TEMPORAL-SPATIAL EVOLUTION AND INFLUENCING FACTORS OF AGRICULTURAL CARBON EMISSIONS IN ANHUI PROVINCE, 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.

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.

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.

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.

Introduction: Strengthening the early warning of greenhouse gas emissions from agriculture is an important way to achieve Goal 13 of the Sustainable Development Goals. Agricultural carbon emissions are an important part of greenhouse gases, and accelerating the development of green and low-carbon agriculture is of great significance for China to achieve high-quality economic development and the goal of “carbon neutrality in peak carbon dioxide emissions”.Methods: By measuring the total agricultural carbon emissions in China and seven administrative regions from 2000 to 2021, the paper analyzes the influencing factors of agricultural carbon emissions in China by using STIRPAT environmental pressure model, and on this basis, predicts the peak trend of agricultural carbon emissions in China under different development scenarios by using the extreme learning machine model optimized by genetic algorithm.Results: The results showed that the extreme learning machine model improved by the genetic algorithm can overcome the shortcoming that the extreme learning machine model is easy to fall into the local optimal solution, thus obtaining higher prediction accuracy. At the same time, from 2000 to 2021, the total agricultural carbon emissions in China showed a continuous fluctuation trend, and due to the constraints of the agricultural economic level, agricultural industrial structure, and agricultural human capital, the agricultural carbon emissions showed spatial differentiation. It is worth noting that, in the context of green development, the agricultural carbon emissions of the seven regions in China all have the potential to achieve the “peak carbon dioxide emissions” goal in 2030, with only a slight difference at the peak.Discussion: The research results of this paper provide evidence for the government to formulate flexible, accurate, reasonable and appropriate agricultural carbon reduction policies, which is helpful to strengthen the exchanges and cooperation of regional agricultural and rural carbon reduction and fixation, and actively and steadily promote China's agriculture to achieve the goal of “peak carbon dioxide emissions carbon neutrality”.

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.

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.

Research has found that the transfer of agricultural land in China has to some extent led to agricultural carbon emissions. Therefore, it is urgent to systematically analyze the reasons for carbon emissions caused by agricultural land transfer, find ways to mitigate the increase in agricultural carbon emissions, and achieve low-carbon and sustainable development of agriculture. This article analyzes the relationship between agricultural land transfer, rural human capital, and agricultural carbon emissions in 30 sample provinces in China based on property rights incentives and scale operation theory, using the system GMM model, adjustment model, and threshold model. The results indicate that the transfer of agricultural land has, to some extent, intensified agricultural carbon emissions, with an increase of 0.003 units per unit of agricultural land transfer intensity. Rural human capital has mitigated the carbon emissions resulting from agricultural land transfer and played a corrective role. Under varying levels of rural human capital, there exists a dual threshold effect on the impact of agricultural land transfer on carbon emission intensity, exhibiting a pattern of ‘ineffectiveness-promotion-inhibition’. The analysis of regional heterogeneity reveals significant differences in the relationship between agricultural land transfer and carbon emissions between major grain-producing areas and non-grain-producing areas. It is worth noting that in the northern region, the transfer of agricultural land exacerbates carbon emissions, whereas in the southern region, higher levels of rural human capital effectively curb the growth of carbon emissions. Furthermore, the impact of agricultural land transfer on carbon emissions is not confined to specific regions, indicating that its environmental consequences transcend administrative boundaries and spread geographically, displaying distinct time-dependent characteristics.

How does agricultural specialization affect carbon emissions in China?

The focus of world governance on climate change has been on the industrial and transport sectors, yet the agricultural sector produces a lot of greenhouse gases, and this has always been ignored. This paper focuses on China, one of the world’s largest agricultural countries, and it investigates its agriculture carbon emission from a new perspective of the internal structure of it, which is relatively under-researched. Carbon metrology, the emission factor method and kernel density estimations are used to analyze China’s agricultural carbon emissions structure and its regional differences and its dynamic evolution characteristics. We find that: (1) China’s total amount of agricultural carbon emissions showed a ladder-like upward trend, but the growth rate of it has gradually slowed down; the inter-provincial heterogeneity of the agricultural carbon emissions was obvious. (2) From the standpoint of the grain functional areas, the annual total amount of agricultural carbon emissions and the amount of carbon emissions of each carbon source in the major grain producing areas were significantly higher than those in the major grain sales areas and the production–sales balance areas, and the carbon emission intensity in the major grain producing areas was the lowest overall. (3) In regard to the internal structure, China’s agricultural carbon emissions mainly came from the livestock and poultry, rice planting and agricultural energy sectors; the proportion of carbon emissions that were caused by the agricultural materials, agricultural energy and soil increased in general, and the inter-provincial differences between them expanded, while the inter-provincial differences between livestock and poultry gradually decreased. The proportion of carbon emissions from the six major agricultural carbon sources showed a convergence trend, and their kernel density had a right tail phenomenon. Our research deepens the understanding of China’s agricultural carbon emission structure, contributes to the rational optimization of the agricultural structure, and helps the agriculture sector and the rural areas to reach the carbon peak.

In the context of the era when new quality productivity is promoting the construction of an agricultural power, sorting out the rising path of agricultural carbon in the Yangtze River Economic Belt and clarifying issues, such as agricultural carbon height and carbon decoupling are important to promoting the "double carbon" process in the agricultural field. A statistical model of agricultural carbon emissions was established to measure the agricultural carbon emissions of 11 provinces （municipalities） in the Yangtze River Economic Belt from 2000 to 2022. On the basis of clarifying the spatial and temporal distribution characteristics and reduction of agricultural carbon emission intensity, the LMDI model was used to analyze agricultural carbon emissions. Second, we constructed models of decoupling in terms of speed and quantity and explored the decoupling relationship between the most influential factors with the strongest driving effect and agricultural carbon emissions. The results showed that during the study period, the total agricultural carbon emissions in the Yangtze River Economic Belt first increased and then decreased, and the major emission sources were straw burning, grain planting, livestock breeding, and agricultural material input. The level of economic development was the major factor leading to the increase in agricultural carbon emissions. Although the improvement of agricultural production efficiency inhibited the increase in carbon emissions caused by the growth of output value to a certain extent, the total inhibitory effect was lesser than the total promotion effect. From the perspective of double decoupling, the region has not yet reached a stable and strong decoupling state between agricultural carbon emissions and economic income growth of farmers. Based on this, suggestions for pollution reduction and carbon sequestration, clean production, and green transformation are put forward, respectively, for the planting industry, breeding industry, and agricultural product supply chain in the upstream, midstream, and downstream regions.

Managing agricultural water pollution (AWP) and agricultural carbon emissions (ACE) together is crucial for addressing the global water resources crisis and climate challenges. Traditional water quality indicators are limited in large-scale evaluations of AWP. The common trends of ACE and AWP, as well as the spatial heterogeneity of their common driving factors also remain unclear. This study introduces a novel framework for analyzing the synergistic reduction of AWP and ACE from the perspective of agricultural grey water footprint (AGWF) and examines disparities in common driving factors across areas with differing levels of economic development and pollution intensities in Zhejiang Province. The results indicate that ACE and AGWF in Zhejiang showed an upward trend from 2010 to 2012, followed by a significant decline from 2013 to 2020. A consistent synergistic reduction trend in grey water footprint and carbon emissions was identified in both the planting and livestock husbandry sectors across Zhejiang. Socio-economic factors jointly influenced the reductions in ACE and AGWF, with technological level and the labor-to-research-and-development (labor-R&D) ratio being the primary drivers, accounting for 79.41% and 78.38% of these reductions, respectively. The impact of agricultural R&D expenditure intensity on AGWF and ACE exhibited spatiotemporal heterogeneity and sectoral disparities. The key to promoting the synergistic reduction of AGWF and ACE lies in advancing the research, development, and application of green agricultural technologies especially in regions where such technologies are not yet fully developed. The results provide a theoretical framework and practical implementation for the integrated management of AWP and ACE, as well as sustainable agricultural policy formulation.

It is vital to explore the relationship between regional agricultural carbon emissions and economic growth for promoting the research of the agricultural Environmental Kuznets Curve (EKC) in China. Based on this, this research first uses the carbon emissions coefficient method to measure Fujian’s agricultural carbon emissions from 2000 to 2016, and uses the EKC model to explore the evolutionary relationship between regional agricultural carbon emissions and economic growth. Research shows that chemical fertilizers are the main cause of agricultural carbon emissions in Fujian. The agricultural carbon footprints and carbon emission intensity in the research area have not yet met the “inverted U-shape” assumed by EKC. Based on this, the author proposes that Fujian should continue to deepen the adjustment of the agricultural supply-side structure and take the road of green and low-carbon agricultural development. Fujian should implement technology reforms and improve the utilization of fertilizers in agricultural production and other emission reduction measures

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.

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