Green development is the cognition of geography to human-nature nexus under the background of the new era.As China is facing various eco-environment problems,green development has become a key approach towards ecologi...Green development is the cognition of geography to human-nature nexus under the background of the new era.As China is facing various eco-environment problems,green development has become a key approach towards ecological progress,and it is ultimately an explicit means to respond to support sustainable development in China.Quantifying green development performance is essential to track efforts towards sustainability and guide policymakers.However,applying the balanced property of’Economy-Ecology-Society’of green development to its performance assessment is rarely discussed.Here we elaborated the connotation of green development and developed a quantification model with coupling coordination degree to assess green development performance of the largest old industrial base of China,Northeast China.We found that the green development performance has been improved from a score of 0.443 in 2003 to 0.530 in 2019 but the disparities of green development performance were enlarging over time,especially for the cities in Heilongjiang.A positive spatial autocorrelation phenomenon of green development performance was confirmed,and Low-Low clusters in the northeastern Heilongjiang and High-High clusters in the central-eastern Liaoning were discovered.This study suggests the need to track the spatio-temporal dynamics of green development performance to provide references for achieving sustainable development goals in northeast China and other regions.展开更多
PM_（2.5） has become an increasing public concern recently because of its visibility reduction and severe health risks. For the whole year of 2013, hourly PM_（2.5） data of 496 monitoring sites scattered in 74 citie...PM_（2.5） has become an increasing public concern recently because of its visibility reduction and severe health risks. For the whole year of 2013, hourly PM_（2.5） data of 496 monitoring sites scattered in 74 cities of China are collected to analyze temporal and spatial variability of PM_（2.5） concentration. Different temporal scales（seasonal variation, monthly variation and daily variation） and spatial scales（urban versus rural, typical areas and national scale） are discussed. Results show that PM_（2.5） concentration changes significantly in both long-term and short-term scales. An apparent bimodal pattern exists in daily variation of PM_（2.5） concentration and the daytime peak appears around 10：00 am while the lowest concentration appears around 16：00 pm. Spatial autocorrelation analysis and Ordinary Kriging are used to characterize spatial variability. Moran＇s I of PM_（2.5） concentration in three typical regions, the Beijing-Tianjin-Hebei region, the Yangtze River Delta region and the Pearl River Delta region, is 0.906, 0.693, 0.746, respectively, which indicates that PM_（2.5） is strong spatial correlated. Spatial distribution of annual PM_（2.5） concentration simulated by Ordinary Kriging shows that 7.94 million km2（83%） areas fail in meeting the requirement of China＇s National Ambient Air Quality Standards Level-2（35 mg/m3） and there are at least three concentrated highly polluted areas across the country.展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.41771138,41571152,41871158,41771179)National Science and Technology Basic Resources Survey Project(No.2017FY101303-1)。
文摘Green development is the cognition of geography to human-nature nexus under the background of the new era.As China is facing various eco-environment problems,green development has become a key approach towards ecological progress,and it is ultimately an explicit means to respond to support sustainable development in China.Quantifying green development performance is essential to track efforts towards sustainability and guide policymakers.However,applying the balanced property of’Economy-Ecology-Society’of green development to its performance assessment is rarely discussed.Here we elaborated the connotation of green development and developed a quantification model with coupling coordination degree to assess green development performance of the largest old industrial base of China,Northeast China.We found that the green development performance has been improved from a score of 0.443 in 2003 to 0.530 in 2019 but the disparities of green development performance were enlarging over time,especially for the cities in Heilongjiang.A positive spatial autocorrelation phenomenon of green development performance was confirmed,and Low-Low clusters in the northeastern Heilongjiang and High-High clusters in the central-eastern Liaoning were discovered.This study suggests the need to track the spatio-temporal dynamics of green development performance to provide references for achieving sustainable development goals in northeast China and other regions.
基金Supported by the National Natural Science Foundation of China(41571385)
文摘PM_（2.5） has become an increasing public concern recently because of its visibility reduction and severe health risks. For the whole year of 2013, hourly PM_（2.5） data of 496 monitoring sites scattered in 74 cities of China are collected to analyze temporal and spatial variability of PM_（2.5） concentration. Different temporal scales（seasonal variation, monthly variation and daily variation） and spatial scales（urban versus rural, typical areas and national scale） are discussed. Results show that PM_（2.5） concentration changes significantly in both long-term and short-term scales. An apparent bimodal pattern exists in daily variation of PM_（2.5） concentration and the daytime peak appears around 10：00 am while the lowest concentration appears around 16：00 pm. Spatial autocorrelation analysis and Ordinary Kriging are used to characterize spatial variability. Moran＇s I of PM_（2.5） concentration in three typical regions, the Beijing-Tianjin-Hebei region, the Yangtze River Delta region and the Pearl River Delta region, is 0.906, 0.693, 0.746, respectively, which indicates that PM_（2.5） is strong spatial correlated. Spatial distribution of annual PM_（2.5） concentration simulated by Ordinary Kriging shows that 7.94 million km2（83%） areas fail in meeting the requirement of China＇s National Ambient Air Quality Standards Level-2（35 mg/m3） and there are at least three concentrated highly polluted areas across the country.
