A sustainable green–blue revolution in Pakistan: a new perspective from quantile regression analysis

Samia Zahra (), Syed Ale Raza Shah () and Ramez Badeeb
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Samia Zahra: Higher Education Archives and Libraries Department
Syed Ale Raza Shah: Xi’an Jiaotong University

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2023, vol. 25, issue 12, No 53, 14975-15002

Abstract: Abstract The paper explores the impact of the green–blue revolution on environmental sustainability proxied by production-based carbon emission and agro-environmental footprint in Pakistan between 1976 and 2020. The basic objective of this study is to evaluate and compare the impact of green–blue revolution on sustainable environment in different quantiles. Johansen co-integration test with trace and max eigenvalues determines the long-run relationship between green–blue revolution and environmental degradation in Pakistan. Using median quantile regression analysis, we find that agricultural machinery, pesticides, and aquaculture production are positive and significant factors of production-based carbon emissions with 0.49, 0.1, and 0.09 coefficient values, respectively, while fertilizers, agricultural credit, and HYV (high yield variety) seeds are not major determinants of production-based carbon emission. At the same time, high variety seeds and fertilizers enhance the agro-environmental footprint in Pakistan with 0.23 and 0.28 coefficients, respectively, by applying median quantile regression. Similarly, agricultural machinery, pesticides, agricultural credit, and aquaculture production are not the major determinants of agro-environmental footprint by using median quantile regression. However, the response of exogenous variables varies from quantile to quantile in the case of both environmental proxies. These results discuss implications for fruitful and effective policies and recommendations for a sustainable environment.

Keywords: Green revolution; Blue revolution; Production-based carbon emission; Agro-environmental footprint; Quantile regression (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10668-022-02698-5

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