Multiple Cropping System Expansion: Increasing Agricultural Greenhouse Gas Emissions in the North China Plain and Neighboring Regions

Global warming is a key issue that is related to the sustainable development of various countries, and agricultural sectors are particularly vulnerable to the effects of climate change and increasing climate variability. To obtain a better understanding of agricultural greenhouse gas (GHG) emissions, the estimation method proposed by the Intergovernmental Panel on Climate Change was used to estimate agricultural GHG emissions in 31 provinces in China with respect to five factors: agricultural energy consumption, agricultural farmland utilization, crop cultivation, ruminant feeding, and straw burning. To analyze emission reduction strategy interactions as well as the spillover of agricultural technical information between regions, we used the spatial Durbin model and further explored the different channels of technology spillover. The results obtained were as follows: (1) ruminant feeding and straw burning are the major sources of agricultural GHG emissions in China; (2) emission reduction strategies interact in the various regions, and imitation behaviors are increasing; (3) the correlation of agricultural GHG emission reduction in the different regions in China is not only limited to direct imitation behaviors, and it also reflects the spillover of technical information, i.e., agricultural technological progress plays an important role in the regional linkages of agricultural GHG emissions; (4) a shortening of the economic distance facilitates agricultural technology exchanges between regions. Therefore, to reduce agricultural GHG emissions, it is recommended that all regions should establish regional cooperative emission reduction mechanisms via agricultural technical cooperation.

Agricultural materials input (fertilizer and pesticide, etc.), together with straw burning, rice planting, and livestock breeding, constitute the sources of agricultural greenhouse gas (GHG) emissions. However, most related studies have discussed the total amount of agricultural GHG emissions or the role of straw burning and rice planting in agricultural GHG emissions and few studies on agricultural GHG emissions from Agricultural materials. Based on the data of 31 provinces in China from 2003 to 2018, this paper explored the evolution process of agricultural GHG emissions from Agricultural materials. Our research turned up some interesting findings. For example, firstly, Agricultural materials play an increasingly important role in agricultural GHG emissions. Agricultural GHG emissions due to Agricultural materials account for an increasing proportion of the total agricultural GHG emissions. Secondly, there are regional differences in the evolution trend of agricultural GHG emissions caused by agricultural materials. Especially after the urbanization rate broke through the critical line of 50% around 2010 in China.

To achieve the goal of "carbon peak and neutrality," the strict requirements for greenhouse gas (GHG) emissions control in the agricultural sector were recommended in relevant plans for Beijing during the 14th Five-Year Plan period. Through collecting agricultural activity data and calculating and screening the emission factors, the amount and emission characteristics of agricultural GHG emissions in Beijing in 2020 were estimated and set as the baseline condition. On this basis, the GHG emissions in 2025 with optimized measurements implemented, which were selected in combination with the natural conditions and planting-breeding mode of Beijing, were set as the reduction condition. The emission reduction potential and its distribution during the 14th Five-Year Plan Period were predicted simultaneously. Meanwhile, the reduction effects on the GHG emissions of optimized measurements were evaluated. In addition, relevant policy recommendations on GHG reduction were proposed accordingly. The results revealed that the total agricultural GHG emissions in Beijing were estimated to be 456000 t (CO2-eq) in 2020, primarily from sources of animal intestinal fermentation and manure management, with contribution rates of 50.7% and 26.7%, respectively. Spatially, it was mainly distributed in districts with large livestock and poultry breeding scales, such as Shunyi District, Miyun District, and Yanqing District, etc. It was predicted that in 2025, the total agricultural GHG emissions would be 349000 t (CO2-eq), and the emission reduction potential in the 14th Five-Year Plan period would be 107000 t (CO2-eq). Animal intestinal fermentation would be the emission source with the largest reduction potential (60000 tons, CO2-eq), followed by the emission source of animal manure management (37000 tons, CO2-eq). Adjusting fodder composition and optimizing manure management were analyzed to be the most effective optimized measurements for agricultural GHG emission reduction. Moreover, the emission reduction potential of CH4 would be greater than that of N2O. The emission reduction potential would be mainly distributed in Miyun District, Shunyi District, Yanqing District, Fangshan District, Tongzhou District, and other suburbs with large livestock and poultry breeding scales, accounting for more than 10% of the total emission reduction potential for each. These regions with large emission reduction potential should be prioritized and then the assessments should be extended to the whole city. The measurements were recommended as follows:① the research and promotion of technologies such as fodder optimization and the efficient treatment of manure should be strengthened, ② the scope of the combination of planting and breeding model should be expanded to promote the development of circular agriculture, and ③ relevant standards, guidelines, and specifications for green and low-carbon agriculture should be formulated, and the regulatory and policy system for synergy reduction of agricultural pollution and GHG should be developed.

Dynamic computable general equilibrium simulation of agricultural greenhouse gas emissions in China

An evaluation of the effect of greenhouse gas accounting methods on a marginal abatement cost curve for Irish agricultural greenhouse gas emissions

Comprehending the spatial-temporal characteristics, contributions, and evolution of driving factors in agricultural non-CO2 greenhouse gas (GHG) emissions at a macro level is pivotal in pursuing temperature control objectives and achieving China's strategic goals related to carbon peak and carbon neutrality. This study employs the Intergovernmental Panel on Climate Change (IPCC) carbon emissions coefficient method to comprehensively evaluate agricultural non-CO2 GHG emissions at the provincial level. Subsequently, the contributions and spatial-temporal evolution of six driving factors derived from the Kaya identity were quantitatively explored using the Logarithmic Mean Divisia Index (LMDI) and Geographical and Temporal Weighted Regression (GTWR) methods. The results revealed that the distribution of agricultural non-CO2 GHG emissions is shifting from the central provinces to the northwest regions. Moreover, the dominant driving factors of agricultural non-CO2 GHG emissions were primarily economic factor (EDL) with positive impact (cumulative promotion is 2939.61 million metric tons (Mt)), alongside agricultural production efficiency factor (EI) with negative impact (cumulative reduction is 2208.98 Mt). Influence of EDL diminished in the eastern coastal regions but significantly impacted underdeveloped regions such as the northwest and southwest. In the eastern coastal regions, EI gradually became the absolute dominant driver, demonstrating a rapid reduction effect. Additionally, a declining birth rate and rural-to-urban population migration have significantly amplified the driving effects of the population factor (RP) at a national scale. These findings, in conjunction with the disparities in geographic and socioeconomic development among provinces, can serve as a guiding framework for the development of a region-specific roadmap aimed at reducing agricultural non-CO2 GHG emissions.

The selection of effective carbon reduction strategies and the management of agricultural greenhouse gas (GHG) emissions are critical issues in climate change mitigation. Different climate actions can lead to varied pathways for agricultural GHG emissions. This study constructs a Computable General Equilibrium (CGE) model for Chinese agriculture to identify which measures can contribute to achieving established climate governance objectives, exploring potential net emission pathways for agricultural GHG. On this basis, we provide a rationale for selecting emission reduction measures. Our findings indicate that: (1) Carbon taxation is an indispensable climate action for achieving China’s “dual carbon” goals and net-zero emissions, necessitating combination with other mitigation strategies; (2) Carbon sequestration, non-agricultural carbon taxation, and CCUS measures can alter the net emission trajectory of agricultural GHG, and carbon sequestration shows the most significant impact; (3) Based on the ‘dual carbon’ or net-zero emission goals, China’s agricultural GHG emission pathway might exhibit a flat M-shaped characteristic, whereas intensifying carbon sequestration efforts could lead to an inverted V-shaped trajectory. Our results offer decision-making support for the formulation of GHG emission reduction measures in China.

Searching for solutions to mitigate greenhouse gas emissions by agricultural policy decisions — Application of system dynamics modeling for the case of Latvia

Agricultural greenhouse gas emission reduction plays an important role in addressing global climate warming. Researching and revealing the spatial and temporal characteristics, as well as the influencing mechanisms of agricultural greenhouse gas emissions, is of great significance for achieving the goals of green and low-carbon development in agriculture. This study examines the agricultural greenhouse gas emissions from 31 provinces （municipalities, autonomous regions） in China from 2000 to 2020. Through the use of geographic detectors, spatial econometric analysis, and other methods, it explores the spatiotemporal evolution characteristics and driving factors of agricultural greenhouse gas emissions. The results indicated the following： ① From 2000 to 2020, agricultural greenhouse gas emissions in China showed a development process of "slow increase - sharp increase - sharp decrease." ② The spatial heterogeneity of agricultural greenhouse gas emissions was significant, forming three high emission areas in space： the central high emission area centered on Henan, the southern high emission area centered on Guangdong, and the southwestern high emission area centered on Sichuan. The center of gravity showed a trend of shifting northward and westward. ③ Rural population, regional gross domestic product, and agricultural output value were the dominant driving factors causing spatial heterogeneity of agricultural greenhouse gas emissions. ④ Agricultural greenhouse gas emissions had spatial spillover effects. When formulating agricultural greenhouse gas reduction targets, it is necessary to adopt a coordinated control strategy among different regions.

To achieve energy conservation and the reduction of agricultural greenhouse gas (GHG) emissions, the Ministry of Agriculture of the People’s Republic of China in 2015 made Zhejiang Province the first national low-carbon and circular agriculture pilot province. Specialized policies were conducted in Zhejiang Province for reducing agricultural GHG emissions. In this study, we collected the GHG emission sources data of each city in Hubei and Zhejiang Provinces and calculated the estimated agricultural GHG emission of each city from 2011 to 2020 by using the recommended method from the Intergovernmental Panel on Climate Change (IPCC). Following this, we evaluated the impact of the pilot policies on the agricultural GHG emission in the treatment province, Zhejiang, by implementing the difference-in-differences (DID) analysis. The empirical results showed that after considering variables such as income level, rural employment, and average power of agricultural machinery, the agricultural GHG emissions in Zhejiang Province decreased significantly after 2015, compared with Hubei Province, driven by the low-carbon and circular agriculture pilot policies. Furthermore, agricultural GHG emissions can be effectively reduced by increasing national financial investment and administrative orders; however, excessive administrative orders and forced transformation of the agricultural system are likely to harm farmers’ interests in the process of policy implementation.

The decision in 2006 to abolish the agricultural tax, which had lasted for thousands of years, contributed to the prosperity of agriculture, and with it the growing importance of soil N2O emissions in China. However, most of the previous literature ignored soil N2O emissions due to their too small share in total agricultural greenhouse gas (GHG) emissions. This paper attempts to take soil N2O emissions as an important variable in the measurement of agricultural green total factor productivity (AGTFP), which incorporates environmental pollution into the analytical framework of agricultural production efficiency. Three impressive results were found. Firstly, soil N2O emissions play an increasingly important role in agricultural GHG emissions. The proportion of soil N2O emissions in agricultural GHG emissions increased from 4.52% in 1998 to 4.83% in 2006, and then to 5.36% in 2016. Secondly, the regional difference of soil N2O emissions in AGTFP is visible. In 2016, although soil N2O emissions accounted for a small proportion (about 5%) of the total agricultural GHG emissions in China, the AGTFP including soil N2O emissions was much lower than that excluding soil N2O emissions, especially in areas with high agricultural and population density. Finally, over time, soil N2O emissions have had an increasing effect on AGTFP. Compared with 1998–2006, the impact of excluding soil N2O emissions on AGTFP in 2007–2016 was more evident than that including soil N2O emissions.

A critical assessment of provincial-level variation in agricultural GHG emissions in China

Balancing crop production and greenhouse gas (GHG) emissions from agriculture soil requires a better understanding and quantification of crop GHG emissions intensity, a measure of GHG emissions per unit crop production. Here we conduct a state-of-the-art estimate of the spatial-temporal variability of GHG emissions intensities for wheat, maize, and rice in China from 1949 to 2012 using an improved agricultural ecosystem model (Dynamic Land Ecosystem Model-Agriculture Version 2.0) and meta-analysis covering 172 field-GHG emissions experiments. The results show that the GHG emissions intensities of these croplands from 1949 to 2012, on average, were 0.10-1.31kgCO2 -eq/kg, with a significant increase rate of 1.84-3.58×10-3 kgCO2 -eqkg-1 year-1 . Nitrogen fertilizer was the dominant factor contributing to the increase in GHG emissions intensity in northern China and increased its impact in southern China in the 2000s. Increasing GHG emissions intensity implies that excessive fertilizer failed to markedly stimulate crop yield increase in China but still exacerbated soil GHG emissions. This study found that overfertilization of more than 60% was mainly located in the winter wheat-summer maize rotation systems in the North China Plain, the winter wheat-rice rotation systems in the middle and lower reaches of the Yangtze River and southwest China, and most of the double rice systems in the South. Our simulations suggest that roughly a one-third reduction in the current N fertilizer application level over these "overfertilization" regions would not significantly influence crop yield but decrease soil GHG emissions by 29.60%-32.50% and GHG emissions intensity by 0.13-0.25kgCO2 -eq/kg. This reduction is about 29% and 5% of total agricultural soil GHG emissions in China and the world, respectively. This study suggests that improving nitrogen use efficiency would be an effective strategy to mitigate GHG emissions and sustain China's food security.

Agriculture plays a central role in maintaining food security and achieving sustainable development for human society. It is a challenge for the agricultural sector to mitigate greenhouse gas (GHG) emissions and maintain agricultural production. However, dual-level uncertainties exist in the processes of agricultural GHG accounting and emission reduction management. In this research, an integrated approach for identifying adaptation strategies in agricultural GHG emission reduction management was developed through incorporating life cycle analysis (LCA) of agricultural production into a general mathematical programming model. This approach strengthened the applicability of LCA and the comprehensiveness of programming models in generating agricultural adaptive actions under different GHG emission restriction targets. Also, dual-level uncertainties of LCA and adaptation management can be effectively addressed. A case study was proposed to illustrate application of the approach in Dalian City, China. The results indicated that farming patterns in eight districts would change significantly. The total area of maize fields would account for the primary proportion (i.e., 40–45 %) in its agricultural sector. Rice, peanut and cabbage fields would be the minor contributors in terms of GHG emissions. In addition to effective rainfall (i.e., [156, 259] mm/ha), a certain amount of water would be supplied for agricultural irrigation to maximize the city’s agricultural yields. Compared with other agricultural crops, rice fields would need the largest amount of irrigation water (i.e., [153.72, 277.98] Mt) to meet the requirements of local government plans.

Agricultural systems contribute nearly one-third of global anthropogenic GHG emissions and are an important source of GHGs globally. The clarification of the GHG emission pattern from agriculture is of paramount importance in the establishment of an agricultural emission reduction mechanism and the realization of China’s dual-carbon target. Based on the life cycle assessment method (LCA), this paper comprehensively quantifies the greenhouse gas emissions from the agricultural system in China, encompassing rice, wheat, and corn cultivation as well as animal husbandry including cows, horses, donkeys, mules, camels, pigs, and sheep. The analysis covers the period 2000–2020 and examines the spatial distribution, temporal trends, and structural changes in the greenhouse gas emissions within China’s agriculture sector. The main results are as follows: (1) from 2000 to 2020, China witnessed a consistent upward trajectory in its total agricultural GHG emissions, exhibiting an average annual growth rate of 0.73%. Notably, methane (CH4) emissions emerged as the largest contributor, displaying an overall fluctuating pattern. Carbon dioxide (CO2) emissions demonstrated intermittent increases with a noteworthy annual growth rate of 3%, signifying the most rapid expansion within this context. Conversely, nitrous oxide (N2O) emissions experienced decline over the specified period. (2) GHG emissions from cultivation demonstrate an upward trajectory, primarily driven by the CH4 emissions originating from rice cultivation and CO2 resulting from straw incineration. Conversely, GHG emissions stemming from animal husbandry declined, with the primary source being CH4 emissions arising from animal enteric fermentation. Agricultural N2O emissions predominantly arise due to manure management and nitrogen fertilizer application. (3) Agricultural greenhouse gas emissions exhibit significant variations in spatial distribution, gradually concentrating towards the North China Plain, the middle and lower reaches of the Yangtze River, and the northeast. Specifically, agricultural CH4 emissions are progressively concentrated in China’s pivotal rice-growing regions, encompassing the middle and lower reaches of the Yangtze River Plain, as well as livestock breeding areas like Inner Mongolia. Agricultural CO2 emissions primarily concentrate in dryland crop production zones such as North China and Northeast China. Meanwhile, Agricultural N2O emissions predominantly occur in Inner Mongolia and the North China Plain. China’s agricultural greenhouse gas emissions in 2020 show a significant spatial clustering effect, with hotspots primarily concentrated in Shandong, Anhui, Henan, and other regions and cold spots focused in the western and southern areas. The emission patterns of agricultural GHGs are closely intertwined with farming practices, regional development levels, and national policy; hence, tailored measures for emission reduction should be formulated based on specific crop types, livestock categories, agricultural production activities, and regional development characteristics.