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The purpose of this study is to investigate the determinants of agricultural insurance adoption by farmers of the state of São Paulo, Brazil.

Primary data from the 2015/2016 crop season was collected from a sample of 175 farmers. Logit econometric models were applied to identify the variables that affect the probability of agricultural insurance adoption.

The empirical results show that the education level, access to technical assistance, use of management tools and farm size positively affect the probability of adopting agricultural insurance. In addition, farmers who produce soybean and/or corn are more likely to use insurance. On the other hand, the higher the farmers’ propensity to take risk the lower the likelihood of using insurance.

The empirical analysis is based on cross-sectional data of a sample of 175 farmers of the state of São Paulo. The use of panel data with a larger sample of farmers, considering a period of years, could provide additional information.

To the best of the knowledge, this is the first empirical analysis about determinants of agricultural insurance adoption by Brazilian farmers, considering behavioral factors. The findings provide useful insights for policymakers in formulating risk management programs in the Brazilian agricultural markets. A better understanding about the determinants of insurance adoption is also relevant for private companies that sell insurance to farmers. Therefore, the paper may contribute with the diffusion of rural insurance as risk management tool in Brazilian agriculture.

This research aims to review literature on development finance and its challenge and to examine blended learning and insurance as a catalysts of development finance. In particular, this paper provides new insights and practical examples of blended finance and insurance.

This research basically relies on literature review and case study to show the value of the emerging methods of blended learning in development finance and insurance system.

Basic finding in this paper includes new insight of blended finance and insurance as a partnership between public and private sector, which offers new arena for academic research and practice.

As the research relies on literature review and authors' insight, originality may not be valued so much, but if may be introducing or creating new ideas or thinking about development finance or international development cooperation where relevant data or experience is still lacking.

This paper aims to investigate the effect of using indigenous forecasts (IFs) and scientific forecasts (SFs) on pastoralists’ adaptation methods in Rwenzori region, Western Uganda.

Data were collected using a household survey from 270 pastoralists and focus group discussions. The multivariate probit model was used in the analysis.

The results revealed that pastoralists using of IF only more likely to be non-farm enterprises and livestock sales as adaptation strategies. Pastoralists using both SF and IF were more likely to practise livestock migration.

Other factors found to be important included land ownership, land tenure, gender, education level, non-farm and productive assets, climate-related risks and agricultural extension access.

Increasing the number of weather stations in pastoral areas would increase the predictive accuracy of scientific climate information, which results in better adaptive capacity of pastoralists. Active participation of pastoral households in national meteorological dissemination processes should be explored.

A two-prong approach that supports both mobile and sedentary pastoralism should be adopted in rangeland development policies.

This study has shown the relevance of IFs in climate change adaptation methods of pastoralists. It has also shown that IFs compliment SFs in climate change adaptation in pastoralism.

With the Chinese marine economy developing rapidly, the environmental problem has been occurring frequently, which needs green finance that supports energy conservation, environmental protection, and sustainable development to solve.

In this paper, the entropy method is used to measure the development level of green finance, the DEA-ML index is used to measure the green total factor productivity which is used to indicate the high-quality development level of the marine economy in 11 coastal provinces (cities), then the grey correlation degree between them whose result shows that there is a certain correlation between the two variables is calculated. The fixed-effect model was used to analyze the relationship between them.

The results show that the development level of green finance can promote the high-quality development of the marine economy, but there are still some problems in the process of green finance supporting the marine economy.

This paper seeks new growth drivers, green finance, for the high-quality development of the marine economy, which few scholars have studied.