Dynamic impacts of economic growth, energy use, urbanization, agricultural productivity, and forested area on carbon emissions: New insights from Kazakhstan. (2022) - Raihan, Asif; Tuspekova, Almagul
Highlights: Improving environmental quality through reducing emissions is the central pillar of climate change mitigation and achieving sustainable development goals. The empirical results indicate that increased economic growth, energy use, and urbanization lead to environmental degradation while agricultural productivity and forested area improve the environment quality in Kazakhstan. Several unit root tests (ADF, DF-GLS, and P-P), cointegration tests (ARDL bounds test, Johansen test, and Engle-Granger test), and cointegration regression models (DOLS, FMOLS, CCR), and diagnostic tests have been utilized to confirm the accuracy of the results. This is a pioneering attempt to reveal the dynamic impacts of urbanization, agricultural productivity, and forested area on CO2 emissions in Kazakhstan. This study provides recommendations for designing effective policies related to the low-carbon economy, promoting renewable energy use, sustainable urban development, climate-smart agriculture, and sustainable forest management that would reduce emissions as well as the negative impacts of global climate change. Abstract: The study aims to investigate the dynamic impacts of economic growth, energy use, urbanization, agricultural productivity, and forested area on carbon dioxide (CO2 ) emissions in Kazakhstan. Time series data from 1996 to 2020 were utilized by employing the Dynamic Ordinary Least Squares (DOLS) approach. The Autoregressive Distributed Lag (ARDL) bounds test revealed evidence of cointegration among the variables in the long run which has been verified by the Johansen cointegration test and Engle-Granger cointegration test. The empirical findings revealed that a 1% increase in economic growth, energy use, and urbanization cause an increase in CO2 emissions by 0.14%, 0.81%, and 1.28% in Kazakhstan. Conversely, a 1% increase in agricultural productivity and the forested area may lead to CO2 emissions reduction by 0.34% and 2.59%, respectively in the long run. The estimated results are robust to alternative estimators such as fully modified least squares (FMOLS) and canonical cointegrating regression (CCR). In addition, the pairwise Granger causality test was utilized to capture the causal linkage between the variables. This article put forward policy recommendations in the areas of low-carbon economy, renewable energy, sustainable urban development, climate-smart agriculture, Graphical Abstract: Graphical Abstract Image, graphical abstract .