基于密切值改进TOPSIS的低碳经济评价研究

低碳经济是应对气候变化的必然选择,如何全面客观地评价一个国家或经济体低碳经济发展水平成为低碳经济研究中迫切需要解决的问题.本文充分考虑低碳经济发展规划和目标,采取修正的TOPSIS方法构建低碳经济评价模型.针对传统TOPSIS方法中存在两个不足:一是相对贴近度计算中将被评价对象到"正理想解"和到"负理想解"的距离直接相加,正向指标和负向指标直接相加是不合理的;二是传统的TOPSIS方法忽略了被评价对象与"正理想解"和"负理想解"的距离的权重分配问题.本文提出利用密切值法对传统TOPSIS方法进行修正,解决到正负理想解两个距离的组合问题.首先,通过密切值的引入将被评价对象到"正理想解"和"负理想解"的距离转化为同向指标,保证了两个距离可以进行运算;然后,对两个距离进行归一化,保证两个距离在相同的数量级上,保证备选方案到正理想解的距离和到负理想解的距离起相当的作用,再根据决策者的偏好,对两个归一化的距离进行权重分配,保证两个距离分配的权重确实体现了决策者的意愿;最后,通过对秦皇岛市的低碳经济评价验证修正TOPSIS评价模型在低碳经济评价下的可行性和有效性,解决了评价指标有目标约束下的低碳经济评价问题.

Research on the low-Carbon Economy Evaluation Based on Osculating Value Improvement TOPSIS

TOPSIS is a sorting method that is close to the ideal solution. It determines the ranking of the evaluated object according to distance between the evaluated object to "ideal solution" and distance between the evaluated object to "negative ideal solution". There are two shortcomings in traditional TOPSIS: first, the distance from the evaluated object to the "ideal solution" and to the "negative ideal solution" is added directly in the calculation of relative closeness, which is unreasonable to add the positive index and the negative index directly; the other is that the traditional TOPSIS method ignores the weight distribution problem of the distance between the evaluated object and "ideal solution" and "negative ideal solution". In view of the above two problems, it proposes to use the osculating value method to modify the traditional TOPSIS method to solve the combination problem of two distances to positive and negative ideal solutions. Firstly, the distance from the evaluated object to "ideal solution" and "negative ideal solution" is transformed into the same direction index through osculating value, which ensures that the two distances can be calculated. Then, normalize the two distances to ensure that they are in the same order of magnitude, and that the distance between the alternatives and the ideal solution is equivalent to the distance between the alternatives and the negative ideal solution. Then, according to the preference of the decision-maker, distribute the weight of the two normalized distances to ensure that the weight of the two distances distribution really reflects the will of the decision-maker. Finally, the feasibility and effectiveness of the improved method are verified by an empirical example.