Spatially and temporally varying relationships between ecological footprint and influencing factors in China’s provinces Using Geographically Weighted Regression (GWR)

Wu, Decun

doi:10.1016/j.jclepro.2020.121089

Cited by 99 publications

(48 citation statements)

References 46 publications

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“…As the result of the current pursuit of becoming innovation-driven, China's future economic growth will be driven mainly by the benefits of scientific and technological innovation (Wei et al, 2015;Wu, 2020). Since advanced technological innovations tend to be greener, higher levels of green innovation are an important driver of China's sustainable development (Ramdhani et al, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Effect of green innovation efficiency on ecological footprint in 283 Chinese Cities from 2008 to 2018

Dai

2021

Environ Dev Sustain

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The slack-based measure (SBM) model was used in this study to calculate the urban green innovation efficiency (GIE) of Chinese 283 cities during 2008-2018, and the night light data from the defense meteorological satellite program/operational linescan system (DMSP/OLS) were used to characterize the economic development level. On the basis, efforts were made to analyze how ecological footprint is affected by urban GIE at varying economic development levels under the Hansen threshold regression model and reveal the mechanism for ecological footprint to receive influence from urban GIE through the mediation effect model. The results show that: (1) The improvement in the urban GIE of the investigated cities during the study period has a negative double threshold in influencing ecological footprint throughout China. However, with higher economic development level, the inhibitory effect gradually weakens, with the elastic coefficient changing from − 0.3046 and − 0.2132 to − 0.1392 at a 1% significant level. (2) The inhibitory effect on ecological footprint from urban GIE is spatially heterogeneous in Chinese cities. In eastern cities other than central and western cities, urban GIE exerts the strongest inhibitory effect on ecological footprint, with the corresponding coefficient being − 0.3972 at a 1% significant level. Moreover, the inhibition in eastern and central regions is strengthened with higher economic development level. Nevertheless, before crossing the second threshold, the inhibitory effect of urban GIE on ecological footprint in western China does not appear, with the coefficient being 0.1899 and 0.1379, respectively, with at a 1% significant level. (3) Industrial structure and energy structure play a mediating role in the effect of urban GIE on ecological footprint. By contrast, population aggregation and infrastructure are important driving forces for the increase of ecological footprint.

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Section: Literature Reviewmentioning

confidence: 99%

Effect of green innovation efficiency on ecological footprint in 283 Chinese Cities from 2008 to 2018

Dai

2021

Environ Dev Sustain

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“…A few studies have suggested that landscape based parameters are closely associated and influenced by any changes in neighbouring areas (Fotheringham et al, 1998). Also, landscape-level spatial correlation and spatial heterogeneity between risk and parameters become a critical aspect that cannot be addressed through simple OLS methods (Wu, 2020). Therefore, this study has adopted a spatial GWR to capture the spatial heterogeneity, if any, between the dependent and independent variables using fixed Gaussian Kernel 6 method, which makes a significant contribution to estimating local coefficients.…”

Section: Methodsmentioning

confidence: 99%

“…R-square value was used to understand the explanatory power. Pseudo t value was considered to check the significance of the local parameters (Wu, 2020). ANOVA test was carried out to test the null hypothesis that the GWR model has no significant improvement than OLS (Nakaya, 2009).…”

Section: Estimation Of Local Associationmentioning

confidence: 99%

Spatio-temporal Assessment of Landscape Ecological Risk and Associated Drivers: A Case Study of Delhi

Mondal

Sharma

Kundu

et al. 2021

Environment and Urbanization ASIA

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Urbanization is considered as the key driver for land use and land cover (LULC) changes across the globe and Delhi is no exception to this phenomenon. The population of Delhi has almost doubled from 8.4 million in 1991 to 16.3 million in 2011. Correspondingly, the built-up area has also increased from 336.82 to 598.22 km 2 during 1999–2018. This urban expansion has led to emergence of serious ecological risk and fragmentation of the landscape. In this context, it is imperative to analyse the level of risks induced by such urban expansion and its underlying associations with other factors. This article quantifies the LULC changes in Delhi during 1999–2018 using Landsat 5 (TM) and Landsat 8 (OLI) data. A spatio-temporal sprawl induced risk assessment index has been developed by combining landscape fragmentation score and land use land cover vulnerability score. The landscape fragmentation score was based on four landscape metrics, whereas the vulnerability score was computed from LULC data. The article also assesses the association between risk areas and economic activities, environmental and infrastructural amenities that are considered key drivers of urban expansion in Delhi. To estimate spatio-temporal variability between risk areas and key drivers, ordinary least square regression and geographical weighted regression (GWR) were used. The GWR results reveal that sprawl-induced ecological risk in Delhi is strongly associated with economic activity, infrastructural accessibility and environmental amenities. This spatial empirical assessment also shows that urban growth incentives or services such as roads, metro rail, schools and hospitals can also create pressure on the landscape if local authorities arbitrarily provide these services across space without considering the associated risks.

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“…Regarding the prevalence of COVID-19, it is clear that the variables are spatially correlated and according to this, we should consider the spatial correlation between independent variables and dependent variables. Fotheringham proposes geographical weighted regression (GWR) which consider spatial heterogeneity, geographic coordinates and core function to carry out local regression estimation on adjacent subsamples of each group (Wu, 2020). The geographically weighted regression (GWR) model expands the classical regression framework that effectively addresses issues of spatial heterogeneity by enabling the variable coe cients to change with the spatial locations (Sun and Xu, 2016).…”

Section: Methodsmentioning

confidence: 99%

The Spatial Predicting of COVID-19 Incidence and Its Mortality Based On OLS and GWR Models in Iran

Ghanbari

Baradaran

Ahmadi

et al. 2021

Preprint

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Background: Within six months of the COVID-19 outbreak, 350279 people were infected, and 20125 people died of COVID-19 in Iran. There is an urgent need to find the most accurate effective indicators on this disease's outbreak in order to control and predict. Methods: We examined the effect of 36 demographic, economic, environmental, health infrastructure, social, and topographic independent variables on the COVID-19 infection and mortality rates using the ordinary least squares (OLS) model in ArcGIS 10.5. Regarding adjusted R-squared>0/7, we selected 20 variables for COVID-19 infection rate and 16 variables for the mortality rate. The collinearity problem between the selected variables resolved after using the variance inflation factor (VIF). Then, we performed the OLS and geographically weighted regression (GWR) models in ArcGIS 10.5.Results: Having a large number of men, having a large population, lack of specialist doctors, lack of hospital, having a large urban population, having a large number of people aged 65 and over or older individuals, and high natural mortality rate had the most prominent impact on the COVID-19 infection increasing rate. Also, lack of ICU beds, low number of insured people, lack of subspecialist physicians, and lack of hospital beds had the most prominent impact on increasing of COVID-19 mortality. Then the variables with VIF above 7.5 were removed and finally, high incoming immigrants rate and lack of nurses were identified as two independent variables to predict COVID-19 infection rate. In addition, high incoming immigrants rate and high number of doctor consultation were recognized as two variables to predict mortality rate due to COVID-19. The results of the Akaike information criterion (AIC) and adj.R2 showed that both models were appropriate for these analyses.Conclusions: Based on our results, there would be a considerable increase in COVID-19 infection in Kerman, Esfahan, and Kermanshah provinces. In addition, there would be a remarkable decrease in COVID-19 infection in Khuzestan, Lorestan, Azarbayjan Shargi, and Tehran provinces. Regarding COVID-19 mortality, there would be a substantial rise in Fars and Khorasan Razavi provinces. Moreover, our analyses predicted a considerable diminish in COVID-19 mortality in Tehran, Ardebil, Zanjan, Gilan, Golestan, Lorestan, Khuzestan, Bushehr, and Hormozgan provinces.

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Spatially and temporally varying relationships between ecological footprint and influencing factors in China’s provinces Using Geographically Weighted Regression (GWR)

Cited by 99 publications

References 46 publications

Effect of green innovation efficiency on ecological footprint in 283 Chinese Cities from 2008 to 2018

Effect of green innovation efficiency on ecological footprint in 283 Chinese Cities from 2008 to 2018

Spatio-temporal Assessment of Landscape Ecological Risk and Associated Drivers: A Case Study of Delhi

The Spatial Predicting of COVID-19 Incidence and Its Mortality Based On OLS and GWR Models in Iran

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