区域森林公园生态效率实证分析——以华东地区为例
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摘要
采用Malmquist-Luenberger指数法对华东地区森林公园生态效率值进行测定的结果表明:从时序范围看,2011—2016年华东地区森林公园生态效率的均值为1. 006,但从2015年起进入下滑态势,说明华东地区森林公园的生态效率已经达到有效水平,但在扩大森林公园规模的同时忽视了对环境的有效保护以及资源的合理配置;从空间差异角度看,只有浙江与安徽两省的森林公园生态效率值未达到有效水平(分别为0. 851和0. 871),其余各省(市)的森林公园生态效率值均大于1,说明区域内存在一定的差异性;技术进步指数下降是制约MalmquistLuenberger指数提升的主要原因。由此可见,加大技术投入,促成交流合作机制,形成特有的竞争优势等,对提高区域森林公园生态效率是十分重要的。
(1)Background——By the end of 2017,there were 3505 forest parks in China,with a total planned area of 202. 819 million hm2,an increase of 113 and 14. 152 million hm2 when compared with the end of 2016. With the continuous expansion of the scale of forest parks,how to reduce environmental pollution and waste of resources in the construction process has become one of the tasks that must be tackled in the development of forest parks.(2)Methods——The research data mainly comes from the China Statistical Yearbook 2012-2017,the"State of the Environment of Seven Provinces and Cities in East China"and the website of the State-owned Forest Farm and Seed Management Department. The Malmquist-Luenberger index method was used to dynamically analyze the changes of ecological efficiency of forest parks in seven provinces of East China. The main formulas and calculation methods were referred to the research results of Chung et al.(3) Results——(1)From the perspective of time series changes,MLEFFCH is on the rise during the period of 2011-2015,with an average annual growth rate of 6. 1%,while MLTECH has been in a downward trend,restricting the rise of the ML index; especially in 2016,technological progress and technological efficiency dropped significantly,which directly led to the decline of the ML index. Since 2015,local governments have increased their investments in forest park construction. The area of forest parks in East China has increased by94300 hm2 by the end of 2016. Energy consumption increased by 142600 tons of standard coal during this round of construction. It can be seen that during the period of 2015-2016,the provinces and cities in East China lacked effective planning and overall coordination in the process of forest park production and operation,resulting in the overall level of internalization of forest parks not reaching an optimal state. In order to make the ecological benefits of forest parks continue to rise steadily,the investment in technology in the production and operation of forest parks in East China still needs to continue increasing.(2)There are some obvious differences among the three indexes of the seven provinces and cities. The average ML index is 1. 006,of which only Zhejiang province and Anhui province have not reached 1,while the other five provinces and cities have exceeded1.It can be seen that the production and management of forest parks have not been neglected in the process of economic development in East China.(4) Conclusions and Discussions——First,during the period from 2011 to 2016,the ecological efficiency index values of forest parks in East China was unstable. Second,there are still some spatial differences in the ecological efficiency of the seven provinces and cities, among which Shandong Province is the highest( 1. 120),and Zhejiang Province is the lowest( 0. 851). Third,the decline in the technology advancement index directly constrains the rise in the productivity index,which drops by an average of 0. 037 per year. In order to solve these issues,we should promote the technological progress in the production,operation and construction of forest parks in East China,improve the technical efficiency of the ecological construction of forest parks,strengthen the exchange and cooperation of forest parks in the region,and promote the coordination anddevelopment of the seven provinces and cities in East China. Provinces and cities also should combine their own characteristics of forest parks to form their own unique and competitive advantages.
(1)Background——By the end of 2017,there were 3505 forest parks in China,with a total planned area of 202. 819 million hm2,an increase of 113 and 14. 152 million hm2 when compared with the end of 2016. With the continuous expansion of the scale of forest parks,how to reduce environmental pollution and waste of resources in the construction process has become one of the tasks that must be tackled in the development of forest parks.(2)Methods——The research data mainly comes from the China Statistical Yearbook 2012-2017,the"State of the Environment of Seven Provinces and Cities in East China"and the website of the State-owned Forest Farm and Seed Management Department. The Malmquist-Luenberger index method was used to dynamically analyze the changes of ecological efficiency of forest parks in seven provinces of East China. The main formulas and calculation methods were referred to the research results of Chung et al.(3) Results——(1)From the perspective of time series changes,MLEFFCH is on the rise during the period of 2011-2015,with an average annual growth rate of 6. 1%,while MLTECH has been in a downward trend,restricting the rise of the ML index; especially in 2016,technological progress and technological efficiency dropped significantly,which directly led to the decline of the ML index. Since 2015,local governments have increased their investments in forest park construction. The area of forest parks in East China has increased by94300 hm2 by the end of 2016. Energy consumption increased by 142600 tons of standard coal during this round of construction. It can be seen that during the period of 2015-2016,the provinces and cities in East China lacked effective planning and overall coordination in the process of forest park production and operation,resulting in the overall level of internalization of forest parks not reaching an optimal state. In order to make the ecological benefits of forest parks continue to rise steadily,the investment in technology in the production and operation of forest parks in East China still needs to continue increasing.(2)There are some obvious differences among the three indexes of the seven provinces and cities. The average ML index is 1. 006,of which only Zhejiang province and Anhui province have not reached 1,while the other five provinces and cities have exceeded1.It can be seen that the production and management of forest parks have not been neglected in the process of economic development in East China.(4) Conclusions and Discussions——First,during the period from 2011 to 2016,the ecological efficiency index values of forest parks in East China was unstable. Second,there are still some spatial differences in the ecological efficiency of the seven provinces and cities, among which Shandong Province is the highest( 1. 120),and Zhejiang Province is the lowest( 0. 851). Third,the decline in the technology advancement index directly constrains the rise in the productivity index,which drops by an average of 0. 037 per year. In order to solve these issues,we should promote the technological progress in the production,operation and construction of forest parks in East China,improve the technical efficiency of the ecological construction of forest parks,strengthen the exchange and cooperation of forest parks in the region,and promote the coordination anddevelopment of the seven provinces and cities in East China. Provinces and cities also should combine their own characteristics of forest parks to form their own unique and competitive advantages.
引文
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[11]王兆峰,霍菲菲.基于VAR模型的湖南武陵山片区旅游产业生态效率影响因素分析[J].中南林业科技大学学报,2018,38(11):136-144.
[12]钟永德.旅游业碳排放计量与生态效率评价[J].旅游学刊,2016,31(9):11-12.
[13]蒋素梅,幸岭.旅游业生态效率研究:以昆明市为例[J].旅游研究,2014,6(2):14-19.
[14]姚治国,陈田.旅游生态效率模型及其实证研究[J].中国人口·资源与环境,2015,25(11):13-120.
[15]任阳军.基于DEA方法的中国区域建筑业生态效率研究[J].长沙大学学报,2015,29(5):77-79.
[16]汪克亮,黄晴晴,孟祥瑞.基于环境压力的矿业城市工业生态效率[J].系统工程,2017,35(2):36-44.
[17]王恩旭,武春友.基于超效率DEA模型的中国省际生态效率时空差异研究[J].管理学报,2011,8(3):443-450.
[18]周国梅,彭昊,曹凤中.循环经济和工业生态效率指标体系[J].城市环境与城市生态,2003(6):201-203.
[19]付丽娜,陈晓红,冷智花.基于超效率DEA模型的城市群生态效率研究:以长株潭“3+5”城市群为例[J].中国人口·资源与环境,2013,23(4):169-175.
[20]崔文田,高宇,张博. Malmquist指数与Malmquist-Luenberger指数的比较研究[J].西安工程科技学院学报,2005(2):152-156,161.
[21] CHUNG Y H,FRE R,GROSSKOPF S. Productivity and undesirable outputs:A directional distance function approach[J]. Journal of Environmental Management,1997,51(3),229-240.
[22]丁振民,黄秀娟,朱佳佳.中国省域森林公园旅游效率测算与旅游收入增长潜力研究[J].林业经济问题,2016,36(6):571-576.
[23]洪小燕.基于DEA模型的森林公园经营效率及其空间差异分析[J].中国林业经济,2015(5):48-51,78.
[24]刘东霞.基于Malmquist指数法的中国省域森林公园运营效率动态实证分析[J].林业经济问题,2014,34(3):229-235.
[25]陈珂,李雪婷,朴慧兰.中国林业生态效率与生态生产率的测度研究[J].林业经济问题,2016,36(2):115-120.
[26]罗能生,李佳佳,罗富政.中国城镇化进程与区域生态效率关系的实证研究[J].中国人口·资源与环境,2013,23(11):53-60.
[27] KOSKELA M. Measuring eco-efficiency in the Finnish forest industry using public data[J]. Journal of Cleaner Production,2015,98:316-327.
[28] NIEMINEN E,LINKE M,TOBLER M,et al. EU COST Action628:life cycle assessment(LCA)of textile products,eco-efficiency and definition of best available technology(BAT)of textile processing[J]. Journal of Cleaner Production,2007,15(13-14):1259-1270.
[29]黄秀娟,林秀治.我国森林公园旅游效率及其影响因素[J].林业科学,2015,51(2):137-146.
[2]场圃总站. 2016年森林公园建设经营情况[EB/OL].(2017-04-27)[2018-09-25]. http://www. Forestry. gov. cn/slgy/2452/97225/7. html.
[3] WBCSD. Eco-efficient Leadership for Improved Economic and Environmental Performance[R]. Geneva:WBCSD,1996:3-16.
[4] HU W,GUO Y,TIAN J,et al. Eco-efficiency of centralized wastewater treatment plants in industrial parks:A slack-based data envelopment analysis[J]. Resources,Conservation and Recycling,2019,141:176-186.
[5] WANG D,WAN K,YANG J. Measurement and evolution of eco-efficiency of coal industry ecosystem in China[J]. Journal of Cleaner Production,2019,209:803-818.
[6] LIU X,GUO P,GUO S. Assessing the eco-efficiency of a circular economy system in China's coal mining areas:Emergy and data envelopment analysis[J]. Journal of Cleaner Production,2019,206:1101-1109.
[7] LONG X,ZHAO X,CHENG F. The comparison analysis of total factor productivity and eco-efficiency in China's cement manufactures[J]. Energy Policy,2015,81:61-66.
[8] LONG X,SUN M,CHENG F,et al. Convergence analysis of eco-efficiency of China's cement manufacturers through unit root test of panel data[J]. Energy,2017,134:709-717.
[9]侯孟阳,姚顺波. 1978—2016年中国农业生态效率时空演变及趋势预测[J].地理学报,2018,73(11):2168-2183.
[10]潘丹,应瑞瑶.中国农业生态效率评价方法与实证:基于非期望产出的SBM模型分析[J].生态学报,2013,33(12):3837-3845.
[11]王兆峰,霍菲菲.基于VAR模型的湖南武陵山片区旅游产业生态效率影响因素分析[J].中南林业科技大学学报,2018,38(11):136-144.
[12]钟永德.旅游业碳排放计量与生态效率评价[J].旅游学刊,2016,31(9):11-12.
[13]蒋素梅,幸岭.旅游业生态效率研究:以昆明市为例[J].旅游研究,2014,6(2):14-19.
[14]姚治国,陈田.旅游生态效率模型及其实证研究[J].中国人口·资源与环境,2015,25(11):13-120.
[15]任阳军.基于DEA方法的中国区域建筑业生态效率研究[J].长沙大学学报,2015,29(5):77-79.
[16]汪克亮,黄晴晴,孟祥瑞.基于环境压力的矿业城市工业生态效率[J].系统工程,2017,35(2):36-44.
[17]王恩旭,武春友.基于超效率DEA模型的中国省际生态效率时空差异研究[J].管理学报,2011,8(3):443-450.
[18]周国梅,彭昊,曹凤中.循环经济和工业生态效率指标体系[J].城市环境与城市生态,2003(6):201-203.
[19]付丽娜,陈晓红,冷智花.基于超效率DEA模型的城市群生态效率研究:以长株潭“3+5”城市群为例[J].中国人口·资源与环境,2013,23(4):169-175.
[20]崔文田,高宇,张博. Malmquist指数与Malmquist-Luenberger指数的比较研究[J].西安工程科技学院学报,2005(2):152-156,161.
[21] CHUNG Y H,FRE R,GROSSKOPF S. Productivity and undesirable outputs:A directional distance function approach[J]. Journal of Environmental Management,1997,51(3),229-240.
[22]丁振民,黄秀娟,朱佳佳.中国省域森林公园旅游效率测算与旅游收入增长潜力研究[J].林业经济问题,2016,36(6):571-576.
[23]洪小燕.基于DEA模型的森林公园经营效率及其空间差异分析[J].中国林业经济,2015(5):48-51,78.
[24]刘东霞.基于Malmquist指数法的中国省域森林公园运营效率动态实证分析[J].林业经济问题,2014,34(3):229-235.
[25]陈珂,李雪婷,朴慧兰.中国林业生态效率与生态生产率的测度研究[J].林业经济问题,2016,36(2):115-120.
[26]罗能生,李佳佳,罗富政.中国城镇化进程与区域生态效率关系的实证研究[J].中国人口·资源与环境,2013,23(11):53-60.
[27] KOSKELA M. Measuring eco-efficiency in the Finnish forest industry using public data[J]. Journal of Cleaner Production,2015,98:316-327.
[28] NIEMINEN E,LINKE M,TOBLER M,et al. EU COST Action628:life cycle assessment(LCA)of textile products,eco-efficiency and definition of best available technology(BAT)of textile processing[J]. Journal of Cleaner Production,2007,15(13-14):1259-1270.
[29]黄秀娟,林秀治.我国森林公园旅游效率及其影响因素[J].林业科学,2015,51(2):137-146.