Search Results (93)

Inland waters, serving as crucial carbon sinks and pivotal conduits within the global carbon cycle, are essential targets for carbon assessment under global warming and carbon neutrality initiatives. However, the extensive spatial distribution and inherent sampling challenges pose fundamental difficulties for monitoring dissolved organic carbon (DOC) in these systems. Since 2010, remote sensing has catalyzed a technological revolution in inland water DOC monitoring, leveraging its advantages for rapid, cost-effective long-term observation. In this critical review, we systematically evaluate research progress over the past two decades to assess the performance of remote sensing products and existing methodologies in DOC retrieval. We provide a detailed examination of diverse remote sensing data sources, outlining their application characteristics and limitations. By tracing uncertainties in retrieval outcomes, we identify atmospheric correction, spatial heterogeneity, and model and data deficiencies as primary sources of uncertainty. Current retrieval approaches—direct, indirect, and machine learning (ML) methods—are thoroughly scrutinized for their features, effectiveness, and application contexts. While ML offers novel solutions, its application remains nascent, constrained by limited waterbody-specific samples and model constraints. Furthermore, we discuss current challenges and future directions, focusing on data optimization, feature engineering, and model refinement. We propose that future research should (1) employ integrated satellite–air–ground observations and develop tailored atmospheric correction for inland waters to reduce data noise; (2) develop deep learning architectures with branch networks to extract DOC’s intrinsic shortwave absorption and longwave anti-interference features; and (3) incorporate dynamic biogeochemical processes within study regions to refine retrieval frameworks using biogeochemical indicators. We also advocate for multi-algorithm collaborative prediction to overcome the spectral paradox and unphysical solutions arising from the single data-driven paradigm of traditional ML, thereby enhancing retrieval reliability and interpretability. Full article

The accurate and large-scale mapping of seagrass meadows is essential, as these meadows form primary habitats for marine organisms and large sinks for blue carbon. Image data available for mapping these habitats are often scarce or are acquired through multiple surveys and instruments, resulting in images of varying spatial and spectral characteristics. This study presents an unsupervised domain adaptation (UDA) strategy that combines histogram-matching with the transformer-based SegFormer model to address these challenges. Unoccupied aerial vehicle (UAV)-derived imagery (3-cm resolution) was used for training, while orthophotos from airplane surveys (12.5-cm resolution) served as the target domain. The method was evaluated across three Danish estuaries (Horsens Fjord, Skive Fjord, and Lovns Broad) using one-to-one, leave-one-out, and all-to-one histogram matching strategies. The highest performance was observed at Skive Fjord, achieving an F1-score/IoU = 0.52/0.48 for the leave-one-out test, corresponding to 68% of the benchmark model that was trained on both domains. These results demonstrate the potential of this lightweight UDA approach to generalization across spatial, temporal, and resolution domains, enabling the cost-effective and scalable mapping of submerged vegetation in data-scarce environments. This study also sheds light on contrast as a significant property of target domains that impacts image segmentation. Full article

To achieve regional sustainable development, the low-carbon transformation of agriculture is essential, as it serves both as a significant carbon source and as a potential carbon sink. This study calculated the agricultural carbon emissions in Liaocheng from 2010 to 2022 by analyzing processes including crop cultivation, animal husbandry, and agricultural input. Additionally, a simulation model of the water–energy–food–carbon nexus (WEFC-Nexus) for Liaocheng’s agricultural production process was developed. Using Vensim PLE 10.0.0 software, this study constructed a WEFC-Nexus model encompassing four major subsystems: economic development, agricultural production, agricultural inputs, and water use. The model explored four policy scenarios: business-as-usual scenario (S1), ideal agricultural development (S2), strengthening agricultural investment (S3), and reducing agricultural input costs (S4). It also forecast the trends in carbon emissions and primary sector GDP under these different scenarios from 2023 to 2030. The conclusions were as follows: (1) Total agricultural carbon emissions exhibited a three-phase trajectory, namely, “rapid growth (2010–2014)–sharp decline (2015–2020)–gradual rebound (2021–2022)”, with sectoral contributions ranked as livestock farming (50%) > agricultural inputs (27%) > crop cultivation (23%). (2) The carbon emissions per unit of primary sector GDP (CEAG) for S2, S3, and S4 decreased by 8.86%, 5.79%, and 7.72%, respectively, compared to S1. The relationship between the carbon emissions under the four scenarios is S3 > S1 > S2 > S4. The relationship between the four scenarios in the primary sector GDP is S3 > S2 > S4 > S1. S2 can both control carbon emissions and achieve growth in primary industry output. Policy recommendations emphasize reducing chemical fertilizer use, optimizing livestock management, enhancing agricultural technology efficiency, and adjusting agricultural structures to balance economic development with environmental sustainability. Full article

The development of a forestry carbon sink project is an important way to achieve carbon neutrality and carbon reduction, and the collective forest carbon sink project is an important part of China’s forestry carbon sink project. As the main management entity of collective forests, whether farmers are willing to produce forestry carbon sinks is directly related to the implementation effect of the project. In this paper, a partial equilibrium model of farmers’ forestry production behavior was established based on production function and utility function, and the path to enhance farmers’ willingness to produce forestry carbon sink through forestry carbon sink projects was analyzed in combination with forest ecological management theory. In terms of empirical analysis, the PSM-DID econometric model was established based on the survey data of LY in Zhejiang Province, China, and the following conclusions were drawn: (1) With the receipt of revenues from forestry carbon sequestration projects and partial cost-sharing by the government, farmers’ participation in forestry carbon sink projects can save investment in forest land management. (2) The saved forestry production costs and forestry carbon sink project subsidies can make up for the loss of farmers’ timber income, so that the net income of forestry will not be significantly reduced. (3) The forestry production factors saved by farmers can be transferred to non-agricultural sectors and increase non-agricultural net income, so that the net income of rural households participating in forestry carbon sink projects will increase. The forestry carbon sink project can improve the utility level of farmers and increase the willingness of farmers to produce forestry carbon sinks by delivering income to farmers and saving forestry production factors. This study demonstrates that a well-designed forestry carbon sink compensation mechanism, combined with an optimized allocation of production factors, can effectively enhance farmers’ willingness to participate. This insight is also applicable to countries or regions that rely on small-scale forestry operations. Full article

Accurate estimation of soil organic carbon (SOC) content is crucial for assessing terrestrial ecosystem carbon stocks. Although traditional methods offer relatively high estimation accuracy, they are limited by poor timeliness and high costs. Combining measured data, remote sensing technology, and machine learning (ML) algorithms enables rapid, efficient, and accurate large-scale prediction. However, single ML models often face issues like high feature variable redundancy and weak generalization ability. Integrated models can effectively overcome these problems. This study focuses on the Weigan–Kuqa River oasis (Wei-Ku Oasis), a typical arid oasis in northwest China. It integrates Sentinel-2A multispectral imagery, a digital elevation model, ERA5 meteorological reanalysis data, soil attribute, and land use (LU) data to estimate SOC. The Boruta algorithm, Lasso regression, and its combination methods were used to screen feature variables, constructing a multidimensional feature space. Ensemble models like Random Forest (RF), Gradient Boosting Machine (GBM), and the Stacking model are built. Results show that the Stacking model, constructed by combining the screened variable sets, exhibited optimal prediction accuracy (test set R² = 0.61, RMSE = 2.17 g∙kg⁻¹, RPD = 1.61), which reduced the prediction error by 9% compared to single model prediction. Difference Vegetation Index (DVI), Bare Soil Evapotranspiration (BSE), and type of land use (TLU) have a substantial multidimensional synergistic influence on the spatial differentiation pattern of the SOC. The implementation of TLU has been demonstrated to exert a substantial influence on the model’s estimation performance, as evidenced by an augmentation of 24% in the R² of the test set. The integration of Boruta–Lasso combination screening and Stacking has been shown to facilitate the construction of a high-precision SOC content estimation model. This model has the capacity to provide technical support for precision fertilization in oasis regions in arid zones and the management of regional carbon sinks. Full article

The establishment of carbon capture, utilization, and storage supply chains at the national level is crucial for meeting global decarbonization targets: they have been suggested as a solution to maintain the global temperature rise below 2 °C relative to preindustrial levels. Optimizing these systems requires a balance of economic viability with environmental impact, but this is a challenge due to diverse operational limitations. This paper introduces an optimization framework that integrates life cycle assessment with a source-sink model while combining the geographical storage and conversion pathways of carbon dioxide into high-value chemicals. This study explores the economic and environmental outcomes of national carbon capture, utilization, and storage networks, considering several constraints, such as carbon dioxide reduction goals, product market demand, and renewable hydrogen availability. The framework is utilized in Germany as a case study, presenting three case studies to maximize overall annual profit and life cycle greenhouse gas reduction. In all analyzed scenarios, the results indicate a clear trade-off between profitability and emission reductions: profit-driven strategies are characterized by increased emissions, while environmental strategies have higher costs despite the environmental benefit. In addition, cost-optimal cases prefer high-profit utilization routes (e.g., gasoline through methane reforming) and cost-effective capture technologies, leading to significant profitability. On the other hand, climate-optimal approaches require diversification, integrating carbon dioxide storage with conversion pathways that exhibit lower emissions (e.g., gasoline, acetic acid, methanol through carbon dioxide hydrogenation). The proposed method significantly contributes to developing and constructing more sustainable, large-scale carbon projects. Full article

Accurate classification of salt marsh vegetation is vital for conservation efforts and environmental monitoring, particularly given the critical role these ecosystems play as carbon sinks. Understanding and quantifying the extent and types of habitats present in Ireland is essential to support national biodiversity goals and climate action plans. Unmanned Aerial Vehicles (UAVs) equipped with optical sensors offer a powerful means of mapping vegetation in these areas. However, many current studies rely on single-sensor approaches, which can constrain the accuracy of classification and limit our understanding of complex habitat dynamics. This study evaluates the integration of Red-Green-Blue (RGB), Multispectral Imaging (MSI), and Hyperspectral Imaging (HSI) to improve species classification compared to using individual sensors. UAV surveys were conducted with RGB, MSI, and HSI sensors, and the collected data were classified using Random Forest (RF), Spectral Angle Mapper (SAM), and Support Vector Machine (SVM) algorithms. The classification performance was assessed using Overall Accuracy (OA), Kappa Coefficient (k), Producer’s Accuracy (PA), and User’s Accuracy (UA), for both individual sensor datasets and the fused dataset generated via band stacking. The multi-camera approach achieved a 97% classification accuracy, surpassing the highest accuracy obtained by a single sensor (HSI, 92%). This demonstrates that data fusion and band reduction techniques improve species differentiation, particularly for vegetation with overlapping spectral signatures. The results suggest that multi-sensor UAV systems offer a cost-effective and efficient approach to ecosystem monitoring, biodiversity assessment, and conservation planning. Full article

The ocean is one of the world’s largest anthropogenic carbon dioxide (CO₂) sinks, but closing the carbon budget is logistically difficult and expensive, and uncertainties in carbon fluxes and reservoirs remain. One specific challenge is that measuring the CO₂ flux at the air–sea interface usually requires costly sensors or analyzers (USD > 30,000), which can limit observational capacity. Our group has developed and validated a low-cost ΔpCO₂ system, able to measure both pCO₂^water and pCO₂^air, for USD ~1400 to combat this limitation. The device is equipped with Internet of Things (IoT) capabilities and built around a USD ~100 pCO₂ K30 sensor at its core. Our Sensor for the Exchange of Atmospheric CO₂ with Water (SEACOW) may be placed in an observational network with traditional pCO₂ sensors or ∆pCO₂ sensors to extend the spatial coverage and resolution of monitoring systems. After calibration, the SEACOW reports atmospheric pCO₂ measurements that are within 2–3% of the measurements made with a calibrated LI-COR LI-850. We also demonstrate the SEACOW’s ability to capture diel pCO₂ cycling in seagrass, provide recommendations for SEACOW field deployments, and provide additional technical specifications for the SEACOW and for the K30 itself (e.g., air- and water-side 99.3% response time; 5.7 and 29.6 min, respectively). Full article

Urban green spaces are increasingly recognized for their potential to mitigate climate change by reducing atmospheric concentrations of greenhouse gases, especially carbon dioxide (CO₂). However, enhancing carbon sequestration efficiency in limited urban green areas remains a significant challenge for sustainable urban planning. Trees are among the most cost-effective and efficient natural carbon sinks, surpassing other types of land cover in terms CO₂ absorption and storage. The present study aimed to evaluate the carbon sequestration potential of four native tree species, Pongamia pinnata, Azadirachta indica, Melia azedarach, and Dalbergia sissoo, in urban parks across Multan City, Pakistan. A total of 456 trees of selected species within six parks of Multan City were inventoried to estimate the biomass and carbon stock using species-specific allometric equations. Soil organic carbon at two soil depths beneath the canopy of each tree was also estimated using Walkley–Black method. The findings revealed that the highest mean tree biomass (2.16 Mg ha⁻¹), carbon stock (1.04 Mg ha⁻¹) and carbon sequestration (3.80 Mg ha⁻¹) were estimated for Dalbergia sissoo, while Melia azedarach exhibited the lowest (0.12 Mg ha⁻¹, 0.06 Mg ha⁻¹ & 0.23 Mg ha⁻¹, respectively) across all six parks. The soil carbon stocks ranged from 48.86 Mg ha⁻¹ to 61.68 Mg ha⁻¹ across all study sites. These findings emphasize the importance of species selection in urban green planning for carbon sequestration. Strategic planting of effective native trees like Dalbergia sissoo can mitigate climate change and provide urban forest ecosystem services. Full article

In this study, the CO₂ reduction benefits of wind energy in the transition towards a carbon-neutral energy system are explored. The marginal benefits of wind energy in replacing CO₂ emissions in electricity generation are gradually declining as carbon-emission-reduction targets are fulfilled. However, there is still the potential to reduce emissions by replacing fossil fuels in other energy sectors via electrification. Using the Finnish TIMES-VTT energy system model, this study simulates the impacts of different wind energy scenarios between 2030 and 2050, analyzing the effects of adding or removing 5 TWh of wind energy on power generation. Our findings indicate that the reduction benefits of wind energy vary over time, stemming initially from the generation of electricity but they are increasingly being driven by electrification through lowered electricity prices, and fuel switching, like the replacement of bioenergy in heating and fuel production. Between the years 2030 and 2050, an average marginal emission reduction of 180–270 gCO₂eq/kWh was seen, rising to 250–320 gCO₂eq/kWh if the impact on reduced carbon sinks through wood chip use was taken into account. Issues using marginal, substitution impacts from simulations are discussed; however, no straightforward methods for capturing the cumulative benefits of assets over their lifetime exist. In transitioning towards a net-zero-carbon energy system, other issues like costs, land use, and social aspects will become more relevant than emission substitution. Full article

Various Higher Education Institutions (HEIs) set themselves goals to become carbon neutral through the implementation of different reduction strategies such as the replacement of fossil-fueled vehicles with electric cars. However, even if all reduction measures are taken, residual GHG emissions will still remain. Therefore, most HEIs have to compensate for the remaining emissions by, for example, buying carbon credits. However, due to growing criticism of carbon credit purchases, HEIs need to explore options for establishing carbon sinks on their own premises to offset their remaining, unavoidable emissions. This study aimed to assess the CO₂ footprint of Hochschule Geisenheim University (HGU) as an exemplary HEI, identify emission hot-spots, and investigate the potential of biomass utilization for achieving carbon neutrality or even negative emissions. The analysis found that HGU’s main emissions were scope 1 emissions, primarily caused by on-site heat supply. The research determined that conversion to a wood chip-based heating system alone was insufficient to achieve climate neutrality, but this goal could be achieved through additional carbon dioxide removal (CDR). By operating a pyrolysis-based bivalent heating system, the study demonstrated that heat demand could be covered while producing sufficient C-sink certificates to transform HGU into the first carbon-negative HEI, at a comparable price to conventional combustion systems. Surplus C-sink certificates could be made available to other authorities or ministries. The results showed that bivalent heating systems can play an important role in HEI transitions to CO₂ neutrality by contributing significantly to the most urgent challenge of the coming decades: removing CO₂ from the atmosphere to limit global warming to as far below 2 °C as possible at nearly no extra costs. Full article

The production of green agricultural products and carbon sink compensation play a crucial role in mitigating climate change. Farmers’ behaviors are influenced by both social norms and personal norms. This study aims to explore how these norms shape farmers’ carbon sink compensation behaviors and to provide a theoretical basis for formulating effective policies and incentive mechanisms. A mixed-methods approach was adopted in this study, involving in-depth interviews with 13 agricultural workers and a survey of 409 individuals from China, Japan, and South Korea who are or were engaged in agriculture-related work. The results indicate that the activation of personal norms is primarily driven by economic costs rather than mere moral responsibility. Subjective norms serve as a significant mediator between personal norms and behavior. Social norms indirectly influence behavior through policy guidance and community support. Based on these findings, specific strategies to strengthen personal norms, optimize social norms, and improve policy incentives were proposed to enhance farmers’ willingness to participate in carbon sink compensation and promote sustainable low-carbon agriculture. To effectively promote farmers’ participation in carbon sink compensation, it is necessary to foster a positive social atmosphere at the community level while addressing farmers’ personal needs by enhancing environmental awareness and engagement through policy guidance and incentives. This study employs grounded theory, combining open, axial, and selective coding to thoroughly analyze the interaction between social and personal norms and their positive impact on farmers’ behavior, specifically regarding green agricultural product carbon sink compensation. Concrete policy and community-level pathways are proposed, providing clear guidance for both theory and practice. Full article

Soil carbon sequestration (SCS) is a nature-based, low-cost climate mitigation strategy that also contributes to the climate adaptation of agricultural systems. Some land-use and land-management practices potentially lead to an enhancement of the soil organic carbon (SOC) sink, such as no-till, the use of cover crops, leaving residues on fields, improving the variety of legume species in grasslands and reducing grazing intensity. However, uncertainties remain both in estimating and measuring the impact of the application of certain practices, as these vary with the soil, climate and historic land use. IPCC (Intergovernmental Panel on Climate Change) guidelines are commonly used to estimate SOC and SOC sequestration potentials at different tiers. Here, the IPCC’s tier 1 methodology was applied to estimate (1) the sequestration potential of nine mitigation practices and (2) the emission or sequestration potential of four current land-change trends for n = 7092 unique agricultural sites in mainland Portugal. The conversion of irrigated crops to improved grasslands resulted in the highest average unit sequestration (1.05 tC ha⁻¹ yr⁻¹), while cropland conversion to poor degraded pasture (abandonment) resulted in the highest unit SOC loss (−0.08 tC ha⁻¹ yr⁻¹). The abandonment of cropland results in a national SOC loss of up to 0.09 MtC yr⁻¹, while the improvement of poor degraded pastures has the highest national sequestration potential, equal to 0.6 MtC yr⁻¹ (2.2 MtCO_2eq yr⁻¹), about 4% of Portugal’s emissions in 2021, if applied in all managed areas. The results enable a comparison between different practices and land uses; however, to enhance accuracy, a higher tier methodology tailored to the Portuguese context should be developed. Full article

A good quantitative knowledge of regional sources and sinks of atmospheric carbon dioxide (CO₂) is essential for understanding the global carbon cycle. It is also a key prerequisite for elaborating cost-effective national strategies to achieve the goals of the Paris Agreement. However, available estimates of CO₂ fluxes for many regions of the world remain uncertain, despite significant recent progress in the remote sensing of terrestrial vegetation and atmospheric CO₂. In this study, we investigate the feasibility of inferring reliable regional estimates of the net ecosystem exchange (NEE) using column-averaged dry-air mole fractions of CO₂ (XCO₂) retrieved from Orbiting Carbon Observatory-2 (OCO-2) observations as constraints on parameters of the widely used Vegetation Photosynthesis and Respiration model (VPRM), which predicts ecosystem fluxes based on vegetation indices derived from multispectral satellite imagery. We developed a regional-scale inverse modeling system that applies a Bayesian variational optimization algorithm to optimize parameters of VPRM coupled to the CHIMERE chemistry transport model and which involves a preliminary transformation of the input XCO₂ data that reduces the impact of the CHIMERE boundary conditions on inversion results. We investigated the potential of our inversion system by applying it to a European region (that includes, in particular, the EU countries and the UK) for the warm season (May–September) of 2021. The inversion of the OCO-2 observations resulted in a major (more than threefold) reduction of the prior uncertainty in the regional NEE estimate. The posterior NEE estimate agrees with independent estimates provided by the CarbonTracker Europe High-Resolution (CTE-HR) system and the ensemble of the v10 OCO-2 model intercomparison (MIP) global inversions. We also found that the inversion improves the agreement of our simulations of XCO₂ with retrievals from the Total Carbon Column Observing Network (TCCON). Our sensitivity test experiments using synthetic XCO₂ data indicate that the posterior NEE estimate would remain reliable even if the actual regional CO₂ fluxes drastically differed from their prior values. Furthermore, the posterior NEE estimate is found to be robust to strong biases and random uncertainties in the CHIMERE boundary conditions. Overall, this study suggests that our approach offers a reliable and relatively simple way to derive robust estimates of CO₂ ecosystem fluxes from satellite XCO₂ observations while enhancing the applicability of VPRM in regions where eddy covariance measurements of CO₂ fluxes are scarce. Full article

Thailand has committed to achieving carbon neutrality by 2050, targeting the power generation sector, which contributes 35% of the country’s CO₂ emissions, as a critical area for intervention. This study explores the transition toward carbon neutrality in power generation, focusing on fossil-fuel-based plants, particularly lignite and natural gas, which remain central to Thailand’s electricity production. A key strategy adopted by the Electricity Generating Authority of Thailand (EGAT) is “Sink Co-creation”, which includes the deployment of Carbon Capture and Storage (CCS) technologies in existing and future lignite power plants, leveraging favorable storage conditions. Additionally, natural gas power plants exhibit significant CCS potential through source–sink matching mechanisms. This study finds that the total greenhouse gas (GHG) emission reductions from fossil-fuel-based power plants could reach 17.07 MtCO₂. Of this total, lignite power plants are projected to achieve a reduction of 3.79 MtCO₂ by 2036, while natural gas power plants are expected to contribute an additional 13.28 MtCO₂ in reductions by 2050. However, the realization of these reductions faces significant challenges, including the high costs associated with CCS implementation and limited investor interest, underscoring the critical need for sustained government support and policy incentives to facilitate progress toward carbon neutrality. Full article