Both economic development level and environmental factors have significant impacts on life expectancy at birth (LE). This paper takes LE as the research object and selects nine economic and environmental indicators with various impacts on LE. Based on a dataset of economic and environmental indicators of 20 countries from 2004 to 2016, our research uses the Pearson Correlation Coefficient to evaluate the correlation coefficients between the indicators, and we use multiple regression models to measure the impact of each indicator on LE. Based on the results from models and calculations, this study conducts a comparative analysis of the influencing mechanisms of different indicators on LE in both developed and developing countries, with conclusions as follow: (1) GDP per capita and the percentage of forest area to land area have a positive impact on LE in developed countries; however, they have a negative impact on LE in developing countries. Total public expenditure on education as a percentage of GDP and fertilizer consumption have a negative impact on LE in developed countries; however, they have a positive impact on LE in developing countries. Gini coefficient and average annual exposure to PM_2.5 have no significant effect on LE in developed countries; however, they have a negative impact on LE in developing countries. Current healthcare expenditures per capita have a negative impact on LE in developed countries, and there is no significant impact on LE in developing countries. (2) The urbanization rate has a significant positive impact on LE in both developed countries and developing countries. Carbon dioxide emissions have a negative impact on LE in both developed and developing countries. (3) In developed countries, GDP per capita has the greatest positive impact on LE, while fertilizer consumption has the greatest negative impact on LE. In developing countries, the urbanization rate has the greatest positive impact on LE, while the Gini coefficient has the greatest negative impact on LE. To improve and prolong LE, it is suggested that countries should prioritize increasing GDP per capita and urbanization level. At the same time, countries should also work on reducing the Gini coefficient and formulating appropriate healthcare and education policies. On the other hand, countries should balance between economic development and environmental protection, putting the emphasis more on environmental protection, reducing environmental pollution, and improving the environment’s ability of self-purification.

Reducing drought vulnerability is a basis to achieve sustainable development in agriculture. The study focuses on agricultural drought vulnerability in China by selecting 12 indicators from two aspects: drought sensitivity and resilience to drought. In this study, the degree of agricultural drought vulnerability in China has been evaluated by entropy weight method and weighted comprehensive scoring method. The influencing factors have also been analyzed by a contribution model. The results show that: (1) From 1978 to 2018, agricultural drought vulnerability showed a decreasing trend in China with more less vulnerable to mildly vulnerable cities, and less highly vulnerable cities. At the same time, there is a trend where highly vulnerable cities have been converted to mildly vulnerable cities, whereas mildly vulnerable cities have been converted to less vulnerable cities. (2) This paper analyzes the influencing factors of agricultural drought vulnerability by dividing China into six geographic regions. It reveals that the contribution rate of resilience index is over 50% in the central, southern, and eastern parts of China, where agricultural drought vulnerability is relatively low. However, the contribution rate of sensitivity is 75% in the Southwest and Northwest region, where the agricultural drought vulnerability is relatively high. Among influencing factors, the multiple-crop index, the proportion of the rural population and the forest coverage rate have higher contribution rate. This study carries reference significance for understanding the vulnerability of agricultural drought in China and it provides measures for drought prevention and mitigation.

The adoption of conservation tillage technology can improve the production efficiency of black soils (mollisols), and it has great significance to ensure the sustainable development of agriculture. This paper takes farmers in the black soil region of Jilin Province as the research object, uses 442 survey data of farmers in seven municipal areas in the black soil region of Jilin Province, constructs a logistic-ISM model, first determines the influencing factors of farmers’ adoption of conservation tillage technology, and then analyzes the hierarchical structure of each influencing factor. The results show that: (1) among the eight significant influencing factors of farmers’ adoption of conservation tillage technology, age, whether they know the government’s subsidies for conservation tillage and the number of labor force are the deep-rooted factors; (2) Education level, whether you know that the government is promoting conservation tillage, and the planting area are intermediate level factors; (3) whether they have received the technical services of conservation tillage and whether the cultivated land is scattered is the direct factors. Based on the significance analysis of the influencing factors of farmers’ adoption of conservation tillage technology and the research on the action mechanism of the influencing factors of farmers’ adoption of conservation tillage technology, this paper puts forward policy suggestions to improve the extension system of conservation tillage technology, improve the implementation of land transfer and subsidy policies, strengthen the ability of rural socialized services, and strengthen the publicity of black soils protection.

The article focuses on the spatial complexity of agricultural green development (AGD) in different regions. The article first constructs an evaluation index system for the level of AGD from four dimensions: Social development, economic benefits, resource input, and ecological environment. Then, the article uses an improved entropy weight method to evaluate the level of AGD with panel data of 31 provinces in China from 2007 to 2018. Finally, on the basis of Moran Index and the Spatial Durbin Model, the article analyzes the spatial heterogeneity of the factors that affect the green development of agriculture in China. The results show that: (1) From 2007 to 2018, the overall level of AGD shows a fluctuating upward trend in China, and there are differences among provinces. The level of AGD in the three major regions presents the characteristics of Eastern > Central > Western; (2) China’s provincial AGD level has an obvious positive autocorrelation in spatial distribution, showing significant spatial agglomeration characteristics in space; (3) the four factors of urbanization level, agricultural mechanization level, scientific and technological R&D investment, and arable area, have different effects on the level of AGD in three major regions. This study provides a reference for understanding the status of China’s agricultural green development level and policy recommendations on how to improve the level of agricultural green development. The results imply that some effective policy measures, such as prompting the integrated development of the three major industries and optimizing the industrial structure, should be taken to coordinate “green” with “development” from national and regional perspectives.

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