【作者】 张慧；

【导师】 张子龙；

【作者基本信息】 兰州大学 ， 地理学·人文地理学， 2023， 硕士

【摘要】 改革开放以来,中国经济快速增长,经济实力显著提高。与此同时,生态破坏、环境污染加剧、能源消耗过高等问题日益凸显。党的二十大报告指出要加快构建新发展格局,着力推动高质量发展,推进生态优先、节约集约、绿色低碳发展。生态效率阐明了经济效益、资源效益与生态环境效益之间的关系,可有效衡量区域可持续发展水平,是高质量发展和绿色新发展理念的重要体现。县域是中国经济功能完整、运行相对独立的基本空间单元,也是国土生态环境的主要承载空间,更是推进生态文明建设与高质量发展政策落实的关键行政单元。从县域尺度对中国生态效率的时空演变及其影响因素进行研究,有助于更精细地刻画中国生态效率的时空格局特征,揭示生态效率演变的驱动因素,以期为制定差别化的生态效率提升政策,着力推进高质量发展,推动生态文明建设,促进人与自然和谐共生提供借鉴和参考。本研究利用包含非期望产出的Super-SBM模型和Malmquist指数模型,构建了考虑能源消费和PM2.5排放的生态效率测度指标体系。首先从静态和动态两个方面分析了2000-2020年中国县域尺度生态效率的时空变化特征,并结合投入产出松弛率探讨了生态效率相对无效的原因。其次,利用α收敛和β收敛分析了中国县域生态效率的空间差异及其收敛性。最后利用面板回归模型,分析了生态效率的影响因素。主要结论如下:(1)中国县域单元生态效率整体偏低,大致呈金字塔分布,空间上呈现“西高东低、南高北低”的分布格局;八大经济区县域生态效率水平呈现明显差异。造成县域生态效率水平偏低的主要原因是投入指标冗余率偏高,其中大西北、东北、北部沿海经济区为冗余度高值区,东部沿海、南部沿海经济区为冗余度低值区。(2)2000-2020年中国县域生态效率整体保持增长趋势,且大多数县域生态效率呈波动增长趋势,生态效率增长动力主要来源于技术进步。八大经济区县域生态效率均保持增长趋势,其中,黄河中游经济区县域生态效率增长最快,东北经济区县域生态效率增长最慢。(3)中国整体和八大经济区内部县域生态效率均存在α收敛和绝对β收敛,但不同经济区县域生态效率的收敛速度存在差异,其中收敛最快的是东北经济区县域,最慢的是南部沿海经济区县域。2000-2020年我国县域生态效率低水平区域通过较高的增长速度追赶生态效率较高区域,使县域生态效率差距不断缩小。(4)县域经济发展水平和生态效率呈“倒U型”曲线关系,工业化水平越高、人口规模越大、政府干预越多,县域生态效率水平越高,而生态条件越好的县域,生态效率水平越低。更多还原

【Abstract】 Since the reform and opening-up policy,China’s economy has grown rapidly,and its economic strength has significantly improved.However,at the same time,ecological destruction,environmental pollution,and excessive energy consumption have become increasingly prominent issues.The report of the 20 th National Congress of the Communist Party of China pointed out that it is necessary to accelerate the establishment of a new development pattern,focus on promoting high-quality development,and advance eco-prioritization,conservation,intensification,green,and low-carbon development.Ecological efficiency clarifies the relationship between economic benefits,resource benefits,and ecological environmental benefits,which can effectively measure the level of sustainable development in a region and is an important manifestation of the concept of high-quality development and green new development.County-level regions are the basic spatial units of China’s economy with complete functions and relatively independent operations.They are also the main carriers of the national ecological environment,and a key administrative unit for promoting ecological civilization construction and the implementation of high-quality development policies.Researching the spatiotemporal evolution of China’s ecological efficiency and its influencing factors at the county level can help to more finely depict the spatiotemporal pattern characteristics of China’s ecological efficiency,reveal the driving factors of ecological efficiency evolution,and provide references and guidance for formulating differentiated policies to improve ecological efficiency,promoting high-quality development,advancing ecological civilization construction,and promoting harmonious coexistence between humans and nature.This study employs a Super-SBM model with unexpected output and a Malmquist index model to construct an ecological efficiency measurement indicator system that considers energy consumption and PM2.5 emissions.Firstly,the spatiotemporal characteristics of ecological efficiency at the county level in China from 2000 to 2020 were analyzed from both static and dynamic perspectives.The reasons for the relative inefficiency of ecological efficiency were discussed in combination with input-output slack variables.Secondly,the spatial differences and convergence of ecological efficiency in Chinese counties were analyzed using α convergence and β convergence.Finally,a panel regression model was used to analyze the influencing factors of ecological efficiency.The main conclusions are as follows:(1)Overall,the ecological efficiency of county-level units in China is relatively low and roughly follows a pyramid distribution.Spatially,it shows a distribution pattern of “high in the west,low in the east,high in the south,and low in the north”.The ecological efficiency levels of the eight economic regions vary significantly.The main reason for the low level of ecological efficiency in counties is the high redundancy of input indicators.The regions with high redundancy are the Northwestern,Northeastern,and Northern coastal economic zones,while the regions with low redundancy are the Eastern and Southern coastal economic zones.(2)From 2000 to 2020,the ecological efficiency of county-level units in China showed an overall increasing trend,with most counties experiencing fluctuating growth in ecological efficiency.The driving force behind the growth of ecological efficiency mainly came from technological progress.The ecological efficiency of the eight major economic regions in the counties has maintained a growing trend.Among them,the ecological efficiency of counties in the middle reaches of the Yellow River Economic Zone has grown the fastest,while the ecological efficiency of counties in the Northeast Economic Zone has grown the slowest.(3)The overall ecological efficiency of China’s county-level units and within the eight major economic regions both exhibit α-convergence and absolute β-convergence,but the convergence speed of ecological efficiency in different economic regions varies.Among them,county-level units in the Northeast Economic Region show the fastest convergence,while those in the Southern Coastal Economic Region show the slowest convergence.From 2000 to 2020,China’s counties with low ecological efficiency have been catching up with those with higher ecological efficiency at a relatively high growth rate,leading to a continuous narrowing of the gap in ecological efficiency among counties.(4)The economic development level and ecological efficiency of county-level regions show an inverted U-shaped curve relationship.The higher the level of industrialization,the larger the population,and the greater the government intervention,the higher the ecological efficiency of the county-level regions.Conversely,the countylevel regions with better ecological conditions have lower ecological efficiency levels.更多还原