摘要
环境政策的实施效果关系到雾霾长效治理目标的实现,而政策执行过程中是否出现问题直接表现为政策实施效果。在此背景下,如何量化政策效应以及挖掘政策实施过程中的问题是文章的主要目标。为此,文章选取北京、兰州、天津、太原作为研究样本,以空气首要污染物PM_(2.5)颗粒物浓度作为研究对象,运用Prophet模型预测反事实状态下潜在的PM_(2.5)浓度,对比实际状况下的PM_(2.5)浓度,量化研究中共十九大环境政策对PM_(2.5)防治的效果。研究发现:(1)中共十九大一揽子环境政策对于PM_(2.5)的防治具有正向效应,但同时也显示出一定的区域异质性;(2)雾霾治理过程中存在"两会蓝"现象和政策执行乏力的问题。基于实证研究结果,文章提出相应的政策建议,对于新常态下雾霾防治具有一定的启示意义。
Since the reform and opening up, China has achieved rapid economic development accompanied by environmental degradation. The smog pollution with PM_(2.5) as the primary pollutant not only seriously threatens people's daily life and health, but also has many negative impacts on China's social economy. At present, China is in a critical period of highquality economic development, and the government plays an indispensable role in solving environmental problems such as smog. At the 18 th National Congress of the Communist Party of China, environmental protection was pushed to a new height, where the prevention and control of atmospheric pollution is a top priority. At the convening of the 19 th National Congress of the Communist Party of China, the central government put forward higher requirements for the prevention and control of atmospheric pollution. Xi Jinping, the General Secretary, clearly stated in his report that we should "focus on solving outstanding environmental problems" and pointed out that "continuous implementation of air pollution prevention and control actions should ensure the blue sky". In recent years, governments at all levels have invested heavily from financial subsidies to legislation to ensure air pollution control. The implementation effect of environmental protection policies and measures will directly affect the realization of policy objectives. Meanwhile,there may be unexpected problems in the implementation of environmental policies. If these problems can be discovered in time, timely adjustment of relevant policies will have an important impact on the government's lowering the economic cost of governance and achieving the goal of governance. In view of this, how to quantify the effect of environmental policies, properly adjust environmental policies, and achieve the goal of long-term smog governance is the core question that this paper attempts to answer. In this paper, we select Beijing, Lanzhou, Tianjin and Taiyuan as the samples. In view of the limitations of the macro policy effect research method, this paper proposes to predict the potential 2.5 concentration change under the counterfactual state based on the machine learning algorithm, compare the actual 2.5 concentration, and quantify the effect of the environmental policies proposed on the 19 th National People's Congress on 2.5 prevention and control. Considering the complexity of the data characteristics of the 2.5 concentration sequence,this paper chooses the traditional ARIMA and HW to establish the prediction system of 2.5, and selects the nonlinear random forest(RF)and Prophet models for comparison. We finally choose the Prophet algorithm with better precision, and based on this, we predict the potential 2.5 concentration in the counterfactual state. The study finds that:(1)The environmental policies proposed on the 19 th National People's Congress has a positive effect on the prevention and control of 2.5, but it also shows a certain regional heterogeneity; environmental protection policies reduce the smog concentration in each city, thus improving the quality of the environment. The municipality has a more perfect policy system and faster response capability than other provincial capital cities.(2)There may be a phenomenon of the "political blue sky" and weak policy implementation in the process of smog management. The "temporary blue sky" is at the expense of more serious retaliatory pollution after the political incident. It is contrary to the concept of sustainable development, which is not conducive to the long-term management of smog, and also causes huge waste of resources in the country. For the phenomenon of the "temporary blue sky",this paper proposes policy recommendations from three aspects: environmental law enforcement,industrial transformation, and governance mechanisms. For the problem of the lack of governance,the government needs to further open up the environmental governance market and inject new vitality into the management of smog.
引文
[1]陈诗一,陈登科.能源结构、雾霾治理与可持续增长[J].环境经济研究,2016,(1).
[2]陈晔婷,邢文祥,朱锐.中国高技术企业“走出去”对研发效率的影响——基于合成控制法的研究[J].世界经济研究,2016,(8).
[3]崔亮亮,李新伟,耿兴义,等. 2013年济南市大气PM2.5污染及雾霾事件对儿童门诊量影响的时间序列分析[J].环境与健康杂志,2015,(6).
[4]戴李杰,张长江,马雷鸣.基于机器学习的PM2.5短期浓度动态预报模型[J].计算机应用,2017,(11).
[5]冯仁杰,吴然,钟佩瑢,等.孕期大气颗粒物暴露对新生儿低出生体重影响的Meta分析[J].中华预防医学杂志,2017,(3).
[6]何小钢.结构转型与区际协调:对雾霾成因的经济观察[J].改革,2015,(5).
[7]胡玉筱,段显明.基于高斯烟羽和多元线性回归模型的扩散和预测研究[J].干旱区资源与环境,2015,(6).
[8]黄启才.自贸试验区设立促进外商直接投资增加了吗——基于合成控制法的研究[J].宏观经济研究,2018,(4).
[9]冷艳丽,杜思正.产业结构、城市化与雾霾污染[J].中国科技论坛,2015,(9).
[10]李永友,文云飞.中国排污权交易政策有效性研究——基于自然实验的实证分析[J].经济学家,2016,(5).
[11]李云燕,王立华,王静,等.京津冀地区雾霾成因与综合治理对策研究[J].工业技术经济,2016,(7).
[12]刘甲炎,范子英.中国房产税试点的效果评估:基于合成控制法的研究[J].世界经济,2013,(11).
[13]彭斯俊,沈加超,朱雪.基于ARIMA模型的PM2.5预测[J].安全与环境工程,2014,(6).
[14]石庆玲,郭峰,陈诗一.雾霾治理中的“政治性蓝天”——来自中国地方“两会”的证据[J].中国工业经济,2016,(5).
[15]孙兆彬,安兴琴,崔甍甍,等.北京地区颗粒物健康效应研究——沙尘天气、非沙尘天气下颗粒物(PM2.5、PM10)对心血管疾病入院人次的影响[J].中国环境科学,2016,(8).
[16]王兵,戴敏,武文杰.环保基地政策提高了企业环境绩效吗?——来自东莞市企业微观面板数据的证据[J].金融研究,2017,(4).
[17]王桂芝,顾赛菊,陈纪波.基于投入产出模型的北京市雾霾间接经济损失评估[J].环境工程,2016,(1).
[18]王玲玲,柏如海,章琦,等.西安市2010–2013年大气污染对育龄妇女妊娠结局的影响[J].中华流行病学杂志,2016,(11).
[19]王勖之,曾沛,刘永辉.上海市PM2.5浓度的分析与预测[J].数学的实践与认识,2017,(15).
[20]温晋锋,王赟.雾霾治理立法:趋向与反思[J].人民论坛,2016,(14).
[21]吴建南,秦朝,张攀.雾霾污染的影响因素:基于中国监测城市PM2.5浓度的实证研究[J].行政论坛,2016,(1).
[22]席鹏辉,梁若冰.油价变动对空气污染的影响:以机动车使用为传导途径[J].中国工业经济,2015,(10).
[23]谢元博,陈娟,李巍.雾霾重污染期间北京居民对高浓度PM2.5持续暴露的健康风险及其损害价值评估[J].环境科学,2014,(1).
[24]杨海兵,沈洁,贾秋放,等.大气污染物和气象因素对心脑血管疾病影响的研究进展[J].中华疾病控制杂志,2010,(3).
[25]于文金,吴雁,黄亦露,等.河北省雾霾波动变化特征及成因研究[J].大气科学学报,2016,(4).
[26]岳利萍,马瑞光.基于排放权核算的雾霾治理创新[J].人文杂志,2016,(4).
[27]赵绍阳,尹庆双,臧文斌.医疗保险补偿与患者就诊选择——基于双重差分的实证分析[J].经济评论,2014,(1).
[28]周景坤,杜磊.国外雾霾防治税收政策及启示[J].理论导刊,2015,(12).
[29]张燕萍,刘旭辉,任展宏,等.太原市大气污染对妊娠结局的影响[J].环境与健康杂志,2007,(3).
[30]张立辉,德格吉日夫,梁洪源.我国施工扬尘排污费征收制度研究[J].环境工程,2016,(2).
[31]Antanasijevi?D Z,Pocajt V V,Povrenovi?D S,et al. PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization[J]. Science of the Total Environment,2013,443:511–519.
[32]BellML,EbisuK,BelangerK.AmbientairpollutionandlowbirthweightinConnecticutand Massachusetts[J]. Environmental Health Perspectives,2007,115(7):1118–1124.
[33]Díaz-Robles L A,Ortega J C,Fu J S,et al. A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas:The case of Temuco,Chile[J]. Atmospheric Environment,2008,42(35):8331–8340.
[34]Elbayoumi M,Ramli N A,Md Yusof N F F,et al. Spatial and seasonal variation of particulate matter(PM10and PM2.5)in Middle Eastern classrooms[J]. Atmospheric Environment,2013,80:389–397.
[35]Fang G C,Zhuang Y J,Kuo Y C,et al. Ambient air metallic elements(Mn,Fe,Zn,Cr,Cu,and Pb)pollutants sources study at a rural resident area near Taichung Thermal Power Plant and Industrial Park:6-month observations[J]. Environmental Earth Sciences,2016,75(7):587.
[36]Gao Y,Guo X Y,Ji H B,et al. Potential threat of heavy metals and PAHs in PM2.5 in different urban functional areas of Beijing[J]. Atmospheric Research,2016,178–179:6–16.
[37]Lee K Y,Wong C K C,Chuang K J,et al. Methionine oxidation in albumin by fine haze particulate matter:An in vitro and in vivo study[J]. Journal of Hazardous Materials,2014,274:384–391.
[38]Lin K P,Pai P F,Yang S L. Forecasting concentrations of air pollutants by logarithm support vector regression with immune algorithms[J]. Applied Mathematics and Computation,2011,217(12):5318–5327.
[39]Marshall J D,Nethery E,Brauer M. Within-urban variability in ambient air pollution:Comparison of estimation methods[J]. Atmospheric Environment,2008,42(6):1359–1369.
[40]Perez P. Combined model for PM10 forecasting in a large city[J]. Atmospheric Environment,2012,60:271–276.
[41]Taylor S J,Letham B. Forecasting at scale[J]. The American Statistician,2018,72(1):37–45.
[42]Wang D Y,Wei S,Luo H Y,et al. A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine[J]. Science of the Total Environment,2017,580:719–733.
[43]Yu L A,Dai W,Tang L. A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting[J]. Engineering Applications of Artificial Intelligence,2016,47:110–121.
[44]Zhang X Y,Wang Y Q,Niu T,et al. Atmospheric aerosol compositions in China:Spatial/temporal variability,chemical signature,regional haze distribution and comparisons with global aerosols[J]. Atmospheric Chemistry and Physics,2012,12(2):779–799.
[45]Zhou Q P,Jiang H Y,Wang J Z,et al. A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network[J]. Science of the Total Environment,2014,496:264–274.
[46]Zheng G,Duan F K,Ma Y,et al. Episode-based evolution pattern analysis of haze pollution:Method development and results from Beijing,China[J]. Environmental Science&Technology,2016,50(9):4632.
(1)ARIMA·HW和RF算法结果将用于比较分析。
