Current Issue
2022, Volume 32, Issue 5
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2022,
32(5):
745-758.
doi: 10.1007/s11769-022-1298-9
Abstract:
Building a Green Silk Road by integrating the Sustainable Development Goals (SDGs) is one of the Belt and Road Initiative (BRI) visions, but the BRI faces enormous challenge that is the conflict between economic development and ecological sustainability. Understanding the current scale and trend of the impact of human activities on the ecosystem is the preliminary work to ensure that human activities do not exceed the ecological carrying capacity under the BRI. This study evaluated the ecosystem pressure in countries along the Belt and Road (B&R) from 2000–2017 based on the supply-consumption balance relationship of ecological resources. Net primary productivity (NPP) is taken as the measure of ecological resources, and the supply level and consumption intensity of ecological resources is estimated based on remote sensing data and statistical data, respectively. Results show that thirteen countries with overconsumed ecological resources concentrated in the West Asia/Middle East. Although the intensity of the ecological resource consumption correlated with ecological resource endowments, the ecosystem pressure was determined by social development dependence on the ecological resources at the same ecological resource endowments level. Nearly 80% of countries along the B&R suffered from significantly increased (P < 0.05) ecosystem pressure during 2000–2017, since most of the countries along the B&R were developing countries, and their economic development was highly dependent on ecological resources. Some West Asia/Middle East countries successfully mitigated the ecosystem pressure by importing feed for livestock. Likewise, the Southeast Asian islands benefitted from the import of agricultural products. The results highlight that the BRI should reduce the dependence of social development demands on local ecological resources by international trade for ensuring the increasing ecosystem pressure trend within the ecological carrying capacity.
Building a Green Silk Road by integrating the Sustainable Development Goals (SDGs) is one of the Belt and Road Initiative (BRI) visions, but the BRI faces enormous challenge that is the conflict between economic development and ecological sustainability. Understanding the current scale and trend of the impact of human activities on the ecosystem is the preliminary work to ensure that human activities do not exceed the ecological carrying capacity under the BRI. This study evaluated the ecosystem pressure in countries along the Belt and Road (B&R) from 2000–2017 based on the supply-consumption balance relationship of ecological resources. Net primary productivity (NPP) is taken as the measure of ecological resources, and the supply level and consumption intensity of ecological resources is estimated based on remote sensing data and statistical data, respectively. Results show that thirteen countries with overconsumed ecological resources concentrated in the West Asia/Middle East. Although the intensity of the ecological resource consumption correlated with ecological resource endowments, the ecosystem pressure was determined by social development dependence on the ecological resources at the same ecological resource endowments level. Nearly 80% of countries along the B&R suffered from significantly increased (P < 0.05) ecosystem pressure during 2000–2017, since most of the countries along the B&R were developing countries, and their economic development was highly dependent on ecological resources. Some West Asia/Middle East countries successfully mitigated the ecosystem pressure by importing feed for livestock. Likewise, the Southeast Asian islands benefitted from the import of agricultural products. The results highlight that the BRI should reduce the dependence of social development demands on local ecological resources by international trade for ensuring the increasing ecosystem pressure trend within the ecological carrying capacity.
2022,
32(5):
759-775.
doi: 10.1007/s11769-022-1299-8
Abstract:
The grassland in the Hindu Kush Himalayan (HKH) region is one of the largest and most biodiverse mountain grassland types in the world, and its ecosystem service functions have profound impacts on the sustainable development of the HKH region. Monitoring the spatiotemporal distribution of grassland aboveground biomass (AGB) accurately and quantifying its response to climate change are indispensable sources of information for sustainably managing grassland ecosystems in the HKH region. In this study, a pure vegetation index model (PVIM) was applied to estimate the long-term dynamics of grassland AGB in the HKH region during 2000–2018. We further quantified the response of grassland AGB to climate change (temperature and precipitation) by partial correlation and variance partitioning analyses and then compared their differences with elevation. Our results demonstrated that the grassland AGB predicted by the PVIM had a good linear relationship with the ground sampling data. The grassland AGB distribution pattern showed a decreasing trend from east to west across the HKH region except in the southern Himalayas. From 2000 to 2018, the mean AGB of the HKH region increased at a rate of 1.57 g/(m2·yr) and ranged from 252.9 (2000) to 307.8 g/m2 (2018). AGB had a positive correlation with precipitation in more than 80% of the grassland, and temperature was positively correlated with AGB in approximately half of the region. The change in grassland AGB was more responsive to the cumulative effect of annual precipitation, while it was more sensitive to the change in temperature in the growing season; in addition, the influence of climate varied at different elevations. Moreover, compared with that of temperature, the contribution of precipitation to grassland AGB change was greater in approximately 60% of the grassland, but the differences in the contribution for each climate factor were small between the two temporal scales at elevations over 2000 m. An accurate assessment of the temporal and spatial distributions of grassland AGB and the quantification of its response to climate change are of great significance for grassland management and sustainable development in the HKH region.
The grassland in the Hindu Kush Himalayan (HKH) region is one of the largest and most biodiverse mountain grassland types in the world, and its ecosystem service functions have profound impacts on the sustainable development of the HKH region. Monitoring the spatiotemporal distribution of grassland aboveground biomass (AGB) accurately and quantifying its response to climate change are indispensable sources of information for sustainably managing grassland ecosystems in the HKH region. In this study, a pure vegetation index model (PVIM) was applied to estimate the long-term dynamics of grassland AGB in the HKH region during 2000–2018. We further quantified the response of grassland AGB to climate change (temperature and precipitation) by partial correlation and variance partitioning analyses and then compared their differences with elevation. Our results demonstrated that the grassland AGB predicted by the PVIM had a good linear relationship with the ground sampling data. The grassland AGB distribution pattern showed a decreasing trend from east to west across the HKH region except in the southern Himalayas. From 2000 to 2018, the mean AGB of the HKH region increased at a rate of 1.57 g/(m2·yr) and ranged from 252.9 (2000) to 307.8 g/m2 (2018). AGB had a positive correlation with precipitation in more than 80% of the grassland, and temperature was positively correlated with AGB in approximately half of the region. The change in grassland AGB was more responsive to the cumulative effect of annual precipitation, while it was more sensitive to the change in temperature in the growing season; in addition, the influence of climate varied at different elevations. Moreover, compared with that of temperature, the contribution of precipitation to grassland AGB change was greater in approximately 60% of the grassland, but the differences in the contribution for each climate factor were small between the two temporal scales at elevations over 2000 m. An accurate assessment of the temporal and spatial distributions of grassland AGB and the quantification of its response to climate change are of great significance for grassland management and sustainable development in the HKH region.
2022,
32(5):
776-791.
doi: 10.1007/s11769-022-1300-6
Abstract:
Identifying and monitoring the spatiotemporal patterns of potentially contaminated land (PCL) in China is a key concern of ecological governance. However, the dynamics of PCL’s expansion remain unclear nationwide. Integrating high-resolution remote sensing images, a land-use/cover change database, crawler data from websites, and other multisource data, we produced a new dataset of China’s PCL in 1990, 2000, 2010, and 2020 using data fusion technology. Then we analyzed the spatiotemporal patterns of China’s PCL from 1990 to 2020. Our study shows that the acquired vector dataset of China’s PCL is of high quality and reliability, with an overall accuracy of 93.21%. The area of China’s PCL has kept growing for the past 30 years, and the growth rate was especially rapid during 2000–2010, 2.32 and 6.13 times as rapid as that during 1990–2000 and 2010–2020, respectively. PCL has also been trending toward higher aggregation over markedly enlarged areas and has transferred progressively from north and southeast of China to northwest and southwest of China and Qinghai-Tibet Plateau. The patterns of China’s PCL have been driven by the joint factors of policies, mineral resources, economy, and others, among which policies and the economy have contributed more prominently to the long-term transition. Our study promotes the access to high-quality spatial data of PCL to facilitate environmental governance of mine wastes, pollution and land management.
Identifying and monitoring the spatiotemporal patterns of potentially contaminated land (PCL) in China is a key concern of ecological governance. However, the dynamics of PCL’s expansion remain unclear nationwide. Integrating high-resolution remote sensing images, a land-use/cover change database, crawler data from websites, and other multisource data, we produced a new dataset of China’s PCL in 1990, 2000, 2010, and 2020 using data fusion technology. Then we analyzed the spatiotemporal patterns of China’s PCL from 1990 to 2020. Our study shows that the acquired vector dataset of China’s PCL is of high quality and reliability, with an overall accuracy of 93.21%. The area of China’s PCL has kept growing for the past 30 years, and the growth rate was especially rapid during 2000–2010, 2.32 and 6.13 times as rapid as that during 1990–2000 and 2010–2020, respectively. PCL has also been trending toward higher aggregation over markedly enlarged areas and has transferred progressively from north and southeast of China to northwest and southwest of China and Qinghai-Tibet Plateau. The patterns of China’s PCL have been driven by the joint factors of policies, mineral resources, economy, and others, among which policies and the economy have contributed more prominently to the long-term transition. Our study promotes the access to high-quality spatial data of PCL to facilitate environmental governance of mine wastes, pollution and land management.
2022,
32(5):
792-807.
doi: 10.1007/s11769-022-1301-5
Abstract:
Algal blooms in lakes have become a common global environmental problem. Nowadays, remote sensing is widely used to monitor algal blooms in lakes due to the macroscopic, fast, real-time characteristics. However, it is often difficult to distinguish between algal blooms and aquatic vegetation due to their similar spectral characteristics. In this paper, we used modified vegetation presence frequency index (VPF) based on Moderate-resolution Imaging Spectroradiometer (MODIS) imagery to distinguish algal blooms from aquatic vegetation, and analyzed the spatial and temporal variations of algal blooms and aquatic vegetation from a phenological perspective for five large natural lakes with frequent algal bloom outbreaks in China from 2019 to 2020. We simplified the VPF method to make it with a higher spatial transferability so that it could be applied to other lakes in different climatic zones. Through accuracy validation, we found that the modified VPF method can effectively distinguish between algal blooms and aquatic vegetation, and the results vary from lake to lake. The highest accuracy of 97% was achieved in Hulun Lake, where the frequency of algal outbreaks is low and the extent of aquatic vegetation is stable, while the lowest accuracy of 76% was achieved in Dianchi Lake, which is rainy in summer and the lake is small. Analyses suggests that the time period when algal blooms occur most frequently might not coincide with that when they have the largest area. However, in most cases these two are close in terms of time period. The modified VPF method has a broad scope of application, is easy to implement, and has a high practical value. Furthermore, the method could be established using only a small amount of measured data, which is useful for water quality monitoring on large spatial scales.
Algal blooms in lakes have become a common global environmental problem. Nowadays, remote sensing is widely used to monitor algal blooms in lakes due to the macroscopic, fast, real-time characteristics. However, it is often difficult to distinguish between algal blooms and aquatic vegetation due to their similar spectral characteristics. In this paper, we used modified vegetation presence frequency index (VPF) based on Moderate-resolution Imaging Spectroradiometer (MODIS) imagery to distinguish algal blooms from aquatic vegetation, and analyzed the spatial and temporal variations of algal blooms and aquatic vegetation from a phenological perspective for five large natural lakes with frequent algal bloom outbreaks in China from 2019 to 2020. We simplified the VPF method to make it with a higher spatial transferability so that it could be applied to other lakes in different climatic zones. Through accuracy validation, we found that the modified VPF method can effectively distinguish between algal blooms and aquatic vegetation, and the results vary from lake to lake. The highest accuracy of 97% was achieved in Hulun Lake, where the frequency of algal outbreaks is low and the extent of aquatic vegetation is stable, while the lowest accuracy of 76% was achieved in Dianchi Lake, which is rainy in summer and the lake is small. Analyses suggests that the time period when algal blooms occur most frequently might not coincide with that when they have the largest area. However, in most cases these two are close in terms of time period. The modified VPF method has a broad scope of application, is easy to implement, and has a high practical value. Furthermore, the method could be established using only a small amount of measured data, which is useful for water quality monitoring on large spatial scales.
2022,
32(5):
808-823.
doi: 10.1007/s11769-022-1302-4
Abstract:
Ecological risk assessment (ERA) is an indispensable method for systematic monitoring of World Heritage Sites (WHSs) exposed to various anthropogenic factors and natural disasters. Remote sensing (RS) and geographical information systems (GIS) can eliminate many limitations in traditional ERA methods. In this study, changes in ecological risk at Huangshan Mountain, the first mixed WHS in China, over the period of 1984–2019 were explored using remote sensing images and products by considering both natural disasters and human disturbance. Results show that of the four land cover types in Huangshan Mountain, namely water, forest, building and farmland, the main land cover type is forest. During the 35 yr, lands categorised at low or relatively low ecological risk levels are dominant in Huangshan Mountain, with the lowest and highest ERIs (ecological risk index) in 1990 and 2010, respectively. The areas at the five ecological risk levels have declined as follows: relatively low > low > medium > relatively high > high. Changes in ecological risks are closely related to changes in land cover and natural disasters. Even though major natural disasters may affect the ecological risk level in the whole region, changes in land cover caused by human activities will shift the ecological risk level in some areas. Our attempts can be modified and applied to other sites, and offer policy implications for protection and preservation of WHSs.
Ecological risk assessment (ERA) is an indispensable method for systematic monitoring of World Heritage Sites (WHSs) exposed to various anthropogenic factors and natural disasters. Remote sensing (RS) and geographical information systems (GIS) can eliminate many limitations in traditional ERA methods. In this study, changes in ecological risk at Huangshan Mountain, the first mixed WHS in China, over the period of 1984–2019 were explored using remote sensing images and products by considering both natural disasters and human disturbance. Results show that of the four land cover types in Huangshan Mountain, namely water, forest, building and farmland, the main land cover type is forest. During the 35 yr, lands categorised at low or relatively low ecological risk levels are dominant in Huangshan Mountain, with the lowest and highest ERIs (ecological risk index) in 1990 and 2010, respectively. The areas at the five ecological risk levels have declined as follows: relatively low > low > medium > relatively high > high. Changes in ecological risks are closely related to changes in land cover and natural disasters. Even though major natural disasters may affect the ecological risk level in the whole region, changes in land cover caused by human activities will shift the ecological risk level in some areas. Our attempts can be modified and applied to other sites, and offer policy implications for protection and preservation of WHSs.
2022,
32(5):
824-833.
doi: 10.1007/s11769-022-1303-3
Abstract:
Depending on various government policies, COVID-19 (Corona Virus Disease-19) lockdowns have had diverse impacts on global aerosol concentrations. In 2022, Changchun, a provincial capital city in Northeast China, suffered a severe COVID-19 outbreak and implemented a very strict lockdown that lasted for nearly two months. Using ground-based polarization Light Detection and Ranging (LiDAR), we detected real-time aerosol profile parameters (EC, extinction coefficient; DR, depolarization ratio; AOD, aerosol optical depth), as well as air-quality and meteorological indexes from 1 March to 30 April in 2021 and 2022 to quantify the effects of lockdown on aerosol concentrations. The period in 2022 was divided into three stages: pre-lockdown (1–10 March), strict lockdown (11 March to 10 April), and partial lockdown (11–30 April). The results showed that, during the strict lockdown period, compared with the pre-lockdown period, there were substantial reductions in aerosol parameters (EC and AOD), and this was consistent with the concentrations of the atmospheric pollutants PM2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 μm) and PM10 (particulate matter with an aerodynamic diameter ≤ 10 μm), and the O3 concentration increased by 8.3%. During the strict lockdown, the values of EC within 0–1 km and AOD decreased by 16.0% and 11.2%, respectively, as compared to the corresponding period in 2021. Lockdown reduced the conventional and organized emissions of air pollutants, and it clearly delayed the time of seasonal emissions from agricultural burning; however, it did not decrease the number of farmland fire points. Considering meteorological factors and eliminating the influence of wind-blown dust events, the results showed that reductions from conventional organized emission sources during the strict lockdown contributed to a 30% air-quality improvement and a 22% reduction in near-surface extinction (0–2 km). Aerosols produced by urban epidemic prevention and disinfection can also be identified using the EC. Regarding seasonal sources of agricultural straw burning, the concentrated burning induced by the epidemic led to the occurrence of heavy pollution from increased amounts of atmospheric aerosols, with a contribution rate of 62%. These results indicate that there is great potential to further improve air quality in the local area, and suggest that the comprehensive use of straw accompanied by reasonable planned burning is the best way to achieve this.
Depending on various government policies, COVID-19 (Corona Virus Disease-19) lockdowns have had diverse impacts on global aerosol concentrations. In 2022, Changchun, a provincial capital city in Northeast China, suffered a severe COVID-19 outbreak and implemented a very strict lockdown that lasted for nearly two months. Using ground-based polarization Light Detection and Ranging (LiDAR), we detected real-time aerosol profile parameters (EC, extinction coefficient; DR, depolarization ratio; AOD, aerosol optical depth), as well as air-quality and meteorological indexes from 1 March to 30 April in 2021 and 2022 to quantify the effects of lockdown on aerosol concentrations. The period in 2022 was divided into three stages: pre-lockdown (1–10 March), strict lockdown (11 March to 10 April), and partial lockdown (11–30 April). The results showed that, during the strict lockdown period, compared with the pre-lockdown period, there were substantial reductions in aerosol parameters (EC and AOD), and this was consistent with the concentrations of the atmospheric pollutants PM2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 μm) and PM10 (particulate matter with an aerodynamic diameter ≤ 10 μm), and the O3 concentration increased by 8.3%. During the strict lockdown, the values of EC within 0–1 km and AOD decreased by 16.0% and 11.2%, respectively, as compared to the corresponding period in 2021. Lockdown reduced the conventional and organized emissions of air pollutants, and it clearly delayed the time of seasonal emissions from agricultural burning; however, it did not decrease the number of farmland fire points. Considering meteorological factors and eliminating the influence of wind-blown dust events, the results showed that reductions from conventional organized emission sources during the strict lockdown contributed to a 30% air-quality improvement and a 22% reduction in near-surface extinction (0–2 km). Aerosols produced by urban epidemic prevention and disinfection can also be identified using the EC. Regarding seasonal sources of agricultural straw burning, the concentrated burning induced by the epidemic led to the occurrence of heavy pollution from increased amounts of atmospheric aerosols, with a contribution rate of 62%. These results indicate that there is great potential to further improve air quality in the local area, and suggest that the comprehensive use of straw accompanied by reasonable planned burning is the best way to achieve this.
2022,
32(5):
834-851.
doi: 10.1007/s11769-022-1304-2
Abstract:
Landslide distribution and susceptibility mapping are the fundamental steps for landslide-related hazard and disaster risk management activities, especially in the Himalaya region which has resulted in a great deal of death and damage to property. To better understand the landslide condition in the Nepal Himalaya, we carried out an investigation on the landslide distribution and susceptibility using the landslide inventory data and 12 different contributing factors in the Dailekh district, Western Nepal. Based on the evaluation of the frequency distribution of the landslide, the relationship between the landslide and the various contributing factors was determined. Then, the landslide susceptibility was calculated using logistic regression and statistical index methods along with different topographic (slope, aspect, relative relief, plan curvature, altitude, topographic wetness index) and non-topographic factors (distance from river, normalized difference vegetation index (NDVI), distance from road, precipitation, land use and land cover, and geology), and 470 (70%) of total 658 landslides. The receiver operating characteristic (ROC) curve analysis using 198 (30%) of total landslides showed that the prediction curve rates (area under the curve, AUC) values for two methods (logistic regression and statistical index) were 0.826, and 0.823 with success rates of 0.793, and 0.811, respectively. The values of R-Index for the logistic regression and statistical index methods were 83.66 and 88.54, respectively, consisting of high susceptible hazard classes. In general, this research concluded that the cohesive and coherent natural interplay of topographic and non-topographic factors strongly affects landslide occurrence, distribution, and susceptibility condition in the Nepal Himalaya region. Furthermore, the reliability of these two methods is verified for landslide susceptibility mapping in Nepal’s central mountain region.
Landslide distribution and susceptibility mapping are the fundamental steps for landslide-related hazard and disaster risk management activities, especially in the Himalaya region which has resulted in a great deal of death and damage to property. To better understand the landslide condition in the Nepal Himalaya, we carried out an investigation on the landslide distribution and susceptibility using the landslide inventory data and 12 different contributing factors in the Dailekh district, Western Nepal. Based on the evaluation of the frequency distribution of the landslide, the relationship between the landslide and the various contributing factors was determined. Then, the landslide susceptibility was calculated using logistic regression and statistical index methods along with different topographic (slope, aspect, relative relief, plan curvature, altitude, topographic wetness index) and non-topographic factors (distance from river, normalized difference vegetation index (NDVI), distance from road, precipitation, land use and land cover, and geology), and 470 (70%) of total 658 landslides. The receiver operating characteristic (ROC) curve analysis using 198 (30%) of total landslides showed that the prediction curve rates (area under the curve, AUC) values for two methods (logistic regression and statistical index) were 0.826, and 0.823 with success rates of 0.793, and 0.811, respectively. The values of R-Index for the logistic regression and statistical index methods were 83.66 and 88.54, respectively, consisting of high susceptible hazard classes. In general, this research concluded that the cohesive and coherent natural interplay of topographic and non-topographic factors strongly affects landslide occurrence, distribution, and susceptibility condition in the Nepal Himalaya region. Furthermore, the reliability of these two methods is verified for landslide susceptibility mapping in Nepal’s central mountain region.
2022,
32(5):
852-866.
doi: 10.1007/s11769-022-1297-x
Abstract:
The increasing impact of global warming and human activities has exacerbated the ecological environment in the Three-River-Source National Park Region (TNPR). Understanding the temporal and spatial evolution of landscape ecological vulnerability (LEV) and its influencing factors are crucial to the implementation of environmental management. Here, we aimed to: 1) construct a LEV assessment model integrating landscape structure and function; 2) analyze the temporal and spatial evolution of TNPR’s LEV from 1995 to 2015; 3) use geographic detectors to reveal the regional influence factors of TNPR’s LEV. The main findings were: 1) grasslands, water, and bare land are important landscapes of TNPR, accounting for 98.37% of the total area. During the study period, there were significant differences in the area of different landscapes; except for desert, shrub, and urban land, the other landscape areas showed a decreasing trend. 2) During the study period, the LEV of TNPR showed a downward trend; except for grasslands, the ecological vulnerability of the other landscapes decreased steadily. Furthermore, a pattern of conversion from high to low vulnerability grade was observed in the study area. In terms of spatial distribution, the LEV level shows a trend of high at both ends (east and west) and low in the middle. 3) Overall, the impact of natural factors on the ecological vulnerability of the TNPR was significantly higher than that of human factors. In conclusion, our study provides a scientific basis for landscape structure optimization and the management of regional ecological vulnerability.
The increasing impact of global warming and human activities has exacerbated the ecological environment in the Three-River-Source National Park Region (TNPR). Understanding the temporal and spatial evolution of landscape ecological vulnerability (LEV) and its influencing factors are crucial to the implementation of environmental management. Here, we aimed to: 1) construct a LEV assessment model integrating landscape structure and function; 2) analyze the temporal and spatial evolution of TNPR’s LEV from 1995 to 2015; 3) use geographic detectors to reveal the regional influence factors of TNPR’s LEV. The main findings were: 1) grasslands, water, and bare land are important landscapes of TNPR, accounting for 98.37% of the total area. During the study period, there were significant differences in the area of different landscapes; except for desert, shrub, and urban land, the other landscape areas showed a decreasing trend. 2) During the study period, the LEV of TNPR showed a downward trend; except for grasslands, the ecological vulnerability of the other landscapes decreased steadily. Furthermore, a pattern of conversion from high to low vulnerability grade was observed in the study area. In terms of spatial distribution, the LEV level shows a trend of high at both ends (east and west) and low in the middle. 3) Overall, the impact of natural factors on the ecological vulnerability of the TNPR was significantly higher than that of human factors. In conclusion, our study provides a scientific basis for landscape structure optimization and the management of regional ecological vulnerability.
2022,
32(5):
867-882.
doi: 10.1007/s11769-022-1305-1
Abstract:
Among cancers, lung cancer is the most common cause of death in China. For the prevention and control of lung cancer, it is necessary to investigate the spatial and temporal distribution of lung cancer mortality, as well as the changes in the trend and the affecting mechanism. Based on statistics and auto-correlation analysis, this paper studied the spatial and temporal distribution of lung cancer mortality in Yuhui District, Bengbu, Huaihe River Basin, from 2017 to 2020. In addition, Spearman’s Rank Correlation Assessment Model and Geographic Detector Model were used to examine the relationship between environmental factors and lung cancer mortality to identify impact factors and their mechanisms. The findings indicated that: 1) from the characteristics of temporal distribution, the number of lung cancer deaths exhibited a linear growth tendency, with the highest mortality in winter; 2) from the characteristics of spatial distribution, lung cancer mortality showed a strong spatial agglomeration form, concentrating on two clustering areas, located in the old city and the central city of Bengbu, near the Huaihe River; 3) from the point of view of the whole research area, there were 15 impact factors with significant correlation in the built and natural environment factors. The significant impacting factors in the built environment included land use, road traffic, spatial form and blue-green space, which could indirectly affect lung cancer mortality, while air pollution and temperature constituted the significant impacting factors in the natural environment; 4) the influence of screened environmental factors on lung cancer mortality was different. Spatial stratified heterogeneity assessment, the interaction among environmental factors demonstrated statistical significance, it was found that the interaction between environmental factors in pairs had a significant enhancement effect on lung cancer mortality. To some extent, urban planning and policies could reduce lung cancer mortality.
Among cancers, lung cancer is the most common cause of death in China. For the prevention and control of lung cancer, it is necessary to investigate the spatial and temporal distribution of lung cancer mortality, as well as the changes in the trend and the affecting mechanism. Based on statistics and auto-correlation analysis, this paper studied the spatial and temporal distribution of lung cancer mortality in Yuhui District, Bengbu, Huaihe River Basin, from 2017 to 2020. In addition, Spearman’s Rank Correlation Assessment Model and Geographic Detector Model were used to examine the relationship between environmental factors and lung cancer mortality to identify impact factors and their mechanisms. The findings indicated that: 1) from the characteristics of temporal distribution, the number of lung cancer deaths exhibited a linear growth tendency, with the highest mortality in winter; 2) from the characteristics of spatial distribution, lung cancer mortality showed a strong spatial agglomeration form, concentrating on two clustering areas, located in the old city and the central city of Bengbu, near the Huaihe River; 3) from the point of view of the whole research area, there were 15 impact factors with significant correlation in the built and natural environment factors. The significant impacting factors in the built environment included land use, road traffic, spatial form and blue-green space, which could indirectly affect lung cancer mortality, while air pollution and temperature constituted the significant impacting factors in the natural environment; 4) the influence of screened environmental factors on lung cancer mortality was different. Spatial stratified heterogeneity assessment, the interaction among environmental factors demonstrated statistical significance, it was found that the interaction between environmental factors in pairs had a significant enhancement effect on lung cancer mortality. To some extent, urban planning and policies could reduce lung cancer mortality.
2022,
32(5):
883-895.
doi: 10.1007/s11769-022-1306-0
Abstract:
Evaluating urban land use efficiency (ULUE) provides insights into the interactions between land use systems and their external environment. Specifically, changes in ULUE are important for monitoring urban transformation in developing countries. In this study, using a traditional input-output index model, we incorporated slack-based measurements and undesirable outputs into a SBM-UN (slack-based measure-undesirable) model to investigate ULUE within the context of increasing environmental restrictions in China. The model was used to estimate the ULUE of 26 cities in the highly developed urban agglomeration of the Yangtze River Delta from 2000 to 2018. The average ULUE in the Yangtze River Delta was relatively low compared to that of developed city regions in the European Union (EU) and North America and exhibited a U-shaped curve over the study period. Incorporating undesirable outputs, such as environmental pollution, into the model reduced ULUE by 19.06%. ULUE varied spatially, with the kernel density estimation exhibiting a bimodal distribution. Efficiency decomposition analysis showed that scale efficiency made a greater contribution to ULUE than pure technical efficiency. Based on our findings, recommended approaches to improve ULUE include optimizing factor allocation, reducing undesirable outputs, and increasing the effective output per land unit. The study suggests that ULUE and the SBM-UN model are useful planning tools for sustainable urban development.
Evaluating urban land use efficiency (ULUE) provides insights into the interactions between land use systems and their external environment. Specifically, changes in ULUE are important for monitoring urban transformation in developing countries. In this study, using a traditional input-output index model, we incorporated slack-based measurements and undesirable outputs into a SBM-UN (slack-based measure-undesirable) model to investigate ULUE within the context of increasing environmental restrictions in China. The model was used to estimate the ULUE of 26 cities in the highly developed urban agglomeration of the Yangtze River Delta from 2000 to 2018. The average ULUE in the Yangtze River Delta was relatively low compared to that of developed city regions in the European Union (EU) and North America and exhibited a U-shaped curve over the study period. Incorporating undesirable outputs, such as environmental pollution, into the model reduced ULUE by 19.06%. ULUE varied spatially, with the kernel density estimation exhibiting a bimodal distribution. Efficiency decomposition analysis showed that scale efficiency made a greater contribution to ULUE than pure technical efficiency. Based on our findings, recommended approaches to improve ULUE include optimizing factor allocation, reducing undesirable outputs, and increasing the effective output per land unit. The study suggests that ULUE and the SBM-UN model are useful planning tools for sustainable urban development.
2022,
32(5):
896-914.
doi: 10.1007/s11769-022-1307-z
Abstract:
Accelerating the promotion of high-quality development of tourism (HQDT) is of great significance to the sustainable development of tourism. This paper defined the concept of HQDT, and then built an evaluation system for HQDT measurement to analyze the spatio-temporal evolution characteristics of China’s HQDT based on provincial panel data from 2010 to 2019, using Geodetector to explore the similarities and differences between driving factors of HQDT and tourism development scale (TDS). The results show that: 1) Taking the development concepts of innovation, coordination, green, openness and sharing as the guidance, and considering the organic unity of quantity and quality, the evaluation index system of the HQDT consists of six dimensions of economic stability, innovation driving, coordination and linkage, green and sustainability, openness and cooperation, and sharing and harmony, which respectively represent the basis, momentum, means, orientation, direction and purpose of the HQDT; 2) The level of China’s HQDT shows an upward trend, presenting the characteristics of eastern region > central region > western region > northeastern region in 2019. The regional differences in China’s HQDT show a downward trend, and the intra-regional differences have replaced the inter-regional differences as the main source of regional differences; 3) China’s HQDT shows the characteristics of higher in the east and lower in the west along the Hu line, while the improvement speed of HQDT shows the characteristics of faster in the west and slower in the east, making the decline of east-west differentiation of China’s HQDT and the movement of the gravity center towards southwest; 4) Both HQDT and TDS are obviously driven by tourism capital investment and regional consumption. In terms of differences, the HQDT is more driven by government guidance, innovation driving force, and opening up, while the main driving factors of TDS are more biased toward capital elements and hardware facilities, including informatization, tourism resource, traffic, and eco-environment.
Accelerating the promotion of high-quality development of tourism (HQDT) is of great significance to the sustainable development of tourism. This paper defined the concept of HQDT, and then built an evaluation system for HQDT measurement to analyze the spatio-temporal evolution characteristics of China’s HQDT based on provincial panel data from 2010 to 2019, using Geodetector to explore the similarities and differences between driving factors of HQDT and tourism development scale (TDS). The results show that: 1) Taking the development concepts of innovation, coordination, green, openness and sharing as the guidance, and considering the organic unity of quantity and quality, the evaluation index system of the HQDT consists of six dimensions of economic stability, innovation driving, coordination and linkage, green and sustainability, openness and cooperation, and sharing and harmony, which respectively represent the basis, momentum, means, orientation, direction and purpose of the HQDT; 2) The level of China’s HQDT shows an upward trend, presenting the characteristics of eastern region > central region > western region > northeastern region in 2019. The regional differences in China’s HQDT show a downward trend, and the intra-regional differences have replaced the inter-regional differences as the main source of regional differences; 3) China’s HQDT shows the characteristics of higher in the east and lower in the west along the Hu line, while the improvement speed of HQDT shows the characteristics of faster in the west and slower in the east, making the decline of east-west differentiation of China’s HQDT and the movement of the gravity center towards southwest; 4) Both HQDT and TDS are obviously driven by tourism capital investment and regional consumption. In terms of differences, the HQDT is more driven by government guidance, innovation driving force, and opening up, while the main driving factors of TDS are more biased toward capital elements and hardware facilities, including informatization, tourism resource, traffic, and eco-environment.
2022,
32(5):
915-930.
doi: 10.1007/s11769-022-1308-y
Abstract:
The dislocation between regional innovation and economic development directly influences the economic effect of regional innovation. However, no in-depth researches have been made on how to solve this problem. Using data from Henan Province, China, employing geographical detector technology, this paper focuses on testing whether the industry-university-research cooperation can contribute to coordinating the relation between regional innovation and economic development. It is shown that: 1) the industry-university-research cooperation in Henan Province is increasing gradually, and the network presents a core-edge structure, and the coupling degree between regional innovation and economic development is spatially unbalanced, which is similar to the spatial distribution of the intensity of industry-university-research cooperation; 2) as an important approach to effectively connect scientific researches with market demands, the industry-university-research cooperation can help form an interactive, interconnected, coupled and coordinated virtuous relation between regional innovation and economic development. Compared with the cooperation between organizations of the same type and the separate innovation of organizations, the improvement of the industry-university-research cooperation level can better coordinate the relation between regional innovation and economic development; 3) the cooperative innovation model between enterprises and universities can better promote the coupling between regional innovation and economic development, compared with many industry-university-research cooperation models. For underdeveloped areas lacking local knowledge base, industry-university-research cooperation should be considered as a long-term development strategy, especially using the knowledge sources of external universities and scientific research institutions to enhance innovation capability and achieve economic growth.
The dislocation between regional innovation and economic development directly influences the economic effect of regional innovation. However, no in-depth researches have been made on how to solve this problem. Using data from Henan Province, China, employing geographical detector technology, this paper focuses on testing whether the industry-university-research cooperation can contribute to coordinating the relation between regional innovation and economic development. It is shown that: 1) the industry-university-research cooperation in Henan Province is increasing gradually, and the network presents a core-edge structure, and the coupling degree between regional innovation and economic development is spatially unbalanced, which is similar to the spatial distribution of the intensity of industry-university-research cooperation; 2) as an important approach to effectively connect scientific researches with market demands, the industry-university-research cooperation can help form an interactive, interconnected, coupled and coordinated virtuous relation between regional innovation and economic development. Compared with the cooperation between organizations of the same type and the separate innovation of organizations, the improvement of the industry-university-research cooperation level can better coordinate the relation between regional innovation and economic development; 3) the cooperative innovation model between enterprises and universities can better promote the coupling between regional innovation and economic development, compared with many industry-university-research cooperation models. For underdeveloped areas lacking local knowledge base, industry-university-research cooperation should be considered as a long-term development strategy, especially using the knowledge sources of external universities and scientific research institutions to enhance innovation capability and achieve economic growth.
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