2022 Vol. 32, No. 4
Display Method:
2022, 32(4): 549-562.
doi: 10.1007/s11769-022-1284-2
Abstract:
The Belt and Road Initiative (BRI) has aroused rich discussions about the possible increase in carbon dioxide emission under the arduous global carbon dioxide emission reduction task. Adopting the methods of input-output technique and complex network analysis, we first construct a fairer method to trace carbon dioxide emission transfer based on global value chains, then trace the source of carbon dioxide emission transfer to the Silk Roads countries with a long-term multiple regional input-output database. We find that, first, after the proposal of the BRI, the total direct carbon dioxide emissions of the Silk Roads countries and China’s proportion of carbon dioxide emission transfer to the other Silk Roads countries have both declined. Second, the Silk Roads countries are generally the net receivers of carbon dioxide emission transfer, and the inflow is mainly distributed in Southeast Asian countries and core countries in other sub-regions. Then, the transfer of carbon dioxide emission accepted by the Silk Roads countries comes mostly from large developing countries, such as China, Russia, and India, and developed countries, such as the United States, Japan, and Germany. The products are mainly concentrated in energy and chemical industries, as well as heavy industries, such as mining and quarrying, and metal products. We suggest that, due to the high degree of spatial and industrial concentrations of carbon dioxide emission transfer, it is necessary to make targeted policies for these countries and industries to reduce these transfers.
The Belt and Road Initiative (BRI) has aroused rich discussions about the possible increase in carbon dioxide emission under the arduous global carbon dioxide emission reduction task. Adopting the methods of input-output technique and complex network analysis, we first construct a fairer method to trace carbon dioxide emission transfer based on global value chains, then trace the source of carbon dioxide emission transfer to the Silk Roads countries with a long-term multiple regional input-output database. We find that, first, after the proposal of the BRI, the total direct carbon dioxide emissions of the Silk Roads countries and China’s proportion of carbon dioxide emission transfer to the other Silk Roads countries have both declined. Second, the Silk Roads countries are generally the net receivers of carbon dioxide emission transfer, and the inflow is mainly distributed in Southeast Asian countries and core countries in other sub-regions. Then, the transfer of carbon dioxide emission accepted by the Silk Roads countries comes mostly from large developing countries, such as China, Russia, and India, and developed countries, such as the United States, Japan, and Germany. The products are mainly concentrated in energy and chemical industries, as well as heavy industries, such as mining and quarrying, and metal products. We suggest that, due to the high degree of spatial and industrial concentrations of carbon dioxide emission transfer, it is necessary to make targeted policies for these countries and industries to reduce these transfers.
2022, 32(4): 563-579.
doi: 10.1007/s11769-022-1286-0
Abstract:
Short-term temperature fluctuations (STFs), including amplitude and frequency fluctuations, are one of the main features of weather and play vital roles in determining the type of ecosystem present. Although temperature fluctuations at different time scales have been extensively discussed, the research on week-scale STFs is lacking. In this study, we developed a method, that can quantify the amplitude and frequency of STFs by the thresholds from all years. We used this method to quantify the amplitude and frequency of the 7-d STFs from 1951 to 2019 across China. Our results indicate that the amplitude of the STF was much higher in the eastern part of China than in the western part, while the frequency of the STF was higher in the middle part than in the southern and northern parts; furthermore, the STF was highly dependent on internal factors such as topography. The long-term STF mainly showed a decreasing trend before 1990, which implies that temperature became increasingly stable from the 1950s to the 1990s. The main influencing factors were related to topography since the trends were relatively consistent in space. A case study in Taihu Lake showed that an unstable STF in winter and summer resulted in a smaller bloom area in the following spring and autumn. Our method could eliminate seasonal effects and is capable of analyzing STFs at scales ranging from days to years. Quantifications of the amplitude and frequency also make the STF indicators more comprehensive. Furthermore, the STF increased significantly across most of China after 1990, which implies that temperature is becoming increasingly unstable. The drivers of these STFs are related to human impacts since the trends are different in space.
Short-term temperature fluctuations (STFs), including amplitude and frequency fluctuations, are one of the main features of weather and play vital roles in determining the type of ecosystem present. Although temperature fluctuations at different time scales have been extensively discussed, the research on week-scale STFs is lacking. In this study, we developed a method, that can quantify the amplitude and frequency of STFs by the thresholds from all years. We used this method to quantify the amplitude and frequency of the 7-d STFs from 1951 to 2019 across China. Our results indicate that the amplitude of the STF was much higher in the eastern part of China than in the western part, while the frequency of the STF was higher in the middle part than in the southern and northern parts; furthermore, the STF was highly dependent on internal factors such as topography. The long-term STF mainly showed a decreasing trend before 1990, which implies that temperature became increasingly stable from the 1950s to the 1990s. The main influencing factors were related to topography since the trends were relatively consistent in space. A case study in Taihu Lake showed that an unstable STF in winter and summer resulted in a smaller bloom area in the following spring and autumn. Our method could eliminate seasonal effects and is capable of analyzing STFs at scales ranging from days to years. Quantifications of the amplitude and frequency also make the STF indicators more comprehensive. Furthermore, the STF increased significantly across most of China after 1990, which implies that temperature is becoming increasingly unstable. The drivers of these STFs are related to human impacts since the trends are different in space.
2022, 32(4): 580-591.
doi: 10.1007/s11769-022-1287-z
Abstract:
In order to investigate the dynamic evolution of the sandy land-lake-vegetation landscape in Songnen Sandy Land (SSL) and its response to climate change and human activities, the distribution pattern, evolution, and driving mechanisms of the landscape were analyzed based on Landsat satellite images and meteorological and socio-economic data during 1980–2020. The results indicate that the area of sandy land exhibited an upward fluctuation during the last 40 yr, with a net increase of 251.75 km2 at an increment rate of 3.80%/10 yr. The lake area also exhibited an upward fluctuation, with a net increase of 1200.95 km2 at an increment rate of 20.42%/10 yr. Vegetation coverage decreased by 2633.30 km2, with areas of low vegetation coverage exhibiting a trend of initial decline and subsequent increase, areas of medium vegetation coverage showed an upward fluctuation, and areas of high vegetation coverage showed a trend of initial increase and subsequent decrease, with overall changes of –0.67%/yr, 1.12%/yr, and 0.17%/yr, respectively. The relationships between sandy land, lakes, and vegetation coverage were significant, with areas of sandy land and low vegetation coverage showing the strongest correlation. The dynamic evolution of landscape is controlled by regional climatic and socio-economic factors, with socio-economic factors as the first principal component contributing up to 59.64%.
In order to investigate the dynamic evolution of the sandy land-lake-vegetation landscape in Songnen Sandy Land (SSL) and its response to climate change and human activities, the distribution pattern, evolution, and driving mechanisms of the landscape were analyzed based on Landsat satellite images and meteorological and socio-economic data during 1980–2020. The results indicate that the area of sandy land exhibited an upward fluctuation during the last 40 yr, with a net increase of 251.75 km2 at an increment rate of 3.80%/10 yr. The lake area also exhibited an upward fluctuation, with a net increase of 1200.95 km2 at an increment rate of 20.42%/10 yr. Vegetation coverage decreased by 2633.30 km2, with areas of low vegetation coverage exhibiting a trend of initial decline and subsequent increase, areas of medium vegetation coverage showed an upward fluctuation, and areas of high vegetation coverage showed a trend of initial increase and subsequent decrease, with overall changes of –0.67%/yr, 1.12%/yr, and 0.17%/yr, respectively. The relationships between sandy land, lakes, and vegetation coverage were significant, with areas of sandy land and low vegetation coverage showing the strongest correlation. The dynamic evolution of landscape is controlled by regional climatic and socio-economic factors, with socio-economic factors as the first principal component contributing up to 59.64%.
2022, 32(4): 592-605.
doi: 10.1007/s11769-022-1288-y
Abstract:
Spatio-temporal dynamic monitoring of soil moisture is highly important to management of agricultural and vegetation ecosystems. The temperature-vegetation dryness index based on the triangle or trapezoid method has been used widely in previous studies. However, most existing studies simply used linear regression to construct empirical models to fit the edges of the feature space. This requires extensive data from a vast study area, and may lead to subjective results. In this study, a Modified Temperature-Vegetation Dryness Index (MTVDI) was used to monitor surface soil moisture status using MODIS (Moderate-resolution Imaging Spectroradiometer) remote sensing data, in which the dry edge conditions were determined at the pixel scale based on surface energy balance. The MTVDI was validated by field measurements at 30 sites for 10 d and compared with the Temperature-Vegetation Dryness Index (TVDI). The results showed that the R2 for MTVDI and soil moisture obviously improved (0.45 for TVDI, 0.69 for MTVDI). As for spatial changes, MTVDI can also better reflect the actual soil moisture condition than TVDI. As a result, MTVDI can be considered an effective method to monitor the spatio-temporal changes in surface soil moisture on a regional scale.
Spatio-temporal dynamic monitoring of soil moisture is highly important to management of agricultural and vegetation ecosystems. The temperature-vegetation dryness index based on the triangle or trapezoid method has been used widely in previous studies. However, most existing studies simply used linear regression to construct empirical models to fit the edges of the feature space. This requires extensive data from a vast study area, and may lead to subjective results. In this study, a Modified Temperature-Vegetation Dryness Index (MTVDI) was used to monitor surface soil moisture status using MODIS (Moderate-resolution Imaging Spectroradiometer) remote sensing data, in which the dry edge conditions were determined at the pixel scale based on surface energy balance. The MTVDI was validated by field measurements at 30 sites for 10 d and compared with the Temperature-Vegetation Dryness Index (TVDI). The results showed that the R2 for MTVDI and soil moisture obviously improved (0.45 for TVDI, 0.69 for MTVDI). As for spatial changes, MTVDI can also better reflect the actual soil moisture condition than TVDI. As a result, MTVDI can be considered an effective method to monitor the spatio-temporal changes in surface soil moisture on a regional scale.
2022, 32(4): 606-619.
doi: 10.1007/s11769-022-1289-x
Abstract:
Chromophoric dissolved organic matter (CDOM) is a key component of organic matter that contributes to the ecological functioning of lakes. The lakes in Taihu Lake Basin play an important role in maintaining regional ecological stabilities; however, the optical characteristics of the CDOM in the upstream and downstream lakes in this basin have not yet been systematically studied. Here, the optical characteristics of CDOM in ten lakes of upstream and downstream of the Taihu Lake Basin were studied using UV-Visible and excitation-emission matrix spectroscopies. Three different fluorophores consisting of two humic-like components (C1, C2) and one protein-like component (C3) were identified by parallel factor analysis. Soil or surface erosion was responsible for the higher abundance of C1 in the upstream lakes, and increased biological activities accounted for the higher abundance of C3 in the downstream lakes. Rainfall erosion in the wet season led to an increase in CDOM. We also found that the photodegradation and flocculation degree, which played a significant role in reducing CDOM, were higher in downstream lakes than in upstream lakes. Optical analysis of CDOM provides a promising method for monitoring water qualities (e.g., total phosphorus and potassium permanganate index) in each lake. Reductions in soil or surface erosion in the upstream are needed to improve water quality.
Chromophoric dissolved organic matter (CDOM) is a key component of organic matter that contributes to the ecological functioning of lakes. The lakes in Taihu Lake Basin play an important role in maintaining regional ecological stabilities; however, the optical characteristics of the CDOM in the upstream and downstream lakes in this basin have not yet been systematically studied. Here, the optical characteristics of CDOM in ten lakes of upstream and downstream of the Taihu Lake Basin were studied using UV-Visible and excitation-emission matrix spectroscopies. Three different fluorophores consisting of two humic-like components (C1, C2) and one protein-like component (C3) were identified by parallel factor analysis. Soil or surface erosion was responsible for the higher abundance of C1 in the upstream lakes, and increased biological activities accounted for the higher abundance of C3 in the downstream lakes. Rainfall erosion in the wet season led to an increase in CDOM. We also found that the photodegradation and flocculation degree, which played a significant role in reducing CDOM, were higher in downstream lakes than in upstream lakes. Optical analysis of CDOM provides a promising method for monitoring water qualities (e.g., total phosphorus and potassium permanganate index) in each lake. Reductions in soil or surface erosion in the upstream are needed to improve water quality.
2022, 32(4): 620-642.
doi: 10.1007/s11769-022-1290-4
Abstract:
Reference evapotranspiration (ET0) is a vital component in hydrometeorological research and is widely applied to various aspects, such as water resource management, hydrological modeling, irrigation deployment, and understanding and predicting the influence of hydrologic cycle variations on future climate and land use changes. Quantifying the influence of various meteorological variables on ET0 is not only helpful for predicting actual evapotranspiration but also has important implications for understanding the impact of global climate change on regional water resources. Based on daily data from 69 meteorological stations, the present study analyzed the spatiotemporal pattern of ET0 and major contributing meteorological variables to ET0 from 1960 to 2017 by the segmented regression model, Mann-Kendall test, wavelet analysis, generalized linear model, and detrending method. The results showed that the annual ET0 declined slightly because of the combined effects of the reduction in solar radiation and wind speed and the increase in vapor pressure deficit (VPD) and average air temperature in the Loess Plateau (LP) during the past 58 yr. Four change points were detected in 1972, 1990, 1999, and 2010, and the annual ET0 showed a zigzag change trend of ‘increasing-decreasing-increasing-decreasing-increasing’. Wind speed and VPD played a leading role in the ET0 changes from 1960 to 1990 and from 1991 to 2017, respectively. This study confirms that the dominant meteorological factors affecting ET0 had undergone significant changes due to global climate change and vegetation greening in the past 58 years, and VPD had become the major factor controlling the ET0 changes on the LP. The data presented herein will contribute to increasing the accuracy of predictions on future changes in ET0.
Reference evapotranspiration (ET0) is a vital component in hydrometeorological research and is widely applied to various aspects, such as water resource management, hydrological modeling, irrigation deployment, and understanding and predicting the influence of hydrologic cycle variations on future climate and land use changes. Quantifying the influence of various meteorological variables on ET0 is not only helpful for predicting actual evapotranspiration but also has important implications for understanding the impact of global climate change on regional water resources. Based on daily data from 69 meteorological stations, the present study analyzed the spatiotemporal pattern of ET0 and major contributing meteorological variables to ET0 from 1960 to 2017 by the segmented regression model, Mann-Kendall test, wavelet analysis, generalized linear model, and detrending method. The results showed that the annual ET0 declined slightly because of the combined effects of the reduction in solar radiation and wind speed and the increase in vapor pressure deficit (VPD) and average air temperature in the Loess Plateau (LP) during the past 58 yr. Four change points were detected in 1972, 1990, 1999, and 2010, and the annual ET0 showed a zigzag change trend of ‘increasing-decreasing-increasing-decreasing-increasing’. Wind speed and VPD played a leading role in the ET0 changes from 1960 to 1990 and from 1991 to 2017, respectively. This study confirms that the dominant meteorological factors affecting ET0 had undergone significant changes due to global climate change and vegetation greening in the past 58 years, and VPD had become the major factor controlling the ET0 changes on the LP. The data presented herein will contribute to increasing the accuracy of predictions on future changes in ET0.
2022, 32(4): 643-664.
doi: 10.1007/s11769-022-1291-3
Abstract:
To investigate the spatial synergy between agricultural development level and transport superiority degree in grain-producing areas of the central Jilin Province and the driving mechanism between them, 22 counties in the central Jilin Province were used as the research units to calculate agricultural development level and transport superiority degree using the entropy weighting method, weighted travel time and raster cost distance. The spatial econometric model was used to analyze the mechanism of the mutual influence between the two. The main conclusions are as follows. 1) Agricultural development level around Changchun, the provincial capital city, and in the areas of Changchun-Jilin and Changchun-Gongzhuling is high, whereas the development level of the counties in the southwest is low. 2) Transport superiority degree of each county has improved, the overall connectivity of the road networks has been optimized, and the level of transportation accessibility has shown the development trend of ‘centralization’, exhibiting the characteristics of proximity diffusion. 3) Locally, there is spatial heterogeneity in the mutual driving effects of the two, with six main patterns. 4) The theoretical conditions of von Thunen’s agricultural location have changed under the conditions of market economy due to the improvement of the transport networks, the new model of agricultural development, and the changes of the market system.
To investigate the spatial synergy between agricultural development level and transport superiority degree in grain-producing areas of the central Jilin Province and the driving mechanism between them, 22 counties in the central Jilin Province were used as the research units to calculate agricultural development level and transport superiority degree using the entropy weighting method, weighted travel time and raster cost distance. The spatial econometric model was used to analyze the mechanism of the mutual influence between the two. The main conclusions are as follows. 1) Agricultural development level around Changchun, the provincial capital city, and in the areas of Changchun-Jilin and Changchun-Gongzhuling is high, whereas the development level of the counties in the southwest is low. 2) Transport superiority degree of each county has improved, the overall connectivity of the road networks has been optimized, and the level of transportation accessibility has shown the development trend of ‘centralization’, exhibiting the characteristics of proximity diffusion. 3) Locally, there is spatial heterogeneity in the mutual driving effects of the two, with six main patterns. 4) The theoretical conditions of von Thunen’s agricultural location have changed under the conditions of market economy due to the improvement of the transport networks, the new model of agricultural development, and the changes of the market system.
2022, 32(4): 665-675.
doi: 10.1007/s11769-022-1292-2
Abstract:
Climate change can affect wind erosion power and hence induce changes in wind erosion rates. In this study, the wind erosion climate factor (C-factor), proposed by the Food and Agriculture Organization of the United Nations, was used to assess the impact of changes in climate on wind erosion climatic erosivity. The Mann-Kendall test was employed to detect trends in the C-factor during the period of 1961–2017 in the farming-pastoral zone of northern China. Sensitivity analysis was used to determine the sensitivity of the C-factor to changes in key climate factors. Furthermore, a comparison of the contributions of different climate factors was carried out to understand their impact on changes in the C-factor. The results indicated that most of the surveyed region exhibited decreasing trends in wind speed at a confidence level of 90%, while maximum and minimum temperatures showed increasing trends throughout the study area. As a consequence of decreasing wind speed, the annual C-factor exhibited significant decreasing trends, with a mean slope of –0.58/yr. Seasonal analysis revealed that in most regions, the changes in the C-factor had significant decreasing trends in spring, winter, and autumn, while in more than two-thirds of the study area, no significant change trends in the C-factor were detected in summer at a confidence level of 90%. Sensitivity analysis showed that the C-factor was most sensitive to wind speed, and that the sensitivity coefficients from July to September were much higher than those in other months. Contribution analysis revealed that, for most stations, wind speed (with greater values of sensitivity coefficients) was the dominant factor in the change of C-factor, while for some stations, the minimum temperature made the most contribution to the C-factor’s change due to its dramatic changes during the study period. Although the minimum temperature sensitivity coefficient was the lowest of all the sensitivity coefficients, it is urgent to evaluate the expected impact of minimum temperature due to its possible changes in the future.
Climate change can affect wind erosion power and hence induce changes in wind erosion rates. In this study, the wind erosion climate factor (C-factor), proposed by the Food and Agriculture Organization of the United Nations, was used to assess the impact of changes in climate on wind erosion climatic erosivity. The Mann-Kendall test was employed to detect trends in the C-factor during the period of 1961–2017 in the farming-pastoral zone of northern China. Sensitivity analysis was used to determine the sensitivity of the C-factor to changes in key climate factors. Furthermore, a comparison of the contributions of different climate factors was carried out to understand their impact on changes in the C-factor. The results indicated that most of the surveyed region exhibited decreasing trends in wind speed at a confidence level of 90%, while maximum and minimum temperatures showed increasing trends throughout the study area. As a consequence of decreasing wind speed, the annual C-factor exhibited significant decreasing trends, with a mean slope of –0.58/yr. Seasonal analysis revealed that in most regions, the changes in the C-factor had significant decreasing trends in spring, winter, and autumn, while in more than two-thirds of the study area, no significant change trends in the C-factor were detected in summer at a confidence level of 90%. Sensitivity analysis showed that the C-factor was most sensitive to wind speed, and that the sensitivity coefficients from July to September were much higher than those in other months. Contribution analysis revealed that, for most stations, wind speed (with greater values of sensitivity coefficients) was the dominant factor in the change of C-factor, while for some stations, the minimum temperature made the most contribution to the C-factor’s change due to its dramatic changes during the study period. Although the minimum temperature sensitivity coefficient was the lowest of all the sensitivity coefficients, it is urgent to evaluate the expected impact of minimum temperature due to its possible changes in the future.
2022, 32(4): 676-685.
doi: 10.1007/s11769-022-1293-1
Abstract:
Soil is the essential part for agricultural and environmental sciences, and soil salinity and soil water content are both the important influence factors for sustainable development of agriculture and ecological environment. Digital camera, as one of the most popular and convenient proximal sensing instruments, has its irreplaceable position for soil properties assessment. In this study, we collected 52 soil samples and photographs at the same time along the coast in Yancheng City of Jiangsu Province. We carefully analyzed the relationship between soil properties and image brightness, and found that soil salt content had higher correlation with average image brightness value than soil water content. From the brightness levels, the high correlation coefficients between soil salt content and brightness levels concentrated on the high brightness values, and the high correlation coefficients between soil water content and brightness levels focused on the low brightness values. Different significance levels (P) determined different brightness levels related to soil properties, hence P value setting can be an optional way to select brightness levels as the input variables for modeling soil properties. Given these information, random forest algorithm was applied to develop soil salt content and soil water content inversion models using randomly 70% of the dataset, and the rest data for testing models. The results showed that soil salt content model had high accuracy (Rv2 = 0.79, RMSEv = 12 g/kg, and RPDv = 2.18), and soil water content inversion model was barely satisfied (Rv2 = 0.47, RMSEv = 3.04%, and RPDv = 1.38). This study proposes a method of modeling soil properties with a digital camera. Combining unmanned aerial vehicle (UAV), it has potential popularization and application value for precise agriculture and land management.
Soil is the essential part for agricultural and environmental sciences, and soil salinity and soil water content are both the important influence factors for sustainable development of agriculture and ecological environment. Digital camera, as one of the most popular and convenient proximal sensing instruments, has its irreplaceable position for soil properties assessment. In this study, we collected 52 soil samples and photographs at the same time along the coast in Yancheng City of Jiangsu Province. We carefully analyzed the relationship between soil properties and image brightness, and found that soil salt content had higher correlation with average image brightness value than soil water content. From the brightness levels, the high correlation coefficients between soil salt content and brightness levels concentrated on the high brightness values, and the high correlation coefficients between soil water content and brightness levels focused on the low brightness values. Different significance levels (P) determined different brightness levels related to soil properties, hence P value setting can be an optional way to select brightness levels as the input variables for modeling soil properties. Given these information, random forest algorithm was applied to develop soil salt content and soil water content inversion models using randomly 70% of the dataset, and the rest data for testing models. The results showed that soil salt content model had high accuracy (Rv2 = 0.79, RMSEv = 12 g/kg, and RPDv = 2.18), and soil water content inversion model was barely satisfied (Rv2 = 0.47, RMSEv = 3.04%, and RPDv = 1.38). This study proposes a method of modeling soil properties with a digital camera. Combining unmanned aerial vehicle (UAV), it has potential popularization and application value for precise agriculture and land management.
2022, 32(4): 686-706.
doi: 10.1007/s11769-022-1294-0
Abstract:
The 14th Five-Year Plan period is a critical period for China to achieve high-quality development. Based on super-efficiency slacks-based measure (SBM) model, grey-related analysis (GRA) and other models, this paper studies the heterogeneity of the coupling relationship among technological innovation, industrial transformation and environmental efficiency in the Huaihai Economic Zone during the period of 2005‒2019. In addition, it analyzes the coupling mechanism of single and binary systems to the ternary system, which is of great significance for the collaborative symbiosis among systems. The findings are as follows. 1) The technological innovation, industrial transformation and environmental efficiency (TIE) systems of the Huaihai Economic Zone had significant spatial-temporal heterogeneity. Although their evaluation value fluctuated, the development trends are all positive. Ultimately, technological innovation is characterized by being high in the northeast and low in the southwest around Xuzhou, while other systems are relatively staggered in space. 2) The coupling of TIE systems is in transition, lack of orderly integration and benign interaction. However, the developing trend of interaction is also upward, and a spatial pattern driven by Xuzhou and Linyi as the dual cores has gradually formed. Moreover, the coupling is mostly manifested as outdated technological innovation and industrial transformation. Except for the final coordination of regenerative cities, the other resource types are all in transition. Cities in all traffic locations are still in transition. The overall system interaction of cities on Longhai Line (Lanzhou-Lianyungang Railway) is relatively optimal, and cities on Xinshi Line (Xinxiang-Rizhao Railway) are accelerating toward synergy. 3) The coupling status of TIE systems depends on the development of the single system and the interaction of the binary (2E) system. The coupling is closely related to technological innovation and Technology-Industry system, and is hindered by the inefficient interaction of Technology-Environment system. Specifically, the synergy of regenerative cities is attributed to the advantage of a single system and the effective integration of 2E systems. Beneficial from the advantages of environmental efficiency, the cities on Xinshi Line promote the synergy of the 2E and TIE systems. Therefore, while the Huaihai Economic Zone stimulates the development potential of the single and 2E systems, it is necessary to amplify the superimposition effect of systems in accordance on the basis of resource and location.
The 14th Five-Year Plan period is a critical period for China to achieve high-quality development. Based on super-efficiency slacks-based measure (SBM) model, grey-related analysis (GRA) and other models, this paper studies the heterogeneity of the coupling relationship among technological innovation, industrial transformation and environmental efficiency in the Huaihai Economic Zone during the period of 2005‒2019. In addition, it analyzes the coupling mechanism of single and binary systems to the ternary system, which is of great significance for the collaborative symbiosis among systems. The findings are as follows. 1) The technological innovation, industrial transformation and environmental efficiency (TIE) systems of the Huaihai Economic Zone had significant spatial-temporal heterogeneity. Although their evaluation value fluctuated, the development trends are all positive. Ultimately, technological innovation is characterized by being high in the northeast and low in the southwest around Xuzhou, while other systems are relatively staggered in space. 2) The coupling of TIE systems is in transition, lack of orderly integration and benign interaction. However, the developing trend of interaction is also upward, and a spatial pattern driven by Xuzhou and Linyi as the dual cores has gradually formed. Moreover, the coupling is mostly manifested as outdated technological innovation and industrial transformation. Except for the final coordination of regenerative cities, the other resource types are all in transition. Cities in all traffic locations are still in transition. The overall system interaction of cities on Longhai Line (Lanzhou-Lianyungang Railway) is relatively optimal, and cities on Xinshi Line (Xinxiang-Rizhao Railway) are accelerating toward synergy. 3) The coupling status of TIE systems depends on the development of the single system and the interaction of the binary (2E) system. The coupling is closely related to technological innovation and Technology-Industry system, and is hindered by the inefficient interaction of Technology-Environment system. Specifically, the synergy of regenerative cities is attributed to the advantage of a single system and the effective integration of 2E systems. Beneficial from the advantages of environmental efficiency, the cities on Xinshi Line promote the synergy of the 2E and TIE systems. Therefore, while the Huaihai Economic Zone stimulates the development potential of the single and 2E systems, it is necessary to amplify the superimposition effect of systems in accordance on the basis of resource and location.
2022, 32(4): 707-723.
doi: 10.1007/s11769-022-1295-z
Abstract:
Changes in natural geographic features and landscape patterns directly influence the hydrology and non-point source pollution processes in the watershed; however, to slow down non-point source pollution, it is necessary to distinguish their effects. But the non-point source pollution process is interactional as a result of multiple factors, and the collinearity between multiple independent variables limits our ability of reason diagnosis. Thus, taking the Burhatong River Basin, Northeast China as an example, the methods of hydrological simulation, geographic detectors, and redundancy analysis have been combined to determine the impact of natural geographic features and landscape patterns on non-point source pollution in the watershed. The Soil & Water Assessment Tool (SWAT) has been adopted to simulate the spatial and temporal distribution characteristics of total nitrogen and total phosphorus in the watershed. The results show that the proportions of agricultural land and forest area and the location-weighted landscape contrast index (LWLI) are the main indicators influencing the rivers total nitrogen and total phosphorus. The interaction of these indicators with natural geographic features and landscape configuration indicators also significantly influences the changes in total nitrogen (TN) and total phosphorus (TP). Natural geographical features and landscape patterns have different comprehensive effects on non-point source pollution in the dry and wet seasons. TN and TP loads are affected mainly by the change in landscape pattern, especially in the wet season. Although the ecological restoration program has improved forest coverage, the purification effect of increased forest coverage on the water quality in the watershed may be offset by the negative impact of increased forest fragmentation. The high concentration and complexity of farmland patches increase the risk of non-point source pollution spread to a certain extent.
Changes in natural geographic features and landscape patterns directly influence the hydrology and non-point source pollution processes in the watershed; however, to slow down non-point source pollution, it is necessary to distinguish their effects. But the non-point source pollution process is interactional as a result of multiple factors, and the collinearity between multiple independent variables limits our ability of reason diagnosis. Thus, taking the Burhatong River Basin, Northeast China as an example, the methods of hydrological simulation, geographic detectors, and redundancy analysis have been combined to determine the impact of natural geographic features and landscape patterns on non-point source pollution in the watershed. The Soil & Water Assessment Tool (SWAT) has been adopted to simulate the spatial and temporal distribution characteristics of total nitrogen and total phosphorus in the watershed. The results show that the proportions of agricultural land and forest area and the location-weighted landscape contrast index (LWLI) are the main indicators influencing the rivers total nitrogen and total phosphorus. The interaction of these indicators with natural geographic features and landscape configuration indicators also significantly influences the changes in total nitrogen (TN) and total phosphorus (TP). Natural geographical features and landscape patterns have different comprehensive effects on non-point source pollution in the dry and wet seasons. TN and TP loads are affected mainly by the change in landscape pattern, especially in the wet season. Although the ecological restoration program has improved forest coverage, the purification effect of increased forest coverage on the water quality in the watershed may be offset by the negative impact of increased forest fragmentation. The high concentration and complexity of farmland patches increase the risk of non-point source pollution spread to a certain extent.
2022, 32(4): 724-744.
doi: 10.1007/s11769-022-1296-y
Abstract:
As a traditional agricultural country, China has always prioritized agricultural development, and has increasingly focused on green and sustainable agricultural development. Based on the inter-provincial panel data for China from 1997 to 2019, this study divided these data into five periods according to the Five-Year Plan (FYP) of China, measured the agricultural eco-efficiency (AEE) values using the Super-SBM model, and then determined the spatial association network of the inter-provincial AEE of China using the improved gravity model. Finally, social network analysis (SNA) was used to further analyze the evolution process of AEE, and we developed a framework of how multidimensional proximity, which includes geographical, economic, technological, cognitive, and institutional proximity, made an influence on the formation of AEE spatial relation network. The findings indicated that: 1) in 1997−2019, the AEE in China was present in some spatial and temporal differences characteristics at the provincial scale, and we specifically found that national macro-regulation and policy incentives played a positive role in the long-term development of AEE. 2) The spatial correlation of AEE development among provincial regions were becoming closer and exhibits obvious spatial correlation and spillover effects. The evolution of the AEE network has clearly observable trends of hierarchization and aggregation, and the complexity of the correlation network continues to increase and exhibits spatial clustering characteristics that are dense in the east and sparse in the west. The network structure has changed from monocentric radiation to a multicentric network, and network nodes select the more advantageous nodes with which to connect. 3) Finally, the geographical proximity had a significant negative effect; the economic, technological, and institutional proximities were all observed to contribute to the AEE network formation, and cognitive proximity did not significantly influence this network formation.
As a traditional agricultural country, China has always prioritized agricultural development, and has increasingly focused on green and sustainable agricultural development. Based on the inter-provincial panel data for China from 1997 to 2019, this study divided these data into five periods according to the Five-Year Plan (FYP) of China, measured the agricultural eco-efficiency (AEE) values using the Super-SBM model, and then determined the spatial association network of the inter-provincial AEE of China using the improved gravity model. Finally, social network analysis (SNA) was used to further analyze the evolution process of AEE, and we developed a framework of how multidimensional proximity, which includes geographical, economic, technological, cognitive, and institutional proximity, made an influence on the formation of AEE spatial relation network. The findings indicated that: 1) in 1997−2019, the AEE in China was present in some spatial and temporal differences characteristics at the provincial scale, and we specifically found that national macro-regulation and policy incentives played a positive role in the long-term development of AEE. 2) The spatial correlation of AEE development among provincial regions were becoming closer and exhibits obvious spatial correlation and spillover effects. The evolution of the AEE network has clearly observable trends of hierarchization and aggregation, and the complexity of the correlation network continues to increase and exhibits spatial clustering characteristics that are dense in the east and sparse in the west. The network structure has changed from monocentric radiation to a multicentric network, and network nodes select the more advantageous nodes with which to connect. 3) Finally, the geographical proximity had a significant negative effect; the economic, technological, and institutional proximities were all observed to contribute to the AEE network formation, and cognitive proximity did not significantly influence this network formation.
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