摘要
基于环境效率影响因素理论和社会嵌入性理论,概括了环境效率影响因素的社会嵌入性的认知嵌入、关系嵌入、结构嵌入、文化嵌入、政治嵌入。以资源环境经济因素为层一变量,以社会嵌入性因素为层二变量,运用多层统计模型解决了中国省域环境效率影响因素的作用路径问题;利用中国省域的1998年到2013年每个省的资本存量、人力资本、能源消耗、用水量、污染排放指数、国内总产值数据,基于超效DEA方法,测算了中国省域的环境效率;实证分析了嵌入性视角下中国省域环境效率的影响因素。结果表明:中国省域之间的环境效率存在较大的差异;环境效率的整体差异中,高达90%的份额是由各省环境效率的社会嵌入性不同造成的。显著性直接影响因素为:人均GDP、物流关系嵌入正向影响环境效率,第二产业比重、节能性政治嵌入负向影响环境效率;显著性间接影响因素为:自然保护性政治嵌入增加将削弱第二产业比重与环境效率之间的负向关系,资金流关系嵌入增加将加强天然气使用比重与环境效率之间的正向关系;既是显著性直接影响又是显著性间接影响因素为:认知嵌入既能直接正向影响环境效率又能削弱天然气使用比重与环境效率之间的正向关系。因此,中国在解决环境问题时,不仅要考虑资源环境经济因素,还要考虑社会场景因素(嵌入性因素)对环境效率的影响,并且协调好二者之间的关系,才能实现环境效率的长期稳定提高。此外,本文也是建立适合社会嵌入性理论实证分析方法的一种尝试,实证结果有助于改善新经济社会学的社会嵌入性理论缺乏实证分析的局面。
Based on the relevant literature and social embeddedness theory, the paper firstly outlines the five social embedded factors influencing environmental efficiency: cognitive, relationship, structural, cultural, and political embedding. It then analyzes the path influencing the provincial environmental efficiency in China, taking resource, environment and economic factors as the first level of variables, and social embedded factors as the second, by using multilevel statistical model. And then it calculates the provincial environmental efficiency in China using Super-Efficient DEA method, and the provincial data from 1998 to 2013, including capital stock, human capital, energy consumption, water consumption, pollution index, and GDP. Finally, from the perspective of embeddedness, it empirically analyzes the factors influencing the provincial environmental efficiency in China. The research shows that there is a big difference in the provincial environmental efficiency in China, and 90% of the difference stems from social embeddedness. The significant and direct factors influencing the provincial environmental efficiency in China include per capita GDP, logistics relationship embedding, proportion of secondary industry in GDP, and energy policy embedding. Among them, the first two have a positive impact on the environmental efficiency, while the latter two have a negative impact. The significant and indirect factors include natural protection policy embedding, and capital flow relationship embedding. The increase of the former will weaken the negative relationship between the proportion of secondary industry and environmental efficiency, and the increase of the latter will strengthen the positive relationship between the proportion of natural gas in overall energy use and environmental efficiency. The cognitive embedding has a significant, direct, and positive impact on the environmental efficiency, while weakening the positive relationship between the proportion of natural gas and environmental efficiency. Therefore, it' s necessary to considerate not only the impact of resource, environment and economic factors, but that of the social scene factors ( embedded factors ) on environmental efficiency, and to coordinate the relationship between the two, in order to steadily improve the environmental efficiency in a long-term. Additionally, this paper paves a way for the empirical analysis of the social embeddedness theory in the new economic sociology, and provide a new perspective for the research of the environmental problems.
出处
《中国人口·资源与环境》
CSSCI
CSCD
北大核心
2016年第8期79-87,共9页
China Population,Resources and Environment
基金
国家自然科学基金项目"交叉分类累加方法与合并方法的多层统计模型理论及其应用研究"(批准号:71261004)
关键词
环境效率
社会嵌入性
多层统计模型
中国省域
environmental efficiency
social embedding
muhilevel statistical model
China' s provinces