Regional Land Research Articles

Multi-scale variability of southeastern Australian wind resources

Abstract. There is growing need to understand wind variability in various regions throughout the world, including in relation to wind energy resources. Here we examine wind variability in southeastern Australia in relation to the El Niño–Southern Oscillation (ENSO) as a dominant mode of atmospheric and oceanic variability for this region. The analysis covers variability from seasonal to diurnal timescales for both land and maritime regions of relevance to wind energy generation. Wind speeds were obtained from the 12 km grid length Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) reanalysis, with a focus on wind at a typical hub-height of 100 m above the surface. Results show spatiotemporal variations in how ENSO influences wind speeds, including consistency in these variations over the wind speed distribution. For example, ENSO-related variations in mean winds were mostly similar in sign to ENSO-related variations in weak winds, noting uncertainties for strong winds given available data. Diurnal variability in wind speed was larger for summer than winter and for land than ocean regions, with the diurnal cycle maxima typically occurring in the afternoon and evening rather than morning, plausibly associated with sensible heating of air above land following solar radiation. Localised variations in the diurnal cycle were identified around mountains and coastal regions. The results show some indication of ENSO influences on the diurnal variability. These findings are intended to help enhance scientific understanding on wind variability, including in relation to ENSO, and to contribute information towards practical guidance in planning such as for use in energy sector applications.

Deformation-Adapted Spatial Domain Filtering Algorithm for UAV Mining Subsidence Monitoring

Underground coal mining induces surface subsidence, leading to disasters such as damage to buildings and infrastructure, landslides, and surface water accumulation. Preventing and controlling disasters in subsidence areas and reutilizing land depend on understanding subsidence regularity and obtaining surface subsidence monitoring data. These data are crucial for the reutilization of regional land resources and disaster prevention and control. Subsidence hazards are also a key constraint to mine development. Recently, with the rapid advancement of UAV technology, the use of UAV photogrammetry for surface subsidence monitoring has become a significant trend in this field. The periodic imagery data quickly acquired by UAV are used to construct DEM through point cloud filtering. Then, surface subsidence information is obtained by differencing DEM from different periods. However, due to the accuracy limitations inherent in UAV photogrammetry, the subsidence data obtained through this method are characterized by errors, making it challenging to achieve high-precision ground surface subsidence monitoring. Therefore, this paper proposes a spatial domain filtering algorithm for UAV photogrammetry combined with surface deformation caused by coal mining based on the surface subsidence induced by coal mining and combined with the characteristics of the surface change. This algorithm significantly reduces random error in the differential DEM, achieving high-precision ground subsidence monitoring using UAV. Simulation and field test results show that the surface subsidence elevation errors obtained in the simulation tests are reduced by more than 50% compared to conventional methods. In field tests, this method reduced surface subsidence elevation errors by 39%. The monitoring error for surface subsidence was as low as 8 mm compared to leveling survey data. This method offers a new technical pathway for high-precision surface subsidence monitoring in mining areas using UAV photogrammetry.

Spatio-temporal dynamics of net primary productivity and the economic value of Spartina alterniflora in the coastal regions of China

This study employs an improved Carnegie–Ames–Stanford Approach (CASA) model to calculate the Net Primary Productivity (NPP) of Spartina alterniflora (SA) and various other land use/land cover types (LULC) across coastal China over multiple years. The research aims to provide significant theoretical and practical insights into carbon sink research in coastal zones, sustainable development, and resource management. Key findings include identifying the first εmax value of 2.219 g C/MJ for SA, addressing a critical data gap in CASA modeling research on invasive plants. SA's NPP exhibited higher values in Shanghai and Zhejiang due to factors such as genetic diversity, invasion duration, and tidal dynamics. In contrast, other LULC exhibited higher NPP values in southern and inland regions, characterized by greater vegetation cover and favorable growing conditions. In 2020, SA and other LULC sequestered 16.352 kt C and 0.821*106 kt C, respectively. From 2000 to 2020, the average annual NPP and total carbon storage of SA and other LULC increased significantly, primarily driven by Shanghai and deciduous needleleaf forests, respectively. Seasonal NPP trends followed summer> spring> autumn> winter, influenced by climate conditions and plant life activities. Economic assessments in 2020 estimated SA's carbon storage value at RMB0.409 billion (Market Value method) or RMB5.562 billion (Carbon Tax method), with RMB2.054 billion attributed to oxygen release values, underscoring its economic and ecological potential. Among other LULC, evergreen broadleaf forests showed the highest carbon storage value (RMB183.463 billion). The study emphasizes the critical role of all LULC in carbon storage and oxygen release, advocating for targeted conservation and land management strategies. It suggests that managing SA should balance stringent control in high-risk areas, lenient measures in low-risk areas, eradication of scattered populations, and maximizing ecological benefits in retention areas, with continuous monitoring and adaptive management strategies to balance conservation and development efforts.

Measuring ecological effects and policy mechanisms of urban land use transition – a case study of Xi'an, China

ABSTRACT Measuring ecological effects and policy mechanisms of urban Land Use Transition (LUT) is the key to understanding regional environmental changes and achieving sustainable urban developments. This paper illustrates and demonstrates a formal method for measuring the ecological effect of LUT characterized by Ecological Security Index (ESI) and Ecosystem Value (ESV), and enhanced with a correlational analysis between the ecological effect model and urban policy mechanism. Using Xi'an, a major capital city of western China as a case study, based on multi-temporal Thematic Mapper (TM) remote sensing images, Digital Elevation Model (DEM) and relevant statistical data, the results show that the method using ESI and ESV, together with the Land Use Transition Probability Matrix (LUTPM) can comprehensively measure the ecological effects of LUT process in Xi'an from 2000 to 2020. The results show that due to the impact of urban expansion, regional construction land increased significantly (by 67.07%), while farmland decreased considerably (by 13.12%). The farmland and forestland around the main urban area of Xi'an were centrally converted into construction land, yet the mutual transition of other types of land was relatively scattered. The total amount of ESV has slightly increased from 40.1 billion yuan to 42.8 billion yuan, and the proportion of ecological service function value presents a decreasing trend in regulation function (65%), support function (22%), supply function (9%), and cultural function (4%). The ESI value slightly decreased from 0.517 to 0.512, and overall it is still in a state of ecological security. The trend of ESV and ESI decreasing from mountainous areas to plains indicates that high-intensity human activities in plain areas have had a significant impact on the ecological environment. The correlation analysis between policy mechanisms and ecological effects indicates that ecological protection policies have improved the ecological environment and maintain ecological security (high ESV-high ESI mode), and may also have limited economic activities and been detrimental to the security of urban composite ecosystems (high ESV-low ESI mode). Spatial expansion policies have driven the rapid growth of construction land and threatened ecological security (low ESV-low ESI mode). Zoning adjustment policies have led to the transformation of ecological land to construction land, threatening ecological security and reducing ESV (low ESV-low ESI mode), and may also have promoted industrial upgrading and economic development, providing economic assurance for improving ecological security (low ESV-high ESI mode). These comprehensive findings provide valuable insights for understanding the ecological effects and policy mechanisms of LUT in Xi'an for achieving sustainable urban development, and demonstrate and explore a novel method to more appropriately and effectively measure LUT by combining ESV and ESI.

Spatial heterogeneity analysis of biased land resource supply policies on housing prices and innovation efficiency

To rationally allocate land resources, the government has formulated biased land resource supply policies. However, this policy will promote the fluctuation of regional housing prices, thereby affecting regional innovation efficiency. To elucidate the intrinsic logical relationship among these factors, this study investigates the influence of biased land resource supply policies on housing prices and innovation across 31 Chinese provinces over a 16-year period. It places particular emphasis on probing into the spatial disparities in this impact. The innovation of this paper lies in the development of a theoretical analysis model, which creatively utilizes the regional land supply status as an instrumental variable to address the endogeneity problem. At the same time, a double robustness test was conducted by substituting the explanatory variables and employing the fixed effects model. The results of the TSLS regression indicate: (1) The land resource supply policy, which prioritizes the central and western regions, has resulted in an increase in housing prices across the majority of regions in China, with the eastern region experiencing a more rapid rise compared to the central and western regions. (2) The accelerated growth of housing prices in the eastern region is likely to exert a more pronounced inhibitory effect on innovation compared to the central and western regions. In summary, China’s land resource supply policy, which favors the central and western regions, has exacerbated the rise in housing prices in the eastern region, and rising housing prices will inhibit the innovation efficiency of the region. Hence, it is imperative for the government to continuously optimize and adjust land resource supply policies, ensuring their alignment with population migration patterns, stabilizing the real estate market, while also considering the innovation efficiency of regional development.

Future Reference Evapotranspiration Trends in Shandong Province, China: Based on SAO-CNN-BiGRU-Attention and CMIP6

One of the primary factors in the hydrological cycle is reference evapotranspiration (ET0). The prediction of ET0 is crucial to manage irrigation water in agriculture under climate change; however, little research has been conducted on the trends of ET0 changes in Shandong Province. In this study, to estimate ET0 in the entire Shandong Province, 245 sites were chosen, and the monthly ET0 values during 1901–2020 were computed using the Hargreaves–Samani formula. A deep learning model, termed SAO-CNN-BiGRU-Attention, was utilized to forecast the monthly ET0 during 2021–2100, and the predictions were compared to two CMIP6 climate scenarios, SSP2-4.5 and SSP5-8.5. The hierarchical clustering results revealed that Shandong Province encompassed three homogeneous regions. The ET0 values of Clusters H1 and H2, which were situated in inland regions and major agricultural areas, were the highest. The SAO-CNN-BiGRU-Attention and SSP5-8.5 forecasting results generally displayed a monotonically growing trend during the forecast period in the three regions; however, the SAO-CNN-BiGRU-Attention model displayed a declining tendency at a few points. According to the SAO-CNN-BiGRU-Attention and SSP5-8.5 results, during 2091–2100, H1, H2, and H3 will reach their peaks; the SSP2-4.5 results show that H1, H2, and H3 will peak in 2031–2040. At the end of the forecast period, for Clusters H1, H2, and H3, the prediction rate of SAO-CNN-BiGRU-Attention increased by 1.31, 1.56%, and 1.80%, respectively, whereas SSP2-4.5’s prediction rate increased by 0.31%, 0.95%, and 1.57%, respectively, and SSP5-8.5’s prediction rate increased by 10.88%, 10.76%, and 10.69%, respectively. The prediction results of SAO-CNN-BiGRU-Attention were similar to those of SSP2-4.5 (R2 > 0.96). The SAO-CNN-BiGRU-Attention deep learning model can be used to forecast future ET0.

Characteristics, Impact Factors and Potential Sources of PM2.5 Pollution for Hainan Island

Based on the environmental monitoring data and meteorological observational data in Hainan Island from 2015 to 2021, the PM2.5-polluted characteristics, influencing factors, and potential contributing regions were analyzed using the backward trajectory simulation, cluster analysis, potential source analysis function （PSCF）, and concentration weight trajectory （CWT） methods. The results showed that PM2.5 in Hainan Island had an obvious seasonal variation, with the highest in winter （22.6 μg·m-3）, followed by that in autumn and spring （17.38 and 16.53 μg·m-3, respectively）, with the lowest in summer （9.79 μg·m-3）. In the past seven years, there were 30 days in Hainan Island in which PM2.5 concentration exceeded the standard. The annual average and four seasons of PM2.5 showed a significant downward trend, and the climatic change rates were -0.97 （annual mean）, -1.09 （spring）, -0.61 （summer）, -0.83 （autumn）, and -1.25 （winter） μg·（m3·a）-1. PM2.5 in Hainan Island was highly correlated with gaseous pollutants, with correlation coefficients of 0.471 （SO2）, 0.633 （NO2）, 0.479 （CO）, and 0.773 （O3-8h）, all passing a significance level of 0.01. PM2.5 was positively correlated with average wind speed and atmospheric pressure and negatively correlated with precipitation, relative humidity, sunshine duration, average temperature, and total solar radiation. Among them, average temperature, relative humidity, and total solar radiation were the main dominant meteorological factors on PM2.5 in Hainan Island. Backward trajectory and potential source analysis revealed that PM2.5 concentration was high （≥20 μg·m-3） in winter and autumn, which was influenced by airflow from inland regions, and Fujian, Zhejiang, Hunan, Jiangxi, Guangdong, and Guangxi provinces were the main potential sources of PM2.5 in Hainan Island.

Use of the SNOWED Dataset for Sentinel-2 Remote Sensing of Water Bodies: The Case of the Po River.

The paper demonstrates the effectiveness of the SNOWED dataset, specifically designed for identifying water bodies in Sentinel-2 images, in developing a remote sensing system based on deep neural networks. For this purpose, a system is implemented for monitoring the Po River, Italy's most important watercourse. By leveraging the SNOWED dataset, a simple U-Net neural model is trained to segment satellite images and distinguish, in general, water and land regions. After verifying its performance in segmenting the SNOWED validation set, the trained neural network is employed to measure the area of water regions along the Po River, a task that involves segmenting a large number of images that are quite different from those in SNOWED. It is clearly shown that SNOWED-based water area measurements describe the river status, in terms of flood or drought periods, with a surprisingly good accordance with water level measurements provided by 23 in situ gauge stations (official measurements managed by the Interregional Agency for the Po). Consequently, the sensing system is used to take measurements at 100 "virtual" gauge stations along the Po River, over the 10-year period (2015-2024) covered by the Sentinel-2 satellites of the Copernicus Programme. In this way, an overall space-time monitoring of the Po River is obtained, with a spatial resolution unattainable, in a cost-effective way, by local physical sensors. Altogether, the obtained results demonstrate not only the usefulness of the SNOWED dataset for deep learning-based satellite sensing, but also the ability of such sensing systems to effectively complement traditional in situ sensing stations, providing precious tools for environmental monitoring, especially of locations difficult to reach, and permitting the reconstruction of historical data related to floods and draughts. Although physical monitoring stations are designed for rapid monitoring and prevention of flood or other disasters, the developed tool for remote sensing of water bodies could help decision makers to define long-term policies to reduce specific risks in areas not covered by physical monitoring or to define medium- to long-term strategies such as dam construction or infrastructure design.

Optimal location to land banking practices in urban-rural informal land market continuum of Ghana

Land banking practices in complex informal land markets are growing in developing countries. However, the land banking (LB) literature predominantly focuses on publicly driven land banks operating within formal land markets. Against this backdrop, this study investigates optimal locations for LB projects in Ghana's complex informal land markets from the perspective of private and semi-public real estate developers. Utilising a two-stage research process, first, the study developed a conceptual framework by using: (1) suppositions regarding space under economic geography; and (2) theoretical suppositions on the use of LB and its influence on LB locational choices uncovered from an interpretive hermeneutic literature review. The second stage focused on an empirical assessment of the conceptual framework by taking four urbanised regions in Ghana. The case study stage uses primary data from 30 interviewees selected using purposive and snowball sampling, while secondary data comprised land bank inventories from the regional Lands Commissions of the case study regions. Results revealed land title security as the primary factor determining optimal locations for land banks. There are significant challenges related to land title security in urban and inner parts of peri-urban areas. These challenges are aggravating the transformation of agricultural lands into residential lands in developers' preferred land bank locations. Based on the ongoing land transformation occurrences, the study underscores the need for policy responses that enhance title security to encourage developers to diversify their land banking locational preferences beyond solely greenfield sites to a mix of green and urban brownfield sites.

Can we develop a more targeted approach to mitigating seismic risk?

The recent high death tolls caused by large earthquakes are a further indication that earthquakes remain one of the most destructive natural hazards in the world and can seriously threaten the achievement of disaster reduction goals. To effectively reduce the existing seismic risk, the limited available mitigation resources should be allocated to areas with the most severe potential risk. However, identifying localized concentrations of risk requires detailed studies. Here, we propose a strategy to delineate regional high seismic risk zone at a fine resolution and with high confidence. We demonstrate this strategy by using the seismic hazard and loss estimation results for earthquake scenarios with a magnitude of Mw 7.5 for the Jiaocheng fault of the Shanxi Rift System, China. Our analyses reveal that the delineated zone accounts for only ~7% of the regional land area but for ~85% of the total financial loss. We recommend prioritizing seismic risk mitigation measures in such high-risk zones, especially for densely populated cities in seismically active areas, to better meet the disaster risk reduction targets in the Sendai Framework.

Can digital financial inclusion facilitate intergenerational income mobility? Evidence from China

We present an empirical study on the impact of digital finance development on intergenerational income mobility. Combining nationally representative Chinese household survey data with indices for digital financial inclusion (DFI), we find that the development of digital finance leads to a lower correlation between children's income and their fathers’ income, significantly enhancing intergenerational mobility. Specifically, a one standard deviation increase in the DFI index is associated with a decrease in intergenerational income elasticity by 0.04–0.05. The effect is particularly pronounced in rural and inland regions. Furthermore, we find that digital finance development stimulates household entrepreneurial activities, which in turn enhance the role of digital finance in reducing intergenerational income correlation.

Ethnic conflicts and their relationship with temperature change in the eastern Qinghai-Tibet Plateau and its vicinity during the past 2000 years

In the context of global warming, the study of climate change and its impacts has garnered significant attention. The Qinghai-Tibet Plateau, with its unique natural environment and socio-cultural significance, plays a crucial role in climate change and the development of the Chinese nation. Investigating the historical interactions, development, and socio-climatic background of the nomadic groups on the Qinghai-Tibet Plateau and agricultural ethnic groups in eastern China contributes to understanding the intrinsic mechanisms of how climate change affects the social interconnectivity of different ethnic groups. This paper is founded on an analysis of 450 conflict events between these groups, as reflected in ancient Chinese war chronicles and ethnic relations annals, creating a decadal conflict sequence from the Eastern Han to the Qing Dynasty (25AD-1910AD). The study conducts a correlation analysis and phase-wise statistical comparison between the climate research findings and the ethnic conflict sequence, specifically examining their relationship with temperature. The main findings are: (1) From the Eastern Han to the Qing Dynasty, a total of 876 conflicts occurred between the ethnic groups in the eastern Qinghai-Tibet Plateau and its vicinity (averaging 4.6 conflicts per decade), with 62.1% initiated by the Ethnic groups on the Qinghai-Tibet Plateau and 66.6% won by the Ethnic groups in eastern China; (2) The frequency of conflicts is primarily correlated with temperatures in the Eastern region, with a less distinct correlation in the Qinghai-Tibet Plateau. During overall warmer periods, conflict incidents were more frequent, with a higher proportion initiated by the Ethnic groups on the Qinghai-Tibet Plateau. Conversely, in colder periods, there were fewer conflicts, with an increased proportion initiated by the Ethnic groups in eastern China; (3) Unlike conflicts between the Ethnic groups in northwestern and eastern China, the Ethnic groups on the Qinghai-Tibet Plateau's external expansion was not a primary objective, indicating that the conflicts were mainly driven by regional economic strength and land productivity. The temperature can reduce land productivity by limiting the growth potential of crops and livestock or by reducing the advance to restrict their production. As a result, water resources, as a condition of land productivity variation, and climate became significant backgrounds for ethnic interactions.

The Influence of Income Inequality on Air Environmental Quality: An Analysis of the Inland Region in China

The Influence of Income Inequality on Air Environmental Quality: An Analysis of the Inland Region in China

The Faustini Permanently Shadowed Region on the Moon

Faustini crater (41 km diameter) hosts a large (664 km2) permanently shadowed region (PSR) with a high potential to harbor water-ice deposits. One of the 13 candidate Artemis III landing areas contains a portion of the crater rim and proximal ejecta. The ShadowCam instrument aboard the Korea Pathfinder Lunar Orbiter provides detailed images of the PSR within Faustini. We characterize the terrain and thermal environment within the Faustini PSR from ShadowCam images, Lunar Reconnaissance Orbiter thermal measurements and laser ranging, and thermal modeling. Our mapping revealed three distinct areas of the floor of Faustini based on elevations, slopes, and surface roughness. These units broadly correlate with temperatures; thus, they may be influenced by variations in volatile sublimation. Crater retention and topographic diffusion rates appear to be asymmetric across the floor, likely due to differences in maximum and average temperatures. Several irregular depressions and a pronounced lobate-rim crater are consistent with subsurface ice. However, differences in the thicknesses of deposited materials on the floor may also explain the asymmetry. Additionally, zones of elevated surface roughness across Faustini appear to result from overprinted crater ray segments, possibly from Tycho and Jackson craters. Mass wasting deposits and pitting on opposite sides of the crater wall may have resulted from the low-angle delivery of material ejected by the Shackleton crater impact event, suggesting that the Artemis III candidate landing region named “Faustini Rim A” will contain material from Shackleton.

Flood sediments as a source of replenishment of alluvial soils with plant nutrition elements

Flood sedimentation in conditions of regular floodplain flooding is a key factor in the formation of alluvial soil. The nature of the passage of hollow waters and their composition affect the quantitative and qualitative characteristics of flood sediments, while having a significant impact on the agroecological state of the floodplain agricultural landscape. The aim of the research was to study the role of flood sediments in enriching the underlying alluvial soil with nutrients. The experiment was conducted on reclaimed arable lands of the Ryazan region in 2023. Determination of the sediment load level and sampling on the surface were carried out using plastic sedimentation samplers fixed on the soil surface, located at a distance of 200 m from each other. The installation was carried out on March 23, the duration of sediment collection was 42 days before the descent of the hollow waters. The average sediment load level of 15.4 t/ha was established, the agrochemical properties of flood sediments and underlying soil were studied, the structure of organic matter, macro– and microelements with flood sediments and their removal from the corn harvest to silage was analyzed. Together with sediments the soil received (kg/ha): total nitrogen – 107.80, total phosphorus – 43.10, total potassium – 104.70, organic matter – 2464,0, mobile phosphorus 15.20, exchangeable potassium 17.80; trace elements (g/ha): boron – 11.86, molybdenum – 1.54, zinc – 122.74, manganese – 1215.06, copper – 200.20, cobalt – 52.05. With a corn harvest for silage of 36.1 kg/ha, the takeaway was: N – 105 kg/ha, P2O5 – 33 kg/ha, K2O – 105 kg/ha. The conducted studies indicate the undoubted agronomic value of flood sediments, they can partially meet the need for fertilizers, therefore, it is necessary to take into account the mass of the substances used to carry out appropriate adjustments to the fertilizer application system. In 2023, the mass of incoming substances with flood sediments completely covered the need for nitrogen; for mobile phosphorus the need decreased to 17.8 kg/ha, for exchangeable potassium the need decreased to 87.2 kg/ha.

Assessing production, processing and utilization of sorghum in West Pokot County, Kenya

Sorghum is one of the most important and under-utilized cereal crops in the Arid and Semi-Arid land (ASAL) regions of Kenya. The crop is grown in drought-prone marginal agricultural areas of Western Central and Eastern and coastal regions of Kenya. The diverse biotic and abiotic constraints including climate changes reduce sorghum productivity. Sorghum technology generators continue promoting suitable management practices and innovations to cushion and improve the resilience of smallholder farmers against the adverse effects of climate change. These interventions contribute to the attainment of not only household and national food security but also enhance incomes. Despite the increasing sorghum production and utilization in West Pokot County, there is limited knowledge of the status of production, processing, and utilization technologies. The aim of this study was to assess the current sorghum production, processing, utilization technology, innovations, and management practices among smallholder farmers. A household survey was undertaken using a jointly developed structured questionnaire by a multi- and inter-disciplinary team of researchers. Using a multi-stage sampling technique, 599 households were randomly selected and interviewed. Data collected were on household and farm characteristics including technologies. The study revealed that compared to maize and pasture, land allocated to sorghum enterprise was relatively low ranging from 0.32 acres to 0.80 acres with an average of 0.73 acres. The adoption of various sorghum technological components was also low. Adoption of improved sorghum varieties was less than 7% while fertilizer use was less than 20%. These contribute to low crop yields of about 140kg per acre. In addition, value addition was also low. Sorghum use was limited to ugali (56%) and porridge (39%) against the diverse value-added products including bread baking. and industrial processing of pure and blended products. More farmer and stakeholders training need to be done. In order to fully upgrade the sorghum value chain; there is the need to have targeted sorghum policies focusing on a range of activities along the value chain. These interventions could be integrated into the West Pokot County Integrated Development Plan. Key words: Sorghum, Production, Marketing, Utilization, Challenges, Opportunities, West Pokot

Temporal and spatial variations of land carbon loss caused by animal-sourced foods consumption in China and the driving factors

The booming animal-sourced foods consumption is a crucial factor driving regional land carbon loss due to the expanded feed use, especially in countries with rapid economic growth. Here, we contribute to revealing the land carbon loss of increased animal-sourced foods consumption in China. Results show that animal-sourced foods consumption in China increased significantly during 2000–2020, especially for beef, dairy and poultry which increased by more than 200 %. As a result, land carbon loss of animal-sourced foods consumption in China has increased by approximately 42 % during the period. Spatially, land carbon loss of animal-sourced foods consumption in most eastern provinces intensified, while that in most western areas declined. The rise of animal-sourced foods consumption per capita was the prominent positive driver for the increase of land carbon loss, while the decline of land carbon loss intensity was the most negative factor. Overall, the booming consumption for animal-sourced foods in China had the dual effects on promoting human dietary and triggering land carbon loss. Therefore, it is essential to formulate strategies for coordinating animal-sourced foods demand and land carbon conservation.

Climate-adaptive battery solutions for renewable microgrids: A case study in Indian coastal and inland regions

The utilization of renewable energy has the potential to alleviate global warming, reduce carbon emissions in the environment, and offer sustainable energy solutions to remote regions. Presently, 13 % of the worldwide population resides in isolated inland and coastal areas where electricity remains inaccessible. Consequently, implementing microgrids powered by renewable energy sources becomes essential to enhance electricity accessibility in these remote locations. This study aims to identify efficient and cost-effective renewable energy technologies suitable for deployment inland and coastal areas. The techno-economic feasibility of renewable energy systems is being evaluated in two distinct locations: Yadavole (inland) and Uppada (coastal) villages in the Indian state of Andhra Pradesh. The energy analysis encompasses various sources, including photovoltaic, diesel generators, wind turbines, flywheels, and batteries (Li-ion and lead-acid). The HOMER software conducts all necessary modeling and simulations for economic analysis and optimal system sizing. According to the simulation results, the most viable microgrid configurations for the inland region, Yadavole, are photovoltaic/diesel and generator/Li-ion. In contrast, for the coastal region, Uppada, photovoltaic/diesel, and generator/lead-acid configurations prove to be the most promising configurations. Moreover, a MATLAB model of the proposed system is created to analyze energy quality. Given the superior performance of Li-ion batteries in elevated temperatures typically found inland, this study recommends a diversified range of battery solutions. Specifically, Li-ion batteries are recommended for inland areas, while lead-acid batteries are recommended for coastal regions. This approach aims to efficiently store renewable power by the specific climatic conditions of each area. It is crucial to systematically analyze region-specific renewable energy technologies to provide valuable insight to stakeholders involved in investment and energy policy decisions.

The coordinated development of higher vocational education, scientific and technological innovation, and sustainable economic development: Spatial distribution, influencing factors and future prediction

High-quality development in China requires higher vocational education, scientific and technological innovation, and sustainable economic development. The spatial distribution patterns of these factors show higher levels in the east and coastal areas compared to the west and inland regions, emphasizing the need for coupling coordination with the social economy. This study examines the impact of sustainable economic development on the coupling coordination degree using the spatial Durbin model. The results show a positive promotion and spillover effect, with regional variations. The main factors affecting the difference in coupling coordination are the amount of technology market contracts, fiscal expenditure on science and technology, patent application authorizations, tertiary industry output value, and the number of R&D institutions. According to the grey prediction model, the coupling coordination degree is expected to increase from 2022 to 2025, but achieving primary coordination may still be challenging in some areas. Therefore, strategies that utilize regional characteristics for coordinated development should be developed to improve the level of coupling coordination and create a mutually beneficial environment.

A Comprehensive Study on Winter PM2.5 Variation in the Yangtze River Delta: Unveiling Causes and Pollution Transport Pathways

To thoroughly investigate the impact of meteorological conditions and emission changes on winter PM2.5 variation in the Yangtze River Delta (YRD) from 2015 to 2019, we leveraged advanced modeling techniques, namely, the Weather Research and Forecasting (WRF) model and the Nested Air Quality Prediction Model System (NAQPMS). The results revealed that a notable trend of high-PM2.5-concentration regions shifted from coastal areas towards to the inland regions. While emission reduction can effectively reduce the concentration of PM2.5, meteorological changes exert a significant impact on PM2.5 concentration. Unfavorable meteorological changes in 2018 and 2019 emerged as crucial factors driving PM2.5 pollution in the region (up 0~50 µg·m−3). Our findings also shed light on the potential sources and transport pathways of PM2.5 pollution in key cities within the YRD, indicating that the coastal channel of Hebei–Shandong–Jiangsu and the inland channel bordering Hebei, Henan, Shandong, and Anhui serve as major contributors. Light and moderate pollution was predominantly influenced by the medium-distance coastal channel (48~70%). Remarkably, short-distance inland (19~54%) and coastal transportation (33~53%) channels emerged as the primary causes of severe PM2.5 pollution in the YRD. To effectively combat this issue, it is imperative to bolster key control and prevention measures in these regions.