Innovative measurement, trade-off-synergy relationship and influencing factors for agricultural net carbon emissions and effective supply of agricultural products in China

Under the requirements for high-quality development, the coordinated promotion of agricultural carbon emission reduction and agricultural product supply guarantee in China is crucial to hold the bottom line of national food security as well as promote agricultural green transformation and development. Based on such situation, from the perspective of decoupling effect, driving factors and the prediction, this paper uses panel data of 30 provinces in China from 2011 to 2020, takes the carbon emission formula, the “two-stage rolling” Tapio decoupling elasticity coefficient method, the spatial Durbin model and the Grey model optimized by the Simpson formula background value to quantify the relationship between agricultural carbon emission and agricultural product supply, analyze the driving effects of agricultural carbon emission reduction and agricultural product increase, and predict the decoupling state of agricultural carbon emission and agricultural product supply between 2021 and 2025, so as to draw a scientific basis that is conducive to the coordinated promotion of agricultural carbon emission reduction and agricultural product supply guarantee in China. The result shows that: (1) The decoupling state of agricultural carbon emission and agricultural product supply shows generally “the eastern and central regions are better than the western regions” in China, and the decoupling state has improved significantly year by year. Green technology innovation (GTI), agricultural carbon emission and agricultural product supply in China have significant spatial differences and spatial auto-correlation, which shows the spatial factors cannot be ignored; (2) Green technology innovation and agricultural carbon emission in local and adjacent provinces are both in an inverted “U-shaped” relationship, meaning that high level green technology innovation is an effective way to reduce carbon emission. Though green technology innovation and agricultural product supply in local and adjacent provinces are both in a positive “U-shaped” relationship, but the minimum value of lnGTI is greater than 0, which indicates that current level of green technology has been raised to a certain level, effectively improving the output of agricultural products; (3) Compared with those in 2016–2020 in China, it is projected that in 2021–2025 the decoupling state of agricultural carbon emission and agricultural product supply will be improved significantly, and the provinces below the optimal state will leave the extremely unreasonable strong negative decoupling state, mainly show recessionary decoupling and recessionary connection. Our findings provide Chinese decision-makers with corresponding references to formulate accountable and scientific regional policies in order to achieve high-quality development of agriculture and realize “Double carbon” target in China.

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.

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 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.

Regional development, agricultural industrial upgrading and carbon emissions: What is the role of fiscal expenditure? —-Evidence from Northeast China

Modern agriculture contributes significantly to greenhouse gas emissions, and agriculture has become the second biggest source of carbon emissions in China. In this context, it is necessary for China to study the nexus of agricultural economic growth and carbon emissions. Taking Jilin province as an example, this paper applied the environmental Kuznets curve (EKC) hypothesis and a decoupling analysis to examine the relationship between crop production and agricultural carbon emissions during 2000–2018, and it further provided a decomposition analysis of the changes in agricultural carbon emissions using the log mean Divisia index (LMDI) method. The results were as follows: (1) Based on the results of CO2 EKC estimation, an N-shaped EKC was found; in particular, the upward trend in agricultural carbon emissions has not changed recently. (2) According to the results of the decoupling analysis, expansive coupling occurred for 9 years, which was followed by weak decoupling for 5 years, and strong decoupling and strong coupling occurred for 2 years each. There was no stable evolutionary path from coupling to decoupling, and this has remained true recently. (3) We used the LMDI method to decompose the driving factors of agricultural carbon emissions into four factors: the agricultural carbon emission intensity effect, structure effect, economic effect, and labor force effect. From a policymaking perspective, we integrated the results of both the EKC and the decoupling analysis and conducted a detailed decomposition analysis, focusing on several key time points. Agricultural economic growth was found to have played a significant role on many occasions in the increase in agricultural carbon emissions, while agricultural carbon emission intensity was important to the decline in agricultural carbon emissions. Specifically, the four factors’ driving direction in the context of agricultural carbon emissions was not stable. We also found that the change in agricultural carbon emissions was affected more by economic policy than by environmental policy. Finally, we put forward policy suggestions for low-carbon agricultural development in Jilin province.

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

Agriculture is the second largest source of carbon emissions in the world. To achieve the strategic goals of “carbon peaking” and “carbon neutrality”, how to effectively control agricultural carbon emissions has become a focus of the Chinese government. As China’s most critical agricultural policy in the early 21st century, assessing the impact of rural tax-and-fees reform (RTFR) on agricultural carbon emissions has vital theoretical and practical implications. Based on panel data of 31 Chinese provinces from 2000 to 2019, this paper constructs a continuous difference-in-differences (CDID) model to identify the effects of RTFR on agricultural carbon emissions, and further tests the mechanisms and heterogeneity of the reform to achieve agricultural carbon emission reduction. The results demonstrate that the reform can effectively reduce the agricultural carbon intensity and improve agricultural carbon efficiency, with the effects of −6.35% and 6.14%, respectively. Moreover, the dynamic effect test shows that the impact of RTFR on agricultural carbon intensity and carbon efficiency is persistent. Furthermore, the mechanism analysis indicates that RTFR achieves the improvement of agricultural operation efficiency and the reduction of agricultural carbon emissions through the expansion of land operation area, the increase of productive investment in agriculture, and the special transfer payment from the central government. However, the impact of RTFR on local government revenue is not conducive to realizing the reform’s carbon reduction effect. The heterogeneity analysis illustrates that the reform policy effects differ in natural climatic conditions, topographical conditions, and crop cultivation structure. The RTFR mostly has a significant negative impact on the carbon emissions generated from material inputs and agricultural production. Therefore, to address the climate change crisis and improve the environmental efficiency of agricultural production, it is necessary to both reduce peasants’ tax burden and implement institutional construction efforts, to further promote the transformation of agricultural production to a low-carbon model.

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.

This study analyzed the carbon reduction effects of water-saving irrigation based on panel data of Chinese provinces from 2010 to 2020. Carbon emissions from irrigation were calculated and decomposed using the Malmquist index and LMDI. Results indicate that, first, the accounting results show a downward trend in estimated agricultural irrigation carbon emissions over the study period under a fixed-parameter framework. The average irrigation carbon intensity exhibits a declining pattern, particularly after the mid-2010s, with differences between provinces narrowing. Second, water-saving irrigation is associated with lower levels of estimated agricultural irrigation carbon emissions within the accounting framework by improving water-use efficiency and reducing irrigation water consumption per unit area, ultimately leading to a decrease in total carbon emissions. Finally, the carbon reduction effects are more pronounced and stable in major grain-producing regions. This study highlights regional heterogeneity in the emission-accounting outcomes associated with water-saving irrigation, which may provide descriptive evidence for discussions on region-specific irrigation management under different regional contexts.

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".

How does agricultural specialization affect carbon emissions in China?

IntroductionTo investigate the spatiotemporal evolution of agricultural carbon emissions and carbon absorption, analyse the spatiotemporal variations in the carbon balance, delineate carbon-offsetting regions, and formulate low-carbon development strategies tailored to various major functional zones, this study aims to promote coordinated regional ecological and environmental governance.MethodsThis study takes a perspective based on major functional zones, focuses on 17 cities in Hubei Province, studies the spatiotemporal variations in agricultural carbon budgets and carbon offsets in each city from the perspective of functional zoning and proposes a spatial optimization scheme for reducing carbon emissions.Results and discussionThe results show that both agricultural carbon emissions and carbon absorption in Hubei Province gradually increased, although the agricultural carbon budgets varied significantly among cities. Arable lands were the main agricultural carbon sinks in Hubei Province. Overall, carbon emissions exhibited declining core–periphery zonation, with Xiangyang, Jingzhou, and Huanggang serving as the centre (high emissions) and the cities of Shennongjia, Enshi, and Yichang serving as the periphery (low emissions). Carbon absorption displayed a U-shaped distribution, with high values in the east, south, and west and low values in the centre and north. The cities of Yichang, Jingmen, and Huanggang were the peak carbon sink areas. In recent years, the coordination between the agricultural carbon emissions and carbon budgets in Hubei Province has gradually improved, and agricultural carbon absorption and emissions have become increasingly balanced. Seven carbon-positive, five carbon-neutral, and five carbon-negative areas were identified in the province. Based on these findings, differentiated carbon emission reduction strategies were proposed to promote coordinated and low-carbon agriculture.

Based on the panel data of prefecture-level cities and above and provincial-level cities in the Yangtze River Economic Belt, respectively, this study measured the agricultural carbon emissions, carbon sinks, and carbon offset rates from 2006 to 2021 and analyzed their evolution characteristics. Based on the STIRPAT model and ridge regression analysis, this study identified the factors affecting agricultural carbon emissions in the 11 provinces and municipalities in the Yangtze River Economic Zone, combined them with the scenario analysis method to predict the agricultural carbon emissions under the baseline scenario in the period of 2022 to 2025, and analyzed the process of "carbon peaking." Simultaneously, this study predicted the agricultural carbon sinks of 11 provinces and cities from 2022 to 2025 and then speculated their agricultural "carbon neutral" process under the framework of agricultural carbon compensation rate, so as to summarize the effective paths for different provinces and cities to achieve agricultural "carbon peak and carbon neutral." The results showed that： ① Changes in agricultural carbon emissions in the Yangtze River Economic Zone during the observation period followed an inverted U-shape and peaked in 2015 at 33 312.65×104 tons. The fluctuation of agricultural carbon sinks was relatively small, with an overall upward trend. The upward trend of the agricultural carbon offset rate was obvious, but it still belonged to the "net carbon emission" region. ② Regional differences of agricultural carbon offsetting rate were prominent, and there was a polarization phenomenon, with "net carbon sink" cities significantly less than "net carbon emission" cities. ③ Shanghai, Zhejiang, and Sichuan reached the peak of agricultural carbon emissions in 2006, which Anhui and Chongqing reached in 2012, and the rest of the provinces and municipalities showed a clear upward trend. ④ Anhui, Chongqing, Sichuan, and Yunnan crossed the "agricultural carbon neutral line" and achieved agricultural carbon neutrality. Jiangsu was expected to achieve this in 2026-2030, whereas the remaining provinces and municipalities faced greater difficulties.

This article utilizes panel data from 2005 to 2020, covering 21 cities in Sichuan Province, to empirically examine the relationship between agricultural industry concentration and carbon emissions. The findings reveal a clear inverted U-shaped relationship between agricultural industry agglomeration and carbon emissions. This relationship also exhibits temporal lag and regional disparities. In Sichuan Province, the link between agricultural industry agglomeration and carbon emissions follows this inverted U-shaped pattern, emphasizing the need for a comprehensive understanding of agglomeration's role in shaping emissions. Carbon emissions in agriculture display strong temporal path dependence, underscoring the importance of timely policies for carbon reduction. Local governments should adapt their strategies to regional peculiarities, promoting the growth of local agricultural industries through increased scale and agglomeration. A well-planned distribution of agricultural industries across regions is essential for sustainable development.