2023 Vol. 33, No. 2
Display Method:
2023, 33(2): 189-204.
doi: 10.1007/s11769-023-1340-6
Abstract:
Urban shrinkage is a global phenomenon, and it will coexist with urban growth for many years. At the same time, the network connection between cities continuously improved due to the construction of the transportation and information networks. However, the relationship between urban network externalities and urban population growth/shrinkage remains unclear. Therefore, based on high-speed railway (HSR) flow data, a spatial econometric model is used to explore the mechanism behind urban population growth and shrinkage from the perspective of network externalities in China. The results indicate that: 1) the urban network experiences a certain clubbing effect. Growing cities that are strongly connected are concentrated along China’s main railway lines and the southeastern coastal areas, while shrinking cities that are weakly connected are distributed at the periphery of the network. 2) Moreover, the network externality disregards spatial distance and together with the agglomeration externality influences the growth and shrinking of cities. 3) Urban economic development still promotes the development of Chinese cities. However, the improvement of the urban economy has a negative cross-regional spillover effect on neighboring cities due to urban competition. 4) Lastly, Local spillovers of urban network externalities are positive, while cross-regional ones are negative. Consequently, the government needs to promote the construction of multi-dimensional network connections between cities to promote cities’ sustainable development. This study reveals the relationship between urban network externalities and urban development, enriches the theories of network externalities and urban growth/shrinkage, and provides a reference for regional coordinated development.
Urban shrinkage is a global phenomenon, and it will coexist with urban growth for many years. At the same time, the network connection between cities continuously improved due to the construction of the transportation and information networks. However, the relationship between urban network externalities and urban population growth/shrinkage remains unclear. Therefore, based on high-speed railway (HSR) flow data, a spatial econometric model is used to explore the mechanism behind urban population growth and shrinkage from the perspective of network externalities in China. The results indicate that: 1) the urban network experiences a certain clubbing effect. Growing cities that are strongly connected are concentrated along China’s main railway lines and the southeastern coastal areas, while shrinking cities that are weakly connected are distributed at the periphery of the network. 2) Moreover, the network externality disregards spatial distance and together with the agglomeration externality influences the growth and shrinking of cities. 3) Urban economic development still promotes the development of Chinese cities. However, the improvement of the urban economy has a negative cross-regional spillover effect on neighboring cities due to urban competition. 4) Lastly, Local spillovers of urban network externalities are positive, while cross-regional ones are negative. Consequently, the government needs to promote the construction of multi-dimensional network connections between cities to promote cities’ sustainable development. This study reveals the relationship between urban network externalities and urban development, enriches the theories of network externalities and urban growth/shrinkage, and provides a reference for regional coordinated development.
2023, 33(2): 205-220.
doi: 10.1007/s11769-023-1336-2
Abstract:
The extant literature on international immigrants has discussed migrants’ entrepreneurial activities in the context of Western countries but has paid little attention to return-home entrepreneurial intention (RHEI). Rural migrant workers (RMWs) in China, who used to promote rural development by remittances and were characterized by similarities with early international migrants, have gradually returned to their hometowns to initiate entrepreneurial activities. Based on the structured questionnaire conducted in 2015 and 2020 in Anhui Province, China, this article combines the concept of mixed embeddedness with the idea of double-layered embeddedness and analyzes the impacts of the social, economic and institutional context in RMWs’ hometowns and migration destinations on RMWs’ RHEI by using binary logistic regression. The article shows that the social, economic, and institutional environments of RMWs’ hometowns and migration destinations have effects on their RHEI. The embeddedness in the economic and informal institutional context in RMWs’ RHEI is even more important than personal characteristics. Compared with migration destinations, RMWs’ hometowns exert a more influential effect on their RHEI. However, that does not mean that the role of migration destinations can be undervalued. Actually, the better the social, economic, and institutional environments of migration destinations RMWs moved into is, the higher entrepreneurial intention they will have after returning to their hometowns. The article proposes a modified framework in combination of mixed embeddedness with double-layer embeddedness and proves that it is suitable for analyzing RMWs’ RHEI. The framework has important implications for strengthening China’s RMWs to return home to start their own businesses.
The extant literature on international immigrants has discussed migrants’ entrepreneurial activities in the context of Western countries but has paid little attention to return-home entrepreneurial intention (RHEI). Rural migrant workers (RMWs) in China, who used to promote rural development by remittances and were characterized by similarities with early international migrants, have gradually returned to their hometowns to initiate entrepreneurial activities. Based on the structured questionnaire conducted in 2015 and 2020 in Anhui Province, China, this article combines the concept of mixed embeddedness with the idea of double-layered embeddedness and analyzes the impacts of the social, economic and institutional context in RMWs’ hometowns and migration destinations on RMWs’ RHEI by using binary logistic regression. The article shows that the social, economic, and institutional environments of RMWs’ hometowns and migration destinations have effects on their RHEI. The embeddedness in the economic and informal institutional context in RMWs’ RHEI is even more important than personal characteristics. Compared with migration destinations, RMWs’ hometowns exert a more influential effect on their RHEI. However, that does not mean that the role of migration destinations can be undervalued. Actually, the better the social, economic, and institutional environments of migration destinations RMWs moved into is, the higher entrepreneurial intention they will have after returning to their hometowns. The article proposes a modified framework in combination of mixed embeddedness with double-layer embeddedness and proves that it is suitable for analyzing RMWs’ RHEI. The framework has important implications for strengthening China’s RMWs to return home to start their own businesses.
2023, 33(2): 221-232.
doi: 10.1007/s11769-023-1333-5
Abstract:
The vigorous development of information and communications technology has accelerated reshaping of the financial industry. The COVID-19 pandemic has further catalyzed the demand for digital financial services. Digital financial inclusion relies on information technology to overcome spatial limitations. In this case, the research question is whether it adheres to the spatial laws governing conventional financial activities. This study uses exploratory spatial data analysis and a geographical detector to elucidate the spatiotemporal characteristics and factors influencing digital financial inclusion at the county level in China (Data don’t include that of Hong Kong, Macao and Taiwan of China) from 2014 to 2020. The research findings indicate: first, China’s county-level digital financial inclusion is generally increasing and exhibits significant spatial autocorrelation. Second, population density, level of traditional financial development, government regulation, and education level are key determinants of China’s county-level digital financial inclusion. Third, policies should be differentiated by region to narrow the spatial gap in digital financial inclusion. The results provide a reference for other developing countries on using digital technology to develop financial inclusion.
The vigorous development of information and communications technology has accelerated reshaping of the financial industry. The COVID-19 pandemic has further catalyzed the demand for digital financial services. Digital financial inclusion relies on information technology to overcome spatial limitations. In this case, the research question is whether it adheres to the spatial laws governing conventional financial activities. This study uses exploratory spatial data analysis and a geographical detector to elucidate the spatiotemporal characteristics and factors influencing digital financial inclusion at the county level in China (Data don’t include that of Hong Kong, Macao and Taiwan of China) from 2014 to 2020. The research findings indicate: first, China’s county-level digital financial inclusion is generally increasing and exhibits significant spatial autocorrelation. Second, population density, level of traditional financial development, government regulation, and education level are key determinants of China’s county-level digital financial inclusion. Third, policies should be differentiated by region to narrow the spatial gap in digital financial inclusion. The results provide a reference for other developing countries on using digital technology to develop financial inclusion.
2023, 33(2): 233-249.
doi: 10.1007/s11769-023-1341-5
Abstract:
Innovation capitalization is a new concept in innovation geography research. Extant research on a city scale has proven that innovation is an important factor affecting housing prices and verified that innovation has a capitalization effect. However, few studies investigate the spatial heterogeneity of innovation capitalization. Thus, case verification at the urban agglomeration scale is needed. Therefore, this study proposes a theoretical framework for the spatial heterogeneity of innovation capitalization at the urban agglomeration scale. Examining the Guangdong-Hong Kong-Macao Greater Bay Area (GHMGBA), China as a case study, the study investigated the spatial heterogeneity of the influence of high-tech firms, representing innovation, on housing prices. This work verified the spatial heterogeneity of innovation capitalization. The study constructed a data set influencing housing prices, comprising 11 factors in 5 categories (high-tech firms, convenience of living facilities, built environment, the natural environment, and the fundamentals of the districts) for 419 subdistricts in the GHMGBA. On the global scale, the study finds that high-tech firms have a significant and positive influence on housing prices, with the housing price increasing by 0.0156% when high-tech firm density increases by 1%. Furthermore, a semi-geographically weighted regression (SGWR) analysis shows that the influence of high-tech firms on housing prices has spatial heterogeneity. The areas where high-tech firms have a significant and positive influence on housing prices are mainly in the Guangzhou-Foshan metropolitan area, western Shenzhen-Dongguan, north-central Zhongshan-Nansha district, and Guangzhou—all areas with densely distributed high-tech firms. These results confirm the spatial heterogeneity of innovation capitalization and the need for further discussion of its scale and spatial limitations. The study offers implications for relevant GHMGBA administrative authorities for spatially differentiated development strategies and housing policies that consider the role of innovation in successful urban development.
Innovation capitalization is a new concept in innovation geography research. Extant research on a city scale has proven that innovation is an important factor affecting housing prices and verified that innovation has a capitalization effect. However, few studies investigate the spatial heterogeneity of innovation capitalization. Thus, case verification at the urban agglomeration scale is needed. Therefore, this study proposes a theoretical framework for the spatial heterogeneity of innovation capitalization at the urban agglomeration scale. Examining the Guangdong-Hong Kong-Macao Greater Bay Area (GHMGBA), China as a case study, the study investigated the spatial heterogeneity of the influence of high-tech firms, representing innovation, on housing prices. This work verified the spatial heterogeneity of innovation capitalization. The study constructed a data set influencing housing prices, comprising 11 factors in 5 categories (high-tech firms, convenience of living facilities, built environment, the natural environment, and the fundamentals of the districts) for 419 subdistricts in the GHMGBA. On the global scale, the study finds that high-tech firms have a significant and positive influence on housing prices, with the housing price increasing by 0.0156% when high-tech firm density increases by 1%. Furthermore, a semi-geographically weighted regression (SGWR) analysis shows that the influence of high-tech firms on housing prices has spatial heterogeneity. The areas where high-tech firms have a significant and positive influence on housing prices are mainly in the Guangzhou-Foshan metropolitan area, western Shenzhen-Dongguan, north-central Zhongshan-Nansha district, and Guangzhou—all areas with densely distributed high-tech firms. These results confirm the spatial heterogeneity of innovation capitalization and the need for further discussion of its scale and spatial limitations. The study offers implications for relevant GHMGBA administrative authorities for spatially differentiated development strategies and housing policies that consider the role of innovation in successful urban development.
2023, 33(2): 250-270.
doi: 10.1007/s11769-023-1342-4
Abstract:
This study constructs a quasi-natural experiment based on the expansion of the Yangtze River Delta urban agglomeration (YRDUA) of China in 2010 to investigate the impact and inner mechanism of urban agglomeration expansion on fine particulate matter (PM2.5) concentrations through propensity scores in difference-in-differences models (PSM-DID) using panel data from 286 prefecture-level cities in China from 2003 to 2016. The results show that 1) urban agglomeration expansion contributes to an overall decrease in PM2.5 concentration, which is mainly achieved from the original cities. For the new cities, on the other hand, the expansion significantly increases the local PM2.5 concentration. 2) In the long term, the significant influence of urban agglomeration expansion on PM2.5 concentration lasts for three years and gradually decreases. A series of robustness tests confirm the applicability of the PSM-DID model. 3) Cities with weaker government regulation, a better educated population and higher per capita income present stronger PM2.5 reduction effects. 4) Urban agglomeration expansion affects the PM2.5 concentration mainly through industrial transfer and population migration, which cause a decrease in the PM2.5 concentration in the original cities and an increase in the PM2.5 concentration in the new cities. Corresponding policy suggestions are proposed based on the conclusions.
This study constructs a quasi-natural experiment based on the expansion of the Yangtze River Delta urban agglomeration (YRDUA) of China in 2010 to investigate the impact and inner mechanism of urban agglomeration expansion on fine particulate matter (PM2.5) concentrations through propensity scores in difference-in-differences models (PSM-DID) using panel data from 286 prefecture-level cities in China from 2003 to 2016. The results show that 1) urban agglomeration expansion contributes to an overall decrease in PM2.5 concentration, which is mainly achieved from the original cities. For the new cities, on the other hand, the expansion significantly increases the local PM2.5 concentration. 2) In the long term, the significant influence of urban agglomeration expansion on PM2.5 concentration lasts for three years and gradually decreases. A series of robustness tests confirm the applicability of the PSM-DID model. 3) Cities with weaker government regulation, a better educated population and higher per capita income present stronger PM2.5 reduction effects. 4) Urban agglomeration expansion affects the PM2.5 concentration mainly through industrial transfer and population migration, which cause a decrease in the PM2.5 concentration in the original cities and an increase in the PM2.5 concentration in the new cities. Corresponding policy suggestions are proposed based on the conclusions.
2023, 33(2): 271-286.
doi: 10.1007/s11769-023-1343-3
Abstract:
Carbon storage of terrestrial ecosystems plays a vital role in advancing carbon neutrality. Better understanding of how land use changes affect carbon storage in urban agglomeration will provide valuable guidance for policymakers in developing effective regional conservation policies. Taking the Pearl River Delta Urban Agglomeration (PRDUA) in China as an example, we examined the heterogeneous response of carbon storage to land use changes in 1990–2018 from a combined view of administrative units and physical entities. The results indicate that the primary change in land use was due to the expansion of construction land (5897.16 km2). The carbon storage in PRDUA decreased from 767.34 Tg C in 1990 to 725.42 Tg C in 2018 with a spatial pattern of high wings and the low middle. The carbon storage loss was largely attributed to construction land expansion (55.74%), followed by forest degradation (54.81%). Changes in carbon storage showed significant divergences in different sized cities and hierarchical boundaries. The coefficients of geographically weighted regression (GWR) reveal that the alteration in carbon storage in Guangzhou City was more responsive to changes in construction land (−0.11) compared to other cities, while that in Shenzhen was mainly affected by the dynamics of forest land (8.32). The change in carbon storage was primarily influenced by the conversion of farmland within urban extent (5.05) and the degradation of forest land in rural areas (5.82). Carbon storage changes were less sensitive to the expansion of construction land in the urban center, urban built-up area, and ex-urban built-up area, with the corresponding GWR coefficients of 0.19, 0.04, and 0.02. This study necessitates the differentiated protection strategies of carbon storage in urban agglomerations.
Carbon storage of terrestrial ecosystems plays a vital role in advancing carbon neutrality. Better understanding of how land use changes affect carbon storage in urban agglomeration will provide valuable guidance for policymakers in developing effective regional conservation policies. Taking the Pearl River Delta Urban Agglomeration (PRDUA) in China as an example, we examined the heterogeneous response of carbon storage to land use changes in 1990–2018 from a combined view of administrative units and physical entities. The results indicate that the primary change in land use was due to the expansion of construction land (5897.16 km2). The carbon storage in PRDUA decreased from 767.34 Tg C in 1990 to 725.42 Tg C in 2018 with a spatial pattern of high wings and the low middle. The carbon storage loss was largely attributed to construction land expansion (55.74%), followed by forest degradation (54.81%). Changes in carbon storage showed significant divergences in different sized cities and hierarchical boundaries. The coefficients of geographically weighted regression (GWR) reveal that the alteration in carbon storage in Guangzhou City was more responsive to changes in construction land (−0.11) compared to other cities, while that in Shenzhen was mainly affected by the dynamics of forest land (8.32). The change in carbon storage was primarily influenced by the conversion of farmland within urban extent (5.05) and the degradation of forest land in rural areas (5.82). Carbon storage changes were less sensitive to the expansion of construction land in the urban center, urban built-up area, and ex-urban built-up area, with the corresponding GWR coefficients of 0.19, 0.04, and 0.02. This study necessitates the differentiated protection strategies of carbon storage in urban agglomerations.
2023, 33(2): 287-303.
doi: 10.1007/s11769-023-1334-4
Abstract:
Cultivated land pressure represents a direct reflection of grain security. Existing relevant studies rarely approached the spatiotemporal pattern of cultivated land pressure or the spatial heterogeneity of its influencing factors from the level of economic zones. Taking the Huaihai Economic Zone (HEZ), China for case analysis, this study investigated the spatiotemporal pattern of cultivated land pressure in diverse periods from 2000 to 2018 based on a modified cultivated land pressure index and spatial correlation models. On this basis, it explored the influencing factors of the spatial differentiation of cultivated land pressure in the late stage of the study using geographical detector as well as multi-scale geographically weighted regression model. The results indicated that: 1) in the study period, the global cultivated land pressure index of the study area decreased gradually, but cultivated land pressure increased locally in a significant way, especially in the central and southern Shandong Province; 2) the spatial pattern of cultivated land pressure manifested global clustering features. Hot and secondary-hot spots presented a narrowing and clustering trend, whereas cold and secondary-cold spots manifested a spreading and clustering trend; 3) average slope, the proportion of non-grain crops, population urbanization rate, and multiple cropping index have significant effects on the spatial differentiation of cultivated land pressure. The former three factors were positively correlated with cultivated land pressure, and the last factor was negatively correlated with cultivated land pressure; and 4) the amount of cultivated land has increased in the central and southern Shandong Province through land consolidation which, nonetheless, failed to improve the grain production. In regards to major grain producing areas similar to the HEZ in China, the authors suggest that great importance should be given to the balance of the quality and quantity of cultivated land, the optimization of agricultural production factors and the rational control of non-grain crops, thus providing a powerful guarantee for grain security in China.
Cultivated land pressure represents a direct reflection of grain security. Existing relevant studies rarely approached the spatiotemporal pattern of cultivated land pressure or the spatial heterogeneity of its influencing factors from the level of economic zones. Taking the Huaihai Economic Zone (HEZ), China for case analysis, this study investigated the spatiotemporal pattern of cultivated land pressure in diverse periods from 2000 to 2018 based on a modified cultivated land pressure index and spatial correlation models. On this basis, it explored the influencing factors of the spatial differentiation of cultivated land pressure in the late stage of the study using geographical detector as well as multi-scale geographically weighted regression model. The results indicated that: 1) in the study period, the global cultivated land pressure index of the study area decreased gradually, but cultivated land pressure increased locally in a significant way, especially in the central and southern Shandong Province; 2) the spatial pattern of cultivated land pressure manifested global clustering features. Hot and secondary-hot spots presented a narrowing and clustering trend, whereas cold and secondary-cold spots manifested a spreading and clustering trend; 3) average slope, the proportion of non-grain crops, population urbanization rate, and multiple cropping index have significant effects on the spatial differentiation of cultivated land pressure. The former three factors were positively correlated with cultivated land pressure, and the last factor was negatively correlated with cultivated land pressure; and 4) the amount of cultivated land has increased in the central and southern Shandong Province through land consolidation which, nonetheless, failed to improve the grain production. In regards to major grain producing areas similar to the HEZ in China, the authors suggest that great importance should be given to the balance of the quality and quantity of cultivated land, the optimization of agricultural production factors and the rational control of non-grain crops, thus providing a powerful guarantee for grain security in China.
2023, 33(2): 304-319.
doi: 10.1007/s11769-023-1338-0
Abstract:
Mastering the pattern of food loss caused by droughts and floods aids in planning the layout of agricultural production, determining the scale of drought and flood control projects, and reducing food loss. The Standardized Precipitation Evapotranspiration Index is calculated using monthly meteorological data from 1984 to 2020 in Shandong Province of China and is used to identify the province’s drought and flood characteristics. Then, food losses due to droughts and floods are estimated separately from disaster loss data. Finally, the relationship between drought/flood-related factors and food losses is quantified using methods such as the Pearson correlation coefficient and linear regression. The results show that: 1) there is a trend of aridity in Shandong Province, and the drought characteristic variables are increasing yearly while flood duration and severity are decreasing. 2) The food losses caused by droughts in Shandong Province are more than those caused by floods, and the area where droughts and floods occur frequently is located in Linyi City. 3) The impact of precipitation on food loss due to drought/flood is significant, followed by potential evapotranspiration and temperature. 4) The relationship between drought and flood conditions and food losses can be precisely quantified. The accumulated drought duration of one month led to 1.939 × 104 t of grain loss, and an increase in cumulative flood duration of one month resulted in 1.134 × 104 t of grain loss. If the cumulative drought severity and average drought peak increased by one unit, food loss due to drought will increase by 1.562 × 104 t and 1.511 × 106 t, respectively. If the cumulative flood severity and average flood peak increase by one unit, food loss will increase by 8.470 × 103 t and 1.034 × 106 t, respectively.
Mastering the pattern of food loss caused by droughts and floods aids in planning the layout of agricultural production, determining the scale of drought and flood control projects, and reducing food loss. The Standardized Precipitation Evapotranspiration Index is calculated using monthly meteorological data from 1984 to 2020 in Shandong Province of China and is used to identify the province’s drought and flood characteristics. Then, food losses due to droughts and floods are estimated separately from disaster loss data. Finally, the relationship between drought/flood-related factors and food losses is quantified using methods such as the Pearson correlation coefficient and linear regression. The results show that: 1) there is a trend of aridity in Shandong Province, and the drought characteristic variables are increasing yearly while flood duration and severity are decreasing. 2) The food losses caused by droughts in Shandong Province are more than those caused by floods, and the area where droughts and floods occur frequently is located in Linyi City. 3) The impact of precipitation on food loss due to drought/flood is significant, followed by potential evapotranspiration and temperature. 4) The relationship between drought and flood conditions and food losses can be precisely quantified. The accumulated drought duration of one month led to 1.939 × 104 t of grain loss, and an increase in cumulative flood duration of one month resulted in 1.134 × 104 t of grain loss. If the cumulative drought severity and average drought peak increased by one unit, food loss due to drought will increase by 1.562 × 104 t and 1.511 × 106 t, respectively. If the cumulative flood severity and average flood peak increase by one unit, food loss will increase by 8.470 × 103 t and 1.034 × 106 t, respectively.
2023, 33(2): 320-332.
doi: 10.1007/s11769-023-1339-z
Abstract:
Air pollution is a problem that directly affects human health, the global environment and the climate. The air quality index (AQI) indicates the degree of air pollution and effect on human health; however, when assessing air pollution only based on AQI monitoring data the fact that the same degree of air pollution is more harmful in more densely populated areas is ignored. In the present study, multi-source data were combined to map the distribution of the AQI and population data, and the analyze their pollution population exposure of Beijing in 2018 was analyzed. Machine learning based on the random forest algorithm was adopted to calculate the monthly average AQI of Beijing in 2018. Using Luojia-1 nighttime light remote sensing data, population statistics data, the population of Beijing in 2018 and point of interest data, the distribution of the permanent population in Beijing was estimated with a high precision of 200 m × 200 m. Based on the spatialization results of the AQI and population of Beijing, the air pollution exposure levels in various parts of Beijing were calculated using the population-weighted pollution exposure level (PWEL) formula. The results show that the southern region of Beijing had a more serious level of air pollution, while the northern region was less polluted. At the same time, the population was found to agglomerate mainly in the central city and the peripheric areas thereof. In the present study, the exposure of different districts and towns in Beijing to pollution was analyzed, based on high resolution population spatialization data, it could take the pollution exposure issue down to each individual town. And we found that towns with higher exposure such as Yongshun Town, Shahe Town and Liyuan Town were all found to have a population of over 200 000 which was much higher than the median population of townships of 51 741 in Beijing. Additionally, the change trend of air pollution exposure levels in various regions of Beijing in 2018 was almost the same, with the peak value being in winter and the lowest value being in summer. The exposure intensity in population clusters was relatively high. To reduce the level and intensity of pollution exposure, relevant departments should strengthen the governance of areas with high AQI, and pay particular attention to population clusters.
Air pollution is a problem that directly affects human health, the global environment and the climate. The air quality index (AQI) indicates the degree of air pollution and effect on human health; however, when assessing air pollution only based on AQI monitoring data the fact that the same degree of air pollution is more harmful in more densely populated areas is ignored. In the present study, multi-source data were combined to map the distribution of the AQI and population data, and the analyze their pollution population exposure of Beijing in 2018 was analyzed. Machine learning based on the random forest algorithm was adopted to calculate the monthly average AQI of Beijing in 2018. Using Luojia-1 nighttime light remote sensing data, population statistics data, the population of Beijing in 2018 and point of interest data, the distribution of the permanent population in Beijing was estimated with a high precision of 200 m × 200 m. Based on the spatialization results of the AQI and population of Beijing, the air pollution exposure levels in various parts of Beijing were calculated using the population-weighted pollution exposure level (PWEL) formula. The results show that the southern region of Beijing had a more serious level of air pollution, while the northern region was less polluted. At the same time, the population was found to agglomerate mainly in the central city and the peripheric areas thereof. In the present study, the exposure of different districts and towns in Beijing to pollution was analyzed, based on high resolution population spatialization data, it could take the pollution exposure issue down to each individual town. And we found that towns with higher exposure such as Yongshun Town, Shahe Town and Liyuan Town were all found to have a population of over 200 000 which was much higher than the median population of townships of 51 741 in Beijing. Additionally, the change trend of air pollution exposure levels in various regions of Beijing in 2018 was almost the same, with the peak value being in winter and the lowest value being in summer. The exposure intensity in population clusters was relatively high. To reduce the level and intensity of pollution exposure, relevant departments should strengthen the governance of areas with high AQI, and pay particular attention to population clusters.
2023, 33(2): 333-350.
doi: 10.1007/s11769-023-1344-2
Abstract:
For mankind’s survival and development, water, energy, and food (WEF) are essential material guarantees. In China, however, the spatial distribution of WEF is seriously unbalanced and mismatched. Here, a collaborative governance mechanism that aims at nexus security needs to be urgently established. In this paper, the Yellow River Basin in China with a representative WEF system, was selected as a case. Firstly, a comprehensive framework for WEF coupling coordination was constructed, and the relationship and mechanism between them were analyzed theoretically. Then, we investigated the spatiotemporal characteristics and driving mechanisms of the coupling coordination degree (CCD) with a composite evaluation method, coupling coordination degree model, spatial statistical analysis, and multiscale geographic weighted regression. Finally, policy implications were discussed to promote the coordinated development of the WEF system. The results showed that: 1) WEF subsystems showed a significant imbalance of spatial pattern and diversity in temporal changes; 2) the CCD for the WEF system varied little and remained at moderate coordination. Areas with moderate coordination have increased, while areas with superior coordination and mild disorder have decreased. In addition, the spatial clustering phenomenon of the CCD was significant and showed obvious characteristics of polarization; and 3) the action of each factor is self-differentiated and regionally variable. For different factors, GDP per capita was of particular importance, which contributed most to the regional development’s coupling coordination. For different regions, GDP per capita, average yearly precipitation, population density, and urbanization rate exhibited differences in geographical gradients in an east-west direction. The conclusion can provide references for regional resource allocation and sustainable development by enhancing WEF system utilization efficiency.
For mankind’s survival and development, water, energy, and food (WEF) are essential material guarantees. In China, however, the spatial distribution of WEF is seriously unbalanced and mismatched. Here, a collaborative governance mechanism that aims at nexus security needs to be urgently established. In this paper, the Yellow River Basin in China with a representative WEF system, was selected as a case. Firstly, a comprehensive framework for WEF coupling coordination was constructed, and the relationship and mechanism between them were analyzed theoretically. Then, we investigated the spatiotemporal characteristics and driving mechanisms of the coupling coordination degree (CCD) with a composite evaluation method, coupling coordination degree model, spatial statistical analysis, and multiscale geographic weighted regression. Finally, policy implications were discussed to promote the coordinated development of the WEF system. The results showed that: 1) WEF subsystems showed a significant imbalance of spatial pattern and diversity in temporal changes; 2) the CCD for the WEF system varied little and remained at moderate coordination. Areas with moderate coordination have increased, while areas with superior coordination and mild disorder have decreased. In addition, the spatial clustering phenomenon of the CCD was significant and showed obvious characteristics of polarization; and 3) the action of each factor is self-differentiated and regionally variable. For different factors, GDP per capita was of particular importance, which contributed most to the regional development’s coupling coordination. For different regions, GDP per capita, average yearly precipitation, population density, and urbanization rate exhibited differences in geographical gradients in an east-west direction. The conclusion can provide references for regional resource allocation and sustainable development by enhancing WEF system utilization efficiency.
2023, 33(2): 351-362.
doi: 10.1007/s11769-023-1345-1
Abstract:
Cotton is a revenue source for cotton-producing countries; as the second-largest crop in Pakistan, it significantly contributes to its economy. Over the past few decades, cotton productivity has become unstable in Pakistan, and climate change is one of the main factors that impact cotton yield. Due to climate change, it becomes very important to understand the change trend and its impact on cotton yield at the regional level. Here, we investigate the relationship of standardized cotton yield variability with the variability of climate factors using a 15-yr moving window. The piecewise regression was fitted to obtain the trend-shifting point of climate factors. The results show that precipitation has experienced an overall decreasing trend of –0.64 mm/yr during the study period, with opposing trends of –1.39 mm/yr and 1.52 mm/yr before and after the trend-shifting point, respectively. We found that cotton yield variability increased at a rate of 0.17%/yr, and this trend was highly correlated with the variability of climate factors. The multiple regression analysis explains that climate variability is a dominant factor and controlled 81% of the cotton production in the study area from 1990 to 2019, while it controlled 73% of the production from 1990 to 2002 and 84% from 2002 to 2019. These findings reveal that climate factors affact the distinct spatial pattern of changes in cotton yield variability at the tehsil level.
Cotton is a revenue source for cotton-producing countries; as the second-largest crop in Pakistan, it significantly contributes to its economy. Over the past few decades, cotton productivity has become unstable in Pakistan, and climate change is one of the main factors that impact cotton yield. Due to climate change, it becomes very important to understand the change trend and its impact on cotton yield at the regional level. Here, we investigate the relationship of standardized cotton yield variability with the variability of climate factors using a 15-yr moving window. The piecewise regression was fitted to obtain the trend-shifting point of climate factors. The results show that precipitation has experienced an overall decreasing trend of –0.64 mm/yr during the study period, with opposing trends of –1.39 mm/yr and 1.52 mm/yr before and after the trend-shifting point, respectively. We found that cotton yield variability increased at a rate of 0.17%/yr, and this trend was highly correlated with the variability of climate factors. The multiple regression analysis explains that climate variability is a dominant factor and controlled 81% of the cotton production in the study area from 1990 to 2019, while it controlled 73% of the production from 1990 to 2002 and 84% from 2002 to 2019. These findings reveal that climate factors affact the distinct spatial pattern of changes in cotton yield variability at the tehsil level.
2023, 33(2): 363-376.
doi: 10.1007/s11769-023-1332-6
Abstract:
Human-wildlife conflict (HWC) negatively impacts both humans and wildlife. Attitudes of local residents have been critical in promoting wildlife conservation. It is therefore necessary to understand the characteristics of HWC and identify influential factors on attitudes towards conservation to implement conservation strategies efficiently. This research focused on features of human-elephant interactions, while attitudes and values regarding the small population of Asian elephants (Elephas maximus) in Nangunhe National Nature Reserve (NNR), Yunnan, China. The total of 327 valid questionnaires were gathered around the area where Asian elephants were distributed. Logistic regression models were employed to analyze the correlations among five predictor variables (‘Area’, ‘Family size’, ‘Annual income’, ‘Quantity of family members in non-primary industries’ and ‘Experiencing loss or not’) and three response variables (‘Attitude towards elephants’, ‘Perception of the values of elephants’ and ‘Attitude towards tourism development’). The study area was densely forested with tea plants, rubber trees, corns and sugarcane. There, 25.99% of respondents reported the experience of human-elephant conflict (HEC), with crop raiding and cash crop damages being the major conflict types. To demonstrate respect for elephants and to mitigate HEC, a unique custom called ‘Giving tribute to elephants’ was developed long ago. Respondents’ township with an official annual festival of ‘Giving Tribute to Elephants’ (odds ratio (OR) = 2.75, P = 1.73 × 10−6) and higher annual income (OR = 2.09, P = 5.45 × 10−5) significantly contributed to forming a more positive attitude towards elephants, whereas HEC itself have contributed to a more negative attitude (OR = 0.50, P = 3.29 × 10−3). Therefore, we propose that: 1) reducing human-elephant conflict by testing multiple mitigation measures and adopting the most effective one of them; 2) enhancing local livelihoods through the development of ecological products and ecotourism; and 3) preserving and developing the Wa culture in this region. The study area deserves more attention and further research to explore and obtain endorsement from the public to achieve coexistence between human and wildlife.
Human-wildlife conflict (HWC) negatively impacts both humans and wildlife. Attitudes of local residents have been critical in promoting wildlife conservation. It is therefore necessary to understand the characteristics of HWC and identify influential factors on attitudes towards conservation to implement conservation strategies efficiently. This research focused on features of human-elephant interactions, while attitudes and values regarding the small population of Asian elephants (Elephas maximus) in Nangunhe National Nature Reserve (NNR), Yunnan, China. The total of 327 valid questionnaires were gathered around the area where Asian elephants were distributed. Logistic regression models were employed to analyze the correlations among five predictor variables (‘Area’, ‘Family size’, ‘Annual income’, ‘Quantity of family members in non-primary industries’ and ‘Experiencing loss or not’) and three response variables (‘Attitude towards elephants’, ‘Perception of the values of elephants’ and ‘Attitude towards tourism development’). The study area was densely forested with tea plants, rubber trees, corns and sugarcane. There, 25.99% of respondents reported the experience of human-elephant conflict (HEC), with crop raiding and cash crop damages being the major conflict types. To demonstrate respect for elephants and to mitigate HEC, a unique custom called ‘Giving tribute to elephants’ was developed long ago. Respondents’ township with an official annual festival of ‘Giving Tribute to Elephants’ (odds ratio (OR) = 2.75, P = 1.73 × 10−6) and higher annual income (OR = 2.09, P = 5.45 × 10−5) significantly contributed to forming a more positive attitude towards elephants, whereas HEC itself have contributed to a more negative attitude (OR = 0.50, P = 3.29 × 10−3). Therefore, we propose that: 1) reducing human-elephant conflict by testing multiple mitigation measures and adopting the most effective one of them; 2) enhancing local livelihoods through the development of ecological products and ecotourism; and 3) preserving and developing the Wa culture in this region. The study area deserves more attention and further research to explore and obtain endorsement from the public to achieve coexistence between human and wildlife.
2023, 33(2): 377-392.
doi: 10.1007/s11769-023-1337-1
Abstract:
Vegetation is an important factor linking the atmosphere, water, soil, and biological functions, and it plays a specific role in the climate change response and sustainable development of regional economies. However, little information is available on vegetation vulnerability and its driving mechanism. Therefore, studying temporal and spatial change characteristics of vegetation and their corresponding mechanisms is important for assessing ecosystem stability and formulating ecological policies in the Kherlen River Basin. We used Moderate-resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) remote sensing images from 2000 to 2020 to analyse temporal changes in NDVI with the autoregressive moving average model (ARMA) and the breaks for additive season trend (BFAST) in the basin and to assess natural, anthropogenic and topographic factors with the Geodetector model. The results show that: 1) the long NDVI time series remained stable in the Kherlen River Basin from 2000 to 2020, with a certain significant mutation period from 2013 to 2017; 2) the coefficient of variation (CV) in the analysis of the spatial NDVI was generally constant, mainly at the level of 0.01–0.07, and the spatial NDVI change was minimally impacted by external interference; and 3) temperature and precipitation are the key factors affecting the NDVI in the basin, and changes in local hydrothermal conditions directly affect the local NDVI. The results of this study could provide a scientific basis for the effective protection of the ecological environment and will aid in understanding the influence of vegetation change mechanisms and the corresponding factors.
Vegetation is an important factor linking the atmosphere, water, soil, and biological functions, and it plays a specific role in the climate change response and sustainable development of regional economies. However, little information is available on vegetation vulnerability and its driving mechanism. Therefore, studying temporal and spatial change characteristics of vegetation and their corresponding mechanisms is important for assessing ecosystem stability and formulating ecological policies in the Kherlen River Basin. We used Moderate-resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) remote sensing images from 2000 to 2020 to analyse temporal changes in NDVI with the autoregressive moving average model (ARMA) and the breaks for additive season trend (BFAST) in the basin and to assess natural, anthropogenic and topographic factors with the Geodetector model. The results show that: 1) the long NDVI time series remained stable in the Kherlen River Basin from 2000 to 2020, with a certain significant mutation period from 2013 to 2017; 2) the coefficient of variation (CV) in the analysis of the spatial NDVI was generally constant, mainly at the level of 0.01–0.07, and the spatial NDVI change was minimally impacted by external interference; and 3) temperature and precipitation are the key factors affecting the NDVI in the basin, and changes in local hydrothermal conditions directly affect the local NDVI. The results of this study could provide a scientific basis for the effective protection of the ecological environment and will aid in understanding the influence of vegetation change mechanisms and the corresponding factors.
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