Research on Spatial-Temporal Characteristics of Agricultural Carbon Emissions in Guangdong Province and the Relationship with Economic Growth

Liu, Li Hui; Xin, Huan Ping

doi:10.4028/www.scientific.net/amr.1010-1012.2072

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…In 1993, Panayotou first introduced and named the relationship between economic growth and environmental conditions as the environmental Kuznets curve (EKC), indicating that per capita income had a strong inverted "U" curve relationship with the level of environmental pollution [46]. However, Liu and Xin confirmed that the evolutionary trend between economic growth and agricultural carbon emissions showed an "N" curve, indicating that agricultural carbon pollution becomes more serious than before as the economy continues to expand [47]. In addition, Tian et al also demonstrated that when per capita income of agriculture increased by 1 unit, agricultural carbon emissions would increase by 0.354 units [9].…”

Section: Per Capita Disposable Income Of Rural Residentsmentioning

confidence: 99%

Spatial-Temporal Characteristics of the Driving Factors of Agricultural Carbon Emissions: Empirical Evidence from Fujian, China

Chen

et al. 2019

Energies

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With the development of agricultural modernization, the carbon emissions caused by the agricultural sector have attracted academic and practitioners’ circles’ attention. This research selected the typical agricultural development province in China, Fujian, as the research object. Based on the carbon emission sources of five main aspects in agricultural production, this paper applied the latest carbon emission coefficients released by Intergovernmental Panel on Climate Change of the UN (IPCC) and World Resources Institute (WRI), then used the ordered weighted aggregation (OWA) operator to remeasure agricultural carbon emissions in Fujian from 2008–2017. The results showed that the amount of agricultural carbon emissions in Fujian was 5541.95 × 103 tonnes by 2017, which means the average amount of agricultural carbon emissions in 2017 was 615.78 × 103 tonnes, with a decrease of 13.13% compared with that in 2008. In terms of spatial distribution, agricultural carbon emissions in the eastern coastal areas were less than those in the inland regions. Among them, the highest agricultural carbon emissions were in Zhangzhou, Nanping, and Sanming, while the lowest were in Xiamen, Putian, and Ningde. In addition, this paper selected six influencing variables, the research and development intensity, the proportion of agricultural labor force, the added value of agriculture, the agricultural industrial structure, the per capita disposable income of rural residents, and per capita arable land area, to clarify further the impacts on agricultural carbon emissions. Finally, geographically- and temporally-weighted regression (GTWR) was used to measure the direction and degree of the influences of factors on agricultural carbon emission. The conclusion showed that the regression coefficients of each selected factor in cities were positive or negative, which indicated that the impacts on agricultural carbon emission had the characteristics of geospatial nonstationarity.

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Section: Per Capita Disposable Income Of Rural Residentsmentioning

confidence: 99%

Spatial-Temporal Characteristics of the Driving Factors of Agricultural Carbon Emissions: Empirical Evidence from Fujian, China

Chen

et al. 2019

Energies

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“…Regarding the assessment of EUE, other energy attributes such as NE, EPF, EP, and SE are meaningful indicators [64]. In multi-biological systems that contain heterotrophic organisms, the proportion of NE is a key determinant of the viability and complexity of the resulting community structure [65,66].…”

Section: Discussionmentioning

confidence: 99%

Dynamic Energy Use Efficiency, Carbon Input, and Agricultural Benefits of Multiple Cropping in Southern China—A Case Study from Guangdong Province

Zhang,

Deng,

et al. 2024

Agriculture

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Background: With the purpose of exploring the development of new quality productive forces in Guangdong, the present study hypothesized that reducing energy and carbon inputs was beneficial for increasing Guangdong’s multi-cropping agricultural energy output and economic returns. Methods: The energy use efficiency of crop production and the corresponding carbon input and agricultural benefit trends from 2011 to 2021 were examined by linear regression analysis for Guangdong Province, China. The corresponding development trends were also predicted using a grey model. Results: The results indicated that the total energy output increased by 12.50%, wherein the energy productivity levels of peanuts, vegetables, mulberry, and fruits increased greatly (51.27–106.17%), while the total energy input and the total carbon input decreased by 12.17% and 6.37%, respectively. Moreover, the energy input per carbon input decreased by 6.19%, while the energy output per carbon input increased by 20.15%. Both energy-related indicators and economic-related indicators all had substantially increased (28.08–44.97% and 83.86–120.91%, respectively). Grey model predictions show that the agricultural output value increased steadily under the current agricultural policy of reducing fossil energy input. Conclusions: The current low-carbon and high-output agricultural model is beneficial for increasing Guangdong’s multi-cropping agricultural economic returns and mitigating greenhouse effects.

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“…However, there are a few studies on the investigation of EKC in the agriculture sector (Li & Zheng, 2011;Liu & Xin, 2014;Xiong et al, 2016;Xu & Lin, 2017;Andreoni & Galmarini, 2012;Liu et al, 2017;Zafeiriou & Azam, 2017;Gokmenoglu & Taspinar, 2018;Zhang et al, 2019); according to our intensive literature survey, there is no study so far that evaluated the effect of the income inequality on environmental quality in the agriculture sector, even for Iran. So, in the present study, we investigate the potential relationship between income inequality and environmental quality in the agriculture sector of Iran.…”

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confidence: 99%

The Potential Relationship Between Income Inequality and Environmental Quality in Iran: The Panel Quantile Regression Approach

Shabani

Hayati

Pishbahar

et al. 2023

REDR

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This paper addresses the relationship between income inequality and environmental quality in the agriculture sector as the most related sector to the environment. In this context, we used a panel data set for 28 provinces of Iran during 2003-2017 and implemented panel quantile regression. To choose the best econometric specification the Taylor diagram is used. To explore whether the effect on income inequality on CO2 emission is different for rich and poor provinces, we consider the GINI × GDP interaction term in the model. Results confirmed that EKC in the agriculture sector of Iran, and a major and significant effect of energy consumption on CO2 emission. Findings indicated a threshold per capita income ($ 17.60 thousand) from which the effect of income inequality on carbon emissions changes. Based on these results, Marginal Properties to Emit (MPE) is more significant for poor people in low-income provinces than rich ones. Therefore, the government should adopt appropriate policies to increase the income of farmers so that the policy of income distribution, along with social justice, also improves the quality of the environment.

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Research on Spatial-Temporal Characteristics of Agricultural Carbon Emissions in Guangdong Province and the Relationship with Economic Growth

Cited by 5 publications

References 4 publications

Spatial-Temporal Characteristics of the Driving Factors of Agricultural Carbon Emissions: Empirical Evidence from Fujian, China

Spatial-Temporal Characteristics of the Driving Factors of Agricultural Carbon Emissions: Empirical Evidence from Fujian, China

Dynamic Energy Use Efficiency, Carbon Input, and Agricultural Benefits of Multiple Cropping in Southern China—A Case Study from Guangdong Province

The Potential Relationship Between Income Inequality and Environmental Quality in Iran: The Panel Quantile Regression Approach

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