中国钢铁行业的环境效率评价：基于过程水平的数据包络分析。
Environmental efficiency evaluation of China's iron and steel industry: A process-level data envelopment analysis.
机构信息
State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing 100084, China; Industrial Energy Saving and Green Development Assessment Center, Tsinghua University, Beijing 100084, China.
State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing 100084, China; Industrial Energy Saving and Green Development Assessment Center, Tsinghua University, Beijing 100084, China.
出版信息
Sci Total Environ. 2020 Mar 10;707:135903. doi: 10.1016/j.scitotenv.2019.135903. Epub 2019 Dec 10.
To resolve the increasingly higher energy and environmental pressures, the evaluation of environmental efficiency in China's iron and steel industry is essential for identifying a precise energy conservation and emission reduction path. However, current studies have only focused on the efficiency evaluation in national, regional, or enterprise level, lacking the analysis of different processes. Therefore, the objective of this research is to conduct a process-level data envelopment analysis (DEA) to evaluate the environmental efficiency of China's iron and steel industry. Totally, 54 enterprises are contained, as the input-output structure of 5 processes: sintering, coking, ironmaking, steelmaking, and steel rolling are set specifically in this study. In addition, to compare the effects to the efficiency results of different DEA methods, Banker, Charnes & Cooper (BCC) model, Slack-based Measure (SBM) model, and Bootstrap-DEA methods are adopted. Finally, a regression model is used to investigate the key environmental protection strategies influencing the environmental efficiency. The results show that: (1) Within different methods, the average efficiency scores from SBM model are lower than the ones from BCC model, and the Bootstrap-DEA method also has a negative modification. (2) Regional efficiency difference exists, as the enterprises in South China perform best in sintering and coking processes but have the lowest overall efficiency scores. (3) Most enterprises have one or more short board processes. 12 enterprises are the enterprises with individual low environmental efficiency process, while other 25 are the enterprises with imbalanced environmental performances. (4) The coefficient factor between environmental protection investment and the efficiency scores are positive, but the factors of proportion of environmental protection staffs, and whether the enterprise has environmental protection research are negative. In sum, this study is hoped to contribute to formulating more precise environmental management measures in China's iron and steel industry.
为了解决日益增长的能源和环境压力，评估中国钢铁行业的环境效率对于确定精确的节能和减排路径至关重要。然而，目前的研究仅关注于国家、地区或企业层面的效率评估，缺乏对不同工艺的分析。因此，本研究的目的是进行过程层面的数据包络分析（DEA），以评估中国钢铁行业的环境效率。总共包含了 54 家企业，具体设定了 5 个过程的投入产出结构：烧结、炼焦、炼铁、炼钢和轧钢。此外，为了比较不同 DEA 方法对效率结果的影响，采用了 Banker、Charnes & Cooper（BCC）模型、基于松弛的测度（SBM）模型和 Bootstrap-DEA 方法。最后，使用回归模型研究了影响环境效率的关键环境保护策略。结果表明：（1）在不同方法内，SBM 模型的平均效率得分低于 BCC 模型，Bootstrap-DEA 方法也有负修正。（2）存在区域效率差异，华南地区的企业在烧结和炼焦过程中表现最好，但整体效率得分最低。（3）大多数企业存在一个或多个短板过程。有 12 家企业是个别环境效率低的过程企业，而其他 25 家企业是环境绩效不平衡的企业。（4）环境保护投资与效率得分之间的系数因子为正，但环境保护人员比例和企业是否有环境保护研究的因子为负。总之，本研究有望为中国钢铁行业制定更精确的环境管理措施做出贡献。
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