Greening Trend Research Articles

Alternating dominant effects of temperature and precipitation along elevational gradient on the alpine and subalpine vegetation activities in southwestern China

Elevation plays a pivotal role in modulating hydroclimatic dynamics, changing vegetation activity and shaping vegetation distribution patterns in alpine regions. Nevertheless, a conspicuous knowledge gap persists regarding the interaction between climate drivers on vegetation activity. In this study, we investigated the spatio-temporal patterns of the Normalized Difference Vegetation Index (NDVI), a proxy for vegetation activity, and examined its interactions with temperature (T) and precipitation (P) along elevational gradient (EG) in the Alpine and Sub-alpine Woodlands of Southwest China (ASWSC) during 2001–2018. We found that approximately 24% vegetation in ASWSC exhibited significant greening trend, as a result of regional warming and slight wetting during the study period. The greening and browning rates were both elevational asymmetric, with higher rates at lower elevations. We also illustrated an alternating domination of T - P on vegetation activity along EG. This suggested a roughly antagonistic effect between the two drivers within the whole vegetation elevation range except for a synergistic effect in the sub-alpine forests zone between 2300 - 4200 m. The antagonistic effect tended to be more pronounced in warm and dry years, while the synergistic effect was more evident in cold and wet years. Specifically for different vegetation types, we found an insignificant T - P interactive effect along EG in indigenous evergreen needleleaf forest (ENF) and evergreen broadleaf forest (EBF), in contrast to the significant synergistic effect in the regenerated deciduous broadleaf forest (DBF) and mixed forest (MF) and antagonistic effect in shrubland and grassland types. In summary, our study revealed alternating-dominant effects of temperature and precipitation on vegetation activity along EG. These findings will deepen our understanding of vegetation resistance and resilience to climate change and eventually contribute to establishing a scientific foundation for effective ecosystem management and conservation practices in alpine region.

Identification of ecologically sensitive zones affected by climate change and anthropogenic activities in Southwest China through a NDVI-based spatial-temporal model

Revealing the driving mechanism of climatic change and anthropogenic activities on vegetation variation and identifying the ecologically sensitive zones in Southwest China are critical for formulating regional ecological restoration strategies. Herein, a NDVI-based spatial–temporal model in Southwest China from 2000 to 2021 under the influences of climatic change and anthropogenic activities alone and in combination was determined based on MODIS NDVI and in situ meteorological data using Theil-Sen median analysis, the Mann–Kendall significance test, residual analysis, and relative analysis. The results showed that on the annual scale, the NDVI, predicted NDVI, and residual NDVI significantly increased at rates of 0.0016 yr−1, 0.0008 yr−1, and 0.0008 yr−1 in Southwest China from 2000 to 2021, respectively. Moreover, the NDVI, predicted NDVI, and residual NDVI significantly (P < 0.05) increased in the Guangxi Hills (GXH), Hengduan Mountains (HDM), Sichuan Basin (SCB), Yunnan-Guizhou Plateau (YGP), Zoige Plateau (ZP) and Tibetan Plateau (TP). The areas with an increase in the NDVI, predicted NDVI, and residual NDVI accounted for 81.45 %, 79.73 %, and 71.20 % of Southwest China, respectively. The NDVI variation in Southwest China was dominated by the combined effects of climatic change and anthropogenic activities, accounting for 83.22 % of the total study area. In addition, 62.76 % of the study area exhibited a greening trend. The contributions of climatic change were greater than those of anthropogenic activities for greening areas, and the opposite is true for browning areas. Innovatively, ecologically sensitive zones were identified through the Theil-Sen median analysis and Mann–Kendall significance test and accounted for 16.78 % of the total study area. Overall, this study proposed a NDVI-based spatial–temporal mode to investigate the driving mechanism of driving forces on NDVI variation, identify ecologically sensitive zones and suggest a management framework in Southwest China.

Do knowledge sharing and big data analytics capabilities matter for green absorptive capacity and green entrepreneurship orientation? Implications for green innovation

PurposeThis study is the first to examine how big data analytics (BDA) capabilities affect green absorptive capacity (GAC) and green entrepreneurship orientation (GEO). It uses the dynamic capability view, BDA and knowledge-sharing literature. There is a lack of studies addressing the BDA–GAC and BDA–GEO relationships and their potential impact on green innovation. Continuing the ongoing research discussion, a few studies examined the vital implications of knowledge sharing (KS) on GAC, GEO and green innovation.Design/methodology/approachThe study used a cross-sectional and stratified random sampling technique to collect data through self-administered surveys among Chinese manufacturing firm employees. The study applied SmartPLS to analyze the obtained data.FindingsThe findings revealed that BDA capabilities positively influence GAC and GEO. In addition, GEO and KS positively impact green innovation. The KS recorded a positive impact on GAC and GEO. Furthermore, GAC and GEO recorded a partial mediating effect.Practical implicationsThe study acknowledges that GAC is the backbone of a firm green entrepreneurial orientation, which needs to be aligned with BDA capabilities to anticipate future green business trends. GAC's help drives GEO's green business agenda. KS plays a strategic role in developing GAC, fostering GEO and improving green innovation.Originality/valueThe study highlights the necessity of aligning BDA capabilities to fit firms' GEO green business agendas. This study focuses on the role of BDA capabilities in developing firms' green dynamics capabilities (e.g. GAC), which helps GEO drive superior green business growth. KS develops GAC and boosts GEO to enhance green innovation.

Multiple Greenness Indexes Revealed the Vegetation Greening during the Growing Season and Winter on the Tibetan Plateau despite Regional Variations

Vegetation is an essential component of terrestrial ecosystems and supplies multiple ecosystem benefits and services. Several indices have been used to monitor changes in vegetation communities using remotely-sensed data. However, only a few studies have conducted a comparative analysis of different indices concerning vegetation greenness variation. Additionally, there have been oversights in assessing the change in greenness of evergreen woody species. In this study, we used the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the near-infrared reflectance of terrestrial vegetation (NIRv), and the leaf area index (LAI) data derived from MODIS data to examine spatial and temporal change in vegetation greenness in the growing season (May–September) and then evaluated the evergreen vegetation greenness change using winter (December–February) greenness using trend analysis and consistency assessment methods between 2000 and 2022 on the Tibetan Plateau, China. The results found that vegetation greenness increased in 80% of pixels during the growing season (northeastern, central-eastern, and northwestern regions). Nevertheless, a decline in the southwestern and central-southern areas was identified. Similar trends in greenness were also observed in winter in about 80% of pixels. Consistency analyses based on the four indexes showed that vegetation growth was enhanced by 29% and 30% of pixels in the growing season and winter, respectively. Further, there was relatively strong consistency among the different vegetation indexes, particularly between the NIRv and EVI. The LAI was less consistent with the other indexes. These findings emphasize the importance of selecting an appropriate index when monitoring long-term temporal trends over large spatial scales.

Building information modeling (BIM) in green buildings: a state-of-the-art bibliometric review

PurposeThis study evaluates the knowledge structure of building information modeling (BIM) in green buildings. Buildings are one of the main contributors to carbon emissions, and implementing BIM in green buildings is seen as an indispensable approach to mitigate environmental and climate change issues.Design/methodology/approachThrough a bibliometric analysis, 297 publications retrieved from the Web of Science (WoS) were analyzed to explore their intellectual structure.FindingsBibliographic coupling analysis produced four clusters on current and emerging trends, while co-word analysis produced four clusters on future BIM and green building trends. Current and emerging trends revolve around BIM adoption in green and existing buildings, life cycle analysis (LCA) and sustainable rating tools. Future trends related to BIM and performance analysis and optimization, the BIM framework for green building design and construction, overcoming barriers and maximizing benefits in BIM adoption.Research limitations/implicationsThe implications of this study are relevant to all BIM and green building stakeholders, including developers, engineers, architects, occupants, tenants and the whole community.Originality/valueThis study examines the crucial integration of BIM and green building within the more extensive construction and building field scope.

Shifts in the trends of vegetation greenness and photosynthesis in different parts of Tibetan Plateau over the past two decades

Ecosystems on the Tibetan Plateau (TP) are fragile and sensitive to climate change and thus provide insight into the climate regulations on vegetation structure and function. In this study, we utilized the Normalized Difference Vegetation Index (NDVI) and solar-induced chlorophyll fluorescence (SIF) to explore vegetation dynamics during the growing season from 2000 to 2021, and further investigated how climate change has influenced them. Based on both NDVI and SIF, significant vegetation greening trends were observed in northeastern TP across the study period due to continuous increases in both temperature and water availability. Browning trends were observed in southwestern TP due to decreasing precipitation and enhanced warming stress during 2000–2007, but the trends reversed after 2007. In the southeastern region, we also detected a turning point of a more prominent greening trend in 2016. Moreover, changes in SIF exhibited greater magnitude compared to NDVI, implying vegetation photosynthesis is more sensitive to climate change than greenness, potentially due to variations in light use efficiency. This study strengthens our understanding of climate regulations on vegetation structure and function under different hydrothermal conditions and highlights the need for additional research on the differences between greenness and photosynthesis.

Exploring NDVI change patterns across the Tibetan Plateau at the hillslope scale using geomorphons

ABSTRACTWithin the Tibetan Plateau (TP), grassland degradation is of great concern, and there is a need to better understand the spatial variability and landscape patterns in grassland degradation across the TP. This study explores potential patterns in grassland degradation, estimated using Landsat-derived normalized difference vegetation index (NDVI) growing season change products generated by a prior study. Shuttle Radar Topography Mission (SRTM) digital terrain model (DTM)-derived geomorphons, topographic slope and the topographic position index (TPI) were used to explore grassland degradation and assess whether changes in growing season NDVI are associated with specific grasslands or landforms. An increase in NDVI within the TP was observed especially during the 1990 to 2018 and 2000 to 2018 time periods. Higher growing season median NDVI change values were found for the Southeast Tibet Shrublands and Meadows (SETSM) and TP Alpine Shrublands and Meadows (TPASM) grasslands in comparison to the other grassland types analysed, suggesting a more pronounced greening trend in the eastern portion of the plateau in comparison to the western portion. Small differences in NDVI change were observed for different geomorphon-based landforms, while topographic slope and TPI showed only modest correlations with NDVI change for all time periods and grassland types. More localized patterns of grassland degradation were masked by the widespread greening trends. Further analysis of only pixels that experienced a decreasing NDVI magnitude of less than or equal to −0.1 also generally suggested more change in the SETSM and TPASM. While this study generally supports the use of geomorphons as an analysis and aggregating unit for studying change patterns at the hillslope scale, more research is required to fully grasp the dynamics of landscape change at the hillslope scale in the TP.

A Landscape Restoration Initiative Reverses Desertification with High Spatiotemporal Variability in the Hinterland of Northwest China

Although we are in an era of enormous global commitments to ecological restoration (the UN Decade on ER; the Bonn Challenge), little attention has been paid to the importance of sustained commitment to individual restoration initiatives and few resources have been dedicated to monitoring, especially the long-term and broad-scale evaluations that would allow us to understand how basin-scale restoration can result in complex spatiotemporal patterns. Remote sensing offers a powerful tool for evaluating restoration initiatives focused on water management in arid regions, where changes in vegetation growth can be tracked visually with measures like the generalized difference vegetation index (GDVI). In this paper, we evaluate the Comprehensive Treatment Program of the Shiyang River Basin (CTSRB), a landscape restoration initiative in China’s northwest, using a widely available remote sensing tool, showing how it can reveal the causes of fluctuating changes in restoration success. We focus on spatiotemporal variation, studying a time series from 2001 to 2020 using regression, trend, and stability analyses for six different divisions of the study region (the study area as a whole, the irrigated areas, the periphery of the irrigated regions, and the districts of Ba, Quanshan, and Hu) to evaluate the effects of the restoration initiative. The study period was divided into four equal-length phases based on the implementation timeline of the CTSRB, which includes one pre- and post-intervention interval and two stages of the CTSRB. We found that the CTSRB has played a positive role in the restoration of vegetation in the Minqin Basin, especially desert vegetation. However, the positive effects were not obvious in the first CTSRB period, which was characterized by a decline in vegetation growth likely caused by the strategy of “close the pumping-wells, transform the land”, which reversed a greening trend caused by the unsustainable irrigation of wasteland prior to the project’s initiation. During the second phase of the CTSRB, vegetation in the regions of “transform the land” gradually improved, and the growth of desert vegetation gradually improved and expanded as a result of more water flowing out of agricultural zones. The rate of vegetation recovery slowed down during the final phase of the CTSRB, and some areas even experienced a decline in the GDVI. Overall, our findings show that the CTSRB, by integrating water management and allowing for uninterrupted ecological restoration, drove complex regional changes in the GDVI, including successful restoration of desert vegetation. The spatiotemporal variable we observed underscores the importance of long-term commitment to arid land restoration initiatives and the importance of even longer-term monitoring using tools like remote sensing.

Spatio-temporal variation in leaf area index in the Yan Mountains over the past 40 years and its relationship to hydrothermal conditions

Changes in hydrothermal conditions have significant effects on vegetation, but there is still a lack of understanding of how vegetation responds to land surface (surface temperature and soil moisture) and meteorological (temperature and precipitation) conditions in mountain regions. This study examined the trends of leaf area index (LAI) in the Yan Mountains over the last four decades using Global Land Surface Satellite (GLASS) data. The results showed a persistent increase of LAI (greening) over 20 % to 80 % of the study area in growing season, spring, summer and autumn. Anthropogenic activities caused the greening trend by crop management before 2000 and afforestation after 2000. The increasing rate of LAI varied with elevation, and the most significant increase occurred in areas between 300 and 900 m, and the lowest increase occurred in areas below 300 m. Moreover, we found that LAI was negatively correlated with land surface temperature and soil moisture, but positively correlated with precipitation and air temperature. The time-lag effect was found between hydrothermal factors and LAI in the past four decades. There was a time lag of 2–3 months between LAI changes and temperature/precipitation during the early and late stages of the growing season, and a time lag of 0–1 month during the middle stage. Specifically, there was no time lag in vegetation response to surface soil moisture, and a time lag of 2–3 months in vegetation response to land surface temperature from July to October. Our findings provide insights into how vegetation adapts to land surface and climatic hydrothermal conditions in mountain regions and can be used by governments to develop policies for ecological protection.

Spatiotemporal characteristics and natural forces of grassland NDVI changes in Qilian Mountains from a sub-basin perspective

The Qilian Mountains (QLMs) encompass a diverse alpine grassland ecosystem undergoing notable changes due to global climate change. Understanding the driving forces behind these changes is crucial for effective regional vegetation restoration and management, particularly given the contrasting climatic conditions between the eastern and western regions of the QLMs. This study investigated the spatiotemporal evolution of grassland NDVI and the influence of 13 variables on NDVI changes in six distinct QLM sub-basins from 1990 to 2019 using Landsat data and the Geodetector model, respectively. Results revealed a consistent greening trend in all sub-basins, albeit with varying slopes. Notably, the Qinghai Lake basin exhibited a recent slowdown in NDVI, highlighting the need for enhanced vegetation restoration efforts, especially in permafrost-covered areas. Environmental factors affecting NDVI differed among basins. In the western QLMs, sunshine duration and relative humidity emerged as key factors influencing NDVI distribution, while in the eastern basins, altitude and air temperature played significant roles. This study underscored the importance of hydrothermal conditions, both horizontally and vertically, in shaping grassland NDVI. Other factors, such as vegetation types, soil characteristics, and permafrost degradation, also influenced NDVI, while slope and slope aspects had a limited impact. Furthermore, the research identified threshold-dependent responses of NDVI to the interaction of these factors. These findings emphasized the need to customize vegetation restoration strategies based on the specific environmental conditions of each basin within the QLMs. This nuanced understanding of the distinct driving characteristics between the eastern and western QLMs enables more effective and targeted approaches to restoring and managing grassland ecosystems in this region.

Increased sensitivity of vegetation to soil moisture and its key mechanisms in the Loess Plateau, China

AbstractSoil moisture (SM) plays a crucial role in connecting various ecological processes in terrestrial ecosystems, as vegetation growth heavily relies on SM availability. Despite many studies evaluating the impacts of SM on vegetation activity, changes in vegetation sensitivity to SM are still poorly understood. In this study, we evaluated the spatiotemporal changes of vegetation sensitivity to SM across the Loess Plateau (LP) region from 1982 to 2010 using satellite‐based normalised difference vegetation index (NDVI) and reanalysis SM data. Our results showed a significant increase in NDVI (slope = 0.0015/year, p &lt; 0.001) and a slight decrease in SM (slope = −0.0001 m3/m3/year, p = 0.7) over the past 29 years, indicating a greening trend in the LP. By establishing a multiple linear model, we found that the sensitivity of vegetation dynamics to SM has significantly increased during the same period (slope = 0.029, p &lt; 0.05). Spatially, vegetation in over half (51.2%) of the total area showed enhanced sensitivity to SM. Further analysis using the Lindeman–Merenda–Gold method showed that atmospheric CO2 was the major contributor (27.20%) to the sensitivity changes, followed by nitrogen deposition (23.70%) and air temperature (20.11%). Ecosystem structure changes (represented by non‐tree land cover fractions) and precipitation shifts contributed 16.13% and 12.83%, respectively, to sensitivity changes. These findings provide a better understanding of the spatiotemporal pattern and underlying mechanisms of ecosystem sensitivity to SM in the greening and warming LP.

Duty ratio drive prediction model of lifetime degradation for organic light emitting diode-on-silicon microdisplay.

A duty ratio drive prediction (DRDP) model of luminance degradation for organic light emitting diodes (OLED) microdisplay is proposed in this paper. The traditional stretched exponential decay (SED) model is not applicable for OLED driven by duty ratio. The DRDP model introduces the duty ratio as the variables affecting the lifetime of OLED. By fitting the undetermined coefficients with the measured luminance data, the quantitative relationships among the initial luminance, duty ratio, and OLED lifetime are obtained. Meanwhile, the model quantifies the phenomenon of spontaneous luminance recovery, which occurs when OLED switches from bright to dark. Finally, the DRDP model is used to compensate the luminance degradation of OLED driven by duty ratio. The experimental results show that the average prediction accuracy of DRDP model for white, red, green, and blue (W/R/G/B) OLED degradation trend is 0.9623. The average prediction accuracy of W/R/G/B OLED lifetime is 0.6119, which is greater than that of SED model. The lifetime is extended by 89.83% after compensation.

Green Supply Chain Management Research Trends and Linkages to UN Sustainable Development Goals

Growing awareness about environmental responsibility among businesses and policymakers has increased interest in Green Supply Chain Management (GSCM). For the first time, using the SDG mapping algorithms, this article focused on 7009 publications from 2013 to 2022 mapped explicitly to 17 SDGs. This study uses bibliometric and science mapping techniques to create a detailed mapping between GSCM research and SDGs. This study created an SDG citation network to show how specific SDGs are related to GSCM practices. The most mapped SDGs in this study are SDG 9 (Industry, Innovation, and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 17 (Partnership for the Goals). Our results showed that GSCM research mainly focuses on three thematic areas: industry 4.0 and sustainable supply chains, GSCM practice coordination, and sustainable environmental policy management. This also includes integrating the circular economy into GSCM, sustainable sourcing, supply chain resilience, digital transformation in GSCM for better environmental performance, and the social impact of GSCM. A shift towards integrating sustainability into supply chain processes has been found. Environmental management, performance monitoring, and new technologies like Industry 4.0 and AI have been used to support the SDGs. International interest and collaboration in GSCM research can be seen from the bibliographic coupling study covering countries like Europe, Western Countries, the Middle East, Southeast Asia, and Eastern Europe. This study shows how GSCM can directly address global challenges identified by the United Nations by mapping GSCM research to specific SDGs.

Exploring the Impact of the Strategy of Sustainable Consumer Behavior on Consumers Consumption Decisions

As global citizens awareness of environmental issues is increasing daily, a recently invented idea called sustainable consumer behavior becomes popular. There are also some debates about whether this new shopping pattern will become mainstream or die out becomes a primary question. Based on past research about sustainable consumer behavior and green products, some patterns and trends about them could be revealed. This paper compiles research regarding sustainable consumer behavior after 2000 and tries to come to a conclusion about the effect this marketing strategy has on consumers shopping behavior. Among all the essays, 15 essays from Google Scholar were used and analyzed in this essay. As a result, it can be found that researchers hold an optimistic view but with certain critics regarding sustainable consumer behavior. Currently, this new trend cannot bring much profit, but its high potential and popularity make it worthy of being expected. This research would be beneficial for decision-makers promoting sustainable consumer behavior.

China’s Ecological Engineering in the Anthropocene

China is the largest contributor to global greening trends over the past two decades, pursuing large-scale tree planting and revegetation initiatives. This type of ecological engineering is controversial, given concerns about China’s authoritarian environmentalism. This essay examines such Chinese efforts and how they diverge from Western environmental approaches based on nature preservation. Chinese environmentalism is based on a tradition that does not delineate nature from culture, the natural from the engineered. This distinction has consequences for global environmental governance in the Anthropocene as China promotes its approach to the global South.

Land use significantly improved grassland degradation and desertification states in China over the last two decades

China possesses extensive grasslands primarily located in dryland regions, which are highly susceptible and fragile to climate change and human intervention, making them prone to degradation and desertification. In recent decades, China has implemented numerous ecological projects to improve the states of ecosystems. Additionally, recent studies have revealed the critical roles of rising CO2 on dryland greening. However, it is still limited to understand the contributions of anthropogenic recovery and CO2 fertilization, as well as other climate factors, to the dynamics of grassland degradation and desertification in China. To address these gaps, we employed a two-step approach to differentiate between grassland degradation and desertification as distinct processes across the grasslands and sparsely vegetated lands in China. This involved assessing degradation within existing grassland areas and identifying the conversion of grasslands into desert regions. The study period of 2000–2020 was examined to determine the occurrence of grassland desertification, which suggested a significant decrease in the desertification area in China. Subsequently, the time series of the Enhanced Vegetation Index (EVI) during the growing season was analyzed to track vegetation dynamics. Since the beginning of the 21st century, a significant greening trend has been observed in approximately 97% of the study area. Furthermore, we quantified the effects of anthropogenic climate change (ACC), climate change, and land use change on grassland degradation and desertification in China. The analyses indicated that 50.7% of the observed vegetation changes in the study area from 2000 to 2020 were primarily driven by land use, followed by the effects of rising CO2, accounting for 42.1% of the changes. These findings provided some insights on developing regionally-targeted strategies for grassland conservation in China.

Change in observed long-term greening across Switzerland – evidence from a three decades NDVI time-series and its relationship with climate and land cover factors

ABSTRACT Environmental changes are significantly modifying terrestrial vegetation dynamics, with serious consequences on Earth system functioning and provision of ecosystem services. Land conditions are an essential element underpinning global sustainability frameworks, such as the Sustainable Development Goals (SDGs), requiring effective solutions to assess the impacts of changing land conditions induced by various driving forces. At the global scale, long-term increase of vegetation greening has been widely reported notably in seasonally snow-covered ecosystems as a response to warming climate. However, greening trends at the national scale have received less attention, although countries like Switzerland are prone to important changing climate conditions. Hereby, we used a 35-year satellite-derived annual and seasonal time-series of Normalized Difference Vegetation Index (NDVI) to assess vegetation greenness evolution at different spatial and temporal scales across Switzerland and related them to temperature, precipitation, and land cover to investigate possible responses of changing climatic conditions. Results indicate that there is a statistically significant greening trend at the national scale with an NDVI mean increasing slope of 0.6%/year for the 61% significant pixels across Switzerland. In particular, the seasonal mean NDVI shows an important break for winter, autumn and spring seasons starting from 2010, potentially indicating a critical point of changing land conditions. At biogeographical scale, we observed an apparent clustering (Jura-Plateau; Northern-Southern Alps; Eastern-Western Alps) related to landscape characteristics, while forested land cover classes are more responsive to NDVI changes. Finally, the NDVI values are mostly a function of temperature at elevations below the tree line rather than precipitation. The findings suggest that multi-annual and seasonal NDVI can be a valuable indicator to monitor vegetation conditions at different scales and can provide complementary observations for national statistics on the ecological state of vegetation to monitor land affected by changing environmental conditions. This work is aiming at strengthening the insights into the driving factors of vegetation change and supporting monitoring changing land conditions to provide guidance for effective and efficient environmental management and sustainable development policy advice at the national scale.

Effects of Climate Variability and Human Activities on Vegetation Dynamics across the Qinghai–Tibet Plateau from 1982 to 2020

In recent years, vegetation on the Qinghai–Tibet Plateau (QTP) has undergone significant greening. However, the causal factors underpinning this phenomenon, whether attributable to temperature fluctuations, precipitation patterns, or anthropogenic interventions, remain a subject of extensive scholarly debate. This study conducted a comprehensive analysis of the evolving vegetation across the QTP. The National Oceanic and Atmospheric Administration Climate Data Record Advanced Very High Resolution Radiometer Normalized Vegetation Difference Index (NOAA CDR AVHRR NDVI) dataset was employed to elucidate the intricate relationship between climatic variables and human activities driving vegetative transformations. The findings were as follows: The NDVI on the QTP has exhibited a significant greening trend at a rate of 0.0013/a (per year). A minor decline, accounting for only 17.6% of grasslands, was observed, which was primarily concentrated in the northwestern and northern regions. Through residual analysis, climate change was found to be the predominant driver, explaining 70.6% of the vegetation variability across the plateau. Concurrently, noticeable trends in temperature and precipitation increases were observed on the QTP, with the southern region demonstrating improved sensitivity to precipitation alterations. In summary, these results substantiate that a confluence of climatic warming, enhanced moisture availability, and a reduction in livestock population collectively creates an environment conducive to enhanced vegetation vigor on the QTP. This study highlights the significance of acknowledging the dual influence of climate and human agency in shaping vegetative dynamics, which is a critical consideration for informed land management strategies and sustainable development initiatives on this ecologically pivotal plateau.

Optimizing Energy Efficiency of Dielectric Materials’ Electrodischarge Dispersion as One Sustainable Development Green Trend

Increasing the energy efficiency of production processes is closely related to minimizing the impact on the environment and is one of the priorities of the concept of sustainable development. Electric discharge is an effective tool for multilevel grinding of non-metallic materials in various working fluids and obtaining coarse and fine suspensions. We introduce the technique for calculating the electrotechnological parameters necessary for energy-efficient electric discharge dispersion. This technique considers the strength characteristics of the crushed material (dispersed phase) and the electrical conductivity of the working fluid (dispersed medium). It is also essential to consider the energy stored in the capacitor bank, the energy criterion, the critical value of the working fluid’s electrical strength, the radius of the high-voltage electrode point, and the distance from the discharge channel axis to the disintegration object. All this allows obtaining a given granulometric composition of the dispersed phase with minimal energy consumption. Experiments confirmed the validity of the proposed calculation technique. We obtained the water-brown coal suspension with a given dispersion two times faster and consumed four times less energy in comparison with the known methods that did not take into account the electrical conductivity of the working liquid and the mechanical strength of the crushed material.

Seasonal variations and drivers of energy fluxes and partitioning along an aridity gradient in temperate grasslands of Northern China

Surface energy exchange is vital because it affects microclimate, water cycle and biogeochemical processes in the canopy-atmosphere interface. In this study, seasonal variations and regulators of energy fluxes and partitioning were investigated based on a long-term eddy flux observation dataset (12 towers, 82 site-years in total) along an aridity gradient in temperate grasslands of Northern China. Our results showed that the net radiation (Rn) and all energy components, including latent heat (LE), sensible heat (H) and ground heat (G0) fluxes, presented unimodal seasonal patterns across different grassland types, with Rn and LE peaked in the middle growing season but H and G0 at the beginning of the growing season. The energy partitioning shifted from the H-dominated to the LE-dominated period accompanied vegetation phenological development, thus a U-shaped seasonal pattern of Bowen ratio (β, H/LE) was observed. Prolonged LE-dominated period and great peak of LE flux enhanced LE/Rn proportion and decreased β from desert steppes to typical steppes and meadow steppes. Climate and vegetation factors co-regulated the seasonal patterns of energy fluxes and partitioning, with enhanced effects of LAI and canopy surface conductance (gc) from desert steppes to typical and meadow steppes, but a reverse pattern of land-atmosphere coupling via vegetation physiological regulation. Meanwhile, enhanced sensitivity of β in response to variations of climate and vegetation factors was also observed with increasing aridity. Our founding highlighted the role of vegetation especially physiological regulation in determining the seasonal patterns of energy fluxes and partitioning in water-limited grasslands. Whereas, the great sensitivity of energy partitioning to vegetation changing with increasing aridity also emphasized that it is vital to pay attention to the response of energy exchange in the desert steppes to the greening trend in Northern China under future climate scenarios.