统计与数据科学学院知识库

“生态兴则文明兴，生态衰则文明衰”。党的十八大报告强调，要把生态文明建设作为一项重大任务，纳入“五位一体”总体布局体系，以促进经济、政治、文化、社会的协调发展，实现可持续发展。通过衡量经济增长的生态效益，我们可以更好地评估它对生态环境的影响，这对于构建一个健康的生态文明来说是非常重要的。因此，为继续走可持续发展道路及推动绿色GDP，必须研究生态效率测算及其影响因素。

本文主要包括以下几个方面。首先，查阅梳理相关的国内外生态效率文献，构建生态效率测度体系，使用超效率DEA-SBM方法，对中国30个省（市、区）2005-2020年进行了生态效率的测算，其中指标体系数据来源于《中国统计年鉴》及各省份统计年鉴、《中国环境统计年鉴》、国家统计局网站及相关部门发布的统计报表；其次，从动力学的角度，建立能源消耗-经济增长-投资水平-环境污染的动力学模型，对内在机理进行研究，根据建立的模型，运用非线性动力学方法，进行数值模拟分析，在得到稳定性的情况下，选取测算生态效率的指标作为实证数据，建立生态效率动力学模型，以2006-2021年作为样本数据对模型进行参数拟合及实证分析，并给出提高生态效率的策略分析：最后，研究生态效率的影响因素，影响因素指标来自经济、社会、环境和资源四个方面，通过整理学习历史文献得到指标体系，通过动态因子分析法选取核心指标，并选取 Tobit 回归模型进行分析。

生态效率测算结果表明：从省际层面来看，北京、天津、上海、和部分南方省市生态效率值排在前面，而西北地区部分省市排名最末，从区域来看，东部地区生态效率均值最优，中部地区生态效率均值最低。生态效率动力学系统分析表明：动力学系统在不同的环境规制下和不同的能源消耗水平下，会有不同的稳定性，根据逐步对系统的控制，给出实证分析，并对提高生态效率提供相应建议。

影响因素分析结果表明：经济发展水平对生态效率有促进作用，产业结构、科技水平及对外开放水平的系数为负数的系数与生态效率关系显著负相关，城镇化对于生态效率的提高抑制作用，环境规制的系数为5.540，对生态效率产生了显著的促进作用。

"When ecology thrives, civilization prospers; when ecology deteriorates, civilization declines." The report to the 18th National Congress of the Communist Party of China stressed that ecological progress should be taken as a major task and incorporated into the "five-sphere integrated" overall layout system, so as to promote coordinated economic, political, cultural and social development and achieve sustainable development. By measuring the ecological benefits of economic growth, we can better assess its impact on the ecological environment, which is very important for building a healthy ecological civilization. Therefore, in order to continue to take the path of sustainable development and promote green GDP, it is necessary to study the measurement of ecological efficiency and its influencing factors.

This article mainly includes the following aspects. Firstly, by consulting and reviewing relevant domestic and foreign literature on ecological efficiency, a measurement system for ecological efficiency was constructed, and DEA-SBM technology was used to evaluate the ecological efficiency of 30 provinces (cities, districts) in China from 2005 to 2020. The indicator system data comes from the "China Statistical Yearbook" and the statistical yearbooks of various provinces from 2006 to 2021, the "China Environmental Statistical Yearbook", the website of the National Bureau of Statistics, and statistical bulletins and reports issued by relevant departments. Secondly, from a dynamic perspective, a dynamic model of energy consumption-economic growth-investment level-environmental pollution was established to study the internal mechanism. Based on the established model, numerical mode analysis was carried out using nonlinear dynamic methods. After obtaining stability, the indicators for measuring ecological efficiency were selected as empirical data to establish a dynamic model of ecological efficiency. Data from 2005 to 2020 were selected for parameter fitting and empirical analysis of the model, and a strategy analysis for improving ecological efficiency was given. Finally, the influencing factors of ecological efficiency were studied, and the indicator system was obtained by summarizing and studying historical literature from the economic, social, environmental, and resource aspects. The core indicators were selected through dynamic factor analysis, and the Tobit regression model was selected for analysis.

The results of ecological efficiency measurement show that, from the provincial level, Beijing, Tianjin, Shanghai, and some southern provinces and cities rank first in terms of ecological efficiency, while some provinces and cities in the northwest region rank lowest. From a regional perspective, the eastern region has the highest average ecological efficiency, while the central region has the lowest average ecological efficiency. The dynamic system analysis of ecological efficiency shows that the dynamic system has different stability under different environmental regulations and different energy consumption levels. Based on the gradual control of the system, empirical analysis is provided, and corresponding suggestions are provided to improve ecological efficiency. The analysis results of influencing factors indicate that the level of economic development has a promoting effect on ecological efficiency, and coefficients with negative coefficients for industrial structure, technological level, and level of openness have a significant negative correlation with ecological efficiency. Urbanization has a inhibiting effect on the improvement of ecological efficiency, and the coefficient of environmental regulation is 5.540, which has a significant promoting effect on ecological efficiency.