event 12 Apr 2023

Research Article // The Nexus between Agriculture, Water, Energy and Environmental Degradation in Central Asia—Empirical Evidence Using Panel Data Models

By Olimjon Saidmamatov and colleagues. There is a positive long-term impact of economic growth, water productivity, energy consumption and electricity production on CO2 emissions while agriculture value added and trade openness have a negative and statistically significant influence on CO2 emissions in Central Asia.

Energies 16 03206 g001 550

Graphical representation of the findings of Panel Data Models.

Abstract

The primary aspiration of this paper is to learn about the effects of economic growth, energy consumption, agriculture and irrigation water consumption and agriculture productivity on environmental pollution in five countries of Central Asia. The data cover the period from 1992 to 2020 by applying panel data models, namely the Panel FMOLS, Panel DOLS and Panel ARDL-PMG approaches. The results indicate that there is a positive long-term impact of economic growth, water productivity, energy consumption and electricity production on CO2 emissions while agriculture value added and trade openness have a negative and statistically significant influence on CO2 emissions in Central Asia. Country specific short-run coefficients from Panel ARDL reveal that energy consumption is the main driver for rise in the level of CO2 emissions in the countries under the study. Indeed, country level analysis generates unique nexus correlation among agriculture, energy and environmental degradation in each country of Central Asia.

Published

April 2023

By

MDPI

Citation

Saidmamatov, O., Tetreault, N., Bekjanov, D., Khodjaniyazov, E., Ibadullaev, E., Sobirov, Y., & Adrianto, L. R. (2023). The Nexus between Agriculture, Water, Energy and Environmental Degradation in Central Asia—Empirical Evidence Using Panel Data Models. Energies, 16(7), 3206. MDPI AG. Doi: 10.3390/en16073206.

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The Nexus between Agriculture, Water, Energy and Environmental Degradation in Central Asia—Empirical Evidence Using Panel Data Models.

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