Carbon Balance and Management最新文献

Emergence of blockchain technology has disrupted a number of economic sectors, particularly financial institutions, with significant effects on their operations. This paper investigates the impact of asset tokenization on the issuance and trading process of financial assets, specifically bonds. It examines the effect of tokenizing the High Yield Bond on the Ethereum blockchain across two key dimensions: On costs, a comparative cost–benefit analysis is conducted before and after tokenization, and on green sustainability, through a comparative analysis on the carbon footprint of the bond before and after Ethereum’s merge to proof of stake. The results show that Tokenization improves cost-savings, and it promotes a greener, more sustainable approach when using the Ethereum blockchain post-transition to proof of stake.

Harvested wood and paper products can store large amounts of carbon long-term but also contribute to carbon emissions once discarded. Currently, several tools are used for inventory and reporting carbon in wood and paper products in the U.S. Carbon in wood and paper is tracked from initial manufacturing, through its lifetime, and final fate (e.g., dumps, landfills, incinerated, or recycled). Once discarded into landfills, a portion of wood and paper is assumed permanently stored; however, carbon storage of specific products can vary widely which influences carbon storage and emissions estimates. Using historical California harvest data and state-level inventory model, HWP-C vR, this research built model capacity for expanding and refining waste parameters, such as product-level decay half-lives and proportions of permanent carbon storage to reduce waste parameter uncertainty. By updating the proportion of carbon permanently stored in landfilled wood and paper products and by adding product-specific discard pathways, carbon in solid waste disposal sites cumulatively increased moderately by about 11.77 MMT CO₂Eq and emissions decreased by about 11.18 MMT CO₂Eq in California from 1953 to 2020. Updated parameters furthermore made it possible to compare product-level carbon storage and emissions within landfills such as newspaper, office paper, coated paper, cardboard, plywood, and lumber. The cumulative wood product categories resulted in similar amounts of carbon compared with paper products – 28.21 MMT CO₂Eq (0.415 MMT CO₂Eq annually) and 27.39 MMT CO₂Eq (0.403 MMT CO₂Eq annually) respectively; however, the carbon storage of wood products was much higher than paper, with 164.07 MMT CO₂Eq (2.413 MMT CO₂Eq annually) stored compared with 31.41 MMT CO₂Eq (0.462 MMT CO₂Eq annually) respectively. These carbon emissions and storage estimates illustrate the value in understanding carbon dynamics at the product-level particularly when considering climate impacts from landfill emissions even after product disposal.

Net ecosystem productivity (NEP) in croplands is a core indicator of carbon exchange between agroecosystems and the atmosphere, directly reflecting net carbon budgets and sequestration capacity. To resolve its spatiotemporal patterns and dominant controls, we combined remote-sensing, land-cover, and meteorological datasets with the Boreal Ecosystem Productivity Simulator (BEPS) coupled to a Geostatistical Model of Soil Respiration (GSMSR) to simulate cropland NEP across China’s three major plains—the Northeast China Plain (NCP), Huang–Huai–Hai Plain (HHHP), and Middle–Lower Yangtze Plain (MLYP)—during 2000–2020. An interpretable machine-learning framework (XGBoost–SHAP) was used to quantify factor responses and regional heterogeneity. The results show that: (1) From 2000 to 2020, cropland NEP across the three major plains increased overall but exhibited a pronounced north–south gradient: cropland NEP increases were larger and more widespread in NCP and HHHP, while persistent negative changes were concentrated in southern MLYP. (2) Analysis of influencing factors revealed that interannual variations in agricultural NEP from 2000 to 2020 were jointly regulated by hydro temperature factors, atmospheric composition, and soil and farm management factors. The important ranking of factors varies with regional heterogeneity. (3) Regionally, NCP was primarily driven by annual mean temperature and surface soil moisture, HHHP was dominated by annual mean temperature and carbon dioxide, while MLYP was influenced mainly by annual mean temperature and PM_2.5. Threshold effects were observed for all factors. Notably, declining PM_2.5 concentrations exerted a positive influence on interannual variations in cropland NEP. This study can provide scientific basis for safeguarding food security and advancing sustainable agricultural development and offer reference for formulating cross-regional policies on enhancing carbon sequestration in croplands and implementing zoned management.

Water-carbon coupling shapes the stability of terrestrial carbon sinks, yet the magnitude, direction, and timing of interactions between surface runoff (Q) and gross primary productivity (GPP) can differ among hydroclimatic regimes and shift over time. This study evaluates Q and GPP coupling in the Minjiang River Basin, a monsoon-influenced mountain-to-basin transition with strong topographic mediation. Using the China Natural Runoff Dataset version 1.0, gap-filled MODIS GPP, CRU meteorology, and MODIS vegetation indices, patterns from micro subbasins to the full basin were quantified with Mann-Kendall trend tests, spatial Pearson correlations, five-year moving correlations, and random forest attribution. Q and GPP show pronounced spatial heterogeneity, with higher Q in the middle and lower basin and increasing GPP from north to south, while basin-scale trends are modest and largely not significant. Spatial coupling forms a persistent north-negative and south-positive dipole. Decoupling is strongest and most extensive from 2001 to 2010, whereas from 2007 to 2018 it shows weaker negative correlations and expanding positive coupling. Moving window analyses indicate strengthening coupling in most subbasins and sign reversals in some. Attribution identifies precipitation as the dominant driver of Q across subbasins, while GPP is jointly regulated by temperature and vegetation structure, with the relative influence shifting from climate toward structure between periods as NDVI and LAI increase in importance. Atmospheric moisture and vegetation also gain influence on Q in the later period. These findings provide transferable diagnostics for identifying where and when water and carbon coupling is likely to weaken or strengthen in mountain plain transitions and highlight vegetation structural levers for carbon-relevant water management.

Global warming has led to pronounced differences in photosynthesis and respiration between sun and shade leaves. However, assessments of the resulting disparities in carbon sink potential and contributions remain limited, and the underlying mechanisms have yet to be systematically elucidated. This study used three carbon sink indicators—gross primary productivity (GPP), sun leaf GPP (GPPsun), and shade leaf GPP (GPPshade)—to reveal the spatiotemporal dynamics and driving mechanisms of carbon sink in the terrestrial ecosystems of southern China. The Lindeman–Merenda–Gold (LMG) model was applied to quantify the relative contributions of climate change to carbon sink variations. The results showed the following: (1) GPP, GPPsun, and GPPshade exhibited increasing but fluctuating trends during the period 2001–2020, with growth rates reaching 10.88, 5.69, and 5.19 g C m⁻² yr⁻¹, respectively. However, GPPshade increased faster than GPPsun in 44.79% of the study area. (2) GPPshade/GPP showed an increasing trend (0.0003 yr^-1), with a mean value of 0.54. The average contribution of shade leaf to the carbon sink was 1.79 times higher than that of sun leaf. (3) Declining solar radiation (SR) dominated this shift. The contribution rates of SR to GPP, GPPsun, and GPPshade were 28.01%, 24.55%, and 34.52%, respectively. SR was the primary driver in 37.46%, 31.02%, and 50.19% of the entire study area. (4) In areas with decreased SR, GPPsun exhibited slow growth, and GPPshade decreased. In areas with increased SR, GPPshade surged, while GPPsun growth decelerated significantly. Shade leaf carbon sink emerged as the dominant contributor to the overall enhancement of vegetation carbon sink. These findings demonstrate a key mechanism—increased GPPshade potential driven by SR decline, suppression of GPPsun, and a resulting restructuring of carbon sink dynamics. This study provides a theoretical support for enhancing terrestrial ecosystem carbon sink and offers valuable insights for advancing global carbon neutrality objectives.

Against the backdrop of China’s “dual-carbon” objectives, this study examines the impact of artificial intelligence (AI) on urban carbon emission efficiency (CEE). Taking the National New Generation Artificial Intelligence Innovation and Development Pilot Zone (NNGAIIDPZ) as a quasi-natural experiment, we employ a multi-period difference-in-differences (DID) approach based on panel data from 274 Chinese prefecture-level cities spanning the period from 2011 to 2022. The empirical results indicate that AI adoption significantly enhances urban CEE, and these findings remain robust across parallel trend tests, placebo tests, and alternative model specifications. Mechanism analysis further shows that green technological innovation and the agglomeration of innovative talent serve as crucial transmission channels through which AI improves carbon emission efficiency. Moreover, heterogeneity analysis reveals that the carbon-reducing effect of AI is more pronounced in non-resource-based cities and in China’s central region. At the same time, it is comparatively weaker in resource-based cities and western regions. Overall, this study provides robust empirical support for AI-enabled low-carbon governance and the formulation of regionally differentiated policy strategies.

Background

High-resolution carbon emission checking and the tracking of carbon flows within and between cities are crucial for carbon emissions reduction and regional coordinated development. However, current research in both directions remains insufficient and requires further exploration. This study selected two big and tight interacted cities from the urban agglomeration along China’s longest and largest river. The research employs two methodologies, the first monitors carbon emissions in high resolution and the second checks the indirect carbon flow hotspots between the two cities.

Results

Results found that both cities showed a significant increasing trend for total carbon emissions from 2000 to 2020. The peak for Nanjing was in 2018, 2648.09 × 10⁴ t, and Changsha’s peak was in 2017, reaching 2202.51.15 × 10⁴ t. Industry is always the main contributor to carbon emissions, but the percentage is decreasing. Generally, carbon emissions in Nanjing are distributed more spatially intensively than in Changsha. There was a similar regularity of carbon emissions for both cities from urban residential, commercial and traffic sectors Through population flow and trade, Changsha pulled 2.65 × 10⁴ t carbon emissions in Nanjing, and the reversed amount that Changsha pulled in Nanjing is 1.87 × 10⁴ t. Generally, Nanjing has a slight advantage over Changsha regarding the higher carbon emissions produced there

Conclusions

This research holds significant implications for the development of low—carbon cities and the promotion of coordinated regional development.

Agroforestry systems (AFS) offer valuable ecological services and multifunctionality, yet there are gaps regarding interactions between tree species (particularly native ones) and food crops in the early stages of AFS across the Congo Basin. Acacia auriculiformis (exotic), and Pentaclethra macrophylla (native) are among the tree species grown in association with food crops in the Congo Basin. However, the knowledge of the carbon storage of these systems is limited, which would encourage their adoption. This study assessed the above-ground carbon (AGC) and soil organic carbon stock (SOCS) in AFS involving A. auriculiformis and P. macrophylla intercropped with cassava, maize, and peanut food crops. Research was conducted in the Lobilo watershed using a multifactorial design with two tree species, four planting densities (T1: 2500 trees × ha⁻¹, T2: 625 trees × ha⁻¹, T3: 278 trees × ha⁻¹; and T0: monoculture), and three intercrops (cassava, maize and peanut). Tree diameter at breast height (DBH) was measured at 1.3 m above the ground and recorded per plot, then integrated into allometric equations to estimate biomass and AGC. Soil samples were collected at 30 cm depth to determine SOCS. Data was analyzed using a mixed-effect model. Results revealed that A. auriculiformis stores more AGC than P. macrophylla. In addition, the planting density of 625 trees × ha⁻¹ and peanut food crops favored AGC sequestration over other planting densities and food crops. Regarding SOCS, agroforestry plots store more carbon than monocropping. Hence, A. auriculiformis intercropped with food crops improve carbon storage at the first stage while P. macrophilla, the local species, required more time to perform this flux. These findings have important policy implications for sustainable land use and climate adaptation in the Congo Basin. This study supports the integration of tailored agroforestry systems into national climate-smart agriculture strategies and land restoration policies. We recommend that policymakers promote agroforestry practices that include these species—particularly at a density of 625 trees × ha⁻¹ intercropped with peanuts—as viable options to enhance carbon storage, improve food production, and reduce the deforestation pressure caused by slash-and-burn agriculture.

The biodiversity and ecosystem functioning (BEF) relationships can offer insights for management of forest plantations. Most evidences on BEF relationships from field observations and experiments focus on short-term (≤ 50 years) forest development. However, how tree diversity and aboveground biomass co-evolve during long-term development remain poorly understood. Addressing this knowledge gap can improve the diversity-based management for long-term forest carbon sequestration. We employed a process-based Ecosystem Demography model (ED-2.2) to simulate three categories of forest attributes over approximately one century (2004–2100) in subtropical coniferous forests in China. These included: (1) three plant functional type (PFT) diversity metrics (Simpson, Shannon–Wiener, and Pielou evenness metrics); (2) three structural diversity metrics for DBH [standard deviation (SD), coefficient of variation (CV), and Gini coefficient (Gini)]; as well as (3) aboveground biomass stock (AGBS) and production (AGBP). The modeling results were evaluated using observations from forest inventory. The results showed that (1) PFT diversity (particularly Simpson’s and Shannon–Wiener diversity) and structural diversity (notably the SD and Gini for DBH) generally exhibited approximately U-shaped and hump-shaped trajectories, respectively. AGBS increased gradually before slightly declining, while AGBP rose rapidly then entered gradual decline. Notably, seasonal warming and precipitation stress may lead to severe tree mortality, potentially altering long-term trajectories of tree diversity and AGBS/AGBP in subtropical coniferous forests. The BEF relationships generally strengthened over time during approximately one century. Among the six tree diversity metrics, SD (95%) and Gini (79%) for DBH demonstrated the highest proportion of significant BEF relationships across years. (2) The stand density and mean tree size relationships generally followed the Yoda’s power law (– 1.44 vs. – 1.50). The significant effects of stand density on BEF relationships were predominantly negative and concentrated in the first half period of simulation. In contrast, the significant effects of mean tree size on BEF relationships were primarily positive, especially in the first half period. Mean tree size emerged as a stronger driver of BEF relationships than stand density, with significant effects detected in 67% vs. 56% of cases, respectively. To some extent, mean tree size and stand density modulated the long-term BEF relationships. This study provides insights on how tree diversity and aboveground biomass co-evolve during forest succession using modelling evidence, highlighting the importance of tree size-dependent processes in shaping BEF relationships during forest development.

Background

The Amazonian forests are increasingly threatened due to continuous changes in land use, particularly deforestation. This study aimed to quantify and analyze the vertical distribution of soil glomalin and its relationship with carbon, climate, and soil properties across three forest types of the Peruvian Amazon. A total of 18 plots were selected and sampled in forests with different vegetation cover types: deforested, disturbed, and primary forest. The vertical variation of total glomalin (TG), easily extractable glomalin (EEG), and the number of arbuscular mycorrhizal fungal (AMF) spores was estimated, as it was the relationships of these variables with soil depth, physical-chemical properties, and climate conditions.

Results

The mean values for TG, EEG, and AMF showed vertical variations in the three forest cover types, with high values in disturbed forests and degraded soils. Overall, higher mean values were found in the surface soil layers compared to the deep layers. TG, EEG, and AMF were positively corelated with soil organic carbon (SOC) and soil organic matter (SOM). Moreover, the total nitrogen (N), SOC, OM, total phosphorus (P), and soil water content (SWC) presented higher values in the topsoil than the deep layers.

Conclusions

The highest production of glomalin in disturbed forests is probably a response to degradation processes. This work is a contribution to expand knowledge about glomalin dynamics in forest soils of the Amazon rainforest and provides essential information for future soil ecosystem restoration practices in tropical forests.