摘要:随着资源紧缺、环境污染日益严峻，生态创新成为各国追逐的政治目标，研究我国不同工业行业的生态创新效率对我国的创新发展和可持续发展具有重要意义。从生态创新内涵出发，构建包括环境效益和经济效益在内的生态创新效率评价指标体系。以2009-2015年我国34个工业行业面板数据为样本，利用生产要素密集度将选取的34个工业行业归类为资源密集型行业、劳动密集型行业和资本密集型行业。运用基于实数编码加速遗传算法的投影寻踪分类模型进行综合评价，该方法可以依据最佳投影方向来判断各评价指标对综合评价目标的贡献大小和方向，利用投影指标值实现对34个行业的统一分类和评价。结果表明：不同要素密集度行业的生态创新效率存在显著差异，资本密集型行业的生态创新效率最高，资源密集型行业和劳动密集型行业的生态创新效率较为接近，但都低于行业整体平均水平，其中尤以劳动密集型行业中的有色金属矿采选业的生态创新效率最低；生态创新研发人力、财力的投入以及创新活动所带来的经济效益对生态创新效率的提升影响较大，而仪器和设备等物质投入对生态创新效率影响较小。



Abstract:The shortage of resources and the increasingly severe environmental pollution have made the eco-innovation become a political goal pursued by countries all over the world. The study on the eco-innovation efficiency of the different industrial sectors in China will be of great significance for the innovative and sustainable development. Starting from the connotation of eco-innovation, an assessment index system of eco-innovation efficiency was established, including environmental benefits and economic benefits. The panel data of 34 industrial sectors from 2009 to 2015 in China was taken as a sample and the 34 industrial sectors selected were classified into resource-intensive industries, labor-intensive industries and capital-intensive industries by using the production factor intensity in this study. The projection pursuit classification model based on real number encoded accelerating genetic algorithm was used for comprehensive assessment, while this method can judge the contribution size and direction of each assessment index to the comprehensive assessment objective according to the optimal projection direction, as well as realize the unified classification and assessment for the 34 industrial sectors through using the projection index value. The results show that the eco-innovation efficiency varies significantly in the industries with different factor intensities. The eco-innovation efficiency of the capital-intensive industries is the highest, while that of the resource-intensive industries and labor-intensive industries are close to each other but both are lower than the overall average of the industry. Among them, the non-ferrous metal mining and dressing industry in labor-intensive industries shows the lowest eco-innovation efficiency. The input of manpower and financial resources in the research and development of eco-innovation and the economic benefits brought by innovation activities have a great influence on the improvement of eco-innovation efficiency, while the input of materials, such as instruments and equipment, etc., have less influence on that.





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