Search Results (3,517)

Industrial structure upgrading serves as an important driving force for the sustained and healthy development of the economy, and it has a positive effect on reducing carbon emission intensity. This study uses provincial panel data from China from 2004 to 2019, starting from the dual perspectives of green total factor productivity and labor misallocation, and employs a four-stage mediation regression model to estimate the mechanism of industrial structure upgrading on carbon emission intensity. The research findings show that: for every 1% increase in industrial structure upgrading, carbon emission intensity will decrease by 0.296%; the central region shows the most significant effect, followed by the western region, while the eastern region shows no significant effect. From the view of the influencing mechanism, industrial structure upgrading will promote green total factor productivity and labor misallocation. When each of the two mediating variables increase by 1%, carbon emission intensity will decrease by 0.12% and 0.054%, respectively. Under the influence of industrial structure upgrading, the inhibitory effects of green total factor productivity and labor misallocation on carbon emission intensity have weakened, and the two factors have made it difficult to form a mediating superposition effect within the sample period. The research conclusion provides the policy implications for China to continuously adhere to industrial structure upgrading, pay attention to improving green total factor productivity, and enhance the low-carbon technical level of workers to achieve the “dual carbon” goals. Full article

Achieving carbon reduction is essential in advancing China’s dual carbon goals and promoting a green transformation in the transportation sector. Changes in energy structure and intensity constitute key drivers for sustainable and low-carbon development in this field. To explore the spatial spillover effects of the energy structure and intensity on the green transition of transportation, this study constructs a panel dataset of 30 Chinese provinces from 2007 to 2020. It employs a super-efficiency SBM model, non-parametric kernel density estimation, and a spatial Markov chain to verify and quantify the spatial spillover effects of green total factor productivity (GTFP) in the transportation sector. A dynamic spatial Durbin model is then used for empirical estimation. The main findings are as follows: (1) GTFP in China’s transportation sector exhibits a distinct spatial pattern of “high in the east, low in the west”, with an evident path dependence and structural divergence in its evolution; (2) GTFP displays spatial clustering characteristics, with “high–high” and “low–low” agglomeration patterns, and the spatial Markov chain confirms that the GTFP levels of neighboring regions significantly influence local transitions; (3) the optimization of the energy structure significantly promotes both local and neighboring GTFP in the short term, although the effect weakens over the long term; (4) a reduction in energy intensity also exerts a significant positive effect on GTFP, but with clear regional heterogeneity: the effects are more pronounced in the eastern and central regions, whereas the western and northeastern regions face risks of negative spillovers. Drawing on the empirical findings, several policy recommendations are proposed, including implementing regionally differentiated strategies for energy structure adjustment, enhancing transportation’s energy efficiency, strengthening cross-regional policy coordination, and establishing green development incentive mechanisms, with the aim of supporting the green and low-carbon transformation of the transportation sector both theoretically and practically. Full article

In this work, a miniaturized, automated, and cost-effective ELISA device is designed and implemented, without the utilization of conventional techniques such as pipetting or microfluidic valve technologies. The device has dimensions of 24 cm × 19 cm × 14 cm and weighs <3 kg. The total hardware cost of the device is estimated to be approximately $1200, which can be further reduced through optimization during scale-up production. Three-dimensional printed disposable parts, including the reagent reservoir disk and the microfluidic connector, have also been developed. IL-6 is used as a model system to demonstrate how the device provides an ELISA measurement. The cost per test is estimated to be <$10. The compactness, automated operation, along with the cost-effectiveness of this ELISA device, makes it suitable for point-of-care applications in resource-limited regions. Full article

Burned area (BA) mapping and fire severity assessment are essential for understanding fire occurrence patterns, formulating post-fire restoration strategies and evaluating vegetation recovery processes. However, existing BA datasets are primarily derived from coarse-resolution satellite imagery and often lack sufficient consideration of fire severity. To address these limitations, this study utilized dense time-series Landsat imagery available on the Google Earth Engine, applying the qualityMosaic method to generate annual composites of minimum normalized burn ratio values. These composites imagery enabled the rapid identification of fire sample points, which were subsequently used to train a random forest classifier for estimating per-pixel burn probability. Pixels with a burned probability greater than 0.9 were selected as the core of the BA, and used as candidate seeds for region growing to further expand the core and extract complete BA. This two-stage extraction method effectively balances omission and commission errors. To avoid the repeated detection of unrecovered BA, this study developed distinct correction rules based on the differing post-fire recovery characteristics of forests and grasslands. The extracted BA were further categorized into four fire severity levels using the delta normalized burn ratio. In addition, we conducted a quantitative validation of the BA mapping accuracy based on Sentinel-2 data between 2015 and 2023. The results indicated that the BA mapping achieved an overall accuracy of 93.90%, with a Dice coefficient of 82.04%, and omission and commission error rates of 26.32% and 5.25%, respectively. The BA dataset generated in this study exhibited good spatiotemporal consistency with existing products, including MCD64A1, FireCCI51, and GABAM. The BA fluctuated significantly between 1985 and 2010, with the highest value recorded in 1987 (13,315 km²). The overall trend of BA showed a decline, with annual burned areas remaining below 2000 km² after 2010 and reaching a minimum of 92.8 km² in 2020. There was no significant temporal variation across different fire severity levels. The area of high-severity burns showed a positive correlation with the annual total BA. High-severity fire-prone zones were primarily concentrated in the northeastern, southeastern, and western parts of the study area, predominantly within grasslands and forest–grassland ecotone regions. Full article

Artisanal fishing is a central pillar of the Angolan economy, particularly in the southern province of Namibe, where it serves as the primary economic activity for numerous coastal communities. However, these communities face significant challenges, including competition from expanding industrial fisheries and inadequate infrastructure at fishing centers, which hampers the storage, preservation, and transportation of catches. These limitations contribute to post-harvest losses and the reduced market value of products, despite the region’s rich diversity of pelagic and demersal resources. This study evaluated the economic viability of artisanal fishing in Namibe under three production scenarios, varying in catch levels and the inclusion of fish processing activities such as dried fish and fishmeal production. Scenario A (pessimistic) assumed a 10% reduction in production compared to the best estimates; Scenario B (intermediate) was based on average reported catches; and Scenario C (optimistic) considered a 10% increase in catches, accounting for seasonal and environmental variability. Results indicated that artisanal fishing was economically viable under all scenarios, with the Internal Rate of Return (IRR) consistently exceeding the Minimum Attractive Rate of Return (MARR) of 7.5%. IRR values ranged from 34.30% (Scenario A, without by-product commercialization) to 106.28% (Scenario C, with dried fish and fishmeal production and sales), representing a more than threefold increase in profitability. This substantial gain underscores the transformative potential of processing by-products into higher-value commodities, enabling integration into larger-scale and more liquid markets. Such value addition supports the concept of a proximity economy by promoting short production cycles, reducing intermediaries, and strengthening local value chains. Beyond financial returns, the findings suggest broader socioeconomic benefits, including local economic growth, job creation, and the preservation of traditional production knowledge. The payback period was less than four years in all cases, decreasing to 1.94 years in the most favorable scenario. By-products such as dried fish and fishmeal exhibit commodity-like characteristics due to their higher commercial value, increasing demand, and potential integration into regional and animal feed markets. In conclusion, diversifying marketing strategies and maximizing the use of fish resources can significantly enhance the economic sustainability of artisanal fishing, foster socioeconomic inclusion, and support the development of artisanal fishing communities in Namibe. Full article

Leaf area index (LAI) is a crucial parameter reflecting the crown structure of the grassland. Accurately obtaining LAI is of great significance for estimating carbon sinks in grassland ecosystems. However, spectral noise interference and pronounced spatial heterogeneity within vegetation canopies constitute significant impediments to achieving high-precision LAI retrieval. This study used hyperspectral sensor mounted on an unmanned aerial vehicle (UAV) to estimate LAI in a typical grassland, Hulun Lake Basin. Multiple machine learning (ML) models were constructed to reveal a relationship between hyperspectral data and grassland LAI using two input datasets, namely spectral transformations and vegetation indices (VIs), while SHAP (SHapley Additive ExPlanation) interpretability analysis was further employed to identify high-contribution features in the ML models. The analysis revealed that grassland LAI has good correlations with the original spectrum at 550 nm and 750 nm–1000 nm, first and second derivatives at 506 nm–574 nm, 649 nm–784 nm, and vegetation indices including the triangular vegetation index (TVI), enhanced vegetation index 2 (EVI2), and soil-adjusted vegetation index (SAVI). In the models using spectral transformations and VIs, the random forest (RF) models outperformed other models (testing R² = 0.89/0.88, RMSE = 0.20/0.21, and RRMSE = 27.34%/28.98%). The prediction error of the random forest model exhibited a positive correlation with measured LAI magnitude but demonstrated an inverse relationship with quadrat-level species richness, quantified by Margalef’s richness index (MRI). We also found that at the quadrat level, the spectral response curve pattern is influenced by attributes within the quadrat, like dominant species and vegetation cover, and that LAI has positive relationship with quadrat vegetation cover. The LAI inversion results in this study were also compared to main LAI products, showing a good correlation (r = 0.71). This study successfully established a high-fidelity inversion framework for hyperspectral-derived LAI estimation in mid-to-high latitude grasslands of the Hulun Lake Basin, supporting the spatial refinement of continental-scale carbon sink models at a regional scale. Full article

Global warming impacts agricultural production and food security, particularly in high-latitude regions with high temperature sensitivity. As a major grain-producing area in China and one of the fastest-warming regions globally, Northeast China (NEC) has received considerable research attention. However, the existing literature lacks sufficient exploration of the spatiotemporal heterogeneity in climate change impacts. Based on data on rice, corn, and soybean yields, as well as temperature, rainfall, and sunshine duration in NEC from 1993 to 2022, this study employs Sen’s slope estimation, the Mann–Kendall (MK) test, spatial autocorrelation analysis, and the Geographically and Temporally Weighted Regression (GTWR) model to analyze the spatiotemporal evolution of grain yields and their responses to climate change. The results show that ① 1993–2022 witnessed an overall rise in grain yields per unit area in NEC, with Liaoning growing fastest. Rice yields increased regionally; corn yields rose in Liaoning and Jilin, while soybean yields increased only in Liaoning. During the growing season, rainfall trended upward with fluctuations, temperatures rose steadily, and sunshine duration declined in Heilongjiang. ② Except for corn and soybeans in the early period, other crops exhibited significant yield spatial agglomeration. High–high agglomeration areas first expanded, then shrank, eventually shifting northward to the region of Jilin Province. ③ Climatic factors show marked spatiotemporal heterogeneity in impacts: positive effect areas of rainfall and temperature expanded northward; sunshine duration’s influence weakened, but its negative effect areas spread. ④ Differences in crop responses are closely linked to their physiological characteristics, regional climate evolution, and agricultural adaptation measures. This study provides a scientific basis for formulating region-specific agricultural adaptation strategies to address climate change in NEC. Full article

To advance global climate governance, this study investigates the carbon emission efficiency (CEE) of 110 Chinese resource-based cities (RBCs) using a super-efficiency SBM-GML model combined with kernel density estimation and spatial analysis (2006–2022). Spatial Durbin model (SDM) and geographically and temporally weighted regression (GTWR) further elucidate the driving mechanisms. The results show that (1) RBCs achieved modest CEE growth (3.8% annual average), driven primarily by regenerative cities (4.8% growth). Regional disparities persisted due to decoupling between technological efficiency and technological progress, causing fluctuating growth rates; (2) CEE exhibited high-value clustering in the northeastern and eastern regions, contrasting with low-value continuity in the central and western areas. Regional convergence emerged through technology diffusion, narrowing spatial disparities; (3) energy intensity and government intervention directly hinder CEE improvement, while rigid industrial structures and expanded production cause negative spatial spillovers, increasing regional carbon lock-in risks. Conversely, trade openness and innovation level promote cross-regional emission reductions; (4) the influencing factors exhibit strong spatiotemporal heterogeneity, with varying magnitudes and directions across regions and development stages. The findings provide a spatial governance framework to facilitate improvements in CEE in RBCs, emphasizing industrial structure optimization, inter-regional technological alliances, and policy coordination to accelerate low-carbon transitions. Full article

Mapping the oil palm (Elaeis guineensis), the globally leading oil-bearing crop and a crucial industrial commodity, is of vital importance for food security and raw material supply. However, existing remote sensing approaches for oil palm mapping present several methodological challenges including temporal resolution constraints, suboptimal feature parameterization, and limitations in age structure assessment. This study addresses these gaps by systematically optimizing temporal, spatial, and textural parameters for enhanced oil palm mapping and age structure analysis through integration of Landsat 4/5/7/8/9, Sentinel-2 multispectral, and Sentinel-1 radar data (LSMR). Analysis of oil palm distribution and dynamics in Malaysia revealed several key insights: (1) Methodological optimization: The integrated LSMR approach achieved 94% classification accuracy through optimal parameter configuration (3-month temporal interval, 3-pixel median filter, and 3 × 3 GLCM window), significantly outperforming conventional single-sensor approaches. (2) Age estimation capabilities: The adapted LandTrendr algorithm enabled precise estimation of the plantation establishment year with an RMSE of 1.14 years, effectively overcoming saturation effects that limit traditional regression-based methods. (3) Regional expansion patterns: West Malaysia exhibits continued plantation expansion, particularly in Johor and Pahang states, while East Malaysia shows significant contraction in Sarawak (3.34 × 10⁵ hectares decline from 2019–2023), with both regions now converging toward similar topographic preferences (100–120 m elevation, 6–7° slopes). (4) Age structure concerns: Analysis identified a critical “replanting gap” with 13.3% of plantations exceeding their 25-year optimal lifespan and declining proportions of young plantations (from 60% to 47%) over the past five years. These findings provide crucial insights for sustainable land management strategies, offering policymakers an evidence-based framework to balance economic productivity with environmental conservation while addressing the identified replanting gap in one of the world’s most important agricultural commodities. Full article

This article examines the production costs of cocoa farming in Côte d’Ivoire, West Africa, taking into account the opportunity cost approach. To this end, surveys were conducted among 228 farmers in three regions, Bonon, Soubré and Biankouma, following an east–west gradient. The estimated costs of using family labor and land were based on the opportunity cost approach. The financial costs associated with production were also taken into account. Comparative analyses between different localities and cropping systems highlighted specific workload characteristics. Finally, a principal component analysis (PCA) was used to profile producers according to their income levels and profits. The findings showed that family labor was the main component of cocoa production costs. Prices paid to farmers did not always cover all production costs, with 38% of farmers producing at a loss, and this was contingent on the agro-ecological zone. Furthermore, the agroforestry system proved to be more economical in terms of labor than the full-sun system. These results underline the relevance of the opportunity cost approach in assessing production costs and setting cocoa selling prices. This should lead to a review of public price-setting mechanisms to ensure fair remuneration for family labor. Full article

Chlorpyrifos (CPF) is a widely used organophosphate insecticide; however, global concerns exist regarding its potential health risks, particularly developmental neurotoxicity. This study aimed to determine CPF residues in locally sourced tomatoes and cucumbers and assess the potential chronic and acute dietary risks associated with their consumption by the adult population of Armenia. As part of the national residue monitoring program, samples of the two most commonly consumed vegetables (tomato and cucumber) were collected from various regions of Armenia and analyzed using gas chromatography–tandem mass spectrometry (GC-MS/MS). Two databases were used for dietary exposure assessment: one containing CPF residue levels and another containing individual food consumption data from a food frequency questionnaire completed by 1329 Armenian residents. Chronic risk was assessed using the Margin of Exposure (MOE), while acute risk was evaluated using the Hazard Quotient (HQ) and the Hazard Index (HI). CPF residues were detected in 15% of tomato and 28.6% of cucumber samples, with a mean content of 0.003 mg/kg. Deterministic and probabilistic assessments indicated no health concern (i.e., MOE > 300 and >1000, HQ and HI < 1) for the general adult population at current exposure levels. However, higher cumulative risk estimates obtained for high-consumption groups emphasize the significance of these studied vegetables as notable contributors to overall CPF intake. The findings indicate the importance of establishing vegetable-specific maximum residue levels, strengthening monitoring, and considering vulnerable population groups in future research. Broader assessments, including other plant-origin products, are recommended to ensure comprehensive risk assessment and support science-based policy decisions for improved food safety and public health protection in Armenia. Full article

Water and land resources, as the material foundation of food production, are essential for national food security. Current research has not yet explored the spatiotemporal features of water and land resources ecological security (WLRES) at the urban scale. To fill this gap, this study evaluated WLRES across 180 cities in China’s main grain-producing areas (MGPAs) from 2005 to 2020. A WLRES evaluation system was developed based on the DPSIR framework and the CRITIC method. The Moran’s I and kernel density estimation were utilized to analyze the spatial distribution, variation trends, and spatial autocorrelation of WLRES from different scales. The results demonstrate the following: (1) WLRES in the MGPAs exhibited a fluctuating upward trend, transitioning from “relatively low ecological security” to “moderate ecological security.” (2) The spatial distribution of WLRES was characterized by higher values in the northeast and southwest regions and lower values in the central region, with spatial heterogeneity gradually intensifying. (3) From 2005 to 2016, WLRES exhibited significant positive spatial autocorrelation: cities with high ecological-security levels were concentrated in the northern region, whereas those with low ecological-security levels were clustered in the central and southern of Huang-Huai-Hai Basin. Over time, this positive spatial autocorrelation weakened and eventually vanished. Our research can provide feasible policy references for improving the sustainable development of WLRES in the MGPAs. Full article

Pinus taeda plantations have been facing declining productivity in South America, especially due to competition for natural resources such as light, water, and nutrients. Competition with spontaneous vegetation in the early years is one of the main constraints on growth and biomass allocation in trees. However, the best method and timing for weed control and its impact on the productivity of Pinus taeda plantations are unknown. This study aims to evaluate whether weed control increases the growth and above-ground biomass production of Pinus taeda plantations in southern Brazil. This study was conducted at two sites with five-year-old Pinus taeda plantations in southern Brazil, with each being submitted to different weed control methods. This study was conducted in randomized blocks, with nine treatments: (i) NC—no weed control, i.e., weeds always present; (ii) PC—physical weed control; (iii) CC–T—chemical weed control in the total area; (iv) CC–R—chemical weed control in rows (1.2 m wide); (v) C6m, (vi) C12m, (vii) C18m, and (viii) C24m—weed control up to 6, 12, 18, and 24 months after planting; and (ix) COC—company operational weed control. The following parameters were evaluated: the floristic composition and weed biomass, height, diameter, stem volume, needle biomass, branches, bark, and stemwood of Pinus taeda. Control of the weed competition, especially by physical means (PC), and chemical control over the entire area (CC–T) promoted significant gains in the growth and above–ground biomass production of Pinus taeda at five years of age, particularly at the Caçador site. The results reinforce the importance of using appropriate strategies for managing weed control to maximize productivity, especially before canopy closure. In addition, the strong correlation between growth variables and the total biomass and stemwood indicates the possibility of obtaining indirect estimates through dendrometric measurements. The results contribute to the improvement of silvicultural management in subtropical regions of southern Brazil. Full article

The upper reaches of the Yangtze River face the challenge of balancing livestock development and ecological protection. As a significant livestock production region in China, optimizing the livestock ecological efficiency (LEE) of Sichuan Province (SP) is of strategic importance for regional sustainable development. Livestock carbon emissions and related pollution indices were utilized as undesirable output indicators within the super-efficiency SBM model to measure SP’s LEE over the 2010–2022 period. Kernel density estimation was combined with the Theil index to analyze spatiotemporal variation characteristics. A STIRPAT model was constructed to explore the influencing factors of SP’s LEE, and a grey forecasting GM (1,1) model was employed for prediction. Key findings reveal the following: (1) LEE increased by 25.9%, with high-efficiency regions expanding from 19.0% to 57.1%; (2) regional disparities persist, driven by labor redundancy and environmental governance gaps; (3) per capita GDP, industrial agglomeration, and technology advancement significantly promoted efficiency, while government subsidies and carbon intensity suppressed it. Projections show LEE reaching 0.923 by 2035. Key recommendations include the following: (1) implementing region-specific strategies for resource optimization, (2) restructuring agricultural subsidies to incentivize emission reduction, and (3) promoting cross-regional technology diffusion. These provide actionable pathways for sustainable livestock management in ecologically fragile zones. Full article

Forest ecosystems act as major carbon sinks, highlighting the need for the accurate estimation of aboveground biomass (AGB). The Global Ecosystem Dynamic Investigation (GEDI), a full-waveform spaceborne LiDAR system developed by NASA, provides detailed global observations of three-dimensional forest structures, playing a critical role in quantifying biomass and carbon storage. However, its performance has not yet been assessed in the Mediterranean forest ecosystems of Southern Italy. Therefore, the objectives of this study were to (i) evaluate the utility of the GEDI L4A gridded aboveground biomass density (AGBD) product in the Apulia region by comparing it with the Apulia AGBD map, and (ii) develop GEDI-derived AGBD models using multiple GEDI metrics. The results indicated that the GEDI L4A gridded product significantly underestimated AGBD, showing large discrepancies from the reference data (RMSE = 40.756 Mg/ha, bias = −30.075 Mg/ha). In contrast, GEDI-derived AGBD models using random forest (RF), geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR) demonstrated improved accuracy. Among them, the MGWR model emerged as the optimal choice for AGBD estimation, achieving the lowest RMSE (14.059 Mg/ha), near-zero bias (0.032 Mg/ha), and the highest R² (0.714). Additionally, the MGWR model consistently outperformed other models across four different plant functional types. These findings underscore the importance of local calibration for GEDI data and demonstrate the capability of the MGWR model to capture scale-dependent relationships in heterogeneous landscapes. Overall, this research highlights the potential of the GEDI to estimate AGBD in the Apulia region and its contribution to enhanced forest management strategies. Full article