Examining the drivers of agricultural carbon emissions in Africa: an application of FMOLS and DOLS approaches

Zwane, Talent; Udimal, Thomas Bilaliib; Pakmoni, Lariba

doi:10.1007/s11356-023-25173-8

Cited by 13 publications

(3 citation statements)

References 78 publications

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“…Contrarily, Rehman, Ma [19] shows insignificant impact on cereal production. Further, Emenekwe, Onyeneke [2], Koondhar, Udemba [20], Pickson, He [18], Sui and Lv [21], and Zwane, Udimal [22] reveal a positive impact of climate on cereal production. Koondhar, Aziz [23] and Chandio, Akram [16] show that a one-way Granger causality exists from cereal production to CO2 emissions.…”

Section: Introductionmentioning

confidence: 95%

Cereal production amidst fertilizer usage, cereal cropland area, and farm labor in Nigeria: A novel dynamic ARDL stimulations approach

Abdullahi,

Ibrahim,

Ahmad

et al. 2023

Preprint

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Nigeria is the most populous country in Africa, and the basic foods for Nigerians are cereal crops, including maize, rice, sorghum, millet, and wheat. However, their productivity is significantly affected by population pressure, poor utilization of cropland, and fertilizers. Against this backdrop, this study examines the relationship between cereal production, cereal cropland area, fertilizer usage, and the rural population (farm labor). The study utilizes the novel dynamic autoregressive distributed lag stimulations (DYARDLS) model and analyzes annual time series data for Nigeria from 1980 to 2021. The results of the unit root test suggest that the chosen variables are stationary both at the level and in the first difference. Furthermore, the bound test affirms that all variables are cointegrated, with a significance level of 1%. The results from DYARDLS show that in the long run, a percentage change in rural population and cereal cropland area boosts cereal food production by 0.018% and 0.51%, respectively. Meanwhile, a 1% change in the food production index exacerbates cereal output by 0.25% in the long run and 1.06% in the short run. We also find that fertilizer consumption could improve cereal production in both the short and long run, but the results are insignificant. In conclusion, we demonstrate that our study variables are the decisive determining factors of cereal productivity and cannot be disregarded in the mission to attain food security. JEL Codes: O13, O33

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Section: Introductionmentioning

confidence: 95%

Cereal production amidst fertilizer usage, cereal cropland area, and farm labor in Nigeria: A novel dynamic ARDL stimulations approach

Abdullahi,

Ibrahim,

Ahmad

et al. 2023

Preprint

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“…Under the constraints of the "double carbon" target, it is essential to clarify the main sources of CEs generated by household consumption to guarantee the green transformation of HCPs. For a long time, scholars have mainly attributed CO 2 emissions to the industrial sector [3][4][5][6], ignoring those from household consumption [7,8]. In recent years, the proportion of CEs caused by household consumption has been increasing annually [9][10][11], and the household sector has become the second largest energy consumption and carbon emission sector, ranking only after the industrial sector [12].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Green Transformation and Driving Factors of Household Consumption Patterns in China from the Perspective of Carbon Emissions

Shang,

Shen,

Guo

2024

Sustainability

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Over the years, the household sector has become the main source of carbon emissions. Thus, it is crucial to study the green transformation of household consumption patterns (HCPs) and its driving factors from the perspective of carbon emissions (CEs). This study used the Tapio decoupling method to analyze the green transformation of HCPs, employed the logarithmic mean divisia index (LMDI) method to find the driving factors of green transformation of HCPs, and conducted marginal effect analysis to identify the marginal effects of the major driving factors of green transformation of HCPs, based on the China Family Panel Studies (CFPS) database from 2012 to 2018. It was found through statistical analysis that dominant types of direct HCPs included electricity and fuel, and dominant types of indirect HCPs included necessities, health, transportation, and education. The results of empirical analysis indicated that direct household consumption structure and the per capita residential area promoted the green transformation of HCPs, while direct household per square meter residential consumption and per capita net income inhibited it. Furthermore, other factors had varying positive or negative impacts on the green transformation of HCPs, depending on regions, income levels, and urban–rural areas. The results of marginal effect analysis suggested that the marginal effects of residential area on per capita household carbon emissions (HCEs) present a trend from increasing to decreasing, while the marginal effect of household income on per capita HCEs presents an increasing trend. However, the marginal effect of household size on per capita HCEs presents a decreasing trend only for the low-income group and the western region when household size increased from five to six persons. This paper enriches the research on the green transformation of HCPs, and provides references for the formulation of green transformation policies for HCPs in different regions, income levels, and urban–rural areas.

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“…Concerning the main influencing factors of land use carbon emissions, cropland and building land predominantly contribute to carbon emissions. The regression model is a common method to analyze the influencing factors of cropland carbon emissions [26][27][28], and the LMDI model is used more in the analysis of building land carbon emissions [29][30][31]. The low-carbon optimization of land use is mainly to set different low-carbon development scenarios and simulate the future land use pattern [32], including early models like CA-Markov [33][34][35] model, CLUS-S [36,37] model, Flus [38,39] model, and the newer models represented by the PLUS [40][41][42] model.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Land Use Gravity Center Change and Carbon Emission Impact in Chengdu Plain of China from 2006 to 2022

Wu,

Pu,

Dong

et al. 2024

Land

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As the economic center and major grain-producing area in Southwest China, the calculation of the carbon budget and the protection of cultivated land in the Chengdu Plain are of vital significance for China to achieve a carbon peak strategy and ensure food security. For the purpose of clarifying the trend of land use focus and carbon emissions in the Chengdu Plain, the carbon peak level of land use in 33 counties in the Chengdu Plain was explored. Based on the gravity center model and IPCC carbon emission coefficient method, the changing trend of land use gravity center and carbon emission in Chengdu Plain from 2006 to 2022 was clarified. PLS regression model and LMDI model were used to explore the main influencing factors of the carbon emission of cropland and the carbon emission of building land. PLUS model was used to simulate future land use patterns and carbon emissions. (1) The center of gravity of cropland, building land, water, and other and unused land shifted to the northeast by 4.23 km, 5.46 km, 8.44 km, and 31.58 km, respectively, and that of forest and grass shifted to the southeast by 11.12 km and 3.41 km, respectively. For major food crops, the centers of gravity of rice and maize moved northeastward by 15.47 km and 7.52 km, respectively, while wheat moved southwestward by 17.77 km. (2) From 2006 to 2022, carbon emissions from land use in the 33 counties of the Chengdu Plain are all on the rise, with a total increase of 13.552 million tons, and carbon sinks in the 31 counties continue to decline, with a total decrease of 0.691 million tons. (3) Under the natural scenario, carbon sink scenario, and carbon reduction scenario, the carbon emissions from land use decrease by 0.5391 million tons, 3.4728 million tons, and 4.5265 million tons from 2022, respectively. Among the 33 counties in the Chengdu Plain, 11 counties did not achieve carbon peak under the natural scenario, 5 counties did not achieve carbon peak under the carbon sink scenario, and all the counties achieved carbon peak under the carbon sink scenario. During the study period, there was a serious loss of cropland in the Chengdu Plain, mainly to building land in the central part of the Chengdu Plain and to forests within the Longmen Mountain, Longquan Mountain, and Leshan City, and there is a need to strengthen cropland protection in this region in the future. Under the natural scenario, carbon sink scenario, and carbon reduction scenario, land use in the Chengdu Plain region can achieve carbon peak, and the carbon reduction model will be more helpful for the counties to achieve carbon peak.

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Examining the drivers of agricultural carbon emissions in Africa: an application of FMOLS and DOLS approaches

Cited by 13 publications

References 78 publications

Cereal production amidst fertilizer usage, cereal cropland area, and farm labor in Nigeria: A novel dynamic ARDL stimulations approach

Cereal production amidst fertilizer usage, cereal cropland area, and farm labor in Nigeria: A novel dynamic ARDL stimulations approach

Analysis of Green Transformation and Driving Factors of Household Consumption Patterns in China from the Perspective of Carbon Emissions

Analysis of Land Use Gravity Center Change and Carbon Emission Impact in Chengdu Plain of China from 2006 to 2022

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