Evaluating the impacts of technological progress on agricultural energy consumption and carbon emissions based on multi-scenario analysis.

Digital inclusive finance can help to achieve agricultural carbon reduction through effective resource allocation, financial innovation, and digital networks. This study empirically tested the role of digital inclusive finance in agricultural carbon emissions reduction using a two-way fixed-effects model that was based on panel data of 30 provinces from 2011 to 2019 in China. The data and statistics showed that China's total agricultural carbon emissions were still growing and had not yet reached their peak. This empirical study found that digital inclusive finance had a significant effect on the reduction in agricultural carbon emissions. Specifically, for every one-level increase in the digital financial inclusion development (DFII) level, the province's total agricultural carbon emissions (TACC), agricultural greenhouse gas carbon emissions (ACGC), and agricultural carbon source carbon emissions (ACSC) decreased by 0.31, 0.38, and 0.25%, respectively, but there was no significant decrease in agricultural energy use carbon emissions (ACEC)1. Furthermore, the first- and second-order lagged terms of digital inclusive finance still had significant agricultural carbon reduction effects, reducing TACC by 0.30 and 0.29%, respectively. To better utilize the agricultural carbon emissions reduction effect of digital inclusive finance, we should further support the development of digital inclusive finance; promote education on, and the breadth and depth of digital inclusive finance; encourage cooperation between digital inclusive finance and low-carbon enterprises to reduce the financing constraints of agricultural low-carbon enterprises; and stimulate the R&D and sales of low-carbon technologies.

Can financial agglomeration curb carbon emissions reduction from agricultural sector in China? Analyzing the role of industrial structure and digital finance

Sensitive zone of global climate change has been formed in China, and it has become a hot topic how can agriculture ensure food security and the supply of important agricultural products while achieving the “Dual Carbon” goal in the country. Based on such background, this paper uses the IPCC carbon emission calculation method, environmental input-output model and economic-water-carbon coefficient method to measure agricultural net carbon emissions, adopts bivariate spatial auto-correlation analysis and SYS-GMM to explore separately the relationship between agricultural net carbon emissions and effective supply of agricultural products, as well as the carbon reduction effect, growth effect and reasonable range of green technology innovation. The results show that: (1) China's agricultural net carbon emissions reveal a spatial distribution of “higher in the east than in the west than in the center” and a temporal characteristic of increasing year by year; China's effective supply of agricultural products shows an increasing trend and a spatial distribution of “higher in the east than in the center than in the west” in 2006–2012 and “higher in the east than in the west than in the center” in 2013–2020. (2) In 2006, 2010, 2015 and 2020, the number of provinces that belong to low-low agglomeration trade-off zone, low-high agglomeration synergy zone, non-significant zone, high-low agglomeration non-trade-off-synergy zone and high-high agglomeration trade-off zone averagely accounted for 12.500 %, 30.000 %, 26.667 %, 9.167 % and 21.667 % of the totality, respectively. (3) The carbon reduction and production growth effects of green technology innovation both show an inverted “U-shape”, and green technology innovation is conducive to both reducing agricultural net carbon emissions and improving supply of agricultural products when it is within a reasonable range of greater than 0.930. (4) Green technology innovation not only has significant spatial and temporal heterogeneity impact, but also exhibits a differential effect on productive agricultural carbon emissions, agricultural trade carbon emissions, agricultural carbon sinks, total output of agricultural products and agricultural net imports in international trade. Therefore, it is proposed that China should establish and improve green technology innovation incubation platforms, guide all participants to ensure the investment and application of green technology products within a reasonable range, formulate and implement regional differential policies and plan in accordance with local conditions, drive ultimately coordinated promotion of agricultural carbon emission reduction and product supply guarantee and lay an important foundation for achieving high-quality economic development and efficient ecological protection.

The "carbon peaking and carbon neutrality goals" has put forward new requirements for China's agricultural carbon emission reduction. It is easy to ignore the carbon emission transfer caused by agricultural trade if the reduction responsibility of carbon emission is merely defined from the supply side. Therefore, it is necessary to conduct in-depth research on agricultural carbon transfer for reasonably dividing the responsibility of agricultural carbon reduction in different provinces. In this study, the cross-section data of 31 provincial-level administrative regions in China in 2015, 2018 and 2021 were used to calculate the agricultural carbon emissions of each province from the production side, and the agricultural carbon transfer model was applied to study the spatial transfer characteristics of agricultural carbon emissions. The results show that: (1) In 2015, 2018, and 2021, the net carbon transfer in Chinese agriculture was 125.76 million tons, 132.49 million tons, and 133.02 million tons, respectively, accounting for 11.97%, 13.31%, and 13.61% of agricultural carbon emissions respectively. (2) The net input area of agricultural carbon emissions formed a spatial distribution pattern of four major regions which are concentrated in the southeast coastal areas, and the gap of net input of emissions was narrowing among the regions. Shanghai, Zhejiang, and Fujian are the regions with the largest net agricultural carbon input among the net input regions. The net agricultural carbon input increased from 43.00 million tons in 2015 to 52.71 million tons in 2021. In Guangdong and Guangxi, agricultural carbon emissions decreased from 41.34 million tons in 2015 to 35.61 million tons in 2021. In Sichuan, Chongqing, and Guizhou, agricultural carbon emissions decreased from 22.98 million tons in 2015 to 14.20 million tons in 2021. Beijing and Tianjin are the regions with the smallest net agricultural carbon input among the four net input regions, with the net agricultural carbon input increasing from 12.53 million tons in 2015 to 13.92 million tons in 2021. (3) The net output area of agricultural carbon emissions also formed a spatial distribution pattern of four major regions, and they were concentrated in the north of China with the center of gravity of net output shifting to the north. In 2015, Heilongjiang and Jilin were the regions with the largest net carbon output among the four net output regions. The net agricultural carbon output increased from 38.45 million tons in 2015 to 39.44 million tons in 2021. In Xinjiang and Gansu, the net agricultural carbon output increased from 15.87 million tons in 2015 to 23.37 million tons in 2021. In Inner Mongolia, the net agricultural carbon output increased from 17.03 million tons in 2015 to 23.05 million tons in 2021. Henan and Anhui have consistently maintained a high level of net agricultural carbon output, the net agricultural carbon output decreased from 35.54 million tons in 2015 to 25.68 million tons in 2021. On the whole, the spatial transfer of agricultural carbon emissions in China shows the characteristics of "north carbon transport to south" bounded by the Yangtze River. This paper believes that agricultural policies of carbon emission reduction should be formulated at both ends of agricultural supply and demand due to the spatial transfer of agricultural carbon emissions, which is not only conducive to stabilizing the production enthusiasm of major agricultural production provinces, but also conducive to controlling carbon emissions in output and input regions. For this purpose, the study puts forward countermeasures and suggestions to promote the reduction of agricultural carbon emission in different provinces, so as to better leverage the green and low-carbon development in the agricultural field under the guidance of the "carbon peaking and carbon neutrality goals".

Ensuring the synergistic advancement of agricultural pollution reduction and carbon emission mitigation, along with sustainable development, is crucial for achieving the ‘dual carbon’ target and modernizing agriculture. To ensure sustainable agricultural development, this study employs a coupling coordination model to explore the synergistic effects of pollution reduction and carbon emission mitigation in Henan Province, considering the agricultural carbon emissions (ACEs), agricultural non-point source pollution (ANP), and the gross value of agricultural output (GVAO) of 18 cities in Henan from 2010 to 2022 as endogenous variables. A panel vector autoregression (PVAR) model is utilized to analyze the interactive responses between agricultural pollution reduction and carbon emission mitigation and agricultural economic development. The results indicate that the degree of synergy between ACE and ANP in Henan Province has shown a trend towards higher values and a diminishing polarization phenomenon between 2010 and 2022, with most regions having degrees of synergy at higher levels. Furthermore, the interactive response relationships between agricultural pollution reduction and carbon emission mitigation and agricultural economic development reveals that the GVAO in Henan Province has a significant positive impact on both ACE and ANP, and that agricultural pollution reduction and carbon emission mitigation are constrained by agricultural economic development, with no significant bidirectional causal relationship observed overall and a lack of positive interaction in the long term. Finally, ACE, ANP, and GVAO in Henan Province exhibit a strong self-reinforcing mechanism, particularly ACE and GVAO, which show a pronounced self-growth trend. Overall, Henan Province should fully utilize the synergistic effects of agricultural pollution reduction and carbon emission mitigation to achieve coordinated progress in agricultural pollution reduction and carbon emission mitigation, as well as green and sustainable development of the agricultural economy.

As the world’s largest emitter of greenhouse gases, China is currently facing severe pressure to reduce emissions. As a large agricultural country, its agricultural carbon emissions accounted for 17% of total carbon emissions in recent years, which is much higher than the international average. It is urgent to control agricultural carbon emissions. At the same time, the development of agriculture is inseparable from the support of agricultural science and technology, and the progress of agricultural science and technology is also the source of power for agricultural economic growth. This article uses Stata 12.0 to study agricultural carbon emissions, agricultural technological progress and agricultural economic development in western provinces in China. The results show that the progress of agricultural science and technology plays an important role in the development of low-carbon agriculture. It can not only help reduce agricultural carbon emissions, but also promote the development of agricultural economy, providing an important reference for the development of low-carbon agriculture.

Carbon emissions, energy consumption and economic growth: Evidence from the agricultural sector of China's main grain-producing areas

Energy market volatility will have systemic effects on agricultural production, energy consumption and carbon emissions. This paper aims to evaluate the impacts of energy price on agricultural production, energy consumption, and carbon emission in China. To achieve the objective, this paper, firstly, constructed a price endogenous partial equilibrium model, and then designed four scenarios of energy price fluctuations, finally evaluating the impacts of energy price fluctuations on agricultural production and its energy consumption and carbon emission. The results revealed that: (1) The impacts on agricultural production are very limited, but higher energy price will result in producers’ welfare loss by 0.6% to 1.4%, under different scenarios. (2) Energy price drives negative impacts on agricultural energy consumption and carbon emission, 1.6%/3.2% and 1.3%/2.6%, respectively, in low/high amplitude scenarios. (3) Heterogeneous impacts are confirmed in the regional analysis; South China is simulated to be the most sensitive area. To mitigate the impacts from energy price and reduce carbon emission in agriculture, several policy implications have recently been proposed, including strengthening supervision of the energy market, constructing an energy saving price-setting mechanism, launching policy instruments to improve energy efficiencies and facilitate cleaner farming techniques, and formulating specific measurements of energy saving and emission reduction for different regions.

How does agricultural specialization affect carbon emissions in China?

The development of low-carbon agriculture is an effective way to maintain sustainable development of agriculture. By using Kaya’s identities to study the factors affecting China’s agricultural energy consumption carbon emissions, it is found that technical factors, low-carbon agricultural factors, rural living standards, and indirect urbanization. Factors and population size factors are important factors influencing the carbon emissions of agricultural energy consumption. Based on this, the paper uses LMDI index analysis method to decompose the above factors and the contribution rate. It is found that both technical factors and low-carbon technology factors can reduce agricultural carbon emissions, and the emission reduction ability of agricultural low-carbon technology factors is stronger than that. Agricultural technology factors. The total population change can drive carbon emissions positively, but the driving force is not strong. Urbanization indicators are driving a weaker positive for agricultural carbon emissions. To this end, the paper proposes that in the process of developing modern agriculture, it is necessary to strengthen the research and development of low-carbon agriculture technology, and promote the development of urbanization in an orderly manner in order to achieve the goal of low-carbon agriculture and sustainable development.

The development of low-carbon agriculture systems has been a global consensus to reduce carbon emissions in the agricultural sector for addressing climate change challenges. This fact brings the need to study the agricultural carbon emissions (ACEs). Studies focusing on calculating the spatiotemporal changes of ACEs and analyzing the main factors for ACE changes have been conducted. The agricultural technology inputs (ATIs) as an important factor to influence ACEs have been identified. The traditional linear model was the commonly used method to study the relationship between ATIs and ACEs, whereas the impact of ATIs on ACEs in different areas might be complex and nonlinear due to the differences in trade openness causing different development levels of agricultural technologies. Therefore, this study aims to investigate the effect of trade openness on the relationship between ATIs and ACEs using a panel threshold model and put forward policy implications for the low-carbon agriculture development. The analysis was based on data from a panel of 31 provinces of China during 2003-2018. The results show that ATIs and ACEs increased from 2003 to 2018 and the spatial distribution of ATIs was similar to that of ACEs. The ATIs had a positive effect on ACEs with a significant single-threshold effect from trade openness. When the trade openness was below the threshold (0.1425), the positive effect of ATIs on ACEs was significant (coefficient, 0.117), whereas, when the trade openness was above the threshold (0.1425), the positive effect of ATIs on ACEs significantly decreased (coefficient, 0.062). Furthermore, industrial structure and agricultural economic development were the positive drivers of ACEs, while trade openness, education level of rural workers, R&D funding, and natural disasters had negative relationships with ACEs. The results provide valuable references for understanding ACE drivers and developing low-carbon agriculture with the consideration of ATIs and trade openness.

As an important industry in ecologically fragile areas, the synergy of agricultural pollution control and carbon reduction is vital for the balanced development of industries and regional synergy. This paper aims to explore the synergistic result of agricultural pollution control and carbon reduction in ecologically fragile areas so as to clarify the weak links and solve carbon pollution in ecologically fragile areas. Leveraging the 2006–2021 municipal data of ecologically fragile areas, this paper calculates the coupling coordination degree (CCD) of agricultural non-point source pollution and agricultural carbon emission in ecologically fragile areas; calculates the decoupling relationship between agricultural carbon emissions, pollutants, and gross agricultural output based on the Tapio decoupling index; and quantitatively depicts the synergy of agricultural pollution control and carbon reduction in ecologically fragile areas. From 2006 to 2021, agricultural carbon emissions in ecologically fragile areas depicted a fluctuating and increasing trend. Agricultural non-point source pollution depicted an “inverted U-shaped” growth trend. The emission trends of agricultural carbon emissions and agricultural pollutants depict that although agricultural pollutants and carbon emissions are homologous, there is heterogeneity in the trend and change in emissions. The synergistic results of agricultural pollution control and carbon reduction show a fluctuating upward trend in ecologically fragile areas, and the coordination degree of ecologically fragile areas increased from 0.32 to 0.89, indicating that the level of coordinated development between agricultural pollution control and carbon reduction increased significantly. Taking into account the decoupling effect, the decoupling state of agricultural carbon pollution synergistic economic growth in ecologically fragile areas has changed from negative decoupling to strong decoupling to weak decoupling, mainly in the state of strong decoupling, negative decoupling of expansion, and weak decoupling; in addition, the synergistic capacity of agricultural pollution control and carbon reduction needs to be further optimized. Based on the research results, there is some room for improvement in agricultural carbon pollution synergy in ecologically fragile areas, and regions should strengthen regional cooperation.

Promoting low-carbon agriculture is vital for climate action and food security. State farms serve as crucial agricultural production bases in China and are essential in reducing China’s carbon emissions and boosting emission efficiency. This study calculates the carbon emissions of state farms across 29 Chinese provinces using the IPCC method from 2010 to 2022. It also evaluates emission efficiency with the Super-Slack-Based Measure (Super-SBM model) and analyzes influencing factors using the Logarithmic Mean Divisia Index (LMDI) method. The findings suggest that the three largest carbon sources are rice planting, chemical fertilizers, and land tillage. Secondly, agricultural carbon emissions in state farms initially surge, stabilize with fluctuations, and ultimately decline, with higher emissions observed in northern and eastern China. Thirdly, the rise of agricultural carbon emission efficiency is driven primarily by technological progress. Lastly, economic development and industry structure promote agricultural carbon emissions, while production efficiency and labor scale reduce them. To reduce carbon emissions from state farms in China and improve agricultural carbon emission efficiency, the following measures can be taken: (1) Improve agricultural production efficiency and reduce carbon emissions in all links; (2) Optimize the agricultural industrial structure and promote the coordinated development of agriculture; (3) Reduce the agricultural labor scale and promote the specialization, professionalization, and high-quality development of agricultural labor; (4) Accelerate agricultural green technology innovation and guide the green transformation of state farms. This study enriches the theoretical foundation of low-carbon agriculture and develops a framework for assessing carbon emissions in Chinese state farms, offering guidance for future research and policy development in sustainable agriculture.

Clarifying the spatiotemporal characteristics of agricultural carbon emissions and influencing factors in China is crucial. A system for measuring agricultural carbon emissions was established, thus evaluating the level of carbon emissions in China and its provinces. Moreover, the dynamic evolution of agricultural carbon emissions in China and the regions on both sides of the Hu Line was analyzed, then investigated factors affecting agricultural carbon emissions by the LMDI model. The results indicate that the total amount and intensity of agricultural carbon emissions showed an upward and then a downward trend in China from 2001 to 2021. The peaks were 330.72 million tons and 1.98 tons\\ha, respectively. Agricultural carbon intensity in provinces was mostly Low-Low Cluster and the range of High-High Cluster has decreased. Inter-provincial disparities in agricultural carbon emissions were also gradually narrowing. These show that the effect of agricultural carbon emissions reduction was obvious in China. It is important to note that carbon emissions from energy consumption in agriculture and agricultural material inputs were substantial, accounting for about 95% of the total. Agricultural carbon emissions were restricted by the agricultural production efficiency, changes in industrial structure, rural population size, and agricultural industrial structure, but were promoted by the level of economy and urbanization. Therefore, we recommend enhancing inter-provincial synergistic collaboration to create agricultural carbon emissions reduction pathways with unique features. It is also essential to maximize agricultural production efficiency and grasp the direction of green and low-carbon. We also suggest that the Chinese government should accelerate the in-depth adjustment and transformation and upgrading of the industrial structure, thereby reducing agricultural carbon emissions at source.

Carbon emission reduction is gaining increasing attention worldwide. This paper focuses on how the development of digital agriculture contributes to agricultural carbon emission reduction. To this end, the spatial characteristics, spillover effects, and driving factors of digital agriculture on agricultural carbon emissions are explored using panel data of 31 regions in China from 2011 to 2019 using a spatial econometric model and STIRPAT model with the extension of an ARDL method that was utilized to demonstrate the linkage amid variables. The results show that digital agriculture development reduces agricultural carbon emissions. Firstly, the results remain robust after estimation using the replacement weight method and the explanatory variable substitution method. Agricultural technological progress, agricultural industry structure, and rural education level all contribute to the reduction of agricultural carbon emissions in a region. Secondly, agricultural carbon emissions in the neighboring regions have a negative relationship with the agricultural industry structure in the region and a positive relationship with rural education level and agricultural technological level. Finally, strengthening the exchange of digital agriculture between regions and leveraging the intermediary effect of digital inclusive finance can effectively enhance the carbon emission reduction effect.