The peak value of carbon emissions in the Beijing-Tianjin-Hebei region based on the STIRPAT model and scenario design

The main objective of this paper was seeking suitable scenarios for the Beijing-Tianjin-Hebei region, where both socio-economic development and low-carbon targets would be achieved. Potential driven factors of carbon emissions, including population, affl uence, urbanization level, technology level, industrial construction, and energy consumption constructio n were selected to build an extended stochastic impacts by regression on population, affl uence, and technology (STIRPAT) model, where ridge regression was applied to ensure its stability. The STIRPAT model showed the signifi cance of each independent variable, which was the foundation of CO2 emissions’ prediction. Furthermore, eight scenarios were established to explore the possible carbon footprints and the maximum of CO2 in the period from 2013 to 2050. This paper fi nally proposed the strategies that can be applied to reduce future carbon emissions in the Beijing-Tianjin-Hebei region. Applying reasonable policies about improvement of technological level, and adjustment of industry and energy consumption structures is a critical factor for the control of CO2 emissions.

[1]  András Deák,et al.  Performance of the ridge regression method as applied to complex linear and nonlinear models , 2003 .

[2]  Zhaohua Wang,et al.  An empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China , 2012 .

[3]  Klaus Hubacek,et al.  A "carbonizing dragon": China's fast growing CO2 emissions revisited. , 2011, Environmental science & technology.

[4]  P E Waggoner,et al.  A framework for sustainability science: A renovated IPAT identity , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Jiekun Song,et al.  Study on Influencing Factors of Carbon Emissions from Energy Consumption of Shandong Province of China from 1995 to 2012 , 2014, TheScientificWorldJournal.

[6]  Thong Ngee Goh,et al.  Adaptive ridge regression system for software cost estimating on multi-collinear datasets , 2010, J. Syst. Softw..

[7]  D. Stern.  The Rise and Fall of the Environmental Kuznets Curve , 2004 .

[8]  E. Rosa,et al.  Effects of population and affluence on CO2 emissions. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Nan Zhou,et al.  China's Energy and Carbon Emissions Outlook to 2050 , 2011 .

[10]  David A. Belsley,et al.  Conditioning Diagnostics: Collinearity and Weak Data in Regression , 1991 .

[11]  Bangzhu Zhu,et al.  Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China , 2013 .

[12]  P. Ehrlich,et al.  IMPACT OF POPULATION GROWTH , 1971, Science.

[13]  Dongxiao Niu,et al.  Modeling CO 2 emissions from fossil fuel combustion using the logistic equation , 2011 .

[14]  Chuanglin Fang,et al.  Urbanisation, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s provinces , 2014 .

[15]  Dora Marinova,et al.  Analysis of the Environmental Impact of China based on STIRPAT Model , 2008 .

[16]  Taha B. M. J. Ouarda,et al.  Automated regression-based statistical downscaling tool , 2008, Environ. Model. Softw..

[17]  Nicholas F. Gray,et al.  Comparative performance of six carbon footprint models for use in Ireland , 2009 .

[18]  Chuanguo Zhang,et al.  Panel estimation for urbanization, energy consumption and CO2 emissions: A regional analysis in China , 2012 .

[19]  Soumyananda Dinda.  Environmental Kuznets Curve Hypothesis: A Survey , 2004 .

[20]  J. Sun,et al.  The nature of CO2 emission Kuznets curve , 1999 .

[21]  E. Rosa,et al.  STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts , 2003 .

[22]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[23]  A. E. Hoerl,et al.  Ridge Regression: Applications to Nonorthogonal Problems , 1970 .

[24]  A. E. Hoerl,et al.  Ridge regression: biased estimation for nonorthogonal problems , 2000 .

[25]  D. Ross Jeffery,et al.  A comparative study of two software development cost modeling techniques using multi-organizational and company-specific data , 2000, Inf. Softw. Technol..

[26]  Fikri Akdeniz,et al.  The moments of the operational almost unbiased ridge regression estimator , 2004, Appl. Math. Comput..

[27]  Tao Wang,et al.  China's Energy Transition: Pathways for Low Carbon Development , 2009 .

[28]  Thomas Dietz,et al.  Rethinking the Environmental Impacts of Population , Affluence and Technology ’ , 2003 .