行业异质性视角下我国工业生态创新效率评价
|
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。
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.
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.
引文
[1] 董培培,霍聪佳.行业异质性视角下绿色创新效率研究.河南科技大学学报:社会科学版,2018,36(2):85- 90.
[2] Fussler C,James P.Driving Eco-innovation:A Breakthrough Discipline for Innovation and Sustainability.London:Pitman,1996.
[3] J?nicke M,Jacob K.Lead markets for environmental innovations:a new role for the nation state.Global Environmental Politics,2004,4(1):29- 46.
[4] Foxon T J,Pearson P J G.Towards improved policy processes for promoting innovation in renewable electricity technologies in the UK.Energy Policy,2007,35(3):1539- 1550.
[5] Kemp,René.Ten themes for eco-innovation policies in Europe.Sapiens,2011,4(4.2):224- 231.
[6] 韩晶.中国区域绿色创新效率研究.财经问题研究,2012,(11):130- 137.
[7] 殷群,程月.我国绿色创新效率区域差异性及成因研究.江苏社会科学,2016,(2):64- 69.
[8] 刘明广.区域创新系统绿色创新效率的空间分布及收敛性研究.工业技术经济,2017,36(4):10- 18.
[9] Mazzanti M,Zoboli R.Embedding environmental innovation in local production systems:SME strategies,networking and industrial relations:evidence on innovation drivers in industrial districts.International Review of Applied Economics,2009,23(2):169- 195.
[10] Chadha A.Overcoming competence lock-in for the development of radical eco-innovations:the case of biopolymer technology.Industry and Innovation,2011,18(3):335- 350.
[11] 华振.中国绿色创新绩效研究——与东北三省的比较分析.技术经济,2011,30(7):30- 34,41- 41.
[12] 曹霞,于娟.绿色低碳视角下中国区域创新效率研究.中国人口·资源与环境,2015,25(5):10- 19.
[13] 冯志军.中国工业企业绿色创新效率研究.中国科技论坛,2013,(2):82- 88.
[14] 任耀,牛冲槐,牛彤,姚西龙.绿色创新效率的理论模型与实证研究.管理世界,2014,(7):176- 177.
[15] 刘明广.区域绿色创新效率评价与收敛性研究——基于组合DEA与空间计量视角.科技管理研究,2017,37(17):257- 266.
[16] 沈能,赵增耀,周晶晶.生产要素拥挤与最优集聚度识别——行业异质性的视角.中国工业经济,2014,(5):83- 95.
[17] 王锋正,郭晓川.环境规制强度、行业异质性与R&D效率——源自中国污染密集型与清洁生产型行业的实证比较.研究与发展管理,2016,28(1):103- 111.
[18] 孙少勤,郭琴琴.基于行业异质性的中国工业碳排放影响因素分析.企业经济,2014,(9):103- 108.
[19] 秦楠,刘李华,孙早.环境规制对就业的影响研究——基于中国工业行业异质性的视角.经济评论,2018,(1):106- 119.
[20] 周四军,廖芳芳,李丹玉.考虑行业异质性的我国工业能源效率分析.产经评论,2017,8(1):31- 44.
[21] Kruscal J B.Linear transformation of multivariate data to reveal clustering//Shepard R N,Romney A K,Nerlove S B,eds.Multidimensional Scaling:Theory and Applications in the Behavioral Sciences Vol.1.Theory.New York:Seminar Press,1972.
[22] 楼文高,杨雪梅,张卫.区域智力资本评价的投影寻踪模型及其实证研究.情报杂志,2010,29(7):113- 116,126- 126.
[23] 孔晓妮,邓峰.中国各省区绿色创新效率评价及其提升路径研究——基于影响因素的分析.新疆大学学报:哲学·人文社会科学版,2015,43(4):14- 18.
[24] 钱丽,肖仁桥,陈忠卫.我国工业企业绿色技术创新效率及其区域差异研究——基于共同前沿理论和DEA模型.经济理论与经济管理,2015,(1):26- 43.
[25] 胡剑波,刘辉.我国区域工业生态创新效率评价——基于SBM模型和CCR模型的比较分析.科技管理研究,2014,34(14):47- 52.
[26] 肖仁桥,丁娟,钱丽.绿色创新绩效评价研究述评.贵州财经大学学报,2017,(2):100- 110.
[27] 毕克新,杨朝均,黄平.中国绿色工艺创新绩效的地区差异及影响因素研究.中国工业经济,2013,(10):57- 69.
[28] 刘冬华,李俊杰,陈亮.中国工业生态技术创新效率评价——基于2011—2014年省际面板数据.吉林工商学院学报,2017,33(1):5- 13.
[29] 杨立生,王倩,柴鑫.基于SBM-DEA模型的企业绿色持续创新效率研究.云南财经大学学报,2018,34(5):102- 112.
[30] 金菊良,丁晶.遗传算法及其在水科学中的应用.成都:四川大学出版社,2000:36- 40.
[31] 封志明,郑海霞,刘宝勤.基于遗传投影寻踪模型的农业水资源利用效率综合评价.农业工程学报,2005,21(3):66- 70.
[2] Fussler C,James P.Driving Eco-innovation:A Breakthrough Discipline for Innovation and Sustainability.London:Pitman,1996.
[3] J?nicke M,Jacob K.Lead markets for environmental innovations:a new role for the nation state.Global Environmental Politics,2004,4(1):29- 46.
[4] Foxon T J,Pearson P J G.Towards improved policy processes for promoting innovation in renewable electricity technologies in the UK.Energy Policy,2007,35(3):1539- 1550.
[5] Kemp,René.Ten themes for eco-innovation policies in Europe.Sapiens,2011,4(4.2):224- 231.
[6] 韩晶.中国区域绿色创新效率研究.财经问题研究,2012,(11):130- 137.
[7] 殷群,程月.我国绿色创新效率区域差异性及成因研究.江苏社会科学,2016,(2):64- 69.
[8] 刘明广.区域创新系统绿色创新效率的空间分布及收敛性研究.工业技术经济,2017,36(4):10- 18.
[9] Mazzanti M,Zoboli R.Embedding environmental innovation in local production systems:SME strategies,networking and industrial relations:evidence on innovation drivers in industrial districts.International Review of Applied Economics,2009,23(2):169- 195.
[10] Chadha A.Overcoming competence lock-in for the development of radical eco-innovations:the case of biopolymer technology.Industry and Innovation,2011,18(3):335- 350.
[11] 华振.中国绿色创新绩效研究——与东北三省的比较分析.技术经济,2011,30(7):30- 34,41- 41.
[12] 曹霞,于娟.绿色低碳视角下中国区域创新效率研究.中国人口·资源与环境,2015,25(5):10- 19.
[13] 冯志军.中国工业企业绿色创新效率研究.中国科技论坛,2013,(2):82- 88.
[14] 任耀,牛冲槐,牛彤,姚西龙.绿色创新效率的理论模型与实证研究.管理世界,2014,(7):176- 177.
[15] 刘明广.区域绿色创新效率评价与收敛性研究——基于组合DEA与空间计量视角.科技管理研究,2017,37(17):257- 266.
[16] 沈能,赵增耀,周晶晶.生产要素拥挤与最优集聚度识别——行业异质性的视角.中国工业经济,2014,(5):83- 95.
[17] 王锋正,郭晓川.环境规制强度、行业异质性与R&D效率——源自中国污染密集型与清洁生产型行业的实证比较.研究与发展管理,2016,28(1):103- 111.
[18] 孙少勤,郭琴琴.基于行业异质性的中国工业碳排放影响因素分析.企业经济,2014,(9):103- 108.
[19] 秦楠,刘李华,孙早.环境规制对就业的影响研究——基于中国工业行业异质性的视角.经济评论,2018,(1):106- 119.
[20] 周四军,廖芳芳,李丹玉.考虑行业异质性的我国工业能源效率分析.产经评论,2017,8(1):31- 44.
[21] Kruscal J B.Linear transformation of multivariate data to reveal clustering//Shepard R N,Romney A K,Nerlove S B,eds.Multidimensional Scaling:Theory and Applications in the Behavioral Sciences Vol.1.Theory.New York:Seminar Press,1972.
[22] 楼文高,杨雪梅,张卫.区域智力资本评价的投影寻踪模型及其实证研究.情报杂志,2010,29(7):113- 116,126- 126.
[23] 孔晓妮,邓峰.中国各省区绿色创新效率评价及其提升路径研究——基于影响因素的分析.新疆大学学报:哲学·人文社会科学版,2015,43(4):14- 18.
[24] 钱丽,肖仁桥,陈忠卫.我国工业企业绿色技术创新效率及其区域差异研究——基于共同前沿理论和DEA模型.经济理论与经济管理,2015,(1):26- 43.
[25] 胡剑波,刘辉.我国区域工业生态创新效率评价——基于SBM模型和CCR模型的比较分析.科技管理研究,2014,34(14):47- 52.
[26] 肖仁桥,丁娟,钱丽.绿色创新绩效评价研究述评.贵州财经大学学报,2017,(2):100- 110.
[27] 毕克新,杨朝均,黄平.中国绿色工艺创新绩效的地区差异及影响因素研究.中国工业经济,2013,(10):57- 69.
[28] 刘冬华,李俊杰,陈亮.中国工业生态技术创新效率评价——基于2011—2014年省际面板数据.吉林工商学院学报,2017,33(1):5- 13.
[29] 杨立生,王倩,柴鑫.基于SBM-DEA模型的企业绿色持续创新效率研究.云南财经大学学报,2018,34(5):102- 112.
[30] 金菊良,丁晶.遗传算法及其在水科学中的应用.成都:四川大学出版社,2000:36- 40.
[31] 封志明,郑海霞,刘宝勤.基于遗传投影寻踪模型的农业水资源利用效率综合评价.农业工程学报,2005,21(3):66- 70.