Agricultural Industrial Structure Research Articles

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.

The role of highway construction in influencing agricultural green total factor productivity in China: agricultural industry structure transformation perspective

Highway construction is vital in agricultural production, directly influencing food security and public health. This study utilized the Epsilon-based measurement-global-Malmquist-model (EBM-GML) and the spatial econometrics model with 31 Chinese provinces’ panel data from 2002 to 2020 to investigate the impacts and the mechanisms of low-grade and high-grade highways on the agricultural green total factor productivity (AGTFP). Research findings indicated that (1) low-grade and high-grade highway construction significantly promote the AGTFP through technological progress, and the high-grade highway exerts a significant positive spillover effect on the adjacent areas’ AGTFP. (2) The mechanisms of low-grade and high-grade roads on the AGTFP are heterogenous. Low-grade roads could improve the AGTFP by encouraging the rationalization of the agricultural industry structure. High-grade highways promote the AGTFP by upgrading the industry structure. And each-grade road could promote the AGTFP by stimulating the integration of planting and breeding. (3) Both low-grade and high-grade highways improved the AGTFP in non-main-grain-producing areas but inhibited it in main-grain-producing areas. Therefore, this study provided practical policy recommendations for green development in China’s agricultural sector and valuable insights for other developing countries.

Effects of environmental regulation on adjustment of agricultural industrial structure--An empirical study based on panel data of 31 provinces in China.

Using the panel data of 31 provinces in China between 2000 and 2018, this study theoretically and empirically analyses the impact of environmental regulations on the adjustment of the agriculture industrial structure from the perspectives of rationalisation and optimisation. Overall, variability in the impact of environmental regulation on the adjustment of the agricultural industrial structure is identified: a negative influence on the rationalisation of agricultural industrial structure and a positive influence on the optimisation of agricultural industrial structure. The impact of environmental regulations on the adjustment of agricultural industrial structure also reflects "large country characteristics": environmental regulations are more significant at the medium and low industrial structure levels. Environmental regulation significantly impacts the rationalisation of agricultural industrial structure in central and western regions and its optimisation in eastern and central regions. Through the panel threshold model test, this paper further finds that the effect of environmental regulations on the adjustment of agricultural industrial structure is not invariant. However, there is a non-linear relationship with significant threshold characteristics. Based on the above results, some countermeasures are suggested.

Decomposition of agriculture-related non-CO2 greenhouse gas emissions in Chengdu: 1995–2020

Methane (CH4) and nitrous oxide (N2O) emissions originating from agricultural systems exert a significant influence on climate change. The exploration of potential driving factors and emission trends based on a city is important for policy-makers and land managers, however has been rarely known. We aim to investigate the potential driving factors behind five non-CO2 sources within Chengdu's agricultural sector spanning 1995 to 2020, including rice cultivation (CH4), agricultural land (N2O), manure management (CH4 and N2O), and enteric fermentation (CH4), and project non-carbon dioxide (CO2) greenhouse gas (GHG) emissions in Chengdu's agricultural sector for the period from 2021 to 2035. The logarithmic mean Divisia index (LMDI) decomposition model was adopted to figure out the influencing factors, while Monte Carlo simulation and scenario analysis were applied to predict the agricultural non-CO2 GHG emissions from 2021 to 2035. Results reveal that non-CO2 GHG emissions from Chengdu's agriculture exhibit fluctuations and declines, with N2O emissions from agricultural land being the most substantial source. Driving actors such as emission intensity (EI), agricultural industrial structure (IS), and rural population (RP) contribute to carbon emission reductions, resulting in reductions of 8.10, 4.25, and 0.91 million tons (Mt), respectively. Conversely, the regional economic development level (EDL), and urbanization rate (URB) are the primary drivers behind increased agricultural non-CO2 GHG emissions, leading to emissions of 8.91 and 2.35 Mt, respectively. Ultimately, the predictive analysis demonstrates that, under the technological breakthrough scenario, Chengdu's agricultural non-CO2 GHG emissions are projected to peak in 2025, with an expected value of only 2.28 Mt. This study offered practical guidance for mitigating non-CO2 GHG emissions in city-level agriculture.

Research on the Path of Village Tourism and Cultural Industry Development from the Perspective of Rural Revitalization Taking Peasant Painting in JinShan District as an Example

Under the background of rural revitalization strategy, all parts of China are trying to adjust the agricultural industry structure and promote the development of rural economy. In this context, the study of new business mode of rural tourism is of great significance. This study takes the peasant paintings of Fengjing ancient town in Jinshan District as the background and the development path of new rural tourism as the research direction, explores how to integrate "agriculture + tourism + education" in small rural areas, and deeply analyzes the methods and approaches of developing rural tourism in the context of rural revitalization. At the same time, Shanghai's composite cultural and creative tourism products are used to retain tourists, increase market share and create a new rural tourism industry. This paper mainly obtains effective information through case studies and in-depth interviews, and digs into the current changes of rural revitalization through an in-depth understanding of the protection status of Jinshan peasant paintings and other intangible cultural heritage in Shanghai, so as to address the problems under the new rural tourism industry in Fengjing Ancient Town of Jinshan District and make suggestions. I hope to focus on promoting new rural tourism, providing new directions for rural revitalization, implementing rural revitalization strategies, promoting rural tourism, and further improving the development of new rural tourism in Shanghai.

Prediction of agricultural carbon emissions in China based on a GA-ELM model

Introduction: Strengthening the early warning of greenhouse gas emissions from agriculture is an important way to achieve Goal 13 of the Sustainable Development Goals. Agricultural carbon emissions are an important part of greenhouse gases, and accelerating the development of green and low-carbon agriculture is of great significance for China to achieve high-quality economic development and the goal of “carbon neutrality in peak carbon dioxide emissions”.Methods: By measuring the total agricultural carbon emissions in China and seven administrative regions from 2000 to 2021, the paper analyzes the influencing factors of agricultural carbon emissions in China by using STIRPAT environmental pressure model, and on this basis, predicts the peak trend of agricultural carbon emissions in China under different development scenarios by using the extreme learning machine model optimized by genetic algorithm.Results: The results showed that the extreme learning machine model improved by the genetic algorithm can overcome the shortcoming that the extreme learning machine model is easy to fall into the local optimal solution, thus obtaining higher prediction accuracy. At the same time, from 2000 to 2021, the total agricultural carbon emissions in China showed a continuous fluctuation trend, and due to the constraints of the agricultural economic level, agricultural industrial structure, and agricultural human capital, the agricultural carbon emissions showed spatial differentiation. It is worth noting that, in the context of green development, the agricultural carbon emissions of the seven regions in China all have the potential to achieve the “peak carbon dioxide emissions” goal in 2030, with only a slight difference at the peak.Discussion: The research results of this paper provide evidence for the government to formulate flexible, accurate, reasonable and appropriate agricultural carbon reduction policies, which is helpful to strengthen the exchanges and cooperation of regional agricultural and rural carbon reduction and fixation, and actively and steadily promote China's agriculture to achieve the goal of “peak carbon dioxide emissions carbon neutrality”.

Application of water resource economic management model in agricultural structure adjustment

Abstract Taking the watershed of Nanchang city as an example, through extensive investigations, the socio-economic status, water environment status, and development status of the agricultural industry in the watershed of Nanchang city were evaluated, and the coupling relationship between industrial structure and water environment was analyzed. The dynamic simulation of agricultural industrial structure adjustment in the Nanchang city basin under the constraints of the water environment was carried out to provide scientific decision-making on water resource shortage and other issues in the Nanchang city basin and to effectively solve the problem of water resource shortage. The research results showed that under the constraints of Scheme 2, the growth rate of agricultural output was in a relatively good development process, which was 8.56% in the short term, 6.65% in the medium term, and 10.33% in the long term. It is necessary to conduct research on the agricultural industrial structure in Nanchang city, which is also conducive to the protection of the water environment.

The Impact of Environmental Regulation on Cultivated Land Use Eco-Efficiency: Evidence from China

In the context of tightening resource and environmental constraints, quantitative measurement and influencing factors of cultivated land use eco-efficiency (CLUE) have become hot topics in current academic research. Existing studies primarily focus on the influence of natural, social, and economic factors on CLUE but ignore exploring the impact mechanism and effect of environmental policies on CLUE. Therefore, this study aims to explore the impact of environmental regulations on CLUE. To achieve this objective, a super-efficiency slack-based measure (super-SBM) model is used to calculate the CLUE for 31 provinces (municipalities and autonomous regions) in China from 2000 to 2009. Additionally, the intermediary effect model and the threshold effect model are used to empirically investigate the transmission mechanism and nonlinear characteristics between environmental regulation and the CLUE. The results show that: (1) the temporal dynamics of CLUE exhibit a pattern of initial fluctuating decline followed by gradual growth in China as a whole and across its eastern, central, and western regions. (2) Environmental regulation has a significant negative impact on CLUE, and the effect exhibits a nonlinear characteristic of decreasing marginal effects. (3) Agricultural technological innovation and agricultural industrial structure play a mediating role between environmental regulation and CLUE, reducing the negative impact of environmental regulation on CLUE. This study provides some implications for formulating scientifically sound environmental policies to optimize land use and enhance resource utilization efficiency.

Retracted: Research on the Optimization of Agricultural Industry Structure Based on Genetic Algorithm

Retracted: Research on the Optimization of Agricultural Industry Structure Based on Genetic Algorithm

Impact of ecological governance policies on county ecosystem change in national key ecological functional zones: A case study of Tianzhu County, Gansu Province

Policy is a key element of ecological governance, however, insufficient attention has been given to how different policies and regulations in a systematic way affect ecosystem processes and functions. This study evaluated the impact of ecological policies in the national key ecological functional areas (NKEFAs) since 1990, taking Tianzhu County as a case study. We first evaluated the change in policy intensity through quantitative analysis of policy texts, then analyzed the change in the ecosystems with carbon storage, habitat quality, ecosystem service and water conservation. Finally, the partial least squares (PLS) was used to quantify the effective path for and effect of policies on the ecosystem through adjustments in agriculture, animal husbandry and population. The results showed that policies have a significant direct impact on the agricultural planting industry, animal husbandry and population size and structure, and have a significant indirect impact on the whole ecosystem. The research demonstrated the usefulness of applying PLS-SEM and ecosystem function indicators to evaluate the impact of ecological governance policies on ecosystems in NKEFAs. The research can provide reference for the ecological policy making of NKEFAs.

Spatial Network and Driving Factors of Agricultural Green Total Factor Productivity in China

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.

Analyzing the Relationship between Green Finance and Agricultural Industrial Upgrading: A Panel Data Study of 31 Provinces in China

This study examines the impact of green finance on optimizing China’s agricultural industrial structure. It emphasizes the importance of innovative green financial services, improved efficiency, tailored regional approaches, and enhanced foresight to foster a high-level development in the agricultural sector amid China’s economic transformation. Based on the provincial panel data of 31 provinces in China from 2012 to 2021, this study empirically tests the effect mechanism of green finance on the optimization process of the agricultural industrial structure by constructing a fixed-effects model. This study finds that green finance can effectively promote the development of the optimization of the agricultural industrial structure. Under the current trend of China’s economic structural transformation and optimization, we suggest that China should innovate green financial service products, improve the efficiency of green finance, and enhance the depth of green financial services for the optimization of the agricultural industrial structure. It is required to strengthen foresight and improve relevant laws, regulations, policies, and standards. To help green finance be better promoted, a high-level development of the agricultural industrial structure is required.

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.

Does agricultural factor misallocation hinder agricultural green production efficiency? Evidence from China

The sustainable development of agriculture is challenged by two major issues: increasing resource constraints and environmental pollution. Sustainable agricultural development is achievable by improving green total factor productivity from the perspective of resource allocation. To promote the green development of agriculture, this paper utilizes the SBM super-efficiency mode and thus calculates the agricultural resource misallocation index and agricultural green production efficiency index in China between 2001 and 2019. Furthermore, this paper discusses the temporal and spatial evolution characteristics of agricultural green production efficiency, using a fixed model and spatial econometric models to estimate the influence effect of agricultural resource misallocation on green production efficiency. Below are the results. First, China's agricultural green total factor productivity is growing at an impressive rate, with high efficiency in the northeast, northwest, and southeast coastal areas and low efficiency in the central and inland areas. Second, agricultural capital misallocation, labor misallocation, and land misallocation all negatively impact agricultural green production efficiency. Accordingly, the misallocation of agricultural factors will hamper the growth of agricultural green production efficiency in this region and also in the surrounding areas. Third, the indirect impact on the own agricultural green production efficiency exceeds its direct impact on neighboring regions' efficiency. Fourth, the mechanisms are the upgrading of agricultural industry structure and green technology innovation. According to the findings, reducing resource misallocation can substantially enhance agricultural green productivity, which is an imperative step in addressing agricultural green production. Hence, policies should be formulated that highlight the regional allocation of agricultural production factors and green production-oriented concept of agricultural production. Also, the government should promote the transformation and upgrading of the agricultural industrial structure, as well as the application of green agricultural technologies.

A Leading Role of Water Resources and Animal Husbandry in Environmental Sustainability: A Case Study of China.

Animal husbandry is an important emission source of greenhouse gas. In order to discover the real situation of carbon emission in China's animal husbandry scientifically, the paper measured and calculated carbon emission in China's animal husbandry from 1997 to 2017 on the basis of soil and water resources. In addition, analyzing its time-order characters, structural characters, driving factors and decoupling relationships are all done in this treatise. Major findings are as follows: (1) The carbon emission of China's animal husbandry in 2017 was 374.3528 million tons, an increase of 17.8066 million tons over 1997, with the average annual growth rate of 0.24% and the average annual carbon emission of 398.7817 million tons; (2) There was a decreasing trend in carbon emission of intestinal fermentation in China's animal husbandry while there was an increasing trend in carbon emission of manure emission in China's animal husbandry; (3) The carbon emission of China's animal husbandry peaked in 2006 and went through three phases of up-down-steady between 1997 and 2017; (4) The contribution of cattle, pig, sheep, other large livestock, poultry and rabbits to China's animal husbandry carbon emissions decreased in turn, and the average contribution of cattle, pigs and sheep to China's animal husbandry carbon emissions was as high as 98.15%. (5) Five factors reducing carbon emission of China's animal husbandry were carbon intensity, agricultural industrial structure, agricultural population-water resources matching degree, agricultural water-soil resources and per capita cultivated land area. Two factors increasing carbon emission of China's animal husbandry were population and economic benefits of agriculture per unit agricultural population; (6) There was a generally weak decoupling between carbon emission in China's animal husbandry and animal husbandry's economic growth from 1997 to 2017.

Carbon Emissions from Agricultural Inputs in China over the Past Three Decades

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

Analysis of Landscape Patterns Changes and Driving Factors of the Guangdong Chaoan Fenghuangdancong Tea Cultural System in China

Guangdong Chaoan Fenghuangdancong Tea (GCFT) Cultural System is the second batch of China’s Nationally Important Agricultural Heritage Systems (China-NIAHS), identified by the Ministry of Agriculture and Rural Affairs in 2014 as having rich biodiversity, valuable knowledge of indigenous technology, and unique ecological and cultural landscape. Under the dual background of rapid urbanization and agricultural industry structure transformation, China-NIAHS-GCFT is facing the reality of structural changes in land use/cover and landscape patterns. Therefore, it is important to systematically portray land use/land cover (LULC) changes in China-NIAHS-GCFT sites and clarify the spatial pattern differences due to the impact of China-NIAHS-GCFT recognition on tea garden areas and the tea industry. This study was conducted in Chaozhou City, Guangdong Province, where GCFT is located, to compare and analyze the LULC characteristics of the core area of the heritage site (Chaoan, Chaozhou) and the control area (Raoping, Chaozhou) before and after recognition. We assessed the spatial variation in tea garden area and the intrinsic driving mechanisms of the change by integrating social factors, such as China-NIAHS-GCFT recognition, and natural factors, such as elevation, precipitation, and temperature. The results show that: (1) Around 2010, the change in LULC of the core and control areas progressed from slight changes to dramatic changes, mainly shifting from natural to anthropogenic landscapes. The decrease in the cropland and grassland and the increase in built-up land in the core area were obviously larger than those in the control area. (2) Before and after GCFT was recognized as China-NIAHS in 2014, the changing pattern of tea garden shifts from “basically stable and small growth” to a trend of “substantial expansion”. Specifically, the recognition brought about tea garden area expansion and tea industry development in the core area, especially Fenghuang. Meanwhile, a radiating effect extends to the control area, especially the townships adjacent to Fenghuang. (3) Similar natural climatic conditions of temperature and precipitation in the two regions provide a basic growing environment for tea trees; however, elevation was the key natural resource condition affecting the distribution of tea gardens. The elevation conditions of the core area are more suitable for growth of tea trees compared to the control area.

The impact of green trade barriers on agricultural green total factor productivity: Evidence from China and OECD countries

In the context of emphasizing on the development of green agriculture globally, this research examines the impact of green trade barriers on agricultural green total factor productivity (AGTFP) based on panel data from China and OECD countries from 2005 to 2019. The strong evidence shows that the green trade barriers have a positive effect on improving the country’s AGTFP. In addition, agricultural trade scale will reduce the impact of green trade barriers on AGTFP. The smaller the agricultural trade scale, the greater improvement effect of green trade barriers on AGTFP, otherwise the trade scale will inhibit it. Finally, the empirical evidence offers suggestions for government, policy makers, such as strengthen the monitoring and supervision of green trade barriers, optimize agricultural industry structure, actively integrate with international standards, and continuously improve agricultural technology and health standards.

The spatial spillover effect and impact paths of agricultural industry agglomeration on agricultural non-point source pollution: A case study in Yangtze River Delta, China

The rapid development of Chinese agriculture has brought about serious agricultural non-point source pollution (ANPSP), and the reduction of non-point source pollution is of great practical importance to realize the green transformation of agriculture, the ecological livability of the countryside and the well-being of farmers. Agriculture industrial agglomeration is widespread throughout the world, and it is generally accepted that there are positive economic effects. However, there is still a debate on whether positive or negative externalities dominate the environmental effects of agriculture industrial agglomeration. Therefore, it is of theoretical significance to correctly identify the nonlinear relationship and spatial spillover effects between agriculture industrial agglomeration and ANPSP. As a region with the rapid development of agricultural intensification in China, the environmental effect of agricultural agglomeration in the Yangtze River Delta is more complex compared with other industries, and It has strong research value to realize the coordinated development of agricultural industry clustering and green agriculture. Based on panel data of 37 cities in the Yangtze River Delta from 2000 to 2019, this paper uses the inventory analysis method to measure the agricultural non-point source pollution (ANPSP) of each city. Then, for the law of spatial evolution of ANPSP, the Spatial Durbin Model (SDM) was developed to analyze the specific impact of agricultural industrial agglomeration on ANPSP. The results show that: (1) the ANPSP of the Yangtze River Delta had a downward trend during 2000–2019, with high-high agglomeration and low-low agglomeration characteristics. (2) The effect of agricultural industrial agglomeration on ANPSP showed an inverted U-shape, which was consistent with the EKC curve. However, after considering the spatial spillover effect, it shifted to a U-shape. (3) Economic, structural and technological effects are the main driving effects for the mitigation of ANPSP, with concentration effects increasing pollution. Therefore, in the future, the moderate-scale business should be encouraged to develop, optimize and adjust the agricultural industrial structure, stimulate technological progress and innovation, enhance the spatial spillover capacity of technological and structural effects, and improve the capacity of nonpoint source pollution management.

How does agricultural industrial structure upgrading affect agricultural carbon emissions? Threshold effects analysis for China.

China continues to emphasize the importance of reducing agricultural carbon emissions and promoting the upgrading of its agricultural industry structure. However, the relationship between the two is rarely examined. This study aims to investigate the impact of upgrading the agricultural industry structure on agricultural carbon emissions. A two-stage instrumental technique and a threshold regression model are used in this study's analysis. The results indicate that agricultural industrial structure upgrading reduces agricultural carbon emissions by a statistically significant amount above the threshold of 0.378. The examination of the underlying mechanism shows agricultural energy efficiency and off-farm work as mediators of the nonlinear relationship between agricultural industrial structure upgrading and agricultural carbon emissions. Only when the agricultural energy efficiency and off-farm work thresholds are surpassed can improving the structure of the agricultural industry minimize agricultural carbon emissions. Analysis of heterogeneity indicates that the threshold for lowering agricultural carbon emissions is greater in northern China, but the potential for reduction is greater.