Driving force analysis and prediction of ecological footprint in urban agglomeration based on extended STIRPAT model and shared socioeconomic pathways (SSPs)

Li, Ziheng; Murshed, Muntasir; Yan, Pengdong

doi:10.1016/j.jclepro.2022.135424

Cited by 34 publications

(6 citation statements)

References 60 publications

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“…They transformed the IPAT model into a stochastic model called the STIRPAT model, where the statistical causes of environmental degradation are evaluated (Chikaraishi et al, 2015). Meng et al (2012) improved upon and broadened the STIRPAT model by adding other elements such as quadratic terms or various P, A, or T. Currently, the STIRPAT model has been successfully used to analyze the effects of various dynamic forces on the diversity of environmental degradation (Ma et al, 2022;Usman et al, 2022;Zhao et al, 2022;Zhu et al, 2022;Li et al, 2023;Yang et al, 2023;Yu et al, 2023). The STIRPAT model can be expressed as the following equation in its exponential form:…”

Section: Theoretical and Empirical Reviewmentioning

confidence: 99%

“…The STIRPAT model is altered by the addition of PAT decomposition variables, and the effects of these components on the environment are examined. "T" is also decomposable in the STIRPAT model, much like P and A Li et al, 2023). In this study, CEPI is used to quantify pollutant emission (I); and GDP per capita captures the affluence of an economy (A) (Poumanyvong and Kaneko, 2010;Hao et al, 2016;Qayyum Khan et al, 2018;Kusumawardani and Dewi, 2020).…”

Section: Theoretical and Empirical Reviewmentioning

confidence: 99%

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The role of institutional quality in assessing the environmental externality of financial inclusion: A DCCE approach

Latif

Safdar

Liaquat

et al. 2023

Front. Environ. Sci.

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Controlling environmental contamination requires the use of environmental regulation. The growth of green finance depends on digital finance. The objectives of the study are threefold: first, to explore the impact of digital financial inclusion in deriving climate change; second, to trace the shape of the financial inclusion-based environmental Kuznets curve; and third, to investigate the intersecting effect of digital financial inclusion and institutional quality on environmental quality. Using panel data from 48 Asian economies between 1996 and 2020, heterogeneity, non-stationarity, and cross-sectional dependence are addressed using an econometric method called “dynamic common correlated effects (DCCE).” The empirical evidence confirms a significant relationship between environmental performance and financial inclusiveness. Furthermore, the findings also validated the inverted U-shape environmental Kuznets curve based on financial inclusiveness. Our research suggests that a strong institutional framework has the potential to mitigate the long-term negative consequences of financial inclusion on the environment. To establish coordinated control of environmental quality, the government fully utilizes the environmental regulation and digital inclusive finance environmental governance. Consequently, to achieve environmental sustainability, policymakers in Asian countries should develop policies that enhance financial inclusion and institutional quality.

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Section: Theoretical and Empirical Reviewmentioning

confidence: 99%

Section: Theoretical and Empirical Reviewmentioning

confidence: 99%

The role of institutional quality in assessing the environmental externality of financial inclusion: A DCCE approach

Latif

Safdar

Liaquat

et al. 2023

Front. Environ. Sci.

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show abstract

“…SSPs cover five scenarios: sustainable path (SSP1), middleof-the-road path (SSP2), regional rivalry path (SSP3), inequality path (SSP4), and fossilfuel development path (SSP5). Because it assists climate change research and facilitates comparisons between different studies, a multi-scenario analysis based on SSPs has been widely used in various fields [44]. However, few studies have explored the evolution of the urban spatial form under different SSP scenarios from the perspective of allometric scaling laws of urban systems.…”

Section: Introductionmentioning

confidence: 99%

Coupling Random Forest, Allometric Scaling, and Cellular Automata to Predict the Evolution of LULC under Various Shared Socioeconomic Pathways

2023

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Accurately estimating land-use demand is essential for urban models to predict the evolution of urban spatial morphology. Due to the uncertainties inherent in socioeconomic development, the accurate forecasting of urban land-use demand remains a daunting challenge. The present study proposes a modeling framework to determine the scaling relationship between the population and urban area and simulates the spatiotemporal dynamics of land use and land cover (LULC). An allometric scaling (AS) law and a Markov (MK) chain are used to predict variations in LULC. Random forest (RF) and cellular automata (CA) serve to calibrate the transition rules of change in LULC and realize its micro-spatial allocation (MKCARF-AS). Furthermore, this research uses several shared socioeconomic pathways (SSPs) as scenario storylines. The MKCARF-AS model is used to predict changes in LULC under various SSP scenarios in Jinjiang City, China, from 2020 to 2065. The results show that the figure of merit (FoM) and the urban FoM of the MKCARF-AS model improve by 3.72% and 4.06%, respectively, compared with the MKCAANN model during the 2005–2010 simulation period. For a 6.28% discrepancy between the predicted urban land-use demand and the actual urban land-use demand over the period 2005–2010, the urban FoM degrades by 21.42%. The growth of the permanent urban population and urban area in Jinjiang City follows an allometric scaling law with an exponent of 0.933 for the period 2005–2020, and the relative residual and R2 are 0.0076 and 0.9994, respectively. From 2020 to 2065, the urban land demand estimated by the Markov model is 19.4% greater than the urban area predicted under scenario SSP5. At the township scale, the different SSP scenarios produce significantly different spatial distributions of urban expansion rates. By coupling random forest and allometric scaling, the MKCARF-AS model substantially improves the simulation of urban land use.

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“…Accompanying the rapid urbanisation in China (Zhang et al 2022a), a large number of anthropogenic activities have spawned excess industrial wastewater, municipal sewage, and agricultural runoff pollution, which threaten water quality safety and human health (Bouaroudj et al 2019, Li et al 2023, Zhang et al 2022b). As one of the most critical environmental issues, river eutrophication has not been properly resolved worldwide due to the imbalance between urbanization and water ecosystem management (Dou et al 2021, Grygoruk et al 2015, Kim et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Spatial dynamics of phosphorus in the natural-rural-suburban-urban river sediment continuum (Qinhuai River basin, China): Insights into concentration, fractionation, and environmental risk assessment of phosphorus

Cao,

Zhu,

Gao

et al. 2023

Preprint

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Rapid urbanisation, due to an imbalance in fitness with technological levels, has resulted in the accumulation of phosphorus (P) in river sediments, which is prone to environmental problems such as eutrophication of water bodies. However, little is known about phosphorus's geochemical composition and environmental risks in the natural-rural-suburban-urban river sediment continuum, which is an urgent need. This study investigated the concentration and fractionation of P using sequential P extraction and their influencing factors by introducing the PLS-SEM model (Partial Least Squares Structural Equation Model) along this continuum from the Qinhuai River. The results showed that the sedimentary TP concentrations and P fractions nearly increased dramatically with increasing the urbanization gradient. This spatial heterogenicity of P along a river was attributed mainly to land use patterns and sediment properties (nutrients ＞metals ＞ grain size). In addition, the highest values of TP, BAP (biologically active P) and PPI (P pollution index) were found in urban sediments among four regions, indicating a higher environmental risk of P release, which may increase the risk of eutrophication in overlying water bodies. Collectively, this work improves the understanding of the spatial dynamics of P in the natural-rural-urban river sediment continuum, highlights the need to control P pollution in urban sediments and provides a scientific basis for the future usage and disposal of P in sediments.

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Driving force analysis and prediction of ecological footprint in urban agglomeration based on extended STIRPAT model and shared socioeconomic pathways (SSPs)

Cited by 34 publications

References 60 publications

The role of institutional quality in assessing the environmental externality of financial inclusion: A DCCE approach

The role of institutional quality in assessing the environmental externality of financial inclusion: A DCCE approach

Coupling Random Forest, Allometric Scaling, and Cellular Automata to Predict the Evolution of LULC under Various Shared Socioeconomic Pathways

Spatial dynamics of phosphorus in the natural-rural-suburban-urban river sediment continuum (Qinhuai River basin, China): Insights into concentration, fractionation, and environmental risk assessment of phosphorus

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