Revealing inter-regional water-energy-carbon transfer driven by land use has great significance for realizing multiple objectives of resource collaborative optimization and carbon mitigation, yet land-use WEC accounting at the provincial scale and the efficiency of inter-regional WEC transfer driven by embodied land flow remain insufficiently explored in China. Accordingly, a theoretical framework for water-energy-carbon accounting and inter-regional transfer based on land use was established. Then the water and energy use and carbon emissions of cropland, forest land, grassland, water area, and construction land in 30 Chinese provinces were estimated and their spatial-temporal patterns were analyzed. The efficiency and spatial characteristics of inter-provincial water-energy-carbon transfer driven by embodied land flow were discussed. The results showed that the land-use water consumption was relatively stable from 2005 to 2020, while land-use energy consumption and carbon emissions exhibited an increasing trend. Embodied land mainly flowed from underdeveloped areas to developed areas, and there was regional variability in the related water-energy-carbon transfer. The energy-carbon transfer patterns driven by embodied land flow were similar. Embodied land consumption was concentrated in cropland, forest land, and grassland. Water consumption driven by embodied land flow was primarily concentrated in cropland. Energy consumption and carbon emissions driven by embodied land flow were mainly concentrated in construction land. Generally, the embodied land flow-related water-energy-carbon transfer efficiency has improved, with the proportion of efficient transfer increasing by 2.03%, 2.08%, and 2.82%, respectively. Water transfer between different land use types was more efficient than energy-carbon transfer, and water-energy-carbon transfer associated with construction land was inefficient. Water-energy-carbon transfer driven by embodied land flow could alleviate resource and environmental pressures, while its efficiency could still be enhanced. Therefore, in the future, land resource allocation should be optimized based on regional coordination and integrated water-energy-carbon management to enhance the efficiency of cross-regional water-energy-carbon transfer, thereby achieving efficient resource utilization and environmental sustainability. Overall, this study provides quantitative evidence for the resource and environmental impacts of cross-regional land resource allocation and offers new insights for synergistic resource optimization from a remote coupling perspective.
The forest soil carbon pool is a core component of the terrestrial carbon cycle, and its quantity and quality are largely regulated by forest types, thereby influencing the carbon sequestration capacity of forest ecosystems. The Qinghai-Xizang Plateau is one of the regions most sensitive to global climate change, experiencing warming rates higher than the global average and pronounced ecological vulnerability. Among its subregions, southeastern Xizang is characterized by extensive forest cover and prominent ecological functions. Accordingly, elucidating the differentiation characteristics and driving mechanisms of soil carbon pool quantity and quality among different forest types in this region is of great significance for accurately evaluating regional carbon sink capacity. This study focused on multiple representative forest types in southeastern Xizang, including coniferous forests, broad-leaved forests, and conifer-broadleaf mixed forests, to systematically compare the distribution characteristics of soil organic carbon (SOC) and its fractions among different forest stands, and to explore the variation patterns and driving factors of the carbon pool management index (CPMI). The results showed significant differences in soil organic carbon and its fractions among forest types in southeastern Xizang (p < 0.05). The Cupressus gigantea forest exhibited consistently higher levels of soil organic carbon and its fractions, indicating a strong carbon accumulation capacity, whereas both Pinus densata forest and Pinus yunnanensis-Populus davidiana mixed forest showed relatively lower values overall. The carbon pool management index varied markedly among forest types. The Cupressus gigantea forest showed the highest carbon pool index (CPI) and carbon pool management index (CPMI) (p < 0.05), indicating the best soil carbon pool quality. Driving factor analysis revealed that soil organic carbon (SOC) and carbon pool index (CPI) were primarily regulated by available nitrogen (AN), available potassium (AK), electrical conductivity (EC), and soil water content (WC). The carbon pool management index (CPMI) was mainly driven by field capacity (FC) and total nitrogen (TN), whereas carbon pool activity (A) and the carbon pool activity index (AI) were more dependent on available phosphorus (AP) and total phosphorus (TP). Redundancy analysis (RDA) showed that the first two ordination axes together explained 92.8% of the total variation, with the first axis accounting for 88.1% and the second axis for 4.7%, indicating that environmental factors can effectively explain the variation in soil carbon pool quantity and quality. This study revealed the spatial differentiation patterns and distinct driving mechanisms of soil carbon pool quantity and quality in alpine forests, providing a scientific basis for evaluating forest carbon pool quality and guiding regional carbon sequestration enhancement and management.
As a major contributor to carbon emissions among various land-use types, the expansion and spatial distribution of construction land are critical factors in regional carbon management strategies. Although considerable research has independently examined construction land growth control and regional carbon emission assessments, few studies have integrated these aspects to guide construction land expansion within the framework of carbon peaking strategies. This study proposes an innovative framework that integrates carbon emission considerations into the management of regional construction land expansion. Using Changsha City as a case study, this research analyzes the spatiotemporal dynamics of construction land expansion and associated carbon emissions from 1990 to 2020, exploring their interdependencies. To project future trends, three scenarios-natural growth, high-emission expansion, and low-emission development-were developed to simulate the impacts of construction land changes on carbon emissions by 2030. The study evaluates the carbon emission consequences of urban expansion and proposes mitigation strategies within a low-carbon development framework. The findings indicate that: (1) From 1990 to 2020, construction land in Changsha City expanded by 660.24 km², primarily encroaching upon adjacent cultivated and forested lands. During the same period, carbon emissions increased by 1.3963 × 10⁸ t, showing a strong positive correlation with construction land expansion; (2) By 2030, carbon emissions are projected to reach 2.396 × 10⁸ t, 2.582 × 10⁸ t, and 1.639 × 10⁸ t under the natural growth, high-emission, and low-emission scenarios, respectively, reflecting increases of 53.21%, 65.11%, and 4.81% relative to 2020 levels; (3) Under both the natural growth and high-emission scenarios, construction land expansion is likely to intensify its adverse impact on the regional ecosystem, thereby reducing ecological stability. In contrast, the low-emission development scenario is projected to promote significant improvements in ecosystem health and resilience. This study offers critical insights for territorial spatial planning and construction land management within the context of the dual-carbon strategy, presenting a viable pathway for reconciling land expansion with ecological sustainability.
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. (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. 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.
The Qinling Mountains contain the largest forest ecosystem in central China. Examining the spatiotemporal variations of urban carbon lock-in and the pathways for unlocking it on the northern and southern piedmont of the Qinling hinterland is of great significance for achieving carbon balance in central and western China. Based on panel data from seven cities on the northern and southern piedmont of the Qinling Mountains from 2008 to 2022, we mea-sured regional carbon lock-in levels and carbon budgets from a land-use perspective, and investigated the spatio-temporal trends. We applied fuzzy-set qualitative comparative analysis to identify the high-carbon and low-carbon configuration effects of regional carbon lock-in at both macro and micro levels. The results showed that the degree of carbon lock-in in cities on the northern and southern piedmont of the Qinling Mountains increased from 1.79 to 5.61 and exhibited a certain degree of spatial clustering between 2008 and 2022. Net carbon emissions ranged from 31.22 Mt to 113.14 Mt, while carbon sinks remained in the range of 18 Mt to 21 Mt. The ratio of total carbon emission from construction land to that from cropland was 2.96:1. At the macro scale, regional carbon lock-in could be attributed to three configuration types: weak carbon sink function, gap in regulatory function, and misaligned industrial structure. At the micro scale, we identified nine high-carbon and ten low-carbon configurations. The main drivers of carbon emissions from natural ecosystem, construction land, and cropland were environmental regulation, industrial structure, and cropping structure, respectively. The degree of carbon lock-in in cities on the northern and southern piedmont of the Qinling Mountains followed a "slow-fast-slow" growth pattern. Spatially, it was characterized by lower in the south and higher in the north, with clustering that diffused from core cities to surrounding areas. On the basis of implementing overarching environmental policies, each region should select appropriate enhancement pathways in line with resource endowments and carbon lock-in drivers, so as to achieve the goal of carbon unlocking. 秦岭是我国中部最大的森林生态系统,开展秦岭腹地南北麓城市碳锁定的时空演变和碳解锁路径研究,对中国中西部区域碳平衡具有重要意义。本研究基于2008—2022年秦岭南北麓7个城市的面板数据,通过土地利用视角测度各地区碳锁定水平和碳收支情况,并探究其时空变化趋势,最后,运用模糊集定性比较方法从宏观和微观两个维度探究区域碳锁定的高碳(低碳)组态效应。结果表明: 2008—2022年间,秦岭南北麓城市碳锁定程度从1.79上涨至5.61,并存在一定的空间聚集现象,净碳排放量为3122.03万～11314.43万t,碳汇量保持在1800万～2100万t,建设用地与耕地总碳排放之比为2.96∶1。在宏观上,区域碳锁定成因可归纳为碳汇功能乏力、监管功能缺失以及产业结构失调3个组态类型。在微观上,共识别出9条高碳和10条低碳组态构型,其中,自然生态系统、建设用地、耕地的碳排放驱动因素分别为环境规制、产业结构、农作物种植结构。秦岭南北麓城市碳锁定程度在研究期间上呈“慢-快-慢”的增长趋势,在空间上表现为南低北高、中心城市向四周发散的聚集特征,各区域在落实宏观环境政策的基础上,应根据自己的资源禀赋和碳锁定驱动因子,选择适当的提升路径从而达到碳解锁的目标。.
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.
Water scarcity is becoming increasingly severe, while the demand for stable and high-yield wheat production continues to rise. Under these circumstances, achieving the dual objectives of water conservation and yield enhancement through precise water management represents a critical challenge for sustainable agriculture, particularly in arid oasis regions.In this study, we investigated the dynamics of endogenous hormones and carbon metabolism in the basal first and second internodes (I1 and I2) of wheat stems under drip irrigation conditions. Special attention was given to the roles of non-structural carbohydrates (NSC) and structural carbohydrates (SC) in regulating stem development. The objective was to elucidate how variations in hormonal regulation and carbon allocation contribute to improvements in wheat grain yield as well as stem lodging-related traits. Two wheat cultivars differing in water sensitivity (XC6 and XC22) were assigned to the main plots. Subplots were subjected to regulated deficit irrigation at two stages (tillering, T and jointing, J) with two levels of water: mild deficit (60-65% FC, FC is field water holding capacity, T1, J1) and moderate deficit (45-50% FC, T2, J2). Following the completion of deficit irrigation, we rehydrated to 75-80% FC. A fully irrigated treatment (75-80% FC, CK) served as the control. Relationships among these physiological indicators, yield components, and stem lodging-related traits were analyzed. The results showed that the T1 treatment significantly enhanced endogenous hormone concentrations and hormonal ratios (gibberellins, GA; zeatin + zeatin riboside, Z + ZR; gibberellin/indole-3-acetic acid, GA/IAA, and zeatin + zeatin riboside/abscisic acid, (Z + ZR)/ABA). Moreover, T1 markedly stimulated the activities of key enzymes involved in sucrose and fructan metabolism, thereby promoting the accumulation of NSC in wheat stems. Consequently, T1 promoted greater grain yield (1.79%-14.01%). In addition, T1 achieved the highest productivity while maintaining superior water-saving efficiency. The endogenous hormones of I1 and the promotion of NSC metabolism were more effective. In contrast, the J1 treatment predominantly activated enzymes associated with lignin biosynthesis and cellulose synthesis, thereby promoting the deposition of SC in the stems. This process significantly enhanced stems filling degree and breaking strength (28.12%-164.86%). And the strengthening effect was more pronounced in I1 than in I2. XC6 exhibited superior hormonal balance, carbon metabolic capacity, and lodging-related stem properties compared with XC22. Correlation and variable importance in projection (VIP) analyzed further revealed that grain number per spike, thousand-kernel weight, gibberellin (GA) in both basal internodes (I1 and I2) and sucrose fructosyltransferase (SST) activity, the hormonal ratio (Z + ZR)/ABA of I1 were the major contributors to yield formation. In contrast, sucrose content (Suc) in both I1 and I2, along with cinnamyl alcohol dehydrogenase (CAD), phenylalanine ammonia-lyase (PAL), and cellulose content (CC) in I1 had the greatest influence on stem filling degree and breaking strength. Overall, the T1 treatment enhanced endogenous hormone accumulation, improved hormonal coordination. And T1 also enhanced stems NSC metabolism. These led to increase yield. In contrast, the J1 was linked to improved stem lodging-related traits, corresponding to increased lignin and cellulose metabolism. Collectively, these findings provide a physiological basis for achieving both water conservation and high yield in wheat production through precise irrigation management in arid oasis regions.
In the context of global climate change, understanding economic efficiency disparities between high-carbon and low-carbon industries is crucial for advancing low-carbon transitions and improving carbon governance. This study examines heterogeneity in corporate carbon emission management and economic performance across Chinese industries and identifies key drivers of firms' transformation capacity. Using a panel dataset of 633 listed enterprises from eight industries in China over 2010-2021, we classify firms into high- and low-carbon groups based on their emissions profiles and benchmark four machine-learning models-Random Forest, XGBoost, LightGBM, and Decision Tree-to capture nonlinear relationships and evaluate the relative importance of environmental and financial indicators. Random Forest delivers the best performance, achieving a classification accuracy of 95.7% (rounded) and strong discriminatory ability (AUC = 0.989). Feature-importance results consistently show that carbon emissions are the most influential variable, followed by total liabilities and total assets, while profitability-related indicators (e.g., operating revenue and gross profit margin) also contribute to distinguishing firms' carbon profiles and performance differences. Overall, high-carbon enterprises appear to face greater transition barriers due to higher abatement cost exposure and tighter balance-sheet constraints, whereas low-carbon firms may be better positioned to benefit from policy incentives and market opportunities. These findings highlight the pivotal role of financial health in enabling low-carbon transformation and underscore the need for differentiated policy design. Policy implications include targeted transition finance and more flexible allowance allocation mechanisms for high-carbon enterprises, alongside continued incentives for technological innovation and market expansion in low-carbon sectors. JEL CLASSIFICATION: Q56; G30; C55; Q43; L60.
Understanding the coordinated changes in soil carbon and nitrogen is essential for evaluating ecosystem responses to environmental change, particularly in ecologically fragile alpine regions such as the Qilian Mountains. In this study, the denitrification-decomposition (DNDC) model was used to assess the spatiotemporal dynamics of soil organic carbon density (SOCD) and total nitrogen density (STND) in the 0-30 cm soil layer from 1975 to 2024. The results revealed that SOCD and STND were higher in the northern and east-central grasslands and lower in the southwestern regions. Both stocks exhibited fluctuating but overall increasing trends, with notable increases aligned with major ecological protection policies in China. To better understand the coupling of soil carbon and nitrogen, we constructed a composite indicator called soil carbon and nitrogen density (SCND) using principal component analysis. This indicator captures the synergistic accumulation of organic carbon and total nitrogen driven by shared ecological processes and was further used to explore its associations with environmental factors, enabling an integrated assessment of soil carbon-nitrogen dynamics. The results revealed that elevation and soil bulk density were the main direct drivers of carbon and nitrogen accumulation, both of which exerted negative effects, whereas the other factors acted through indirect pathways. These findings underscore the importance of topography and soil structure in regulating carbon and nitrogen dynamics. It is recommended to plant deep-rooted grass species, limit heavy machinery, and maintain long-term ecological protection to prevent declines after initial gains from interventions. In addition, the carbon-to-nitrogen (C/N) ratio showed increasing spatial heterogeneity over time, with high values in the western and central regions, where nitrogen input can be enhanced by introducing legumes or applying organic fertilizers. In the northern and southeastern areas, grazing exclusion or low-intensity grazing is recommended to promote organic matter accumulation. Vertically, the C/N ratio decreased with soil depth, indicating strong carbon and nitrogen coupling within the soil profile. Overall, this study highlights the coordinated dynamics of soil carbon and nitrogen in the Qilian Mountain grasslands, providing valuable insights for the sustainable management and resilience improvement of grasslands in this region under changing environmental conditions.
As a core metric for climate policy, the scientific estimation of carbon social costs is crucial for formulating mitigation strategies. However, traditional integrated assessment models predominantly focus on the global aggregate, failing to adequately account for regional heterogeneity, sectoral characteristics, and strategic interactions between regions. They also lack systematic integration of ESG principles. To address this, this paper examines regional and sectoral carbon social costs driven by ESG development. Through cooperative and non-cooperative games, we improve the integrated economic-environmental-climate development model, take the eight economic regions in China as an example, get the carbon social cost of each economic region and typical important industries, and obtain the key parameters and the evolution law of carbon social cost. The model categorizes the carbon emissions after the implementation of emission reduction policies under the ESG perspective into direct and indirect emissions. It studies the economic impacts of the two types of emissions before and after the implementation of emission reduction policies, and conducts research on the top four typical important industries (industry, construction, transportation, and power) that rank among the top four global CO2 emitters, to obtain the analytical solution of the social cost of carbon in the region and the typical important industries. In addition, this paper numerically simulates the social cost of carbon for the four industries under the baseline scenario, cooperative game scenario, non-cooperative game scenario, and temperature limitation scenario. The study shows that the social cost of carbon in the northern, southern and eastern coastal economic regions is higher than that in other economic regions, the social cost of carbon in the industrial and electric power industries in each economic region is higher than that in the building and transportation industries, and the more stringent the temperature limit is, the higher the social cost of carbon is in the economic regions.
As the environmental problems caused by the greenhouse effect become more and more serious, and the forest as the largest carbon pool can effectively slow down the greenhouse effect, it is particularly important to accurately predict the carbon storage of the forest. In order to accurately estimate the biomass and carbon storage of Quercus mongolica in Northeast China, the biomass allocation pattern of Q. mongolica was analyzed. In this study, data of 175 Q. mongolica trees in Heilongjiang, Jilin, Liaoning and eastern Inner Mongolia were collected, including aboveground organ biomass, DBH, tree height, age and climatic factors, as well as published carbon content data of different organs. In this study, the biomass allocation pattern of individual Q. mongolica was analyzed. An additively compatible aboveground biomass and carbon storage model and an algebraically controlled aggregation model were established using nonlinear simultaneous equations. After selecting the aggregate biomass compatibility model, climate factors were added to establish a compatibility model containing climate factors. In addition, the root-stem ratio model was used to construct the underground compatible biomass and carbon storage model. The adjusted R2adj values of the final established aboveground components and aboveground total biomass and carbon storage models were between 0.7048 and 0.9618, the total relative error ( TRE ) was within ± 1%, and the average prediction error ( MPE ) was below 10%, which met the modeling accuracy standard. The belowground biomass models showed adjusted R²adj values between 0.7702 and 0.7801, TRE ≤ 1%, and MPE < 15%. This study elucidated the biomass allocation pattern of individual Q. mongolica. All the developed models meet the accuracy requirements and can be applied to predict the biomass and carbon storage of Q. mongolica in Northeast China. In the compatibility model with climate factors, the accuracy of leaf and branch models has been greatly improved, indicating that the addition of climate factors in the independent model can greatly improve the accuracy of each component model, which can provide a theoretical basis for the establishment of each component model in the compatibility model of other tree species.
The progress of the digital economy and low-carbon economy (hereinafter “both economies”) in China currently shows a digitalization trend and decarbonization urgency, and their intrinsic connections are becoming increasingly evident. Study on coordination of both economies is of crucial importance for China. In this study, an index system was constructed to measure the development level of both economies, and the entropy method was employed to calculate the index based on the data panel of China’s 282 prefecture-level cities during 2012–2021. Then, the coordinated development level of both economies (CDL) was assessed by the coupling coordination degree model. In addition, we conduct a sensitivity test on the weight setting of the composite index in the coupling coordination degree model to verify the robustness of CDL measurement to alternative weight specifications. Results indicate that CDL has steadily improved annually and is generally in the basic coordination stage. Cities in the basic maladjustment stage and the basic coordination stage still show considerable room for further improvement. The CDL across China’s four regions exhibits a pattern of “Eastern > Northeastern > Central > Western”. Next, the regional differences of CDL were decomposed by the Dagum Gini coefficient, revealing a decreasing trend in overall differences. Within-regional differences is the primary source of regional differences. More specifically, within-regional disparities rank from high to low as Western, Eastern, Northeastern, and Central; and the between-regional disparities, from high to low, are Western–Northeastern, Eastern–Western, Central–Western, Eastern–Central, Eastern–Northeastern, and Central–Northeastern. Accordingly, further empirical analysis using the spatial Durbin model identified several driving mechanisms for CDL. In the spatial econometric setting, we further perform robustness checks with alternative spatial weight matrices, including a geographic adjacency matrix, an economic distance matrix, and a gravity-model nested matrix, and we also report the decomposition of spatial effects. Moreover, we introduce a one-period lag of the dependent variable to estimate a dynamic spatial Durbin model, capturing path dependence in CDL and testing the robustness of the conclusions. Results demonstrate that government guidance, market regulation, technological innovation, and structural optimization mechanisms all promote CDL significantly, while the openness mechanism has a significant inhibiting effect. The direct effects are consistent with the above results, while the indirect effects indicate positive spatial spillovers from government guidance and market regulation, and a significant negative spatial spillover from openness; these findings remain broadly stable after replacing spatial weight matrices and after introducing the dynamic term, suggesting strong robustness in identifying the driving mechanisms. These findings can be interpreted through the technology–institution–structure analysis framework. Specifically, technological integration highlights digital technology empowering low-carbon technology innovation; institutional guarantee emphasizes synergy between government policies and market mechanisms; and structural transformation underscores the synergistic upgrading of industrial digitalization and low-carbonization.
This study is one of the few contextual and integrative empirical studies examining how artificial intelligence marketing efforts (AI MEs) can shape consumers' green purchasing behavior (GPB), particularly among Generation Z consumers in developing countries, and thus contribute to green consumption and brand element relationships. This study investigates the direct and mediating effects of both AI MEs (information, customisation, interaction, and accessibility) and brand elements (brand preference, brand experience, and brand trust) on consumers' GPBs based on the SOR model. An analysis based on surveys (n = 609; SEM-ANN) revealed that AI MEs significantly affected brand elements and GPB, and that brand experience and brand preference were significant mediators in the relationship between AI MEs and GPB. The study also found a significant relationship between brand elements and GPB in the present study. The ANN analysis showed that the most important variables in explaining GPB were brand preference, AI MEs, and brand experience. By integrating AI marketing and brand elements into the conceptualisation of GPB, this study contextually enriches and integrates the limited body of knowledge on sustainable consumer behaviour. The findings offer new theoretical insights and practical guidance for policymakers and businesses aiming to leverage AI to promote environmentally responsible consumption.
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 PM2.5. Threshold effects were observed for all factors. Notably, declining PM2.5 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.
Understanding the spatiotemporal dynamics of terrestrial ecosystem carbon sinks, as well as the underlying driving mechanisms, is crucial for guiding regional carbon neutrality policies. Using Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data, field measurements data, and multi-source environmental data, we estimated net primary productivity in subtropical zone from 2000 to 2020 with the Carnegie-Ames-Stanford approach (CASA) model, and assessed net ecosystem production (NEP) by subtracting heterotrophic respiration. Regression analysis, coefficient of variation, Hurst exponent, and geodetector were applied to examine the spatiotemporal patterns and driving forces of NEP. The results identified distinct spatial heterogeneity in NEP across the study area, characterized by a west-south high and east-low gradient, with moderate levels in the north. The NEP exhibited positive persistence (H > 0.5) in 73.2% of the study area. Notably, natural forest areas showed strong persistent improvement (H > 0.65), whereas the Chang-Jiu urban agglomeration was characterized by strong persistent degradation (H < 0.35). The elevation range of 550-750 m exhibited the peak carbon sink capacity (345.6 g C m⁻² year⁻¹); Normalized difference vegetation index and elevation, with the q value of 0.37 and 0.34 respectively, were identified as the key individual factors influencing NEP variation. The strongest interactive effect on NEP variation was detected between soil type and land use type (q = 0.586). This evidence, combined with the impact of the climate-land use interaction on NEP, implies that synergistic management of these factors could enhance carbon sink potential. Our research reveals that the carbon sink dynamics in subtropical zone are governed by the interaction of topographic, climate, and human activity. Future efforts must implement zonal management strategies (e.g., conserving mountainous areas and promoting forest-grain intercropping on plains) to bolster forest carbon sinks.
Despite decades of study, current research on grazing management’s impacts on ecosystem health and its socioeconomic drivers remains too limited in scope and scale to enable adaptive, evidence-based decision making by producers. There is a pressing need for interdisciplinary research that collects ecosystem data at broader spatial and temporal scales while incorporating working farms and ranches. Such efforts are critical for informing grazing decisions and understanding grazinglands’ potential to deliver ecosystem services, including climate mitigation, water cycling, resilience, and rural livelihoods. The Metrics, Management, and Monitoring (3M) project addresses this need through a novel social-ecological framework that integrates biophysical, socioeconomic, and management data across U.S. grazinglands. The project combines controlled experiments at four intensively monitored “hubs” with data from 59 producer-managed farms and ranches. Its core objectives are to: (1) assess the social-ecological health of grazinglands across diverse ecoregions, (2) refine monitoring approaches to improve scalability and accuracy, and (3) integrate producer-led data to balance experimental rigor with real-world relevance. Over 50 scientists collaborate on 3M to evaluate how grazing strategies affect soil carbon, water dynamics, CO₂ fluxes, plant communities, productivity, social wellbeing, and producer economics. These insights support the development of ecosystem models and decision-support tools to help producers make evidence-based choices. Beyond data generation, 3M offers a scalable research model that bridges ecological and social sciences to support adaptive, informed grazing management. This integrated framework provides a transferable template for studying any working landscape where human and ecological systems are deeply interconnected. The online version contains supplementary material available at 10.1186/s13021-026-00431-7.
Dryland ecosystems, which cover nearly half of the Earth's terrestrial surface, play a considerable role in global carbon dynamics yet remain underrepresented in carbon stock assessments. This study evaluates organic carbon stocks in six protected areas within the hyper-arid AlUla County, Saudi Arabia, focusing on aboveground biomass (AGB) of herbaceous plants, trees and shrubs, as well as soil organic carbon (SOC). Across six protected areas, 172 plots were sampled using species- and growth form-specific allometric equations and soil cores (to 30 cm depth) to estimate organic carbon stocks for eight distinct habitat types. Mean total organic carbon (TOC) stocks ranged from 2.054 ± 0.379 t.ha−1 in basaltic rock or ‘harrats’ habitat, to 12.831 ± 1.921 t.ha−1 in abandoned agricultural lands. SOC accounted for more than 95% of average TOC stocks across all habitat types, except in arid thorn woodlands where SOC contributed 53.71% to the TOC stocks. Arid thorn woodlands also had the highest AGB carbon stocks (1.755 ± 0.564 t.ha⁻1), with trees comprising 54.61% of the AGB carbon pool. Organic carbon stocks in hyper-arid AlUla are predominantly soil-based, while AGB contributes little to the TOC stocks except in habitats with persistent woody vegetation. These patterns align with the lower end of reported ranges for other hyper-arid systems and establish an empirical foundation for future research on carbon storage in hyper-arid ecosystems of the Arabian Peninsula. The online version contains supplementary material available at 10.1186/s13021-026-00416-6.
The growing frequency and extent of wildfires constitute a significant environmental challenge, posing serious threats to ecosystems, biodiversity, and human livelihoods. This study presents a comprehensive wildfire susceptibility assessment for El Tarf Province, one of the most fire-prone yet understudied regions in Algeria. Long-term Landsat imagery (1995-2024) combined with four machine learning algorithms was used to produce high-resolution susceptibility maps and identify the key environmental and bioclimatic drivers of wildfire occurrence. Ten conditioning factors representing topographic, vegetative, edaphic, and climatic conditions were integrated, with elevation, Enhanced Vegetation Index (EVI), wind speed, and precipitation emerging as dominant predictors. Among the tested models, Random Forest achieved the highest predictive performance (ROC-AUC = 0.897), closely followed by XGBoost (0.896), while LightGBM provided an optimal balance between accuracy (0.875) and computational efficiency. Logistic Regression, though simpler, performed reasonably well (0.794). The Landsat-derived wildfire inventory comprised approximately 622,221 burned pixels and was subsequently split into a pre-2017 training set (72.8%) and a post-2017 testing set (27.2%) to evaluate model generalization over time. Spatial block cross-validation was applied to reduce spatial autocorrelation and enhance model generalization. This methodological framework, combining spatial and temporal validation, temporal hold-out, and spatial blocking, strengthens the robustness and reliability of wildfire susceptibility modeling. Interpretability analyses based on SHAP values, Gini importance, and permutation importance identified the contributions of underexplored variables, including vegetation type, soil type, and soil organic carbon (SOC). The resulting susceptibility maps provide valuable insights for spatial planning and ecosystem management, supporting evidence-based strategies to enhance environmental resilience and biodiversity conservation in Mediterranean landscapes.
China is currently the world's largest emitter of carbon dioxide and also one of the countries making the greatest efforts to reduce emissions. The Central Yunnan Urban Agglomeration, located in southwest China, sits at the geometric center connecting China with South and Southeast Asia. Positioned at the convergence of the Belt and Road Initiative and the Yangtze River Economic Belt, it represents a typical plateau-based, ecologically livable urban cluster. Anthropogenic emissions at the county administrative level are crucial for achieving carbon neutrality goals, as reduction targets can be effectively decomposed to subnational units. However, existing research has primarily focused on the provincial or national level, with limited studies examining the spatiotemporal interaction characteristics of carbon emissions at the county level. This paper examines the Central Yunnan Urban Agglomeration, employing Exploratory Spatio-Temporal Data Analysis (ESTDA) and Tapio spatial econometric methods. Based on a remote sensing image inversion dataset, it quantifies the spatio-temporal dynamics of county-level carbon emissions within the agglomeration from 2006 to 2021, along with the decoupling of emissions from economic growth during this 15-year period. Spatio-temporal interaction patterns of per capita carbon emissions across counties were analyzed using LISA metrics (path length, curvature, mean activity direction), spatiotemporal transition matrices (transition probabilities, transition types, transition indices), and spatiotemporal network graphs. Results indicate that per capita energy consumption carbon emissions in counties within the Central Yunnan Urban Agglomeration exhibit strong spatial clustering stability and path dependency characteristics. From 2006 to 2021, Type IV transitions (self-sustaining transitions where neither the region itself nor its adjacent units undergo spatial association type changes) dominated, accounting for 65.31%. This phenomenon may be linked to the rigidity of local energy consumption structures and the slow pace of industrial restructuring. However, the proportion of such transitions has shown a declining trend in recent years. By constructing a synergy index based on the LISA time-path covariance correlation coefficient of per capita carbon emissions in adjacent counties and visualizing it through the LISA spatiotemporal network, it was found that the region predominantly exhibited positive correlations (synergistic growth) from 2006 to 2021, with a pronounced trend of synergistic evolution. This formed a weak synergistic development network centered on Chenggong County, reflecting the core county's significant radiating and driving role in the regional low-carbon synergy process. Furthermore, this study identifies four decoupling states between per capita carbon emissions and per capita GDP: weak decoupling, strong decoupling, negative growth decoupling, and strong negative decoupling. Among these, weak decoupling is the most prevalent state, indicating that economic growth in most counties still relies to some extent on increased carbon emissions, and the low-carbon transition process requires further deepening.