The literature on economic sanctions suggests that the choice of policy instrument, for example trade sanctions, financial sanctions, or military intervention, is endogenous to the political process and, in particular, to the policy outcome sought by the sanctioner. But the choice of instrument also affects the outcome of the sanctions. Therefore, the sanctions policy outcome and the probabilities of the sanctioner's adoption of different sanctions instruments are jointly determined. To capture this endogeneity, multivariate probit and logit models are estimated using data from Hufbauer, Schott & Elliott, with the random utilities to the sanctioner of choosing military action, trade sanctions, and financial sanctions as dependent variables. The expected probabilities of choosing these alternatives are then incorporated as explanatory variables in predicting the success of the sanctions in attaining their political objectives. This procedure generates simultaneous estimates of the determinants of both instrument choice and sanctions success. The empirical results indicate that military force is less likely to be used against an economically healthy and politically stable target than against a more vulnerable target. However, military action is encouraged by third-country assistance to the sanctioned country and by a high cost of sanctions to the sanctioner. Financial sanctions are more likely to be used against a target that receives third-country support but are less likely against an economically healthy country. Sanctions success is positively correlated with the degree of warmth in relations between sanctioner and target prior to the sanctions; negatively correlated with the size of the sanctioner relative to the target; and negatively correlated with the economic health and political stability of the target. There is no evidence that third-country assistance to the target diminishes the effectiveness of sanctions or that the cost of sanctions to either the target or the sanctioner has a strong effect on the sanctions outcome.

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With the acceleration of urbanization, the urban heat island effect has brought a significant impact on the urban environment, and the urban green space can effectively alleviate the heat island effect, but the specific influence intensity is yet to be further studied [1]. In this study, 65 urban green spaces of different sizes and different types within Fifth Ring Road of Beijing were selected through big data analysis to explore the effects of different influence factors such as area, perimeter, shape index and vegetation coverage on its internal temperature, and then the influence intensity of urban green space on heat island effect were studied and analyzed. The results show that: 1) when the green space area is 1-10 hectares, the average temperature inside the green space will decrease with the increase of the area, and the temperature will decrease by about 0.1 °C for each increase of 1 hectare; when the area is larger than 10 hectares, the average temperature inside the green space will basically not change; 2) the internal temperature of green space does not change with the perimeter and shape index of green space; 3) when the green space area is equal, the internal temperature of the green space will decrease about 0.158 °C when the vegetation coverage increases by 1% (i.e., there is a negative correlation between the green space vegetation coverage and the internal temperature).

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