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Agricultural production contributes to the increase in global carbon emissions. It is crucial to improve output and reduce carbon emissions in the context of agricultural modernization, for which improved carbon emission efficiency is key. However, the role of agricultural modernization in promoting agricultural carbon emission efficiency is not clear. Hence, the aim of this article is to analyze the spatiotemporal evolution of agricultural modernization and agricultural carbon emission efficiency in China from 2000 to 2019 and to reveal the relationship between agricultural modernization and agricultural carbon emission efficiency. The results showed that (1) in China, the overall level of agricultural modernization has been steadily increasing, and the regional differences are widening, showing a spatial pattern characterized by a gradual decline from the eastern and central regions to the western region. (2) China's agricultural carbon emission efficiency continues to grow but has not achieved a data envelopment analysis (DEA) effect, with the eastern and western regions having higher agricultural carbon efficiency than the central region. The regional differences first narrow and then expand. (3) Agricultural modernization significantly promotes agricultural carbon emission efficiency in both the province and the neighboring provinces, and the interprovincial spillover effect exceeds the direct effect within the province. A nonlinear correlation exists between agricultural modernization and agricultural carbon emission efficiency. [ABSTRACT FROM AUTHOR]

As an important energy base in China, Shanxi Province, in its economic development, has depended heavily on coal resources. However, enormous coal consumption produces a large amount of carbon dioxide and an aggravated ecological problem. In this paper, the super efficiency slack-based measure and data envelopment analysis (SBM-DEA) model and Malmquist index were used to calculate the energy consumption and carbon emission efficiency of 11 prefecture-level cities in Shanxi in the period from 2000 to 2020. The results were as follows: (1) The primary form of energy consumption in Shanxi Province is the burning of coal, and the carbon emission efficiencies of the 11 prefecture-level cities are quite different. The technical efficiency, technological progress, and total factor productivity of Taiyuan and Shuozhou were found to be greater than 1 compared to other cities in Shanxi. (2) On the whole, although the carbon emission rate of Shanxi Province has slowed down, it still faces the problems of a large total emission base, high carbon emission intensity, and low efficiency of the unit carbon emission output. Industrial structure is the key factor restricting improvements in the efficiency of carbon emissions in Shanxi Province. (3) From the spatial and temporal evolution of the carbon emission efficiency in Shanxi Province, we can see that the carbon emission efficiency of the whole province gradually dispersed and improved from the central and northern regions to the southern regions during the period of 2000–2020. In order to achieve a low-carbon economy, Shanxi Province must optimize its industrial structure, accelerate research and development in low-carbon technologies, adjust the energy structure, and promote the construction of an energy supply system with clean low-carbon energy as the main source. [ABSTRACT FROM AUTHOR]

In 2021, The People's Republic of China proposed goals for peaking carbon dioxide emissions before 2030 and carbon neutrality before 2060, in the 15 counties (districts) of the Main Stream Area of the Yellow River Basin in Gansu that plays an important role in ecological protection and green development. Next the CO2 equivalents were converted according to the IPCC2 standard, the total agricultural GHG emissions was calculated, the relationship with the agricultural output value was analyzed, and the discretization of the space was analyzed by the coefficient of variation and standard deviation. Firstly, the total agricultural GHG emissions in 15 counties (districts) of the Main Stream Area of the Yellow River Basin increased 55.54% in 2000–2019, and 2.35% annually, roughly divided into three stages: the rapid growth period (2000–2008), the slow decline period (2009–2014) and the rapid decline period (2015–2019). The economic efficiency is significantly improved, with an average annual decline of 6.49%, roughly divided into three stages: the slow-descent stage (2000–2004), the period of slow-growth stage (2005–2008) and the period of fast-decline (2009–2019). Secondly, based on the characteristics of the total GHG emissions, Maqu County has the largest GHG emissions increase, from 26.8842 kt in 2000 to 38.9603 kt, in 2019, an increase of 44.92%, while the smallest GHG emissions, in Anning District, decreased 87.33% from 111 t in 2000 to 14.1 t in 2019; In the rate of increase in the total GHG emissions, Dongxiang County had the largest rate of increase from 2000 to 2019, an increase of 160.28% and an average annual increase of 4.90%. The smallest rate of decrease in GHG emissions was seen in Chengguan District, where they decreased 92.11% from 2000 to 2019, an average annual decrease of 11.93%. The characteristics of agricultural GHG emissions intensity is a significant declining trending and agricultural production efficiency has been significantly improved. Finally, to provide a basis for the formulation of differentiated agricultural energy conservation and emissions reduction policies, reduce agricultural GHG emissions intensity and reduce the use efficiency of resources by formulating differentiated emission targets, tasks and incentive measures. [ABSTRACT FROM AUTHOR]

China is a large agricultural country with a high level of agricultural carbon emissions. Whether market prices can be used in agricultural production as a means of agricultural carbon emissions reduction is of great significance to improve the allocation of agricultural production factors and expand large-scale production. This paper applies an autoregressive distributed lag–pooled mean group(ARDL–PMG) model to evaluate the relationship between agricultural production factor prices, food consumption prices, and agricultural carbon emissions, using Chinese provincial panel data from 1994 to 2018. The results show that agricultural carbon emissions and agricultural production factor prices show environmental Kuznets curve (EKC) characteristics; agricultural carbon emissions and food prices show a U-shaped curve; and agricultural production factors are positively correlated with food price in both directions in the long-term. The results of Granger causality tests show that price is the cause of agricultural carbon emissions; the price of agricultural production factors and the price of food consumption are mutually causal. Such results have implications for price, agriculture, and environmental policies. The analysis implies that the market price can be applied to agricultural carbon reduction, which will help policymakers to implement effective price policies in order to reduce agricultural carbon emissions. One implication is that promoting the marketization of agricultural production factors and reducing price distortions will be conducive to carbon emissions reduction in agriculture, which in turn will increase food consumption prices. Therefore, subsidies are needed at the consumption end, which will eventually achieve further carbon emissions reduction at the production and consumption ends. [ABSTRACT FROM AUTHOR]