Anthropogenic Carbon Emissions Research Articles

Promoting low-carbon land use: from theory to practical application through exploring new methods

Cities are main carbon emissions generators. Land use changes can not only affect terrestrial ecosystems carbon, but also anthropogenic carbon emissions. However, carbon monitoring at a spatial level is still coarse, and low-carbon land use encounters the challenge of being unable to adjust at the patch scale. This study addresses these limitations by using land-use data and various auxiliary data to explore new methods. The approach involves developing a high-resolution carbon monitoring model and investigating a patch-scale low-carbon land use model by integrating high carbon sink/source images with the Future Land Use Simulation model. Between 2000 and 2020, the results reveal an increasing trend in both carbon emissions and carbon sinks in the Shangyu district. Carbon sinks can only offset approximately 3% of the total carbon emissions. Spatially, the north exhibits net carbon emissions, while the southern region functions more as a carbon sink. A total of 14.5% of the total land area witnessed a change in land-use type, with the transfer-out of cropland constituting the largest area at 96.44 km2, accounting for 50% of the total transferred area. Land-use transfer resulted in an annual increase of 77.72 × 104 t in carbon emissions between 2000 and 2020. Through land-use structure optimisation, carbon emissions are projected to increase by only 7154 t C/year from 2000 to 2030, significantly lower than the amount between 2000 and 2020. Further low-carbon land optimisation at the patch scale can enhance the carbon sink by 129.59 t C/year. The conclusion drawn is that there is considerable potential to reduce carbon emissions through land use control. The new methods developed in our study can effectively contribute to high-resolution carbon monitoring in spatial contexts and support low-carbon land use, promoting the application of low-carbon land use from theory to practice. This will provide technological guidance for land use planning, city planning, and so forth.

Bilayer Porous Electrocatalysts for Stable and Selective Electrochemical Reduction of CO2 to Formate in the Presence of Flue Gas Containing NO and SO2.

The development of stable and selective electrocatalysts for converting CO2 to value-added chemicals or fuels has gained much interest in terms of their potential to mitigate anthropogenic carbon emissions. Most of the electrocatalysts are tested under pure CO2; however, industrial outlet flue gas contains numerous impurities, such as NO and SO2, which poison the electrocatalysts and alter the product selectivity. Developing electrocatalysts that are resistant to such impurities is essential for commercial implementation. Herein, we prepared bilayer porous electrocatalysts, namely, Sn, Bi, and In, on porous Cu foam mesh (Sn/Cu-f, Bi/Cu-f, and In/Cu-f) by a two-step electrodeposition process and employed these electrodes for the electrochemical reduction of CO2 to formate. It was observed that the bilayer porous electrocatalysts exhibited high CO2 reduction activity compared to catalysts coated on a Cu mesh. Among bilayer porous electrocatalysts, Sn/Cu-f and Bi/Cu-f electrocatalysts showed more than 80% faradaic efficiency (FE) toward formate production, with a formate partial current density of around -16 and -10.4 mA cm-2, respectively, at -1.02 V vs RHE. In/Cu-f electrocatalyst showed nearly 40% formate FE with formate partial current density of -15 mA cm-2 at -1.22 V vs RHE. We investigated the effect of NO and SO2 impurities (500 ppm of NO, 800 ppm of SO2, and 500 ppm of NO + 800 ppm of SO2) on these electrocatalysts' selectivity and stability toward formate. It was observed that the Bi/Cu-f electrocatalyst showed 50 h stability with 80 ± 5% formate FE, and Sn/Cu-f showed 18 h stability with above 80 ± 5% efficiency in the presence of NO and SO2 mixed with CO2. Furthermore, we studied the effect of CO2 concentration with Sn/Cu-f and Bi/Cu-f catalysts in the range of 15-100% CO2, for which formate FEs of 45-80% were observed.

Implications of Defect Density and Polymer Interactions for CO2 Capture on Amine-Functionalized MIL-101(Cr).

Rising anthropogenic carbon emissions have dire environmental consequences, necessitating remediative approaches, which includes use of solid sorbents. Here, aminopolymers (poly(ethylene imine) (PEI) and poly(propylene imine) (PPI)) are supported within solid mesoporous MIL-101(Cr) to examine effects of support defect density on aminopolymer-MOF interactions for CO2 uptake and stability during uptake-regeneration cycles. Using simulated flue gas (10 % CO2 in He), MIL-101(Cr)-ρhigh (higher defect density) shows 33 % higher uptake capacity per gram adsorbent than MIL-101(Cr)-ρlow (lower defect density) at 308 K, consistent with increased availability of undercoordinated Cr adsorption sites at missing linker defects. Increasing aminopolymer weight loadings (10-50 wt.%) within MIL-101(Cr)-ρlow and MIL-101(Cr)-ρhigh increases amine efficiencies and CO2 uptake capacities relative to bare MOFs, though both incur CO2 diffusion limitations through confined, viscous polymer phases at higher (40-50 wt.%) loadings. Benchmarked against SBA-15, lower polymer packing densities (PPI>PEI), weaker and less abundant van der Waals interactions between aminopolymers and pore walls, and open framework topology increase amine efficiencies. Interactions between amines and Cr defect sites incur amine efficiency losses but grant higher thermal and oxidative stability during uptake-regeneration cycling. Finally, >25 % higher CO2 uptake capacities are achieved for aminopolymer/MIL-101(Cr)-ρhigh under humid conditions, demonstrating promise for realistic applications.

Assessing the impact of urban expansion on carbon emission

Urban expansion is not only a driving force for economic growth and development but also a perilous element for anthropogenic carbon emission coinciding with population growth, urban expansion, land use pattern, urban sprawl. The study compares urban expansion and its impact on the carbon emission pattern for Rajshahi, a city of Bangladesh where land cover data is an important determinant. At first, the study aims to evaluate the pattern of urban expansion and the second step quantifies the carbon emission trend. The satellite images are used for land cover detection of urban expansion and Support Vector Machine (SVM) algorithm is applied for its classification. Similar carbon emission data are examined to relate carbon emission quantification rate by remote sensing algorithm. It indicates that urban expansion significantly explains carbon emission by comparing with similar secondary data to extract carbon emission and energy consumption. After accentuating urban expansion and carbon emission rate, the spatiotemporal pattern is analyzed in 16 different directions revealing that the urban expansion and emission trend expands to north-western and north-eastern directions of Rajshahi, turning it into a low carbon absorbed city. The responsible landmarks are real estate housing complex, educational institutions, student dormitories, industrial areas, brickfields and mango wholesale business. In order to understand the effects of uncontrolled urban expansion, this study will assist policymakers and urban planners in developing a comprehensive and well-organized strategy.

Energy efficiency of hydrogen production via gasification from lignocellulosic residual biomass blend

Facing the challenges of environmental sustainability and energy security caused by anthropogenic carbon emissions, there is a need to adopt cleaner energy generation technologies, leveraging Colombia's existing national resources. In this context, hydrogen emerges as a promising source of renewable energy. Therefore, this project explores the use of a blend of residual lignocellulosic biomass as raw material for hydrogen production through gasification for energy purposes. Initially, a screening of residual lignocellulosic biomass in the study region was conducted, a blend was selected, and a simulation of the synthesis gas production process was carried out prospectively using Aspen Plus Dynamics® software. The results revealed that, by using the selected biomass blend, a synthesis gas with a hydrogen molar fraction of 38.7% and an ER of 0.19 was obtained. According to sensitivity analyses, the optimal parameters identified to achieve this hydrogen concentration were: gasification temperature of 707°C, oxygen flows of 484 kg/h, steam at 420 kg/h, and gasification pressure of 1 atm. These findings support the potential of the studied lignocellulosic biomass blend as an alternative for hydrogen production, while also offering an opportunity for the valorization of lignocellulosic residual biomass.

Significant Reduction of Unequal Population Exposure to Climate Extremes by Achieving the Carbon Neutrality

AbstractClimate extremes, such as hot temperature and heavy precipitation events, have devastating effects on human societies. As the planet gets warmer, they have become more intense and more frequent. To avoid irreversible damages from climate extremes, many countries have committed to achieving net‐zero anthropogenic carbon emissions, or carbon neutrality, by the 2050s. Here, we quantify the impact of carbon neutrality on population exposure to climate extremes using multi‐model projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) Shared Socioeconomic Pathway (SSP)1‐1.9 and SSP3‐7.0 scenarios. It is found that the increasing population exposure to hot‐temperature and heavy‐precipitation extremes under SSP3‐7.0 scenario can be substantially reduced by 87%–98% in the late 21st century by achieving the carbon neutrality based on SSP1‐1.9 scenario. The benefits of carbon neutrality are particularly pronounced in Africa and Asia. The potential benefits of carbon neutrality are also significant in North America, Europe, and Oceania, where a reduction in climate extremes is more than twice as important as population decline in reducing population exposure to climate extremes. These results provide important scientific support for ongoing efforts to achieve net‐zero carbon emissions by the 2050s to reduce potential climate risk and its inequity across continents.

Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager

Industrial heat sources (IHSs) are key contributors to anthropogenic heat, air pollution, and carbon emissions. Accurately and automatically detecting their production areas (IHSPAs) on a large scale is vital for environmental monitoring and decision making, yet this is challenged by the lack of high-resolution thermal data. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data with the highest resolution (30 m) in the civilian field and a three-band advantage were first introduced to detect IHSPAs. In this study, an IHSPA identification model using multi-features extracted from SDGSAT-1 TIS and Landsat OLI data and support vector machine (SVM) was proposed. First, three brightness temperatures and four thermal radiation indices using SDGSAT-1 TIS and Landsat OLI data were designed to enlarge the temperature difference between IHSPAs and the background. Then, 10 features combined with three indices from Landsat OLI images with the same spatial resolution (30 m) and stable data were extracted. Second, an IHSPA identification model based on SVM and multi-feature extraction was constructed to identify IHSPAs. Finally, the IHS objects were manually delineated and verified using the identified IHSPAs and Google Earth images. Some conclusions were obtained from different comparisons in Wuhai, China: (1) IHSPA identification based on SVM using thermal and optical features can detect IHSPAs and obtain the best results compared with different features and identification models. (2) The importance of using thermal features from the SDGSAT-1 TIS to detect IHSPAs was demonstrated by different importance analysis methods. (3) Our proposed method can detect more IHSs, with greater spatial coverage and smaller areas, compared with the methods of Ma and Liu. This new way to detect IHSPAs can obtain higher-spatial-resolution emissions of IHSs on a large scale and help decision makers target environmental monitoring, management, and decision making in industrial plant processing.

Total organic carbon measurements reveal major gaps in petrochemical emissions reporting.

Anthropogenic organic carbon emissions reporting has been largely limited to subsets of chemically speciated volatile organic compounds. However, new aircraft-based measurements revealed total gas-phase organic carbon emissions that exceed oil sands industry-reported values by 1900% to over 6300%, the bulk of which was due to unaccounted-for intermediate-volatility and semivolatile organic compounds. Measured facility-wide emissions represented approximately 1% of extracted petroleum, resulting in total organic carbon emissions equivalent to that from all other sources across Canada combined. These real-world observations demonstrate total organic carbon measurements as a means of detecting unknown or underreported carbon emissions regardless of chemical features. Because reporting gaps may include hazardous, reactive, or secondary air pollutants, fully constraining the impact of anthropogenic emissions necessitates routine, comprehensive total organic carbon monitoring as an inherent check on mass closure.

Green Consumption, Environmental Regulation and Carbon Emissions—An Empirical Study Based on a PVAR Model

China’s proposed double carbon goal makes it urgent to promote green consumption and green lifestyles. The present study selected separate economic indicators of urban and rural areas and assigned different weights in the construction of a green consumption indicator system. Based on data from 30 provinces (excluding Tibet) between 2003 and 2019, this study investigated the connections between green consumption, environmental regulations, and carbon emissions, analyzing their mechanism. This study found that green consumption will reduce anthropogenic carbon emissions in the short term but will result in low carbon emissions in the long term. Environmental rules have a definite long-term impact on green consumption, as evidenced by the “U”-shaped trend they follow. Second, this study found that the level of green consumption exhibits a rising and then falling trend on the vegetation’s capacity to sequester carbon, and the impeding force will become stronger over time. Third, this study found that green consumption innately has a degree of inertia and self-enhancement bias.

Beware of trees: Pine afforestation of a naturally treeless habitat reduces flower and pollinator diversity

Planting three billion trees to capture carbon and seeking measures to reverse pollinator decline are two key pledges of the EU Biodiversity Strategy for 2030. Although planting trees could be adequate to restore biodiversity in degraded landscapes and mitigate anthropogenic carbon emissions, it can also negatively impact biodiversity of naturally treeless habitats. To explore whether these two pledges might conflict, we focused on the European dry heathland, a treeless habitat frequent in the southwestern Iberian Peninsula, where it is locally known as herriza. The herriza stands out by its high plant biodiversity and profuse flowering that supports a wide range of insect pollinators. Yet, the herriza has been heavily afforested with pine trees until the onset of the 21st century. This past activity provides a unique natural experiment to assess the effect of afforestation on flower abundance and associated pollinator diversity. We conducted a two-year field study of the diversity and abundance of flowers and pollinators in five selected sites, each consisting of two adjacent plots of open and afforested herriza. Afforested herriza plots had consistently lower diversity and abundance of flowers and insect pollinators than open herriza plots. Our results highlight the negative impact of afforestation of a treeless habitat on its flowering pattern and associated insect pollinator guilds. We thus suggest seeking alternatives to afforesting naturally treeless habitats in order to avoid conflicts between carbon sequestration and pollinator conservation.

Climate change determines the sign of productivity trends in US forests

Forests are integral to the global land carbon sink, which has sequestered ~30% of anthropogenic carbon emissions over recent decades. The persistence of this sink depends on the balance of positive drivers that increase ecosystem carbon storage-e.g., CO2 fertilization-and negative drivers that decrease it-e.g., intensifying disturbances. The net response of forest productivity to these drivers is uncertain due to the challenge of separating their effects from background disturbance-regrowth dynamics. We fit non-linear models to US forest inventory data (113,806 plot remeasurements in non-plantation forests from ~1999 to 2020) to quantify productivity trends while accounting for stand age, tree mortality, and harvest. Productivity trends were generally positive in the eastern United States, where climate change has been mild, and negative in the western United States, where climate change has been more severe. Productivity declines in the western United States cannot be explained by increased mortality or harvest; these declines likely reflect adverse climate-change impacts on tree growth. In the eastern United States, where data were available to partition biomass change into age-dependent and age-independent components, forest maturation and increasing productivity (likely due, at least in part, to CO2 fertilization) contributed roughly equally to biomass carbon sinks. Thus, adverse effects of climate change appear to overwhelm any positive drivers in the water-limited forests of the western United States, whereas forest maturation and positive responses to age-independent drivers contribute to eastern US carbon sinks. The future land carbon balance of forests will likely depend on the geographic extent of drought and heat stress.

High-frequency dynamics of pH, dissolved oxygen, and temperature in the coastal ecosystems of the Tanga-Pemba Seascape: implications for upwelling-enhanced ocean acidification and deoxygenation

Ocean acidification, deoxygenation, and warming are three interconnected global change challenges caused by increased anthropogenic carbon emissions. These issues present substantial threats to marine organisms, ecosystems, and the survival of coastal communities depending on these ecosystems. Coastal upwelling areas may experience significant declines in pH, dissolved oxygen (DO), and temperature levels during upwelling events, making marine organisms and ecosystems in these areas more susceptible to ocean acidification and deoxygenation. Understanding the dynamics of pH, DO, and temperature in coastal upwelling areas is essential for evaluating the susceptibility of resident organisms and ecosystems to lower pH and DO conditions occurring during upwelling events. To accomplish this, we used the pH and the DO loggers to measure high-frequency data for pH and DO, respectively, over six months in the open ocean and for a 24-hour cycle within the mangrove, seagrass, and coral reef ecosystems of the Tanga-Pemba Seascape (T-PS) during the northeast monsoon season. Our findings revealed the occurrence of multiple upwelling events, with varying durations, that result in significant declines in pH, DO, and temperature within the seascape. This is the first study to confirm the occurrence of multiple upwelling events in the T-PS. Moreover, the study has revealed a pH threshold value of 7.43 for ocean acidification in the T-PS. This is the first study to report a threshold value for ocean acidification in coastal upwelling areas of the Western Indian Ocean (WIO). Furthermore, it revealed that the extremely low levels of pH that occurred during upwelling events were above the pH threshold value of 7.43 for ocean acidification, while the extremely low levels of DO fell below the oxygen threshold value of 4.6 mg/L for deoxygenation. During upwelling events, seagrass and coral reef ecosystems, but not mangrove ecosystems, demonstrated elevated mean hourly values of pH and DO compared to those of the open ocean. These findings show that marine organisms and ecosystems in the T-PS are frequently exposed to lower pH and DO conditions due multiple upwelling events. However, their susceptibility to these conditions is reduced to some extent by the presence of seagrass meadows within these interconnected systems.

A review of anthropogenic ground-level carbon emissions based on satellite data

A review of anthropogenic ground-level carbon emissions based on satellite data

How does industrial transformative process shape the spatial dynamics of urban development?

Rapid global expansion of built-up areas and industrialization has led to significant environmental challenges, particularly in terms of anthropogenic carbon emissions. Consequently, it has become crucial to comprehensively understand the spatial dynamics of urban built-up areas and the associated socioeconomic transitions. This understanding serves as a critical foundation for formulating effective land use and industrial policies that not only promote economic sustainability but also contribute to the pursuit of environment related- sustainable development goals (SDGs). However, our knowledge about the dynamic relationship between urban industrial structure and built-up area patterns is limited. To address this gap, we use partial redundancy analysis (PRDA) to quantify the relative impact of key industrial employment sectors. The results indicate that the transformation of industrial structure has a significant influence on the spatial dynamics and morphologies of urban built-up areas. In addition, we found that the decline in agricultural population is associated with an increase in spatial isolation, while the growth of the manufacturing population affects changes in spatial extent and regularity. Furthermore, the combined impact of industrial and economic drivers accounts for up to 24.4% of the dynamics of urban built-up areas, with industrial transformation having a 3.82% greater impact than economic growth. These findings provide a valuable understanding of the interconnectedness between the spatial evolution of built-up areas and socioeconomic transitions, not only in China but also in other city clusters worldwide. Moreover, they might contribute to the knowledge of carbon neutrality pathways, further promoting sustainable development efforts.

Land-use carbon emissions and built environment characteristics: A city-level quantitative analysis in emerging economies

Rapid urbanization and subsequent urban land expansions have led to land use changes, emerging as a prominent source of anthropogenic carbon emissions in the developing world. While numerous studies have quantified land-use carbon emissions (LUCE), there is still a lack of rigorous statistical analyses examining the driving factors of LUCE, particularly in the post-2015 era, when stringent environmental pollution regulations and carbon emission reduction policies have been implemented worldwide. This study aims to assess city-level LUCE in emerging economies and to further investigate the drivers of inter-city variations in LUCE through an extended STIRPAT model, taking Guangdong Province in China as an example. The results indicate a substantial increase in LUCE from 2000 to 2020, reflecting a surge in energy consumption associated with land-use transitions from natural land to construction land. The growing disparity between direct and indirect carbon emissions underscores the increasing significance of construction land planning and management in achieving emission reduction goals. Furthermore, our findings from the extended STIRPAT model shed light on carbon neutrality pathways by revealing the potential for LUCE reduction through adopting sustainable built environment planning.

Potential and critical pathways of CO2 emissions reduction: A city-based study

China has put forward the "Dual Carbon" target, carbon peak by 2030 and carbon neutrality by 2060. It is necessary to study the current situation of carbon emissions, carbon reduction potential, and decarbonization paths of industrial cities in China. Taking Wuhan, a typical industrial city, as an object, anthropogenic carbon emissions and distribution in Wuhan in the last 20 years are analyzed. The extended STIRPAT and the gray relation models are adopted to construct a decomposition model for discerning the driving factors of carbon emissions in Wuhan. Besides, a scenario-driven carbon emission projection system with five development scenarios is established, where carbon sinks from the ecosystem and CCUS technology and the decoupling status of “CO2 emissions-GDP” are considered, to assess Wuhan's carbon emissions reduction potential and decarbonization pathways by 2060. The results show that Wuhan's carbon emissions mainly come from the industrial and transportation sectors and that the increase in the share of tertiary industry and the decrease in carbon intensity are the main factors curbing Wuhan's carbon emissions. To realize the “Dual Carbon” target, Wuhan's decarbonization development should start with the ISI (Industrial Structure improving) scenario and focus mainly on the development of tertiary industry between 2022 and 2035. Between 2035 and 2045, the decarbonization development transitions to the BID (Comprehensively improving) scenario, which requires simultaneous accelerating improvement of the city's economy and energy structure. Finally, carbon neutrality can be achieved between 2050 and 2060 through ecosystem carbon sinks and technology carbon sinks.

Study on the driving factors and decoupling effect of carbon emission from pig farming in China—based on LMDI and Tapio model

AbstractThe livestock sector accounts for 18% of total anthropogenic carbon emissions and is an important source of global greenhouse gas (GHG) emissions. China occupies a large proportion of total livestock carbon emissions worldwide, especially in the pig industry, which is significant to China's agricultural economy and also a key area for China to achieve the "Carbon peaking and Carbon neutrality goals." This study uses the life cycle approach to calculate the GHG emission status of China's pig farming industry from 2001 to 2020, and then, we establish a logarithmic mean Divisia index (LMDI) model to identify the main driving factors and a Tapio decoupling model to analyze its decoupling status. We decompose the emission sources as well as decoupling index into five drivers: technological progress, livestock structure, policy bias, affluence, and population. The results reveal that the carbon emission of China's pig industry is in a weak growth trend and overall in a weak decoupling state but has volatility, which is closely related to the "Pig Cycle" in China. Decomposition analysis shows that increasing affluence and population growth are the main drivers of GHG emissions; simultaneously, technological progress, livestock structure, and policy bias are the main drivers of emission reduction. Meanwhile, technological and policy factors positively contribute to the decoupling status, while affluence level, population, and livestock structure changes negatively inhibit the decoupling status. The study concludes that technological advances, optimized economic structures, the guidance of green consumption patterns, and the solution to the "Pig Cycle" problem are crucial to further reduce GHG emissions from China's pig industry; meanwhile, technological changes have a dominant role in promoting carbon decoupling in pig farming.

Applying real options with reinforcement learning to assess commercial CCU deployment

Carbon capture and utilization (CCU), which emerged as a means to reduce anthropogenic carbon emissions, has been highlighted to close the carbon cycle and combat climate change. CCU involves utilizing or converting captured CO2 to create value-added products that can replace or supplement fossil fuel-derived products. In order to meet climate goals, commercial-scale CCU facilities need to be built and their capacities increased, but barriers to large-scale CCU deployment still exist, primarily caused by large uncertainties in product/energy prices, markets, technologies, and policies. Conventional techno-economic analysis (TEA) methods cannot appropriately assess viability of CCU deployment projects which include dynamic uncertainties and deployment strategies. More flexible methods that can account for both time-varying uncertainties and dynamic capacity building are needed. We propose a systematic framework for the evaluation of commercial CCU deployment using the theory of real options and reinforcement learning (RL). RL is needed as considering options of adding capacities or delaying/abandoning the project at multiple time points under dynamic uncertainties lead to a large-scale stochastic optimal control problem which cannot be solved using conventional optimization methods like stochastic programming. The framework consists of three steps: surrogate modeling, uncertainty modeling, and assessment modeling. The framework is dependent on the type of CCU technology, uncertainties, real options and RL algorithm. We demonstrate the application of the framework through a case study of CO2 hydrogenation-to-methanol process in Europe, which is a late-stage, i.e., high technology readiness level (TRL), technology.

Carbon Capture and Storage: From Global Cycles to Global Solutions

Anthropogenic carbon emissions have overwhelmed the natural carbon cycle, leading to a dramatic increase in atmospheric CO2 concentration. The rate of this increase may be unprecedented in Earth’s history and is leading to a substantial increase in global temperatures, ocean acidification, sea level rise and potentially human health challenges. In this Geochemical Perspectives we review the natural carbon cycle and its link to global climate. Notably, as directly observed by field observations summarised in this volume, there is a natural negative feedback loop between increasing global temperature, continental weathering rates, and CO2 that has tended to limit Earth climate changes over geological time scales. Due to the rapid increase in atmospheric carbon concentrations, global average temperatures have increased by more than 1.2 °C since the start of the industrial revolution. One way to slow or even arrest this increasing global average temperature is through Carbon Capture and Storage (CCS). Carbon dioxide can be captured either from large industrial point sources or directly from the atmosphere. Taking account of the natural carbon cycle, the most secure approach to storing captured CO2 is by reacting it with mafic or ultramafic rocks to form stable carbonate minerals, a process referred to as “mineral carbonation”. Although mineral carbonation can occur and be accelerated at the Earth’s surface, due to the required scale and required time frames it is most effective in the subsurface. This subsurface mineralisation approach was developed into an industrial scale process through an academic-industrial collaboration called CarbFix. The history of CarbFix, from its beginnings as a concept through its installation as an industrial process is presented in detail. This Geochemical Perspectives concludes with an assessment of the future of subsurface mineralisation as a means to help address the global warming challenge, as well as a detailed list of potential research directions that need to be addressed to further upscale and optimise this carbon storage approach.

Depth-dependent driver of global soil carbon turnover times

Soil carbon fixation has the potential to offset anthropogenic carbon emissions and mitigate climate change. However, the carbon fixation capacity still remains uncertain at the global scale, and little is known about the patterns and controls of soil carbon turnover times. Here we synthesize 5188 radiocarbon measurements at the global scale, and random forest models are applied to assess the key drivers of soil carbon turnover times in different soil layers. We find that across the globe, the mean soil carbon turnover time (τ) is 4178 ± 106 years (mean ± standard error), but the turnover time varies significantly across different regions and land cover types, with the longest values of τ being observed in tundra and the shortest in temperate forests. Longer soil carbon turnover times are observed in the northern permafrost regions, where the mean τ value is nearly twice that of the non-permafrost regions. Furthermore, τ is generally longer in subsoil than that in topsoil across all ecosystems. Moreover, we find the key drivers of τ are depth-dependent. The most important factors affecting topsoil τ are microbes (bacteria, fungi), while soil mineral protection is the major contributor to subsoil τ. These results highlight the necessity to integrate depth-specific soil carbon turnover time and its associated drivers in carbon cycling models into future climate change scenarios.