Crop Production and Agricultural Carbon Emissions: Relationship Diagnosis and Decomposition 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.

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

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.

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.

How does agricultural specialization affect carbon emissions in China?

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.

Agricultural activities constitute the second-largest contributor to greenhouse gas emissions. Proactively mitigating agricultural carbon emissions is crucial for safeguarding the ecological en-vironment, improving agricultural productivity, and fostering long-term ecological sustainability. This paper employs bibliometric analysis to examine the research status, hot topics, and devel-opment trends of agricultural carbon emissions in China over the past 2 decades. Based on Citespace software, the study primarily conducts visual analysis on 660 academic articles on ag-ricultural carbon emissions collected from the China National Knowledge Infrastructure (CNKI) between 2001 and May 2023, including publications indexed in Peking University Chinese Core Journals (PKU Core), Chinese Social Sciences Citation Index and Chinese Science Citation Database. The analysis covers publication quantity, author cooperation, institution cooperation, keyword co-occurrence, keyword clustering, keyword burst, keyword timeline, and keyword timezone. Research results indicate: (1) From the annual publication volume changes perspective, research on China’s agricultural carbon emissions demonstrates a rapid upward trend in the new era, with increasing research interest. (2) The core net-work of research authors has been established, primarily concentrated in agricultural and forestry universities, and the core network of institutions in this field is gradually forming. However, collaboration networks between authors and research institutions are relatively dispersed, necessitating strengthened collaboration among institutions. (3) Current research on agricultural carbon emissions predominantly focuses on the challenges of reducing agricultural carbon emissions in China under the “dual carbon” goals, measures, and pathways to achieve agricultural carbon emission reductions; performance evaluation of agricultural carbon emissions, factors affecting these emissions, and their reduction potential; as well as the relationship between agricultural carbon emissions and agricultural economic growth. Future research should delve deeper into the precise accounting of agricultural carbon emissions under the “dual carbon” goals, their underlying mechanisms, and issues related to precise and differentiated agricultural carbon reduction strategies. (4) The development trajectory of domestic agricultural carbon emissions research shows a period of germination from 2001 to 2009, a development stage from 2010 to 2015, and a deepening stage from 2016 to 2023, with a notable increase in publications in 2021, signifying a new upward phase in research output.

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.

IntroductionReducing carbon emissions from agriculture is essential to ensuring food security and human prosperity. As a country with approximately 20% of the global population, China has begun actively practicing the low-carbon agricultural development conception. Against the backdrop of disruptive technologies that continue to be integrated into various industries, the massive application of agricultural robots has opened the way to intelligent agriculture. This paper tries to answer whether there is some non-linear nexus between the application of agricultural robots and agricultural carbon emissions in China. As an essential tool for carbon emission reduction in China, does environmental regulation moderate the nexus between agricultural robot applications and agricultural carbon emissions? If so, how does this effect manifest itself?MethodsThis work takes China as an example by collecting macro-regional panel data from 30 provinces from 2006 to 2019. The environmental Kuznets curve theory is extended to agricultural carbon emissions, and we carried out empirical tests utilizing the panel fixed effects model and the moderating effects model.ResultsThis study verifies the inverted U-shaped nexus between agricultural robotics applications and agricultural carbon emissions in Chinese provinces, i.e., the agricultural carbon emissions (ACE)-Kuznets curve holds. The higher the level of formal environmental regulation, the larger the peak of the ACE-Kuznets curve and the more the inflection point is pushed back. The higher the level of informal environmental regulation, the lower the peak of the ACE-Kuznets curve and the later the inflection point.DiscussionThe findings in this paper represent the first exploration of the environmental Kuznets curve in agricultural carbon emissions. It is noteworthy that the moderating effect of formal environmental regulation does not lower the peak of the curve as we expect. This appearance is attributed to the reality that China is still in a phase of rising agricultural carbon emissions, which is exacerbated by the overlapping positive effects of agricultural robotics applications and formal environmental regulations. Informal environmental regulation is more effective than formal environmental regulation in reducing agricultural carbon emissions at this stage.

The relationship between agricultural carbon emissions and agricultural economic growth has attracted a significant research attention. A key issue to address in the development of agriculture is the reduction of agricultural carbon emissions while maintaining agricultural economic growth. This study investigated the interactions between agricultural carbon emissions and agricultural economic growth from multiple perspectives based on agricultural carbon emission data from 30 provinces in China measured from 1997 to 2015. Using this dataset, the coupling and decoupling effects of agricultural carbon emissions and the underlying driving factors were explored using a coupling development degree model, the Tapio decoupling assessment model, and a logarithmic mean Divisia index (LMDI) decomposition model. The results were as follows: (1) at the regional scale, the degree of coupling development between agricultural carbon emissions and agricultural economic growth is high in the central region of China and low in the western region. At the provincial scale, the coupling effects of agricultural carbon emissions exhibited four levels: minimal, low, moderate, and high coupling. (2) With the exceptions of Beijing, Zhejiang, Fujian, Guangdong, Inner Mongolia, and Shanghai, the relationships between agricultural carbon emissions and agricultural economic growth in the other 24 provinces were in a weak decoupling state. (3) The effects of agricultural development scale and agricultural technical progress were the major driving factors associated with increases and decreases in agricultural carbon emissions, respectively.

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

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”.

Reducing agricultural carbon emissions is an important aspect of achieving China’s carbon peak and neutrality goals. Different agricultural centrality result in different agriculture status and role in different regions, affecting agricultural carbon emissions. In this study, agricultural centrality is introduced from the perspective of social network analysis. Spatial autocorrelation analysis, geographically and temporally weighted regression (GTWR) and other methods are used to empirically explore the effect of technological progress and agricultural centrality on the spatiotemporal heterogeneity of agricultural carbon emissions. The moderating effect of agricultural centrality on the relationship between technological progress and agricultural carbon emissions is further explored. The results show that 1) during the research period (2001–2019), the agricultural carbon emissions first increased and then decreased, with remarkable spatial agglomeration characteristics, revealing a significant spatial autocorrelation of carbon emissions among provinces; 2) provinces have distinctly uneven characteristics in the social network of agricultural carbon emissions, while the same province shows relative consistency in terms of location centrality and betweenness centrality. Areas with high centrality are the major grain producing areas, and they invariably play an important role in the spatially linked network of agricultural carbon emissions; 3) technological progress has an inhibitory effect on agricultural carbon emissions, and the regression coefficient decreases from western to eastern regions, demonstrating a spatial gradient distribution. The location centrality has a negative effect on agricultural carbon emissions, with significant spatial heterogeneity. The effect of betweenness centrality on agricultural carbon emissions has increased from positive to negative over time, and the promotion of each province’s intermediary role has inhibited the increase of agricultural carbon emissions; 4) both agricultural location centrality and betweenness centrality have significant positive moderating effects on the relationship between technological progress and agricultural carbon emissions. With the increase of location centrality and betweenness centrality, technological progress has an increasingly strong inhibitory effect on agricultural carbon emissions. We put forward targeted suggestions based on different agricultural centrality in order to reduce agricultural carbon emissions and provide directions for achieving the China’s carbon peak and neutrality goals and the Sustainable Development Goals of the United Nations’ Agenda 2030.

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.

Agricultural carbon emission caused by agricultural economic growth has attracted wide attention in academic circles. Based on the input data of agricultural production materials from 1999 to 2014 in Jilin Province, this paper analyzes the decoupling relationship between agricultural carbon emission and economic growth in Jilin Province by using elastic decoupling method. Results show that the average annual growth rate of its total agricultural carbon emission was 4.28% from 1999 to 2014, with fertilizer being the main source and carbon emission from pesticide growing fastest. Meanwhile, weak decoupling is the main feature of agricultural carbon emissions and economic growth in terms of decoupling analysis.