The alpine wetlands in QTP (Qinghai-Tibetan Plateau) have been profoundly impacted along with global climate changes. We employ satellite datasets and climate data to explore the relationships between alpine wetlands and climate changes based on remote sensing data. Results show that:1) the wetland NDVI (Normalized Difference Vegetation Index) and GPP (Gross Primary Production) were more sensitive to air temperature than to precipitation rate. The wetland ET (evapotranspiration) across alpine wetlands was greatly correlated with precipitation rate. 2) Alpine wetlands responses to climate changes varied spatially and temporally due to different geographic environments, variety of wetland formation and human disturbances. 3) The vegetation responses of the Zoige wetland was the most noticeable and related to the temperature, while the GPP and NDVI of the Qiangtang Plateau and Gyaring-Ngoring Lake were significantly correlated with both temperature and precipitation. 4) ET in the Zoige wetland showed a significantly positive trend, while ET in Maidika wetland and the Qiangtang plateau showed a negative trend, implying wetland degradation in those two wetland regions. The complexities of the impacts of climate changes on alpine wetlands indicate the necessity of further study to understand and conserve alpine wetland ecosystems.

The rapid invasion of the plant Spartina alterniflora in coastal wetland areas can threaten the capacity of their soils to store carbon (C), nitrogen (N), and sulfur (S). In this study, we investigated the spatial and temporal distribution patterns of C, N and S of both soil and (native and invasive) plants in four typical coastal wetlands in the core area of the Yancheng National Nature Reserve, China. The results show that the invasive S. alterniflora greatly influenced soil properties and increased soil C, N and S storage capacity:the stock (mean ±standard error) of soil organic carbon (SOC, (3.56 ±0.36) kg/m³), total nitrogen (TN, (0.43 ±0.02) kg/m³), and total sulfur (TS, (0.69 ±0.11) kg/m³) in the S. alterniflora marsh exceeded those in the adjacent bare mudflat, Suaeda salsa marsh, and Phragmites australis marsh. Because of its greater biomass, plant C ((1193.7 ±133.6) g/m²), N ((18.8 ±2.4) g/m²), and S ((9.4 ±1.5) g/m²) storage of S. alterniflora was also larger than those of co-occurring native plants. More biogenic elements circulated in the soil-plant system of the S. alterniflora marsh, and their spatial and temporal distribution patterns were also changed by the S. alterniflora invasion. Soil properties changed by S. alterniflora's invasion thereby indirectly affected the accumulation of soil C, N and S in this wetland ecosystem. The SOC, TN, and TS contents were positively correlated with soil electrical conductivity and moisture, but negatively correlated with the pH and bulk density of soil. Together, these results indicate that S. alterniflora invasion altered ecosystem processes, resulted in changes in net primary production and litter decomposition, and increased the soil C, N and S storage capacity in the invaded ecosystems in comparison to those with native tallgrass communities in the coastal wetlands of East China.

Multiple natural and human factors in estuarine wetlands result in complicated land surface characteristics with distinct spatial and temporal heterogeneities, thereby contributing to the difficulty in identifying spatiotemporal variations and influencing factors of plant diversity. A unique estuarine wetland gradient system (UEWGS) consisting of soil, vegetation, heat, distance, landscape, and anthropogenic gradients was established based on the ecological features of estuarine wetland through remote sensing and field investigation methods. It resolved the complicated land surface characteristics, covered all aspects of factors influencing plant diversity, and possessed distinct spatiotemporal heterogeneities. The Yellow River Delta, the largest estuarine wetland in the northern China, was selected as the study area to demonstrate UEWGS in four seasons in 2017. A total of 123 species were recorded with considerable seasonal difference. Phragmites australis, Suaeda salsa, and Tamarix chinensis were the dominant species, and crop species also played important roles. In single effect, all aspects of gradients exerted significant influences, yet only vegetation gradient possessed significant influences in all seasons. In comprehensive effect, soil, vegetation, heat, and distance gradients showed significant gross influences. Moisture content in soil gradient and net primary productivity in vegetation gradient possessed significant net influences in all seasons and can be considered as the main driving factor and indicator, respectively, of plant diversity. The results validated the significance of UEWGS in revealing the plant diversity spatiotemporal characteristics and influencing factors, and UEWGS possessed universal applicability in the spatiotemporal analysis of plant diversity in estuarine areas.

Climate changes are likely to increase the risk of numerous extreme weather events throughout the world. The objectives of this study were to investigate and analyze the temporal-spatial variability patterns of temperature extremes based on daily maximum (TX) and minimum temperature (TN) data collected from 49 meteorological stations in Xinjiang of China during 1960-2015. These temperature data were also used to assess the impacts of altitude on the temperature extremes. Additionally, possible teleconnections with the large-scale circulation pattern (the El Nino-Southern Oscillation, ENSO and Arctic Oscillation, AO) were investigated. Results showed that all percentile indices had trends consistent with warming in most parts of Xinjiang during 1960-2015, but the warming was more pronounced for indices derived from TN compared to those from TX. The minimum TN and maximum TX increased at rates of 0.16℃/10 yr and 0.59℃/10 yr, respectively during 1960-2015. Accordingly, the diurnal temperature range showed a significant decreasing trend of -0.23℃/10 yr for the whole study area. The frequency of the annual average of the warm events showed significant increasing trends while that of the cold events presented decreasing trends. Over the same period, the number of frost days showed a statistically significant decreasing trend of -3.37 d/10 yr. The number of the summer days and the growing season showed significant increasing trends at rates of 1.96 and 2.74 d/10 yr, respectively. The abrupt change year of each index was from the 1980s to the 1990s, showing that this periodic interval was a transitional phase between cold and warm climate change. Significant correlations of temperature extremes and elevation included the trends of tropical nights, growing season frequency, and cold spell duration indicator. This result also indicated the clear and complex local influence on climatic extremes. In addition, the relationship between each index of the temperature extremes with large-scale atmospheric circulation (ENSO and AO) demonstrated that the influence of ENSO on each index of the temperature extremes was greater than that of the AO in Xinjiang.

Xinjiang is located in the core China's ‘Belt and Road’ development, and northern Xinjiang is an important region for economic development. In recent years, due to the strong influence of global climate change and human disturbance, regional climate instability and ecological-economic-social system sensitivity have grown. In this paper, seasonal, interannual, interdecadal, spatial, abrupt, and periodic variations of temperature and precipitation in northern Xinjiang were analyzed using daily surface air temperature and precipitation data from 49 meteorological stations during 1961-2017. At the same time, the driving factors of climate change are discussed. Methods included linear regression, cumulative anomaly, the Mann-Kendall test, and Morlet wavelet analysis. The results indicated that during the study period, annual mean temperature and annual precipitation increased significantly at rates of 0.35℃/10 yr and 13.25 mm/10 yr, respectively, with abrupt changes occurring in 1994 and 1986. Annual mean temperature and annual precipitation in all four seasons showed increasing trends, with the maximum increases in winter of 0.42℃/10 yr and 3.95 mm/10 yr, respectively. The general climate in northern Xinjiang showed a trend towards increasingly warm and humid. In terms of spatial distribution, the temperature and precipitation in high mountainous areas increased the most, while basins areas increased only slightly. Periodic change analysis showed that annual mean temperature and annual precipitation experienced two climatic shifts from cold to warm and dry to wet, respectively. Population change, economic development and land use change are important factors affecting climate change, and more research should be done in this field.

The burning of crop residues emits large quantities of atmospheric aerosols. Published studies have developed inventories of emissions from crop residue burning based on statistical data. In contrast, this study used satellite-retrieved land-cover data (1 km×1 km) as activity data to compile an inventory of atmospheric pollutants emitted from the burning of crop residues in China in 2015. The emissions of PM₁₀, PM_2.5, VOCs, NO_x, SO₂, CO, and NH₃ from burning crop straw on nonirrigated farmland in China in 2015 were 610.5, 598.4, 584.4, 230.6, 35.4, 3329.3, and 36.1 Gg (1 Gg=10⁹ g), respectively; the corresponding emissions from burning paddy rice residues were 234.1, 229.7, 342.3, 57.5, 57.5, 1122.1, and 21.5 Gg, respectively. The emissions from crop residue burning showed large spatial and temporal variations. The emissions of particulate matter and gaseous pollutants from crop residue burning in nonirrigated farmland were highest in east China, particularly in Shandong, Henan, Anhui, and Sichuan provinces. Emissions from burning paddy rice residue were highest in east and central China, with particularly high levels in Shandong, Jiangsu, Zhejiang, and Hunan provinces. The monthly variations in atmospheric pollutant emissions were similar among different regions, with the highest levels observed in October in north, northeast, northwest, east, and southwest China and in June and July in central and south China. The developed inventory of emissions from crop residue burning is expected to help improve air quality models by providing high-resolution spatial and temporal data.

The relationship between livelihood diversification of farm households and cultivated land utilization has become a core research topic related to global environmental change. Agro-pastoral ecologically-vulnerable areas face challenges such as insufficient ecosystem conservation, low agricultural production, and weak economies. In this study, 215 farm households from Zhengxiangbai Banner, Taibus Banner, and Duolun County of Inner Mongolia were surveyed. The sustainable livelihoods framework of the United Kingdom (UK) Department for International Development (DFID) was used to measure the livelihood capital of these farm households. A one-way analysis of variance (ANOVA) was applied to examine the differences in the livelihood capital of different types households, and a correlation analysis was applied to analyze its impact on cultivated land utilization. Results showed that households with non-farming activities accounted for 64.7% of the total surveyed households, and non-farming employment was becoming more prevalent. Physical and financial capital was the driving factors for livelihood diversity. Each livelihood capital had key factors that affected household farmland use behaviors, such as the age of householder, the labor ratio, proportion of income, farmland scale, number of machines, and these had a significantly positive or negative influence on farmland use. Full-time farming households were more likely to transfer the land into cultivation and invest more labor, while non-farming households with high income were likely to transfer farmland out and invest more money to develop efficient farming or improve the employment skills. The results of this study suggest that policymakers need to fully consider livelihood changes of local households. It is effective to strengthen labor training, create farmland market and improve the efficiency of farmland utilization. We hope to achieve a win-win scenario to improve local economies and ecosystem conservation.

Urban particulate matter 2.5 (PM_2.5) pollution and public health are closely related, and concerns regarding PM_2.5 are widespread. Of the underlying factors, the urban morphology is the most manageable. Therefore, investigations of the impact of urban three-dimensional (3D) morphology on PM_2.5 concentration have important scientific significance. In this paper, 39 PM_2.5 monitoring sites of Beijing in China were selected with PM_2.5 automatic monitoring data that were collected in 2013. This data set was used to analyze the impacts of the meteorological condition and public transportation on PM_2.5 concentrations. Based on the elimination of the meteorological conditions and public transportation factors, the relationships between urban 3D morphology and PM_2.5 concentrations are highlighted. Ten urban 3D morphology indices were established to explore the spatial-temporal correlations between the indices and PM_2.5 concentrations and analyze the impact of urban 3D morphology on the PM_2.5 concentrations. Results demonstrated that road length density (RLD), road area density (RAD), construction area density (CAD), construction height density (CHD), construction volume density (CVD), construction otherness (CO), and vegetation area density (VAD) have positive impacts on the PM_2.5 concentrations, whereas water area density (WAD), water fragmentation (WF), and vegetation fragmentation (VF) (except for the 500 m buffer) have negative impacts on the PM_2.5 concentrations. Moreover, the correlations between the morphology indices and PM_2.5 concentrations varied with the buffer scale. The findings could lay a foundation for the high-precision spatial-temporal modelling of PM_2.5 concentrations and the scientific planning of urban 3D spaces by authorities responsible for controlling PM_2.5 concentrations.

This paper aims to examine the effect of agglomeration on firm level productivity in Iran's food manufacturing by employing a firm level dataset during 1986-2015 among firms for four districts. The empirical results show that agglomeration in north districts are key factors in productivity growth. In this work, we apply a spatial Bayes model that uses hierarchical techniques during the three terms. The productivity clustering map is able to capture such patterns as the high productivity area that appears in the south, north districts of Iran. This paper evaluates the effect of agglomeration on firm productivity in Iran's food industries at district level. We find that regional market potential is the strong predictor of productivity; moreover, industrial agglomeration has a productivity-augmenting impact.

As innovation and technological change have become increasingly important for the competitiveness and sustainable growth of firms, cooperative innovation is now crucial for traditional industries in the context of globalization. This paper proposes a framework for analyzing the spatial pattern of cooperative innovation for traditional industries in developing countries. Based on in-depth interviews with 35 firms in the oil equipment manufacturing industry in Dongying City, China, this study argues that different firms in the innovation pyramid have various innovation activity preferences and spatial patterns. Firms with high innovation abilities tend to cooperate with various partners that are geographically dispersed and continuously expanding, while firms with inferior abilities usually cooperate with nearby fixed partners. Due to the differences in innovation environment and actor locations, firms tend to make different choices regarding innovation types and models, which highlight the importance of personnel training and basic scientific research at the global scale and practical product research and development at the national scale. Additionally, talent flow is the most important way to realize relationships for firm innovation activity.

The economic transformation of the old industrial bases is a key research topic among geographers in China. In this paper, we propose that the concept of regional economic resilience (RER) has unique theoretical value in analyzing the economic transformation of the old industrial bases. We constructed an analytical framework and an index system and applied the conceptual tools to study the evolution of RER in the old industrial base of Liaoning Province in China, which is currently subjected to not only sudden shocks but ‘slow burn’-longer term processes of change that may nevertheless affect the regional economy. There are four main findings:first, the evolution of RER in Liaoning can be divided into four stages from 2000 to 2015. Liaoning is currently in its conservation-release period, and the next stage will be a release-reorganization period. Second, the RER of the majority of the studied cities is lower than the average value for Liaoning, and this is mainly attributed to the relatively weak vulnerability-resistance and adaptability-transformation capacity of these cities. Third, the RER levels of the 14 cities in Liaoning differ significantly. At the first level is Shenyang and Dalian, at the second level is Dandong and Yingkou, and the third level comprises the remaining cities. Fourth, regional economic resilience is mainly determined by vulnerability-resistance, which indirectly reflects Liaoning's lack of adaptability-transformation capacity, and the ability of the region to renew or create a new development path is weak.

This study analyzed the spatial-temporal heterogeneity of green development efficiency and its influencing factors in the growing Xuzhou Metropolitan Area for the period 2000-2015. The slacks-based measure (SBM) model, spatial autocorrelation, and the geographically weighted regression (GWR) model were used to conduct the analysis. The conclusions were as follows:first, the overall efficiency of green development of the Xuzhou Metropolitan Area decreased, the regional differences and spatial agglomeration shrunk and differences within the region were the main contributors to the regional differences of green development efficiency. Second, the counties with high-efficiency green development were distributed along the coast, and along the routes of the Beijing-Shanghai and the Eastern Longhai railways. A developing axis of the high-efficiency counties was the main feature of the spatial pattern for green development efficiency. Third, regarding spatial correlation and green development efficiency, the High-High type counties in the Xuzhou Metropolitan Area formed a centralized distribution corridor along the inter-provincial border areas of Henan and Jiangsu, whereas the Low-Low type counties were concentrated in the external, marginal parts of the metropolitan area. Fourth, the major factors (ranked in decreasing order of impact) influencing green development efficiency were innovation, government regulations, the economic development level, energy consumption, and industrial structure. These factors exerted their influence to varying extents; the influence of the same factor had different effects in different regions and obvious spatial differences were observed for the different regions.

The emergence of rapid transit, primarily represented by high-speed railway (HSR), while reshaping the regional traffic patterns, leads to the reconstruction and redistribution of population and industry. This leads to either shrinkage or expansion of urban scale. However, research on the influence mechanisms of the urban scale has mostly concentrated on historical, economic and social factors. The influence of traffic factors is rarely mentioned in current research. Therefore, this study examines Northeast China, where the change in urban scale is most significant, to discuss the spatial impact of high-speed railway on the urban scale. This is of great significance in terms of enriching current understanding of the factors affecting the urban scale. The results included the following:1) The high-speed railway produced considerable space-time convergence effects, however, simultaneously aggravated the imbalance in traffic development in Northeast China. The increase in accessibility presents attenuation characteristics from the high-speed railway. Additionally, the high-speed railway has changed the mode of cooperation between cities in the provinces, inter-regional and inter-provincial cooperation models gradually become popular. 2) The change rate of accessibility and the urban scale present significant spatial coupling phenomena, with the change rate of the Harbin-Dalian trunk lines and its surroundings being more significant. 3) There are predominantly four modes of the influence of high-speed railway on the urban scale, which make difference city present expansion or shrinkage.