2021
DOI: 10.3390/en14248339
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Impact of Agriculture and Energy on CO2 Emissions in Zambia

Abstract: The world has experienced increased impacts of anthropogenic global warming due to increased emissions of greenhouse gases (GHGs), which include carbon dioxide (CO2). Anthropogenic activities that contribute to CO2 emissions include deforestation, usage of fertilizers, and activities related to mining and energy production. The main objective of this paper was to assess the impacts of agriculture and energy production on CO2 emissions in Zambia. This research used econometric analysis, specifically the Autoreg… Show more

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Cited by 26 publications
(13 citation statements)
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“…This change is most distinct in the reduction of retail shops, the higher competition due to the emergence and the development of various new store formats [ 3 , 4 ]. Environmental and health problems have increased the interest of researchers and practitioners in investigating the factors that affect organic food and agriculture consumption [ 5 , 6 , 7 ], specifically e. g. problems of health and environmental impact of protein or minerals consumption etc.…”
Section: Introductionmentioning
confidence: 99%
“…This change is most distinct in the reduction of retail shops, the higher competition due to the emergence and the development of various new store formats [ 3 , 4 ]. Environmental and health problems have increased the interest of researchers and practitioners in investigating the factors that affect organic food and agriculture consumption [ 5 , 6 , 7 ], specifically e. g. problems of health and environmental impact of protein or minerals consumption etc.…”
Section: Introductionmentioning
confidence: 99%
“…At the same time, some of the literature discusses the impact of household energy consumption on agricultural green production. It is found that household energy cleaning can enhance farmers’ environmental awareness [ 31 ], control agricultural source pollution [ 32 ], and improve climate conditions [ 29 ], thereby promoting agricultural green production. This work used micro-data from China as a sample to explore the relationship between cleaner household energy and agricultural green production.…”
Section: Discussionmentioning
confidence: 99%
“…There are some studies that discussed the effects of CHE on AGP. The long-term use of non-clean energy by households produces large amounts of CO 2 , triggering climate extremes that have a negative impacts on crop yields [ 28 ], forcing farmers to stop deforestation; protect the soil, vegetation, and water sources; and restore sustainable agricultural production [ 29 ]. Accelerating clean energy supply is one of the vital factors for green agriculture, hence CHE promotes AGP [ 30 ].…”
Section: Introductionmentioning
confidence: 99%
“…After carrying out the unit root test and finding out the order of integration of the variables, the ARDL Bounds test is used as it is capable of running estimation for time series on variables which has the order of integration such as I(0) and I(1) or even a mixture of both but strictly not of a higher order I(2) [ 49 ]. This procedure goes beyond the limitations of Engle and Granger [ 47 ], and Johansen and Jeselius [ 53 ], which constrains the cointegration steps only to variables with the same order of integration as the ARDL The Bounds test can run a regression with variables of order I(0), I(1), or a combination of both and hence making it superior, a proposition that has been supported by several scholars pertaining to models with similar time series properties [ 49 , 54 , 55 , 56 , 57 , 58 , 59 ]. The optimal lag determination criteria adopted for each of the variables were computed automatically using the Akaike Information Criterion (AIC) [ 60 , 61 ], because it can suit the small sample sizes and also reduce any chances of underestimating the lags in the sample as it improves chances of determining the correct lag length unlike the other methods such as the Sequential modified LR test statistic, Final prediction error, Schwarz information criterion, and Hannan–Quinn information criterion [ 60 , 61 ].…”
Section: Methodsmentioning
confidence: 99%