Do natural resources, economic growth, human capital, and urbanization affect the ecological footprint? A modified dynamic ARDL and KRLS approach

被引:87
|
作者
Zhou, Runyu [1 ]
Abbasi, Kashif Raza [2 ,3 ]
Salem, Sultan [4 ]
Almulhim, Abdulaziz, I [5 ]
Alvarado, Rafael [6 ]
机构
[1] Anhui Univ Finance & Econ, Sch Int Trade & Econ, Bengbu 233030, Peoples R China
[2] Shanghai Univ, Sch Econ, 99 Shangda Rd,Baoshan Campus, Shanghai 200444, Peoples R China
[3] ILMA Univ, Fac Management Sci, Dept Business Adm, Karachi, Pakistan
[4] Univ Birmingham, Coll Social Sci, Birmingham Business Sch, Dept Econ, Birmingham B15 2TT, W Midlands, England
[5] Imam Abdulrahman Bin Faisal Univ, Coll Architecture & Planning, Dept Urban & Reg Planning, POB 1982, Dammam 31451, Saudi Arabia
[6] Univ Espiritu Santo, Esai Business Sch, Samborondon 091650, Ecuador
关键词
Ecological footprint; Natural resources; GDP; Human capital; Urbanization; DARDL; KUZNETS CURVE HYPOTHESIS; UNIT-ROOT; ENERGY-CONSUMPTION; CARBON EMISSIONS; COAL CONSUMPTION; POLICY SHOCKS; COINTEGRATION; ELECTRICITY; PERSISTENCE; AFRICA;
D O I
10.1016/j.resourpol.2022.102782
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The interaction between the abundance of natural resources and environmental depletion has significant ecological consequences. Nonetheless, this area is not adequately studied, and numerous results are apparent throughout the literature. For massive economic development, it is vital to recognize the role of human capital, urbanization, and natural resources. Hence it is important to consider various factors that can play a constructive role in environmental sustainability. Therefore, this study investigates the relationship between total natural resources (TNR), gross domestic product (GDP), human capital index (HCI) and urbanization (URB) with ecological footprint (EFP) in Pakistan from 1980 to 2018. The research uses the latest versions of dynamic Autoregressive Distributed Lag (ARDL) simulations model. The key benefit of dynamic ARDL is to estimate positive and negative shifts between the selected variables with an immediate visual illustration over the short and long period. In addition, the Kernel-based Regularized Least Squares (KRLS) machine learning method is used to test robustness. The results show that the rise in TNR has a long-term detrimental influence on EFP. However, upsurge in GDP and HCI increases EFP in the long-term. Lastly, URB observed an important and detrimental long-term impact on the EFP. The KRLS also support the hypothesis. This study suggest a policies to the planners and government officials for managing rapid urbanization and minimizing its urban, environmental and economic challenges.
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页数:13
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