西北地区可再生能源与碳排放关系的实证研究
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摘要
为研究西北五省区二氧化碳排放与可再生能源的关系,通过收集陕西、甘肃、青海、宁夏、新疆5个省区1995—2014年的数据,对人均碳排放进行测算,建立面板均衡模型,运用单位根检验、面板协整检验和完全修正最小二乘回归法,探索可再生能源消费与二氧化碳排放之间的长期均衡关系;之后利用格兰杰因果关系检验,验证二者之间的动态因果关系。研究结果表明,西北地区可再生能源消费与人均二氧化碳、人均GDP间均存在双向因果关系,长期均衡模型的系数显示,可再生能源消费的增加会促进碳排放的降低,而人均GDP表现出相反的作用。因此,西北五省区的政府应该鼓励可再生能源消费,充分发挥地区可再生能源存储和开发优势,有助于早日实现西北区域的减排目标。
For the purpose of studying the relationship between carbon dioxide emission and renewable energy in the five northwest provinces, the per capita carbon emission is calculated to establish the panel equilibrium model by collecting the data of Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang in 1995—2014, and the long-term equilibrium relationship between the renewable energy consumption and the carbon dioxide emission is explored by applying the unit root test, panel co-integration test and the completely-revised least square regression method. Later, the Granger causality is utilized to verify their dynamic causalities. The research result indicates that the bidirectional causality exists among the renewable energy consumption, per capita carbon dioxide and per capita GDP in the Northwest China, and the coefficient of the long-term equilibrium model shows that the increase of renewable energy consumption would reduce the carbon emission, while the per capita GDP has the opposite effect. Hence, the government in the five northwest provinces shall encourage the renewable energy consumption and fully exert the storage and development advantage of regional renewable energy, which is conductive to achieving the emission reduction target in the northwest region at an early date.
For the purpose of studying the relationship between carbon dioxide emission and renewable energy in the five northwest provinces, the per capita carbon emission is calculated to establish the panel equilibrium model by collecting the data of Shaanxi, Gansu, Qinghai, Ningxia and Xinjiang in 1995—2014, and the long-term equilibrium relationship between the renewable energy consumption and the carbon dioxide emission is explored by applying the unit root test, panel co-integration test and the completely-revised least square regression method. Later, the Granger causality is utilized to verify their dynamic causalities. The research result indicates that the bidirectional causality exists among the renewable energy consumption, per capita carbon dioxide and per capita GDP in the Northwest China, and the coefficient of the long-term equilibrium model shows that the increase of renewable energy consumption would reduce the carbon emission, while the per capita GDP has the opposite effect. Hence, the government in the five northwest provinces shall encourage the renewable energy consumption and fully exert the storage and development advantage of regional renewable energy, which is conductive to achieving the emission reduction target in the northwest region at an early date.
引文
[ 1 ] 刘雅薇.基于STIRPAT模型的中国二氧化碳排放驱动因素分析:来自30个省市面板数据的实证研究[D].武汉:华中师范大学,2014.
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[14] 李正辉,崔衍安.中国碳排放演化特征的实证研究[J].统计与决策,2011,27(11):122-126.
[15] 程豪.碳排放怎么算:《2006年IPCC国家温室气体清单指南》[J].中国统计,2014,62(11):28-30.
[16] Levin A,Lin C,Chu C.Unit Root Tests in Panel Data:Asymptotic and Finite——Sample Properties[J].Journal of Econometrics,2002,108:1-24.
[17] Breitung J?rg.The Local Power of Some Unit Root Tests for Panel Data[J].Sfb Discussion Papers,1999,15(15):161-177.
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[20] KAO C.Spurious Regression and Residual-based Tests for Cointegration in Panel Data[J].Journal of Econometrics,1999:1-44.
[21] Kao,Chihwa,Chiang,Min-Hsien.On the Estimation and Inference of a Cointegrated Regression in Panel Data[J].Chihwa Kao,2000,15(1):109--141.
[22] Pedroni P.Critical Value for Cointegration Tests in Heterogeneous Panels with Multiple Regressors[J].Oxf Bull Econ Stat,1999:61653-61678.
[ 2 ] 邓小乐,孙慧.基于STIRPAT模型的西北五省区碳排放峰值预测研究[J].生态经济,2016,32(9):36-41.
[ 3 ] 戴林.《可再生能源法》解读[J].电力电子,2006,(2):55-55.
[ 4 ] Fatih Cemil,?zbuday,Bahar Celikkol Erbas.How Effective are Energy Efficiency and Renewable Energy in Curbing CO2,Emissions in the Long Run? A Heterogeneous Panel Data Analysis[J].Energy,2015,82:734-745.
[ 5 ] Bilgili F,Ko?ak E,Bulut U.The Dynamic Impact of Renewable Energy Consumption on CO2 Emissions:A Revisited Environmental Kuznets Curve Approach[J].Renewable Sustainable Energy Reviews,2016,54:838-845.
[ 6 ] Zhou Y,Li Y P,Huang G H.Planning Sustainable Electric-power System with Carbon Emission Abatement Through CDM under Uncertainty[J].Applied Energy,2015,140:350-364.
[ 7 ] 娄铖伟,张筱慧,丛鹏伟,等.含柔性软开关的有源配电网故障恢复策略[J].电力系统自动化,2018,42(1):23-31.
[ 8 ] 新华社.西北“风光”无限好:大西北新能源再调查.[DB/OL].(2017-09-14).[ 2018 -10 -18].http://news.bjx.com.cn/html/20170914/849931.shtml.
[ 9 ] 马蓓蓓,鲁春霞,张雷,等.新形势下西北地区碳排放及低碳化发展研究:以陕西省为例[J].资源科学,2010,32(2):223-229.
[10] 刘海龙,石培基,刘海猛,等.西北地区重工业城市工业行业碳排放分析:以兰州市为例[J].生态经济:中文版,2014,30(5):41-45.
[11] Apergis N,Payne J E,Menyah K,et al.On the Causal Dynamics Between Emissions,Nuclear Energy,Renewable Energy,and Economic Growth[J].Ecological Economics,2010,69(11):2255-2260.
[12] A?ssa M S B,Jebli M B,Youssef S B.Output,Renewable Energy Consumption and Trade in Africa[J].Energy Policy,2014,66(17):11-18.
[13] Ito K.CO2 Emissions,Renewable and Non Renew-able Energy Consumption,and Economic Growth:Evidence from Panel Data for Developed Coun-tries[J].International Economics,2016,36(1):553-559.
[14] 李正辉,崔衍安.中国碳排放演化特征的实证研究[J].统计与决策,2011,27(11):122-126.
[15] 程豪.碳排放怎么算:《2006年IPCC国家温室气体清单指南》[J].中国统计,2014,62(11):28-30.
[16] Levin A,Lin C,Chu C.Unit Root Tests in Panel Data:Asymptotic and Finite——Sample Properties[J].Journal of Econometrics,2002,108:1-24.
[17] Breitung J?rg.The Local Power of Some Unit Root Tests for Panel Data[J].Sfb Discussion Papers,1999,15(15):161-177.
[18] Im K S,Pesaran M H,Shin Y.Testing for Unit Roots in Heterogeneous Panels[J].Journal of Econometrics,2003,115:53-74.
[19] Maddala G S,Wu S.A Comparative Study of Unit Root Tests with Panel Data and A New Simple Test[J].Oxford Bulletin of Economics and Statistics,61:63-152.
[20] KAO C.Spurious Regression and Residual-based Tests for Cointegration in Panel Data[J].Journal of Econometrics,1999:1-44.
[21] Kao,Chihwa,Chiang,Min-Hsien.On the Estimation and Inference of a Cointegrated Regression in Panel Data[J].Chihwa Kao,2000,15(1):109--141.
[22] Pedroni P.Critical Value for Cointegration Tests in Heterogeneous Panels with Multiple Regressors[J].Oxf Bull Econ Stat,1999:61653-61678.