摘要
采用"多投入—多产出"的DEA模型,以环境生产函数和方向性距离函数为基础,将污染变量作为坏产出而引入生产函数,在传统生产技术的基础上构造出环境生产技术,对丝绸之路经济带(中国段)17个城市2000—2014年全要素能源效率、成分、影响因素进行测度与分析。研究表明:在环境约束下能源过度使用、废气过度排放导致丝绸之路经济带(中国段)各城市能源效率增长率和能源使用技术效率增长率降低。同时,因为忽视能源减排技术,导致能源使用技术进步增长率降低。因此,通过提高能源利用效率、强化节能减排技术利用、调整产业结构、抑制高能耗行业过快增长、加强丝路沿线城市技术交流与合作等举措有助于提高各城市的全要素能源效率,最终实现丝绸之路经济带的经济增长与能耗降低。
This paper used DEA model of multi input and multi output, based on the environmental production function and directional distance function, and based on the pollution variables as the bad outcomes into production function, constructing environmental production technology based on the traditional production technology to measure and analyzed TFP energy efficiency and the composition and influence factors of 17 cities in the silk road economic belt (China) in 2000 - 2014. The results showed that, under the constraint of environmental factors, exces- sive emissions and excessive use of exhaust gas reduced the city energy efficiency growth rate and the growth rate of technology efficiency of energy use. Because of the neglecting of the use of energy and emission reduction technology led to lower energy using technological progress rate. Therefore, by improving energy efficiency and strengthening the energy - saving emission reduction technology, by the adjustment of industrial structure, inhibiting the excessive growth of high energy consumption industries, strengthening technological exchanges and cooperation along the silk road city,which would help to improve the city's total factor energy efficiency and finally realize a winwin goal of the silk road economic belt of economic growth and reducing the energy consumption.
出处
《资源开发与市场》
CAS
CSSCI
2018年第4期463-470,共8页
Resource Development & Market
基金
陕西省社会科学基金项目"金融支持陕西省能源型产业转型升级的路径与机制研究"(编号:2017D016)
陕西省科技厅资助课题项目"基于丝绸之路经济带建设的陕西地方金融平台构建与政策研究"(编号:2016KRM017)
西安工程大学博士科研启动基金项目"丝绸之路经济带科技交流合作的模式与机制研究"(编号:BS1439)
西安工程大学哲学社会科学项目"基于丝绸之路经济带建设的陕西地方金融平台构建研究"(编号:2015ZXSK17)
关键词
丝绸之路经济带
环境生产函数
全要素能源效率
ML生产率指数
Silk Road Economic Belt
environmental production function
total factor energy efficiency
ML productivity index