IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i12p2237-d121477.html
   My bibliography  Save this article

The STIRPAT Analysis on Carbon Emission in Chinese Cities: An Asymmetric Laplace Distribution Mixture Model

Author

Listed:
  • Shanshan Wang

    (School of Economics and Management, Beihang University, Beijing 100191, China)

  • Tianhao Zhao

    (School of Economics and Management, Beihang University, Beijing 100191, China)

  • Haitao Zheng

    (School of Economics and Management, Beihang University, Beijing 100191, China)

  • Jie Hu

    (School of Economics and Management, Beihang University, Beijing 100191, China)

Abstract

In cities’ policy-making, it is a hot issue to grasp the determinants of carbon dioxide emission in Chinese cities. And the common method is to use the STIRPAT model, where its coefficients represent the influence intensity of each determinants of carbon emission. However, less work discusses estimation accuracy, especially in the framework of non-normal distribution and heterogeneity among cities’ emission. To improve the estimation accuracy, this paper employs a new method to estimate the STIRPAT model. The method uses a mixture of Asymmetric Laplace distributions (ALDs) to approximate the true distribution of the error term. Meantime, a designed two-layer EM algorithm is used to obtain estimators. We test the robustness via the comparison results of five different models. We find that the ALDs Mixture Model is more reliable the others. Further, a significant Kuznets curve relationship is identified in China.

Suggested Citation

  • Shanshan Wang & Tianhao Zhao & Haitao Zheng & Jie Hu, 2017. "The STIRPAT Analysis on Carbon Emission in Chinese Cities: An Asymmetric Laplace Distribution Mixture Model," Sustainability, MDPI, vol. 9(12), pages 1-13, December.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:12:p:2237-:d:121477
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/12/2237/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/9/12/2237/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Shangshan & Xiang, Liming, 2017. "Two-layer EM algorithm for ALD mixture regression models: A new solution to composite quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 136-154.
    2. Haitao Zheng & Jie Hu & Rong Guan & Shanshan Wang, 2016. "Examining Determinants of CO 2 Emissions in 73 Cities in China," Sustainability, MDPI, vol. 8(12), pages 1-17, December.
    3. Wang, Zhaohua & Yin, Fangchao & Zhang, Yixiang & Zhang, Xian, 2012. "An empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China," Applied Energy, Elsevier, vol. 100(C), pages 277-284.
    4. Bo Li & Xuejing Liu & Zhenhong Li, 2015. "Using the STIRPAT model to explore the factors driving regional CO 2 emissions: a case of Tianjin, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 76(3), pages 1667-1685, April.
    5. Mi, Zhifu & Zhang, Yunkun & Guan, Dabo & Shan, Yuli & Liu, Zhu & Cong, Ronggang & Yuan, Xiao-Chen & Wei, Yi-Ming, 2016. "Consumption-based emission accounting for Chinese cities," Applied Energy, Elsevier, vol. 184(C), pages 1073-1081.
    6. Wang, Mingwei & Che, Yue & Yang, Kai & Wang, Min & Xiong, Lijun & Huang, Yuchi, 2011. "A local-scale low-carbon plan based on the STIRPAT model and the scenario method: The case of Minhang District, Shanghai, China," Energy Policy, Elsevier, vol. 39(11), pages 6981-6990.
    7. Xie, Rui & Fang, Jiayu & Liu, Cenjie, 2017. "The effects of transportation infrastructure on urban carbon emissions," Applied Energy, Elsevier, vol. 196(C), pages 199-207.
    8. Dong, Liang & Fujita, Tsuyoshi & Zhang, Hui & Dai, Ming & Fujii, Minoru & Ohnishi, Satoshi & Geng, Yong & Liu, Zhu, 2013. "Promoting low-carbon city through industrial symbiosis: A case in China by applying HPIMO model," Energy Policy, Elsevier, vol. 61(C), pages 864-873.
    9. Zhang, Chuanguo & Lin, Yan, 2012. "Panel estimation for urbanization, energy consumption and CO2 emissions: A regional analysis in China," Energy Policy, Elsevier, vol. 49(C), pages 488-498.
    10. Xu, Lei & Chen, Nengcheng & Chen, Zeqiang, 2017. "Will China make a difference in its carbon intensity reduction targets by 2020 and 2030?," Applied Energy, Elsevier, vol. 203(C), pages 874-882.
    11. Wang, Ping & Wu, Wanshui & Zhu, Bangzhu & Wei, Yiming, 2013. "Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China," Applied Energy, Elsevier, vol. 106(C), pages 65-71.
    12. Panayotou T., 1993. "Empirical tests and policy analysis of environmental degradation at different stages of economic development," ILO Working Papers 992927783402676, International Labour Organization.
    13. Su, Yongxian & Chen, Xiuzhi & Li, Yong & Liao, Jishan & Ye, Yuyao & Zhang, Hongou & Huang, Ningsheng & Kuang, Yaoqiu, 2014. "China׳s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 231-243.
    14. Shao, Shuai & Yang, Lili & Gan, Chunhui & Cao, Jianhua & Geng, Yong & Guan, Dabo, 2016. "Using an extended LMDI model to explore techno-economic drivers of energy-related industrial CO2 emission changes: A case study for Shanghai (China)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 516-536.
    15. Zhi-Fu Mi & Yi-Ming Wei & Bing Wang & Jing Meng & Zhu Liu & Yuli Shan & Jingru Liu & Dabo Guan, 2017. "Socioeconomic impact assessment of China's CO2 emissions peak prior to 2030," CEEP-BIT Working Papers 103, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    16. Dabo Guan & Terry Barker, 2012. "Low-carbon development in the least developed region: a case study of Guangyuan, Sichuan province, southwest China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 62(2), pages 243-254, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Derya Topdag & Tuğçe Acar & İsmail Erkan Celik, 2020. "Estimation of the Global-Scale Ecological Footprint within the Framework of STIRPAT Models: The Quantile Regression Approach," Istanbul Journal of Economics-Istanbul Iktisat Dergisi, Istanbul University, Faculty of Economics, vol. 70(2), pages 339-358, December.
    2. Xing, Licong & Khan, Yousaf Ali & Arshed, Noman & Iqbal, Mubasher, 2023. "Investigating the impact of economic growth on environment degradation in developing economies through STIRPAT model approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    3. Olatunji Abdul Shobande, 2021. "Decomposing the Persistent and Transitory Effect of Information and Communication Technology on Environmental Impacts Assessment in Africa: Evidence from Mundlak Specification," Sustainability, MDPI, vol. 13(9), pages 1-12, April.
    4. Chun Liu & Gui-hua Nie, 2021. "Identifying the Driving Factors of Food Nitrogen Footprint in China, 2000–2018: Econometric Analysis of Provincial Spatial Panel Data by the STIRPAT Model," Sustainability, MDPI, vol. 13(11), pages 1-23, May.
    5. Qudsia Kanwal & Xianlai Zeng & Jinhui Li, 2023. "Measuring the recycling potential of industrial waste for long-term sustainability," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    6. John A. Paravantis & Panagiotis D. Tasios & Vasileios Dourmas & Georgios Andreakos & Konstantinos Velaoras & Nikoletta Kontoulis & Panagiota Mihalakakou, 2021. "A Regression Analysis of the Carbon Footprint of Megacities," Sustainability, MDPI, vol. 13(3), pages 1-24, January.
    7. Ellen Thio & MeiXuen Tan & Liang Li & Muhammad Salman & Xingle Long & Huaping Sun & Bangzhu Zhu, 2022. "The estimation of influencing factors for carbon emissions based on EKC hypothesis and STIRPAT model: Evidence from top 10 countries," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(9), pages 11226-11259, September.
    8. Nihal Ahmed & Franklin Ore Areche & Adnan Ahmed Sheikh & Amine Lahiani, 2022. "Green Finance and Green Energy Nexus in ASEAN Countries: A Bootstrap Panel Causality Test," Energies, MDPI, vol. 15(14), pages 1-13, July.
    9. YoungSeok Hwang & Jung-Sup Um & JunHwa Hwang & Stephan Schlüter, 2020. "Evaluating the Causal Relations between the Kaya Identity Index and ODIAC-Based Fossil Fuel CO 2 Flux," Energies, MDPI, vol. 13(22), pages 1-20, November.
    10. Mansi Wang & Noman Arshed & Mubbasher Munir & Samma Faiz Rasool & Weiwen Lin, 2021. "Investigation of the STIRPAT model of environmental quality: a case of nonlinear quantile panel data analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(8), pages 12217-12232, August.
    11. Ridwan Lanre Ibrahim & Usama Al-Mulali & Kazeem Bello Ajide & Abubakar Mohammed & Fatimah Ololade Bolarinwa, 2022. "Investigating the Mediating Roles of Income Level and Technological Innovation in Africa’s Sustainability Pathways Amidst Energy Transition, Resource Abundance, and Financial Inclusion," Sustainability, MDPI, vol. 14(19), pages 1-27, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiancheng Qin & Hui Tao & Minjin Zhan & Qamar Munir & Karthikeyan Brindha & Guijin Mu, 2019. "Scenario Analysis of Carbon Emissions in the Energy Base, Xinjiang Autonomous Region, China," Sustainability, MDPI, vol. 11(15), pages 1-18, August.
    2. Vélez-Henao, Johan-Andrés & Font Vivanco, David & Hernández-Riveros, Jesús-Antonio, 2019. "Technological change and the rebound effect in the STIRPAT model: A critical view," Energy Policy, Elsevier, vol. 129(C), pages 1372-1381.
    3. Wang, Shaojian & Zeng, Jingyuan & Liu, Xiaoping, 2019. "Examining the multiple impacts of technological progress on CO2 emissions in China: A panel quantile regression approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 103(C), pages 140-150.
    4. Shoufu Lin & Ji Sun & Dora Marinova & Dingtao Zhao, 2017. "Effects of Population and Land Urbanization on China’s Environmental Impact: Empirical Analysis Based on the Extended STIRPAT Model," Sustainability, MDPI, vol. 9(5), pages 1-21, May.
    5. Shuai, Chenyang & Shen, Liyin & Jiao, Liudan & Wu, Ya & Tan, Yongtao, 2017. "Identifying key impact factors on carbon emission: Evidences from panel and time-series data of 125 countries from 1990 to 2011," Applied Energy, Elsevier, vol. 187(C), pages 310-325.
    6. Jianghua Liu & Mengxu Li & Yitao Ding, 2021. "Econometric analysis of the impact of the urban population size on carbon dioxide (CO2) emissions in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(12), pages 18186-18203, December.
    7. Wang, Chen & Engels, Anita & Wang, Zhaohua, 2018. "Overview of research on China's transition to low-carbon development: The role of cities, technologies, industries and the energy system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1350-1364.
    8. Johan-Andrés Vélez-Henao, 2020. "Does urbanization boost environmental impacts in Colombia? An extended STIRPAT–LCA approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(3), pages 851-866, June.
    9. Pengyan Zhang & Jianjian He & Xin Hong & Wei Zhang & Chengzhe Qin & Bo Pang & Yanyan Li & Yu Liu, 2017. "Regional-Level Carbon Emissions Modelling and Scenario Analysis: A STIRPAT Case Study in Henan Province, China," Sustainability, MDPI, vol. 9(12), pages 1-15, December.
    10. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    11. Jinzhao Song & Qing Feng & Xiaoping Wang & Hanliang Fu & Wei Jiang & Baiyu Chen, 2018. "Spatial Association and Effect Evaluation of CO 2 Emission in the Chengdu-Chongqing Urban Agglomeration: Quantitative Evidence from Social Network Analysis," Sustainability, MDPI, vol. 11(1), pages 1-19, December.
    12. Wang, Yuan & Zhang, Xiang & Kubota, Jumpei & Zhu, Xiaodong & Lu, Genfa, 2015. "A semi-parametric panel data analysis on the urbanization-carbon emissions nexus for OECD countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 704-709.
    13. Xinlin Zhang & Yuan Zhao & Qi Sun & Changjian Wang, 2017. "Decomposition and Attribution Analysis of Industrial Carbon Intensity Changes in Xinjiang, China," Sustainability, MDPI, vol. 9(3), pages 1-16, March.
    14. Wang, Wei-Zheng & Liu, Lan-Cui & Liao, Hua & Wei, Yi-Ming, 2021. "Impacts of urbanization on carbon emissions: An empirical analysis from OECD countries," Energy Policy, Elsevier, vol. 151(C).
    15. Rauf, Abdul & Zhang, Jin & Li, Jinkai & Amin, Waqas, 2018. "Structural changes, energy consumption and carbon emissions in China: Empirical evidence from ARDL bound testing model," Structural Change and Economic Dynamics, Elsevier, vol. 47(C), pages 194-206.
    16. Wang, Shaojian & Wang, Jieyu & Fang, Chuanglin & Feng, Kuishuang, 2019. "Inequalities in carbon intensity in China: A multi-scalar and multi-mechanism analysis," Applied Energy, Elsevier, vol. 254(C).
    17. Haitao Zheng & Jie Hu & Rong Guan & Shanshan Wang, 2016. "Examining Determinants of CO 2 Emissions in 73 Cities in China," Sustainability, MDPI, vol. 8(12), pages 1-17, December.
    18. Fang, Chuanglin & Wang, Shaojian & Li, Guangdong, 2015. "Changing urban forms and carbon dioxide emissions in China: A case study of 30 provincial capital cities," Applied Energy, Elsevier, vol. 158(C), pages 519-531.
    19. Feng, Jing-Chun & Yan, Jinyue & Yu, Zhi & Zeng, Xuelan & Xu, Weijia, 2018. "Case study of an industrial park toward zero carbon emission," Applied Energy, Elsevier, vol. 209(C), pages 65-78.
    20. Tianling Zhang & Panda Su & Hongbing Deng, 2021. "Does the Agglomeration of Producer Services and the Market Entry of Enterprises Promote Carbon Reduction? An Empirical Analysis of the Yangtze River Economic Belt," Sustainability, MDPI, vol. 13(24), pages 1-21, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:9:y:2017:i:12:p:2237-:d:121477. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.