Carbon Balance and Management最新文献

Background: Against the backdrop of global warming, the imbalance in agricultural carbon budgets poses a dual threat to ecological security and food security. As a major grain-producing region in China, Hunan Province is confronted with substantial CH₄ emissions derived from rice cultivation, a problem further exacerbated by industrialization, urbanization, and shifts in farming practices. Consequently, investigating the carbon imbalance in Hunan's agricultural cultivation is of great significance for advancing the sustainable development of agriculture in the province. This study constructs and quantifies the agricultural carbon imbalance index (CII), and employs exploratory spatiotemporal data analysis, the PLS-VIP method, and the GTWR model to analyze the spatiotemporal evolution and driving factors of agricultural cultivation carbon imbalance of Hunan Province in China from 2001 to 2022.

Results: (1) The CII for agricultural cultivation in Hunan Province decreased from 0.41 in 2001 to 0.26 in 2022. Its spatiotemporal pattern shifted from "high in the north and low in the south" to "high in the west and low in the east," with the gravity center of CII moving southwestward. (2) Over the study period, the spatial correlation characteristics of CII underwent three stages: significant positive correlation, random distribution, and weak positive correlation. LISA time path and spatiotemporal transition analyses showed that the spatiotemporal clustering pattern of CII remained relatively stable from 2001 to 2011; however, its stability weakened slightly from 2012 to 2022. (3) Key factors influencing the agricultural cultivation CII in Hunan Province include GPA, GST, IARDF, PAOV, FUI, and PUI. These factors exhibit significant spatiotemporal heterogeneity in their effects. For example, the FUI and PUI had significant impacts on CII in the Xiangbei region, whereas their influence was relatively weaker in the Xiangxi region.

Conclusions: To alleviate the persistent carbon imbalance in Hunan's agricultural cultivation systems, differentiated carbon sequestration and emission reduction strategies should be formulated by integrating the significance hierarchy of CII drivers and their spatial heterogeneity patterns. Special emphasis ought to be placed on tackling the re-emergence of carbon imbalance in specific municipal regions, which stems from urban expansion encroaching on farmland and the persistence of traditional cultivation practices. This targeted optimization will effectively facilitate the sustainable and low-carbon development of Hunan's agricultural sector.

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.

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.