An Empirical Study on China’s Regional Carbon Emissions of Agriculture

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.

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.

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”.

How does agricultural specialization affect carbon emissions in China?

Research on Spatial-Temporal Characteristics and Driving Factor of Agricultural Carbon Emissions in China

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.

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.

This research compares the findings of previous papers on agricultural carbon emission in rural China and analyzes the potential driving factors and influencing factors and mechanisms in a meta-analysis. In this paper, we also derive and elaborate on common economic, technological, policy, and social factors that affect agricultural carbon emissions based on a synthesis of published articles in refereed journals from 2000 to 2023. A total of 1,971 documents concerning agricultural carbon emissions in rural China were discovered using keyword searches in the Scopus and CNKI databases. The findings show a constant growth in research production, indicating rising worldwide interest in agricultural carbon emissions in China. We identify influential keywords, authors, and nations that shape the research landscape, emphasizing current worldwide collaboration networks and developing research hotspots. Citation networks highlight the importance of distributing scientific results, particularly significant papers from various years. The study examines the factors influencing agricultural carbon emissions in rural China, providing valuable insights for policymakers and researchers aiming to develop sustainable practices and manage climate change in agriculture.

Global warming has become one of the major threats to the security of human survival, security, and sustainable development. Agricultural production has been widely suspected as one of the main sources of anthropogenic carbon emissions. Analyzing the changing characteristics and influencing factors of agricultural carbon emissions is of great significance for the mitigation of global climate change and the sustainable development in agriculture. Taking China, a large agricultural country, as an example, this study used the empirical model to quantify carbon emissions from agricultural inputs from 1991 to 2019, and analyzed the driving factors using ridge regression. We found that agricultural carbon emissions in China have been on the rise in the past 30 years, but at a markedly slower pace. From 2008 to 2019, the average annual growth rate of agricultural carbon emissions was 1.47%, down significantly from 2.92% between 1991 and 2007. The carbon emissions per unit of planting area showed an overall increasing trend, which grew from 179.35 t ce/km2 to 246.26 t ce/km2, with an average annual growth rate of 1.13%. The carbon emissions per unit of agricultural output mainly showed a decreasing trend, which decreased from 0.52 kg ce/CNY to 0.06 kg ce/CNY, with an average annual rate of change of −7.42%. China’s agricultural carbon emissions were closely related to macro-policies. Fertilizer inputs, agricultural industry structure, and energy use intensity were significantly positively correlated with carbon emission intensity. The degree of urban feedback to rural areas, public investment in agriculture, and large-scale planting were significantly negatively correlated with carbon emission intensity, but the impacts of these factors had a “lag effect”. In order to reduce carbon emissions from agriculture and promote development in green agriculture, we suggest that the government should further increase the degree of urban feedback to rural and public investment in the agricultural sector. In addition, large-scale agricultural production should be encouraged to increase resource efficiency and reduce carbon emissions.

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.

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.

Regional Differences, Convergence Characteristics, and Carbon Peaking Prediction of Agricultural Carbon Emissions in China

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.

The accurate measurement of agricultural carbon emissions and the analysis of the key influential factors and spatial effects are the premise of the rational formulation of agricultural emission reduction policies and the promotion of the regional coordinated governance of reductions in agricultural carbon emissions. In this paper, a spatial autocorrelation model and spatial Dubin model are used to explore the spatiotemporal characteristics, influential factors and spatial effects of agricultural carbon emissions (ACEs). The results show that (1) From 2014 to 2019, the overall carbon emissions of Zhejiang Province showed a downward trend, while the agricultural carbon emission density showed an upward trend. ACEs are mainly caused by rice planting and land management, accounting for 59.08% and 26.17% of the total agricultural carbon emissions, respectively. (2) The ACEs in Zhejiang Province have an obvious spatial autocorrelation. The spatial clustering characteristics of the ACEs are enhanced, and the "H-H" cluster is mainly concentrated in the northeast of Zhejiang, while the "L-L" cluster is concentrated in the southwest. (3) The results of the Dubin model analysis across the whole sample area show that the ACEs exhibit a significant spatial spillover effect. The disposable income per capita in the rural areas of the county significantly promotes the increase in the ACEs in the neighboring counties, and the adjustment of the industrial structure of the county has a positive effect on the agricultural carbon emission reductions in neighboring counties. (4) The grouping results show that there is heterogeneity between 26 counties in the mountainous areas and non-mountainous areas. In the 26 mountainous counties, the urbanization rate, rural population, mechanization level and industrial structure have significant negative spatial spillover effects on the carbon emissions. In the non-mountainous counties, the agricultural economic development level and disposable income per capita of the rural residents have significant spatial spillover effects on the agricultural carbon emissions. These research results can provide a theoretical basis for the promotion of the development of low-carbon agriculture in Zhejiang according to the region and category.

IntroductionThe agricultural sector is the second largest emitter of greenhouse gases, accounting for 23% of global anthropogenic carbon emissions. Analysis of the basic state of carbon emissions from China's agriculture is helpful to achieve carbon reduction targets.MethodsAgricultural carbon emissions were calculated using the emission factor method, based on data from the China Rural Statistical Yearbook and various provincial statistical yearbooks. To analyze spatial patterns, the standard deviation ellipse method and the center of gravity migration model were employed, uncovering the migration path of agricultural carbon emissions. Regional disparities and the driving factors of agricultural carbon emissions were further examined using the Theil index and the Logarithmic Mean Divisia Index (LMDI) model.ResultsThe analysis indicated that the emissions center has gradually shifted towards the central and western regions, reflecting changes in agricultural production activity areas. Intraregional differences are the primary contributors to the imbalance in agricultural carbon emissions, with pronounced disparities in grain production and consumption balance regions. Key influencing factors include agricultural production efficiency, adjustments in agricultural industrial structure, economic structure and output, and urbanization levels. The economic output effect and urbanization effect are identified as the main drivers of increased carbon emissions, while declining production efficiency has hindered emission reduction efforts.ConclusionThe findings provide valuable insights for regional management and policymaking in China's agricultural sector, highlighting the need to enhance production efficiency and optimize agricultural structure to reduce emissions.