Based on the Peking University Digital Financial Inclusion Index and 2011–2018 provincial panel data, this paper discusses the mechanism of digital financial inclusion on agricultural green total factor productivity from both theoretical and empirical perspectives. The result shows that digital financial inclusion can significantly increase China’s agricultural green total factor productivity, and the optimization of the agricultural industry structure can bring a significant “structural growth effect”. A total of 8.42% of the positive effects of digital financial inclusion on agricultural green total factor productivity are realized through the intermediary effect of agricultural industrial structure optimization. Through further research, it is found that digital financial inclusion has regional heterogeneity in the improvement of agricultural green total factor productivity. At the same time, digital financial inclusion of different dimensions will also have a differential impact on the improvement of agricultural green total factor productivity. In order to promote the green development of agriculture, it is necessary to further improve the financial development environment, optimize the structure of the agricultural industry, and formulate development policies for digital inclusive finance in accordance with local conditions.

The implementation of green finance is a powerful measure to promote global carbon emissions reduction that has been highly valued by academic circles in recent years. However, the role of green credit in carbon emissions reduction in China is still lacking testing. Using a set of panel data including 30 provinces and cities, this study focused on the impact of green credit on carbon dioxide emissions in China from 2006 to 2016. The empirical results indicated that green credit has a significantly negative effect on carbon dioxide emissions intensity. Furthermore, after the mechanism examination, we found that the promotion impacts of green credit on industrial structure upgrading and technological innovation are two effective channels to help reduce carbon dioxide emissions. Heterogeneity analysis found that there are regional differences in the effect of green credit. In the western and northeastern regions, the effect of green credit is invalid. Quantile regression results implied that the greater the carbon emissions intensity, the better the effect of green credit. Finally, a further discussion revealed there exists a nonlinear correlation between green credit and carbon dioxide emissions intensity. These findings suggest that the core measures to promote carbon emission reduction in China are to continue to expand the scale of green credit, increase the technology R&D investment of enterprises, and to vigorously develop the tertiary industry.

The consequences of greenhouse gas emissions are a global problem and are felt most clearly in poor countries. Every sector in Ethiopia is affected by greenhouse gas emissions, but the productivity of the agricultural sector is particularly at risk. Although climate change is a long-term phenomenon, no in-depth macro-level studies have been conducted to guide discussion in this area. Therefore, the study fills this gap and carefully examines these impacts over time from 2019 to 2022 using the Vector Auto Regressive Model. Our results show that a 1% increase in fertilizer consumption, agricultural land, nitrous oxide (N2O) emissions, rural population, and area devoted to grain production results in a 0.28, 2.09, 15.92, 5.33, and 1.31 percent increase in agricultural yield in the long-run, respectively. A negative relationship was found between agricultural employment, agricultural methane emissions (CH4), carbon dioxide emissions (CO2), and agricultural productivity with a significance level of 5%. This means that under a black box condition, a one percent increase in agricultural employment, CH₄, and CO₂emissions in the country in the long run will lead to a decrease in agricultural productivity by 5.82, 17.11, and 2.75 percent respectively, as we also found that all regressors except technology adoption had an elastic relationship with agricultural productivity. The short-term error correction estimates show that the coefficient of the “speed of adjustment” term for the expected productivity equation is both statistically significant and negative. The value of the coefficient term of −0.744 shows that an adjustment of 74.4% is made each year to converge the long-run equilibrium level. Therefore, Ethiopia needs to take measures that keep the economy away from sectors that produce a lot of carbon. These must be coordinated at a global level to achieve social change towards a fair and environmentally sustainable future and to increase agricultural productivity.

Based on the 2012 and 2018 China Family Panel Studies (CFPS) data, this paper explores the influence mechanism between rural social security, precautionary savings, and the upgrading of rural residents’ consumption structure in China from theoretical and empirical perspectives. The results show that China’s rural social security can significantly promote the upgrading of rural residents’ consumption structure, and 1.79% of the positive effect of China’s rural social security on the upgrading of rural residents’ consumption structure in China is realized through the mediating effect of precautionary saving; the robustness of the regression results is tested by a placebo test and substitution matching method. Through further research, it is found that the promotion effect of China’s rural social security on the upgrading of rural residents’ consumption structure in China varies by region and age. In order to promote the sustainable upgrading of rural residents’ consumption structure, rural social security policies should be further improved, and new rural social pension insurance policies should be formulated according to local conditions, which will ultimately promote the economic development of rural areas.

Due to the striking gap in agricultural labor productivity (ALP) between China and developed countries, improving agricultural labor productivity is critically important. This study investigates the effect of land rental on agricultural labor productivity from two levels including household agricultural labor productivity and aggregated village-level agricultural labor productivity by employing propensity matching methods and instrumental variables method. Mediation analysis is also applied to explore the influencing mechanism and underlying paths for household agricultural labor productivity improvement. The results analyses based on the nation-wide data sets in 2017 reveal that renting-in land has a significant positive impact on promoting household agricultural labor productivity and renting-out land has significant opposite effects. The mediation analysis indicates that renting-in land affects household ALP indirectly through the land-labor ratio, intermediate inputs, and agricultural assets investment. Moreover, from the village perspective, we further found that the development of the land rental market positively affects the improvement of aggregated agricultural labor productivity of the village. Land rental is an effective channel to stimulate land transferred from lower agricultural labor productivity to higher ones and then promote the resource allocation within the village.