo, hazardous material transportation trucks (HMTTs) have different route selection and driving characteristics compared to traditional freight trucks. These differences lead to unique travel and emission patterns, which in turn affect traffic management strategies and emission control measures. However, existing research predominantly focuses on safety aspects related to individual vehicle behavior, with limited exploration of the broader travel and emission characteristics of HMTTs. To bridge this gap, this study develops a comprehensive framework for analyzing the travel patterns and emissions of HMTTs. The methodology begins by applying a Gaussian mixture distribution model to identify vehicle stop points, eliminating biases associated with subjective settings. Origin–destination (OD) pairs are then determined through stop time clustering, followed by the extraction of travel characteristics using non-negative matrix factorization. Emissions are subsequently calculated based on the identified trip data. The relationship between emissions and land use characteristics is further analyzed using geographically weighted regression (GWR). Crucially, this study leverages data from the BeiDou Satellite Navigation System, focusing on HMTTs operating within Shanghai. The processed data reveal three distinct travel modes of HMTTs, categorized by spatiotemporal patterns: Daytime—Surrounding cities, Early morning—In-city, and Midnight—Scattered. Moreover, unlike other road vehicles, HMTT emissions are heavily influenced by industrial and company-related points of interest (POIs). These findings highlight the significant role of BeiDou Satellite Navigation System data in optimizing HMTT management strategies to reduce emissions and improve overall safety.

Dual-Task Optimization Method for Inverse Design of RGB Micro-LED Light Collimator

Abstract: Miniaturized pixel sizes in near-eye digital displays lead to pixel emission patterns with large divergence angles, necessitating efficient beam collimation solutions to improve the light coupling efficiency. Traditional beam collimation optics, such as lenses and cavities, are wavelength-sensitive and cannot simultaneously collimate red (R), green (G), and blue (B) light. In this work, we employed inverse design optimization and finite-difference time-domain (FDTD) simulation techniques to design a collimator comprised of nano-sized photonic structures. To alleviate the challenges of the spatial incoherence nature of micro-LED emission light, we developed a strategy called dual-task optimization. Specifically, the method models light collimation as a dual task of color routing. By optimizing a color router, which routes incident light within a small angular range to different locations based on its spectrum, we simultaneously obtained a beam collimator, which can restrict the output of the light emitted from the routing destination with a small divergence angle. We further evaluated the collimation performance for spatially incoherent RGB micro-LED light in an FDTD using a multiple-dipole simulation method, and the simulation results demonstrate that our designed collimator can increase the light coupling efficiency from approximately 30% to 60% within a divergence angle of ±20° for all R/G/B light under the spatially incoherent emission.

Industrial air emissions and breast cancer incidence in a United States-wide prospective cohort.

We evaluated air emissions of industrial compounds, many of which have carcinogenic or endocrine disrupting properties, in relation to breast cancer incidence. Using the United States Environmental Protection Agency's Toxics Release Inventory, we quantified air emissions of 28 compounds near Sister Study participants' residences during the 10 years leading up to study enrollment (2003-2006; n=46,150). We used Cox proportional hazards regression to estimate adjusted hazard ratios (HR) and 95% confidence intervals (CI) for associations of residential emission levels of single pollutants with incident breast cancer. We assessed pollutant mixtures using an Exposure Continuum Mapping (ECM) framework and characterized associations using a joint-exposure response function. During follow-up (median=13.4 years), we identified 4,155 breast cancer cases. We observed non-monotonic but elevated associations with breast cancer for emissions within 3km of the residence for nickel compounds (HRquintile5vs.none = 1.3; 95% CI 1.0, 1.6) and trichloroethylene (HRquintile5vs.none = 1.3; 95% CI 1.0, 1.6). ECM identified 25 mixture profiles that explained 72% of the variance in emissions patterns, with most participants experiencing relatively low emissions profiles. The joint-exposure response function suggested that higher incidence of breast cancer occurred among individuals with relatively rare, high emissions profiles; however, the overall trend was not associated with breast cancer (p=0.09). In our study, breast cancer incidence was associated with air emissions of certain industrial carcinogens. Although the overall emissions mixture did not show a trend related to breast cancer, this may not reflect the importance of individual compounds or specific emissions sources.

Peak Assessment and Driving Factor Analysis of Residential Building Carbon Emissions in China’s Urban Agglomerations

Urban agglomerations, as hubs of population, economic activity, and energy consumption, significantly contribute to greenhouse gas emissions. The interconnected infrastructure, energy networks, and shared economic systems of these regions create complex emission dynamics that cannot be effectively managed through isolated city-level strategies. However, these regions also present unique opportunities for innovation, policy implementation, and resource optimization, making them crucial focal points in efforts to reduce carbon emissions. This study examines China’s three major urban agglomerations: the Yangtze River Delta, the Pearl River Delta, and the Beijing–Tianjin–Hebei region. Utilizing data from 2005 to 2020 and a comprehensive evaluation model (BCPCAM), the research offers more profound insights into the socio-economic factors and collaborative mechanisms influencing emission trends, facilitating the development of targeted strategies for sustainable development and carbon neutrality. The findings indicate that (1) economic development and carbon control can progress synergistically to some extent, as economically advanced cities like Beijing and Shanghai have achieved their carbon peaks earlier; (2) natural resource endowment significantly affects urban carbon emissions, with resource-rich cities such as Tangshan and Handan, where fossil fuels dominate the energy mix, facing considerable challenges in reducing emissions; and (3) notable differences exist in the growth patterns of carbon emissions between urban and rural buildings, underscoring the need for tailored carbon reduction policies.

Optimal azimuthal distribution of thermal emission for efficient radiative cooling.

Directional control of thermal emission to enhance radiative cooling has recently attracted significant attention. However, the ideal emission directionality remains questionable and under-explored. This Letter employs a universal formalism to analyze typical emission patterns and their impact on radiative power, equilibrium temperature, and cooling power. Contrary to intuition, the optimal emission pattern is bipolar rather than unipolar toward the zero-degree zenith angle under conditions of reduced atmospheric transmittance and low non-radiative heat transfer coefficient, which coincides well with the intrinsic emission pattern of epsilon-near-zero (ENZ) optical metamaterials. This study lays a theoretical foundation for ENZ metamaterial applications in radiative cooling and demonstrates the potential for enhancing cooling through directional thermal emission.

Leaf Size Determines Damage- and Herbivore-Induced Volatile Emissions in Maize.

Stress-induced plant volatiles play an important role in mediating ecological interactions between plants and their environment. The timing and location of the inflicted damage is known to influence the quality and quantity of induced volatile emissions. However, how leaf characteristics and herbivore feeding behaviour interact to shape volatile emissions is not well understood. Using a high-throughput volatile profiling system with high temporal resolution, we examined how mechanical damage and herbivore feeding on different leaves shape plant-level volatile emission patterns in maize. We then tested feeding patterns and resulting consequences on volatile emissions with two generalist herbivores (Spodoptera exigua and Spodoptera littoralis), and assessed whether feeding preferences are associated with enhanced herbivore performance. We found maize seedlings emit more volatiles when larger leaves are damaged. Larger leaves emitted more volatiles locally, which was the determining factor for higher plant-level emissions. Surprisingly, both S. exigua and S. littoralis preferentially consumed larger leaves, and thus maximize plant volatile emission without apparent growth benefits. Together, these findings provide an ecophysiological and behavioural mechanism for plant volatile emission patterns, with potentially important implications for volatile-mediated plant-environment interactions.

A Comprehensive Approach to CO2 Emissions Analysis in High-Human-Development-Index Countries Using Statistical and Time Series Approaches

Reducing carbon dioxide (CO2) emissions is vital at both global and national levels, given their significant role in exacerbating climate change. CO2 emissions, stemming from a variety of industrial and economic activities, are major contributors to the greenhouse effect and global warming, posing substantial obstacles in addressing climate issues. It is imperative to forecast CO2 emissions trends and classify countries based on their emission patterns to effectively mitigate worldwide carbon emissions. This paper presents an in-depth comparative study on the determinants of CO2 emissions in twenty countries with high Human Development Index (HDI), exploring factors related to economy, environment, energy use, and renewable resources over a span of 25 years. The study unfolds in two distinct phases: initially, statistical techniques such as Ordinary Least Squares (OLS), fixed effects, and random effects models are applied to pinpoint significant determinants of CO2 emissions. Following this, the study leverages supervised and unsupervised time series approaches to further scrutinize and understand the factors influencing CO2 emissions. Seasonal AutoRegressive Integrated Moving Average with eXogenous variables (SARIMAX), a statistical time series forecasting model, is first used to predict emission trends from historical data, offering practical insights for policy formulation. Subsequently, Dynamic Time Warping (DTW), an unsupervised time series clustering approach, is used to group countries by similar emission patterns. The dual-phase approach utilized in this study significantly improves the accuracy of CO2 emissions predictions while also providing a deeper insight into global emission trends. By adopting this thorough analytical framework, nations can develop more focused and effective carbon reduction policies, playing a vital role in the global initiative to combat climate change.

Shrimp mariculture may increase aquatic CO2, CH4, and N2O emissions in semi-indoor and indoor ponds.

This study revealed the greenhouse gas (GHG) emission patterns during shrimp mariculture in both semi-indoor and indoor ponds. In the semi-indoor ponds, the average carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) concentrations of pond water varied from 1373.0±721.7 μatm, 4.4±1.3nM, and 14.2±3.5nM, respectively, at the initiation of stocking to 5296.0±1474.7 μatm, 3.8±0.3nM, and 28.1±17.9nM, respectively, in the harvest stage. In the indoor pond, these varied from 562 μatm, 3.5±0.2nM, and 10.7±0.1nM, respectively, at the initiation of stocking to 4728.0 μatm, 18.2±0.7nM, and 94.1±0.6nM, respectively, in the harvest stage. An upward trend in CO₂ and N₂O concentrations was observed in the water of both semi-indoor and indoor ponds as the culture period progressed. The discharged waters exhibited the highest GHG concentrations, potentially acting as emission hotspots if left untreated. The total average CO₂-equivalent (CO₂-eq) fluxes from pond water during the shrimp mariculture period were 49.8±40.7mg CO₂-eqm-2h-1 in the semi-indoor ponds, with 44% of these GHG emissions attributable to shrimp mariculture practices. In the indoor pond, the fluxes were 101.5±52.6mg CO₂-eqm-2h-1, with 86% of the GHG emissions attributable to shrimp mariculture practices. According to these results, shading effects may increase the GHG emissions from aquaculture pond waters.

Regulation Mechanisms of CO2 Fluxes in Subtropical Mountain Peatlands Based on Long‐Term In Situ Observations at the Dajiuhu Peatland

AbstractThe Dajiuhu peatland in Shennongjia, China, is a highly representative subalpine peatland, emblematic of subtropical mountainous peatlands. Due to the lack of long‐term in situ continuous observations and in‐depth studies on CO2 absorption and emission patterns, the regulation mechanisms of CO2 flux in subtropical peatlands remain unclear. Since July 2015, we have conducted over five years of continuous in situ observations of CO2 fluxes and major environmental factors in the Dajiuhu peatland ecosystem. We calculated the annual average net ecosystem carbon exchange (NEE) of CO2 and decomposed NEE into gross primary productivity (GPP) and ecosystem respiration (Reco), thus examining the peatland ecosystem's absorption and emission of CO2 separately. The results indicate that the annual average NEE from 2016 to 2020 was −283.6 g C m−2 yr−1, reflecting a strong CO2 sink. Our study indicates the regulation mechanisms of CO2 flux in the peatland. Temperature is the most direct factor affecting CO2 absorption and emission, serving as the most important driver of CO2 flux on short time scales. Precipitation only affects CO2 absorption but has a significant impact on NEE, being a key factor in maintaining the peatland's CO2 sink function. Variations in annual precipitation also led to differences in net CO2 absorption between years. Our results illustrate an important role of sub‐tropical mountain peatlands in mitigating the greenhouse effect and maintaining moisture conditions is crucial for protecting its ecological functions.

Temporal-spatial characteristics and driver pattern of methane emission in heterogeneous Chinese cities

Temporal-spatial characteristics and driver pattern of methane emission in heterogeneous Chinese cities

Untangling the impacts of bacterial community on carbon dioxide and nitrous oxide across a drinking water reservoir.

Reservoirs represent a critical component of greenhouse gas (GHG) emissions, yet the intricacies of how biotic and abiotic factors influence GHG dynamics within reservoirs remain largely unexplored. Herein, we investigated the spatiotemporal patterns of CO2 and N2O emissions and the underlying factors in the Danjiangkou Reservoir, Asia's largest artificial freshwater reservoir. We found that this reservoir was a significant source of GHGs to the atmosphere, with peak CO2 emissions observed in autumn (1544.39±652.53μatm) and N2O emissions in winter (32.57±8.87μmol/L). Moreover, we identified crucial bacterial biomarkers that regulate GHG dynamics, and these GHG biomarkers exhibited consistent seasonal patterns with the corresponding GHGs, concerning their abundance and niche breadth. Notably, GHG biomarkers displayed larger effects on the variations of N2O than CO2 emissions, while physiochemical variables were more critical for CO2 dynamics, highlighting the need to consider both biotic and abiotic factors when evaluating GHG emissions from reservoirs. Overall, this study advanced our knowledge of GHG emissions and their driving mechanisms in artificial reservoirs, emphasizing the importance of functional microbes in estimating and managing CO2 and N2O emissions from artificial reservoirs worldwide.

Different technology packages for aluminium smelters worldwide to deliver the 1.5 °C target

Production of aluminium, one of the most energy-intensive metals, is challenging for mitigation efforts. Regional mitigation strategies often neglect the emissions patterns of individual smelters and fail to guide aluminium producers’ efforts to reduce GHG emissions. Here we build a global aluminium GHG emissions inventory (CEADs-AGE), which includes 249 aluminium smelters, representing 98% of global primary aluminium production and 280 associated fossil fuel-based captive power units. We find, despite the installation of more efficient and higher amperage cells, that the share of aluminium production powered by fossil fuel-based captive power units increased from 37% to 49% between 2012 and 2021. Retiring fossil fuel-based captive power plants 10 years ahead of schedule could reduce emissions intensity by 5.0–10.5 tCO2e per tonne of aluminium for dependent smelters. At least 18% of smelting capacity by 2040 and 67% by 2050 must be retrofitted with inert anode technology to achieve net-zero targets.