Dergiler / Atmospheric Pollution Research / 2019 / Cilt: 10 - Sayı: 1

Spatial analysis on carbon emission abatement capacity at provincial level inChina from 1997 to 2014: An empirical study based on SDM model

Sayfa
97–104
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The coefficients of economic development and energy utilization efficiency are also sta-tistically significant, and indicates mutual effects among regions.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Currently, the increasing carbon emissions accompanied with rapid urbanization and industrial development isthe central environmental problem in China.
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Temel Bulgular & Veri:In this paper, the carbon emission abatement index (CACI) con-sisting of equal weighted efficiency and equity indexes is employed tofigure out the allocation of carbonemission mitigation quotient when the inclination and capacity in each province are all taken into consideration.Then, the novel spatial panel data model combined with economic weight matrix is used to estimate the spatialdependence of the carbon emission reduction potential at the provincial level.
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Sonuç & Katkı:The coefficients of economic development and energy utilization efficiency are also sta-tistically significant, and indicates mutual effects among regions.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Özet

Currently, the increasing carbon emissions accompanied with rapid urbanization and industrial development isthe central environmental problem in China. In this paper, the carbon emission abatement index (CACI) con-sisting of equal weighted efficiency and equity indexes is employed tofigure out the allocation of carbonemission mitigation quotient when the inclination and capacity in each province are all taken into consideration.Then, the novel spatial panel data model combined with economic weight matrix is used to estimate the spatialdependence of the carbon emission reduction potential at the provincial level. Moreover, the driving forces areexamined. The result suggests that the provinces with low marginal cost and per capita carbon emissions shouldtake on more burdens regarding carbon emission reduction (e.g. Ningxia, Shanghai, Shanxi, Qinghai andTianjin), while the provinces with low economic development and carbon intensity can be allocated relativelyless loads (e.g. Sichuan, Jiangxi, Guangxi, Anhui and Hunan). Furthermore, the capacity of carbon emissionmitigation is negatively affected by urbanization and positively affected by per capita GDP, population densityand energy intensity. The coefficients of economic development and energy utilization efficiency are also sta-tistically significant, and indicates mutual effects among regions.

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Bu Makaleye Atıf Yap

WANG, S. L. Y., ZHAO, T., WANG, J., FANG, G., XU, K., YUAN, R. (2019). Spatial analysis on carbon emission abatement capacity at provincial level inChina from 1997 to 2014: An empirical study based on SDM model. Atmospheric Pollution Research, Cilt: 10 - Sayı: 1, 97–104. https://ajindex.com/articles/166190

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Kaynakça (15)

  1. Ang, B.W., 2004. Decomposition analysis for policymaking in energy:: which is thepreferred method? Energy Pol. 32, 1131–1139.
  2. Ang, B.W., 2015. LMDI decomposition approach: a guide for implementation. Energy Pol.86, 233–238.
  3. Ang, B.W., Zhang, F.Q., Choi, K.H., 1998. Factorizing changes in energy and environ-mental indicators through decomposition. Energy 23, 489–495
  4. Anselin, L., 1990. Spatial econometric: methods and models. J. Am. Stat. Assoc. 85, 160.Cazorla, M.V., Toman, M.A., 2001. International equity and climate change policy.Integrated Assess. 1, 111–126.
  5. Chang, C.C., 2010. A multivariate causality test of carbon dioxide emissions, energyconsumption and economic growth in China. Appl. Energy 87, 3533–3537.
  6. Chuai, X., Huang, X., Wang, W., Chen, Z., 2012. Temporospatial changes of carbonfootprint based on energy consumption in China. J. Geogr. Sci. 22, 110–124.
  7. Chung, Y.H., Färe, R., Grosskopf, S., 1995. Productivity and undesirable outputs: a di-rectional distance function approach. Microeconomics 51, 229–240.
  8. Conley, T.G., Ligon, E., 2011. Economic distance and cross-country spillovers. J. Econ.Growth 7, 157–187.
  9. Elhorst, J.P., 2010. Applied spatial econometrics: raising the bar. Spatial Econ. Anal. 5,9–28.
  10. Elhorst, J.P., 2015. Matlab software for spatial panels. Int. Reg. Sci. Rev. 37, 389–405.
  11. Fan, T., Luo, R., Xia, H., Li, X., 2015. Using LMDI method to analyze the influencingfactors of carbon emissions in China's petrochemical industries. Nat. Hazards 75,319–332.
  12. Fare, R., Grosskopf, S., Lovell, C.A.K., Yaisawarng, S., 1993. Derivation of shadow pricesfor undesirable outputs: a distance function approach. Rev. Econ. Stat. 75, 374–380.
  13. Feng, K., Davis, S.J., Sun, L., Hubacek, K., 2015. Drivers of the US CO2 emissions 1997-2013. Nat. Commun. 6, 7714.Hamit-Haggar, M., 2012. Greenhouse gas emissions, energy consumption and economicgrowth: a panel cointegration analysis from Canadian industrial sector perspective.Energy Econ. 34, 358–364. IPCC, 2007. IPCC Fourth Assessment Report: Mitigation of Climate Change 2007.Cambridge University Press,
  14. Cambridge. Lesage, J.P., 2009. An introduction to spatial econometrics. Rev. d'Écon. Ind. 123,513–514. National Bureau of Statistics of China, 1998a. China Statistical Yearbooks. ChinaStatistical Press, Beijing. National Bureau of Statistics of China, 1998b. China Statistical Yearbooks. ChinaStatistical Press,
  15. Beijing. Shahbaz, M., Sbia, R., Hamdi, H., Ozturk, I., 2014. Economic growth, electricityconsumption, ur