Agricultural Carbon Emissions Research Articles

Analysis of the effect of digital financial inclusion on agricultural carbon emissions in China

<p style="text-align:justify"><font _mstmutation="1">In the context of China's "dual carbon" goal, digital financial inclusion plays an important role in agricultural carbon reduction. Based on the provincial panel data of 31 provinces in China from 2012 to 2022, this paper analyzes the impact of digital financial inclusion on agricultural carbon emissions and its transmission mechanism. The results show that digital financial inclusion has a significant inhibitory effect on agricultural carbon emissions. From the perspective of mechanism, inclusive digital finance can reduce agricultural carbon emissions through the upgrading of agricultural industrial structure. In addition, in the mechanism of digital inclusive finance affecting agricultural carbon emissions, there is a threshold effect, and only when rural human capital is higher than a certain threshold, digital inclusive finance can show an inhibitory effect on agricultural carbon emissions. Therefore, it is necessary to strengthen the multi-dimensional development of digital technology facilities, the development of digital inclusive finance and the upgrading of agricultural industrial structure, promote the green and low-carbon transformation of agriculture, comprehensively reduce agricultural carbon emissions, and realize the low-carbon and sustainable development of China's agriculture.</font></p>

Decomposition and scenario analysis of agricultural carbon emissions in Heilongjiang, China.

As a key agricultural region in China, Heilongjiang Province has experienced significant carbon emissions over the past few decades. To understand the underlying factors and future trends in these emissions, a comprehensive analysis was conducted from 1993 to 2030. The agricultural carbon emissions from 1993 to 2020 were estimated using the emission factor method. To analyze the influencing factors and future trends of these emissions, the study employed the Logarithmic Mean Divisia Index (LMDI) and integrated it with the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. Results showed that (1) the agricultural carbon emissions in Heilongjiang were primarily driven by rice cultivation, followed by fertilizer production and irrigation electricity. (2) The economic and labor structure effects were the main driving factors of agricultural carbon emissions, while the population, demographic, and intensity effects were the main inhibitors. (3) Agricultural carbon emissions in Heilongjiang Province peaked in 2016 with 69.6 Mt CO2-eq and could subsequently decline by -3.92% to -4.52% between 2020 and 2030 in different scenario simulations. In the future, Heilongjiang Province should prioritize the reduction of agricultural carbon emissions from rice production. Adjusting the planting structure, managing the layout of rice paddies, and promoting the cultivation of dry rice varieties would significantly contribute to mitigating agricultural carbon emissions.

Big data development and agricultural carbon emissions: Exacerbation or suppression? A quasi-natural experiment based on the establishment of the National Big Data Comprehensive Pilot Zone

As a strategic resource, big data has become a key force affecting carbon emission reduction in agriculture. However, its impacts remain controversial, and relevant empirical evidence remains to be explored. Based on quasi-natural experimental analysis, this study explored the impact and mechanism of the construction of the National Big Data Comprehensive Pilot Zone (NBDCPZ) on agricultural carbon emissions (ACE) in China and adopted a difference-in-difference (DID) model using China's provincial panel data from 2003 to 2020. The results showed that the ACE in the NBDCPZ establishment area was significantly reduced by 11.91%, a finding that remained robust following the parallel trend test and the placebo test, among others. Mechanism analysis showed that the ACE was reduced through industrial upgrading and technological innovation. Heterogeneity analysis showed that more pronounced policy gains were achieved in China's central-eastern regions as well as in non-major grain-producing areas compared to western and major grain-producing areas. This research provided supporting evidence for the prospect of big data application in ACE and provided useful guidance regarding the promotion of green and sustainable agricultural development.

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.

Does Scaling Up Pig Farming Promote Carbon Neutrality Among Pig Farmers?

Pig farming is a major source of carbon emissions in China's livestock industry, and the promotion and application of carbon neutral technologies dedicated to carbon reduction and sequestration is a key measure to promote the realization of carbon neutrality in pig farming industry. In the absence of a system for carbon emission reduction in agriculture, it is important to explore whether large-scale pig farming can spontaneously promote the application of carbon neutral technologies by pig farmers. Combined with the technical background of carbon neutrality in pig farming industry and using the field survey data of 468 pig farmers in Shandong Province, this paper empirically analyzed the effect of large-scale pig farming on the carbon neutral behavior of pig farmers. The findings are threefold. Firstly, because of the effect of economies of scale in the application of carbon neutral technologies, large-scale pig farming can promote the carbon neutral behavior of pig farmers. However, the effect of economies of scale in the application of carbon neutral technologies has a boundary, and the annual output of pigs should be maintained at 3000 to 4999. Secondly, among the effects of pig breeding scale on the application and its degree of composting project, state of planting and breeding cycle has no regulating effect. Thirdly, among the effects of pig breeding scale on the application and its degree of biogas engineering, both state of planting and breeding cycle and subsidy for construction of biogas digester have negative regulating effects. In this paper, the pig farming industry is taken as a typical example to reveal the endogenous incentive mechanism of promoting agricultural carbon emission reduction, which provides theoretical reference and experience enlightenment for the formulation of effective policies to promote agricultural carbon neutrality, which are different from industrial industries.

Analysis of the impact of resource misallocation and socialized services on low-carbon agricultural production with DML based on random forest

The substantial increase in agricultural carbon emissions (ACEs) contributes significantly to global warming, and agricultural resource misallocation (ARM) plays a major role in the rise of ACEs. Therefore, the aim of developing modern agriculture is to reduce ARM and promote low-carbon agriculture. To achieve this, the double machine learning model (DML) and moderating effect model are adopted in the present study to explore the correlations between agricultural socialized services (ASS), ARM, and ACEs. Also, the ARM index is calculated using the panel data collected from 31 provinces during the 2004–2020 period in China. The research results are as follows. Firstly, ARM has a significant positive impact on ACEs at the 1% significance level. Secondly, ASS exerts significant negative regulatory effects on ARM and ACEs at the 1% significance level. Lastly, the goodness of fit (R2) reaches 95% and 40% for DML and the fixed effect model (FEM), respectively. Relatively, DML performs better in the goodness of fit according to this study. Furthermore, it is found out in this study that land misallocation, labor misallocation and capital misallocation increase ACEs and impede the development of low-carbon agriculture. Also, as a regulatory variable, ASS mitigates the ARM-induced increase of ACEs. Therefore, these findings provide guidance on the development of low-carbon agriculture for ASS.

Impact and transmission mechanism of digital economy on agricultural energy carbon emission reduction

The digital economy is a powerful driver for accelerating economic growth and an innovative approach to promoting high-quality agricultural development. Utilizing panel data encompassing 30 provincial-level administrative regions in China from 2011 to 2021, this paper quantifies the development level of digital economy and agricultural energy carbon emission intensity, and examines the impact and mechanism of digital economy on agricultural energy carbon emission intensity. The results reveal that: (1) Distinct regional disparities exist in both the development level of the digital economy and the carbon emission intensity of agricultural energy across China from 2011 to 2021, although an overall decreasing trend is observed; (2) The development of digital economy has a significant inhibitory effect on agricultural energy carbon emission intensity, and this conclusion remains robust after a series of tests; (3) Digital economy development can mitigate agricultural energy carbon emission intensity by alleviating misallocation of agricultural capital and promoting green technology innovation. In addition, there are significant regional differences in the impact of the digital economy on the carbon intensity of agricultural energy. These findings contribute to a deeper understanding of the environmental governance implications of the digital economy in agriculture and offer valuable guidance for the effective application of digital technologies towards achieving agricultural emission reduction goals.

Can digital financial inclusion converge the regional agricultural carbon emissions intensity gap?

To explore whether digital finance can reduce agricultural carbon emissions, promote regional convergence, and foster inclusivity in rural revitalization and shared prosperity, this paper uses the provincial-level index of digital financial inclusion to analyze the impact of digital financial inclusion on the intensity of agricultural carbon emissions and the Degum Gini coefficient (D-Gini coefficient) of regional carbon emission intensity in 30 sample provinces from 2010 to 2020. It examines the mechanism of the impact of digital financial inclusion on both variables to understand the underlying factors better. The main conclusions are as follows: (1) Digital financial inclusion significantly reduces the intensity of agricultural carbon emissions and narrows the gap in carbon emission intensity between regions. (2) The unconditional quantile regression coefficients show that the negative coefficients of the digital financial inclusion index and the three-dimensional indices decrease with increasing quantiles. However, the significant effects vary significantly at different quantiles. (3) Technological progress and the government's ability to allocate financial resources play a significant mediating role, and the income gap between urban and rural areas can be further narrowed, as well as the carbon emission intensity gap between provinces. The empirical results are robust and proven by replacing the econometric analysis method, changing the core variables, and other methods.

Coupled coordination and pathway analysis of food security and carbon emission efficiency under climate-smart agriculture orientation

Owing to the contradiction between agricultural production and environmental development, the issues of food security and carbon mitigation cannot be isolated, and achieving coupled and coordinated development is the key to agricultural sustainability. This study adopted the coupled coordination model and dynamic qualitative comparative analysis (dynamic QCA) method to measure the coupled coordination degree (CCD) of the food security index (FSI) and agricultural carbon emission efficiency (ACEE) in 31 provinces of China from 2010 to 2021, seeking paths to achieve high coupled coordination from Climate-Smart Agriculture technology, external environment, and incentive dimensions, and simulating path selection differences under various CSA priority scenarios. The results indicated that the CCD of the FSI and ACEE in China significantly increased year-on-year increase, with significant regional differences primarily reflected in the Northeast > East > West > Central regions. Based on the CSA orientation, the “technology-environmental safeguard” linkage path and the “technology-environment-incentive” hybrid path are proposed. There are differences in CSA practices across regions, which require customization based on their unique socioeconomic, ecological, and political landscapes. When priorities favour food security, the “technology-environment-incentive” hybrid pathway supports high CCD, and as priorities increase, the contribution of CSA technologies, centred on water-saving irrigation, increases and the role of the external environment diminishes. When the priority tendency is to mitigate emissions, both paths can achieve high CCD. As the priority tendency for carbon emissions increases, urbanisation and CSA technologies such as water-saving irrigation and straw return become essential factors contributing to higher coupling coordination, and the role of agriculture-related financial expenditures diminishes. These findings provide policy support for safeguarding food security and low-carbon agriculture.

Does Rural Labor Transfer Impact Chinese Agricultural Carbon Emission Efficiency? A Substitution Perspective of Agricultural Machinery

In this paper, referring to Kaya’s method, the ratio of gross agricultural production (GAP) and agricultural carbon emission is defined as agricultural carbon emission efficiency (ACEE). Considering rural labor transfer (RLT) increases the agricultural machinery intensity (AMI), the two are substitutes for each other and may interact with agricultural carbon emission efficiency (ACEE). We constructed a Simultaneous Equations Model (SEM) of ACEE, RLT and AMI and analyzed the interaction mechanism of these three variables using the Three-Stage Least Squares (3SLS). The following conclusions are drawn. First, RLT and AMI significantly promote the improvement of ACEE, while the improvement of ACEE and AMI further promotes RLT. Secondly, the causal relationship and influence mechanism of ACEE, RLT and AMI are interactive and multi-directional. For example, an increase in AMI promotes ACEE, but an increase in ACEE inhibits an increase in AMI. Finally, China has significant regional heterogeneity, and different regions have different interaction mechanisms. Local governments should consider the local economic base and technological level when implementing policies. This paper extends the analytical framework of ACEE, RLT, and AMI and provides a reference for governments to make policies.

Impacts of geopolitics and policy on Latin American biodiversity and water resources

AbstractLatin America is a major agricultural producer with important natural resources. Efforts have been made to protect sensitive areas but are hindered by agricultural trade disruptions outside the control of individual countries due to globally integrated crop markets. This analysis assesses the effects of two trade shocks, that is, the war in Ukraine and vehicle decarbonization in the United States (US), on biodiversity and water resources in Latin America. Results show that an increase in maize and wheat exports from the region triggered by the war in Ukraine negatively affects biodiversity in Brazil and leads to cropland expansion into drought‐prone areas in Argentina and Chile. For the case of reduced crop exports from Latin America due to US vehicle decarbonization and the corresponding shift away from US maize ethanol, the pressure on arable land in areas of high biodiversity and water stress is eased. As opposed to agricultural carbon emissions, which have global impacts, biodiversity and water issues have a strong local and regional significance. Regulatory frameworks aiming to protect these regions should be forward looking to detect and shield vulnerable areas from future threats. Other changes affecting global agriculture and trade, for example, sustainable aviation fuels in the US or the European Farm‐to‐Fork policy, need to be anticipated for effective policies in Latin America [EconLit Citations: Q13, Q25, Q34, Q56].

Does green finance matter for agricultural carbon abatement? Fresh insight from China.

Green finance constitutes a fundamental financial institutional arrangement aimed at achieving the dual-carbon objective during China's agricultural transition toward low-carbon, which aligns with the pragmatic necessity for reducing agricultural carbon emissions. For this purpose, this study investigates the impact of green finance on agricultural carbon reduction and its underlying mechanism by analyzing panel data from 30 regions using a two-way fixed effects model. Results indicate that green finance significantly promotes agricultural carbon reduction. The promotion effect of green finance on agricultural carbon reduction demonstrates an uneven spatial distribution, declining from eastern to western to central regions. Notably, non-Yangtze River Economic Belt provinces exhibit more pronounced effects compared to the Yangtze River Economic Belt region. Industrial structure upgrading and green innovation serve as crucial pathways for green finance to augment agricultural carbon reduction. Our finding reveals policymakers should implement differentiated green finance implementation strategies to accelerate the restructuring of the agricultural industry and encourage the introduction and use of innovative low-carbon technologies.

An empirical analysis of agricultural and rural carbon emissions under the background of rural revitalization strategy–based on machine learning algorithm

An empirical analysis of agricultural and rural carbon emissions under the background of rural revitalization strategy–based on machine learning algorithm

The research model of forecasting agricultural carbon emissions based on ARIMA-LSTM

To accurately forecast the volume of agricultural carbon emissions in China, this paper proposes a forecasting model that combines the Autoregressive Integrated Moving Average Model (ARIMA) with Long Short-Term Memory networks (LSTM) to better handle both linear and non-linear parts in time series. Using the data on China's agricultural carbon emissions collected from 2000 to 2023 as the research subject, the data from 2000 to 2016 were first used as the training set, and the data from 2017 to 2023 were used as the test set. The Spearman's rank correlation coefficient method was utilized to study the correlation between various types of carbon emissions. Then, the established ARIMA-LSTM forecasting model was applied to predict the emissions in the test samples and make comparisons. The Absolute Relative Error (ARE) was adopted as the evaluation metric for different models, comparing the prediction errors of the ARIMA-LSTM model with those of the ARIMA and LSTM single models through analysis. The results show that the positive correlation between diesel carbon emissions and film carbon emissions was the highest; the absolute relative error of the ARIMA-LSTM model did not exceed 7%, which is significantly lower than the other two comparison models. This research model achieves high-accuracy predictions of carbon emissions, which is beneficial for the control and management of the greenhouse effect.

Can Rural Digitization and the Efficiency of Agricultural Carbon Emissions Be Coupled and Harmonized under the “Dual-Carbon” Goal?

A major driving force behind China’s low-carbon, environmentally friendly development of agriculture and the accomplishment of the “dual-carbon” goal is the digital transformation of rural areas. In this study, on the basis of clarifying the degree of rural digitization and agricultural carbon emissions efficiency in China from 2010 to 2021, the degree of coupled coordination and the spatiotemporal pattern characteristics between the two are examined using the coupled coordination model. Then, the influencing factors are analyzed in depth using the spatial Durbin model. Our findings reveal that, first, in terms of the degree of rural digitization, the index increases overall and the spatial imbalance is obvious, with a spatial distribution pattern of “high in the east and low in the west”. Regarding the efficiency of agricultural carbon emissions, there is an overall “N-shaped” change, which is mainly influenced by technological progress in agricultural production, and the regional annual averages are, in descending order, the Western, Eastern, Northeastern, and Central regions, with obvious regional differences. Second, the coupling coordination index shows a fluctuating upward trend, from “extreme disorder” to “high-level coordination”. Furthermore, there are obvious regional characteristics. The regional growth rates are, in descending order, the Western, Eastern, Central, and Northeastern regions. Third, coupling coordination is jointly influenced by a variety of factors, including government input, educational level, industrial structure, energy use, urbanization rate, living standards, driving temporal patterns, and regional differences. This study not only helps to clarify the relationship between the two, offering a reference for the realization of the “dual-carbon” goal, but also broadens the concepts of the low-carbon and environmentally friendly development of agriculture.

Does carbon reduction and sequestration conflict with food security in rural China?—What, why and how?

Based on panel data of 31 provinces in rural China from 1997 to 2020, this manuscript first applies a carbon reduction and sequestration (CRS) model from the perspective of agricultural carbon emissions and agricultural carbon sinks. We then construct a food security evaluation system to examine the four dimensions of quantity, quality, ecological and economic security. Finally, the study uses a spatial Durbin model to empirically analyze the impact of CRS on food security and the moderating effect of fiscal decentralization. The relevant results: First, from 1997 to 2020, carbon emissions rose from 221.9794 million tons (1997) to 251.1368 million tons (2020), representing an increase of 13.14 %. The total amount of carbon sinks increased from 518.259 million tons (1997) to 758.887 million tons (2020); an increase of 46.43 %. CRS exhibited a fluctuating downward trend, falling from 0.98 (1997) to 0.90 (2020). However, food security showed an increasing trend, rising 0.12 (1997) to 0.32 (2020), with an average annual growth rate of 6.94 %. Second, in the short term, national CRS has had a significantly negative impact on food security, whereas the long term the result is exactly the opposite. In terms of control variables, planting structure, openness to the world, and economic development have significantly positive impact on food security, and urbanization, technological progress, and environmental regulation have significantly negative impact on food security. Regional heterogeneity is evident in the three functional attribute areas. Third, fiscal decentralization can enhance the negative impact of CRS on food security in the short term and weaken the positive impact of CRS on food security in the long term. Similarly, some regional heterogeneity is found among different regions.

How Does Agricultural Green Transformation Improve Residents’ Health? Empirical Evidence from China

Promoting green and sustainable agriculture is of great significance for ensuring food security and addressing global challenges. Meanwhile, health has increasingly become a global concern. Nutrition and health are the purpose of agricultural production. As two major global issues, how agriculture empowers human health has long been discussed. Based on the provincial panel data of China from 2003 to 2021, this paper studies the impact of agricultural green transformation (AGT) on residents’ health and explores its impact mechanism. The empirical results show that: (1) AGT in China has significantly reduced the average mortality rate and maternal mortality rate and significantly increased the average life expectancy, indicating that AGT in China has significantly improved the health level. The conclusions of robustness testing methods such as replacing AGT indicators and controlling endogeneity are still valid; and (2) The impact of AGT on residents’ health depends on the regional economic level, and there is a threshold effect. Compared with low-income areas, the positive effects of AGT in high-income areas on residents’ health are more pronounced; and (3) Agricultural carbon emissions play an intermediary effect between AGT and residents’ health, and AGT can improve residents’ health by reducing carbon emissions. The level of local education development plays a moderating role in the relationship between AGT and residents’ health. Agricultural policy implications include enhancing the ability to protect and utilize agricultural resources, promoting the green and low-carbon transformation of agriculture, and taking a more scientific and systematic approach towards the complex diversity of health risk factors.

Degassing of greenhouse gases from groundwater under different irrigation methods: A neglected carbon source in agriculture

The degassing potential of supersaturated dissolved greenhouse gases (N2O, CO2, and CH4) in groundwater has often been overlooked as a source of emissions in irrigated agriculture. Quantifying the degassing potential and investigating measures are essential for agricultural carbon mitigation. This study estimated for the first time the direct greenhouse gas degassing potential from groundwater under different irrigation methods and explored mitigation strategies in the Guanzhong Basin, Northwest China. The results revealed that while short-term degassing potential from groundwater irrigation exceeds that reported for global inland waters and fertilized soils, the interannual degassing potential (13,819–52,163 t CO2-eq year−1) represents only 0.3–1.1 % of annual emissions from regional agricultural soils. The degassing potential exhibited a trend of over-surface drip irrigation (ODI) > surface drip irrigation (DI) > subsurface drip irrigation (SDI) > flood irrigation (FI) under equivalent irrigation volumes. Notably, SDI emerges as a promising strategy for concurrent water conservation and greenhouse gas mitigation. Switching from over-surface and surface drip irrigation to subsurface drip irrigation yields a reduction of 60.8 % and 46.3 % in carbon emissions per cubic meter of water saved, respectively. This study provides valuable insights for the development of sustainable irrigation practices and emphasizes the importance of integrating groundwater degassing potential into agricultural carbon budgets and emission mitigation strategies.

Rural Digital Economy, Agricultural Green Technology Innovation, and Agricultural Carbon Emissions – Based on Panel Data from 30 Provinces in China between 2012 and 2021

Rural Digital Economy, Agricultural Green Technology Innovation, and Agricultural Carbon Emissions – Based on Panel Data from 30 Provinces in China between 2012 and 2021

Study on the Impact of China's Digital Economy on Agricultural Carbon Emissions

<p _msthash="770" _msttexthash="18511649104"><span lang="EN-US" style="font-size:12.0pt"><span style="font-family:"Times New Roman",serif">Under the background of China's “double carbon” goal, digital economy has become an important way to reduce carbon emissions in China. This paper utilizes the provincial panel data of China from 2012 to 2022, introduces the perspective of agricultural science and technology innovation, empirically examines the impact mechanism of regional digital economy development on agricultural carbon emission through regression analysis model, and portrays the dynamic effect and spillover effect of digital economy development on agricultural carbon emission from both time and space dimensions. The empirical results show that: digital economic development will have a significant inhibitory effect on the intensity of agricultural carbon emissions, and the inhibitory effect will be indirectly affected through the path of agricultural scientific and technological innovation; the impact of digital economic development on the intensity of agricultural carbon emissions there is a time lag effect, the current stage of the digital economic development will still have a strong inhibitory effect on the intensity of agricultural carbon emissions in the future; Digital economic development has a spatial spillover effect, i.e., the development of the regional digital economy will have an inhibitory effect on the intensity of agricultural carbon emissions in neighboring provinces. Based on this, it is proposed to strengthen the construction of digital infrastructure, promote the coordinated development of the digital economy in the region, and formulate policies to reduce carbon emissions in agriculture.</span></span></p>