Coupling coordination between digital village construction and agricultural carbon emissions in China.

Exploring the coupling coordination degree (CCD) between digital village construction (DVC) and agricultural carbon emissions (ACE) is crucial for promoting village revitalization and sustainable agricultural development. Analyzing data from 30 provinces in China in 2011-2020, this paper employs the CCD model, the Dagum Gini coefficient, and the geographic detector for in-depth analysis. The results show that the overall level of CCD gradually increases over time, but the national CCD still remains in a state of "low coordination," and there are apparent spatial differences in the CCD among provinces. In addition, the overall difference in CCD shows a decreasing trend, and the contribution of inter-regional differences has gradually become the most critical source of CCD's regional difference. Finally, the spatial differences of CCD are the result of two-factor interaction, among which the innovation level is the most core driving factor. The above findings provide important implications for China and other developing countries to fully leverage the interaction between village digitalization and agricultural decarbonization to promote sustainable agricultural development.

An integrated framework for improving green agricultural production sustainability in human-natural systems.

Water scarcity, land pollution, and global warming are serious challenges and crises facing the development of sustainable or green agriculture and need to be addressed using efficient and environmentally friendly management strategies. This paper proposed an integrated framework appropriate for agricultural green total factor productivity (AGTFP) assessment coupled with microscopic and mesoscopic perspectives under water-energy-food (WEF) nexus, which generated scientific and reasonable strategies for green and low-carbon agriculture from internal core factors and peripheral environmental impacts to improve green agricultural production sustainability. Taking the Lianshui irrigation district (LID) with three sub-areas as the object, internal core factors were explored by partial least squares regression (PLSR) and the external impact path through partial least squares structural equation modeling (PLS-SEM). Results indicated that AGTFP in LID was the smallest (0.818) compared to the three sub-areas and was in a fluctuating state. Meanwhile, AGTFP which was calculated considering undesirable outputs, was closer to tangible productivity. Resource endowments and technical facilities will promote agricultural production, desirable outputs will stimulate green production, and undesirable outputs can inhibit green production. The external influence pathway was shown to be primary environment - > secondary environment - > economic aspects - > social aspects - > AGTFP. The innovative perspectives presented in this study can facilitate preferable decisions and avoid unintended consequences for human-natural systems.

A study on the potential of digital economy in reducing agricultural carbon emissions.

Agriculture is a significant source of carbon emissions, which have a substantial environmental impact. The digital economy plays a vital role in mitigating these emissions through innovative digital solutions. As a leading agricultural nation, China faces substantial pressure to reduce its agricultural carbon emissions(ACE). This paper aims to thoroughly examine the relationship between the growth of the rural digital economy and ACE. To achieve this, we utilize an extensive panel dataset covering China's provinces from 2011 to 2020, analyzing the dynamic and spatial effects of digital economy development on ACE. The key findings of this research are as follows: (1) The rapid expansion of the digital economy significantly reduces ACE. (2) The impact of digital economic development on lowering ACE varies spatially, with a clear progression from eastern to western regions. (3) The digital economy helps reduce ACE through three specific channels: fostering technological innovation, enhancing scale efficiency management, and providing agricultural financial incentives. Based on these findings, this study proposes policy recommendations to improve digital infrastructure, promote balanced regional development in the digital economy, and optimize the management of agricultural science and technology. These policy insights aim to transform agriculture and achieve the goal of reducing ACE, thereby contributing to broader environmental sustainability.

Can the development of the rural digital economy reduce agricultural carbon emissions? A spatiotemporal empirical study based on China's provinces.

Rapid advancement of the rural digital economy has intensified the demand for leveraging digital tools to foster low-carbon and sustainable agricultural practices, garnering widespread academic and bureaucratic attention. Understanding how the rural digital economy influences agricultural carbon emissions is crucial for unlocking emission reduction potential, facilitating a transition towards sustainable energy usage in rural areas, and nurturing green agricultural development. In this study, we employ the entropy method, a spatial Durbin model, and a panel threshold model to assess the impact of the rural digital economy on agricultural carbon emissions across each province in China from 2010 to 2022. Additionally, we delve into the mechanism through which the rural digital economy facilitates agricultural carbon reduction, particularly in terms of "agricultural socialized services". Our findings reveal several key insights. Firstly, the rural digital economy contributes significantly to reducing agricultural carbon emission intensity. Secondly, there is a non-linear relationship between the rural digital economy and agricultural carbon emissions. With the development of rural digital economy showing a marginal decreasing trend, there is an obvious threshold effect. Thirdly, enhancing agricultural socialized services through the rural digital economy can curb agricultural carbon emissions. Lastly, the carbon reduction effect of the rural digital economy is more significant in more economically developed areas, areas with moderate levels of economic development, and areas with low technological investment; implementation of a "zero growth" policy for fertilizers strengthens this carbon reduction effect. This study sheds light on the mechanisms and effects of agricultural carbon emissions, offering quantitative evidence and theoretical support for the transition towards low-carbon and sustainable agricultural development.

Environmental regulations and agricultural carbon emissions efficiency: Evidence from rural China.

Reducing carbon emissions while maintaining simultaneous economic growth has been the focus of agricultural and environmental management research in recent times. To examine the influence of agricultural environmental regulations and related factors on agricultural carbon emissions efficiency, the entropy method was utilized to weigh each index and develop an index system for evaluating agricultural environmental regulations. This study utilizes the Super Slacked-Based Measure model that takes into account undesirable outputs. The research data used spans the years 2010-2019 and covers 31 provinces in China to calculate the efficiency of agricultural carbon emissions. A spatial Durbin model was employed to investigate the influence of environmental regulations and other influential factors on the efficiency of agricultural carbon emissions. The efficiency levels in the eastern region of China have consistently exceeded the national average, whereas the central region has demonstrated the lowest efficiency levels across the nation. Both the efficiency of agricultural carbon emissions and the intensity of agri-environmental regulations measured in this paper are strongly spatially autocorrelated between provinces. The environmental regulations index on local agricultural carbon emissions efficiency is significantly positive, while the effect on the agricultural carbon emissions efficiency in adjacent areas is not significant. Overall, agricultural environmental regulations effectively enhance agricultural carbon emissions efficiency, which in turn promotes technological innovation and economic growth. At the same time, local governments should actively adopt targeted strategies based on the actual situation of different regions in terms of their resource endowments and differences in the production characteristics of different crops.

Re-measurement and influencing factors of agricultural eco-efficiency under the 'dual carbon' target in China.

Given that agriculture is both a carbon source and sink, the sustainability goals of carbon peaking and neutrality place high demands on the green and low-carbon agricultural development in China, and the exploration of a realistic path for a sustainable agricultural development is urgently needed. Under the above 'dual carbon' target, this study focused on the key issue of how to improve China's agricultural eco-efficiency (AEE) and constructed an innovative AEE indicator system that can reflect carbon constraint and coordinated agricultural economic development, resource use and ecological environment. The super-efficient slack-based measured Data Envelopment Analysis (SBM-DEA) method, which considers undesirable outputs, was applied to re-measure the AEE of 30 provinces and cities in China from 2001 to 2020, and its spatial and temporal evolution was analysed in conjunction with kernel density estimation. The Tobit regression model was used to explore various influencing factors by region. The results show that the AEE re-measurements, which take into account the 'dual carbon' requirement, are significantly better than the traditional AEE. From 2001 to 2020, China had an overall V-shaped fluctuation curve AEE, with a small decline and several inter-annual fluctuations, and exhibited a large potential to rise. China's AEE showed a spatially uneven regional development at different stages of distribution and evident multi-polar differentiation. Inter-provincial differences were observed in China's AEE, and the vicious circle of low-level green and low-carbon agricultural development was difficult to break. Urbanisation had a significant positive effect on national and eastern AEE but a significant negative effect on central AEE. The agricultural carbon offset rate had a significant effect on AEE nationally and in the three regions. Thus, the introduction of 'dual carbon' target effectively drove the development of AEE. Agricultural industry structure inhibited the improvement of AEE nationally and in the western region. Agricultural economic development hindered the national AEE improvement but promoted that of the central region, where China showed an environment Kuznets curve. Hopefully, this study can provide data support and theoretical reference for the green and low-carbon agricultural development and help achieve the 'dual carbon' target.

Agricultural carbon emissions in Zhejiang Province, China (2001-2020): changing trends, influencing factors, and has it achieved synergy with food security and economic development?

Given the huge carbon footprint of agricultural activities, reduction in agricultural carbon emission (ACE) is important to achieve China's carbon peaking and carbon neutrality goals, but it may affect agricultural food security and economic development. Therefore, it is important for scientific carbon reduction measures to understand the multi-year trends and the influencing factors of ACE, and clarify whether the process of ACE affects food security and economic development. This study analyzed the trends of total ACE and ACE caused by different agricultural carbon sources (ACS) from 2001 to 2020 in Zhejiang Province, then we revealed the main influencing factors of ACE based on the logarithmic mean Divisia index (LMDI) model and dissected the relationship between ACE and food security and economic development. Results show that the total ACE fluctuated from 6.10 Mt in 2001 to 3.93 Mt in 2020, and the process included a decrease in 2001-2003 and 2005-2020 and an increase in 2003-2005. The decrease in ACE, from 2001 to 2014, was mainly due to the decline in rice acreage, which contributed 90.38%; from 2014 to 2020, it was by the reduction in the use of fertilizer, diesel, and pesticide, which contributed 83.9%. As drivers, agricultural economic development effect and total population size effect drove 4.25 and 1.54 Mt of ACE, respectively. As inhibitors, planting structure effect, technology development effect, and population structure effect inhibited 3.12, 2.11, and 2.74 Mt of ACE, respectively. With the reduction of ACE, the agricultural economy continued to grow, but the food security situation was pessimistic, indicating that ACE reduction has achieved synergy with economic development, but not with food security.

Effect of agricultural carbon emissions on farmers' health expenditure of China: evidence from the educational threshold effect.

Environmental pollution, especially agricultural carbon emissions (ACE), has led to public health problems to rural areas in China and accompanied by a heavy medical economic burden. However, most studies on carbon dioxide emissions and healthcare expenditures focused on the industrial sector, and the effect of ACE was overlooked. Therefore, studying the effect of ACE on rural residents' healthcare expenditures (NHCE) is not only conducive to accelerating the low-carbon transformation of agriculture but also has important implications for reducing healthcare expenditures. In addition, the effect of ACE on NHCE in different areas might be complex and nonlinear due to differences in years of schooling (EDU) leading to different awareness of environmental protection and health among farmers. Therefore, this paper used the Bayesian quantile regression (BQR) model and the panel threshold model to explore the effect of ACE on NHCE in different areas, based on the panel data of 31 provinces in China from 2007 to 2019. The results showed that ACE and NHCE experienced similar spatial distribution from 2007 to 2019. The BQR estimation results found that ACE had a significant positive effect on NHCE at different quantile levels during the sample period, public health concern, and thereby increasing the medical and economic burden of rural households. Meanwhile, ACE had a positive effect on NHCE with a significant single threshold effect from EDU. Specifically, farmers gradually realize the harm of environmental pollution to health with the continuous improvement of education level, and then ACE aggravated the improvement of NHCE after exceeding the threshold. EDU was more essential for farmers in contiguous poverty (CP) areas than in relatively developed (RD) areas and played an important role between ACE and NHCE. Furthermore, demographic structure, economic development, and public services were also positive driving factors for NHCE. The results of analysis provide a valuable reference for understanding the factors influencing NHCE and enable formulation of ACE emission reduction policies according to local conditions.

New evidence on the impact of No-tillage management on agricultural carbon emissions.

Controlling agricultural carbon emissions contributes to achieving peak carbon emissions and carbon neutrality. However, as a conservation management practice of farmland, the impact of No-tillage management (NTM) on agricultural carbon emissions needs to be further discussed. The main purpose of this paper is to assess the direct effect and spatial spillover effect of NTM on agricultural carbon emissions, revealing the regulating mechanism of NTM on agricultural carbon emissions and the combined application of NTM. Results indicate that NTM reduces agricultural carbon emissions, which is significant in the central and western regions, along with the primary grain, corn, and rice production areas, as well as the northern regions of the Huai River. Furthermore, the spatial spillover analysis reveals that the implementation of NTM increases agricultural carbon emissions in neighboring regions, but financial support and cross-regional services can negatively regulate the relationship between NTM and space agricultural carbon emissions. This paper also finds that combining straw-returning technology and NTM reduces agricultural carbon emissions. Building a cross-regional coordination mechanism, an incentive mechanism, and innovating the conservation tillage model is essential for promoting the NTM and achieving agricultural carbon reduction.

On the impacts of agricultural subsidies on agricultural carbon emissions in China: empirical evidence from microdata of rice production.

China's agricultural subsidy system has increased the investment on the agricultural production factors such as energy and chemistry, which caused an increase of agricultural carbon emissions. Based on the Thousand-Village Survey data from Shanghai University of Finance and Economics in 2013, the focal paper uses ordinary and two-stage least squares (OLS and 2SLS) to systematically investigate the impact and mechanism of agricultural subsidies on agricultural carbon emissions in China. Results show that China's current agricultural subsidy system has a promoting effect on agricultural carbon emissions. Four types of agricultural subsidies, namely, direct grain subsidies, subsidies for improved varieties, comprehensive subsidies for agricultural materials, and agricultural machinery purchase subsidies, impact the agricultural carbon emissions in ascending order. The agricultural subsidies increase the agricultural carbon emissions directly and indirectly. The findings indicate that a new type of agricultural subsidy system should be constructed, which mainly includes subsidies for farmers' out-migrating for work and land transfer, direct subsidies for grain, and subsidies for improved seed varieties. Among them, the direct grain subsidies should be implemented on the size of planting area and subsidies for improved seed varieties on the size of farmland to reduce the agricultural carbon emissions.

Impact of agricultural modernization on agricultural carbon emissions in China: a study based on the spatial spillover effect.

Taking the data of 30 provinces (excluding Hong Kong, Macao, and Taiwan regions and Tibet) at the provincial level from 2010 to 2019 as the research object, this paper analyzes the current situation and characteristics of China's agricultural modernization and response to carbon emissions. Agricultural modernization is decomposed into production modernization, management modernization, and ecological modernization. This study uses the spatial Dobbin model to demonstrate the impact of agricultural modernization on carbon emissions and analyzes the impact of agricultural modernization on carbon emissions in the East. The direct effect and spatial spillover effect of the three western regions are to different degrees. The results show that agricultural carbon emissions are spatially dependent. The development of agricultural modernization and transportation of neighboring provinces and cities will have an impact on agricultural carbon emissions in this region. Therefore, under the background of rural revitalization and low-carbon agriculture, this paper further analyzes the impact of agricultural modernization on the spatial distribution of carbon emissions in the eastern, central, and western regions. Recommendations are proposed with a view to giving better play to the process of agricultural modernization.

Analysis of regional differences and spatial spillover effects of agricultural carbon emissions in China.

In order to realize "double carbon" target in agriculture and high-quality development of the rural economy in China, it is crucial to study the regional differences and spatial spillover effects of agricultural carbon emissions (ACE). This paper measures ACE using panel data of 31 Chinese provinces from 2005 to 2020, examines the spatio-temporal evolution characteristics，the convergence of agricultural carbon emissions, compares and analyzes regional differences, and investigates the spatial correlation and spatial spillover effects. The study found that: (1) Total agricultural carbon emissions over the research period exhibit a rising and then reducing trend, the spatial distribution of total agricultural carbon emissions is described as high in east-central and low in west. The gap of agricultural carbon emissions is gradually declining in the east, and will eventually reach their respective steady-state levels in the west and northeast. (2) There is a strong spatial interprovincial link of ACE, which has a beneficial knock-on effect on the convergence of adjacent provinces. (3) Agricultural industrial structure, urbanization level, the size of the agricultural labor force, and the intensity of the agricultural machinery input all directly affect ACE in this province and indirectly affect ACE in adjacent provinces, with the exception of the negligible coefficient of economic development level on ACE. Hence, pertinent policy suggestions are put out to serve as a guide for reducing ACE.

Impact of Digital Village Construction on Agricultural Carbon Emissions: Evidence from Mainland China.

Reducing agricultural carbon emissions is required to reach the goal of carbon neutrality and mitigate the effects of climate change. With the advent of the digital economy, we aimed to determine if digital village construction can achieve carbon reduction in agriculture. As such, in this study, we used balanced panel data for 30 provinces in China from 2011 to 2020 to conduct an empirical analysis based on measuring the digital village construction level in each province. We found the following: Firstly, digital village construction is conducive to reducing the carbon emitted from agriculture, and the results of further tests showed that the carbon reduction effect of digital villages is mainly based on the reduction in carbon emissions from chemical fertilisers and pesticides. Secondly, the digital village construction has a stronger inhibiting effect on agricultural carbon emissions in major grain-producing areas than in non-major grain-producing areas. The level of rural human capital is the limiting condition for digital village construction to enable green agricultural development; in areas with higher levels of human capital, digital village construction has a significant inhibiting effect on agricultural carbon emissions. The above conclusions are valuable for the future promotion of digital village construction and the design of a green development model for agriculture.

Examining the drivers of agricultural carbon emissions in Africa: an application of FMOLS and DOLS approaches.

The major aim of this study was to investigate the impact of renewable energy consumption and agricultural economic growth on agricultural carbon emissions in Africa for the period 1990-2019. This paper employed panel fully modified ordinary least square (PFMOLS) and dynamic ordinary least square (PDOLS) estimation techniques. The empirical results showed that agriculture growth promote agricultural carbon emissions. More precisely, the results revealed a strong positive and statistical significant impact on agricultural carbon emissions in Africa. On the other hand, the results on quadratic show a negative causal association between agriculture growth and agricultural carbon emissions in Africa. Interestingly, renewable energy consumption was found to have a negative impact on agricultural carbon emissions. On Granger causality, the result shows that there is a unidirectional causality between agriculture growth and agricultural carbon emissions. Based on these findings, we recommend that countries should promote and encourage the use of renewable energy to curb agricultural carbon emissions. There is a need to adopt agricultural practices that have the potential to limit carbon emissions.

Analysis of the Spatial Differentiation and Promotion Potential for Agricultural Eco-Efficiency-Evidence of Pollution's Strong Disposability.

Agricultural eco-efficiency is an important indicator used to measure agriculture's high-quality and sustainable development. Therefore, this paper uses the EBM-Super-ML method with strong disposability of undesired output to calculate Chinese agricultural eco-efficiency and uses a geographical detector to measure the driving force of the factor. The research conclusions are mainly reflected in three aspects. Firstly, from the perspective of eco-efficiency changes, the overall mean value of agricultural eco-efficiency increased by 3.5%, and the regional heterogeneity is significant, with the fastest growth in the eastern region. Secondly, the results of driving force analysis show that the main driving factors of agricultural eco-efficiency divergence are capital inputs, total carbon emissions, labor inputs, agricultural film residues, fertilizer use, and pesticide residues, with driving forces of 0.43, 0.37, 0.34, 0.31, 0.28, and 0.20, respectively. Finally, from the perspective of eco-efficiency improvement potential, the mean value of output improvement potential is 5%, and the input factor is 7%. Among the non-desired outputs, the reduction rate of agricultural films can reach 40%. Among the input factors, labor input has the highest potential for intensive use, while agricultural machinery has a negative effect. Therefore, strengthening the development of the agricultural service industry is of great significance to improve the utilization rate of mechanical equipment and reduce the undesired output of agriculture.

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

Analyzing the impacts of technological progress on agricultural energy consumption and carbon emissions is of great significance for the development of low-carbon agriculture. Most of the existing studies focus on the agricultural sector level and lack of assessment of the impacts of technological progress on agricultural energy use and carbon emissions from the perspective of crops. In this study, we evaluated the impacts of technological progress on the energy consumption and carbon emissions of main crops in China under energy intensity constraints using a price endogenous partial equilibrium model with scenario analysis. We found that China's agriculture will have the highest yield and social welfare in 2025 under the production technological progress scenario, which will be 695.44 million t and 287.91 million yuan. Energy consumption for production will be the least under the energy technology progress scenario, which will be reduced by 9.02 million t ce or 16.01% compared to the baseline scenario. Under energy intensity constraints, synergy progress in production and energy technology will be the most effective way to reduce carbon emissions in the agricultural sector. Compared to the baseline, China's agricultural sector will reduce carbon emissions by 22.18 million t c in 2025 under the synergy scenario, a decrease of 16.18%. Therefore, we suggested that China's agricultural sector should pay more attention to the synergetic development of agricultural energy and production technology to further reduce carbon emissions and promote the development of green agriculture.

Evaluating the Spatiotemporal Characteristics of Agricultural Eco-Efficiency Alongside China's Carbon Neutrality Targets.

Agriculture has the dual effect of contributing to both carbon emissions and sequestration, and thus plays a critical role in mitigating global climate change and achieving carbon neutrality. Agricultural eco-efficiency (AEE) is an important measurement through which we can assess the efforts toward reduced emissions and increased sequestration. The purpose of this study was to understand the relationship between China's target of carbon neutrality and AEE through an evaluative model, so as to improve AEE and ultimately achieve sustainable agricultural development. The Super-SBM model scientifically measures the AEE based on provincial panel data collected between 2000 and 2020. We selected kernel density function and spatial distribution to explore the spatial and temporal evolutionary trends, and used a Tobit model to identify the drivers of AEE. The research shows that (1) China's agricultural system functions as a net carbon sink, with all provinces' agricultural carbon sequestration levels recorded as higher than their carbon emissions from 2000 to 2020. (2) Despite sequestration levels, the level of AEE in China is not high enough, and the average efficiency level from 2000 to 2020 is 0.7726, showing an overall trend where AEE decreased at first and then increased. (3) The AEE of each province is clearly polarized; there are obvious core-periphery characteristics and spatial distribution of clustered contiguous areas. Central provinces generally have lower efficiency, eastern and northeastern provinces have higher efficiency, and northeastern provinces always remain in the high-efficiency group. (4) Influencing factors show that urbanization, upgrading of industrial structure, financial support for agriculture, and mechanization have a significant positive impact on AEE. These findings have important implications for the promotion of the low-carbon green development of Chinese agriculture.

Spatial and Temporal Characteristics and Drivers of Agricultural Carbon Emissions in Jiangsu Province, China.

Scientific measurement and analysis of the spatial and temporal distribution characteristics of agricultural carbon emissions (ACEs) and the influencing factors are important prerequisites for the formulation of reasonable ACEs reduction policies. Compared with previous studies, this paper fully considers the heterogeneity of rice carbon emission coefficients, measures and analyzes the spatial and temporal characteristics of ACEs in Jiangsu Province from three carbon sources, including agricultural land use, rice cultivation, and livestock and poultry breeding, and explores spatial clustering patterns and driving factors, which can provide a reference for agricultural low-carbon production. The results indicate that from 2005 to 2020, Jiangsu's ACEs showed a decreasing trend, with an average annual decrease of 0.32%, while agricultural carbon emission density (ACED) showed an increasing trend, with an average annual increase of 0.16%. The area with the highest values for ACEs is concentrated in the northern region of Jiangsu, while the areas with the highest values for ACED are distributed in the southern region. The spatial clustering characteristics of ACEs have been strengthening. The "H-H" agglomeration is mainly concentrated in Lianyungang and Suqian, while the "L-L" agglomeration is concentrated in Zhenjiang, Changzhou, and Wuxi. Each 1% change in rural population, economic development level, agricultural technology factors, agricultural industry structure, urbanization level, rural investment, and per capita disposable income of farmers causes changes of 0.112%, -0.127%, -0.116%, 0.192%, -0.110%, -0.114%, and -0.123% in Jiangsu's ACEs, respectively. To promote carbon emission reduction in agriculture in Jiangsu Province, we should actively promote the development of regional synergistic carbon reduction, accelerate the construction of new urbanization, and guide the coordinated development of agriculture, forestry, animal husbandry, and fisheries industries.

The role of digital finance in reducing agricultural carbon emissions: evidence from China's provincial panel data.

As a vast agricultural country that emits a high level of agricultural carbon, China faces significant pressure to reduce its agricultural emissions. In recent years, digital finance has become a crucial part of China's financial system and has reshaped China's mode of green finance. Based on the 2011 to 2020 panel data of 31 provinces in China, this study discusses the effect and mechanisms of digital finance on agricultural carbon emissions. A two-way fixed effect model, threshold effect model, mediating effect model, and moderating effect model have been adopted to investigate the nexus of digital finance and agricultural carbon emissions. The results show that: (1) digital finance can reduce agricultural carbon emissions, and this effect is nonlinear, with two thresholds. (2) A reduction of agricultural carbon emissions through digital finance can be realized via digital finance's impact on farmers' entrepreneurship and agricultural technology innovation. (3) Urbanization has a positive moderating effect on digital finance's agricultural carbon emissions reduction effect. Based on the above conclusions, specific recommendations are proposed with regard to digital finance reducing agricultural carbon emissions.

Dynamic Linkage between Aging, Mechanizations and Carbon Emissions from Agricultural Production.

The trend of aging is intensifying and has become a prominent population phenomenon worldwide. The aging population has an important impact on carbon emissions, but at present, there is little research on its ecological consequences, especially the relationship with agricultural carbon emissions. For a long time, China has been dominated by a scattered small-scale peasant economy. Currently, the aging population also means that the agricultural labor force will gradually become scarce, and the agricultural production will face reform. This article is intended to find the long-term impact of aging and mechanization on agricultural carbon emissions and construct a more comprehensive policy framework for sustainable development, hoping to contribute to environmental and ecological protection. The research sample in this article is from 2000 to 2019, covering 30 provinces (cities, autonomous regions) in China. We adopted methods and models including Fully Modified General Least Squares (FMOLS), Dynamic General Least Squares (DOLS), Panel Vector Autoregression (PVAR) model, etc., and used the Granger causality test to determine the causal relationship between variables. Results show that aging is the Granger cause of agricultural carbon emissions and agricultural mechanization. Agricultural carbon emissions and agricultural mechanization have a bidirectional causal relationship. In the short term, agricultural mechanization and aging both have made a great contribution to carbon dioxide emissions from agricultural production. However, in the long term, the impact of aging on agricultural mechanization is significantly negative. Therefore, it is generally beneficial to improve the environmental problems of agricultural production. Our research focuses on the latest background of population trends and global climate issues and finally provides suggestions and a theoretical basis for the formulation of government agricultural policies according to the research conclusions.