Influences of population pressure change on vegetation greenness in China's mountainous areas

Spatial constraints or spatial dynamics? The spatial spillover effect of networks of flood regulation service flows on land-use degree

In the face of frequent floods under climate and environmental changes, it is particularly important to measure the supply and demand of flood regulation services. Using the Hainan Island as an illustrative case, this study constructs a spatial spillover model to examine the spatial correlation mode and evolution of regional land-use degree through the network of ecosystem service flow. The research results show that forests, grasslands, and reservoirs function as the primary suppliers of flood regulation services, with forests contributing significantly to the regulation of floods. High flood risk was identified in the eastern, northern, and western regions of the Hainan Island, corresponding to increased demand for flood regulation services in croplands, towns, and rural settlements within these areas. The flow of flood regulation services within the Hainan Island was found to be directed from the center to the surrounding areas, with medium and high service flows predominantly concentrated in the northern and surrounding regions. The degree of land use on the Hainan Island demonstrated an influence on socio-economic development. Additionally, the flow network of ecological services was identified as a crucial factor in spatial spillovers, reflecting the level of interaction between county units.

Environmental quality and its impact on total fertility rate: an econometric analysis from a new perspective

BACKGROUND

Environmental quality significantly affects various aspects of human existence. This study employs ecological footprint as a proxy to assess the impact of environmental quality on the TFR, measured as births per woman. This study investigates the extent to which ecological footprint indicators impact on the TFR in across 31 countries between from 1990 to 2017.

METHODS

We gathered data on ecological footprints, specifically carbon, agricultural land, grazing land, forest products, and fisheries, from the Global Footprint Network. Information on the TFR, Human Development Index (HDI), and per capita Gross National Income (GNI) were sourced from the World Bank and the United Nations. We applied static panel and quantile regression models to scrutinize the connection between the ecological footprint and TFR, showing how the former influences the latter.

RESULTS

The outcomes reveal that, in both fixed and random effects models, factors including HDI, carbon, and fishing grounds exert a negative influence on TFR, all at a significance level of p < 0.01. Conversely, cropland and forest product footprints exhibited a favorable impact on the TFR (p < 0.01). Furthermore, GNI per capita positively affected the TFR in both models, with a p-value of 0.01. Quantiles regression analysis demonstrated that HDI and carbon footprint had a negative impact on TFR across all quantiles. This statistical significance is maintained for all quantiles, although it is only significant for the carbon footprint up to the 60th quantile, at p < 0.01.

CONCLUSIONS

This study establishes a negative correlation between specific ecological footprint indicators, such as carbon and fishing grounds, and TFR. Conversely, there was a positive correlation between the footprint of forest products and the TFR. The primary conclusion drawn is that there is heterogeneity in the results regarding the relationship between ecological footprint and TFR. Moreover, the ecological footprint indicators considered in this study did not uniformly influence TFR. Each ecological footprint indicator exhibited distinct effects on the TFR, displaying either positive or negative correlation coefficients. Future research endeavors may delve into how ecological footprints impact other population dynamics, such as mortality and migration.

DOM Associates with Greenhouse Gas Emissions in Chinese Rivers under Diverse Land Uses

Growing evidence indicates that rivers are hotspots of greenhouse gas (GHG) emissions and play multiple roles in the global carbon budget. However, the roles of terrestrial carbon from land use in river GHG emissions remain largely unknown. We studied the microbial composition, dissolved organic matter (DOM) properties, and GHG emission responses to different landcovers in rivers (n = 100). The bacterial community was mainly constrained by land-use intensity, whereas the fungal community was mainly controlled by DOM chemical composition (e.g., terrestrial DOM with high photoreactivity). Anthropogenic stressors (e.g., land-use intensity, gross regional domestic product, and total population) were the main factors affecting chromophoric DOM (CDOM). DOM biodegradability exhibited a positive correlation with CDOM and contributed to microbial activity for DOM transformation. Variations in CO2 and CH4 emissions were governed by the biodegradation or photomineralization of dissolved organic carbon derived from autotrophic DOM and were indirectly affected by land use via changes in DOM properties and water chemistry. Because the GHG emissions of rivers offset some of the climatic benefits of terrestrial carbon (or ocean) sinks, intensified urban land use inevitably alters carbon cycling and changes the regional microclimate.

Influence of Urban Agglomeration Expansion on Fragmentation of Green Space: A Case Study of Beijing-Tianjin-Hebei Urban Agglomeration

Loss of green space habitats and landscape fragmentation are important reasons for the decline in environmental quality, degradation of ecosystem functions, and decline in biodiversity. Quantifying the spatio-temporal characteristics of landscape fragmentation of green space and its relationship with urban expansion mode is an important basis for improving urban development mode and enhancing urban ecological functions. For this paper, we took the Beijing–Tianjin–Hebei (BTH) urban agglomeration as the research object, a typical rapidly urbanizing area. Through multi-scale landscape pattern analysis and statistical analysis, the spatial–temporal evolution characteristics of green space fragmentation in the BTH urban agglomeration from 2000 to 2020 and the influence of urban expansion were analyzed, and the land-use situation in 2030 was predicted by the Future Land Use Simulation (FLUS) model. The main conclusions are as follows: The BTH urban agglomeration has developed rapidly in the last 20 years, showing the characteristics of diffusion and corridor development. The intensity and pattern of urban expansion have significantly affected the pattern of green space, leading to the intensification of domestic green space fragmentation. Among them, urban expansion exerts most severe effects on the fragmentation of farmland, followed by grassland and water. The influence of urban expansion on the scale and fragmentation of forestland is limited. The forecast results in 2030 show that built-up areas may continue to occupy green space. The rate of occupation of farmland will slow down while that of grassland will intensify.

Is the grass always greener? Land surface phenology reveals differences in peak and season-long vegetation productivity responses to climate and management

Vegetation phenology-the seasonal timing and duration of vegetative phases-is controlled by spatiotemporally variable contributions of climatic and environmental factors plus additional potential influence from human management. We used land surface phenology derived from the Advanced Very High Resolution Radiometer and climate data to examine variability in vegetation productivity and phenological dates from 1989 to 2014 in the U.S. Northwestern Plains, a region with notable spatial heterogeneity in climate, vegetation, and land use. We first analyzed interannual trends in six phenological measures as a baseline. We then demonstrated how including annual-resolution predictors can provide more nuanced insights into measures of phenology between plant communities and across the ecoregion. Across the study area, higher annual precipitation increased both peak and season-long productivity. In contrast, higher mean annual temperatures tended to increase peak productivity but for the majority of the study area decreased season-long productivity. Annual precipitation and temperature had strong explanatory power for productivity-related phenology measures but predicted date-based measures poorly. We found that relationships between climate and phenology varied across the region and among plant communities and that factors such as recovery from disturbance and anthropogenic management also contributed in certain regions. In sum, phenological measures did not respond ubiquitously nor covary in their responses. Nonclimatic dynamics can decouple phenology from climate; therefore, analyses including only interannual trends should not assume climate alone drives patterns. For example, models of areas exhibiting greening or browning should account for climate, anthropogenic influence, and natural disturbances. Investigating multiple aspects of phenology to describe growing-season dynamics provides a richer understanding of spatiotemporal patterns that can be used for predicting ecosystem responses to future climates and land-use change. Such understanding allows for clearer interpretation of results for conservation, wildlife, and land management.

Modeling Spatiotemporal Population Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in Chongqing, China

The sustained growth of non-farm wages has led to large-scale migration of rural population to cities in China, especially in mountainous areas. It is of great significance to study the spatial and temporal pattern of population migration mentioned above for guiding population spatial optimization and the effective supply of public services in the mountainous areas. Here, we determined the spatiotemporal evolution of population in the Chongqing municipality of China from 2000–2018 by employing multi-period spatial distribution data, including nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). There was a power function relationship between the two datasets at the pixel scale, with a mean relative error of NTL integration of 8.19%, 4.78% less than achieved by a previous study at the provincial scale. The spatial simulations of population distribution achieved a mean relative error of 26.98%, improved the simulation accuracy for mountainous population by nearly 20% and confirmed the feasibility of this method in Chongqing. During the study period, the spatial distribution of Chongqing’s population has increased in the west and decreased in the east, while also increased in low-altitude areas and decreased in medium-high altitude areas. Population agglomeration was common in all of districts and counties and the population density of central urban areas and its surrounding areas significantly increased, while that of non-urban areas such as northeast Chongqing significantly decreased.

North American historical monthly spatial climate dataset, 1901-2016

We present historical monthly spatial models of temperature and precipitation generated from the North American dataset version "j" from the National Oceanic and Atmospheric Administration's (NOAA's) National Centres for Environmental Information (NCEI). Monthly values of minimum/maximum temperature and precipitation for 1901-2016 were modelled for continental United States and Canada. Compared to similar spatial models published in 2006 by Natural Resources Canada (NRCAN), the current models show less error. The Root Generalized Cross Validation (RTGCV), a measure of the predictive error of the surfaces akin to a spatially averaged standard predictive error estimate, averaged 0.94 °C for maximum temperature models, 1.3 °C for minimum temperature and 25.2% for total precipitation. Mean prediction errors for the temperature variables were less than 0.01 °C, using all stations. In comparison, precipitation models showed a dry bias (compared to recorded values) of 0.5 mm or 0.7% of the surface mean. Mean absolute predictive errors for all stations were 0.7 °C for maximum temperature, 1.02 °C for minimum temperature, and 13.3 mm (19.3% of the surface mean) for monthly precipitation.

Decoupling of Land Use Intensity and Ecological Environment in Gansu Province, China

Land is the carrier of the production and living activities of human society and the basis of survival and development of all living organisms. With the continuous development of the social economy, the unreasonable use of land is becoming more and more serious, aggravating the deterioration of the ecological environment. Most studies in this field have mainly focused on land use changes and the corresponding impacts on the ecological environment, but relatively few studies have delinked the relationship between land use intensity and the ecological environment. Based on data on these two factors for Gansu Province from 1998 to 2017, we used the Tapio decoupling model to evaluate the decoupling relationship between land use intensity and ecological environment. From 1998 to 2017, the comprehensive land use intensity in Gansu province increased by 107.77%, and the comprehensive ecological environment index increased by 63.76%. In general, the relationship between land use intensity and ecological environment experienced five states, namely weak decoupling, strong negative decoupling, strong decoupling, expansive negative decoupling, and declining decoupling. During 1999–2013 and 2013–2016, land use intensity and ecological environment had decoupled, and the main reasons were as follows: (1) The Chinese government introduced a series of farmland protection policies and measures, controlled the area of newly added construction land, and reduced urban land expansion; (2) ecological restoration projects for mountains, forests, fields, lakes, and grassland strengthened the environmental protection in Gansu Province; and (3) in the process of economic development, the increased investment of technology and capital improved the land use efficiency, finally realizing the “double growth” of land use intensity and environmental quality. Based on these results, land use intensity and environmental quality are not necessarily contradictory, and a moderate improvement of land use efficiency and environmental protection can probably result in increased land use intensity and higher environmental quality.

Spatiotemporal variations of vegetation and its determinants in the National Key Ecological Function Area on Loess Plateau between 2000 and 2015

China defined 25 National Key Ecological Function Areas in 2010 and adopted various measures to support ecosystem restoration in these areas. During the process of environment policymaking, it is important to observe the variation of vegetation and its driving factors. In this paper, we chose the National Key Ecological Function Area (NKEFA) on Loess Plateau as the study area. Based on MODIS-NDVI data between 2000 and 2015, the trend analysis was used to depict the change in NDVI and the stepwise regression analysis method was used to quantitatively assess its determinants. The results show that: (a) The vegetation coverage in study area was low in the northwest and high in the southeast, corresponding to the distribution of precipitation and temperature. (b) NDVI in the growing season increased remarkably from 0.2841 in 2000 to 0.4199 in 2015 with a linear tendency of 0.085/10a. About 71.22% of the study area experienced an extremely significant increasing of NDVI, while only 0.03% of the total area suffered from significant decreasing of NDVI. (c) Compared to climatic factors, ecosystem conservation policies, and labor transfer contributed more to the vegetation changes in the study area. In order to ensure ecological security and sustainable development in these areas, it is necessary to maintain the continuity of ecological compensation policy. Moreover, developing targeted eco-compensation policies and encouraging farmers to participate in nonfarm employment are effective ways to reach a win-win outcome of reducing the ecosystem pressure and improving the welfare of rural households.

Response of net primary production to land use and climate changes in the middle-reaches of the Heihe River Basin

Net primary production (NPP) supplies matter, energy, and services to facilitate the sustainable development of human society and ecosystem. The response mechanism of NPP to land use and climate changes is essential for food security and biodiversity conservation but lacks a comprehensive understanding, especially in arid and semi-arid regions. To this end, taking the middle-reaches of the Heihe River Basin (MHRB) as an example, we uncovered the NPP responses to land use and climate changes by integrating multisource data (e.g., MOD17A3 NPP, land use, temperature, and precipitation) and multiple methods. The results showed that (a) land use intensity (LUI) increased, and climate warming and wetting promoted NPP. From 2000 to 2014, the LUI, temperature, and precipitation of MHRB increased by 1.46, 0.58°C, and 15.76 mm, respectively, resulting in an increase of 14.62 gC/m2 in annual average NPP. (b) The conversion of low-yield cropland to forest and grassland increased NPP. Although the widespread conversion of unused land and grassland to cropland boosted both LUI and NPP, it was not conducive to ecosystem sustainability and stability due to huge water consumption and human-appropriated NPP. Urban sprawl occupied cropland, forest, and grassland and reduced NPP. (c) Increase in temperature and precipitation generally improved NPP. The temperature decreasing <1.2°C also promoted the NPP of hardy vegetation due to the simultaneous precipitation increase. However, warming-induced water stress compromised the NPP in arid sparse grassland and deserts. Cropland had greater NPP and NPP increase than natural vegetation due to the irrigation, fertilizers, and other artificial inputs it received. The decrease in both temperature and precipitation generally reduced NPP, but the NPP in the well-protection or less-disturbance areas still increased slightly.

Influences of vertical differences in population emigration on mountainous vegetation greenness: A case study in the Taihang Mountains

With the rapid advance of urbanization, rural population emigration has become a key factor that affects the man-land relationship in China's mountainous areas and may have a huge impact on ecological restoration. This study used the NDVI in the growing seasons to analyze the variation trend of vegetation greenness at different elevations in the Taihang Mountains during 2000-2010, employing trend analysis method. Then, we selected 990 samples, each of which was a circular area with a radius of 3 km. On this basis, we quantitatively analyzed the contribution degree of population emigration to this variation trend after eliminating the influences of precipitation, temperature, and other factors. The results showed that rural population emigration was significant in the Taihang Mountains in the past 10 years, with a rural population emigration rate of up to 16.3%; The vegetation in the Taihang Mountains presented a trend of overall improvement, but local deterioration; The results of the regression analysis showed that population emigration had significantly impacts on vegetation greenness at 1% significance level and 1% of population emigration can increase the NDVI variation trend by 0.06%. Furthermore, the impact gradually weakened with increasing elevation.

Influences of population pressure change on vegetation greenness in China's mountainous areas

Mountainous areas in China account for two‐thirds of the total land area. Due to rapid urbanization, rural population emigration in China's mountainous areas is very significant. This raises the question to which degree such population emigration influences the vegetation greenness in these areas. In this study, 9,753 sample areas (each sample measured about 64 square kilometers) were randomly selected, and the influences of population emigration (population pressure change) on vegetation greenness during 2000–2010 were quantitatively expressed by the multivariate linear regression (MLR) model, using census data under the condition of controlling the natural elements such as climatic and landform factors. The results indicate that the vegetation index in the past 10 years has presented an increasing overall trend, albeit with local decrease in some regions. The combined area of the regions with improved vegetation accounted for 81.7% of the total mountainous areas in China. From 2000 to 2010, the rural population significantly decreased, with most significant decreases in the northern and central areas (17.2% and 16.8%, respectively). In China's mountainous areas and in most of the subregions, population emigration has significant impacts on vegetation change. In different subregions, population decrease differently influenced vegetation greenness, and the marginal effect of population decrease on vegetation change presented obvious differences from north to south. In the southwest, on the premise of controlling other factors, a population decrease by one unit could increase the slope of vegetation change by 16.4%; in contrast, in the southeastern, northern, northeastern, and central area, the proportion was about 15.5%, 10.6%, 9.7%, and 7.5%, respectively, for improving the trend of NDVI variation.