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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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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.

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.

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.

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.

Environmental costs should be taken into account when measuring the achievements of China's agricultural development, since the long-term extensive development of agriculture has caused huge environmental pollution. This study took agricultural carbon emissions as an undesired output to estimate the agricultural development efficiency in 31 provinces of China from 1998 to 2016, based on the green total factor productivity, as assessed by the slacks-based measure directional distance function and constructing the global Malmquist-Luenberger index. We measured agricultural carbon emissions in terms of five aspects: agricultural materials, rice planting, soil, livestock and poultry farming, and straw burning, and then compared the green total factor productivity index and the total factor productivity index. The study came to the following conclusions: (1) the green technology efficiency change was smaller than the technology efficiency change at first, but the gap between them is narrowing with time, such that the former is now larger than the latter; (2) the green technology efficiency was in a declining state and the green technology progress was increasing, promoting the green total factor productivity growth, from 1998 to 2016; and (3) China's agricultural green total factor productivity increased by 4.2% annually in the east, 3.4% annually in the central region, and 2.5% annually in the west.

Despite achieving remarkable development, China's agricultural economy has been under severe environmental pressure. Based on previous studies, the present study further considers the sources of agricultural carbon emissions in depth, estimates China's agricultural carbon emissions from 1997 to 2016, and analyzes the agricultural pollution faced by China and its provinces. The study estimates the amount and intensity of agricultural carbon emissions in China from five carbon sources-agricultural materials, rice planting, soil N 2 O, livestock and poultry farming, and straw burning-and analyzes their spatial and temporal characteristics. The following results were obtained: (1) between 1997 and 2016, the amount of agricultural carbon emissions in China generally increased, while the intensity of agricultural carbon emissions decreased; (2) in the same period, the amount of carbon emissions from each category of carbon source generally increased, with the exception of rice planting; however, the amount of emissions fluctuated; (3) the amount and intensity of carbon emissions varied greatly among provinces; (4) the emissions from different categories of carbon source showed different concentration trends and agglomeration forms; (5) China's agricultural carbon emissions showed obvious spatial correlation, which overall was high-high agglomeration; however, its carbon emissions gradually weakened, and the spatial agglomeration of agricultural carbon emissions in each province changed between 1997 and 2016.

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.

In the context of carbon neutrality, the low-carbon transition in agriculture is crucial to achieving carbon mitigation through clean production. The provision of agricultural productive services (APS) is pivotal for modernizing farming practices in China. However, the impact of this on the low-carbon transformation has received limited attention. This research examined the non-linear relationship between agricultural productive services and low-carbon development, including verifying a threshold effect with APS as the threshold variable, employing panel data for 31 provinces in China from 2010 to 2021. The results of the study suggested that the effect of services associated with agricultural productivity on the transition of the agricultural sector to low-carbon practices varied across threshold ranges. Specifically, when the APS exceeded the threshold of 2.4396, a significant suppressive effect was observed on carbon emission intensity. Further analysis revealed that APS indirectly influenced the farmland scale and agricultural technological advancements, thereby promoting the low-carbon transition of China's agriculture. Based on these results, it is recommended to intensify the development of APS in key cereal-producing regions, while emphasizing the harmonious progress of these services in conjunction with large-scale farmland management. [ABSTRACT FROM AUTHOR]

AbstractReducing agricultural carbon emissions (ACEs) is essential to China’s overall carbon-emission-reduction target and agricultural sustainable development. This paper analyzes the impact of agricultural technology progress (ATP) and financial support for agriculture on ACEs from a theoretical level, and empirically tests the effect of ATP on ACEs by spatial econometric model, using China’s provincial data from 2001 to 2019. The regulatory effect model is used to examine the moderating effect of financial support policies on ATP and ACEs. The sample was divided according to natural geographical environment, economic development level and agricultural production structure to study the regional heterogeneity from the spatial perspective. The main conclusions are as follows: Overall, ATP has an inhibitory effect on ACEs, and this effect has a spatial spillover effect. In terms of regulation effect, fiscal support for agriculture policy has an enhanced regulatory effect in the impact of ATP on reducing ACEs. In addition, there is obvious regional heterogeneity in the carbon emission reduction effect of ATP in the west or east of the Hu Huanyong line, western or non-western, major or non-major grain-producing regions. This paper draws the following policy implications: First, increase agricultural technology research and development and expand technology spillover channels; second, establish regional collaborative emissions reduction mechanisms and strengthen regional technical cooperation; third, improve the financial support system for agriculture; Fourth, implement regional heterogeneous agricultural policies in accordance with local conditions.

To obtain a scientific understanding of the spatial and temporal patterns of agricultural eco-efficiency (AEE) as well as its influencing factors in Fujian Province, effective recommendations were provided to promote agricultural green development. The Super-SBM model with undesired output was employed to assess AEE, and the GTWR model was used to analyze the main factors influencing AEE based on data from nine prefecture-level cities in Fujian Province spanning from 2000 to 2020. There are three conclusions to be drawn: Firstly, the AEE of Fujian Province has increased from 0.340 in 2000 to 0.971 in 2020, but it has not yet reached the efficiency frontier due to excessive input of agricultural factors and carbon emissions. The development of AEE across regions is unbalanced with a wide gap between them. Secondly, from a spatial correlation perspective, global correlation of AEE in Fujian Province showed random distribution while local agglomeration existed to varying degrees. Thirdly, AEE in Fujian Province was influenced by various driving factors that changed significantly over different periods and exhibited obvious regional differences. In summary, the AEE of Fujian Province has shown overall improvement; however, its effectiveness remains limited. The disparities among cities are evident, necessitating the formulation of differentiated strategies for agricultural ecological development to enhance regional synergistic driving forces and improve regional AEE. [ABSTRACT FROM AUTHOR]

Comprehensive accounting of carbon emissions and carbon sinks in the agricultural sector is crucial for China to achieve its carbon neutrality goal as early as possible. This paper develops a comprehensive and scientific accounting system to recalculate China’s agriculture sector’s carbon emissions and sinks from 1995 to 2020, taking into account both resource inputs and productive activities. Subsequently, the STIRPAT model is employed to predict alterations in carbon emissions and sinks across different scenarios. The results show that energy consumption, chemical inputs, and farmland soil management have surpassed livestock and poultry breeding as the main contributors to agricultural carbon emissions. Furthermore, this paper classifies 31 provinces in China into five distinct types based on the variations in agricultural carbon emissions and carbon sinks. These types include carbon sink-dominated regions, paddy planting-dominated regions, livestock farming-dominated regions, resource inputs-dominated regions, and composite factor-dominated regions. In addition, the extent of agricultural technology and the magnitude of agricultural development are the key factors impacting China’s agricultural carbon emissions and carbon sinks, respectively. Prior to 2045, agricultural carbon emissions must be directly reduced as much as possible, and their source must be controlled; following that year, the role of carbon sequestration will become more prominent, and the active development of agricultural carbon sinks will be more beneficial in achieving agricultural carbon neutrality.

Carbon emissions represent an important cause of climate change. Agriculture is affected by climate change as well as a major source of carbon emissions. In this study, the PVAR and Han-Phillips GMM dynamic SDM models as well as urban panel data of Northeast China from 2007 to 2021 were used to explore the response mechanisms and spatial effects in the logical framework of "Regional development -Agricultural industrial upgrading-Carbon emissions", and to analyze the effect of fiscal expenditure on agricultural carbon reduction and climate risk management. The results show that: (1) The long-term regulation effect of fiscal expenditure on agricultural carbon reduction is ineffective, because fiscal agricultural expenditure only stimulates industrial upgrading, whereas fiscal environmental expenditure has a long-term stimulating effect on regional development; (2) Fiscal environmental expenditure has a short-term inhibiting effect on agricultural carbon emissions, which is only significant during the lag 1 period; (3) The spatial correlation of agricultural carbon emissions is mainly due to fiscal agricultural expenditure and industrial upgrading, based on the effects of technological spillovers, industrial linkages, and resource crowding between cities; and (4) Regional development and industrial upgrading contribute to agricultural carbon emissions in the long term; however, agricultural carbon emissions threaten long-term development. The above-mentioned results verify that regional development and agricultural industry upgrading depend on the environment in the sample area and indicate that insufficient attention has been paid to the low-carbon development of agriculture in fiscal expenditure. Currently, agricultural carbon reduction in China faces difficult challenges because of the time and spatial interactions, which must be considered in terms of systematic interventions, cross-regional, and development longevity in the future. This study supports the practice of agriculture carbon reduction and provides a new perspective for detailed analysis of the effect of fiscal expenditure on regional and industrial development. [ABSTRACT FROM AUTHOR]

The effects of land transfer on agricultural carbon emissions and their underlying mechanisms must be investigated if we are to achieve sustainable development and environmentally friendly high-quality agricultural development. This research experimentally investigated the spatial impacts of land transfer on agricultural carbon emissions and their underlying causes using multiple econometric models based on provincial panel data covering the years 2010 to 2022. The results allow us to draw the following conclusions: (1) Land transfer significantly inhibits agricultural carbon emissions. This conclusion remained valid after various robustness checks, including a reduction in sample size, change model type, and adjustment of geographical regions. (2) Agricultural socialized services play a positive moderating role in the process of land transfer to curb agricultural carbon emissions. (3) Land transfer has a substantial spillover effect on agricultural carbon emissions, resulting in significantly reduced emissions in the immediate area and nearby regions.

The digital economy, closely linked to agricultural progress, plays a key role in reducing agricultural carbon emissions. By utilizing panel data between 2011 and 2021 from 30 Chinese provincial-level regions, the present work empirically assesses the direct impact and nonlinear spatial spillover effects of the digital economy on agricultural carbon emissions, where methodologies including fixed effects, threshold models, and spatial econometrics are employed. The results were as follows: (1) The digital economy led to remarkable inhibition of agricultural carbon emissions, which was consistent across various robustness checks. (2) The carbon reduction efficiency of the digital economy presented obvious spatial variation, which had a greater negative effect in eastern regions and major grain-producing regions. (3) The digital economy had a nonlinear impact on agricultural carbon emissions, and technological innovation played a threshold effect, showing an “inverted U-shaped” characteristic of increasing first and then decreasing overall. (4) This emission abatement effect also showed a significant spatial spillover aspect, meaning that the digital economy development in one area prominently abates agricultural carbon emissions in adjacent provinces. Our findings might provide a theoretical and empirical foundation for comprehending and addressing agricultural carbon emissions abatement in China from the digital economy perspective.

AbstractAgriculture plays an indispensable role in the economic system of all countries, and the issue of agricultural carbon emissions has become a challenge to the sustainable development of the global economy. Against this backdrop, based on the data of 30 provinces, municipalities directly under the central government and autonomous regions (excluding Hong Kong, Macau and Tibet) in China from 2005 to 2019, this current study comprehensively uses the two-way fixed effect panel model and panel vector autoregressive (PVAR) model to explore the static and dynamic relationships between agricultural mechanization, large-scale operation and agricultural carbon emissions. Analysis show that agricultural mechanization and large-scale operation promote the agricultural carbon emissions, and increasing the level of urbanization will help curb the increase of agricultural carbon emissions. However, in a dynamic perspective, there is a two-way granger causality relationship between agricultural mechanization and agricultural carbon emissions. Agricultural mechanization is an important cause of agricultural carbon emissions and its promoting effect is sustainable, while the short-term effect of large-scale operation on agricultural carbon emissions is positive, and in the long run it helps reduce agricultural carbon emissions. Accordingly, the study recommends that the government should make the following efforts: firstly, speed up the research and development of agricultural machinery driven by clean energy; secondly, guide the effective transfer of land and encourage farmers to reduce the amount of agricultural chemical materials input while improving the use efficiency of pesticide chemical materials; thirdly, uphold the concept of ecological civilization in the process of promoting urbanization.

Accelerating economic globalization is a major driver of the transfer of embodied pollutant emissions from trade. China and the United States are currently the largest importers and exporters of agricultural products, respectively, and are also major producers and consumers of these products. This paper aims to analyze and compare the patterns of embodied agricultural carbon emissions (ACE) in the two countries, which is crucial for understanding how trade influences the transfer of such emissions. In this study, we calculated the embodied ACE of China and the United States from the perspectives of production and consumption for the years 1970, 1980, 1990, 2000, 2010, and 2016 by establishing a multi-regional input–output (MRIO) model. Additionally, we employed the Logarithmic Mean Divisia Index (LMDI) decomposition method to analyze the driving factors behind the changes in embodied ACE over time. The findings indicated that the embodied ACE associated with imports and exports in China and the United States followed a pattern of increase and subsequent decrease during the period 1970–2016, with net imports escalating from −18.79 million tons and −3.62 million tons to 40.35 million tons and 51.22 million tons, respectively. This study identified two main factors contributing to the reduction in embodied ACE in both countries: the declining intensity of embodied ACE per unit of traded products and the diminishing proportion of the primary industry. The growth in GDP per capita, population expansion, and an increase in the proportion of agricultural products in international trade are predicted to promote an increase in embodied ACE imports and exports in both countries. This paper advocates for the reduction of embodied ACE through the continuous promotion of research and application of energy-saving and emission-reduction technologies, an optimized industrial structure, and the implementation of relevant energy-saving and emission-reduction policies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The prediction of regional agricultural carbon emissions is of great significance for regional environmental protection and sustainable development of regional agriculture. The article proposes a PLS-SA-AdaBoost prediction model combining partial least squares (PLS), simulated annealing algorithm (SA) and adaptive boosting algorithm (AdaBoost), which overcomes the shortcomings of a single model with insufficient prediction accuracy. Through an empirical study of agricultural carbon emissions in Fujian Province, China, the article validates the effectiveness of this combined forecasting model. The results show that the combined PLS-SA-AdaBoost forecasting model has higher accuracy and better performance than other models. The article predicts the future trend and range of agricultural carbon emissions in Fujian Province under five different scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

How to control the growth of agricultural carbon emissions while developing tourism has become the primary issue that needs addressing in the development of rural China. This paper explores the impact of tourism on agricultural carbon emissions and further analyses the mediating effect of labor transfer in this relationship. The analysis is evaluated using provincial panel data from 31 provinces in China from 2007 to 2019 with a sequential regression model. The results show that tourism development increases agricultural carbon emissions, which is more pronounced in areas with advanced economies or higher education levels. Second, labor transfer to nonfarming industries suppresses the increase in agricultural carbon emissions caused by tourism. Finally, (i) the impact of tourism on labor transfer in less developed and less educated regions is limited, and (ii) there is the expectation of large‐scale use of agricultural machinery due to labor transfer in economically developed or highly educated areas. The heterogeneity test shows these two reasons lead to a mediating effect of labor transfer on agricultural carbon emissions in different regions. [ABSTRACT FROM AUTHOR]

Agricultural green total factor productivity (AGTFP) is an important indicator to reflect the sustainability level of agriculture. The AGTFP network reflects the spatial correlations of the AGTFP among regions; thus, exploring its network structure and influencing factors can provide targeted policy guidance to the coordinated development of the agriculture sector. This study builds an epsilon-based measurement data envelopment analysis (EBM-DEA) model to calculate 31 provincial AGTFPs in China from 2002 to 2020. Then, social network analysis (SNA) was utilized to explore the characteristics of the AGTFP network, and the quadratic assignment procedure (QAP) was applied to find its external influencing factors. We reached four central conclusions: (1) Overall, there was a gradual upward trend of AGTFP in China during 2002~2020, and the average value rose from 0.75 in 2002 to 0.90 in 2020, but there were some differences among regions. (2) There is a complex and stable network characteristic of AGTFP; the average network density is 0.3753, and the average network efficiency is 0.4714. Meanwhile, some eastern and central areas, such as Henan, Anhui, Hubei, Hebei, Jiangsu, etc., have relatively high centrality and are a bridge in the entire network. (3) The AGTFP network can be divided into eight blocks, including two net beneficial blocks (the central-eastern provinces with high centrality); two net spillover blocks, including the provinces located in the developed urban areas (Beijing, Tianjin, Shanghai, and Jilin), the underdeveloped northwest regions (Ningxia, Qinghai, Xinjiang, and Tibet). The other areas are two-way spillover blocks. (4) Transportation development gap, technological progress gap, and the similarities of the agricultural industry structure are critical factors influencing the AGTFP network. Hence, improving the efficiency of the logistics and transportation industry, promoting technology transfer from developed areas to underdeveloped areas, and developing characteristic agriculture are all conducive to promoting the whole region's AGTFP. Our research provides policy implications for sustainable agricultural development in China and other developing countries. [ABSTRACT FROM AUTHOR]

Reducing agricultural carbon emissions (ACE) is important for the sustainable development of agriculture. Agricultural productive services (APS), a novel form of agricultural technology extension, offer new avenues for promoting sustainable and green agriculture. The present study aims to explore the impact of APS on ACE. In line with the aim of the study, the Kernel density, Moran's I index, spatial Durbin model, and threshold regression model are employed. The findings reveal that APS demonstrate a significant inhibitory effect on ACE, reducing ACE not only within the region itself but also exerting a negative spatial spillover effect on other regions. Furthermore, a non-linear relationship between APS and ACE is observed, characterized by an inverted U-shaped curve with the arable land operating area serving as the threshold. Therefore, to fully harness the inhibitory effect of APS on ACE and to promote environmentally friendly and sustainable agricultural development, policymakers should vigorously develop APS, strengthen regional cooperation, and promote land transfer. Our research can help in understanding the impact of APS on ACE and to promote sustainable agricultural development. [ABSTRACT FROM AUTHOR]

Copyright of Arid Land Geography is the property of Chinese Academy of Sciences, Xinjiang Institute of Ecology & Geography and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Given the background of the rural population ageing, the reduced agricultural non-point source pollution and the decreased agricultural carbon emission, agricultural producer services, as an important bridge between small farmers and modern agriculture, are an important path to ensure food security and the green development of agriculture. Based on panel data of 31 provinces in China from 2003 to 2020, this paper uses Slack-based measure model with undesirable outputs (SBM-undesirable model)to calculate the agricultural ecological efficiency of 31 provinces. Furthermore, the two-stage least squares, the panel threshold model and the spatial Durbin model are used to empirically analyze the influence mechanism and the spatial spillover effect of agricultural productive services on agricultural ecological efficiency. The results show that agricultural producer services have a significant non-linear impact on agricultural ecological efficiency. Rural residents' income and per capita cultivated land area can adjust the relationship between them. The two main ways for agricultural productive services to improve agricultural ecological efficiency are as follows: reducing undesirable outputs such as pesticides, chemical fertilizers and plastic sheeting for agricultural use and improving agricultural production efficiency. In addition, agricultural producer services have a significant positive spatial spillover effect on agricultural ecological efficiency, and the indirect impact elasticity of spatial spillover is higher than the direct impact elasticity. Therefore, to achieve food security and promote the sustainable development of agriculture, it is necessary to vigorously develop agricultural productive services through multiparty cooperation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Strengthening agricultural carbon emissions (ACE) reduction is an inherent requirement to promote the integrated development of ecological greenery in the Yangtze River Delta (YRD), and it has a significant driving effect and benchmarking significance for China to achieve carbon peaking and carbon neutrality. Based on panel data of 41 cities in the YRD from 2001 to 2019, this study reveals the spatial and temporal evolution pattern of ACE and identifies the influencing factors through methods such as CV, SDE, ESDA and SDM. The results are summarized as follows: (1) the ACE in the YRD show a fluctuating downward trend, and the gap between cities tends to widen; (2) the YRD shows emission characteristics dominated by agricultural material input, with a proportion of over 50% over the years, followed by rice cultivation and livestock breeding with the lowest proportion. In addition, Jiangsu and Anhui have higher proportions, followed by Zhejiang, and Shanghai; (3) these cities with high ACE are mainly located in the northwest regions of the YRD, and those with low ACE are mainly located in the southeast regions of the YRD. Furthermore, cities in the YRD have a positive global spatial autocorrelation, and the local spatial agglomeration pattern has strong characteristics of stability and spatial dependence; (4) the agricultural economic development level has an inverted U-shaped effect on ACE in the YRD. Meanwhile, the total rural population, total power of agricultural machinery, and urbanization level have a significant positive impact, while the agricultural planting structure and trade openness level are not significantly affected, but the rural non-farm employment level have a significantly negative impact on ACE. SDM further supports the influence of various factors, and it can be found that the agricultural economic development level and trade openness level exhibit significant negative spillover effects. [ABSTRACT FROM AUTHOR]

All mankind is faced with the severe challenge to deal with climate change. Advocating the concept of “a community with a shared future for mankind,” China has taken the initiative to commit itself to emission reduction and has actively contributed its strength. According to the data on agricultural carbon emissions, it finds that non-carbon dioxide gases dominated agricultural carbon emissions, showing an overall upward trend year by year. However, a downward trend has been seen, and it was near a peak in this regard in recent years. Emissions from energy consumption of agricultural machinery may become the most significant uncertain factor for the carbon dioxide peaking of agriculture, as agriculture in China is yet to be mechanized further. The analysis above has laid a foundation for the emission reduction idea of driving the green transformation of agriculture with low-carbon measures. Specific recommendations are as follows in this regard. Carbon constraint indicators should be added to the 14th Five-Year Plan for the development of agriculture and rural areas. The methodology accounting should be expedited for agricultural carbon emissions. China should make a lot of effort to develop the agricultural carbon market and strive to employ financial means to promote low-carbon agricultural technologies. [ABSTRACT FROM AUTHOR]

Reducing agricultural carbon emissions (ACE) is important for the sustainable development of agriculture. Agricultural productive services (APS), a novel form of agricultural technology extension, offer new avenues for promoting sustainable and green agriculture. The present study aims to explore the impact of APS on ACE. In line with the aim of the study, the Kernel density, Moran’s I index, spatial Durbin model, and threshold regression model are employed. The findings reveal that APS demonstrate a significant inhibitory effect on ACE, reducing ACE not only within the region itself but also exerting a negative spatial spillover effect on other regions. Furthermore, a non-linear relationship between APS and ACE is observed, characterized by an inverted U-shaped curve with the arable land operating area serving as the threshold. Therefore, to fully harness the inhibitory effect of APS on ACE and to promote environmentally friendly and sustainable agricultural development, policymakers should vigorously develop APS, strengthen regional cooperation, and promote land transfer. Our research can help in understanding the impact of APS on ACE and to promote sustainable agricultural development.

Low-carbon development has recently become a growing trend in agricultural modernization. It will provide beneficial guidance for exploring driving factors of agricultural carbon emissions in Hebei Province, a typical agricultural region, and formulate relevant policy on its reduction. Calculating carbon emissions in Hebei from 1995 to 2014 demonstrated that energy and land use accounted for more than 90% of agricultural carbon emissions, and there was an increasing tendency overall with a peak value in 2010 and, to a certain extent lately, a slight decline. This paper employed transformed Kaya identity in light of local actual conditions for selecting eight influencing factors. Meanwhile, the extended STIRPAT model and ridge regression were used to make regression analysis. Results showed that contributing factors were efficiency, agricultural import, urbanization, agricultural mechanization, and population, whose 1% increase caused 0.1852%, 0.1663%, 0.1597%, 0.1573%, and 0.1329% increases in carbon emissions, respectively, while 1% growth in industry structure and agricultural affluence were responsible for 0.1475%, and changing the elastic coefficient of (0.1314-0.2958lnA)% decrease in carbon emissions, respectively, where A represented agricultural output value per capita. Furthermore, there existed an inverted U-shaped EKC between economic progress and carbon emissions. Given the above conclusions, policy recommendations were provided for effectively achieving agricultural carbon emissions reductions. [ABSTRACT FROM AUTHOR]