基于非期望SBM-SVM改进模型的投资有效性预测——以重庆市工业行业为例

对工业行业进行有效投资在一定程度上对经济发展有重要影响，作为能源消耗和环境污染的主要源头，为落实绿色发展理念，推动节能减排工作，提出对绿色工业投资的有效性研究。基于非期望SBM-SVM模型并对其改进，选取重庆市2011—2020年工业企业相关指标作为样本数据，将通过非期望SBM模型得到的评价效率分为有效和无效两类作为结果变量，投入和产出指标作为特征变量，构建SVM模型，对工业投资有效性进行分类预测研究，通过“试错法”、PSO、GA智能优化算法对SVM模型的惩罚因子C和核函数参数g进行寻优。结果显示：PSO方法的寻优效果最佳，准确率从71.88%提高到了88.66%;构建的新非期望SBM-SVM模型在对其改进优化后，进行工业投资有效性分类，具有一定的可行性和适用性。

The Relationship Between Flowering and Fruiting Period of Garden Plants and Urban Lighting: Investigation on Flowering and Fruiting Period of Illuminated Garden Plants in Chongqing Urban Area

The industrial sector is the main source of energy consumption and environmental pollution, and effective investment in the industrial sector has an important impact on economic development to a certain extent. In orderto implement the concept of green development and promote energy conservation and emission reduction, a study on the effectiveness of investment in green industry was proposed. Based on the undesirable SBM-SVM model and its improvement, the relevant indicators of industrial enterprises in Chongqing from 2011 to 2020 were selected as the sample data, and the evaluation efficiency obtained by the undesirable SBM model was divided into two categories: effective investment and ineffective investment as the outcome variables. The input and output indicators were used as characteristic variables to construct an SVM model to study the classification and prediction of industrial investment effectiveness. Through trial-and- error method, PSO, and GA intelligent optimization algorithms, the penalty factor C and kernel function parameter g of the SVM model were optimized. The results showed that the optimization effect of the PSO method was the best, and the accuracy rate was increased from 71.88% to 88.66%. The constructed new undesirable SBM-SVM model has certain feasibility and applicability to classify the effectiveness of industrial investment after improvement and optimization.