Does the Policy of Decoupled Subsidies Improve the Agricultural Economic Resilience?—Evidence from China’s Main Corn Producing Regions

Yang, Qifeng; Zhang, Pingyu; Li, Yuxin; Ning, Jiachen; Chu, Nanchen

doi:10.3390/su151310164

Cited by 4 publications

(1 citation statement)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the studies above, this paper combines the connotation of agricultural economic resilience, introduces the PSR index in the PSR model, and measures the level of agricultural economic resilience in terms of three dimensions, namely, risk resistance ability, adaptive adjustment ability and reconstructive creativity ability [55][56][57]. Risk resistance ability (P) refers to the ability of agriculture to withstand and diversify risks when it suffers shocks.…”

Section: Construction Of the Indicator Systemmentioning

confidence: 99%

Spatio-temporal pattern and the evolution of the distributional dynamics of county-level agricultural economic resilience in China

Li,

Yu,

Deng

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

Because the complexity of the external environment has put great pressure on the agricultural economy, making it vulnerable, it is necessary to promote a system of resilience in the agricultural economy so that Chinese agriculture can continue to persevere in the face of serious external uncertainties. Therefore, this paper investigates the spatio-temporal pattern and evolution of the distributional dynamics of China’s county-level agricultural economic resilience based on 2000–2020 data covering 2,545 counties. The results are as follows: first, from 2000 to 2020, the mean value of China’s county-level agricultural economic resilience showed an obvious upward trend, which indicates that China’s agricultural economy gradually increased its ability to resist risks and continued to develop in a favourable manner. Specifically, the county-level agricultural economic resilience index of the northeast region grew the most significantly, while the index of county units in the western region was relatively low. Second, the centre of gravity of the spatial distribution of China’s agricultural economic resilience gradually migrated to the northwest, showing a dominant direction from northeast to southwest and a tendency to develop from southeast to northwest. Third, the spatial differences in China’s agricultural economic resilience generally showed an upward trend, while county-level differences were the main source of the overall differences, followed by inter-provincial differences, inter-municipal differences and inter-regional differences. Additionally, the contribution of county-level differences to the overall differences fluctuated within the range of 54%-58%. Fourth, there is a possibility of localized convergence in China’s agricultural economic resilience, which is continuous in spatial effects and has obvious positively correlated spatial effects at different times and in different county spaces.

show abstract

Section: Construction Of the Indicator Systemmentioning

confidence: 99%

Spatio-temporal pattern and the evolution of the distributional dynamics of county-level agricultural economic resilience in China

Li,

Yu,

Deng

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

An Empirical Investigation of the “Mezzogiorno Trap” in China’s Agricultural Economy: Insights from Data Envelopment Analysis (2015–2021)

Li,

Yang,

Zou

2023

Agriculture

View full text Add to dashboard Cite

Reducing regional inequality is one of the seventeen Sustainable Development Goals (SDGs) established by the United Nations. However, a persistent regional disparity known as the “Mezzogiorno Trap” presents a significant challenge. The underdeveloped regions that fall into the “Mezzogiorno Trap”, even though they can narrow the gap with other regions through substantial support, see the disparity widen again when the level of assistance starts to decline. This paper proposes a methodology for identifying the “Mezzogiorno Trap”. By employing this approach and combining panel data on Chinese agriculture from 2015 to 2021, it is discovered that despite the overall development of the Chinese agricultural economy during this period, the “Mezzogiorno Trap” still exists. The paper analyzes the reasons behind the “Mezzogiorno Trap” in the Chinese agricultural economy and presents constructive recommendations based on the research findings. The research process demonstrates that this methodology is better suited for studying regional disparities in specific economic sectors, and the obtained results are more stable and reliable.

show abstract

Efficiency and Driving Factors of Agricultural Carbon Emissions: A Study in Chinese State Farms

Han,

Xu,

Zhang

et al. 2024

Agriculture

View full text Add to dashboard Cite

Promoting low-carbon agriculture is vital for climate action and food security. State farms serve as crucial agricultural production bases in China and are essential in reducing China’s carbon emissions and boosting emission efficiency. This study calculates the carbon emissions of state farms across 29 Chinese provinces using the IPCC method from 2010 to 2022. It also evaluates emission efficiency with the Super-Slack-Based Measure (Super-SBM model) and analyzes influencing factors using the Logarithmic Mean Divisia Index (LMDI) method. The findings suggest that the three largest carbon sources are rice planting, chemical fertilizers, and land tillage. Secondly, agricultural carbon emissions in state farms initially surge, stabilize with fluctuations, and ultimately decline, with higher emissions observed in northern and eastern China. Thirdly, the rise of agricultural carbon emission efficiency is driven primarily by technological progress. Lastly, economic development and industry structure promote agricultural carbon emissions, while production efficiency and labor scale reduce them. To reduce carbon emissions from state farms in China and improve agricultural carbon emission efficiency, the following measures can be taken: (1) Improve agricultural production efficiency and reduce carbon emissions in all links; (2) Optimize the agricultural industrial structure and promote the coordinated development of agriculture; (3) Reduce the agricultural labor scale and promote the specialization, professionalization, and high-quality development of agricultural labor; (4) Accelerate agricultural green technology innovation and guide the green transformation of state farms. This study enriches the theoretical foundation of low-carbon agriculture and develops a framework for assessing carbon emissions in Chinese state farms, offering guidance for future research and policy development in sustainable agriculture.

show abstract

Does the Policy of Decoupled Subsidies Improve the Agricultural Economic Resilience?—Evidence from China’s Main Corn Producing Regions

Cited by 4 publications

References 50 publications

Spatio-temporal pattern and the evolution of the distributional dynamics of county-level agricultural economic resilience in China

Spatio-temporal pattern and the evolution of the distributional dynamics of county-level agricultural economic resilience in China

An Empirical Investigation of the “Mezzogiorno Trap” in China’s Agricultural Economy: Insights from Data Envelopment Analysis (2015–2021)

Efficiency and Driving Factors of Agricultural Carbon Emissions: A Study in Chinese State Farms

Contact Info

Product

Resources

About