Environmental pollution and agricultural productivity in Pakistan: new insights from ARDL and wavelet coherence approaches.

Abstract

The most serious challenge to the global facade is figuring out how to mitigate pollution levels without compromising agricultural productivity. The spillover effect of environmental change is predicted to be very high, although it will differ by region and crop. Considering this view, this study tries to address this issue by adopting comprehensive methodologies to assess the influence of carbon dioxide (CO2) emissions, agricultural labor, land, feeds, and fertilizers on agricultural productivity in Pakistan from 1961 to 2018. The autoregressive distributive lag (ARDL) and wavelet transform coherence (WTC) approaches are applied to estimate the long-run and short-run elasticity estimates. The empirical findings discover that CO2 emissions, agricultural land, labor, feed, and fertilizers exert high pressure on agricultural productivity which is backed up by the WTC findings. Furthermore, the gradual shift causality test results reveal the presence of a unidirectional causality relationship between all regressors and agriculture productivity, demonstrating that all the factors significantly influence agriculture productivity. Moreover, these findings are robust to different robustness tests that we perform to test the reliability/accuracy of our core results. From policy perspectives, regulations must be developed to explore a practicable expansion strategy that includes the use of efficient fertilizers and feed at optimal levels, as well as environmental protection through public-private investment in the agricultural sector.