Greening Trend Research Articles

A Bibliometric Analysis of Green Skills Research in Vocational Education: 2018-2022

Green skills (GSs) have become one of the important strategies for achieving sustainable development. It can make the environment effective for social and economic development. The aim of this research is to reveal data on the evolution and development of green skills and trends in the development of green jobs throughout the world. This research uses R Studio and Bibliometrix to analyze 198 papers related to green skills in green jobs published from 2018 to 2022 in the Scopus database using Bibliometrix and visualization mapping methods. The results show a substantial increase in the number of studies GSs in recent years, with the focus area in European and American countries leading this research. Aspects of Will, manufacturing and Energy, and Journals of Green Environmental Management are the first three journals cited in the study of GSs. Studying the cited literature together on environmentally friendly skills includes, among other things, the relationship between other skills and ecosystems with human good health, green construction, evaluation and green management of environmentally friendly competencies, and analysis of specific aspects of environmentally friendly skills. The results of the analysis of green skills grouping keywords show that there is research that concentrates on green skills in the fields of education, ecosystem services, climate change, and biodiversity protection. In conclusion, this research provides a reference for future studies of green skills necessary for the development of a sustainable educational environment, such as a multidisciplinary approach, adaptation of new technologies, partnerships with sustainable technology green industries, and environmental awareness.

Spatial–Temporal Analysis of Greenness and Its Relationship with Poverty in China

Ecological environmental protection and poverty alleviation are of great significance for the study of human–land relationship coordination and sustainable development, and they have also been a focus of attention in China in the past few decades. In this study, we chose 13 contiguous poverty-stricken areas in China as the study area. Using MODIS Leaf Area Index (LAI) data from 2000 to 2020, the spatial–temporal changes in greenness were obtained using the Bayesian spatial–temporal model (BYM). Spatial autocorrelation was used to identify the spatial distribution of poverty using socio-economic statistical data. Driving factors, including natural factors, poverty factors, and the Grain for Green Policy (GTGP), and their influence on greenness were analyzed by using the Geodetector model for detecting spatial differentiation and factors’ interactions. The results showed the following: (1) In 13 contiguous poverty-stricken areas (CPSAs) in China, 59% of the area presented an increasing trend of greenness. (2) In 2000, the high poverty levels with larger MPI values were widely distributed. After 20 years, the overall MPI value was lower, except in some northwest regions with increased MPI values. The spatial autocorrelation of poverty, which relates to the mutual influence of poverty in adjacent areas, also decreased. (3) In the study area, 65.24% of the regions showed strong synergistic effect between greening progress and poverty reduction in the interaction between poverty status and green development. With the improvement of greenness level, the positive correlation between poverty alleviation and ecological environment improvement has become increasingly close. (4) The impacts of interaction factors with the highest q values changed from temperature interacting with precision to regional division interacting with the Grain for Green Policy. The conclusions are that from 2000 to 2020, the impact of natural factors, geographical division, and poverty status on greenness has shown a decreasing trend; The effect of the Grain for Green Policy is gradually increasing; At the same time, the interaction and overlapping effects between the Grain for Green Policy and poverty were increasing. Taking into account the needs of ecological environment, poverty alleviation, and rural revitalization, this research provides valuable reference for formulating and implementing relevant policies based on the actual situation in different regions to promote harmonious coexistence between human-land relationship.

Uncovering true significant trends in global greening

The global greening trend, marked by significant increases in vegetation cover across ecoregions, has attracted widespread attention. However, even robust traditional methods, like the non-parametric Mann-Kendall test, often overlook crucial factors such as serial correlation, spatial autocorrelation, and multiple testing, particularly in spatially gridded data. This oversight can lead to inflated significance of detected spatiotemporal trends. To address these limitations, this research introduces the True Significant Trends (TST) workflow, which enhances the conventional approach by incorporating pre-whitening to control for serial correlation, Theil-Sen (TS) slope for robust trend estimation, the Contextual Mann-Kendall (CMK) test to account for spatial and cross-correlation, and the adaptive False Discovery Rate (FDR) correction. Using AVHRR NDVI data over 42 years (1982–2023), we found that conventional workflow identified up to 50.96% of the Earth's terrestrial land surface as experiencing statistically significant vegetation trends. In contrast, the TST workflow reduced this to 38.16%, effectively filtering out spurious trends and providing a more accurate assessment. Among these significant trends identified using the TST workflow, 76.07% indicated greening, while 23.93% indicated browning. Notably, considering areas (pixels) with NDVI values above 0.15, greening accounted for 85.43% of the significant trends, with browning making up the remaining 14.57%. These findings strongly validate the ongoing global greening of vegetation. They also suggest that incorporating more robust analytical methods, such as the True Significant Trends (TST) approach, could significantly improve the accuracy and reliability of spatiotemporal trend analyses.

Greenwashing and the purchase behavior toward electric motorbikes: The role of eco-literacy

ABSTRACT Nowadays, due to the increasing green trends, numerous businesses in the automobile industry adopt greenwashing as a primary strategy to appear environmentally conscious, despite their production processes like manufacturing lithium-ion batteries, which are causing significant environmental pollution. By combining three theories as signaling, legitimacy, and psychological contract, the study focuses on solving the previous research gaps when examining the influence of greenwashing (GW), green brand image (GBI), green skepticism (GS), green confusion (GC), perceived betrayal (PB), eco-literacy (EL) on the purchase intention (PI) towards electric motorbikes among Vietnamese consumers. The study employed a convenient random sampling method with a sample size of 535 consumers in Vietnam. A quantitative research method and Structural Equation Modeling (SEM) approach were used to analyze the data using SmartPLS software. The findings reveal that GW significantly influences the PI, and GBI, GS, GC, and PB partial mediate the correlation. Additionally, the study discovered the significant moderating role of GW in the correlation between GW to PI, GW to GS, and GW to PB. Based on the findings, the managers can apply marketing strategies to encourage consumers’ purchase intention and avoid greenwashing activities. Then, the theoretical implications and directions for future research have been proposed.

High-mass loaded redox-active lignin functionalized carbonized wood collector to construct sustainable and high-performance supercapacitors

Traditional electrode materials for supercapacitors often face issues like high toxicity, cost, and non-renewability. To address these drawbacks, biomass-based alternatives are being explored, aligning with green development trends. Herein, carbonized wood (CW) with rich pore structure and redox-active lignin are combined to fabricate an all-wood-based sustainable supercapacitor electrode material. Due to its inherent porous structure, CW provides a larger surface area for accommodating active materials ion, enabling the electrode to achieve a higher lignin loading capacity of 2.82–11.68 mg/cm2. Furthermore, the utilization of lignin as a substitute for conventional transition metal-based pseudocapacitor material functionalized CW endows the electrode with exemplary electrochemical performance while guaranteeing the comprehensive sustainability of the electrode. This synergy confers the electrode with exceptional electrical performance, yielding an areal capacitance of 960.7 mF/cm2 at a current density of 1 mA/cm2. The symmetric supercapacitors (SSC) manufactured by this composite electrode can achieve a notable areal energy density of 0.14 mWh/cm2 and a power density of 15.98 mW/cm2, while maintaining an outstanding capacitance retention rate of 81 % after 50,000 cycles at 20 mA/cm2. The manufacture of CW-lignin electrode underscores the potential of utilizing renewable biomass resources as alternatives for developing high-performance energy storage applications, thereby reducing negative environmental impacts.

Greening and browning trend with physio-climatic drivers in Chattogram division, Bangladesh

Greening and browning trend with physio-climatic drivers in Chattogram division, Bangladesh

Changes in Vegetation Greenness and Responses to Land Use Changes in the Yongding River Basin (in North China) from 2002 to 2022

Vegetation is an important component of an ecosystem, fulfilling various ecological functions in areas such as soil and water conservation, climate regulation, and water source maintenance. This study focuses on the Yongding River Basin as a research area. This study used vegetation indices with long time series as a data source in combination with Landsat land use data. This study applied linear trend estimation to analyze the interannual variation trend in vegetation greenness from 2002 to 2022 in the Yongding River Basin and quantitatively analyzed the impact of land use changes on vegetation greenness. The results show that, from 2002 to 2022, the vegetation greenness in the Yongding River Basin has shown an overall increasing trend. The average growth season and the maximum annual normalized difference vegetation index (NDVI) growth rates were 0.006/10a and 0.008/10a, respectively, and the area of increased vegetation greenness accounted for 90% of the total area. During the main growth season (April to October) in the Yongding River Basin, the NDVI generally showed a spatial pattern of being higher in mountainous areas and lower in water areas, with the largest coefficient of variation in vegetation in the river water areas, and the most stable vegetation in forest land. In terms of the changes in vegetation greenness, the contribution rate of arable land was between 36.73% and 38.63%, followed by grassland and forest land, with contribution rates of 26.86% to 27.11% and 23.94% to 26.43%, respectively. The total contribution rate of water areas, construction land, and unused land was around 10.18%. This study can provide a theoretical basis for environmental protection and rational land use in the Yongding River Basin.

Exploration of the Urbanization Process and Its Impact on Vegetation in 125 Resource-Based Cities in China and Comparison with Other Cities

Resource-based cities (RBCs) in China are at a historic juncture in their transformative development. Observing and assessing the role of the resource curse in urban expansion and greening is crucial for the sustainable development of these cities. This study proposes a new framework to extract urban boundary data from 2000 to 2020 in China. Utilizing these data, we analyzed differences in urban expansion intensity and urban vegetation cover between 125 RBCs and 223 non-RBCs. We found the following: (1) While urban areas in China experienced steady growth from 2000 to 2020, the urban area expansion rates of RBCs lagged behind those non-RBCs located in the same geographical areas except in South China, with the lowest annual expansion rate of 1.18% occurring in the Northeast. (2) Although the existing urban areas in some cities show a greening trend, both existing and new urban areas in China are predominantly characterized overall by browning. Surprisingly, RBCs exhibit a stronger greening trend than non-RBCs, particularly in Northwestern China. (3) There is a nuanced interplay and coexistence between resource dependency and urban expansion, with a specific negative correlation when resource dependency is very high or very low. This study provides a novel method and approach for urban boundary delineation. It offers new insights into the developmental characteristics of RBCs, enriching the theoretical framework of resource curse research and supporting the green development of RBCs with robust data.

Planted Forests Greened 7% Slower Than Natural Forests in Southern China Over the Past Forty Years

AbstractForests have seen a strong greening trend worldwide, and previous studies have attributed this mainly to land‐use conversions such as afforestation. However, for the greening of existing forests, the role of human interventions is unclear. Here we paired neighboring natural and planted forests in Southern China to minimize the differences between the forest types and analyzed the vegetation index EVI2 from Landsat over 1987 to 2021. The EVI2 trends observed in natural forests can be seen as mainly responses to large‐scale environmental changes, whereas the difference between the forest types represents the impact caused by human interventions. We found that though the mean EVI2 of planted forests was comparable to that of natural forests, the greening trends were overall 7.0% lower in planted forests. Our results suggest that human interventions associated with planted forests did not accelerate their greening, indicating the necessity for refined policies to enhance future forest greening.

Biodiversity conservation and ecological restoration dominated vegetation dynamics during the 1980s-2010s in Yunnan, China

Changes in both vegetation type and productivity can affect the structure, functioning, and services of terrestrial ecosystems. Understanding vegetation dynamics and their drivers is critical for biodiversity conservation and ecosystem management. Although land cover datasets, climate-vegetation models, and remote sensing vegetation indices have been frequently used to reflect vegetation dynamics, they generally lack biological information about vegetation, such as species compositions, community structures, and succession status. Yunnan, recognized as the most biodiverse province in China, has undergone considerable vegetation changes over recent decades. However, the roles of climate change and human activities remain unclear. This study integrated detailed vegetation maps, climate factors, and vegetation indices obtained in the 1980s (1986–1995) and 2010s (2006–2015) to comprehensively evaluate the coverage transformations and productivity changes of vegetation types in Yunnan and unravel the drivers of decadal vegetation changes. The results indicated that: 1) A greening trend was observed across all vegetation types, particularly in coniferous and temperate forests. 2) The decadal vegetation changes were dominated by: the restoration of savanna and shrubland, cropland expansion, and artificial afforestation, accounting for 23.7 %, 22.9 %, and 19.1 % of all observed changes, respectively. 3) Conservation and restoration efforts dominated vegetation greening in Yunnan (55 %), followed by artificial afforestation (23 %), and agricultural expansion (16 %). By comparing vegetation maps of separated stages, the inclusion of vegetation class information provided critical knowledge for a better understanding of the intrinsic mechanisms behind vegetation greening and range shifting. Our study highlighted the significant role of ecological conservation and restoration policies and practices in influencing the spatiotemporal dynamics of biodiversity and ecosystem functioning at a regional scale, within merely a few decades.

How does greening affect the surface water budget in the Loess Plateau?

Significant vegetation increase in the Loess Plateau (LP) of China could strongly affect the surface water budget. Through the WRF model with the Water Vapor Tracer (WVT) method tracking moisture within the LP, this study conducted three sets of experiments from 1999 to 2018 with GLASS Fractional Vegetation Cover (FVC) data. The results indicate that vegetation has a critical role in partitioning evapotranspiration (ET) into transpiration (Et), canopy evaporation (Ecan), and soil evaporation (Edir), thus regulating terrestrial internal convective precipitation (P). The local P response largely depends on external P (E_P), while the internal P (I_P) contribution remains minor. In summer, the total wet difference of I_P in the LP is about 0.03 mm/day, almost 5 times that of E_P. Greening also causes surface runoff reduction. Thus, in spring and summer, surface water storage (W) decreases due to the greater increase in ET than in P. In autumn, W increases by about 0.06 mm/day due to a large decrease in Ecan, implying confining the increased W to shallower soils, resulting in accelerated loss of deep soil moisture. The greening trend of 2000–2018 contributed to an increase in I_P, which could not offset increased ET and reduced E_P, leading to terrestrial water storage reduction.

Response of alpine vegetation function to climate change in the Tibetan Plateau: A perspective from solar-induced chlorophyll fluorescence

Vegetation change in the Tibetan Plateau (TP) is a crucial indicator of climate change in alpine regions. Previous studies have reported an overall greening trend in the vegetation structure across the TP, especially in its northeastern part, in response to a warming climate. However, variations in the vegetation function and the possible drivers remain poorly understood. Considering the optimal temperature for plants in TP is usually higher than the current temperature, our hypothesis is the function and structure of alpine vegetation have changed synchronously over past few decades. To test this hypothesis, we analyzed satellite-observed solar-induced chlorophyll fluorescence (SIF) and leaf area index (LAI) in the Yellow River source (YRS) region in the northeastern TP to quantify the long-term trends in vegetation functional and structural states, respectively. The results suggest that from 1982 to 2018, SIF increased significantly in 77.71 % of the YRS area, resulting in a significant upward trend of 0.52 × 10−3 mW m−2 nm−1 sr−1 yr−1 (p < 0.001) for the regional-mean SIF. This represents a 16.1 % increase in SIF, which is close in magnitude to the increase in LAI over the same period. The synchronous changes between vegetation function and structure suggest that improved greenness corresponds to a similar level of change in carbon uptake across YRS. Additionally, we used a multiple regression approach to quantify the contribution of climatic factors to SIF changes in YRS. Our analyses show that the increases in SIF were primarily driven by rising temperatures. Spatially, temperature dominated SIF changes in most parts of YRS, except for certain dry parts in the northern and western YRS, where precipitation had a greater impact. Our results are crucial for a comprehensive understanding of climate regulations on vegetation structure and function in high-elevation regions.

Spatial and Temporal Dynamics in Vegetation Greenness and Its Response to Climate Change in the Tarim River Basin, China

Vegetation in ecologically sensitive regions has experienced significant alterations due to global climate change. The underlying mechanisms remain somewhat obscure owing to the spatial heterogeneity of influencing factors, particularly in the Tarim River Basin (TRB) in China. Therefore, this study targets the TRB, analyzing the spatial and temporal dynamics of vegetation greenness and its climatic determinants across multiple spatial scales. Utilizing Normalized Difference Vegetation Index (NDVI) data, vegetation greenness trends over the past 23 years were assessed, with future projections based on the Hurst exponent. Partial correlation and multiple linear regression analyses were employed to correlate NDVI with temperature (TMP), precipitation (PRE), and potential evapotranspiration (PET), elucidating NDVI’s response to climatic variations. Results revealed that from 2000 to 2022, 90.1% of the TRB exhibited an increase in NDVI, with a significant overall trend of 0.032/decade (p &lt; 0.01). The difference in NDVI change across sub-basins and vegetation types highlighted the spatial disparity in greening. Notable greening predominantly occurred near rivers at lower elevations and in extensive cropland areas, with projections indicating continued greening in some regions. Conversely, future trends mainly suggested a shift towards browning, particularly in higher-elevation areas with minimal human influence. From 2000 to 2022, the TRB experienced a gradual increase in TMP, PRE, and PET. The latter two factors were significantly correlated with NDVI, indicating their substantial role in greening. However, vegetation sensitivity to climate change varied across sub-basins, vegetation types, and elevations, likely due to differences in plant characteristics, hydrothermal conditions, and human disturbances. Despite climate change influencing vegetation dynamics in 51.5% of the TRB, its impact accounted for only 25% of the total NDVI trend. These findings enhance the understanding of vegetation ecosystems in arid regions and provide a scientific basis for developing ecological protection strategies in the TRB.

Bibliometric analysis : Green chemistry trends and issues in chemistry education from 2019 to 2024

This study analyzes green chemistry research trends in chemistry education. This study used a quantitative bibliometric approach. The number of publications analyzed is 104 publication documents from 2019 – 2024. This research collects, processes, and filters information in Scopus journals and articles. Metadata results show that the distribution of publication frequency peaked in 2019, with 26 documents identified. The green chemistry research area is dominated by chemistry research (31.3%). The country with the most documents and the most productive in publishing green chemistry is the United States, with 30 papers identified. At the same time, Indonesia is ranked fifth as the most productive country in publishing green chemistry, with 10 documents identified. Canada ranked second with 17 papers, and Germany ranked third with 15 documents. The institutions that contributed the most came from Germany: the University of Bremen, with 11 papers 10.58%, and the University of Toronto with 8 documents (7.69%). The authors with the most citations are Chen Tse-Lun et al., with 245 citations. Meanwhile, when viewed from the number of documents published by the author, Eilks I. has 11 papers with a contribution of 4.91%. There are 5 clusters with the most popping keywords: green chemistry, human, and chemical reaction. Research and publications on this topic have been sparse in the past five years. Surveys and analyses of green chemistry literature are essential because tracking research trends in green chemistry in chemistry education is vital to directing the future.

Investigation of Novel Transition Metal Loaded Hydrochar Catalyst Synthesized from Waste Biomass (Rice Husk) and Its Application in Biodiesel Production Using Waste Cooking Oil (WCO)

The decarbonization of transportation plays a crucial role in mitigating climate change, and biodiesel has emerged as a promising solution due to its renewable and eco-friendly nature. However, in order to maintain the momentum of the “green trend” and ensure energy security, an ecologically friendly pathway is important to produce efficient biodiesel. In this work, activated carbon (AC) obtained from rice husk (RH) is hydrothermally prepared and modified through cobalt transition metal for catalyst support for the transesterification process. The physicochemical characteristics of the synthesized catalysts are examined using XRD, FTIR, SEM and EDS, TGA, and BET, while the produced biodiesel is also characterized using Gas Chromatography and Mass Spectroscopy (GC-MS). To optimize the transesterification process, Fatty Acid Methyl Esters (FAME) are produced by the conversion of waste cooking oil. Response Surface Methodology (RSM) is used to validate temperature (75 °C), the methanol-to-oil molar ratio (1:9), catalyst weight percentage (2 wt.%), and retention time (52.5 min). The highest conversion rate of waste cooking oil (WCO) to biodiesel was recorded at 96.3% and tested as per American Society for Testing and Materials (ASTM) standards. Based on the results, it is clear that cobalt-loaded rice husk-based green catalyst (RHAC-Co) enhanced catalytic activity and yield for biodiesel production. Further research should focus on engine performance evaluation and scaling up of the catalyst by optimizing it for the industrial scale.

Exploring the paradox of densification and greening in China’ s old cities

Urban densification is widely regarded as a key strategy for curbing urban sprawl and optimizing resource efficiency. However, it still threatens urban greenspaces by altering the original land use. The imperative to preserve and restore urban greenspaces has gained increased attention, aligning with the global spotlight on urban resilience. This prompts the crucial question: How will urban densification impact urban greening? Focusing on the old city areas of 159 cities with over one million population in China, we depicted the dynamics of urban greenness using remote sensing data and explored greening trends under urban densification on both horizontal and vertical scales by generalized additive model (GAM). The findings are as follows: (1) As urban horizontal density increased from 68.4 % to 93.1 %, a significant U-shaped trend in urban greenness emerged within the old city areas over the past three decades. Notably, 80 % of these cities experienced turning points before 2010. (2) Almost half of the cities have recovered the urban greenness to 30 years ago, with a growth of 6.58 % on average. While urban densification led to a loss of greenness (-0.0197±0.0182), urban greenspaces countered this with a gain of 0.0049±0.0075, and the original built-up area contributed a gain of 0.0027±0.0284. (3) We decomposed urban densification into horizontal and vertical growth. The horizontal growth coincides with the greenness loss initially, but the rebound occurs when the horizontal density approaches 83 %. Vertical growth, on the other hand, is negatively correlated with urban greenness. Our study depicts the turning point of urban greening in old city areas unveiling the ecological effect of urban densification, which provides a reference for the sustainable development of future urbanization.

Regional Differences, Dynamic Evolution, and Convergence of Global Agricultural Energy Efficiency

Understanding the regional disparities, dynamic evolution, and convergence–divergence characteristics of global agricultural energy efficiency is crucial for enhancing agricultural energy efficiency, ensuring food security, and responding to global green development trends. This paper utilizes 2002–2021 panel data from 144 countries globally, employing the epsilon-based measure–global Malmquist–Luenberger (EBM-GML) model to estimate agricultural energy efficiency, considering unexpected output. The Dagum Gini coefficient, kernel density estimation, spatial Markov matrix, and spatial convergence model are employed to explain the spatial patterns and evolving trends of global and regional agricultural energy efficiency at three levels: regional disparities, dynamic evolution, and convergence. The results indicate significant spatial heterogeneity in global agricultural energy efficiency, with Europe exhibiting the highest efficiency, followed by Asia and the Americas, while Oceania and Africa demonstrate the lowest efficiency. Agricultural energy efficiency globally and in each region continues to improve, with increasing regional disparities, and difficulties in grade transitions in agricultural energy efficiency across regions. Each region exhibits β-convergence characteristics, but the convergence rates vary, and various factors influence growth rates of agricultural energy efficiency differently across regions. Therefore, countries should tailor their strategies based on local conditions, considering their own resource endowments and developmental stages, and strengthen international exchanges and cooperation.

Towards understanding climate change impacts: monitoring the vegetation dynamics of terrestrial national parks in Indonesia

Monitoring vegetation dynamics in terrestrial national parks (TNPs) is crucial for ensuring sustainable environmental management and mitigating the potential negative impacts of short- and long-term disturbances understanding the effect of climate change within natural and protected areas. This study aims to monitor the vegetation dynamics of TNPs in Indonesia by first categorizing them into the regions of Sumatra, Jawa, Kalimantan, Sulawesi, and Eastern Indonesia and then applying ready-to-use MODIS EVI time-series imageries (MOD13Q1) taken from 2000 to 2022 on the GEE cloud-computing platform. Specifically, this research investigates the greening and browning fraction trends using Sen's slope, considers seasonality by analyzing the maximum and minimum EVI values, and assesses anomalous years by comparing the annual time series and long-term median EVI value. The findings reveal significantly increasing greening trends in most TNPs, except Danau Sentarum, from 2000 to 2022. The seasonality analysis shows that most TNPs exhibit peak and trough greenness at the end of the rainy and dry seasons, respectively, as the vegetation response to precipitation increases and decreases. Anomalies in seasonality that is affected by climate change was detected in all of the regions. To increase TNPs resilience, suggested measures include active reforestation and implementation of Assisted Natural Regeneration, strengthen the enforcement of fundamental managerial task, and forest fire management.

Spatiotemporal analysis of tropical vegetation ecosystems and their responses to multifaceted droughts in Mainland Southeast Asia using satellite-based time series

ABSTRACT Drought ranks among the costliest of all climate-related phenomena and manifests in various forms, posing significant challenges in understanding its influence on agriculture and natural ecosystems. Mainland Southeast Asia (MSEA), a significant region of tropical agriculture and vegetation ecosystems, has become increasingly susceptible to drought hazards. In this study, we characterized and assessed vegetation dynamics and their drought impacts using correlation analysis and explainable machine learning methods under different vegetation types and elevation zones during the dry growing seasons from 2000 to 2022. Specifically, we characterized the vegetation dynamics and their trend in space and time. Next, we assessed vegetation-drought responses in consideration of meteorological, hydrological, and agricultural droughts under different land cover types and elevation characteristics. Lastly, we used an explainable machine learning method to quantify the drivers and impacts of multifaceted droughts on natural and undisturbed vegetation ecosystems. Our findings revealed that nearly 70% of the MSEA region experienced a greening trend despite large areas of vegetative damage during the drought years. Vietnam witnessed increasing vegetation condition in most observed years while the declining trend was mainly found in Cambodia and southern Laos. Vegetation-drought responses showed that tropical vegetation had a high sensitivity to drought conditions, and stronger responses were observed in rainfed crop, mixed forest, and deciduous forest at lower altitude areas. In natural and undisturbed ecosystems, short-term meteorological and agricultural drought disturbances accounted for nearly 93% of variations in tropical vegetation. Among the different examined drought indices, the 3-month Standardized Precipitation Evapotranspiration (SPEI-3) and Temperature Condition Index (TCI) were identified as the factors having the largest influence, together explaining about 55% of the observed natural undisturbed vegetation variations. These findings deepen our understanding of tropical vegetation-drought responses and the underlying drivers of natural and undisturbed ecosystems. Such insights could provide valuable information to assist national and local governments in the MSEA region in developing effective drought management and adaptation programs to safeguard tropical agricultural production and natural ecosystems amidst growing climate challenges.

Study on the properties and mechanisms of the glazed hollow bead thermal insulation mortar

With increasing concerns for energy conservation and environmental protection, research on glazed hollow bead thermal insulation mortar is of utmost importance. This type of mortar offers superior thermal insulation, leading to reduced energy consumption and emissions, in line with the current green building trends. This article aims to investigate the impact of varying component proportions on the parameters of thermal insulation mortar through an orthogonal experiment with four factors and three levels: glazed hollow bead, sepiolite, air-entraining agent, and cellulose ether. Additionally, a single-factor experiment is conducted to analyze the influence degree of water-solid ratio and these four factors. The experimental results are then verified through SEM (Scanning Electron Microscope) observation. The research findings indicate that glazed hollow beads have the most significant impact on thermal conductivity and compressive strength, while the air-entraining agent exerts the greatest influence on flexural strength. Specifically, when the content of glazed hollow bead is 2%, sepiolite 1%, air-entraining agent 0.6%, and cellulose ether 0.6%, the thermal conductivity can reach a minimum value of 0.0533W/(m·K). On the other hand, when the content of glazed hollow bead is 1%, sepiolite 2%, air-entraining agent 0.4%, and cellulose ether 0.6%, the compressive strength can achieve a maximum value of 2.4 MPa. These findings provide a solid foundation for further exploration into improving the performance of thermal insulation mortar.