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Peters, Mark; House, Robert M.; Lewandrowski, Jan; McDowell, Howard

doi:10.1023/a:1010684106573

Cited by 33 publications

(4 citation statements)

References 19 publications

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“…Therefore, more and more scholars have begun to pay close attention to agricultural emissions. These studies focus on the calculation of agricultural carbon emissions [9][10][11], regional comparison of agricultural carbon emissions [12][13][14][15][16][17], the relationship between agricultural carbon emissions and economic growth [18][19][20], carbon emission driving factor analysis [21][22][23][24], and carbon emission reduction mechanism and policy [25][26][27][28][29][30][31]. Some scholars have attempted to study the regional equity of agricultural carbon emissions [32], decision mechanisms of agricultural carbon emissions [33], and agriculturally embodied carbon emissions [34].…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Characteristics and LMDI-Based Impact Factor Decomposition of Agricultural Carbon Emissions in Hotan Prefecture, China

2016

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Greenhouse gas emissions from the agricultural ecosystem account for 7%-20% of the world's total greenhouse gas emissions, while approximately 17% of China's carbon emissions are from agriculture. In this study, based on the scientific calculation system of carbon emissions in agriculture, we calculated the carbon emissions of agriculture in the Hotan prefecture between 1999 and 2013 and analyzed their spatial-temporal characteristics; next, we used the LMDI model to study the driving factors of agricultural carbon emissions. The results demonstrated the following: (1) in time series, the agricultural carbon emissions showed three stages of change, i.e., "decline, continued to rise and decline", during the period of 1999 to 2013 in the Hotan prefecture; (2) In space, the carbon emissions from agricultural land use, paddy fields, enteric fermentation, and manure management were different due to the different sizes of cities and counties. The intensity of agricultural carbon emissions was varied and high, but the agricultural production structure, agricultural carbon emissions structure and other aspects had a high degree of consistency and homogeneity in the cities and counties of the Hotan prefecture; (3) Regarding the driving mechanism, the labor factor, agricultural labor productivity, and planting-animal husbandry carbon intensity are the main factors that increase agricultural carbon emissions in the Hotan prefecture. Compared with 1999, three major factors cumulatively achieved a 199.68% carbon emission increment from 2000 to 2013, of which the labor factor cumulatively increased by 120.04%, the agricultural labor productivity factor cumulatively increased by 54.94% and the planting-animal husbandry carbon intensity factor cumulatively increased by 24.70%. The agricultural production structure factor largely inhibited agricultural carbon emissions of the Hotan prefecture, which cut 99.74% of the carbon emissions from 2000 to 2013. Finally, we proposed policy recommendations, including the acceleration of labor transfer, the innovation and promotion of science and technology, the scientific breeding and rational disposal of livestock waste, and the adjustment and optimization of the agricultural industry structure.

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Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Characteristics and LMDI-Based Impact Factor Decomposition of Agricultural Carbon Emissions in Hotan Prefecture, China

2016

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“…First, external factors such as carbon tax, innovation capacity, employment policy, etc., affect agricultural carbon emissions. Peter (2001) [11] concluded that carbon taxes had a negative impact on agricultural carbon emissions based on U.S. agricultural data in 1990 and 2020. Gerlagh (2007) [12] used an endogenous growth model to establish that technological progress can curb agricultural carbon emissions.…”

Section: Influencing Factors Of Agricultural Carbon Emissionsmentioning

confidence: 99%

The Impact of Agricultural Production Efficiency on Agricultural Carbon Emissions in China

Zhu

Huo

2022

Energies

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With the rapid development of China’s economy, China has become the world’s largest carbon emitter. China not only has an obvious growth rate of industrial carbon emissions but also the intensity of agricultural carbon emissions is hovering at a high level. The development of China’s agricultural economy has largely come at the expense of high emissions. Currently, under the background of global warming and difficulty in controlling greenhouse gas emissions, the development of low-carbon agriculture is an important way to realize the harmonious development of the ecological environment and economic growth and to promote the sustainable development of agriculture. The agricultural production efficiency is the main factor affecting the intensity of agricultural carbon emissions. Based on provincial panel data of China from 2010 to 2019, this paper establishes an indicator system and uses the super-efficiency SBM model to measure agricultural production efficiency. The regional agricultural carbon emissions were estimated using carbon-emission-related agricultural production activities. In order to study the nonlinear relationship between agricultural production efficiency and agricultural carbon emission intensity in the narrow sense, this paper uses a threshold regression model with agricultural carbon emissions as the threshold variable. Based on the analysis of China’s agricultural production efficiency and agricultural carbon emissions from 2010 to 2019, an empirical test is conducted through a threshold regression model. The results show an “inverted U-shaped” relationship between agricultural production efficiency and agricultural carbon emission intensity. In areas with high agricultural production efficiency, the improvement of production efficiency can suppress the intensity of agricultural carbon emissions; in areas with low agricultural production efficiency, the improvement of production efficiency increases the intensity of agricultural carbon emissions. Finally, based on the research conclusions, this paper provides feasible suggestions and countermeasures for China’s agricultural carbon emission reduction and improvement of agricultural production efficiency.

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“…26 or multinational level (for developed countries) can significantly reduce global emissions. 27 Using a general equilibrium model, (Golub et al, 2013[28]) find that an international carbon tax equal to USD 27/tCO2eq 28 in developed countries can significantly reduce livestock emissions but that 25% to 35% of this reduction would be offset by carbon leakage in developing countries. They argue that this leakage effect can be eliminated by forest carbon sequestration incentives in developing countriesalthough food security concerns can arise because sequestration encourages forestland to expand and agricultural land to contract.…”

Section: Supply Sidementioning

confidence: 99%

“…At the global level,(Havlík et al, 2014[18]) apply the GLObal BIOsphere Management (GLOBIOM) economic partial equilibrium land use model and finds that a carbon price of USD 10/tCO2eq results in an abatement of 3.2 GtCO2eq per year. (Frank et al, 2018[74]), which use the same model as(Havlík et al, 2014[18]), find that a USD 25/tCO2eq and a USD 100/tCO2eq reduce non-CO2 emissions by around 1 GtCO2eq and 2.6 GtCO2eq per year until 2030 respectively 27. This is a comparative static model and therefore it assumes constant production(Golub et al, 2013[28]).…”

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confidence: 99%

Carbon leakage and agriculture

Arvanitopoulos¹,

Garsous²,

Agnolucci³

2021

OECD Food, Agriculture and Fisheries Papers

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The risks of carbon leakage associated with climate policies in the agricultural sector remains underresearched. Studies to date suggest that carbon pricing policies implemented by a single country, or small group of countries, reduce global emissions but also affect the international competitiveness of these countries' agricultural sectors and induce carbon leakage. While carbon leakage can be prevented with trade-related measures that adjust emissions prices at the border, such measures applied in developed countries could potentially lead to significant welfare losses for developing countries that heavily rely on agricultural exports. That said, important caveats apply to the reviewed studies: i) from an environmental perspective, estimations of carbon leakage rates alone do not offer a comprehensive assessment of how optimally agricultural activities are allocated across countries; ii) most of the studies estimate the effects of additional environmental policies, such as carbon taxes, and ignore the effects of existing policies, including market distorting and potentially environmentally harmful support for agricultural production.

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Untitled

Cited by 33 publications

References 19 publications

Spatial-Temporal Characteristics and LMDI-Based Impact Factor Decomposition of Agricultural Carbon Emissions in Hotan Prefecture, China

Spatial-Temporal Characteristics and LMDI-Based Impact Factor Decomposition of Agricultural Carbon Emissions in Hotan Prefecture, China

The Impact of Agricultural Production Efficiency on Agricultural Carbon Emissions in China

Carbon leakage and agriculture

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