Revisiting the role of forestry, agriculture, and renewable energy in testing environment Kuznets curve in Pakistan: evidence from Quantile ARDL approach

Aziz, Noshaba; Sharif, Arshian; ʻAlī, Raz̤ā; Rong, Kong

Revisiting the role of forestry, agriculture, and renewable energy in testing environment Kuznets curve in Pakistan: evidence from Quantile ARDL approach

2020

Aziz, Noshaba | Sharif, Arshian | ʻAlī, Raz̤ā | Rong, Kong

This paper assesses the Environmental Kuznets curve based on quantile behavior of the relationship between economic growth, forest area, agriculture production, renewable energy, and environmental degradation. The current literature generally used a single indicator to address environmental issues; however single indicator neither measures overall environmental conditions nor does specify that the environment issue is generally diminishing. Our study is the first one that used ecological footprint (EF) as an indicator to test environmental Kuznets curve (EKC) hypothesis for Pakistan by employing recent approach of quantile autoregressive distributed lag (QARDL) initiated by Cho et al. (J Econ 188(1):281–300, 2015). The result of this study validates the EKC hypothesis for Pakistan and shows quantile-dependent relationship, and in that case, using the conventional methods may somewhat lead to biased results. Moreover, the rejection of the null hypothesis of parameter constancy is also confirmed by Wald test. In the long run, the findings of renewable energy consumption and forest area show significant negative effects on ecological footprints, which indicates that by increasing renewable energy usage and forest area, ecological footprints can be minimized. Interestingly, the short-term effects of agricultural production findings on EF show statistically negative results. This illustrates that EF can also be reduced in the agriculture sector by adopting environment-friendly technologies. In order to create efficient policies for environment deterioration, the empirical findings of the current analysis can be used as a guideline for policy implications.

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