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

School of Economics and Management, Beihang University, Beijing 100191, China
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This paper is an extended version of our paper published in the Second Global Conference on Theory and Applications of OR/OM for Sustainability (GCTAOS 2017) with ID: 106.
Sustainability 2017, 9(12), 2237; https://doi.org/10.3390/su9122237
Received: 2 October 2017 / Revised: 18 November 2017 / Accepted: 29 November 2017 / Published: 4 December 2017
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. View Full-Text
Keywords: carbon emission; ALDs mixture model; EM algorithm; heterogeneity; Chinese cities carbon emission; ALDs mixture model; EM algorithm; heterogeneity; Chinese cities
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Wang, S.; Zhao, T.; Zheng, H.; Hu, J. The STIRPAT Analysis on Carbon Emission in Chinese Cities: An Asymmetric Laplace Distribution Mixture Model. Sustainability 2017, 9, 2237.

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